1
|
Mi Y, Marcu SB, Tabirca S, Yallapragada VV. PS-GO parametric protein search engine. Comput Struct Biotechnol J 2024; 23:1499-1509. [PMID: 38633387 PMCID: PMC11021831 DOI: 10.1016/j.csbj.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/01/2024] [Accepted: 04/01/2024] [Indexed: 04/19/2024] Open
Abstract
With the explosive growth of protein-related data, we are confronted with a critical scientific inquiry: How can we effectively retrieve, compare, and profoundly comprehend these protein structures to maximize the utilization of such data resources? PS-GO, a parametric protein search engine, has been specifically designed and developed to maximize the utilization of the rapidly growing volume of protein-related data. This innovative tool addresses the critical need for effective retrieval, comparison, and deep understanding of protein structures. By integrating computational biology, bioinformatics, and data science, PS-GO is capable of managing large-scale data and accurately predicting and comparing protein structures and functions. The engine is built upon the concept of parametric protein design, a computer-aided method that adjusts and optimizes protein structures and sequences to achieve desired biological functions and structural stability. PS-GO utilizes key parameters such as amino acid sequence, side chain angle, and solvent accessibility, which have a significant influence on protein structure and function. Additionally, PS-GO leverages computable parameters, derived computationally, which are crucial for understanding and predicting protein behavior. The development of PS-GO underscores the potential of parametric protein design in a variety of applications, including enhancing enzyme activity, improving antibody affinity, and designing novel functional proteins. This advancement not only provides a robust theoretical foundation for the field of protein engineering and biotechnology but also offers practical guidelines for future progress in this domain.
Collapse
Affiliation(s)
- Yanlin Mi
- School of Computer Science and Information Technology, University College Cork, Cork, Ireland
- SFI Centre for Research Training in Artificial Intelligence, University College Cork, Cork, Ireland
| | - Stefan-Bogdan Marcu
- School of Computer Science and Information Technology, University College Cork, Cork, Ireland
| | - Sabin Tabirca
- School of Computer Science and Information Technology, University College Cork, Cork, Ireland
- Faculty of Mathematics and Informatics, Transylvania University of Brasov, Brasov, Romania
| | - Venkata V.B. Yallapragada
- Centre for Advanced Photonics and Process Analytics, Munster Technological University, Cork, Ireland
| |
Collapse
|
2
|
Hermosilla AM, Berner C, Ovchinnikov S, Vorobieva AA. Validation of de novo designed water-soluble and transmembrane β-barrels by in silico folding and melting. Protein Sci 2024; 33:e5033. [PMID: 38864690 PMCID: PMC11168064 DOI: 10.1002/pro.5033] [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: 11/21/2023] [Revised: 04/14/2024] [Accepted: 05/08/2024] [Indexed: 06/13/2024]
Abstract
In silico validation of de novo designed proteins with deep learning (DL)-based structure prediction algorithms has become mainstream. However, formal evidence of the relationship between a high-quality predicted model and the chance of experimental success is lacking. We used experimentally characterized de novo water-soluble and transmembrane β-barrel designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high-quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction ("in silico melting"), which can reveal differences in the presence of favorable contacts between designs. This study provides a new insight on DL-based structure prediction models explainability and on how they could be leveraged for the design of increasingly complex proteins; in particular membrane proteins which have historically lacked basic in silico validation tools.
Collapse
Affiliation(s)
- Alvaro Martin Hermosilla
- Structural Biology BrusselsVrije Universiteit BrusselBrusselsBelgium
- VIB‐VUB Center for Structural BiologyBrusselsBelgium
| | - Carolin Berner
- Structural Biology BrusselsVrije Universiteit BrusselBrusselsBelgium
- VIB‐VUB Center for Structural BiologyBrusselsBelgium
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship ProgramHarvard UniversityCambridgeMassachusettsUSA
- Present address:
Department of BiologyMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Anastassia A. Vorobieva
- Structural Biology BrusselsVrije Universiteit BrusselBrusselsBelgium
- VIB‐VUB Center for Structural BiologyBrusselsBelgium
- VIB Center for AI and Computational BiologyBelgium
| |
Collapse
|
3
|
Yang H, Wu X, Sun C, Wang L. Unraveling the metabolic potential of biocontrol fungi through omics data: a key to enhancing large-scaleapplication strategies. Acta Biochim Biophys Sin (Shanghai) 2024; 56:825-832. [PMID: 38686460 DOI: 10.3724/abbs.2024056] [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] [Indexed: 05/02/2024] Open
Abstract
Biological control of pests and pathogens has attracted much attention due to its green, safe and effective characteristics. However, it faces the dilemma of insignificant effects in large-scale applications. Therefore, an in-depth exploration of the metabolic potential of biocontrol fungi based on big omics data is crucial for a comprehensive and systematic understanding of the specific modes of action operated by various biocontrol fungi. This article analyzes the preferences for extracellular carbon and nitrogen source degradation, secondary metabolites (nonribosomal peptides, polyketide synthases) and their product characteristics and the conversion relationship between extracellular primary metabolism and intracellular secondary metabolism for eight different filamentous fungi with characteristics appropriate for the biological control of bacterial pathogens and phytopathogenic nematodes. Further clarification is provided that Paecilomyces lilacinus, encoding a large number of hydrolase enzymes capable of degrading pathogen protection barrier, can be directly applied in the field as a predatory biocontrol fungus, whereas Trichoderma, as an antibiosis-active biocontrol control fungus, can form dominant strains on preferred substrates and produce a large number of secondary metabolites to achieve antibacterial effects. By clarifying the levels of biological control achievable by different biocontrol fungi, we provide a theoretical foundation for their application to cropping habitats.
Collapse
|
4
|
Sawada R, Sakajiri Y, Shibata T, Yamanishi Y. Predicting therapeutic and side effects from drug binding affinities to human proteome structures. iScience 2024; 27:110032. [PMID: 38868195 PMCID: PMC11167438 DOI: 10.1016/j.isci.2024.110032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 04/08/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024] Open
Abstract
Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.
Collapse
Affiliation(s)
- Ryusuke Sawada
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Department of Pharmacology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Yuko Sakajiri
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Japan
| | - Tomokazu Shibata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Japan
| |
Collapse
|
5
|
Cheng Z, Bi H, Liu S, Chen J, Misquitta AJ, Yu K. Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters. J Chem Theory Comput 2024. [PMID: 38888427 DOI: 10.1021/acs.jctc.4c00337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This study introduces a comprehensive asymptotic parameter database encompassing atomic multipole moments, polarizabilities, and dispersion coefficients. Leveraging active learning, our database comprehensively represents protein fragments with up to 8 heavy atoms, capturing their conformational diversity with merely 78,000 data points. Additionally, the E(3) neural network (E3NN) is employed to predict the asymptotic parameters directly from the local geometry. The E3NN models demonstrate exceptional accuracy and transferability across all asymptotic parameters, achieving an R2 of 0.999 for both protein fragments and 20 amino acid dipeptide test sets. The long-range electrostatic and dispersion energies can be obtained using the E3NN-predicted parameters, with an error of 0.07 and 0.02 kcal/mol, respectively, when compared to symmetry-adapted perturbation theory (SAPT). Therefore, our force fields demonstrate the capability to accurately describe long-range interactions in proteins, paving the way for next-generation protein force fields.
Collapse
Affiliation(s)
- Zheng Cheng
- School of Mathematical Sciences, Peking University, Beijing 100871, China
- AI for Science Institute, Beijing 100084, P. R. China
| | - Hangrui Bi
- School of Mathematical Sciences, Peking University, Beijing 100871, China
- DP Technology, Beijing 100080, P. R. China
| | - Siyuan Liu
- DP Technology, Beijing 100080, P. R. China
| | - Junmin Chen
- Tsinghua-Berkeley Shenzhen Institute, Shenzhen 518055, Guangdong, P. R. China
- Tsinghua Shenzhen International Graduate School, Shenzhen 518055, Guangdong, P. R. China
| | - Alston J Misquitta
- School of Physics and Astronomy, Queen Mary, University of London, London E1 4NS, U.K
| | - Kuang Yu
- Tsinghua-Berkeley Shenzhen Institute, Shenzhen 518055, Guangdong, P. R. China
- Tsinghua Shenzhen International Graduate School, Shenzhen 518055, Guangdong, P. R. China
| |
Collapse
|
6
|
Krishnan R S, Firzan Ca N, Mahendran KR. Functionally Active Synthetic α-Helical Pores. Acc Chem Res 2024. [PMID: 38875523 DOI: 10.1021/acs.accounts.4c00101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
ConspectusTransmembrane pores are currently at the forefront of nanobiotechnology, nanopore chemistry, and synthetic chemical biology research. Over the past few decades, significant studies in protein engineering have paved the way for redesigning membrane protein pores tailored for specific applications in nanobiotechnology. Most previous efforts predominantly centered on natural β-barrel pores designed with atomic precision for nucleic acid sequencing and sensing of biomacromolecules, including protein fragments. The requirement for a more efficient single-molecule detection system has driven the development of synthetic nanopores. For example, engineering channels to conduct ions and biomolecules selectively could lead to sophisticated nanopore sensors. Also, there has been an increased interest in synthetic pores, which can be fabricated to provide more control in designing architecture and diameter for single-molecule sensing of complex biomacromolecules. There have been impressive advancements in developing synthetic DNA-based pores, although their application in nanopore technology is limited. This has prompted a significant shift toward building synthetic transmembrane α-helical pores, a relatively underexplored field offering novel opportunities. Recently, computational tools have been employed to design and construct α-helical barrels of defined structure and functionality.We focus on building synthetic α-helical pores using naturally occurring transmembrane motifs of membrane protein pores. Our laboratory has developed synthetic α-helical transmembrane pores based on the natural porin PorACj (Porin A derived from Corynebacterium jeikeium) that function as nanopore sensors for single-molecule sensing of cationic cyclodextrins and polypeptides. Our breakthrough lies in being the first to create a functional and large stable synthetic transmembrane pore composed of short synthetic α-helical peptides. The key highlight of our work is that these pores can be synthesized using easy chemical synthesis, which permits its easy modification to include a variety of functional groups to build charge-selective sophisticated pores. Additionally, we have demonstrated that stable functional pores can be constructed from D-amino acid peptides. The analysis of pores composed of D- and L-amino acids in the presence of protease showed that only the D pores are highly functional and stable. The structural models of these pores revealed distinct surface charge conformation and geometry. These new classes of synthetic α-helical pores are highly original systems of general interest due to their unique architecture, functionality, and potential applications in nanopore technology and chemical biology. We emphasize that these simplified transmembrane pores have the potential to be components of functional nanodevices and therapeutic tools. We also suggest that such designed peptides might be valuable as antimicrobial agents and can be targeted to cancer cells. This article will focus on the evolutions in assembling α-helical transmembrane pores and highlight their advantages, including structural and functional versatility.
