1
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Rühle T, Leister D, Pasch V. Chloroplast ATP synthase: From structure to engineering. THE PLANT CELL 2024; 36:3974-3996. [PMID: 38484126 PMCID: PMC11449085 DOI: 10.1093/plcell/koae081] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 12/27/2023] [Indexed: 10/05/2024]
Abstract
F-type ATP synthases are extensively researched protein complexes because of their widespread and central role in energy metabolism. Progress in structural biology, proteomics, and molecular biology has also greatly advanced our understanding of the catalytic mechanism, post-translational modifications, and biogenesis of chloroplast ATP synthases. Given their critical role in light-driven ATP generation, tailoring the activity of chloroplast ATP synthases and modeling approaches can be applied to modulate photosynthesis. In the future, advances in genetic manipulation and protein design tools will significantly expand the scope for testing new strategies in engineering light-driven nanomotors.
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Affiliation(s)
- Thilo Rühle
- Plant Molecular Biology, Faculty of Biology, Ludwig-Maximilians-University Munich, D-82152 Planegg-Martinsried, Germany
| | - Dario Leister
- Plant Molecular Biology, Faculty of Biology, Ludwig-Maximilians-University Munich, D-82152 Planegg-Martinsried, Germany
| | - Viviana Pasch
- Plant Molecular Biology, Faculty of Biology, Ludwig-Maximilians-University Munich, D-82152 Planegg-Martinsried, Germany
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2
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Deichmann M, Hansson FG, Jensen ED. Yeast-based screening platforms to understand and improve human health. Trends Biotechnol 2024; 42:1258-1272. [PMID: 38677901 DOI: 10.1016/j.tibtech.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 04/29/2024]
Abstract
Detailed molecular understanding of the human organism is essential to develop effective therapies. Saccharomyces cerevisiae has been used extensively for acquiring insights into important aspects of human health, such as studying genetics and cell-cell communication, elucidating protein-protein interaction (PPI) networks, and investigating human G protein-coupled receptor (hGPCR) signaling. We highlight recent advances and opportunities of yeast-based technologies for cost-efficient chemical library screening on hGPCRs, accelerated deciphering of PPI networks with mating-based screening and selection, and accurate cell-cell communication with human immune cells. Overall, yeast-based technologies constitute an important platform to support basic understanding and innovative applications towards improving human health.
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Affiliation(s)
- Marcus Deichmann
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Frederik G Hansson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Emil D Jensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark.
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3
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Muir J, Anguiano M, Kim CK. Neuromodulator and neuropeptide sensors and probes for precise circuit interrogation in vivo. Science 2024; 385:eadn6671. [PMID: 39325905 DOI: 10.1126/science.adn6671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 07/01/2024] [Indexed: 09/28/2024]
Abstract
To determine how neuronal circuits encode and drive behavior, it is often necessary to measure and manipulate different aspects of neurochemical signaling in awake animals. Optogenetics and calcium sensors have paved the way for these types of studies, allowing for the perturbation and readout of spiking activity within genetically defined cell types. However, these methods lack the ability to further disentangle the roles of individual neuromodulator and neuropeptides on circuits and behavior. We review recent advances in chemical biology tools that enable precise spatiotemporal monitoring and control over individual neuroeffectors and their receptors in vivo. We also highlight discoveries enabled by such tools, revealing how these molecules signal across different timescales to drive learning, orchestrate behavioral changes, and modulate circuit activity.
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Affiliation(s)
- J Muir
- Center for Neuroscience, University of California, Davis, Davis, CA 95618, USA
- Department of Neurology, School of Medicine, University of California, Davis, Sacramento, CA 95817, USA
| | - M Anguiano
- Neuroscience Graduate Group, University zof California, Davis, Davis, CA 95616, USA
| | - C K Kim
- Center for Neuroscience, University of California, Davis, Davis, CA 95618, USA
- Department of Neurology, School of Medicine, University of California, Davis, Sacramento, CA 95817, USA
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4
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Wang XF, Tang JY, Sun J, Dorje S, Sun TQ, Peng B, Ji XW, Li Z, Zhang XE, Wang DB. ProT-Diff: A Modularized and Efficient Strategy for De Novo Generation of Antimicrobial Peptide Sequences by Integrating Protein Language and Diffusion Models. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2406305. [PMID: 39319609 DOI: 10.1002/advs.202406305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 09/08/2024] [Indexed: 09/26/2024]
Abstract
Antimicrobial peptides (AMPs) are a promising solution for treating antibiotic-resistant pathogens. However, efficient generation of diverse AMPs without prior knowledge of peptide structures or sequence alignments remains a challenge. Here, ProT-Diff is introduced, a modularized deep generative approach that combines a pretrained protein language model with a diffusion model for the de novo generation of AMPs sequences. ProT-Diff generates thousands of AMPs with diverse lengths and structures within a few hours. After silico physicochemical screening, 45 peptides are selected for experimental validation. Forty-four peptides showed antimicrobial activity against both gram-positive or gram-negative bacteria. Among broad-spectrum peptides, AMP_2 exhibited potent antimicrobial activity, low hemolysis, and minimal cytotoxicity. An in vivo assessment demonstrated its effectiveness against a drug-resistant E. coli strain in acute peritonitis. This study not only introduces a viable and user-friendly strategy for de novo generation of antimicrobial peptides, but also provides potential antimicrobial drug candidates with excellent activity. It is believed that this study will facilitate the development of other peptide-based drug candidates in the future, as well as proteins with tailored characteristics.
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Affiliation(s)
- Xue-Fei Wang
- Precision Scientific (Beijing) Co. Ltd., Beijing, 100085, China
| | - Jing-Ya Tang
- Key Laboratory of Biomacromolecules (CAS), National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Science, Beijing, 100049, China
| | - Jing Sun
- Key Laboratory of Biomacromolecules (CAS), National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Biotechnology, School of Life Sciences, Shandong Normal University, Jinan, 250014, China
| | - Sonam Dorje
- Key Laboratory of Biomacromolecules (CAS), National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Science, Beijing, 100049, China
| | - Tian-Qi Sun
- Precision Scientific (Beijing) Co. Ltd., Beijing, 100085, China
| | - Bo Peng
- Key Laboratory of Biomacromolecules (CAS), National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Science, Beijing, 100049, China
| | - Xu-Wo Ji
- Precision Scientific (Beijing) Co. Ltd., Beijing, 100085, China
| | - Zhe Li
- Precision Scientific (Beijing) Co. Ltd., Beijing, 100085, China
| | - Xian-En Zhang
- Key Laboratory of Biomacromolecules (CAS), National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- Faculty of Synthetic Biology, Shenzhen Institute of Advances Technology, Shenzhen, 518055, China
| | - Dian-Bing Wang
- Key Laboratory of Biomacromolecules (CAS), National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
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5
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Savinov A, Swanson S, Keating AE, Li GW. High-throughput discovery of inhibitory protein fragments with AlphaFold. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.19.572389. [PMID: 38187731 PMCID: PMC10769210 DOI: 10.1101/2023.12.19.572389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Peptides can bind to specific sites on larger proteins and thereby function as inhibitors and regulatory elements. Peptide fragments of larger proteins are particularly attractive for achieving these functions due to their inherent potential to form native-like binding interactions. Recently developed experimental approaches allow for high-throughput measurement of protein fragment inhibitory activity in living cells. However, it has thus far not been possible to predict de novo which of the many possible protein fragments bind to protein targets, let alone act as inhibitors. We have developed a computational method, FragFold, that employs AlphaFold to predict protein fragment binding to full-length proteins in a high-throughput manner. Applying FragFold to thousands of fragments tiling across diverse proteins revealed peaks of predicted binding along each protein sequence. Comparisons with experimental measurements establish that our approach is a sensitive predictor of fragment function: Evaluating inhibitory fragments from known protein-protein interaction interfaces, we find 87% are predicted by FragFold to bind in a native-like mode. Across full protein sequences, 68% of FragFold-predicted binding peaks match experimentally measured inhibitory peaks. Deep mutational scanning experiments support the predicted binding modes and uncover superior inhibitory peptides in high throughput. Further, FragFold is able to predict previously unknown protein binding modes, explaining prior genetic and biochemical data. The success rate of FragFold demonstrates that this computational approach should be broadly applicable for discovering inhibitory protein fragments across proteomes.
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Affiliation(s)
- Andrew Savinov
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sebastian Swanson
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Amy E. Keating
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Center for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gene-Wei Li
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
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6
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Wang F, Wang Y, Feng L, Zhang C, Lai L. Target-Specific De Novo Peptide Binder Design with DiffPepBuilder. J Chem Inf Model 2024. [PMID: 39266056 DOI: 10.1021/acs.jcim.4c00975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2024]
Abstract
Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic data set, referred to as PepPC-F, from the abundant protein-protein interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures. We tested DiffPepBuilder on 30 experimentally verified strong peptide binders with available protein-peptide complex structures. DiffPepBuilder was able to effectively recall the native structures and sequences of the peptide ligands and to generate novel peptide binders with improved binding free energy. We subsequently conducted de novo generation case studies on three targets. In both the regeneration test and case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with ProteinMPNN, in terms of sequence and structure recall, interface quality, and structural diversity. Molecular dynamics simulations confirmed that the introduction of disulfide bonds enhanced the structural rigidity and binding performance of the generated peptides. As a general peptide binder de novo design tool, DiffPepBuilder can be used to design peptide binders for given protein targets with three-dimensional and binding site information.
