1
|
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.
Collapse
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
| |
Collapse
|
2
|
Wang X, Li A, Li X, Cui H. Empowering Protein Engineering through Recombination of Beneficial Substitutions. Chemistry 2024; 30:e202303889. [PMID: 38288640 DOI: 10.1002/chem.202303889] [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: 01/04/2024] [Indexed: 02/24/2024]
Abstract
Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.
Collapse
Affiliation(s)
- Xinyue Wang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Anni Li
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Xiujuan Li
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Haiyang Cui
- School of Life Sciences, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| |
Collapse
|
3
|
Chu AE, Lu T, Huang PS. Sparks of function by de novo protein design. Nat Biotechnol 2024; 42:203-215. [PMID: 38361073 DOI: 10.1038/s41587-024-02133-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/09/2024] [Indexed: 02/17/2024]
Abstract
Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.
Collapse
Affiliation(s)
- Alexander E Chu
- Biophysics Program, Stanford University, Palo Alto, CA, USA
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
- Google DeepMind, London, UK
| | - Tianyu Lu
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
| | - Po-Ssu Huang
- Biophysics Program, Stanford University, Palo Alto, CA, USA.
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA.
| |
Collapse
|
4
|
Guo Z, Liu J, Wang Y, Chen M, Wang D, Xu D, Cheng J. Diffusion models in bioinformatics and computational biology. NATURE REVIEWS BIOENGINEERING 2024; 2:136-154. [PMID: 38576453 PMCID: PMC10994218 DOI: 10.1038/s44222-023-00114-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/25/2023] [Indexed: 04/06/2024]
Abstract
Denoising diffusion models embody a type of generative artificial intelligence that can be applied in computer vision, natural language processing and bioinformatics. In this Review, we introduce the key concepts and theoretical foundations of three diffusion modelling frameworks (denoising diffusion probabilistic models, noise-conditioned scoring networks and score stochastic differential equations). We then explore their applications in bioinformatics and computational biology, including protein design and generation, drug and small-molecule design, protein-ligand interaction modelling, cryo-electron microscopy image data analysis and single-cell data analysis. Finally, we highlight open-source diffusion model tools and consider the future applications of diffusion models in bioinformatics.
Collapse
Affiliation(s)
- Zhiye Guo
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Yanli Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Mengrui Chen
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Duolin Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| |
Collapse
|
5
|
Praljak N, Lian X, Ranganathan R, Ferguson AL. ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design. ACS Synth Biol 2023; 12:3544-3561. [PMID: 37988083 PMCID: PMC10911954 DOI: 10.1021/acssynbio.3c00261] [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] [Indexed: 11/22/2023]
Abstract
Deep generative models (DGMs) have shown great success in the understanding and data-driven design of proteins. Variational autoencoders (VAEs) are a popular DGM approach that can learn the correlated patterns of amino acid mutations within a multiple sequence alignment (MSA) of protein sequences and distill this information into a low-dimensional latent space to expose phylogenetic and functional relationships and guide generative protein design. Autoregressive (AR) models are another popular DGM approach that typically lacks a low-dimensional latent embedding but does not require training sequences to be aligned into an MSA and enable the design of variable length proteins. In this work, we propose ProtWave-VAE as a novel and lightweight DGM, employing an information maximizing VAE with a dilated convolution encoder and an autoregressive WaveNet decoder. This architecture blends the strengths of the VAE and AR paradigms in enabling training over unaligned sequence data and the conditional generative design of variable length sequences from an interpretable, low-dimensional learned latent space. We evaluated the model's ability to infer patterns and design rules within alignment-free homologous protein family sequences and to design novel synthetic proteins in four diverse protein families. We show that our model can infer meaningful functional and phylogenetic embeddings within latent spaces and make highly accurate predictions within semisupervised downstream fitness prediction tasks. In an application to the C-terminal SH3 domain in the Sho1 transmembrane osmosensing receptor in baker's yeast, we subject ProtWave-VAE-designed sequences to experimental gene synthesis and select-seq assays for the osmosensing function to show that the model enables synthetic protein design, conditional C-terminus diversification, and engineering of the osmosensing function into SH3 paralogues.
Collapse
Affiliation(s)
- Nikša Praljak
- Graduate Program in Biophysical Sciences, University of Chicago, Chicago, Illinois 60637, United States
| | - Xinran Lian
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Rama Ranganathan
- Center for Physics of Evolving Systems and Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois 60637, United States
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| |
Collapse
|
6
|
Wang J, Chen C, Yao G, Ding J, Wang L, Jiang H. Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review. Molecules 2023; 28:7865. [PMID: 38067593 PMCID: PMC10707872 DOI: 10.3390/molecules28237865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/13/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.