Collapse
Affiliation(s)
- Smrithi Krishnan R
- Transdisciplinary Research Program, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, India-695014
| | - Neilah Firzan Ca
- Transdisciplinary Research Program, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, India-695014
- Manipal Academy of Higher Education, Manipal, Karnataka India-576104
| | - Kozhinjampara R Mahendran
- Transdisciplinary Research Program, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, India-695014
| |
Collapse
|
7
|
Cross JA, Dawson WM, Shukla SR, Weijman JF, Mantell J, Dodding MP, Woolfson DN. A de novo designed coiled coil-based switch regulates the microtubule motor kinesin-1. Nat Chem Biol 2024:10.1038/s41589-024-01640-2. [PMID: 38849529 DOI: 10.1038/s41589-024-01640-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 05/09/2024] [Indexed: 06/09/2024]
Abstract
Many enzymes are allosterically regulated via conformational change; however, our ability to manipulate these structural changes and control function is limited. Here we install a conformational switch for allosteric activation into the kinesin-1 microtubule motor in vitro and in cells. Kinesin-1 is a heterotetramer that accesses open active and closed autoinhibited states. The equilibrium between these states centers on a flexible elbow within a complex coiled-coil architecture. We target the elbow to engineer a closed state that can be opened with a de novo designed peptide. The alternative states are modeled computationally and confirmed by biophysical measurements and electron microscopy. In cells, peptide-driven activation increases kinesin transport, demonstrating a primary role for conformational switching in regulating motor activity. The designs are enabled by our understanding of ubiquitous coiled-coil structures, opening possibilities for controlling other protein activities.
Collapse
Affiliation(s)
- Jessica A Cross
- School of Biochemistry, University of Bristol, Bristol, UK.
- School of Chemistry, University of Bristol, Bristol, UK.
| | | | - Shivam R Shukla
- School of Biochemistry, University of Bristol, Bristol, UK
- School of Chemistry, University of Bristol, Bristol, UK
| | | | - Judith Mantell
- School of Biochemistry, University of Bristol, Bristol, UK
| | - Mark P Dodding
- School of Biochemistry, University of Bristol, Bristol, UK.
- Bristol BioDesign Institute, University of Bristol, Bristol, UK.
| | - Derek N Woolfson
- School of Biochemistry, University of Bristol, Bristol, UK.
- School of Chemistry, University of Bristol, Bristol, UK.
- Bristol BioDesign Institute, University of Bristol, Bristol, UK.
| |
Collapse
|
8
|
Lu P, Tian J. ACDMBI: A deep learning model based on community division and multi-source biological information fusion predicts essential proteins. Comput Biol Chem 2024; 112:108115. [PMID: 38865861 DOI: 10.1016/j.compbiolchem.2024.108115] [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: 04/03/2024] [Revised: 05/15/2024] [Accepted: 05/28/2024] [Indexed: 06/14/2024]
Abstract
Accurately identifying essential proteins is vital for drug research and disease diagnosis. Traditional centrality methods and machine learning approaches often face challenges in accurately discerning essential proteins, primarily relying on information derived from protein-protein interaction (PPI) networks. Despite attempts by some researchers to integrate biological data and PPI networks for predicting essential proteins, designing effective integration methods remains a challenge. In response to these challenges, this paper presents the ACDMBI model, specifically designed to overcome the aforementioned issues. ACDMBI is comprised of two key modules: feature extraction and classification. In terms of capturing relevant information, we draw insights from three distinct data sources. Initially, structural features of proteins are extracted from the PPI network through community division. Subsequently, these features are further optimized using Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Moving forward, protein features are extracted from gene expression data utilizing Bidirectional Long Short-Term Memory networks (BiLSTM) and a multi-head self-attention mechanism. Finally, protein features are derived by mapping subcellular localization data to a one-dimensional vector and processing it through fully connected layers. In the classification phase, we integrate features extracted from three different data sources, crafting a multi-layer deep neural network (DNN) for protein classification prediction. Experimental results on brewing yeast data showcase the ACDMBI model's superior performance, with AUC reaching 0.9533 and AUPR reaching 0.9153. Ablation experiments further reveal that the effective integration of features from diverse biological information significantly boosts the model's performance.
Collapse
Affiliation(s)
- Pengli Lu
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China.
| | - Jialong Tian
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China.
| |
Collapse
|
9
|
Datta Darshan VM, Arumugam N, Almansour AI, Sivaramakrishnan V, Kanchi S. In silico energetic and molecular dynamic simulations studies demonstrate potential effect of the point mutations with implications for protein engineering in BDNF. Int J Biol Macromol 2024; 271:132247. [PMID: 38750847 DOI: 10.1016/j.ijbiomac.2024.132247] [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: 03/07/2024] [Revised: 05/01/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024]
Abstract
Protein engineering by directed evolution is time-consuming. Hence, in silico techniques like FoldX-Yasara for ∆∆G calculation, and SNPeffect for predicting propensity for aggregation, amyloid formation, and chaperone binding are employed to design proteins. Here, we used in silico techniques to engineer BDNF-NTF3 interaction and validated it using mutations with known functional implications for NGF dimer. The structures of three mutants representing a positive, negative, or neutral ∆∆G involving two interface residues in BDNF and two mutations representing a neutral and positive ∆∆G in NGF, which is aligned with BDNF, were selected for molecular dynamics (MD) simulation. Our MD results conclude that the secondary structure of individual protomers of the positive and negative mutants displayed a similar or different conformation from the NTF3 monomer, respectively. The positive mutants showed fewer hydrophobic interactions and higher hydrogen bonds compared to the wild-type, negative, and neutral mutants with similar SASA, suggesting solvent-mediated disruption of hydrogen-bonded interactions. Similar results were obtained for mutations with known functional implications for NGF and BDNF. The results suggest that mutations with known effects in homologous proteins could help in validation, and in silico directed evolution experiments could be a viable alternative to the experimental technique used for protein engineering.
Collapse
Affiliation(s)
- V M Datta Darshan
- Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Andhra Pradesh 515134, India
| | - Natarajan Arumugam
- Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Abdulrahman I Almansour
- Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Venketesh Sivaramakrishnan
- Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Andhra Pradesh 515134, India.
| | - Subbarao Kanchi
- Department of Physics, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Andhra Pradesh 515134, India.
| |
Collapse
|
10
|
Yurtsever A, Hirata K, Kojima R, Miyazawa K, Miyata K, Kesornsit S, Zareie H, Sun L, Maeda K, Sarikaya M, Fukuma T. Dynamics of Molecular Self-Assembly of Short Peptides at Liquid-Solid Interfaces - Effect of Charged Amino Acid Point Mutations. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2400653. [PMID: 38385848 DOI: 10.1002/smll.202400653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Indexed: 02/23/2024]
Abstract
Self-organizing solid-binding peptides on atomically flat solid surfaces offer a unique bio/nano hybrid platform, useful for understanding the basic nature of biology/solid coupling and their practical applications. The surface behavior of peptides is determined by their molecular folding, which is influenced by various factors and is challenging to study. Here, the effect of charged amino acids is studied on the self-assembly behavior of a directed evolution selected graphite-binding dodecapeptide on graphite surface. Two mutations, M6 and M8, are designed to introduce negatively and positively charged moieties, respectively, at the anchoring domain of the wild-type (WT) peptide, affecting both binding and assembly. The questions addressed here are whether mutant peptides exhibit molecular crystal formation and demonstrate molecular recognition on the solid surface based on the specific mutations. Frequency-modulated atomic force microscopy is used for observations of the surface processes dynamically in water at molecular resolution over several hours at the ambient. The results indicate that while the mutants display distinct folding and surface behavior, each homogeneously nucleates and forms 2D self-organized patterns, akin to the WT peptide. However, their growth dynamics, domain formation, and crystalline lattice structures differ significantly. The results represent a significant step toward the rational design of bio/solid interfaces, potent facilitators of a variety of future implementations.
Collapse
Affiliation(s)
- Ayhan Yurtsever
- Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
| | - Kaito Hirata
- Institute for Frontier Science and Initiative, Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
| | - Ryohei Kojima
- Division of Nano Life Science, Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
| | - Keisuke Miyazawa
- Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
| | - Kazuki Miyata
- Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
- Division of Nano Life Science, Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
| | - Sanhanut Kesornsit
- Graduate School of Frontier Science Initiative, Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
| | - Hadi Zareie
- Dentomimetix, Inc., Fluke Hall, University of Washington, Seattle, WA, 98195, USA
| | - Linhao Sun
- Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
| | - Katsuhiro Maeda
- Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
- Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
| | - Mehmet Sarikaya
- Dentomimetix, Inc., Fluke Hall, University of Washington, Seattle, WA, 98195, USA
| | - Takeshi Fukuma
- Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
| |
Collapse
|
11
|
Hoya M, Matsunaga R, Nagatoishi S, Ide T, Kuroda D, Tsumoto K. Impact of single-residue mutations on protein thermal stability: The case of threonine 83 of BC2L-CN lectin. Int J Biol Macromol 2024; 272:132682. [PMID: 38815947 DOI: 10.1016/j.ijbiomac.2024.132682] [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: 10/11/2023] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 06/01/2024]
Abstract
The thermal stability of trimeric lectin BC2L-CN was investigated and found to be considerably altered when mutating residue 83, originally a threonine, located at the fucose-binding loop. Mutants were analyzed using differential scanning calorimetry and isothermal microcalorimetry. Although most mutations decreased the affinity of the protein for oligosaccharide H type 1, six mutations increased the melting temperature (Tm) by >5 °C; one mutation, T83P, increased the Tm value by 18.2 °C(T83P, Tm = 96.3 °C). In molecular dynamic simulations, the investigated thermostable mutants, T83P, T83A, and T83S, had decreased fluctuations in the loop containing residue 83. In the T83S mutation, the side-chain hydroxyl group of serine formed a hydrogen bond with a nearby residue, suggesting that the restricted movement of the side-chain resulted in fewer fluctuations and enhanced thermal stability. Residue 83 is located at the interface and near the upstream end of the equivalent loop in a different protomer; therefore, fluctuations by this residue likely propagate throughout the loop. Our study of the dramatic change in thermal stability by a single amino acid mutation provides useful insights into the rational design of protein structures, especially the structures of oligomeric proteins.
Collapse
Affiliation(s)
- Megumi Hoya
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Sagami Chemical Research Institute, 2743-1 Hayakawa, Ayase, Kanagawa 252-1193, Japan
| | - Ryo Matsunaga
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Satoru Nagatoishi
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Project Division of Advanced Biopharmaceutical Science, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
| | - Teruhiko Ide
- Tosoh Corporation, Hayakawa, 2743-1 Ayase, Kanagawa 252-1123, Japan
| | - Daisuke Kuroda
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan
| | - Kouhei Tsumoto
- Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; Project Division of Advanced Biopharmaceutical Science, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
| |
Collapse
|
12
|
Salgado JCS, Alnoch RC, Polizeli MDLTDM, Ward RJ. Microenzymes: Is There Anybody Out There? Protein J 2024; 43:393-404. [PMID: 38507106 DOI: 10.1007/s10930-024-10193-1] [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] [Accepted: 03/08/2024] [Indexed: 03/22/2024]
Abstract
Biological macromolecules are found in different shapes and sizes. Among these, enzymes catalyze biochemical reactions and are essential in all organisms, but is there a limit size for them to function properly? Large enzymes such as catalases have hundreds of kDa and are formed by multiple subunits, whereas most enzymes are smaller, with molecular weights of 20-60 kDa. Enzymes smaller than 10 kDa could be called microenzymes and the present literature review brings together evidence of their occurrence in nature. Additionally, bioactive peptides could be a natural source for novel microenzymes hidden in larger peptides and molecular downsizing could be useful to engineer artificial enzymes with low molecular weight improving their stability and heterologous expression. An integrative approach is crucial to discover and determine the amino acid sequences of novel microenzymes, together with their genomic identification and their biochemical biological and evolutionary functions.