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Affiliation(s)
- Fanhao Wang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Yuzhe Wang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Laiyi Feng
- Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Changsheng Zhang
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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7
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Wu K, Jiang H, Hicks DR, Liu C, Muratspahic E, Ramelot TA, Liu Y, McNally K, Gaur A, Coventry B, Chen W, Bera AK, Kang A, Gerben S, Lamb MYL, Murray A, Li X, Kennedy MA, Yang W, Schober G, Brierley SM, Gelb MH, Montelione GT, Derivery E, Baker D. Sequence-specific targeting of intrinsically disordered protein regions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.15.603480. [PMID: 39071356 PMCID: PMC11275711 DOI: 10.1101/2024.07.15.603480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
A general approach to design proteins that bind tightly and specifically to intrinsically disordered regions (IDRs) of proteins and flexible peptides would have wide application in biological research, therapeutics, and diagnosis. However, the lack of defined structures and the high variability in sequence and conformational preferences has complicated such efforts. We sought to develop a method combining biophysical principles with deep learning to readily generate binders for any disordered sequence. Instead of assuming a fixed regular structure for the target, general recognition is achieved by threading the query sequence through diverse extended binding modes in hundreds of templates with varying pocket depths and spacings, followed by RFdiffusion refinement to optimize the binder-target fit. We tested the method by designing binders to 39 highly diverse unstructured targets. Experimental testing of ~36 designs per target yielded binders with affinities better than 100 nM in 34 cases, and in the pM range in four cases. The co-crystal structure of a designed binder in complex with dynorphin A is closely consistent with the design model. All by all binding experiments for 20 designs binding diverse targets show they are highly specific for the intended targets, with no crosstalk even for the closely related dynorphin A and dynorphin B. Our approach thus could provide a general solution to the intrinsically disordered protein and peptide recognition problem.
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8
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Gong X, Zhang J, Gan Q, Teng Y, Hou J, Lyu Y, Liu Z, Wu Z, Dai R, Zou Y, Wang X, Zhu D, Zhu H, Liu T, Yan Y. Advancing microbial production through artificial intelligence-aided biology. Biotechnol Adv 2024; 74:108399. [PMID: 38925317 DOI: 10.1016/j.biotechadv.2024.108399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/20/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024]
Abstract
Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.
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Affiliation(s)
- Xinyu Gong
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Jianli Zhang
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Qi Gan
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Yuxi Teng
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Jixin Hou
- School of ECAM, College of Engineering, University of Georgia, Athens, GA 30602, USA
| | - Yanjun Lyu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington 76019, USA
| | - Zhengliang Liu
- School of Computing, The University of Georgia, Athens, GA 30602, USA
| | - Zihao Wu
- School of Computing, The University of Georgia, Athens, GA 30602, USA
| | - Runpeng Dai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yusong Zou
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Xianqiao Wang
- School of ECAM, College of Engineering, University of Georgia, Athens, GA 30602, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington 76019, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tianming Liu
- School of Computing, The University of Georgia, Athens, GA 30602, USA
| | - Yajun Yan
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA.
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9
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Abali Z, Aydin Z, Khokhar M, Ates YC, Gursoy A, Keskin O. PPInterface: A Comprehensive Dataset of 3D Protein-Protein Interface Structures. J Mol Biol 2024; 436:168686. [PMID: 38936693 DOI: 10.1016/j.jmb.2024.168686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 05/25/2024] [Accepted: 06/20/2024] [Indexed: 06/29/2024]
Abstract
The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein-protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein-protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset's utility and accessibility. The dataset and web server can be found at https://3dpath.ku.edu.tr/PPInt/.
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Affiliation(s)
- Zeynep Abali
- Computational Science and Engineering Graduate Program, Koc University, Istanbul 34450, Turkey
| | - Zeynep Aydin
- Computational Science and Engineering Graduate Program, Koc University, Istanbul 34450, Turkey
| | - Moaaz Khokhar
- Computer Engineering, Koc University, Istanbul 34450, Turkey
| | - Yigit Can Ates
- Computer Engineering, Koc University, Istanbul 34450, Turkey
| | - Attila Gursoy
- Computer Engineering, Koc University, Istanbul 34450, Turkey
| | - Ozlem Keskin
- Chemical and Biological Engineering, Koc University, Istanbul 34450, Turkey.
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10
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Lauko A, Pellock SJ, Anischanka I, Sumida KH, Juergens D, Ahern W, Shida A, Hunt A, Kalvet I, Norn C, Humphreys IR, Jamieson C, Kang A, Brackenbrough E, Bera AK, Sankaran B, Houk KN, Baker D. Computational design of serine hydrolases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.29.610411. [PMID: 39257749 PMCID: PMC11384011 DOI: 10.1101/2024.08.29.610411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Enzymes that proceed through multistep reaction mechanisms often utilize complex, polar active sites positioned with sub-angstrom precision to mediate distinct chemical steps, which makes their de novo construction extremely challenging. We sought to overcome this challenge using the classic catalytic triad and oxyanion hole of serine hydrolases as a model system. We used RFdiffusion1 to generate proteins housing catalytic sites of increasing complexity and varying geometry, and a newly developed ensemble generation method called ChemNet to assess active site geometry and preorganization at each step of the reaction. Experimental characterization revealed novel serine hydrolases that catalyze ester hydrolysis with catalytic efficiencies (k cat /K m ) up to 3.8 x 103 M-1 s-1, closely match the design models (Cα RMSDs < 1 Å), and have folds distinct from natural serine hydrolases. In silico selection of designs based on active site preorganization across the reaction coordinate considerably increased success rates, enabling identification of new catalysts in screens of as few as 20 designs. Our de novo buildup approach provides insight into the geometric determinants of catalysis that complements what can be obtained from structural and mutational studies of native enzymes (in which catalytic group geometry and active site makeup cannot be so systematically varied), and provides a roadmap for the design of industrially relevant serine hydrolases and, more generally, for designing complex enzymes that catalyze multi-step transformations.
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Affiliation(s)
- Anna Lauko
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
- These authors contributed equally: Anna Lauko, Samuel J. Pellock
| | - Samuel J Pellock
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- These authors contributed equally: Anna Lauko, Samuel J. Pellock
| | - Ivan Anischanka
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kiera H Sumida
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Chemistry, University of Washington, Seattle, WA, USA
| | - David Juergens
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Molecular Engineering, University of Washington, Seattle, WA, USA
| | - Woody Ahern
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Alex Shida
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Andrew Hunt
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Indrek Kalvet
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Christoffer Norn
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ian R Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Cooper Jamieson
- Department of Chemistry and Biochemistry, University of California, Los Angeles, California, USA
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Evans Brackenbrough
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Asim K Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Banumathi Sankaran
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - K N Houk
- Department of Chemistry and Biochemistry, University of California, Los Angeles, California, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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11
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Weinberg ZY, Soliman SS, Kim MS, Shah DH, Chen IP, Ott M, Lim WA, El-Samad H. De novo-designed minibinders expand the synthetic biology sensing repertoire. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.12.575267. [PMID: 38293112 PMCID: PMC10827046 DOI: 10.1101/2024.01.12.575267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Synthetic and chimeric receptors capable of recognizing and responding to user-defined antigens have enabled "smart" therapeutics based on engineered cells. These cell engineering tools depend on antigen sensors which are most often derived from antibodies. Advances in the de novo design of proteins have enabled the design of protein binders with the potential to target epitopes with unique properties and faster production timelines compared to antibodies. Building upon our previous work combining a de novo-designed minibinder of the Spike protein of SARS-CoV-2 with the synthetic receptor synNotch (SARSNotch), we investigated whether minibinders can be readily adapted to a diversity of cell engineering tools. We show that the Spike minibinder LCB1 easily generalizes to a next-generation proteolytic receptor SNIPR that performs similarly to our previously reported SARSNotch. LCB1-SNIPR successfully enables the detection of live SARS-CoV-2, an improvement over SARSNotch which can only detect cell-expressed Spike. To test the generalizability of minibinders to diverse applications, we tested LCB1 as an antigen sensor for a chimeric antigen receptor (CAR). LCB1-CAR enabled CD8+ T cells to cytotoxically target Spike-expressing cells. We further demonstrate that two other minibinders directed against the clinically relevant epidermal growth factor receptor are able to drive CAR-dependent cytotoxicity with efficacy similar to or better than an existing antibody-based CAR. Our findings suggest that minibinders represent a novel class of antigen sensors that have the potential to dramatically expand the sensing repertoire of cell engineering tools.