Collapse
Affiliation(s)
| | | | | | - Junjie Ding
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
| | - Liangliang Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
| | - Hui Jiang
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (J.W.); (C.C.); (G.Y.)
| |
Collapse
|
7
|
Ingraham JB, Baranov M, Costello Z, Barber KW, Wang W, Ismail A, Frappier V, Lord DM, Ng-Thow-Hing C, Van Vlack ER, Tie S, Xue V, Cowles SC, Leung A, Rodrigues JV, Morales-Perez CL, Ayoub AM, Green R, Puentes K, Oplinger F, Panwar NV, Obermeyer F, Root AR, Beam AL, Poelwijk FJ, Grigoryan G. Illuminating protein space with a programmable generative model. Nature 2023; 623:1070-1078. [PMID: 37968394 PMCID: PMC10686827 DOI: 10.1038/s41586-023-06728-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 10/06/2023] [Indexed: 11/17/2023]
Abstract
Three billion years of evolution has produced a tremendous diversity of protein molecules1, but the full potential of proteins is likely to be much greater. Accessing this potential has been challenging for both computation and experiments because the space of possible protein molecules is much larger than the space of those likely to have functions. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences, and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems that enables long-range reasoning with sub-quadratic scaling, layers for efficiently synthesizing three-dimensional structures of proteins from predicted inter-residue geometries and a general low-temperature sampling algorithm for diffusion models. Chroma achieves protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics and even natural-language prompts. The experimental characterization of 310 proteins shows that sampling from Chroma results in proteins that are highly expressed, fold and have favourable biophysical properties. The crystal structures of two designed proteins exhibit atomistic agreement with Chroma samples (a backbone root-mean-square deviation of around 1.0 Å). With this unified approach to protein design, we hope to accelerate the programming of protein matter to benefit human health, materials science and synthetic biology.
Collapse
Affiliation(s)
| | | | | | | | - Wujie Wang
- Generate Biomedicines, Somerville, MA, USA
| | | | | | | | | | | | - Shan Tie
- Generate Biomedicines, Somerville, MA, USA
| | | | | | - Alan Leung
- Generate Biomedicines, Somerville, MA, USA
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Li Y, Yao Y, Xia Y, Tang M. Searching for protein variants with desired properties using deep generative models. BMC Bioinformatics 2023; 24:297. [PMID: 37480001 PMCID: PMC10362698 DOI: 10.1186/s12859-023-05415-9] [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: 08/15/2022] [Accepted: 07/17/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Protein engineering aims to improve the functional properties of existing proteins to meet people's needs. Current deep learning-based models have captured evolutionary, functional, and biochemical features contained in amino acid sequences. However, the existing generative models need to be improved when capturing the relationship between amino acid sites on longer sequences. At the same time, the distribution of protein sequences in the homologous family has a specific positional relationship in the latent space. We want to use this relationship to search for new variants directly from the vicinity of better-performing varieties. RESULTS To improve the representation learning ability of the model for longer sequences and the similarity between the generated sequences and the original sequences, we propose a temporal variational autoencoder (T-VAE) model. T-VAE consists of an encoder and a decoder. The encoder expands the receptive field of neurons in the network structure by dilated causal convolution, thereby improving the encoding representation ability of longer sequences. The decoder decodes the sampled data into variants closely resembling the original sequence. CONCLUSION Compared to other models, the person correlation coefficient between the predicted values of protein fitness obtained by T-VAE and the truth values was higher, and the mean absolute deviation was lower. In addition, the T-VAE model has a better representation learning ability for longer sequences when comparing the encoding of protein sequences of different lengths. These results show that our model has more advantages in representation learning for longer sequences. To verify the model's generative effect, we also calculate the sequence identity between the generated data and the input data. The sequence identity obtained by T-VAE improved by 12.9% compared to the baseline model.
Collapse
Affiliation(s)
- Yan Li
- School of Information, Yunnan Normal University, Kunming, China
| | - Yinying Yao
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yu Xia
- School of Information, Yunnan Normal University, Kunming, China
| | - Mingjing Tang
- Engineering Research Center of Sustainable Development and Utilization of Biomass Energy, Ministry of Education, Yunnan Normal University, Kunming, China.
- School of Life Science, Yunnan Normal University, Kunming, China.
| |
Collapse
|
10
|
Dürr SL, Levy A, Rothlisberger U. Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins. Nat Commun 2023; 14:2713. [PMID: 37169763 PMCID: PMC10175565 DOI: 10.1038/s41467-023-37870-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 03/29/2023] [Indexed: 05/13/2023] Open
Abstract
Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.70 ± 0.64 Å of experimental locations. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. Metal3D predicts a global zinc density that can be used for annotation of computationally predicted structures and a per residue zinc density that can be used in protein design workflows. Currently trained on zinc, the framework of Metal3D is readily extensible to other metals by modifying the training data.
Collapse
Affiliation(s)
- Simon L Dürr
- Laboratory of Computational Chemistry and Biochemistry,Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Andrea Levy
- Laboratory of Computational Chemistry and Biochemistry,Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry,Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
| |
Collapse
|
11
|
Ziegler C, Martin J, Sinner C, Morcos F. Latent generative landscapes as maps of functional diversity in protein sequence space. Nat Commun 2023; 14:2222. [PMID: 37076519 PMCID: PMC10113739 DOI: 10.1038/s41467-023-37958-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 04/05/2023] [Indexed: 04/21/2023] Open
Abstract
Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.
Collapse
Affiliation(s)
- Cheyenne Ziegler
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Jonathan Martin
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Claude Sinner
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, 75080, USA.
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, 75080, USA.
