1
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Ghafarollahi A, Buehler MJ. ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning. DIGITAL DISCOVERY 2024; 3:1389-1409. [PMID: 38993729 PMCID: PMC11235180 DOI: 10.1039/d4dd00013g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/13/2024] [Indexed: 07/13/2024]
Abstract
Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protein design often rely on AI-based models, such as surrogate models that address end-to-end problems by linking protein structure to material properties or vice versa. However, these models frequently focus on specific material objectives or structural properties, limiting their flexibility when incorporating out-of-domain knowledge into the design process or comprehensive data analysis is required. In this study, we introduce ProtAgents, a platform for de novo protein design based on Large Language Models (LLMs), where multiple AI agents with distinct capabilities collaboratively address complex tasks within a dynamic environment. The versatility in agent development allows for expertise in diverse domains, including knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis. The dynamic collaboration between agents, empowered by LLMs, provides a versatile approach to tackling protein design and analysis problems, as demonstrated through diverse examples in this study. The problems of interest encompass designing new proteins, analyzing protein structures and obtaining new first-principles data - natural vibrational frequencies - via physics simulations. The concerted effort of the system allows for powerful automated and synergistic design of de novo proteins with targeted mechanical properties. The flexibility in designing the agents, on one hand, and their capacity in autonomous collaboration through the dynamic LLM-based multi-agent environment on the other hand, unleashes great potentials of LLMs in addressing multi-objective materials problems and opens up new avenues for autonomous materials discovery and design.
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Affiliation(s)
- Alireza Ghafarollahi
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 USA
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 USA
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 USA
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2
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Schmidt B, Hildebrandt A. From GPUs to AI and quantum: three waves of acceleration in bioinformatics. Drug Discov Today 2024; 29:103990. [PMID: 38663581 DOI: 10.1016/j.drudis.2024.103990] [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: 12/13/2023] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/01/2024]
Abstract
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on the effective use of these powerful technologies. Furthermore, progress in computational techniques and architectures continues to be highly dynamic, involving novel deep neural network models and artificial intelligence (AI) accelerators, and potentially quantum processing units in the future. These are expected to be disruptive for the life sciences as a whole and for drug discovery in particular. Here, we identify three waves of acceleration and their applications in a bioinformatics context: (i) GPU computing, (ii) AI and (iii) next-generation quantum computers.
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Affiliation(s)
- Bertil Schmidt
- Institut für Informatik, Johannes Gutenberg University, Mainz, Germany.
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3
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Zhou B, Zheng L, Wu B, Tan Y, Lv O, Yi K, Fan G, Hong L. Protein Engineering with Lightweight Graph Denoising Neural Networks. J Chem Inf Model 2024; 64:3650-3661. [PMID: 38630581 DOI: 10.1021/acs.jcim.4c00036] [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: 04/19/2024]
Abstract
Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establishes a lightweight graph neural network scheme for protein structures, which efficiently analyzes the microenvironment of amino acids in wild-type proteins and reconstructs the distribution of the amino acid sequences that are more likely to pass natural selection. This distribution serves as a general guidance for scoring proteins toward arbitrary properties on any order of mutations. Our proposed solution undergoes extensive wet-lab experimental validation spanning diverse physicochemical properties of various proteins, including fluorescence intensity, antigen-antibody affinity, thermostability, and DNA cleavage activity. More than 40% of ProtLGN-designed single-site mutants outperform their wild-type counterparts across all studied proteins and targeted properties. More importantly, our model can bypass the negative epistatic effect to combine single mutation sites and form deep mutants with up to seven mutation sites in a single round, whose physicochemical properties are significantly improved. This observation provides compelling evidence of the structure-based model's potential to guide deep mutations in protein engineering. Overall, our approach emerges as a versatile tool for protein engineering, benefiting both the computational and bioengineering communities.
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Affiliation(s)
- Bingxin Zhou
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai National Center for Applied Mathematics (SJTU Center), Shanghai 200240, China
| | - Lirong Zheng
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Banghao Wu
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yang Tan
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
| | - Outongyi Lv
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Kai Yi
- School of Mathematics and Statistics, University of New South Wales, Sydney 2052, Australia
| | - Guisheng Fan
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Liang Hong
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai National Center for Applied Mathematics (SJTU Center), Shanghai 200240, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
- Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai 201203, China
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4
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Krishna R, Wang J, Ahern W, Sturmfels P, Venkatesh P, Kalvet I, Lee GR, Morey-Burrows FS, Anishchenko I, Humphreys IR, McHugh R, Vafeados D, Li X, Sutherland GA, Hitchcock A, Hunter CN, Kang A, Brackenbrough E, Bera AK, Baek M, DiMaio F, Baker D. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 2024; 384:eadl2528. [PMID: 38452047 DOI: 10.1126/science.adl2528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/27/2024] [Indexed: 03/09/2024]
Abstract
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.