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Affiliation(s)
| | | | - Matthew S. Kim
- Tetrad Gradudate Program, UCSF, San Francisco CA
- Cell Design Institute, San Francisco CA
| | - Devan H. Shah
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley, CA
| | - Irene P. Chen
- Gladstone Institutes, San Francisco CA
- Department of Medicine, UCSF, San Francisco CA
| | - Melanie Ott
- Gladstone Institutes, San Francisco CA
- Department of Medicine, UCSF, San Francisco CA
- Chan Zuckerberg Biohub–San Francisco, San Francisco CA
| | - Wendell A. Lim
- Cell Design Institute, San Francisco CA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Center for Cellular Construction, University of California, San Francisco, CA, USA
| | - Hana El-Samad
- Department of Biochemistry & Biophysics, UCSF, San Francisco CA
- Cell Design Institute, San Francisco CA
- Chan Zuckerberg Biohub–San Francisco, San Francisco CA
- Altos Labs, San Francisco CA
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12
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Lv Y, Qi J, Babon JJ, Cao L, Fan G, Lang J, Zhang J, Mi P, Kobe B, Wang F. The JAK-STAT pathway: from structural biology to cytokine engineering. Signal Transduct Target Ther 2024; 9:221. [PMID: 39169031 PMCID: PMC11339341 DOI: 10.1038/s41392-024-01934-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/12/2024] [Accepted: 07/16/2024] [Indexed: 08/23/2024] Open
Abstract
The Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway serves as a paradigm for signal transduction from the extracellular environment to the nucleus. It plays a pivotal role in physiological functions, such as hematopoiesis, immune balance, tissue homeostasis, and surveillance against tumors. Dysregulation of this pathway may lead to various disease conditions such as immune deficiencies, autoimmune diseases, hematologic disorders, and cancer. Due to its critical role in maintaining human health and involvement in disease, extensive studies have been conducted on this pathway, ranging from basic research to medical applications. Advances in the structural biology of this pathway have enabled us to gain insights into how the signaling cascade operates at the molecular level, laying the groundwork for therapeutic development targeting this pathway. Various strategies have been developed to restore its normal function, with promising therapeutic potential. Enhanced comprehension of these molecular mechanisms, combined with advances in protein engineering methodologies, has allowed us to engineer cytokines with tailored properties for targeted therapeutic applications, thereby enhancing their efficiency and safety. In this review, we outline the structural basis that governs key nodes in this pathway, offering a comprehensive overview of the signal transduction process. Furthermore, we explore recent advances in cytokine engineering for therapeutic development in this pathway.
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Affiliation(s)
- You Lv
- Center for Molecular Biosciences and Non-communicable Diseases Research, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China
- Xi'an Amazinggene Co., Ltd, Xi'an, Shaanxi, 710026, China
| | - Jianxun Qi
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100080, China
| | - Jeffrey J Babon
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
| | - Longxing Cao
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, 310024, China
| | - Guohuang Fan
- Immunophage Biotech Co., Ltd, No. 10 Lv Zhou Huan Road, Shanghai, 201112, China
| | - Jiajia Lang
- School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Jin Zhang
- Xi'an Amazinggene Co., Ltd, Xi'an, Shaanxi, 710026, China
| | - Pengbing Mi
- School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
| | - Bostjan Kobe
- School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Brisbane, Queensland, 4072, Australia.
| | - Faming Wang
- Center for Molecular Biosciences and Non-communicable Diseases Research, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China.
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13
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Kovalevskiy O, Mateos-Garcia J, Tunyasuvunakool K. AlphaFold two years on: Validation and impact. Proc Natl Acad Sci U S A 2024; 121:e2315002121. [PMID: 39133843 PMCID: PMC11348012 DOI: 10.1073/pnas.2315002121] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024] Open
Abstract
Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model's capabilities and limitations.
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14
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Zitnik M, Li MM, Wells A, Glass K, Morselli Gysi D, Krishnan A, Murali TM, Radivojac P, Roy S, Baudot A, Bozdag S, Chen DZ, Cowen L, Devkota K, Gitter A, Gosline SJC, Gu P, Guzzi PH, Huang H, Jiang M, Kesimoglu ZN, Koyuturk M, Ma J, Pico AR, Pržulj N, Przytycka TM, Raphael BJ, Ritz A, Sharan R, Shen Y, Singh M, Slonim DK, Tong H, Yang XH, Yoon BJ, Yu H, Milenković T. Current and future directions in network biology. BIOINFORMATICS ADVANCES 2024; 4:vbae099. [PMID: 39143982 PMCID: PMC11321866 DOI: 10.1093/bioadv/vbae099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 05/31/2024] [Accepted: 07/08/2024] [Indexed: 08/16/2024]
Abstract
Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation Not applicable.
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Affiliation(s)
- Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Michelle M Li
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Aydin Wells
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Deisy Morselli Gysi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Statistics, Federal University of Paraná, Curitiba, Paraná 81530-015, Brazil
- Department of Physics, Northeastern University, Boston, MA 02115, United States
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
| | - Sushmita Roy
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Wisconsin Institute for Discovery, Madison, WI 53715, United States
| | - Anaïs Baudot
- Aix Marseille Université, INSERM, MMG, Marseille, France
| | - Serdar Bozdag
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- Department of Mathematics, University of North Texas, Denton, TX 76203, United States
| | - Danny Z Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Kapil Devkota
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Morgridge Institute for Research, Madison, WI 53715, United States
| | - Sara J C Gosline
- Biological Sciences Division, Pacific Northwest National Laboratory, Seattle, WA 98109, United States
| | - Pengfei Gu
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Pietro H Guzzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, 88100, Italy
| | - Heng Huang
- Department of Computer Science, University of Maryland College Park, College Park, MD 20742, United States
| | - Meng Jiang
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Ziynet Nesibe Kesimoglu
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Mehmet Koyuturk
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, United States
| | - Nataša Pržulj
- Department of Computer Science, University College London, London, WC1E 6BT, England
- ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, 08010, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
| | - Anna Ritz
- Department of Biology, Reed College, Portland, OR 97202, United States
| | - Roded Sharan
- School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, United States
| | - Donna K Slonim
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Hanghang Tong
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Xinan Holly Yang
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, United States
| | - Byung-Jun Yoon
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, United States
| | - Haiyuan Yu
- Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, United States
| | - Tijana Milenković
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
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15
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Albanese KI, Petrenas R, Pirro F, Naudin EA, Borucu U, Dawson WM, Scott DA, Leggett GJ, Weiner OD, Oliver TAA, Woolfson DN. Rationally seeded computational protein design of ɑ-helical barrels. Nat Chem Biol 2024; 20:991-999. [PMID: 38902458 PMCID: PMC11288890 DOI: 10.1038/s41589-024-01642-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 05/09/2024] [Indexed: 06/22/2024]
Abstract
Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.
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Affiliation(s)
- Katherine I Albanese
- School of Chemistry, University of Bristol, Bristol, UK
- Max Planck-Bristol Centre for Minimal Biology, University of Bristol, Bristol, UK
| | | | - Fabio Pirro
- School of Chemistry, University of Bristol, Bristol, UK
| | | | - Ufuk Borucu
- School of Biochemistry, University of Bristol, Medical Sciences Building, Bristol, UK
| | | | - D Arne Scott
- Rosa Biotech, Science Creates St Philips, Bristol, UK
| | | | - Orion D Weiner
- Cardiovascular Research Institute, Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | | | - Derek N Woolfson
- School of Chemistry, University of Bristol, Bristol, UK.
- Max Planck-Bristol Centre for Minimal Biology, University of Bristol, Bristol, UK.
- School of Biochemistry, University of Bristol, Medical Sciences Building, Bristol, UK.
- Bristol BioDesign Institute, University of Bristol, Bristol, UK.
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16
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Pillai A, Idris A, Philomin A, Weidle C, Skotheim R, Leung PJY, Broerman A, Demakis C, Borst AJ, Praetorius F, Baker D. De novo design of allosterically switchable protein assemblies. Nature 2024; 632:911-920. [PMID: 39143214 PMCID: PMC11338832 DOI: 10.1038/s41586-024-07813-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 07/11/2024] [Indexed: 08/16/2024]
Abstract
Allosteric modulation of protein function, wherein the binding of an effector to a protein triggers conformational changes at distant functional sites, plays a central part in the control of metabolism and cell signalling1-3. There has been considerable interest in designing allosteric systems, both to gain insight into the mechanisms underlying such 'action at a distance' modulation and to create synthetic proteins whose functions can be regulated by effectors4-7. However, emulating the subtle conformational changes distributed across many residues, characteristic of natural allosteric proteins, is a significant challenge8,9. Here, inspired by the classic Monod-Wyman-Changeux model of cooperativity10, we investigate the de novo design of allostery through rigid-body coupling of peptide-switchable hinge modules11 to protein interfaces12 that direct the formation of alternative oligomeric states. We find that this approach can be used to generate a wide variety of allosterically switchable systems, including cyclic rings that incorporate or eject subunits in response to peptide binding and dihedral cages that undergo effector-induced disassembly. Size-exclusion chromatography, mass photometry13 and electron microscopy reveal that these designed allosteric protein assemblies closely resemble the design models in both the presence and absence of peptide effectors and can have ligand-binding cooperativity comparable to classic natural systems such as haemoglobin14. Our results indicate that allostery can arise from global coupling of the energetics of protein substructures without optimized side-chain-side-chain allosteric communication pathways and provide a roadmap for generating allosterically triggerable delivery systems, protein nanomachines and cellular feedback control circuitry.