- Center for Systems Biology, University of Texas at Dallas, Richardson, TX, 75080, USA.
| |
Collapse
|
12
|
Fatouros PR, Roy U, Sur S. Implications of SARS-CoV-2 spike protein interactions with Zn-bound form of ACE2: a computational structural study. Biometals 2023:10.1007/s10534-023-00491-z. [PMID: 36725769 PMCID: PMC9891659 DOI: 10.1007/s10534-023-00491-z] [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: 07/13/2022] [Accepted: 01/13/2023] [Indexed: 02/03/2023]
Abstract
The COVID-19 pandemic has generated a major interest in designing inhibitors to prevent SARS-CoV-2 binding on host cells to protect against infection. One promising approach to such research utilizes molecular dynamics simulation to identify potential inhibitors that can prevent the interaction between spike (S) protein on the virus and angiotensin converting enzyme 2 (ACE2) receptor on the host cells. In these studies, many groups have chosen to exclude the ACE2-bound zinc (Zn) ion, which is critical for its enzymatic activity. While the relatively distant location of Zn ion from the S protein binding site (S1 domain), combined with the difficulties in modeling this ion has motivated the decision of exclusion, Zn can potentially contribute to the structural stability of the entire protein, and thus, may have implications on S protein-ACE2 interaction. In this study, the authors model both the ACE2-S1 and ACE2-inhibitor (mAb) system to investigate if there are variations in structure and the readouts due to the presence of Zn ion. Although distant from the S1 or inhibitor binding region, inclusion/exclusion of Zn has statistically significant effects on the structural stability and binding free energy in these systems. In particular, the binding free energy of the ACE2-S1 and ACE2-inhibitor structures is - 3.26 and - 14.8 kcal/mol stronger, respectively, in the Zn-bound structure than in the Zn-free structures. This finding suggests that including Zn may be important in screening potentially inhibitors and may be particularly important in modeling monoclonal antibodies, which may be more sensitive to changes in antigen structure.
Collapse
Affiliation(s)
- Peter R. Fatouros
- Department of Chemical and Biomolecular Engineering, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699 USA
| | - Urmi Roy
- Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699 USA
| | - Shantanu Sur
- Department of Biology, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699 USA
| |
Collapse
|
13
|
Clifton BE, Kozome D, Laurino P. Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design. Biochemistry 2023; 62:210-220. [PMID: 35245020 DOI: 10.1021/acs.biochem.1c00757] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The rapid growth of sequence databases over the past two decades means that protein engineers faced with optimizing a protein for any given task will often have immediate access to a vast number of related protein sequences. These sequences encode information about the evolutionary history of the protein and the underlying sequence requirements to produce folded, stable, and functional protein variants. Methods that can take advantage of this information are an increasingly important part of the protein engineering tool kit. In this Perspective, we discuss the utility of sequence data in protein engineering and design, focusing on recent advances in three main areas: the use of ancestral sequence reconstruction as an engineering tool to generate thermostable and multifunctional proteins, the use of sequence data to guide engineering of multipoint mutants by structure-based computational protein design, and the use of unlabeled sequence data for unsupervised and semisupervised machine learning, allowing the generation of diverse and functional protein sequences in unexplored regions of sequence space. Altogether, these methods enable the rapid exploration of sequence space within regions enriched with functional proteins and therefore have great potential for accelerating the engineering of stable, functional, and diverse proteins for industrial and biomedical applications.
Collapse
Affiliation(s)
- Ben E Clifton
- Protein Engineering and Evolution Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna, Okinawa 904-0495, Japan
| | - Dan Kozome
- Protein Engineering and Evolution Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna, Okinawa 904-0495, Japan
| | - Paola Laurino
- Protein Engineering and Evolution Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna, Okinawa 904-0495, Japan
| |
Collapse
|
14
|
Soleymani F, Paquet E, Viktor H, Michalowski W, Spinello D. Protein-protein interaction prediction with deep learning: A comprehensive review. Comput Struct Biotechnol J 2022; 20:5316-5341. [PMID: 36212542 PMCID: PMC9520216 DOI: 10.1016/j.csbj.2022.08.070] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/15/2022] Open
Abstract
Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.
Collapse
Affiliation(s)
- Farzan Soleymani
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
| | - Eric Paquet
- National Research Council, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada
| | - Herna Viktor
- School of Electrical Engineering and Computer Science, University of Ottawa, ON, Canada
| | | | - Davide Spinello
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
| |
Collapse
|
15
|
Chen JC, Chen JP, Shen MW, Wornow M, Bae M, Yeh WH, Hsu A, Liu DR. Generating experimentally unrelated target molecule-binding highly functionalized nucleic-acid polymers using machine learning. Nat Commun 2022; 13:4541. [PMID: 35927274 PMCID: PMC9352670 DOI: 10.1038/s41467-022-31955-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 07/11/2022] [Indexed: 11/09/2022] Open
Abstract
In vitro selection queries large combinatorial libraries for sequence-defined polymers with target binding and reaction catalysis activity. While the total sequence space of these libraries can extend beyond 1022 sequences, practical considerations limit starting sequences to ≤~1015 distinct molecules. Selection-induced sequence convergence and limited sequencing depth further constrain experimentally observable sequence space. To address these limitations, we integrate experimental and machine learning approaches to explore regions of sequence space unrelated to experimentally derived variants. We perform in vitro selections to discover highly side-chain-functionalized nucleic acid polymers (HFNAPs) with potent affinities for a target small molecule (daunomycin KD = 5-65 nM). We then use the selection data to train a conditional variational autoencoder (CVAE) machine learning model to generate diverse and unique HFNAP sequences with high daunomycin affinities (KD = 9-26 nM), even though they are unrelated in sequence to experimental polymers. Coupling in vitro selection with a machine learning model thus enables direct generation of active variants, demonstrating a new approach to the discovery of functional biopolymers.