Collapse
Affiliation(s)
- Jose Carlos Santos Salgado
- Department of Chemistry, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), University of São Paulo, Ribeirão Preto, 14040-900, São Paulo, Brazil.
- Department of Biology, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), University of São Paulo, Ribeirão Preto, 14040-901, São Paulo, Brazil.
| | - Robson Carlos Alnoch
- Department of Biology, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), University of São Paulo, Ribeirão Preto, 14040-901, São Paulo, Brazil
- Department of Biochemistry and Immunology, Faculdade de Medicina de Ribeirão Preto (FMRP), University of São Paulo, Ribeirão Preto, 14049-900, São Paulo, Brazil
| | - Maria de Lourdes Teixeira de Moraes Polizeli
- Department of Biology, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), University of São Paulo, Ribeirão Preto, 14040-901, São Paulo, Brazil
- Department of Biochemistry and Immunology, Faculdade de Medicina de Ribeirão Preto (FMRP), University of São Paulo, Ribeirão Preto, 14049-900, São Paulo, Brazil
| | - Richard John Ward
- Department of Chemistry, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), University of São Paulo, Ribeirão Preto, 14040-900, São Paulo, Brazil
- Department of Biochemistry and Immunology, Faculdade de Medicina de Ribeirão Preto (FMRP), University of São Paulo, Ribeirão Preto, 14049-900, São Paulo, Brazil
| |
Collapse
|
13
|
da Silva LSA, Seman LO, Camponogara E, Mariani VC, Dos Santos Coelho L. Bilinear optimization of protein structure prediction: An exact approach via AB off-lattice model. Comput Biol Med 2024; 176:108558. [PMID: 38754216 DOI: 10.1016/j.compbiomed.2024.108558] [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: 02/27/2024] [Revised: 04/25/2024] [Accepted: 05/05/2024] [Indexed: 05/18/2024]
Abstract
Protein structure prediction (PSP) remains a central challenge in computational biology due to its inherent complexity and high dimensionality. While numerous heuristic approaches have appeared in the literature, their success varies. The AB off-lattice model, which characterizes proteins as sequences of A (hydrophobic) and B (hydrophilic) beads, presents a simplified perspective on PSP. This work presents a mathematical optimization-based methodology capitalizing on the off-lattice AB model. Dissecting the inherent non-linearities of the energy landscape of protein folding allowed for formulating the PSP as a bilinear optimization problem. This formulation was achieved by introducing auxiliary variables and constraints that encapsulate the nuanced relationship between the protein's conformational space and its energy landscape. The proposed bilinear model exhibited notable accuracy in pinpointing the global minimum energy conformations on a benchmark dataset presented by the Protein Data Bank (PDB). Compared to traditional heuristic-based methods, this bilinear approach yielded exact solutions, reducing the likelihood of local minima entrapment. This research highlights the potential of reframing the traditionally non-linear protein structure prediction problem into a bilinear optimization problem through the off-lattice AB model. Such a transformation offers a route toward methodologies that can determine the global solution, challenging current PSP paradigms. Exploration into hybrid models, merging bilinear optimization and heuristic components, might present an avenue for balancing accuracy with computational efficiency.
Collapse
Affiliation(s)
- Luiza Scapinello Aquino da Silva
- Electrical Engineering Graduate Program (PPGEE), Federal University of Parana (UFPR), Coronel Francisco Heraclito dos Santos, Curitiba, 81530-000, Paraná, Brazil.
| | - Laio Oriel Seman
- Department of Automation and Systems Engineering, Federal University of Santa Catarina (UFSC), Engenheiro Agronômico Andrei Cristian Ferreira, Florianópolis, 88040-900, Santa Catarina, Brazil
| | - Eduardo Camponogara
- Department of Automation and Systems Engineering, Federal University of Santa Catarina (UFSC), Engenheiro Agronômico Andrei Cristian Ferreira, Florianópolis, 88040-900, Santa Catarina, Brazil
| | - Viviana Cocco Mariani
- Electrical Engineering Graduate Program (PPGEE), Federal University of Parana (UFPR), Coronel Francisco Heraclito dos Santos, Curitiba, 81530-000, Paraná, Brazil; Mechanical Engineering Graduate Program (PGMec), Federal University of Parana (UFPR), Coronel Francisco Heraclito dos Santos, Curitiba, 81530-000, Paraná, Brazil
| | - Leandro Dos Santos Coelho
- Electrical Engineering Graduate Program (PPGEE), Federal University of Parana (UFPR), Coronel Francisco Heraclito dos Santos, Curitiba, 81530-000, Paraná, Brazil
| |
Collapse
|
14
|
Zhang J, Lin L, Wei W, Wei D. Identification, Characterization, and Computer-Aided Rational Design of a Novel Thermophilic Esterase from Geobacillus subterraneus, and Application in the Synthesis of Cinnamyl Acetate. Appl Biochem Biotechnol 2024; 196:3553-3575. [PMID: 37713064 DOI: 10.1007/s12010-023-04697-2] [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] [Accepted: 08/16/2023] [Indexed: 09/16/2023]
Abstract
Investigation of a novel thermophilic esterase gene from Geobacillus subterraneus DSMZ 13552 indicated a high amino acid sequence similarity of 25.9% to a reported esterase from Geobacillus sp. A strategy that integrated computer-aided rational design tools was developed to select mutation sites. Six mutants were selected from four criteria based on the simulated saturation mutation (including 19 amino acid residues) results. Of these, the mutants Q78Y and G119A were found to retain 87% and 27% activity after incubation at 70 °C for 20 min, compared with the 19% activity for the wild type. Subsequently, a double-point mutant (Q78Y/G119A) was obtained and identified with optimal temperature increase from 65 to 70 °C and a 41.51% decrease in Km. The obtained T1/2 values of 42.2 min (70 °C) and 16.9 min (75 °C) for Q78Y/G119A showed increases of 340% and 412% compared with that in the wild type. Q78Y/G119A was then employed as a biocatalyst to synthesize cinnamyl acetate, for which the conversion rate reached 99.40% with 0.3 M cinnamyl alcohol at 60 °C. The results validated the enhanced enzymatic properties of the mutant and indicated better prospects for industrial application as compared to that in the wild type. This study reported a method by which an enzyme could evolve to achieve enhanced thermostability, thereby increasing its potential for industrial applications, which could also be expanded to other esterases.
Collapse
Affiliation(s)
- Jin Zhang
- State Key Laboratory of Bioreactor Engineering, Newworld Institute of Biotechnology, East China University of Science and Technology, Shanghai, 200237, People's Republic of China
| | - Lin Lin
- School of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai, 201418, People's Republic of China
- Research Laboratory for Functional Nanomaterial, National Engineering Research Center for Nanotechnology, Shanghai, 200241, People's Republic of China
| | - Wei Wei
- State Key Laboratory of Bioreactor Engineering, Newworld Institute of Biotechnology, East China University of Science and Technology, Shanghai, 200237, People's Republic of China.
| | - Dongzhi Wei
- State Key Laboratory of Bioreactor Engineering, Newworld Institute of Biotechnology, East China University of Science and Technology, Shanghai, 200237, People's Republic of China
| |
Collapse
|
15
|
Winnifrith A, Outeiral C, Hie BL. Generative artificial intelligence for de novo protein design. Curr Opin Struct Biol 2024; 86:102794. [PMID: 38663170 DOI: 10.1016/j.sbi.2024.102794] [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: 12/21/2023] [Revised: 01/31/2024] [Accepted: 02/19/2024] [Indexed: 05/19/2024]
Abstract
Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the so-called 'de novo' design problem have recently been brought forward by developments in artificial intelligence. Generative architectures, such as language models and diffusion processes, seem adept at generating novel, yet realistic proteins that display desirable properties and perform specified functions. State-of-the-art design protocols now achieve experimental success rates nearing 20%, thus widening the access to de novo designed proteins. Despite extensive progress, there are clear field-wide challenges, for example, in determining the best in silico metrics to prioritise designs for experimental testing, and in designing proteins that can undergo large conformational changes or be regulated by post-translational modifications. With an increase in the number of models being developed, this review provides a framework to understand how these tools fit into the overall process of de novo protein design. Throughout, we highlight the power of incorporating biochemical knowledge to improve performance and interpretability.
Collapse
Affiliation(s)
- Adam Winnifrith
- Department of Biochemistry, University of Oxford, South Parks Rd, Oxford, OX1 3QU, United Kingdom; Evolvere Biosciences, Innovation Building, Old Road Campus, Oxford, OX3 7FZ, United Kingdom.
| | - Carlos Outeiral
- Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, United Kingdom.
| | - Brian L Hie
- Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, CA 94305, USA; Stanford Data Science, 475 Via Ortega, Stanford CA 94305, USA; Arc Institute, 3181 Porter Dr, Palo Alto, CA, USA.
| |
Collapse
|
16
|
Wang Y, Zhang Y, Su R, Wang Y, Qi W. Antimicrobial therapy based on self-assembling peptides. J Mater Chem B 2024; 12:5061-5075. [PMID: 38726712 DOI: 10.1039/d4tb00260a] [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: 05/30/2024]
Abstract
The emergence of drug-resistant microorganisms has threatened global health, and microbial infections have severely limited the use of medical materials. For example, the attachment and colonization of pathogenic bacteria to medical implant materials can lead to wound infections, inflammation and complications, as well as implant failure, shortening their lifespan and even resulting in patient death. In the era of antibiotic resistance, antimicrobial drug discovery needs to prioritize unconventional therapies that act on new targets or adopt new mechanisms. In this regard, supramolecular antimicrobial peptides have emerged as attractive therapeutic platforms, both as bactericides for combination antibiotics and as delivery vehicles. By taking advantage of their programmable intermolecular and intramolecular interactions, peptides can be modified to form higher-order structures (including nanofibers and nanoparticles) with unique functionality. This paper begins with an analysis of the relationship between peptide self-assembly and antimicrobial activity, describes in detail the research and development of various self-assembled antimicrobial peptides in recent years, and finally explores different combinatorial strategies for self-assembling antimicrobial peptides.
Collapse
Affiliation(s)
- Yuqi Wang
- Chemical Engineering Research Center, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China.
| | - Yexi Zhang
- Chemical Engineering Research Center, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China.
| | - Rongxin Su
- Chemical Engineering Research Center, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China.
- State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, P. R. China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, P. R. China
- Tianjin Key Laboratory of Membrane Science and Desalination Technology, Tianjin University, Tianjin 300072, P. R. China
| | - Yuefei Wang
- Chemical Engineering Research Center, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China.
- State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, P. R. China
- Tianjin Key Laboratory of Membrane Science and Desalination Technology, Tianjin University, Tianjin 300072, P. R. China
| | - Wei Qi
- Chemical Engineering Research Center, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, P. R. China.