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Affiliation(s)
- Arvind Pillai
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
| | - Abbas Idris
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Annika Philomin
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Connor Weidle
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Rebecca Skotheim
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Philip J Y Leung
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Program in Molecular Engineering, University of Washington, Seattle, WA, USA
| | - Adam Broerman
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Cullen Demakis
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure, and Design, University of Washington, Seattle, WA, USA
| | - Andrew J Borst
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Florian Praetorius
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
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17
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Nielsen JC, Hjo Rringgaard C, Nygaard MMR, Wester A, Elster L, Porsgaard T, Mikkelsen RB, Rasmussen S, Madsen AN, Schlein M, Vrang N, Rigbolt K, Dalbo Ge LS. Machine-Learning-Guided Peptide Drug Discovery: Development of GLP-1 Receptor Agonists with Improved Drug Properties. J Med Chem 2024; 67:11814-11826. [PMID: 38977267 DOI: 10.1021/acs.jmedchem.4c00417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Peptide-based drug discovery has surged with the development of peptide hormone-derived analogs for the treatment of diabetes and obesity. Machine learning (ML)-enabled quantitative structure-activity relationship (QSAR) approaches have shown great promise in small molecule drug discovery but have been less successful in peptide drug discovery due to limited data availability. We have developed a peptide drug discovery platform called streaMLine, enabling rigorous design, synthesis, screening, and ML-driven analysis of large peptide libraries. Using streaMLine, this study systematically explored secretin as a peptide backbone to generate potent, selective, and long-acting GLP-1R agonists with improved physicochemical properties. We synthesized and screened a total of 2688 peptides and applied ML-guided QSAR to identify multiple options for designing stable and potent GLP-1R agonists. One candidate, GUB021794, was profiled in vivo (S.C., 10 nmol/kg QD) and showed potent body weight loss in diet-induced obese mice and a half-life compatible with once-weekly dosing.
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Affiliation(s)
| | | | | | - Anita Wester
- Gubra, Ho̷rsholm Kongevej 11B, Ho̷rsholm 2970, Denmark
| | | | | | | | | | | | | | - Niels Vrang
- Gubra, Ho̷rsholm Kongevej 11B, Ho̷rsholm 2970, Denmark
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18
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Krapp LF, Meireles FA, Abriata LA, Devillard J, Vacle S, Marcaida MJ, Dal Peraro M. Context-aware geometric deep learning for protein sequence design. Nat Commun 2024; 15:6273. [PMID: 39054322 PMCID: PMC11272779 DOI: 10.1038/s41467-024-50571-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 07/15/2024] [Indexed: 07/27/2024] Open
Abstract
Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning approach based solely on a geometric transformer of atomic coordinates and element names that predicts protein sequences from backbone scaffolds aware of the restraints imposed by diverse molecular environments. To validate the method, we show that it can produce highly thermostable, catalytically active enzymes with high success rates. This concept is anticipated to improve the versatility of protein design pipelines for crafting desired functions.
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Affiliation(s)
- Lucien F Krapp
- Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Fernando A Meireles
- Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Luciano A Abriata
- Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Jean Devillard
- Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sarah Vacle
- Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Maria J Marcaida
- Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Matteo Dal Peraro
- Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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19
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Yim J, Campbell A, Mathieu E, Foong AYK, Gastegger M, Jiménez-Luna J, Lewis S, Satorras VG, Veeling BS, Noé F, Barzilay R, Jaakkola TS. Improved motif-scaffolding with SE(3) flow matching. ARXIV 2024:arXiv:2401.04082v2. [PMID: 38259348 PMCID: PMC10802670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models have achieved breakthrough success in designing scaffolds for a range of motifs. However, generated scaffolds tend to lack structural diversity, which can hinder success in wet-lab validation. In this work, we extend FrameFlow, an SE(3) flow matching model for protein backbone generation, to perform motif-scaffolding with two complementary approaches. The first is motif amortization, in which FrameFlow is trained with the motif as input using a data augmentation strategy. The second is motif guidance, which performs scaffolding using an estimate of the conditional score from FrameFlow without additional training. On a benchmark of 24 biologically meaningful motifs, we show our method achieves 2.5 times more designable and unique motif-scaffolds compared to state-of-the-art. Code: https://github.com/microsoft/protein-frame-flow.
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20
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Liu C, Wu K, Choi H, Han H, Zhang X, Watson JL, Shijo S, Bera AK, Kang A, Brackenbrough E, Coventry B, Hick DR, Hoofnagle AN, Zhu P, Li X, Decarreau J, Gerben SR, Yang W, Wang X, Lamp M, Murray A, Bauer M, Baker D. Diffusing protein binders to intrinsically disordered proteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.16.603789. [PMID: 39071267 PMCID: PMC11275890 DOI: 10.1101/2024.07.16.603789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Proteins which bind intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) with high affinity and specificity could have considerable utility for therapeutic and diagnostic applications. However, a general methodology for targeting IDPs/IDRs has yet to be developed. Here, we show that starting only from the target sequence of the input, and freely sampling both target and binding protein conformation, RFdiffusion can generate binders to IDPs and IDRs in a wide range of conformations. We use this approach to generate binders to the IDPs Amylin, C-peptide and VP48 in a range of conformations with Kds in the 3 -100nM range. The Amylin binder inhibits amyloid fibril formation and dissociates existing fibers, and enables enrichment of amylin for mass spectrometry-based detection. For the IDRs G3bp1, common gamma chain (IL2RG) and prion, we diffused binders to beta strand conformations of the targets, obtaining 10 to 100 nM affinity. The IL2RG binder colocalizes with the receptor in cells, enabling new approaches to modulating IL2 signaling. Our approach should be widely useful for creating binders to flexible IDPs/IDRs spanning a wide range of intrinsic conformational preferences.
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Affiliation(s)
- Caixuan Liu
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kejia Wu
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Biological Physics, Structure and Design Graduate Program, University of Washington, Seattle, WA, USA
| | - Hojun Choi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Hannah Han
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Xulie Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Joseph L Watson
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Sara Shijo
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98105, USA
| | - Asim K Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Evans Brackenbrough
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Derrick R Hick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Andrew N Hoofnagle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98105, USA
| | - Ping Zhu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xingting Li
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin Decarreau
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Stacey R Gerben
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Wei Yang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Xinru Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Mila Lamp
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Analisa Murray
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Magnus Bauer
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
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21
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Launay R, Chobert SC, Abby SS, Pierrel F, André I, Esque J. Structural Reconstruction of E. coli Ubi Metabolon Using an AlphaFold2-Based Computational Framework. J Chem Inf Model 2024; 64:5175-5193. [PMID: 38710096 DOI: 10.1021/acs.jcim.4c00304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Ubiquinone (UQ) is a redox polyisoprenoid lipid found in the membranes of bacteria and eukaryotes that has important roles, notably one in respiratory metabolism, which sustains cellular bioenergetics. In Escherichia coli, several steps of the UQ biosynthesis take place in the cytosol. To perform these reactions, a supramolecular assembly called Ubi metabolon is involved. This latter is composed of seven proteins (UbiE, UbiG, UbiF, UbiH, UbiI, UbiJ, and UbiK), and its structural organization is unknown as well as its protein stoichiometry. In this study, a computational framework has been designed to predict the structure of this macromolecular assembly. In several successive steps, we explored the possible protein interactions as well as the protein stoichiometry, to finally obtain a structural organization of the complex. The use of AlphaFold2-based methods combined with evolutionary information enabled us to predict several models whose quality and confidence were further analyzed using different metrics and scores. Our work led to the identification of a "core assembly" that will guide functional and structural characterization of the Ubi metabolon.
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Affiliation(s)
- Romain Launay
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France
| | - Sophie-Carole Chobert
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000 Grenoble, France
| | - Sophie S Abby
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000 Grenoble, France
| | - Fabien Pierrel
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000 Grenoble, France
| | - Isabelle André
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France
| | - Jérémy Esque
- Toulouse Biotechnology Institute, TBI, Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France
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22
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Wang J, Watson JL, Lisanza SL. Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models. Cold Spring Harb Perspect Biol 2024; 16:a041472. [PMID: 38438190 PMCID: PMC11216169 DOI: 10.1101/cshperspect.a041472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.
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Affiliation(s)
- Jue Wang
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, Washington 98195, USA
- DeepMind, London EC4A 3BF, United Kingdom
| | - Joseph L Watson
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
| | - Sidney L Lisanza
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
- Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, Washington 98195, USA
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23
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Chaves EJF, Coêlho DF, Cruz CHB, Moreira EG, Simões JCM, Nascimento-Filho MJ, Lins RD. Structure-based computational design of antibody mimetics: challenges and perspectives. FEBS Open Bio 2024. [PMID: 38925955 DOI: 10.1002/2211-5463.13855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 05/17/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024] Open
Abstract
The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure-based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of their structural and functional properties. This review covers the main classes of designed antigen-binding motifs, as well as alternative strategies to develop tailored ones. We discuss the intricacies of different computational protein-protein interaction design strategies, showcased by selected successful cases in the literature. Subsequently, we explore the latest advancements in the computational techniques including the integration of machine and deep learning methodologies into the design framework, which has led to an augmented design pipeline. Finally, we verse onto the current challenges that stand in the way between high-throughput computer design of antibody mimetics and experimental realization, offering a forward-looking perspective into the field and the promises it holds to biotechnology.