Collapse
Affiliation(s)
- Jonathan C. Chen
- grid.66859.340000 0004 0546 1623Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA USA ,grid.38142.3c000000041936754XDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA ,grid.38142.3c000000041936754XHoward Hughes Medical Institute, Harvard University, Cambridge, MA USA
| | - Jonathan P. Chen
- grid.512059.aWork conducted at Uber AI Labs, Uber Technologies, Inc., San Francisco, CA USA ,Meta Platforms, Menlo Park, CA USA
| | - Max W. Shen
- grid.66859.340000 0004 0546 1623Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA USA ,grid.38142.3c000000041936754XDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA ,grid.38142.3c000000041936754XHoward Hughes Medical Institute, Harvard University, Cambridge, MA USA ,grid.116068.80000 0001 2341 2786Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA USA
| | - Michael Wornow
- grid.66859.340000 0004 0546 1623Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA USA ,grid.38142.3c000000041936754XDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA
| | - Minwoo Bae
- grid.66859.340000 0004 0546 1623Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA USA ,grid.38142.3c000000041936754XDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA
| | - Wei-Hsi Yeh
- grid.66859.340000 0004 0546 1623Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA USA ,grid.38142.3c000000041936754XDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA ,grid.38142.3c000000041936754XHoward Hughes Medical Institute, Harvard University, Cambridge, MA USA ,grid.38142.3c000000041936754XProgram in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, MA USA
| | - Alvin Hsu
- grid.66859.340000 0004 0546 1623Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA USA ,grid.38142.3c000000041936754XDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA ,grid.38142.3c000000041936754XHoward Hughes Medical Institute, Harvard University, Cambridge, MA USA
| | - David R. Liu
- grid.66859.340000 0004 0546 1623Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA USA ,grid.38142.3c000000041936754XDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA ,grid.38142.3c000000041936754XHoward Hughes Medical Institute, Harvard University, Cambridge, MA USA
| |
Collapse
|
16
|
Andreini C, Rosato A. Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications. Int J Mol Sci 2022; 23:ijms23147684. [PMID: 35887033 PMCID: PMC9323969 DOI: 10.3390/ijms23147684] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/04/2022] [Accepted: 07/06/2022] [Indexed: 02/04/2023] Open
Abstract
All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures.
Collapse
Affiliation(s)
- Claudia Andreini
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy;
- Magnetic Resonance Center (CERM), Department of Chemistry, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Antonio Rosato
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy;
- Magnetic Resonance Center (CERM), Department of Chemistry, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Correspondence:
| |
Collapse
|
17
|
Kucera T, Togninalli M, Meng-Papaxanthos L. Conditional generative modeling for de novo protein design with hierarchical functions. Bioinformatics 2022; 38:3454-3461. [PMID: 35639661 PMCID: PMC9237736 DOI: 10.1093/bioinformatics/btac353] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 04/20/2022] [Accepted: 05/20/2022] [Indexed: 11/18/2022] Open
Abstract
Motivation Protein design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computational or experimental protocols. At the same time, machine learning has enabled the solving of complex problems by leveraging large amounts of available data, more recently with great improvements on the domain of generative modeling. Yet, generative models have mainly been applied to specific sub-problems of protein design. Results Here, we approach the problem of general-purpose protein design conditioned on functional labels of the hierarchical Gene Ontology. Since a canonical way to evaluate generative models in this domain is missing, we devise an evaluation scheme of several biologically and statistically inspired metrics. We then develop the conditional generative adversarial network ProteoGAN and show that it outperforms several classic and more recent deep-learning baselines for protein sequence generation. We further give insights into the model by analyzing hyperparameters and ablation baselines. Lastly, we hypothesize that a functionally conditional model could generate proteins with novel functions by combining labels and provide first steps into this direction of research. Availability and implementation The code and data underlying this article are available on GitHub at https://github.com/timkucera/proteogan, and can be accessed with doi:10.5281/zenodo.6591379. Supplementary information Supplemental data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Tim Kucera
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland
| | | | | |
Collapse
|
18
|
Talluri S. Algorithms for protein design. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 130:1-38. [PMID: 35534105 DOI: 10.1016/bs.apcsb.2022.01.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.
Collapse
Affiliation(s)
- Sekhar Talluri
- Department of Biotechnology, GITAM, Visakhapatnam, India.
| |
Collapse
|
19
|
Ding W, Nakai K, Gong H. Protein design via deep learning. Brief Bioinform 2022; 23:6554124. [PMID: 35348602 PMCID: PMC9116377 DOI: 10.1093/bib/bbac102] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/26/2022] [Accepted: 03/01/2022] [Indexed: 12/11/2022] Open
Abstract
Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.
Collapse
Affiliation(s)
- Wenze Ding
- School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.,School of Future Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.,MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China.,Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| | - Kenta Nakai
- Institute of Medical Science, the University of Tokyo, Tokyo 1088639, Japan
| | - Haipeng Gong
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China.,Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| |
Collapse
|
20
|
Yu Y, Wang R, Teo RD. Machine Learning Approaches for Metalloproteins. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27041277. [PMID: 35209064 PMCID: PMC8878495 DOI: 10.3390/molecules27041277] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/10/2022] [Accepted: 02/11/2022] [Indexed: 01/10/2023]
Abstract
Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.