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Affiliation(s)
- Rohith Krishna
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Jue Wang
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Woody Ahern
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA
| | - Pascal Sturmfels
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA
| | - Preetham Venkatesh
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Indrek Kalvet
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
| | | | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Ian R Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Ryan McHugh
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
| | - Dionne Vafeados
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Xinting Li
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | | | - Andrew Hitchcock
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - C Neil Hunter
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Alex Kang
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Evans Brackenbrough
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Asim K Bera
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - Minkyung Baek
- School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98105, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98105, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
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5
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Buehler MJ. Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design. ACS ENGINEERING AU 2024; 4:241-277. [PMID: 38646516 PMCID: PMC11027160 DOI: 10.1021/acsengineeringau.3c00058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 04/23/2024]
Abstract
Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design, and manufacturing, including their capacity to work effectively with human language, symbols, code, and numerical data. Here, we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. Moreover, when used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem-solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how fine-tuning endows LLMs with a reasonable understanding of subject area knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty recalling correct information and may hallucinate. We show how this can be addressed using retrieval-augmented Ontological Knowledge Graph strategies. The graph-based strategy helps us not only to discern how the model understands what concepts are important but also how they are related, which significantly improves generative performance and also naturally allows for injection of new and augmented data sources into generative AI algorithms. We find that the additional feature of relatedness provides advantages over regular retrieval augmentation approaches and not only improves LLM performance but also provides mechanistic insights for exploration of a material design process. Illustrated for a use case of relating distinct areas of knowledge, here, music and proteins, such strategies can also provide an interpretable graph structure with rich information at the node, edge, and subgraph level that provides specific insights into mechanisms and relationships. We discuss other approaches to improve generative qualities, including nonlinear sampling strategies and agent-based modeling that offer enhancements over single-shot generations, whereby LLMs are used to both generate content and assess content against an objective target. Examples provided include complex question answering, code generation, and execution in the context of automated force-field development from actively learned density functional theory (DFT) modeling and data analysis.
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Affiliation(s)
- Markus J. Buehler
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Department
of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Department
of Mechanical Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Center
for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
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6
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Luu RK, Buehler MJ. BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306724. [PMID: 38145334 PMCID: PMC10933662 DOI: 10.1002/advs.202306724] [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: 09/15/2023] [Revised: 12/11/2023] [Indexed: 12/26/2023]
Abstract
The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.
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Affiliation(s)
- Rachel K. Luu
- Laboratory for Atomistic and Molecular Mechanics (LAMM)Massachusetts Institute of Technology77 Massachusetts AvenueCambridgeMA02139USA
- Department of Materials Science and EngineeringMassachusetts Institute of Technology77 Massachusetts AvenueCambridgeMA02139USA
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM)Massachusetts Institute of Technology77 Massachusetts AvenueCambridgeMA02139USA
- Center for Computational Science and EngineeringSchwarzman College of ComputingMassachusetts Institute of Technology77 Massachusetts AvenueCambridgeMA02139USA
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7
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Yang J, Li FZ, Arnold FH. Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering. ACS CENTRAL SCIENCE 2024; 10:226-241. [PMID: 38435522 PMCID: PMC10906252 DOI: 10.1021/acscentsci.3c01275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/26/2023] [Accepted: 01/16/2024] [Indexed: 03/05/2024]
Abstract
Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.
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Affiliation(s)
- Jason Yang
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Francesca-Zhoufan Li
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Frances H. Arnold
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91125, United States
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8
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Kim DN, McNaughton AD, Kumar N. Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions. Bioengineering (Basel) 2024; 11:185. [PMID: 38391671 PMCID: PMC10886287 DOI: 10.3390/bioengineering11020185] [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: 12/30/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.
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Affiliation(s)
- Doo Nam Kim
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
| | - Andrew D McNaughton
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
| | - Neeraj Kumar
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
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9
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Ni B, Kaplan DL, Buehler MJ. ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. SCIENCE ADVANCES 2024; 10:eadl4000. [PMID: 38324676 PMCID: PMC10849601 DOI: 10.1126/sciadv.adl4000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024]
Abstract
Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here, we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as the target to enable the discovery of protein materials with superior mechanical properties.