- State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, P. R. China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, P. R. China
- Tianjin Key Laboratory of Membrane Science and Desalination Technology, Tianjin University, Tianjin 300072, P. R. China
| |
Collapse
|
17
|
Rawat S, Singh G, Prasad A. Investigating the Taenia solium Fatty Acid Binding Protein Superfamily for Their Immunological Outlook and Prospect for Therapeutic Targets. ACS OMEGA 2024; 9:22557-22572. [PMID: 38826528 PMCID: PMC11137695 DOI: 10.1021/acsomega.3c09253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/22/2024] [Accepted: 05/07/2024] [Indexed: 06/04/2024]
Abstract
Taenia solium, like other helminthic parasites, lacks key components of cellular machinery required for endogenous lipid biosynthesis. This deficiency compels the parasite to obtain all of its lipid requirements from its host. The passage of lipids across the cell membrane is tightly regulated. To facilitate effective lipid transport, the cestode parasite utilizes certain lipid binding proteins called FABPs. These FABPs bind with the lipid ligands and allow the transport of lipids across the membranes and into the cytosol. Here, by integrating a computational with homology protein prediction tools, we had identified five FABPs in the T. solium proteome. We confirmed their presence by RNA expression analysis of respective genes from the parasite's cysticerci transcript. During the molecular modeling and MD simulation studies, two of them, TsM_000544100 and TsM_001185100, were most stable. Furthermore, they had a robust interaction with the IgG1 molecule, as evidenced by MD simulation. In addition, by employing in silico screening, we had identified potential ligand interacting residues that are present on the probable druggable site. In combination with in vitro cysticidal assays, enalaprilat dihydrate showed efficacy against cysticerci, which suggests that FABPs play a significant role in the cysticercus life cycle. Together, we provided a detailed distribution of all FABPs expressed by T. solium cysticerci and the critical role of TsM_001185100 in cysticercus viability.
Collapse
Affiliation(s)
- Suraj
S. Rawat
- School
of Biosciences and Bioengineering, Indian
Institute of Technology Mandi, Mandi, Himachal Pradesh 175005, India
| | - Gagandeep Singh
- Dayanad
Medical College and Hospital, Ludhiana, Punjab 141001,India
| | - Amit Prasad
- School
of Biosciences and Bioengineering, Indian
Institute of Technology Mandi, Mandi, Himachal Pradesh 175005, India
- Indian
Knowledge System and Mental Health Centre, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh 175005, India
- Centre
for Human-Computer Interaction, Indian Institute
of Technology Mandi, Mandi, Himachal Pradesh 175005, India
| |
Collapse
|
18
|
Doga H, Raubenolt B, Cumbo F, Joshi J, DiFilippo FP, Qin J, Blankenberg D, Shehab O. A Perspective on Protein Structure Prediction Using Quantum Computers. J Chem Theory Comput 2024; 20:3359-3378. [PMID: 38703105 PMCID: PMC11099973 DOI: 10.1021/acs.jctc.4c00067] [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: 01/22/2024] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/06/2024]
Abstract
Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.
Collapse
Affiliation(s)
- Hakan Doga
- IBM Quantum,
Almaden Research Center, San Jose, California 95120, United States
| | - Bryan Raubenolt
- Center
for Computational Life Sciences, Lerner
Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States
| | - Fabio Cumbo
- Center
for Computational Life Sciences, Lerner
Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States
| | - Jayadev Joshi
- Center
for Computational Life Sciences, Lerner
Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States
| | - Frank P. DiFilippo
- Center
for Computational Life Sciences, Lerner
Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States
| | - Jun Qin
- Center
for Computational Life Sciences, Lerner
Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States
| | - Daniel Blankenberg
- Center
for Computational Life Sciences, Lerner
Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States
| | - Omar Shehab
- IBM
Quantum, IBM Thomas J Watson Research Center, Yorktown Heights, New York 10598, United States
| |
Collapse
|
19
|
Qian W, Ma N, Zeng X, Shi M, Wang M, Yang Z, Tsui SKW. Identification of novel single nucleotide variants in the drug resistance mechanism of Mycobacterium tuberculosis isolates by whole-genome analysis. BMC Genomics 2024; 25:478. [PMID: 38745294 PMCID: PMC11094924 DOI: 10.1186/s12864-024-10390-3] [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: 01/11/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Tuberculosis (TB) represents a major global health challenge. Drug resistance in Mycobacterium tuberculosis (MTB) poses a substantial obstacle to effective TB treatment. Identifying genomic mutations in MTB isolates holds promise for unraveling the underlying mechanisms of drug resistance in this bacterium. METHODS In this study, we investigated the roles of single nucleotide variants (SNVs) in MTB isolates resistant to four antibiotics (moxifloxacin, ofloxacin, amikacin, and capreomycin) through whole-genome analysis. We identified the drug-resistance-associated SNVs by comparing the genomes of MTB isolates with reference genomes using the MuMmer4 tool. RESULTS We observed a strikingly high proportion (94.2%) of MTB isolates resistant to ofloxacin, underscoring the current prevalence of drug resistance in MTB. An average of 3529 SNVs were detected in a single ofloxacin-resistant isolate, indicating a mutation rate of approximately 0.08% under the selective pressure of ofloxacin exposure. We identified a set of 60 SNVs associated with extensively drug-resistant tuberculosis (XDR-TB), among which 42 SNVs were non-synonymous mutations located in the coding regions of nine key genes (ctpI, desA3, mce1R, moeB1, ndhA, PE_PGRS4, PPE18, rpsA, secF). Protein structure modeling revealed that SNVs of three genes (PE_PGRS4, desA3, secF) are close to the critical catalytic active sites in the three-dimensional structure of the coding proteins. CONCLUSION This comprehensive study elucidates novel resistance mechanisms in MTB against antibiotics, paving the way for future design and development of anti-tuberculosis drugs.
Collapse
Affiliation(s)
- Weiye Qian
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Nan Ma
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Xi Zeng
- Agricultural Bioinformatics Key Laboratory of Hubei Province and 3D Genomics Research Centre, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Mai Shi
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Mingqiang Wang
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Zhiyuan Yang
- School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China.
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Stephen Kwok-Wing Tsui
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
- Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Hong Kong SAR, China.
| |
Collapse
|
20
|
Węgrzyn E, Mejdrová I, Müller FM, Nainytė M, Escobar L, Carell T. RNA-Templated Peptide Bond Formation Promotes L-Homochirality. Angew Chem Int Ed Engl 2024; 63:e202319235. [PMID: 38407532 DOI: 10.1002/anie.202319235] [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: 12/13/2023] [Revised: 02/19/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024]
Abstract
The world in which we live is homochiral. The ribose units that form the backbone of DNA and RNA are all D-configured and the encoded amino acids that comprise the proteins of all living species feature an all-L-configuration at the α-carbon atoms. The homochirality of α-amino acids is essential for folding of the peptides into well-defined and functional 3D structures and the homochirality of D-ribose is crucial for helix formation and base-pairing. The question of why nature uses only encoded L-α-amino acids is not understood. Herein, we show that an RNA-peptide world, in which peptides grow on RNAs constructed from D-ribose, leads to the self-selection of homo-L-peptides, which provides a possible explanation for the homo-D-ribose and homo-L-amino acid combination seen in nature.
Collapse
Affiliation(s)
- Ewa Węgrzyn
- Department of Chemistry, Institute for Chemical Epigenetics (ICE-M), Ludwig-Maximilians-Universität (LMU) München, Butenandtstrasse 5-13, 81377, Munich, Germany
| | - Ivana Mejdrová
- Department of Chemistry, Institute for Chemical Epigenetics (ICE-M), Ludwig-Maximilians-Universität (LMU) München, Butenandtstrasse 5-13, 81377, Munich, Germany
| | - Felix M Müller
- Department of Chemistry, Institute for Chemical Epigenetics (ICE-M), Ludwig-Maximilians-Universität (LMU) München, Butenandtstrasse 5-13, 81377, Munich, Germany
| | - Milda Nainytė
- Department of Chemistry, Institute for Chemical Epigenetics (ICE-M), Ludwig-Maximilians-Universität (LMU) München, Butenandtstrasse 5-13, 81377, Munich, Germany
| | - Luis Escobar
- Department of Chemistry, Institute for Chemical Epigenetics (ICE-M), Ludwig-Maximilians-Universität (LMU) München, Butenandtstrasse 5-13, 81377, Munich, Germany
| | - Thomas Carell
- Department of Chemistry, Institute for Chemical Epigenetics (ICE-M), Ludwig-Maximilians-Universität (LMU) München, Butenandtstrasse 5-13, 81377, Munich, Germany
| |
Collapse
|
21
|
Lee S, Kim G, Karin EL, Mirdita M, Park S, Chikhi R, Babaian A, Kryshtafovych A, Steinegger M. Petabase-Scale Homology Search for Structure Prediction. Cold Spring Harb Perspect Biol 2024; 16:a041465. [PMID: 38316555 PMCID: PMC11065157 DOI: 10.1101/cshperspect.a041465] [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: 02/07/2024]
Abstract
The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, we conducted a petabase-scale search of the Sequence Read Archive (SRA), resulting in gigabytes of aligned homologs for CASP15 targets. These were merged with default MSAs produced by ColabFold-search and provided to ColabFold-predict. By using SRA data, we achieved highly accurate predictions (GDT_TS > 70) for 66% of the non-easy targets, whereas using ColabFold-search default MSAs scored highly in only 52%. Next, we tested the effect of deep homology search and ColabFold's advanced features, such as more recycles, on prediction accuracy. While SRA homologs were most significant for improving ColabFold's CASP15 ranking from 11th to 3rd place, other strategies contributed too. We analyze these in the context of existing strategies to improve prediction.
Collapse
Affiliation(s)
- Sewon Lee
- School of Biological Sciences, Seoul National University, Gwanak-gu, Seoul 08826, South Korea
| | - Gyuri Kim
- School of Biological Sciences, Seoul National University, Gwanak-gu, Seoul 08826, South Korea
| | | | - Milot Mirdita
- School of Biological Sciences, Seoul National University, Gwanak-gu, Seoul 08826, South Korea
| | - Sukhwan Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, South Korea
| | - Rayan Chikhi
- Institut Pasteur, Université Paris Cité, G5 Sequence Bioinformatics, 75015 Paris, France
| | - Artem Babaian
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | | | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Gwanak-gu, Seoul 08826, South Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul 08826, South Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, South Korea
| |
Collapse
|
22
|
Zheng L, Shen J, Chen R, Hu Y, Zhao W, Leung ELH, Dai L. Genome engineering of the human gut microbiome. J Genet Genomics 2024; 51:479-491. [PMID: 38218395 DOI: 10.1016/j.jgg.2024.01.002] [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: 10/08/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 01/15/2024]
Abstract
The human gut microbiome, a complex ecosystem, significantly influences host health, impacting crucial aspects such as metabolism and immunity. To enhance our comprehension and control of the molecular mechanisms orchestrating the intricate interplay between gut commensal bacteria and human health, the exploration of genome engineering for gut microbes is a promising frontier. Nevertheless, the complexities and diversities inherent in the gut microbiome pose substantial challenges to the development of effective genome engineering tools for human gut microbes. In this comprehensive review, we provide an overview of the current progress and challenges in genome engineering of human gut commensal bacteria, whether executed in vitro or in situ. A specific focus is directed towards the advancements and prospects in cargo DNA delivery and high-throughput techniques. Additionally, we elucidate the immense potential of genome engineering methods to enhance our understanding of the human gut microbiome and engineer the microorganisms to enhance human health.