Collapse
Affiliation(s)
- Yue Yu
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, Jiangsu 215316, China;
- Department of Physics, Duke University, Durham, NC 27708, USA
| | - Ruobing Wang
- Department of Chemistry, Duke University, Durham, NC 27708, USA;
| | - Ruijie D. Teo
- Department of Chemistry, Duke University, Durham, NC 27708, USA;
- UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Correspondence:
| |
Collapse
|
21
|
Lin E, Lin CH, Lane HY. De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update. J Chem Inf Model 2022; 62:761-774. [DOI: 10.1021/acs.jcim.1c01361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, Washington 98195, United States
- Department of Electrical & Computer Engineering, University of Washington, Seattle, Washington 98195, United States
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40447, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
| |
Collapse
|
22
|
Giessel A, Dousis A, Ravichandran K, Smith K, Sur S, McFadyen I, Zheng W, Licht S. Therapeutic enzyme engineering using a generative neural network. Sci Rep 2022; 12:1536. [PMID: 35087131 PMCID: PMC8795449 DOI: 10.1038/s41598-022-05195-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 12/15/2021] [Indexed: 12/31/2022] Open
Abstract
Enhancing the potency of mRNA therapeutics is an important objective for treating rare diseases, since it may enable lower and less-frequent dosing. Enzyme engineering can increase potency of mRNA therapeutics by improving the expression, half-life, and catalytic efficiency of the mRNA-encoded enzymes. However, sequence space is incomprehensibly vast, and methods to map sequence to function (computationally or experimentally) are inaccurate or time-/labor-intensive. Here, we present a novel, broadly applicable engineering method that combines deep latent variable modelling of sequence co-evolution with automated protein library design and construction to rapidly identify metabolic enzyme variants that are both more thermally stable and more catalytically active. We apply this approach to improve the potency of ornithine transcarbamylase (OTC), a urea cycle enzyme for which loss of catalytic activity causes a rare but serious metabolic disease.
Collapse
Affiliation(s)
- Andrew Giessel
- Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA.
| | - Athanasios Dousis
- Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA
| | | | - Kevin Smith
- Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA
| | - Sreyoshi Sur
- Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA
| | - Iain McFadyen
- Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA
| | - Wei Zheng
- Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA
| | - Stuart Licht
- Moderna Therapeutics, 200 Technology Square, Cambridge, MA, 02139, USA.
| |
Collapse
|
23
|
Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol 2022; 23:40-55. [PMID: 34518686 DOI: 10.1038/s41580-021-00407-0] [Citation(s) in RCA: 480] [Impact Index Per Article: 240.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/23/2021] [Indexed: 02/08/2023]
Abstract
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.
Collapse
Affiliation(s)
- Joe G Greener
- Department of Computer Science, University College London, London, UK
| | - Shaun M Kandathil
- Department of Computer Science, University College London, London, UK
| | - Lewis Moffat
- Department of Computer Science, University College London, London, UK
| | - David T Jones
- Department of Computer Science, University College London, London, UK.
| |
Collapse
|
24
|
Rudden LSP, Hijazi M, Barth P. Deep learning approaches for conformational flexibility and switching properties in protein design. Front Mol Biosci 2022; 9:928534. [PMID: 36032687 PMCID: PMC9399439 DOI: 10.3389/fmolb.2022.928534] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/15/2022] [Indexed: 11/30/2022] Open
Abstract
Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibility of proteins, from side-chain motion to larger conformational reshuffling, poses a challenge to design methods, where the ideal approach must consider both the spatial and temporal evolution of proteins in the context of their functional capacity. In this review, we highlight existing methods for protein design before discussing how methods at the forefront of deep learning-based design accommodate flexibility and where the field could evolve in the future.
Collapse
Affiliation(s)
| | | | - Patrick Barth
- *Correspondence: Lucas S. P. Rudden, ; Patrick Barth,
| |
Collapse
|
25
|
Deep generative modeling for protein design. Curr Opin Struct Biol 2021; 72:226-236. [PMID: 34963082 DOI: 10.1016/j.sbi.2021.11.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/01/2021] [Accepted: 11/22/2021] [Indexed: 11/21/2022]
Abstract
Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed that encompass all known protein sequences, model specific protein families, or extrapolate the dynamics of individual proteins. Those generative models can learn protein representations that are often more informative of protein structure and function than hand-engineered features. Furthermore, they can be used to quickly propose millions of novel proteins that resemble the native counterparts in terms of expression level, stability, or other attributes. The protein design process can further be guided by discriminative oracles to select candidates with the highest probability of having the desired properties. In this review, we discuss five classes of generative models that have been most successful at modeling proteins and provide a framework for model guided protein design.
Collapse
|
26
|
Thomas M, Boardman A, Garcia-Ortegon M, Yang H, de Graaf C, Bender A. Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2390:1-59. [PMID: 34731463 DOI: 10.1007/978-1-0716-1787-8_1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.
Collapse
Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Andrew Boardman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Miguel Garcia-Ortegon
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.,Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK
| | - Hongbin Yang
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | | | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.
| |
Collapse
|
27
|
Defresne M, Barbe S, Schiex T. Protein Design with Deep Learning. Int J Mol Sci 2021; 22:11741. [PMID: 34769173 PMCID: PMC8584038 DOI: 10.3390/ijms222111741] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/23/2021] [Accepted: 10/26/2021] [Indexed: 12/21/2022] Open
Abstract
Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.