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Affiliation(s)
- Elton J F Chaves
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
| | - Danilo F Coêlho
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Carlos H B Cruz
- Institute of Structural and Molecular Biology, University College London, UK
| | | | - Júlio C M Simões
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Manassés J Nascimento-Filho
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Roberto D Lins
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
- Fiocruz Genomics Network, Brazil
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24
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Sela M, Church JR, Schapiro I, Schneidman-Duhovny D. RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning. J Chem Inf Model 2024; 64:4630-4639. [PMID: 38829021 PMCID: PMC11200256 DOI: 10.1021/acs.jcim.4c00467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/15/2024] [Accepted: 05/17/2024] [Indexed: 06/05/2024]
Abstract
Microbial rhodopsins (MRs) are a diverse and abundant family of photoactive membrane proteins that serve as model systems for biophysical techniques. Optogenetics utilizes genetic engineering to insert specialized proteins into specific neurons or brain regions, allowing for manipulation of their activity through light and enabling the mapping and control of specific brain areas in living organisms. The obstacle of optogenetics lies in the fact that light has a limited ability to penetrate biological tissues, particularly blue light in the visible spectrum. Despite this challenge, most optogenetic systems rely on blue light due to the scarcity of red-shifted opsins. Finding additional red-shifted rhodopsins would represent a major breakthrough in overcoming the challenge of limited light penetration in optogenetics. However, determining the wavelength absorption maxima for rhodopsins based on their protein sequence is a significant hurdle. Current experimental methods are time-consuming, while computational methods lack accuracy. The paper introduces a new computational approach called RhoMax that utilizes structure-based geometric deep learning to predict the absorption wavelength of rhodopsins solely based on their sequences. The method takes advantage of AlphaFold2 for accurate modeling of rhodopsin structures. Once trained on a balanced train set, RhoMax rapidly and precisely predicted the maximum absorption wavelength of more than half of the sequences in our test set with an accuracy of 0.03 eV. By leveraging computational methods for absorption maxima determination, we can drastically reduce the time needed for designing new red-shifted microbial rhodopsins, thereby facilitating advances in the field of optogenetics.
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Affiliation(s)
- Meitar Sela
- The
Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Jonathan R. Church
- Fritz
Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Igor Schapiro
- Fritz
Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Dina Schneidman-Duhovny
- The
Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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25
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Meador K, Castells-Graells R, Aguirre R, Sawaya MR, Arbing MA, Sherman T, Senarathne C, Yeates TO. A suite of designed protein cages using machine learning and protein fragment-based protocols. Structure 2024; 32:751-765.e11. [PMID: 38513658 PMCID: PMC11162342 DOI: 10.1016/j.str.2024.02.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/22/2024] [Accepted: 02/23/2024] [Indexed: 03/23/2024]
Abstract
Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, self-assembling protein cages. For the generation of docked conformations, we emphasize a protein fragment-based approach, while for sequence design of the de novo interface, a comparison of knowledge-based and machine learning protocols highlights the power and increased experimental success achieved using ProteinMPNN. An analysis of design outcomes provides insights for improving interface design protocols, including prioritizing fragment-based motifs, balancing interface hydrophobicity and polarity, and identifying preferred polar contact patterns. In all, we report five structures for seven protein cages, along with two structures of intermediate assemblies, with the highest resolution reaching 2.0 Å using cryo-EM. This set of designed cages adds substantially to the body of available protein nanoparticles, and to methodologies for their creation.
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Affiliation(s)
- Kyle Meador
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA
| | | | - Roman Aguirre
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA
| | - Michael R Sawaya
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA
| | - Mark A Arbing
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA
| | - Trent Sherman
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA
| | - Chethaka Senarathne
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA
| | - Todd O Yeates
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA; UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA.
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26
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Durojaye OA, Yekeen AA, Idris MO, Okoro NO, Odiba AS, Nwanguma BC. Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations. Int J Biol Macromol 2024; 269:131840. [PMID: 38679255 DOI: 10.1016/j.ijbiomac.2024.131840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 04/13/2024] [Accepted: 04/22/2024] [Indexed: 05/01/2024]
Abstract
The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53. Extensive all-atom molecular dynamics simulations highlighted the stability of the designed peptide in complex with the target, supported by several structural analyses, including RMSD, RMSF, Rg, SASA, PCA, and free energy landscapes. Using the steered molecular dynamics and umbrella sampling simulations, we elucidate the dissociation dynamics of p53, Pep1, and Pep2 from MDM2. Notable differences in interaction profiles were observed, emphasizing the distinct dissociation patterns of each peptide. In conclusion, the results of our umbrella sampling simulations suggest Pep1 as a higher-affinity MDM2 binder compared to p53 and Pep2, positioning it as a potential inhibitor of the MDM2-p53 interaction. Using state-of-the-art protein design tools and advanced MD simulations, this study provides a comprehensive framework for rational in silico design of peptide binders with therapeutic implications in disrupting MDM2-p53 interactions for anticancer interventions.
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Affiliation(s)
- Olanrewaju Ayodeji Durojaye
- MOE Key Laboratory of Membraneless Organelle and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230027, China; School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230027, China; Department of Chemical Sciences, Coal City University, Emene, Enugu State, Nigeria.
| | - Abeeb Abiodun Yekeen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States; Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
| | | | - Nkwachukwu Oziamara Okoro
- Department of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka 410001, Nigeria
| | - Arome Solomon Odiba
- Department of Molecular Genetics and Biotechnology, University of Nigeria, Nsukka, Enugu State 410001, Nigeria; Department of Biochemistry, Faculty of Biological Sciences, University of Nigeria, Nsukka, Enugu State 410001, Nigeria.
| | - Bennett Chima Nwanguma
- Department of Molecular Genetics and Biotechnology, University of Nigeria, Nsukka, Enugu State 410001, Nigeria; Department of Biochemistry, Faculty of Biological Sciences, University of Nigeria, Nsukka, Enugu State 410001, Nigeria.
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27
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Flynn CD, Chang D. Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities. Diagnostics (Basel) 2024; 14:1100. [PMID: 38893627 PMCID: PMC11172335 DOI: 10.3390/diagnostics14111100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 05/22/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.
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Affiliation(s)
- Connor D. Flynn
- Department of Chemistry, Weinberg College of Arts & Sciences, Northwestern University, Evanston, IL 60208, USA
| | - Dingran Chang
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60208, USA
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28
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Aguilera-Puga MDC, Plisson F. Structure-aware machine learning strategies for antimicrobial peptide discovery. Sci Rep 2024; 14:11995. [PMID: 38796582 PMCID: PMC11127937 DOI: 10.1038/s41598-024-62419-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 05/16/2024] [Indexed: 05/28/2024] Open
Abstract
Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico
| | - Fabien Plisson
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico.
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29
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Xie X, Valiente PA, Kim J, Kim PM. HelixDiff, a Score-Based Diffusion Model for Generating All-Atom α-Helical Structures. ACS CENTRAL SCIENCE 2024; 10:1001-1011. [PMID: 38799672 PMCID: PMC11117309 DOI: 10.1021/acscentsci.3c01488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 05/29/2024]
Abstract
Here, we present HelixDiff, a score-based diffusion model for generating all-atom helical structures. We developed a hot spot-specific generation algorithm for the conditional design of α-helices targeting critical hotspot residues in bioactive peptides. HelixDiff generates α-helices with near-native geometries for most test scenarios with root-mean-square deviations (RMSDs) less than 1 Å. Significantly, HelixDiff outperformed our prior GAN-based model with regard to sequence recovery and Rosetta scores for unconditional and conditional generations. As a proof of principle, we employed HelixDiff to design an acetylated GLP-1 D-peptide agonist that activated the glucagon-like peptide-1 receptor (GLP-1R) cAMP accumulation without stimulating the glucagon-like peptide-2 receptor (GLP-2R). We predicted that this D-peptide agonist has a similar orientation to GLP-1 and is substantially more stable in MD simulations than our earlier D-GLP-1 retro-inverse design. This D-peptide analogue is highly resistant to protease degradation and induces similar levels of AKT phosphorylation in HEK293 cells expressing GLP-1R compared to the native GLP-1. We then discovered that matching crucial hotspots for the GLP-1 function is more important than the sequence orientation of the generated D-peptides when constructing D-GLP-1 agonists.
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Affiliation(s)
- Xuezhi Xie
- Donnelly
Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Pedro A Valiente
- Donnelly
Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Jisun Kim
- Donnelly
Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Philip M Kim
- Donnelly
Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department
of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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30
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Torres SV, Valle MB, Mackessy SP, Menzies SK, Casewell NR, Ahmadi S, Burlet NJ, Muratspahić E, Sappington I, Overath MD, Rivera-de-Torre E, Ledergerber J, Laustsen AH, Boddum K, Bera AK, Kang A, Brackenbrough E, Cardoso IA, Crittenden EP, Edge RJ, Decarreau J, Ragotte RJ, Pillai AS, Abedi M, Han HL, Gerben SR, Murray A, Skotheim R, Stuart L, Stewart L, Fryer TJA, Jenkins TP, Baker D. De novo designed proteins neutralize lethal snake venom toxins. RESEARCH SQUARE 2024:rs.3.rs-4402792. [PMID: 38798548 PMCID: PMC11118692 DOI: 10.21203/rs.3.rs-4402792/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Snakebite envenoming remains a devastating and neglected tropical disease, claiming over 100,000 lives annually and causing severe complications and long-lasting disabilities for many more1,2. Three-finger toxins (3FTx) are highly toxic components of elapid snake venoms that can cause diverse pathologies, including severe tissue damage3 and inhibition of nicotinic acetylcholine receptors (nAChRs) resulting in life-threatening neurotoxicity4. Currently, the only available treatments for snakebite consist of polyclonal antibodies derived from the plasma of immunized animals, which have high cost and limited efficacy against 3FTxs5,6,7. Here, we use deep learning methods to de novo design proteins to bind short- and long-chain α-neurotoxins and cytotoxins from the 3FTx family. With limited experimental screening, we obtain protein designs with remarkable thermal stability, high binding affinity, and near-atomic level agreement with the computational models. The designed proteins effectively neutralize all three 3FTx sub-families in vitro and protect mice from a lethal neurotoxin challenge. Such potent, stable, and readily manufacturable toxin-neutralizing proteins could provide the basis for safer, cost-effective, and widely accessible next-generation antivenom therapeutics. Beyond snakebite, our computational design methodology should help democratize therapeutic discovery, particularly in resource-limited settings, by substantially reducing costs and resource requirements for development of therapies to neglected tropical diseases.