Collapse
Affiliation(s)
- Linggang Zheng
- Dr Neher's Biophysics Laboratory for Innovative Drug Discovery/State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China; CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Juntao Shen
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruiyue Chen
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yucan Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Zhao
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Elaine Lai-Han Leung
- Cancer Center, Faculty of Health Science, University of Macau, Macau 999078, China; MOE Frontiers Science Center for Precision Oncology, University of Macau, Macau 999078, China.
| | - Lei Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| |
Collapse
|
23
|
McMaster B, Thorpe C, Ogg G, Deane CM, Koohy H. Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity? Nat Methods 2024; 21:766-776. [PMID: 38654083 DOI: 10.1038/s41592-024-02240-7] [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: 08/23/2023] [Accepted: 03/08/2024] [Indexed: 04/25/2024]
Abstract
T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.
Collapse
Affiliation(s)
- Benjamin McMaster
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Christopher Thorpe
- Open Targets, Wellcome Genome Campus, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Graham Ogg
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | | | - Hashem Koohy
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Alan Turning Fellow in Health and Medicine, University of Oxford, Oxford, UK.
| |
Collapse
|
24
|
Nada H, Kim S, Lee K. PT-Finder: A multi-modal neural network approach to target identification. Comput Biol Med 2024; 174:108444. [PMID: 38636325 DOI: 10.1016/j.compbiomed.2024.108444] [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: 01/02/2024] [Revised: 04/04/2024] [Accepted: 04/07/2024] [Indexed: 04/20/2024]
Abstract
Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.
Collapse
Affiliation(s)
- Hossam Nada
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea
| | - Sungdo Kim
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea
| | - Kyeong Lee
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea.
| |
Collapse
|
25
|
Dutta S, Chhabra R, Muthusamy V, Gain N, Subramani R, Sarika K, Devi EL, Madhavan J, Zunjare RU, Hossain F. Allelic variation and haplotype diversity of Matrilineal ( MTL) gene governing in vivo maternal haploid induction in maize. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2024; 30:823-838. [PMID: 38846462 PMCID: PMC11150217 DOI: 10.1007/s12298-024-01456-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 06/09/2024]
Abstract
Diverse haploid inducer lines with > 6% of haploid induction rate are now routinely used to develop doubled haploid lines. Though MTL gene regulates haploid induction, its molecular characterization and haplotype analysis in maize and its related species have not been undertaken so far. In the present study, the entire 1812 bp long MTL gene was sequenced among two mutant and eight wild-type inbreds. A 4 bp insertion differentiated the mutant from the wild-type allele. Sequence analysis further revealed 103 polymorphic sites including 38 InDels and 65 SNPs. A total of 15 conserved regions were detected, of which exon-4 was the most conserved. Ten gene-based markers specific to MTL revealed the presence of 40 haplotypes among diverse 48 inbreds of exotic and indigenous origin. It generated 20 alleles with an average of two alleles per locus. The mean polymorphic information content was 0.3247 with mean gene diversity of 0.4135. A total of 15 paralogous sequences of MTL were detected in maize genome with 3-7 exons. Maize MTL proteins of both wild-type and mutant were non-polar in nature, and they possessed four domains. R1-nj-based haploid inducer (HI) lines viz., Pusa-HI-101 and Pusa-HI-102 had an average haploid induction rate of 8.45 ± 0.96% and 10.46 ± 1.15%, respectively. Lines wild-type MTL gene did not generate any haploid. In comparison with 27 orthologues of 21 grass species, maize MTL gene had the closest ancestry with Saccharum spontaneum and Sorghum. The information generated here assumes great significance in understanding the diversity of MTL gene and presence of paralogues and orthologues. This is the first report on haplotype analysis and molecular characterization of MTL gene in maize and related grass species. Supplementary Information The online version contains supplementary material available at 10.1007/s12298-024-01456-3.
Collapse
Affiliation(s)
- Suman Dutta
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Rashmi Chhabra
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Vignesh Muthusamy
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Nisrita Gain
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | | | - Konsam Sarika
- ICAR Research Complex for NEH Region, Manipur Centre, Lamphelpat, India
| | - Elangbam L. Devi
- ICAR Research Complex for NEH Region, Sikkim Centre, Gangtok, India
| | - Jayanthi Madhavan
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Rajkumar U. Zunjare
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Firoz Hossain
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| |
Collapse
|
26
|
Braun R, Tfirn M, Ford RM. Listening to life: Sonification for enhancing discovery in biological research. Biotechnol Bioeng 2024. [PMID: 38678506 DOI: 10.1002/bit.28729] [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: 11/07/2023] [Revised: 04/05/2024] [Accepted: 04/16/2024] [Indexed: 05/01/2024]
Abstract
Sonification, or the practice of generating sound from data, is a promising alternative or complement to data visualization for exploring research questions in the life sciences. Expressing or communicating data in the form of sound rather than graphs, tables, or renderings can provide a secondary information source for multitasking or remote monitoring purposes or make data accessible when visualizations cannot be used. While popular in astronomy, neuroscience, and geophysics as a technique for data exploration and communication, its potential in the biological and biotechnological sciences has not been fully explored. In this review, we introduce sonification as a concept, some examples of how sonification has been used to address areas of interest in biology, and the history of the technique. We then highlight a selection of biology-related publications that involve sonifications of DNA datasets and protein datasets, sonifications for data collection and interpretation, and sonifications aimed to improve science communication and accessibility. Through this review, we aim to show how sonification has been used both as a discovery tool and a communication tool and to inspire more life-science researchers to incorporate sonification into their own studies.
Collapse
Affiliation(s)
- Rhea Braun
- Department of Chemical Engineering, School of Engineering and Applied Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Maxwell Tfirn
- Department of Music, Christopher Newport University, Newport News, Virginia, USA
| | - Roseanne M Ford
- Department of Chemical Engineering, School of Engineering and Applied Sciences, University of Virginia, Charlottesville, Virginia, USA
| |
Collapse
|
27
|
Grin I, Maksymenko K, Wörtwein T, ElGamacy M. The Damietta Server: a comprehensive protein design toolkit. Nucleic Acids Res 2024:gkae297. [PMID: 38661218 DOI: 10.1093/nar/gkae297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/22/2024] [Accepted: 04/06/2024] [Indexed: 04/26/2024] Open
Abstract
The growing importance of protein design to various research disciplines motivates the development of integrative computational platforms that enhance the accessibility and interoperability of different design tools. To this end, we describe a web-based toolkit that builds on the Damietta protein design engine, which deploys a tensorized energy calculation framework. The Damietta Server seamlessly integrates different design tools, in addition to other tools such as message-passing neural networks and molecular dynamics routines, allowing the user to perform multiple operations on structural models and forward them across tools. The toolkit can be used for tasks such as core or interface design, symmetric design, mutagenic scanning, or conformational sampling, through an intuitive user interface. With the envisioned integration of more tools, the Damietta Server will provide a central resource for protein design and analysis, benefiting basic and applied biomedical research communities. The toolkit is available with no login requirement through https://damietta.de/.
Collapse
Affiliation(s)
- Iwan Grin
- Interfaculty Institute of Microbiology and Infection Medicine (IMIT), University of Tübingen, Tübingen, Germany
| | - Kateryna Maksymenko
- Max Planck Institute for Biology, Department of Protein Evolution, Tübingen, Germany
| | - Tobias Wörtwein
- Max Planck Institute for Biology, Department of Protein Evolution, Tübingen, Germany
- Division of Translational Oncology, Internal Medicine II, University Hospital Tübingen, Tübingen, Germany
| | - Mohammad ElGamacy
- Max Planck Institute for Biology, Department of Protein Evolution, Tübingen, Germany
- Division of Translational Oncology, Internal Medicine II, University Hospital Tübingen, Tübingen, Germany
| |
Collapse
|
28
|
Walter LJ, Quoika PK, Zacharias M. Structure-Based Protein Assembly Simulations Including Various Binding Sites and Conformations. J Chem Inf Model 2024; 64:3465-3476. [PMID: 38602938 DOI: 10.1021/acs.jcim.4c00212] [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: 04/13/2024]
Abstract
Many biological functions are mediated by large complexes formed by multiple proteins and other cellular macromolecules. Recent progress in experimental structure determination, as well as in integrative modeling and protein structure prediction using deep learning approaches, has resulted in a rapid increase in the number of solved multiprotein assemblies. However, the assembly process of large complexes from their components is much less well-studied. We introduce a rapid computational structure-based (SB) model, GoCa, that allows to follow the assembly process of large multiprotein complexes based on a known native structure. Beyond existing SB Go̅-type models, it distinguishes between intra- and intersubunit interactions, allowing us to include coupled folding and binding. It accounts automatically for the permutation of identical subunits in a complex and allows the definition of multiple minima (native) structures in the case of proteins that undergo global transitions during assembly. The model is successfully tested on several multiprotein complexes. The source code of the GoCa program including a tutorial is publicly available on Github: https://github.com/ZachariasLab/GoCa. We also provide a web source that allows users to quickly generate the necessary input files for a GoCa simulation: https://goca.t38webservices.nat.tum.de.
Collapse
Affiliation(s)
- Luis J Walter
- Center for Functional Protein Assemblies, Technical University of Munich, Ernst-Otto-Fischer-Str. 8, Garching 85748, Germany
| | - Patrick K Quoika
- Center for Functional Protein Assemblies, Technical University of Munich, Ernst-Otto-Fischer-Str. 8, Garching 85748, Germany
| | - Martin Zacharias
- Center for Functional Protein Assemblies, Technical University of Munich, Ernst-Otto-Fischer-Str. 8, Garching 85748, Germany
| |
Collapse
|
29
|
Vargas-Pérez MDLÁ, Devos DP, López-Lluch G. An AlphaFold Structure Analysis of COQ2 as Key a Component of the Coenzyme Q Synthesis Complex. Antioxidants (Basel) 2024; 13:496. [PMID: 38671943 PMCID: PMC11047366 DOI: 10.3390/antiox13040496] [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: 03/12/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 04/28/2024] Open
Abstract
Coenzyme Q (CoQ) is a lipidic compound that is widely distributed in nature, with crucial functions in metabolism, protection against oxidative damage and ferroptosis and other processes. CoQ biosynthesis is a conserved and complex pathway involving several proteins. COQ2 is a member of the UbiA family of transmembrane prenyltransferases that catalyzes the condensation of the head and tail precursors of CoQ, which is a key step in the process, because its product is the first intermediate that will be modified in the head by the next components of the synthesis process. Mutations in this protein have been linked to primary CoQ deficiency in humans, a rare disease predominantly affecting organs with a high energy demand. The reaction catalyzed by COQ2 and its mechanism are still unknown. Here, we aimed at clarifying the COQ2 reaction by exploring possible substrate binding sites using a strategy based on homology, comprising the identification of available ligand-bound homologs with solved structures in the Protein Data Bank (PDB) and their subsequent structural superposition in the AlphaFold predicted model for COQ2. The results highlight some residues located on the central cavity or the matrix loops that may be involved in substrate interaction, some of which are mutated in primary CoQ deficiency patients. Furthermore, we analyze the structural modifications introduced by the pathogenic mutations found in humans. These findings shed new light on the understanding of COQ2's function and, thus, CoQ's biosynthesis and the pathogenicity of primary CoQ deficiency.