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Affiliation(s)
- Bo Ni
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - David L. Kaplan
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
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10
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Satalkar V, Degaga GD, Li W, Pang YT, McShan AC, Gumbart JC, Mitchell JC, Torres MP. Generative β-hairpin design using a residue-based physicochemical property landscape. Biophys J 2024:S0006-3495(24)00070-5. [PMID: 38297834 DOI: 10.1016/j.bpj.2024.01.029] [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] [Revised: 12/20/2023] [Accepted: 01/25/2024] [Indexed: 02/02/2024] Open
Abstract
De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.
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Affiliation(s)
- Vardhan Satalkar
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia
| | - Gemechis D Degaga
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Wei Li
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia
| | - Yui Tik Pang
- School of Physics, Georgia Institute of Technology, Atlanta, Georgia
| | - Andrew C McShan
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
| | - James C Gumbart
- School of Physics, Georgia Institute of Technology, Atlanta, Georgia; School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia
| | - Julie C Mitchell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee.
| | - Matthew P Torres
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia; School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia.
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11
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Ni B, Kaplan DL, Buehler MJ. ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model. ARXIV 2023:arXiv:2310.10605v3. [PMID: 37904735 PMCID: PMC10614992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pre-trained protein language model and maps mechanical unfolding responses to create novel proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are novel, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as target to enable the discovery of protein materials with superior mechanical properties.
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Affiliation(s)
- Bo Ni
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
| | - David L. Kaplan
- Department of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
| | - Markus J. Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
- Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA
- Lead contact
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12
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Wysokowski M, Luu RK, Arevalo S, Khare E, Stachowiak W, Niemczak M, Jesionowski T, Buehler MJ. Untapped Potential of Deep Eutectic Solvents for the Synthesis of Bioinspired Inorganic-Organic Materials. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2023; 35:7878-7903. [PMID: 37840775 PMCID: PMC10568971 DOI: 10.1021/acs.chemmater.3c00847] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/02/2023] [Indexed: 10/17/2023]
Abstract
Since the discovery of deep eutectic solvents (DESs) in 2003, significant progress has been made in the field, specifically advancing aspects of their preparation and physicochemical characterization. Their low-cost and unique tailored properties are reasons for their growing importance as a sustainable medium for the resource-efficient processing and synthesis of advanced materials. In this paper, the significance of these designer solvents and their beneficial features, in particular with respect to biomimetic materials chemistry, is discussed. Finally, this article explores the unrealized potential and advantageous aspects of DESs, focusing on the development of biomineralization-inspired hybrid materials. It is anticipated that this article can stimulate new concepts and advances providing a reference for breaking down the multidisciplinary borders in the field of bioinspired materials chemistry, especially at the nexus of computation and experiment, and to develop a rigorous materials-by-design paradigm.
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Affiliation(s)
- Marcin Wysokowski
- Institute
of Chemical Technology, Faculty of Chemical Technology, Poznan University of Technology, Berdychowo 4, 60965 Poznan, Poland
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
| | - Rachel K. Luu
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
| | - Sofia Arevalo
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
| | - Eesha Khare
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
| | - Witold Stachowiak
- Institute
of Chemical Technology, Faculty of Chemical Technology, Poznan University of Technology, Berdychowo 4, 60965 Poznan, Poland
| | - Michał Niemczak
- Institute
of Chemical Technology, Faculty of Chemical Technology, Poznan University of Technology, Berdychowo 4, 60965 Poznan, Poland
| | - Teofil Jesionowski
- Institute
of Chemical Technology, Faculty of Chemical Technology, Poznan University of Technology, Berdychowo 4, 60965 Poznan, Poland
| | - Markus J. Buehler
- Laboratory
for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
- Center
for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, United States
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Mardikoraem M, Wang Z, Pascual N, Woldring D. Generative models for protein sequence modeling: recent advances and future directions. Brief Bioinform 2023; 24:bbad358. [PMID: 37864295 PMCID: PMC10589401 DOI: 10.1093/bib/bbad358] [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: 06/13/2023] [Revised: 09/08/2023] [Accepted: 09/12/2023] [Indexed: 10/22/2023] Open
Abstract
The widespread adoption of high-throughput omics technologies has exponentially increased the amount of protein sequence data involved in many salient disease pathways and their respective therapeutics and diagnostics. Despite the availability of large-scale sequence data, the lack of experimental fitness annotations underpins the need for self-supervised and unsupervised machine learning (ML) methods. These techniques leverage the meaningful features encoded in abundant unlabeled sequences to accomplish complex protein engineering tasks. Proficiency in the rapidly evolving fields of protein engineering and generative AI is required to realize the full potential of ML models as a tool for protein fitness landscape navigation. Here, we support this work by (i) providing an overview of the architecture and mathematical details of the most successful ML models applicable to sequence data (e.g. variational autoencoders, autoregressive models, generative adversarial neural networks, and diffusion models), (ii) guiding how to effectively implement these models on protein sequence data to predict fitness or generate high-fitness sequences and (iii) highlighting several successful studies that implement these techniques in protein engineering (from paratope regions and subcellular localization prediction to high-fitness sequences and protein design rules generation). By providing a comprehensive survey of model details, novel architecture developments, comparisons of model applications, and current challenges, this study intends to provide structured guidance and robust framework for delivering a prospective outlook in the ML-driven protein engineering field.