Collapse
Affiliation(s)
- Marianne Defresne
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, ANITI, 31077 Toulouse, France; (M.D.); (S.B.)
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France
| | - Sophie Barbe
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, ANITI, 31077 Toulouse, France; (M.D.); (S.B.)
| | - Thomas Schiex
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France
| |
Collapse
|
28
|
Linder J, Seelig G. Fast activation maximization for molecular sequence design. BMC Bioinformatics 2021; 22:510. [PMID: 34670493 PMCID: PMC8527647 DOI: 10.1186/s12859-021-04437-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 10/11/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Optimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation, which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, the current version of the method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence. RESULTS Here, we introduce Fast SeqProp, an improved activation maximization method that combines straight-through approximation with normalization across the parameters of the input sequence distribution. Fast SeqProp overcomes bottlenecks in earlier methods arising from input parameters becoming skewed during optimization. Compared to prior methods, Fast SeqProp results in up to 100-fold faster convergence while also finding improved fitness optima for many applications. We demonstrate Fast SeqProp's capabilities by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor. CONCLUSIONS Fast SeqProp offers a reliable and efficient method for general-purpose sequence optimization through a differentiable fitness predictor. As demonstrated on a variety of deep learning models, the method is widely applicable, and can incorporate various regularization techniques to maintain confidence in the sequence designs. As a design tool, Fast SeqProp may aid in the development of novel molecules, drug therapies and vaccines.
Collapse
Affiliation(s)
- Johannes Linder
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA
| | - Georg Seelig
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, USA
| |
Collapse
|
29
|
Trinquier J, Uguzzoni G, Pagnani A, Zamponi F, Weigt M. Efficient generative modeling of protein sequences using simple autoregressive models. Nat Commun 2021; 12:5800. [PMID: 34608136 PMCID: PMC8490405 DOI: 10.1038/s41467-021-25756-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 08/23/2021] [Indexed: 02/08/2023] Open
Abstract
Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost (by a factor between 102 and 103). Furthermore, the simple structure of our models has distinctive mathematical advantages, which translate into an improved applicability in sequence generation and evaluation. Within these models, we can easily estimate both the probability of a given sequence, and, using the model's entropy, the size of the functional sequence space related to a specific protein family. In the example of response regulators, we find a huge number of ca. 1068 possible sequences, which nevertheless constitute only the astronomically small fraction 10-80 of all amino-acid sequences of the same length. These findings illustrate the potential and the difficulty in exploring sequence space via generative sequence models.
Collapse
Affiliation(s)
- Jeanne Trinquier
- grid.503253.20000 0004 0520 7190Sorbonne Université, CNRS, Institut de Biologie Paris Seine, Biologie Computationnelle et Quantitative LCQB, F-75005 Paris, France ,grid.462608.e0000 0004 0384 7821Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - Guido Uguzzoni
- grid.4800.c0000 0004 1937 0343Department of Applied Science and Technology (DISAT), Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy ,grid.428948.b0000 0004 1784 6598Italian Institute for Genomic Medicine, IRCCS Candiolo, SP-142, I-10060 Candiolo (TO), Italy
| | - Andrea Pagnani
- grid.4800.c0000 0004 1937 0343Department of Applied Science and Technology (DISAT), Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy ,grid.428948.b0000 0004 1784 6598Italian Institute for Genomic Medicine, IRCCS Candiolo, SP-142, I-10060 Candiolo (TO), Italy ,grid.470222.10000 0004 7471 9712INFN Sezione di Torino, Via P. Giuria 1, I-10125 Torino, Italy
| | - Francesco Zamponi
- grid.462608.e0000 0004 0384 7821Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, F-75005 Paris, France
| | - Martin Weigt
- grid.503253.20000 0004 0520 7190Sorbonne Université, CNRS, Institut de Biologie Paris Seine, Biologie Computationnelle et Quantitative LCQB, F-75005 Paris, France
| |
Collapse
|
30
|
Bitard-Feildel T. Navigating the amino acid sequence space between functional proteins using a deep learning framework. PeerJ Comput Sci 2021; 7:e684. [PMID: 34616884 PMCID: PMC8459775 DOI: 10.7717/peerj-cs.684] [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: 03/25/2021] [Accepted: 07/30/2021] [Indexed: 06/13/2023]
Abstract
MOTIVATION Shedding light on the relationships between protein sequences and functions is a challenging task with many implications in protein evolution, diseases understanding, and protein design. The protein sequence space mapping to specific functions is however hard to comprehend due to its complexity. Generative models help to decipher complex systems thanks to their abilities to learn and recreate data specificity. Applied to proteins, they can capture the sequence patterns associated with functions and point out important relationships between sequence positions. By learning these dependencies between sequences and functions, they can ultimately be used to generate new sequences and navigate through uncharted area of molecular evolution. RESULTS This study presents an Adversarial Auto-Encoder (AAE) approached, an unsupervised generative model, to generate new protein sequences. AAEs are tested on three protein families known for their multiple functions the sulfatase, the HUP and the TPP families. Clustering results on the encoded sequences from the latent space computed by AAEs display high level of homogeneity regarding the protein sequence functions. The study also reports and analyzes for the first time two sampling strategies based on latent space interpolation and latent space arithmetic to generate intermediate protein sequences sharing sequential properties of original sequences linked to known functional properties issued from different families and functions. Generated sequences by interpolation between latent space data points demonstrate the ability of the AAE to generalize and produce meaningful biological sequences from an evolutionary uncharted area of the biological sequence space. Finally, 3D structure models computed by comparative modelling using generated sequences and templates of different sub-families point out to the ability of the latent space arithmetic to successfully transfer protein sequence properties linked to function between different sub-families. All in all this study confirms the ability of deep learning frameworks to model biological complexity and bring new tools to explore amino acid sequence and functional spaces.