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Affiliation(s)
- Susana Vázquez Torres
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Melisa Benard Valle
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Stephen P. Mackessy
- Department of Biological Sciences, University of Northern Colorado, Greeley, CO, 80639, USA
| | - Stefanie K. Menzies
- Centre for Snakebite Research & Interventions, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
- Centre for Drugs & Diagnostics, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
- Biomedical & Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, United Kingdom LA1 4YG8
| | - Nicholas R. Casewell
- Centre for Snakebite Research & Interventions, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
- Centre for Drugs & Diagnostics, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Shirin Ahmadi
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Nick J. Burlet
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Edin Muratspahić
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Isaac Sappington
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Max D. Overath
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Esperanza Rivera-de-Torre
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jann Ledergerber
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Andreas H. Laustsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Kim Boddum
- Sophion Bioscience, DK-2750 Ballerup, Denmark
| | - Asim K. Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Evans Brackenbrough
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Iara A. Cardoso
- Centre for Snakebite Research & Interventions, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Edouard P. Crittenden
- Centre for Snakebite Research & Interventions, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Rebecca J. Edge
- Department of Infection Biology and Microbiomes, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, L3 5RF, United Kingdom
| | - Justin Decarreau
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Robert J. Ragotte
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Arvind S. Pillai
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Mohamad Abedi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Hannah L. Han
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Stacey R. Gerben
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Analisa Murray
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Rebecca Skotheim
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lynda Stuart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lance Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Thomas J. A. Fryer
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
- Media Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA
| | - Timothy P. Jenkins
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105,USA
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31
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Yang W, Hicks DR, Ghosh A, Schwartze TA, Conventry B, Goreshnik I, Allen A, Halabiya SF, Kim CJ, Hinck CS, Lee DS, Bera AK, Li Z, Wang Y, Schlichthaerle T, Cao L, Huang B, Garrett S, Gerben SR, Rettie S, Heine P, Murray A, Edman N, Carter L, Stewart L, Almo S, Hinck AP, Baker D. Design of High Affinity Binders to Convex Protein Target Sites. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.592114. [PMID: 38746206 PMCID: PMC11092582 DOI: 10.1101/2024.05.01.592114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
While there has been progress in the de novo design of small globular miniproteins (50-65 residues) to bind to primarily concave regions of a target protein surface, computational design of minibinders to convex binding sites remains an outstanding challenge due to low level of overall shape complementarity. Here, we describe a general approach to generate computationally designed proteins which bind to convex target sites that employ geometrically matching concave scaffolds. We used this approach to design proteins binding to TGFβRII, CTLA-4 and PD-L1 which following experimental optimization have low nanomolar to picomolar affinities and potent biological activity. Co-crystal structures of the TGFβRII and CTLA-4 binders in complex with the receptors are in close agreement with the design models. Our approach provides a general route to generating very high affinity binders to convex protein target sites.
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Affiliation(s)
- Wei Yang
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Derrick R Hicks
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Agnidipta Ghosh
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York 10461, USA
| | - Tristin A Schwartze
- Department of Structural Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Brian Conventry
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Inna Goreshnik
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Aza Allen
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Samer F Halabiya
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Chan Johng Kim
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Cynthia S Hinck
- Department of Structural Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - David S Lee
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Asim K Bera
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Zhe Li
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Yujia Wang
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Thomas Schlichthaerle
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Longxing Cao
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Buwei Huang
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Sarah Garrett
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York 10461, USA
| | - Stacey R Gerben
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Stephen Rettie
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Piper Heine
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Analisa Murray
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Natasha Edman
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Lauren Carter
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Lance Stewart
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Steve Almo
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York 10461, USA
| | - Andrew P Hinck
- Department of Structural Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
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32
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Krishna R, Wang J, Ahern W, Sturmfels P, Venkatesh P, Kalvet I, Lee GR, Morey-Burrows FS, Anishchenko I, Humphreys IR, McHugh R, Vafeados D, Li X, Sutherland GA, Hitchcock A, Hunter CN, Kang A, Brackenbrough E, Bera AK, Baek M, DiMaio F, Baker D. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 2024; 384:eadl2528. [PMID: 38452047 DOI: 10.1126/science.adl2528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/27/2024] [Indexed: 03/09/2024]
Abstract
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.
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Affiliation(s)
- Rohith Krishna
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Jue Wang
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Woody Ahern
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA
| | - Pascal Sturmfels
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA
| | - Preetham Venkatesh
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Indrek Kalvet
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
| | | | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Ian R Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Ryan McHugh
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Dionne Vafeados
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Xinting Li
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | | | - Andrew Hitchcock
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - C Neil Hunter
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Alex Kang
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Evans Brackenbrough
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Asim K Bera
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Minkyung Baek
- School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
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33
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Saikia B, Baruah A. In silico design of misfolding resistant proteins: the role of structural similarity of a competing conformational ensemble in the optimization of frustration. SOFT MATTER 2024; 20:3283-3298. [PMID: 38529658 DOI: 10.1039/d4sm00171k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Most state-of-the-art in silico design methods fail due to misfolding of designed sequences to a conformation other than the target. Thus, a method to design misfolding resistant proteins will provide a better understanding of the misfolding phenomenon and will also increase the success rate of in silico design methods. In this work, we optimize the conformational ensemble to be selected for negative design purposes based on the similarity of the conformational ensemble to the target. Five ensembles with different degrees of similarity to the target are created and destabilized and the target is stabilized while designing sequences using mean field theory and Monte Carlo simulation methods. The results suggest that the degree of similarity of the non-native conformations to the target plays a prominent role in designing misfolding resistant protein sequences. The design procedures that destabilize the conformational ensemble with moderate similarity to the target have proven to be more promising. Incorporation of either highly similar or highly dissimilar conformations to the target conformation into the non-native ensemble to be destabilized may lead to sequences with a higher misfolding propensity. This will significantly reduce the conformational space to be considered in any protein design procedure. Interestingly, the results suggest that a sequence with higher frustration in the target structure does not necessarily lead to a misfold prone sequence. A successful design method may purposefully choose a frustrated sequence in the target conformation if that sequence is even more frustrated in the competing non-native conformations.
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Affiliation(s)
- Bondeepa Saikia
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India.
| | - Anupaul Baruah
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India.
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34
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Nikolaev A, Kuzmin A, Markeeva E, Kuznetsova E, Ryzhykau YL, Semenov O, Anuchina A, Remeeva A, Gushchin I. Reengineering of a flavin-binding fluorescent protein using ProteinMPNN. Protein Sci 2024; 33:e4958. [PMID: 38501498 PMCID: PMC10949330 DOI: 10.1002/pro.4958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/12/2024] [Accepted: 02/18/2024] [Indexed: 03/20/2024]
Abstract
Recent advances in machine learning techniques have led to development of a number of protein design and engineering approaches. One of them, ProteinMPNN, predicts an amino acid sequence that would fold and match user-defined backbone structure. Its performance was previously tested for proteins composed of standard amino acids, as well as for peptide- and protein-binding proteins. In this short report, we test whether ProteinMPNN can be used to reengineer a non-proteinaceous ligand-binding protein, flavin-based fluorescent protein CagFbFP. We fixed the native backbone conformation and the identity of 20 amino acids interacting with the chromophore (flavin mononucleotide, FMN) while letting ProteinMPNN predict the rest of the sequence. The software package suggested replacing 36-48 out of the remaining 86 amino acids so that the resulting sequences are 55%-66% identical to the original one. The three designs that we tested experimentally displayed different expression levels, yet all were able to bind FMN and displayed fluorescence, thermal stability, and other properties similar to those of CagFbFP. Our results demonstrate that ProteinMPNN can be used to generate diverging unnatural variants of fluorescent proteins, and, more generally, to reengineer proteins without losing their ligand-binding capabilities.