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Affiliation(s)
- Mehrsa Mardikoraem
- Michigan State University (MSU)‘s Department of Chemical Engineering and Materials Science
| | - Zirui Wang
- Regeneron Pharmaceuticals, Inc. Having received his B.S. in Chemical Engineering from MSU, he is currently pursuing a M.S. in Computer Science from Syracuse University
| | | | - Daniel Woldring
- MSU’s Department of Chemical Engineering and Materials Science and a member of MSU’s Institute for Quantitative Health Sciences and Engineering
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Badini S, Regondi S, Pugliese R. Unleashing the Power of Artificial Intelligence in Materials Design. MATERIALS (BASEL, SWITZERLAND) 2023; 16:5927. [PMID: 37687620 PMCID: PMC10488647 DOI: 10.3390/ma16175927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023]
Abstract
The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error experimentation. From this perspective, herein we describe how these tools can enable the acceleration and enrichment of each stage of the discovery cycle of novel materials with optimized properties. We begin by outlining the state-of-the-art AI models in materials design, including machine learning (ML), deep learning, and materials informatics tools. These methodologies enable the extraction of meaningful information from vast amounts of data, enabling researchers to uncover complex correlations and patterns within material properties, structures, and compositions. Next, a comprehensive overview of AI-driven materials design is provided and its potential future prospects are highlighted. By leveraging such AI algorithms, researchers can efficiently search and analyze databases containing a wide range of material properties, enabling the identification of promising candidates for specific applications. This capability has profound implications across various industries, from drug development to energy storage, where materials performance is crucial. Ultimately, AI-based approaches are poised to revolutionize our understanding and design of materials, ushering in a new era of accelerated innovation and advancement.
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Voignac D, Belsey S, Wermter E, Paltiel Y, Shoseyov O. Biobased Electronics: Tunable Dielectric and Piezoelectric Cellulose Nanocrystal-Protein Films. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2258. [PMID: 37570575 PMCID: PMC10421335 DOI: 10.3390/nano13152258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 08/13/2023]
Abstract
Cellulose has been a go-to material for its dielectric properties from the onset of capacitor development. The demand for an energy storage solution continues to grow, but the supply remains limited and relies too often on fossil and mined materials. This work proposes a fully sustainable and green method with which to produce dielectric thin films made of renewable and degradable materials. Cellulose nanocrystals (CNC) made an excellent matrix for the dispersion of proteins and the fabrication of robust transparent thin films with enhanced dielectric permittivity. A range of proteins sources, additives and concentrations allowed for us to control the dielectric permittivity from εr = 4 to 50. The proteins screened came from animal and plant sources. The films were formed from drying a water suspension of the CNC and proteins through evaporation-induced self-assembly. This yielded nano-layered structures with very high specific surface areas, ideal for energy storage devices. The resulting films were characterized with respect to the electrical, mechanical, piezoelectric, and optical properties to be compared. Electrically conductive (σ = 1.53 × 103 S/m) CNC films were prepared with carbon nanotubes (CNT). The fabricated films were used to make flexible, sustainable, and degradable capacitors by layering protein-based films between CNC-CNT composite films.
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Affiliation(s)
- Daniel Voignac
- Robert H. Smith Faculty of Agriculture, Food and Environment and Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Rehovot 7610001, Israel; (D.V.); (S.B.)
- Department of Applied Physics and Center for Nanoscience and Nanotechnology, The Hebrew University, Jerusalem 9190401, Israel;
| | - Shylee Belsey
- Robert H. Smith Faculty of Agriculture, Food and Environment and Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Rehovot 7610001, Israel; (D.V.); (S.B.)
| | - Elisabeth Wermter
- Faculty 5, HSB—City University of Applied Sciences, 28199 Bremen, Germany;
| | - Yossi Paltiel
- Department of Applied Physics and Center for Nanoscience and Nanotechnology, The Hebrew University, Jerusalem 9190401, Israel;
| | - Oded Shoseyov
- Robert H. Smith Faculty of Agriculture, Food and Environment and Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Rehovot 7610001, Israel; (D.V.); (S.B.)
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