Collapse
Affiliation(s)
- María de los Ángeles Vargas-Pérez
- Departamento de Fisiología, Anatomía y Biología Celular, Centro Andaluz de Biología del Desarrollo (CABD), CSIC-UPO-JA, Universidad Pablo de Olavide, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Carretera de Utrera km1, 41013 Seville, Spain;
| | - Damien Paul Devos
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-UPO-JA, Universidad Pablo de Olavide, Carretera de Utrera km1, 41013 Seville, Spain;
| | - Guillermo López-Lluch
- Departamento de Fisiología, Anatomía y Biología Celular, Centro Andaluz de Biología del Desarrollo (CABD), CSIC-UPO-JA, Universidad Pablo de Olavide, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Carretera de Utrera km1, 41013 Seville, Spain;
| |
Collapse
|
30
|
Penunuri G, Wang P, Corbett-Detig R, Russell SL. A Structural Proteome Screen Identifies Protein Mimicry in Host-Microbe Systems. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588793. [PMID: 38645127 PMCID: PMC11030372 DOI: 10.1101/2024.04.10.588793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Host-microbe systems are evolutionary niches that produce coevolved biological interactions and are a key component of global health. However, these systems have historically been a difficult field of biological research due to their experimental intractability. Impactful advances in global health will be obtained by leveraging in silico screens to identify genes involved in mediating interspecific interactions. These predictions will progress our understanding of these systems and lay the groundwork for future in vitro and in vivo experiments and bioengineering projects. A driver of host-manipulation and intracellular survival utilized by host-associated microbes is molecular mimicry, a critical mechanism that can occur at any level from DNA to protein structures. We applied protein structure prediction and alignment tools to explore host-associated bacterial structural proteomes for examples of protein structure mimicry. By leveraging the Legionella pneumophila proteome and its many known structural mimics, we developed and validated a screen that can be applied to virtually any host-microbe system to uncover signals of protein mimicry. These mimics represent candidate proteins that mediate host interactions in microbial proteomes. We successfully applied this screen to other microbes with demonstrated effects on global health, Helicobacter pylori and Wolbachia , identifying protein mimic candidates in each proteome. We discuss the roles these candidates may play in important Wolbachia -induced phenotypes and show that Wobachia infection can partially rescue the loss of one of these factors. This work demonstrates how a genome-wide screen for candidates of host-manipulation and intracellular survival offers an opportunity to identify functionally important genes in host-microbe systems.
Collapse
|
31
|
Li T, Hou X, Sun Z, Ma B, Wu X, Feng T, Ai H, Huang X, Li R. Characterization of FBA genes in potato ( Solanum tuberosum L.) and expression patterns in response to light spectrum and abiotic stress. Front Genet 2024; 15:1364944. [PMID: 38686025 PMCID: PMC11057440 DOI: 10.3389/fgene.2024.1364944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 03/29/2024] [Indexed: 05/02/2024] Open
Abstract
Fructose-1, 6-bisphosphate aldolase (FBA) plays vital roles in plant growth, development, and response to abiotic stress. However, genome-wide identification and structural characterization of the potato (Solanum tuberosum L.) FBA gene family has not been systematically analyzed. In this study, we identified nine StFBA gene members in potato, with six StFBA genes localized in the chloroplast and three in the cytoplasm. The analysis of gene structures, protein structures, and phylogenetic relationships indicated that StFBA genes were divided into Class I and II, which exhibited significant differences in structure and function. Synteny analysis revealed that segmental duplication events promoted the expansion of the StFBA gene family. Promoter analysis showed that most StFBA genes contained cis-regulatory elements associated with light and stress responses. Expression analysis showed that StFBA3, StFBA8, and StFBA9 showing significantly higher expression levels in leaf, stolon, and tuber under blue light, indicating that these genes may improve photosynthesis and play an important function in regulating the induction and expansion of microtubers. Expression levels of the StFBA genes were influenced by drought and salt stress, indicating that they played important roles in abiotic stress. This work offers a theoretical foundation for in-depth understanding of the evolution and function of StFBA genes, as well as providing the basis for the genetic improvement of potatoes.
Collapse
Affiliation(s)
- Ting Li
- Center for Crop Biotechnology, College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Xinyue Hou
- Center for Crop Biotechnology, College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Zhanglun Sun
- Center for Crop Biotechnology, College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Bin Ma
- Country College of Life Sciences, Shihezi University, Shihezi, China
| | - Xingxing Wu
- Center for Crop Biotechnology, College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Tingting Feng
- Center for Crop Biotechnology, College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Hao Ai
- Center for Crop Biotechnology, College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Xianzhong Huang
- Center for Crop Biotechnology, College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Ruining Li
- Center for Crop Biotechnology, College of Agriculture, Anhui Science and Technology University, Fengyang, China
| |
Collapse
|
32
|
Capponi S, Wang S. AI in cellular engineering and reprogramming. Biophys J 2024:S0006-3495(24)00245-5. [PMID: 38576162 DOI: 10.1016/j.bpj.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/19/2024] [Accepted: 04/01/2024] [Indexed: 04/06/2024] Open
Abstract
During the last decade, artificial intelligence (AI) has increasingly been applied in biophysics and related fields, including cellular engineering and reprogramming, offering novel approaches to understand, manipulate, and control cellular function. The potential of AI lies in its ability to analyze complex datasets and generate predictive models. AI algorithms can process large amounts of data from single-cell genomics and multiomic technologies, allowing researchers to gain mechanistic insights into the control of cell identity and function. By integrating and interpreting these complex datasets, AI can help identify key molecular events and regulatory pathways involved in cellular reprogramming. This knowledge can inform the design of precision engineering strategies, such as the development of new transcription factor and signaling molecule cocktails, to manipulate cell identity and drive authentic cell fate across lineage boundaries. Furthermore, when used in combination with computational methods, AI can accelerate and improve the analysis and understanding of the intricate relationships between genes, proteins, and cellular processes. In this review article, we explore the current state of AI applications in biophysics with a specific focus on cellular engineering and reprogramming. Then, we showcase a couple of recent applications where we combined machine learning with experimental and computational techniques. Finally, we briefly discuss the challenges and prospects of AI in cellular engineering and reprogramming, emphasizing the potential of these technologies to revolutionize our ability to engineer cells for a variety of applications, from disease modeling and drug discovery to regenerative medicine and biomanufacturing.
Collapse
Affiliation(s)
- Sara Capponi
- IBM Almaden Research Center, San Jose, California; Center for Cellular Construction, San Francisco, California.
| | - Shangying Wang
- Bay Area Institute of Science, Altos Labs, Redwood City, California.
| |
Collapse
|
33
|
Listov D, Goverde CA, Correia BE, Fleishman SJ. Opportunities and challenges in design and optimization of protein function. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00718-y. [PMID: 38565617 DOI: 10.1038/s41580-024-00718-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.
Collapse
Affiliation(s)
- Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Casper A Goverde
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Sarel Jacob Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.
| |
Collapse
|
34
|
Chen J, Zia A, Luo A, Meng H, Wang F, Hou J, Cao R, Si D. Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration. Brief Bioinform 2024; 25:bbae118. [PMID: 38609330 PMCID: PMC11014792 DOI: 10.1093/bib/bbae118] [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: 06/27/2023] [Revised: 01/23/2024] [Accepted: 03/02/2024] [Indexed: 04/14/2024] Open
Abstract
Understanding the protein structures is invaluable in various biomedical applications, such as vaccine development. Protein structure model building from experimental electron density maps is a time-consuming and labor-intensive task. To address the challenge, machine learning approaches have been proposed to automate this process. Currently, the majority of the experimental maps in the database lack atomic resolution features, making it challenging for machine learning-based methods to precisely determine protein structures from cryogenic electron microscopy density maps. On the other hand, protein structure prediction methods, such as AlphaFold2, leverage evolutionary information from protein sequences and have recently achieved groundbreaking accuracy. However, these methods often require manual refinement, which is labor intensive and time consuming. In this study, we present DeepTracer-Refine, an automated method that refines AlphaFold predicted structures by aligning them to DeepTracers modeled structure. Our method was evaluated on 39 multi-domain proteins and we improved the average residue coverage from 78.2 to 90.0% and average local Distance Difference Test score from 0.67 to 0.71. We also compared DeepTracer-Refine with Phenixs AlphaFold refinement and demonstrated that our method not only performs better when the initial AlphaFold model is less precise but also surpasses Phenix in run-time performance.
Collapse
Affiliation(s)
- Jason Chen
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA
| | - Ayisha Zia
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Albert Luo
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA
| | - Hanze Meng
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Fengbin Wang
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Jie Hou
- Department of Computer Science, Saint Louis University, Saint Louis, MO 63103, USA
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA
| | - Dong Si
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA
| |
Collapse
|
35
|
Li L, Chen J, Sun Z. Exploring the shared pathogenic strategies of independently evolved effectors across distinct plant viruses. Trends Microbiol 2024:S0966-842X(24)00058-1. [PMID: 38521726 DOI: 10.1016/j.tim.2024.03.001] [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: 01/17/2024] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 03/25/2024]
Abstract
Plants have developed very diverse strategies to defend themselves against viral pathogens, among which plant hormones play pivotal roles. In response, some viruses have also deployed multifunctional viral effectors that effectively hijack key component hubs to counter or evade plant immune surveillance. Although significant progress has been made toward understanding counter-defense strategies that manipulate plant hormone regulatory molecules, these efforts have often been limited to an individual virus or specific host target/pathway. This review provides new insights into broad-spectrum antiviral responses in rice triggered by key components of phytohormone signaling, and highlights the common features of counter-defense strategies employed by distinct rice-infecting RNA viruses. These strategies involve the secretion of multifunctional virulence effectors that target the sophisticated phytohormone system, dampening immune responses by engaging with the same host targets. Additionally, the review provides an in-depth exploration of various viral effectors, emphasizing tertiary structure-based research and shared host targets. Understanding these conserved characteristics in detail may pave the way for molecular drug design, opening new opportunities to enhance broad-spectrum antiviral trials through precise engineering.