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Affiliation(s)
- Andrey Nikolaev
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Alexander Kuzmin
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Elena Markeeva
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Elizaveta Kuznetsova
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Yury L. Ryzhykau
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
- Frank Laboratory of Neutron PhysicsJoint Institute for Nuclear ResearchDubnaRussia
| | - Oleg Semenov
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Arina Anuchina
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Alina Remeeva
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Ivan Gushchin
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
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35
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Zhang J, Durham J, Qian Cong. Revolutionizing protein-protein interaction prediction with deep learning. Curr Opin Struct Biol 2024; 85:102775. [PMID: 38330793 DOI: 10.1016/j.sbi.2024.102775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/31/2023] [Accepted: 01/05/2024] [Indexed: 02/10/2024]
Abstract
Protein-protein interactions (PPIs) are pivotal for driving diverse biological processes, and any disturbance in these interactions can lead to disease. Thus, the study of PPIs has been a central focus in biology. Recent developments in deep learning methods, coupled with the vast genomic sequence data, have significantly boosted the accuracy of predicting protein structures and modeling protein complexes, approaching levels comparable to experimental techniques. Herein, we review the latest advances in the computational methods for modeling 3D protein complexes and the prediction of protein interaction partners, emphasizing the application of deep learning methods deriving from coevolution analysis. The review also highlights biomedical applications of PPI prediction and outlines challenges in the field.
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Affiliation(s)
- Jing Zhang
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; HaroldC.Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA. https://twitter.com/jzhang_genome
| | - Jesse Durham
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; HaroldC.Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qian Cong
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; HaroldC.Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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36
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Zhang C, Zhang C, Shang T, Zhu N, Wu X, Duan H. HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints. Brief Bioinform 2024; 25:bbae215. [PMID: 38706323 PMCID: PMC11070728 DOI: 10.1093/bib/bbae215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 04/12/2024] [Accepted: 04/18/2024] [Indexed: 05/07/2024] Open
Abstract
In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.
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Affiliation(s)
- Chenhao Zhang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Chengyun Zhang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, China
- AI department, Shanghai Highslab Therapeutics. Inc, Shanghai, 201203, China
| | - Tianfeng Shang
- AI department, Shanghai Highslab Therapeutics. Inc, Shanghai, 201203, China
| | - Ning Zhu
- China Pharmaceutical University, Nanjing, Jiangsu, 211198, China
| | - Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao, 999078, China
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37
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de Haas RJ, Brunette N, Goodson A, Dauparas J, Yi SY, Yang EC, Dowling Q, Nguyen H, Kang A, Bera AK, Sankaran B, de Vries R, Baker D, King NP. Rapid and automated design of two-component protein nanomaterials using ProteinMPNN. Proc Natl Acad Sci U S A 2024; 121:e2314646121. [PMID: 38502697 PMCID: PMC10990136 DOI: 10.1073/pnas.2314646121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/20/2024] [Indexed: 03/21/2024] Open
Abstract
The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.
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Affiliation(s)
- Robbert J. de Haas
- Department of Physical Chemistry and Soft Matter, Wageningen University and Research, Wageningen6078 WE, The Netherlands
| | - Natalie Brunette
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Alex Goodson
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Sue Y. Yi
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Erin C. Yang
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Quinton Dowling
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Hannah Nguyen
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Asim K. Bera
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
| | - Banumathi Sankaran
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA94720
| | - Renko de Vries
- Department of Physical Chemistry and Soft Matter, Wageningen University and Research, Wageningen6078 WE, The Netherlands
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
- HHMI, Seattle, WA98195
| | - Neil P. King
- Department of Biochemistry, University of Washington, Seattle, WA98195
- Institute for Protein Design, University of Washington, Seattle, WA98195
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38
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van Aalen EA, Lurvink JJJ, Vermeulen L, van Gerven B, Ni Y, Arts R, Merkx M. Turning Antibodies into Ratiometric Bioluminescent Sensors for Competition-Based Homogeneous Immunoassays. ACS Sens 2024; 9:1401-1409. [PMID: 38380622 PMCID: PMC10964239 DOI: 10.1021/acssensors.3c02478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/02/2024] [Accepted: 02/08/2024] [Indexed: 02/22/2024]
Abstract
Here we present LUCOS (Luminescent Competition Sensor), a modular and broadly applicable bioluminescent diagnostic platform enabling the detection of both small molecules and protein biomarkers. The construction of LUCOS sensors entails the covalent and site-specific coupling of a bioluminescent sensor component to an analyte-specific antibody via protein G-mediated photoconjugation. Target detection is accomplished through intramolecular competition with a tethered analyte competitor for antibody binding. We established two variants of LUCOS: an inherent ratiometric LUCOSR variant and an intensiometric LUCOSI version, which can be used for ratiometric detection upon the addition of a split calibrator luciferase. To demonstrate the versatility of the LUCOS platform, sensors were developed for the detection of the small molecule cortisol and the protein biomarker NT-proBNP. Sensors for both targets displayed analyte-dependent changes in the emission ratio and enabled detection in the micromolar concentration range (KD,app = 16-92 μM). Furthermore, we showed that the response range of the LUCOS sensor can be adjusted by attenuating the affinity of the tethered NT-proBNP competitor, which enabled detection in the nanomolar concentration range (KD,app = 317 ± 26 nM). Overall, the LUCOS platform offers a highly versatile and easy method to convert commercially available monoclonal antibodies into bioluminescent biosensors that provide a homogeneous alternative for the competitive immunoassay.
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Affiliation(s)
- Eva A. van Aalen
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Joep J. J. Lurvink
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Leandra Vermeulen
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Benice van Gerven
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Yan Ni
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Remco Arts
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
| | - Maarten Merkx
- Laboratory
of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, Eindhoven 5600 MB, The
Netherlands
- Institute
for Complex Molecular Systems, Eindhoven
University of Technology, P.O. Box 513, Eindhoven 5600 MB, The Netherlands
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39
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Hansen AL, Theisen FF, Crehuet R, Marcos E, Aghajari N, Willemoës M. Carving out a Glycoside Hydrolase Active Site for Incorporation into a New Protein Scaffold Using Deep Network Hallucination. ACS Synth Biol 2024; 13:862-875. [PMID: 38357862 PMCID: PMC10949244 DOI: 10.1021/acssynbio.3c00674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/16/2024] [Accepted: 01/23/2024] [Indexed: 02/16/2024]
Abstract
Enzymes are indispensable biocatalysts for numerous industrial applications, yet stability, selectivity, and restricted substrate recognition present limitations for their use. Despite the importance of enzyme engineering in overcoming these limitations, success is often challenged by the intricate architecture of enzymes derived from natural sources. Recent advances in computational methods have enabled the de novo design of simplified scaffolds with specific functional sites. Such scaffolds may be advantageous as platforms for enzyme engineering. Here, we present a strategy for the de novo design of a simplified scaffold of an endo-α-N-acetylgalactosaminidase active site, a glycoside hydrolase from the GH101 enzyme family. Using a combination of trRosetta hallucination, iterative cycles of deep-learning-based structure prediction, and ProteinMPNN sequence design, we designed proteins with 290 amino acids incorporating the active site while reducing the molecular weight by over 100 kDa compared to the initial endo-α-N-acetylgalactosaminidase. Of 11 tested designs, six were expressed as soluble monomers, displaying similar or increased thermostabilities compared to the natural enzyme. Despite lacking detectable enzymatic activity, the experimentally determined crystal structures of a representative design closely matched the design with a root-mean-square deviation of 1.0 Å, with most catalytically important side chains within 2.0 Å. The results highlight the potential of scaffold hallucination in designing proteins that may serve as a foundation for subsequent enzyme engineering.
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Affiliation(s)
- Anders Lønstrup Hansen
- The
Linderstrøm-Lang Centre for Protein Science, Section for Biomolecular
Sciences, Department of Biology, University
of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark
| | - Frederik Friis Theisen
- The
Linderstrøm-Lang Centre for Protein Science, Section for Biomolecular
Sciences, Department of Biology, University
of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark
| | - Ramon Crehuet
- Institute
for Advanced Chemistry of Catalonia (IQAC), CSIC, Carrer Jordi Girona 18-26, 08034 Barcelona, Spain
| | - Enrique Marcos
- Protein
Design and Modeling Lab, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB), CSIC, Baldiri Reixac 10, 08028 Barcelona, Spain
| | - Nushin Aghajari
- Molecular
Microbiology and Structural Biochemistry, CNRS, University of Lyon1, UMR5086, 7 Passage du Vercors, F-69367 Lyon CEDEX 07, France
| | - Martin Willemoës
- The
Linderstrøm-Lang Centre for Protein Science, Section for Biomolecular
Sciences, Department of Biology, University
of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark
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40
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Wu X, Lin H, Bai R, Duan H. Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design. Eur J Med Chem 2024; 268:116262. [PMID: 38387334 DOI: 10.1016/j.ejmech.2024.116262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/06/2024] [Accepted: 02/17/2024] [Indexed: 02/24/2024]
Abstract
Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.
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Affiliation(s)
- Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Huitian Lin
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Renren Bai
- School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, PR China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, PR China.
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41
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Tu G, Fu T, Zheng G, Xu B, Gou R, Luo D, Wang P, Xue W. Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives. J Chem Inf Model 2024; 64:1433-1455. [PMID: 38294194 DOI: 10.1021/acs.jcim.3c01736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.