Collapse
Affiliation(s)
- Tristan Bitard-Feildel
- IBPS, CNRS, Laboratoire de Biologie Computationnelle et Quantitative, Sorbonne Université, Paris, France
- Institut des Sciences du Calcul et de des Données (ISCD), Sorbonne Université, Paris, France
| |
Collapse
|
31
|
Takahashi T, Chikenji G, Tokita K. Lattice protein design using Bayesian learning. Phys Rev E 2021; 104:014404. [PMID: 34412286 DOI: 10.1103/physreve.104.014404] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 06/11/2021] [Indexed: 01/01/2023]
Abstract
Protein design is the inverse approach of the three-dimensional (3D) structure prediction for elucidating the relationship between the 3D structures and amino acid sequences. In general, the computation of the protein design involves a double loop: A loop for amino acid sequence changes and a loop for an exhaustive conformational search for each amino acid sequence. Herein, we propose a novel statistical mechanical design method using Bayesian learning, which can design lattice proteins without the exhaustive conformational search. We consider a thermodynamic hypothesis of the evolution of proteins and apply it to the prior distribution of amino acid sequences. Furthermore, we take the water effect into account in view of the grand canonical picture. As a result, on applying the 2D lattice hydrophobic-polar (HP) model, our design method successfully finds an amino acid sequence for which the target conformation has a unique ground state. However, the performance was not as good for the 3D lattice HP models compared to the 2D models. The performance of the 3D model improves on using a 20-letter lattice proteins. Furthermore, we find a strong linearity between the chemical potential of water and the number of surface residues, thereby revealing the relationship between protein structure and the effect of water molecules. The advantage of our method is that it greatly reduces computation time, because it does not require long calculations for the partition function corresponding to an exhaustive conformational search. As our method uses a general form of Bayesian learning and statistical mechanics and is not limited to lattice proteins, the results presented here elucidate some heuristics used successfully in previous protein design methods.
Collapse
Affiliation(s)
- Tomoei Takahashi
- Graduate School of Informatics, Nagoya University, Nagoya 464-8601, Japan
| | - George Chikenji
- Graduate School of Engineering, Nagoya University, Nagoya 464-8603, Japan
| | - Kei Tokita
- Graduate School of Informatics, Nagoya University, Nagoya 464-8601, Japan
| |
Collapse
|
32
|
Mowbray M, Savage T, Wu C, Song Z, Cho BA, Del Rio-Chanona EA, Zhang D. Machine learning for biochemical engineering: A review. Biochem Eng J 2021. [DOI: 10.1016/j.bej.2021.108054] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
33
|
Principles and Methods in Computational Membrane Protein Design. J Mol Biol 2021; 433:167154. [PMID: 34271008 DOI: 10.1016/j.jmb.2021.167154] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/03/2021] [Accepted: 07/06/2021] [Indexed: 01/13/2023]
Abstract
After decades of progress in computational protein design, the design of proteins folding and functioning in lipid membranes appears today as the next frontier. Some notable successes in the de novo design of simplified model membrane protein systems have helped articulate fundamental principles of protein folding, architecture and interaction in the hydrophobic lipid environment. These principles are reviewed here, together with the computational methods and approaches that were used to identify them. We provide an overview of the methodological innovations in the generation of new protein structures and functions and in the development of membrane-specific energy functions. We highlight the opportunities offered by new machine learning approaches applied to protein design, and by new experimental characterization techniques applied to membrane proteins. Although membrane protein design is in its infancy, it appears more reachable than previously thought.
Collapse
|
34
|
Cao Y, Das P, Chenthamarakshan V, Chen PY, Melnyk I, Shen Y. Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2021; 139:1261-1271. [PMID: 34423306 PMCID: PMC8375603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Designing novel protein sequences for a desired 3D topological fold is a fundamental yet nontrivial task in protein engineering. Challenges exist due to the complex sequence-fold relationship, as well as the difficulties to capture the diversity of the sequences (therefore structures and functions) within a fold. To overcome these challenges, we propose Fold2Seq, a novel transformer-based generative framework for designing protein sequences conditioned on a specific target fold. To model the complex sequence-structure relationship, Fold2Seq jointly learns a sequence embedding using a transformer and a fold embedding from the density of secondary structural elements in 3D voxels. On test sets with single, high-resolution and complete structure inputs for individual folds, our experiments demonstrate improved or comparable performance of Fold2Seq in terms of speed, coverage, and reliability for sequence design, when compared to existing state-of-the-art methods that include data-driven deep generative models and physics-based RosettaDesign. The unique advantages of fold-based Fold2Seq, in comparison to a structure-based deep model and RosettaDesign, become more evident on three additional real-world challenges originating from low-quality, incomplete, or ambiguous input structures. Source code and data are available at https://github.com/IBM/fold2seq.