Collapse
Affiliation(s)
- Lulu Li
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Biotechnology in Plant Protection of MOA of China and Zhejiang Province, Institute of Plant Virology, Ningbo University, Ningbo, 315211, China
| | - Jianping Chen
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Biotechnology in Plant Protection of MOA of China and Zhejiang Province, Institute of Plant Virology, Ningbo University, Ningbo, 315211, China
| | - Zongtao Sun
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Key Laboratory of Biotechnology in Plant Protection of MOA of China and Zhejiang Province, Institute of Plant Virology, Ningbo University, Ningbo, 315211, China.
| |
Collapse
|
36
|
Bagde SR, Kim CY. Architecture of full-length type I modular polyketide synthases revealed by X-ray crystallography, cryo-electron microscopy, and AlphaFold2. Nat Prod Rep 2024. [PMID: 38501175 DOI: 10.1039/d3np00060e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Covering: up to the end of 2023Type I modular polyketide synthases construct polyketide natural products in an assembly line-like fashion, where the growing polyketide chain attached to an acyl carrier protein is passed from catalytic domain to catalytic domain. These enzymes have immense potential in drug development since they can be engineered to produce non-natural polyketides by strategically adding, exchanging, and deleting individual catalytic domains. In practice, however, this approach frequently results in complete failures or dramatically reduced product yields. A comprehensive understanding of modular polyketide synthase architecture is expected to resolve these issues. We summarize the three-dimensional structures and the proposed mechanisms of three full-length modular polyketide synthases, Lsd14, DEBS module 1, and PikAIII. We also describe the advantages and limitations of using X-ray crystallography, cryo-electron microscopy, and AlphaFold2 to study intact type I polyketide synthases.
Collapse
Affiliation(s)
- Saket R Bagde
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.
| | - Chu-Young Kim
- Department of Biochemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
| |
Collapse
|
37
|
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.
Collapse
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
| |
Collapse
|
38
|
Heinz-Kunert SL, Pandya A, Dang VT, Oktawiec J, Nguyen AI. Pore Restructuring of Peptide Frameworks by Mutations at Distal Packing Residues. Biomacromolecules 2024; 25:2016-2023. [PMID: 38362872 DOI: 10.1021/acs.biomac.3c01418] [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: 02/17/2024]
Abstract
Porous framework materials are highly useful for catalysis, adsorption, and separations. Though they are usually made from inorganic and organic building blocks, recently, folded peptides have been utilized for constructing frameworks, opening up an enormous structure-space for exploration. These peptides assemble in a metal-free fashion using π-stacking, H-bonding, dispersion forces, and the hydrophobic effect. Manipulation of pore-defining H-bonding residues is known to generate new topologies, but the impact of mutations in the hydrophobic packing region facing away from the pores is less obvious. To explore their effects, we synthesized variants of peptide frameworks with mutations in the hydrophobic packing positions and found by single-crystal X-ray crystallography (SC-XRD) that they induce significant changes to the framework pore structure. These structural changes are driven by a need to maximize van der Waals interactions of the nonpolar groups, which are achieved by various mechanisms including helix twisting, chain flipping, chain offsetting, and desymmetrization. Even subtle changes to the van der Waals interface, such as the introduction of a methyl group or isomeric replacement, result in significant pore restructuring. This study shows that the dispersion interactions upholding a peptide material are a rich area for structural engineering.
Collapse
Affiliation(s)
- Sherrie L Heinz-Kunert
- Department of Chemistry, University of Illinois Chicago, Chicago, Illinois 60607, United States
| | - Ashma Pandya
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States
| | - Viet Thuc Dang
- Department of Chemistry, University of Illinois Chicago, Chicago, Illinois 60607, United States
| | - Julia Oktawiec
- Department of Chemistry, Northwestern University, Evanston, Illinois 60208, United States
| | - Andy I Nguyen
- Department of Chemistry, University of Illinois Chicago, Chicago, Illinois 60607, United States
| |
Collapse
|
39
|
Singh N, Singh AK. In Silico Structural Modeling and Binding Site Analysis of Cerebroside Sulfotransferase (CST): A Therapeutic Target for Developing Substrate Reduction Therapy for Metachromatic Leukodystrophy. ACS OMEGA 2024; 9:10748-10768. [PMID: 38463293 PMCID: PMC10918841 DOI: 10.1021/acsomega.3c09462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/26/2024] [Accepted: 01/31/2024] [Indexed: 03/12/2024]
Abstract
Cerebroside sulfotransferase (CST) is emerging as an important therapeutic target to develop substrate reduction therapy (SRT) for metachromatic leukodystrophy (MLD), a rare neurodegenerative lysosomal storage disorder. MLD develops with progressive impairment and destruction of the myelin sheath as a result of accumulation of sulfatide around the nerve cells in the absence of its recycling mechanism with deficiency of arylsulfatase A (ARSA). Sulfatide is the product of the catalytic action of cerebroside sulfotransferase (CST), which needs to be regulated under pathophysiological conditions by inhibitor development. To carry out in silico-based preliminary drug screening or for designing new drug candidates, a high-quality three-dimensional (3D) structure is needed in the absence of an experimentally derived three-dimensional crystal structure. In this study, a 3D model of the protein was developed using a primary sequence with the SWISS-MODEL server by applying the top four GMEQ score-based templates belonging to the sulfotransferase family as a reference. The 3D model of CST highlights the features of the protein responsible for its catalytic action. The CST model comprises five β-strands, which are flanked by ten α-helices from both sides as well as form the upside cover of the catalytic pocket of CST. CST has two catalytic regions: PAPS (-sulfo donor) binding and galactosylceramide (-sulfo acceptor) binding. The catalytic action of CST was proposed via molecular docking and molecular dynamic (MD) simulation with PAPS, galactosylceramide (GC), PAPS-galactosylceramide, and PAP. The stability of the model and its catalytic action were confirmed using molecular dynamic simulation-based trajectory analysis. CST response against the inhibition potential of the experimentally reported competitive inhibitor of CST was confirmed via molecular docking and molecular dynamics simulation, which suggested the suitability of the CST model for future drug discovery to strengthen substrate reduction therapy for MLD.
Collapse
Affiliation(s)
- Nivedita Singh
- Department of Dravyaguna,
Faculty of Ayurveda, Institute of Medical
Sciences, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India
| | - Anil Kumar Singh
- Department of Dravyaguna,
Faculty of Ayurveda, Institute of Medical
Sciences, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India
| |
Collapse
|
40
|
Kohyama S, Frohn BP, Babl L, Schwille P. Machine learning-aided design and screening of an emergent protein function in synthetic cells. Nat Commun 2024; 15:2010. [PMID: 38443351 PMCID: PMC10914801 DOI: 10.1038/s41467-024-46203-0] [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: 06/27/2023] [Accepted: 02/16/2024] [Indexed: 03/07/2024] Open
Abstract
Recently, utilization of Machine Learning (ML) has led to astonishing progress in computational protein design, bringing into reach the targeted engineering of proteins for industrial and biomedical applications. However, the design of proteins for emergent functions of core relevance to cells, such as the ability to spatiotemporally self-organize and thereby structure the cellular space, is still extremely challenging. While on the generative side conditional generative models and multi-state design are on the rise, for emergent functions there is a lack of tailored screening methods as typically needed in a protein design project, both computational and experimental. Here we describe a proof-of-principle of how such screening, in silico and in vitro, can be achieved for ML-generated variants of a protein that forms intracellular spatiotemporal patterns. For computational screening we use a structure-based divide-and-conquer approach to find the most promising candidates, while for the subsequent in vitro screening we use synthetic cell-mimics as established by Bottom-Up Synthetic Biology. We then show that the best screened candidate can indeed completely substitute the wildtype gene in Escherichia coli. These results raise great hopes for the next level of synthetic biology, where ML-designed synthetic proteins will be used to engineer cellular functions.
Collapse
Affiliation(s)
- Shunshi Kohyama
- Dept. Cellular and Molecular Biophysics, Max Planck Institute of Biochemistry, Martinsried, D-82152, Germany
| | - Béla P Frohn
- Dept. Cellular and Molecular Biophysics, Max Planck Institute of Biochemistry, Martinsried, D-82152, Germany
| | - Leon Babl
- Dept. Cellular and Molecular Biophysics, Max Planck Institute of Biochemistry, Martinsried, D-82152, Germany
| | - Petra Schwille
- Dept. Cellular and Molecular Biophysics, Max Planck Institute of Biochemistry, Martinsried, D-82152, Germany.
| |
Collapse
|
41
|
Ferreiro D, Branco C, Arenas M. Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation. Bioinformatics 2024; 40:btae096. [PMID: 38374231 PMCID: PMC10914458 DOI: 10.1093/bioinformatics/btae096] [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: 03/22/2023] [Revised: 01/15/2024] [Accepted: 02/16/2024] [Indexed: 02/21/2024] Open
Abstract
MOTIVATION The selection among substitution models of molecular evolution is fundamental for obtaining accurate phylogenetic inferences. At the protein level, evolutionary analyses are traditionally based on empirical substitution models but these models make unrealistic assumptions and are being surpassed by structurally constrained substitution (SCS) models. The SCS models often consider site-dependent evolution, a process that provides realism but complicates their implementation into likelihood functions that are commonly used for substitution model selection. RESULTS We present a method to perform selection among site-dependent SCS models, also among empirical and site-dependent SCS models, based on the approximate Bayesian computation (ABC) approach and its implementation into the computational framework ProteinModelerABC. The framework implements ABC with and without regression adjustments and includes diverse empirical and site-dependent SCS models of protein evolution. Using extensive simulated data, we found that it provides selection among SCS and empirical models with acceptable accuracy. As illustrative examples, we applied the framework to analyze a variety of protein families observing that SCS models fit them better than the corresponding best-fitting empirical substitution models. AVAILABILITY AND IMPLEMENTATION ProteinModelerABC is freely available from https://github.com/DavidFerreiro/ProteinModelerABC, can run in parallel and includes a graphical user interface. The framework is distributed with detailed documentation and ready-to-use examples.
Collapse
Affiliation(s)
- David Ferreiro
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics and Immunology, Universidade de Vigo, 36310 Vigo, Spain
| | - Catarina Branco
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics and Immunology, Universidade de Vigo, 36310 Vigo, Spain
| | - Miguel Arenas
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics and Immunology, Universidade de Vigo, 36310 Vigo, Spain
| |
Collapse
|
42
|
Jänes J, Beltrao P. Deep learning for protein structure prediction and design-progress and applications. Mol Syst Biol 2024; 20:162-169. [PMID: 38291232 PMCID: PMC10912668 DOI: 10.1038/s44320-024-00016-x] [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: 06/26/2023] [Revised: 12/21/2023] [Accepted: 01/11/2024] [Indexed: 02/01/2024] Open
Abstract
Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms that underlie biological processes in living cells and can have practical applications in the study of disease mutations or the discovery of novel drug treatments. Here, we review the progress made in sequence-based prediction of protein structures with a focus on applications that go beyond the prediction of single monomer structures. This includes the application of deep learning methods for the prediction of structures of protein complexes, different conformations, the evolution of protein structures and the application of these methods to protein design. These developments create new opportunities for research that will have impact across many areas of biomedical research.