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Affiliation(s)
- Gao Tu
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Tingting Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | | | - Binbin Xu
- Chengdu Sintanovo Biotechnology Co., Ltd., Chengdu 610200, China
| | - Rongpei Gou
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Ding Luo
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Weiwei Xue
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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42
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Sulea T, Kumar S, Kuroda D. Editorial: Progress and challenges in computational structure-based design and development of biologic drugs. Front Mol Biosci 2024; 11:1360267. [PMID: 38389897 PMCID: PMC10883042 DOI: 10.3389/fmolb.2024.1360267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/01/2024] [Indexed: 02/24/2024] Open
Affiliation(s)
- Traian Sulea
- Human Health Therapeutics Research Centre, National Research Council Canada, Montreal, QC, Canada
| | - Sandeep Kumar
- Computational Protein Design and Modeling, Computational Science, Moderna Therapeutics, Cambridge, MA, United States
| | - Daisuke Kuroda
- Research Center of Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo, Japan
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43
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Notin P, Rollins N, Gal Y, Sander C, Marks D. Machine learning for functional protein design. Nat Biotechnol 2024; 42:216-228. [PMID: 38361074 DOI: 10.1038/s41587-024-02127-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
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Affiliation(s)
- Pascal Notin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Computer Science, University of Oxford, Oxford, UK.
| | | | - Yarin Gal
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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44
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Dolorfino M, Samanta R, Vorobieva A. ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.16.575764. [PMID: 38352434 PMCID: PMC10862708 DOI: 10.1101/2024.01.16.575764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Recent deep-learning (DL) protein design methods have been successfully applied to a range of protein design problems, including the de novo design of novel folds, protein binders, and enzymes. However, DL methods have yet to meet the challenge of de novo membrane protein (MP) and the design of complex β-sheet folds. We performed a comprehensive benchmark of one DL protein sequence design method, ProteinMPNN, using transmembrane and water-soluble β-barrel folds as a model, and compared the performance of ProteinMPNN to the new membrane-specific Rosetta Franklin2023 energy function. We tested the effect of input backbone refinement on ProteinMPNN performance and found that given refined and well-defined inputs, ProteinMPNN more accurately captures global sequence properties despite complex folding biophysics. It generates more diverse TMB sequences than Franklin2023 in pore-facing positions. In addition, ProteinMPNN generated TMB sequences that passed state-of-the-art in silico filters for experimental validation, suggesting that the model could be used in de novo design tasks of diverse nanopores for single-molecule sensing and sequencing. Lastly, our results indicate that the low success rate of ProteinMPNN for the design of β-sheet proteins stems from backbone input accuracy rather than software limitations.
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Affiliation(s)
- Marissa Dolorfino
- Structural Biology Brussel, Vrije Universiteit Brussel, Brussels, Belgium
- VUB-VIB Center for Structural Biology, Brussels, Belgium
| | | | - Anastassia Vorobieva
- Structural Biology Brussel, Vrije Universiteit Brussel, Brussels, Belgium
- VUB-VIB Center for Structural Biology, Brussels, Belgium
- VIB Center for AI and Computational Biology, Belgium
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45
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Vázquez Torres S, Leung PJY, Venkatesh P, Lutz ID, Hink F, Huynh HH, Becker J, Yeh AHW, Juergens D, Bennett NR, Hoofnagle AN, Huang E, MacCoss MJ, Expòsit M, Lee GR, Bera AK, Kang A, De La Cruz J, Levine PM, Li X, Lamb M, Gerben SR, Murray A, Heine P, Korkmaz EN, Nivala J, Stewart L, Watson JL, Rogers JM, Baker D. De novo design of high-affinity binders of bioactive helical peptides. Nature 2024; 626:435-442. [PMID: 38109936 PMCID: PMC10849960 DOI: 10.1038/s41586-023-06953-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 12/07/2023] [Indexed: 12/20/2023]
Abstract
Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.
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Affiliation(s)
- Susana Vázquez Torres
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Philip J Y Leung
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Molecular Engineering, University of Washington, Seattle, WA, USA
| | - Preetham Venkatesh
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Isaac D Lutz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Fabian Hink
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Huu-Hien Huynh
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Jessica Becker
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Andy Hsien-Wei Yeh
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - David Juergens
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Molecular Engineering, University of Washington, Seattle, WA, USA
| | - Nathaniel R Bennett
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Molecular Engineering, University of Washington, Seattle, WA, USA
| | - Andrew N Hoofnagle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Eric Huang
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Marc Expòsit
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Graduate Program in Molecular Engineering, University of Washington, Seattle, WA, USA
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Asim K Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Alex Kang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Joshmyn De La Cruz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Paul M Levine
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Xinting Li
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Mila Lamb
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Stacey R Gerben
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Analisa Murray
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Piper Heine
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Elif Nihal Korkmaz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jeff Nivala
- School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA
| | - Lance Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Joseph L Watson
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
| | - Joseph M Rogers
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA.
- Institute for Protein Design, University of Washington, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
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46
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de Raffele D, Ilie IM. Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning. Chem Commun (Camb) 2024; 60:632-645. [PMID: 38131333 DOI: 10.1039/d3cc04630c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties.
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Affiliation(s)
- Daria de Raffele
- University of Amsterdam, van 't Hoff Institute for Molecular Sciences, Science Park 904, P.O. Box 94157, 1090 GD Amsterdam, The Netherlands.
- Amsterdam Center for Multiscale Modeling (ACMM), University of Amsterdam, P.O. Box 94157, 1090 GD Amsterdam, The Netherlands
| | - Ioana M Ilie
- University of Amsterdam, van 't Hoff Institute for Molecular Sciences, Science Park 904, P.O. Box 94157, 1090 GD Amsterdam, The Netherlands.
- Amsterdam Center for Multiscale Modeling (ACMM), University of Amsterdam, P.O. Box 94157, 1090 GD Amsterdam, The Netherlands
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47
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Tučs A, Ito T, Kurumida Y, Kawada S, Nakazawa H, Saito Y, Umetsu M, Tsuda K. Extensive antibody search with whole spectrum black-box optimization. Sci Rep 2024; 14:552. [PMID: 38177656 PMCID: PMC10767033 DOI: 10.1038/s41598-023-51095-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/30/2023] [Indexed: 01/06/2024] Open
Abstract
In designing functional biological sequences with machine learning, the activity predictor tends to be inaccurate due to shortage of data. Top ranked sequences are thus unlikely to contain effective ones. This paper proposes to take prediction stability into account to provide domain experts with a reasonable list of sequences to choose from. In our approach, multiple prediction models are trained by subsampling the training set and the multi-objective optimization problem, where one objective is the average activity and the other is the standard deviation, is solved. The Pareto front represents a list of sequences with the whole spectrum of activity and stability. Using this method, we designed VHH (Variable domain of Heavy chain of Heavy chain) antibodies based on the dataset obtained from deep mutational screening. To solve multi-objective optimization, we employed our sequence design software MOQA that uses quantum annealing. By applying several selection criteria to 19,778 designed sequences, five sequences were selected for wet-lab validation. One sequence, 16 mutations away from the closest training sequence, was successfully expressed and found to possess desired binding specificity. Our whole spectrum approach provides a balanced way of dealing with the prediction uncertainty, and can possibly be applied to extensive search of functional sequences.
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Affiliation(s)
- Andrejs Tučs
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Tomoyuki Ito
- Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Yoichi Kurumida
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
- Department of Data Science, School of Frontier Engineering, Kitasato University, Sagamihara, Japan
| | - Sakiya Kawada
- Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Hikaru Nakazawa
- Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Yutaka Saito
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
- RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo, 103-0027, Japan
- AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), Tokyo, Japan
- Department of Data Science, School of Frontier Engineering, Kitasato University, Sagamihara, Japan
| | - Mitsuo Umetsu
- Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan.
- RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo, 103-0027, Japan.
| | - Koji Tsuda
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
- RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo, 103-0027, Japan.
- Center for Basic Research on Materials, National Institute for Materials Science (NIMS), Tsukuba, Japan.
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48
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Chen J, Gu Z, Lai L, Pei J. In silico protein function prediction: the rise of machine learning-based approaches. MEDICAL REVIEW (2021) 2023; 3:487-510. [PMID: 38282798 PMCID: PMC10808870 DOI: 10.1515/mr-2023-0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/11/2023] [Indexed: 01/30/2024]
Abstract
Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.
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Affiliation(s)
- Jiaxiao Chen
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Zhonghui Gu
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
- Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, China
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49
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Khakzad H, Igashov I, Schneuing A, Goverde C, Bronstein M, Correia B. A new age in protein design empowered by deep learning. Cell Syst 2023; 14:925-939. [PMID: 37972559 DOI: 10.1016/j.cels.2023.10.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/22/2023] [Accepted: 10/11/2023] [Indexed: 11/19/2023]
Abstract
The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins.
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Affiliation(s)
- Hamed Khakzad
- Université de Lorraine, CNRS, Inria, LORIA, 54000 Nancy, France; École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Ilia Igashov
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Arne Schneuing
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Casper Goverde
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | | | - Bruno Correia
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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Lee GR, Pellock SJ, Norn C, Tischer D, Dauparas J, Anischenko I, Mercer JAM, Kang A, Bera A, Nguyen H, Goreshnik I, Vafeados D, Roullier N, Han HL, Coventry B, Haddox HK, Liu DR, Yeh AHW, Baker D. Small-molecule binding and sensing with a designed protein family. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.565201. [PMID: 37961294 PMCID: PMC10635051 DOI: 10.1101/2023.11.01.565201] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Despite transformative advances in protein design with deep learning, the design of small-molecule-binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling the de novo design of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications.
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