Collapse
Affiliation(s)
- Jürgen Jänes
- Institute of Molecular Systems Biology, ETH Zürich, 8093, Zürich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pedro Beltrao
- Institute of Molecular Systems Biology, ETH Zürich, 8093, Zürich, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| |
Collapse
|
43
|
Mougkogiannis P, Adamatzky A. On interaction of proteinoids with simulated neural networks. Biosystems 2024; 237:105175. [PMID: 38460836 DOI: 10.1016/j.biosystems.2024.105175] [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: 01/09/2024] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 03/11/2024]
Abstract
Proteinoid-neuron networks combine biological neurons with spiking proteinoid microspheres, which are generated by thermal condensation of amino acids. Complex and dynamic spiking patterns in response to varied stimuli make these networks suitable for unconventional computing. This research examines the interaction of proteinoid-neuron networks with function-generator-artificial neural networks (ANN) that may create distinct electrical waveforms. Function-generator- artificial neural network (ANN) stimulates and modulates proteinoid-neuron network spiking activity and synchronisation to encode and decode information. We employ function-generator-ANN to study proteinoid-neuron network nonlinear dynamics and chaos and optimise their performance and energy efficiency. Function-generator-ANN improves proteinoid-neuron networks' computational capacities and robustness and creates unique hybrid systems with electrical devices. We address the benefits as well as the drawbacks of employing proteinoid-neuron networks for unconventional computing with function-generator-ANN.
Collapse
|
44
|
Barton J, Gaspariunas A, Galson JD, Leem J. Building Representation Learning Models for Antibody Comprehension. Cold Spring Harb Perspect Biol 2024; 16:a041462. [PMID: 38012013 PMCID: PMC10910360 DOI: 10.1101/cshperspect.a041462] [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: 11/29/2023]
Abstract
Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. It is challenging to predict the functional and biophysical properties of antibodies from their amino acid sequence alone, requiring numerous experiments for full characterization. Machine learning, specifically deep representation learning, has emerged as a family of methods that can complement wet lab approaches and accelerate the overall discovery and engineering process. Here, we review advances in antibody sequence representation learning, and how this has improved antibody structure prediction and facilitated antibody optimization. We discuss challenges in the development and implementation of such models, such as the lack of publicly available, well-curated antibody function data and highlight opportunities for improvement. These and future advances in machine learning for antibody sequences have the potential to increase the success rate in developing new therapeutics, resulting in broader access to transformative medicines and improved patient outcomes.
Collapse
Affiliation(s)
- Justin Barton
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | | | - Jacob D Galson
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | - Jinwoo Leem
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| |
Collapse
|
45
|
Maniam S, Maniam S. Screening Techniques for Drug Discovery in Alzheimer's Disease. ACS OMEGA 2024; 9:6059-6073. [PMID: 38371787 PMCID: PMC10870277 DOI: 10.1021/acsomega.3c07046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/22/2023] [Accepted: 12/25/2023] [Indexed: 02/20/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive and irreversible impairment of memory and other cognitive functions of the aging brain. Pathways such as amyloid beta neurotoxicity, tau pathogenesis and neuroinflammatory have been used to understand AD, despite not knowing the definite molecular mechanism which causes this progressive disease. This review attempts to summarize the small molecules that target these pathways using various techniques involving high-throughput screening, molecular modeling, custom bioassays, and spectroscopic detection tools. Novel and evolving screening methods developed to advance drug discovery initiatives in AD research are also highlighted.
Collapse
Affiliation(s)
- Sandra Maniam
- Department
of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia
| | - Subashani Maniam
- School
of Science, STEM College, RMIT University, Melbourne, Victoria 3001, Australia
| |
Collapse
|
46
|
Fannjiang C, Listgarten J. Is Novelty Predictable? Cold Spring Harb Perspect Biol 2024; 16:a041469. [PMID: 38052497 PMCID: PMC10835614 DOI: 10.1101/cshperspect.a041469] [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: 12/07/2023]
Abstract
Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal applications ranging from drug development and plastic degradation to carbon sequestration. When designing objects to achieve novel property values with machine learning, one faces a fundamental challenge: how to push past the frontier of current knowledge, distilled from the training data into the model, in a manner that rationally controls the risk of failure. If one trusts learned models too much in extrapolation, one is likely to design rubbish. In contrast, if one does not extrapolate, one cannot find novelty. Herein, we ponder how one might strike a useful balance between these two extremes. We focus in particular on designing proteins with novel property values, although much of our discussion is relevant to machine learning-based design more broadly.
Collapse
Affiliation(s)
- Clara Fannjiang
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
| | - Jennifer Listgarten
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
| |
Collapse
|
47
|
Liu Y, Liu H. Protein sequence design on given backbones with deep learning. Protein Eng Des Sel 2024; 37:gzad024. [PMID: 38157313 DOI: 10.1093/protein/gzad024] [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: 08/16/2023] [Revised: 12/08/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024] Open
Abstract
Deep learning methods for protein sequence design focus on modeling and sampling the many- dimensional distribution of amino acid sequences conditioned on the backbone structure. To produce physically foldable sequences, inter-residue couplings need to be considered properly. These couplings are treated explicitly in iterative methods or autoregressive methods. Non-autoregressive models treating these couplings implicitly are computationally more efficient, but still await tests by wet experiment. Currently, sequence design methods are evaluated mainly using native sequence recovery rate and native sequence perplexity. These metrics can be complemented by sequence-structure compatibility metrics obtained from energy calculation or structure prediction. However, existing computational metrics have important limitations that may render the generalization of computational test results to performance in real applications unwarranted. Validation of design methods by wet experiments should be encouraged.
Collapse
Affiliation(s)
- Yufeng Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Haiyan Liu
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230027, China
- Biomedical Sciences and Health Laboratory of Anhui Province, University of Science and Technology of China, Hefei, Anhui 230027, China
- School of Biomedical Engineering, Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu 215004, China
| |
Collapse
|
48
|
de Bastos Balbe E Gutierres M, Pedebos C, Bacaicoa-Caruso P, Ligabue-Braun R. Structure determination needs to go viral. Amino Acids 2024; 56:3. [PMID: 38286913 PMCID: PMC10824879 DOI: 10.1007/s00726-023-03374-2] [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: 05/29/2023] [Accepted: 11/14/2023] [Indexed: 01/31/2024]
Abstract
Viral diseases are expected to cause new epidemics in the future, therefore, it is essential to assess how viral diversity is represented in terms of deposited protein structures. Here, data were collected from the Protein Data Bank to screen the available structures of viruses of interest to WHO. Excluding SARS-CoV-2 and HIV-1, less than 50 structures were found per year, indicating a lack of diversity. Efforts to determine viral structures are needed to increase preparedness for future public health challenges.
Collapse
Affiliation(s)
- Matheus de Bastos Balbe E Gutierres
- Programa de Pós-Graduação em Biociências (PPGBio), Universidade Federal de Ciências da Saúde de Porto Alegre - UFCSPA, Porto Alegre, Rio Grande do Sul, Brazil
| | - Conrado Pedebos
- Programa de Pós-Graduação em Biociências (PPGBio), Universidade Federal de Ciências da Saúde de Porto Alegre - UFCSPA, Porto Alegre, Rio Grande do Sul, Brazil
| | - Paula Bacaicoa-Caruso
- Programa de Pós-Graduação em Biociências (PPGBio), Universidade Federal de Ciências da Saúde de Porto Alegre - UFCSPA, Porto Alegre, Rio Grande do Sul, Brazil
| | - Rodrigo Ligabue-Braun
- Programa de Pós-Graduação em Biociências (PPGBio), Universidade Federal de Ciências da Saúde de Porto Alegre - UFCSPA, Porto Alegre, Rio Grande do Sul, Brazil.
- Departamento de Farmacociências, Universidade Federal de Ciências da Saúde de Porto Alegre - UFCSPA, Porto Alegre, Rio Grande do Sul, Brazil.
| |
Collapse
|
49
|
Suhail M. Biophysical chemistry behind sickle cell anemia and the mechanism of voxelotor action. Sci Rep 2024; 14:1861. [PMID: 38253605 PMCID: PMC10803371 DOI: 10.1038/s41598-024-52476-8] [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: 11/30/2023] [Accepted: 01/19/2024] [Indexed: 01/24/2024] Open
Abstract
Sickle cell anemia disease has been a great challenge to the world in the present situation. It occurs only due to the polymerization of sickle hemoglobin (HbS) having Pro-Val-Glu typed mutation, while the polymerization does not occur in normal hemoglobin (HbA) having Pro-Glu-Glu peptides. It is also well confirmed that the oxygenated HbS (OHbS) does not participate in the polymerization, while the deoxygenated HbS (dHbS) does, which causes the shape of red blood cells sickled. After polymerization, the blood has a low oxygen affinity. Keeping this fact into consideration, only those drugs are being synthesized that stabilize the OHbS structure so that the polymerization of HbS can be stopped. The literature data showed no systematic description of the changes occurring during the OHbS conversion to dHbS before polymerization. Hence, an innovative reasonable study between HbA and HbS, when they convert into their deoxygenated forms, was done computationally. In this evaluation, physiochemical parameters in HbA/HbS before and after deoxygenation were studied and compared deeply. The computationally collected data was used to understand the abnormal behaviour of dHbS arising due to the replacement of Glu6 with Val6. Consequently, during the presented computational study, the changes occurring in HbS were found opposite/abnormal as compared to HbA after the deoxygenation of both. The mechanism of Voxelotor (GBT-440) action to stop the HbS polymerization was also explained with the help of computationally collected data. Besides, a comparative study between GBT-440 and another suggested drug was also done to know their antisickling strength. Additionally, the effect of pH, CO, CO2, and 2,3-diphosphoglycerate (2,3-DPG) on HbS structure was also studied computationally.
Collapse
Affiliation(s)
- Mohd Suhail
- Department of Chemistry, Siddhartha (PG) College, Aakhlor Kheri, Deoband (Saharanpur), Uttar Pradesh, 247554, India.
| |
Collapse
|
50
|
He J, Liu X, Zhu C, Zha J, Li Q, Zhao M, Wei J, Li M, Wu C, Wang J, Jiao Y, Ning S, Zhou J, Hong Y, Liu Y, He H, Zhang M, Chen F, Li Y, He X, Wu J, Lu S, Song K, Lu X, Zhang J. ASD2023: towards the integrating landscapes of allosteric knowledgebase. Nucleic Acids Res 2024; 52:D376-D383. [PMID: 37870448 PMCID: PMC10767950 DOI: 10.1093/nar/gkad915] [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: 08/21/2023] [Revised: 09/22/2023] [Accepted: 10/06/2023] [Indexed: 10/24/2023] Open
Abstract
Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.
Collapse
Affiliation(s)
- Jixiao He
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xinyi Liu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chunhao Zhu
- College of Pharmacy, Ningxia Medical University, 1160 Shengli Street, Yinchuan, Ningxia 750004, China
| | - Jinyin Zha
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qian Li
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mingzhu Zhao
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jiacheng Wei
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mingyu Li
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chengwei Wu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Junyuan Wang
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Yonglai Jiao
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shaobo Ning
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jiamin Zhou
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Yue Hong
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yonghui Liu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hongxi He
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Mingyang Zhang
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Feiying Chen
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yanxiu Li
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xinheng He
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jing Wu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shaoyong Lu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Kun Song
- Nutshell Therapeutics, Shanghai 201210, China
| | - Xuefeng Lu
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Jian Zhang
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Pharmacy, Ningxia Medical University, 1160 Shengli Street, Yinchuan, Ningxia 750004, China
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
| |
Collapse
|