Collapse
Affiliation(s)
- Yue Cao
- IBM Research
- Texas A&M University
| | | | | | | | | | | |
Collapse
|
35
|
Osadchy M, Kolodny R. How Deep Learning Tools Can Help Protein Engineers Find Good Sequences. J Phys Chem B 2021; 125:6440-6450. [PMID: 34105961 DOI: 10.1021/acs.jpcb.1c02449] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The deep learning revolution introduced a new and efficacious way to address computational challenges in a wide range of fields, relying on large data sets and powerful computational resources. In protein engineering, we consider the challenge of computationally predicting properties of a protein and designing sequences with these properties. Indeed, accurate and fast deep network oracles for different properties of proteins have been developed. These learn to predict a property from an amino acid sequence by training on large sets of proteins that have this property. In particular, deep networks can learn from the set of all known protein sequences to identify ones that are protein-like. A fundamental challenge when engineering sequences that are both protein-like and satisfy a desired property is that these are rare instances within the vast space of all possible ones. When searching for these very rare instances, one would like to use good sampling procedures. Sampling approaches that are decoupled from the prediction of the property or in which the predictor uses only post-sampling to identify good instances are less efficient. The alternative is to use sampling methods that are geared to generate sequences satisfying and/or optimizing the predictor's desired properties. Deep learning has a class of architectures, denoted as generative models, which offer the capability of sampling from the learned distribution of a predicted property. Here, we review the use of deep learning tools to find good sequences for protein engineering, including developing oracles/predictors of a property of the proteins and methods that sample from a distribution of protein-like sequences to optimize the desired property.
Collapse
Affiliation(s)
- Margarita Osadchy
- Department of Computer Science, Jacobs Building, University of Haifa, 199 Aba Houshi Road, Mount Carmel, Haifa, Israel 3498838
| | - Rachel Kolodny
- Department of Computer Science, Jacobs Building, University of Haifa, 199 Aba Houshi Road, Mount Carmel, Haifa, Israel 3498838
| |
Collapse
|
36
|
Ashraf C, Joshi N, Beck DAC, Pfaendtner J. Data Science in Chemical Engineering: Applications to Molecular Science. Annu Rev Chem Biomol Eng 2021; 12:15-37. [PMID: 33710940 DOI: 10.1146/annurev-chembioeng-101220-102232] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science-the application tools for molecular discovery and property optimization at the atomic scale. We summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.
Collapse
Affiliation(s)
- Chowdhury Ashraf
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, USA; ,
| | - Nisarg Joshi
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, USA; ,
| | - David A C Beck
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, USA; , .,eScience Institute, University of Washington, Seattle, Washington 98195, USA
| | - Jim Pfaendtner
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, USA; ,
| |
Collapse
|
37
|
Pearce R, Zhang Y. Deep learning techniques have significantly impacted protein structure prediction and protein design. Curr Opin Struct Biol 2021; 68:194-207. [PMID: 33639355 PMCID: PMC8222070 DOI: 10.1016/j.sbi.2021.01.007] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 01/09/2021] [Accepted: 01/18/2021] [Indexed: 12/26/2022]
Abstract
Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constraint prediction and end-to-end model training has significantly improved the accuracy of protein structure prediction, largely solving the problem at the fold level for single-domain proteins. The field of protein design has also witnessed dramatic improvement, where noticeable examples have shown that information stored in neural-network models can be used to advance functional protein design. Thus, incorporation of deep learning techniques into different steps of protein folding and design approaches represents an exciting future direction and should continue to have a transformative impact on both fields.
Collapse
Affiliation(s)
- Robin Pearce
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
| |
Collapse
|
38
|
Wu Z, Johnston KE, Arnold FH, Yang KK. Protein sequence design with deep generative models. Curr Opin Chem Biol 2021; 65:18-27. [PMID: 34051682 DOI: 10.1016/j.cbpa.2021.04.004] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 12/20/2022]
Abstract
Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.
Collapse
Affiliation(s)
- Zachary Wu
- Division of Chemistry and Chemical Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, 91125, CA, USA
| | - Kadina E Johnston
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, 91125, CA, USA
| | - Frances H Arnold
- Division of Chemistry and Chemical Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, 91125, CA, USA; Division of Biology and Biological Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, 91125, CA, USA
| | - Kevin K Yang
- Microsoft Research New England, 1 Memorial Drive, Cambridge, 02142, MA, USA.
| |
Collapse
|
39
|
Ward MD, Zimmerman MI, Meller A, Chung M, Swamidass SJ, Bowman GR. Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets. Nat Commun 2021; 12:3023. [PMID: 34021153 PMCID: PMC8140102 DOI: 10.1038/s41467-021-23246-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 04/16/2021] [Indexed: 12/05/2022] Open
Abstract
Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of β-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding.
Collapse
Affiliation(s)
- Michael D Ward
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, USA
| | - Maxwell I Zimmerman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, USA
| | - Artur Meller
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, USA
| | - Moses Chung
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, USA
| | - S J Swamidass
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Gregory R Bowman
- Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA.
- Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, USA.
| |
Collapse
|
40
|
Shin JE, Riesselman AJ, Kollasch AW, McMahon C, Simon E, Sander C, Manglik A, Kruse AC, Marks DS. Protein design and variant prediction using autoregressive generative models. Nat Commun 2021; 12:2403. [PMID: 33893299 PMCID: PMC8065141 DOI: 10.1038/s41467-021-22732-w] [Citation(s) in RCA: 120] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 03/26/2021] [Indexed: 12/11/2022] Open
Abstract
The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 105-nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design.
Collapse
Affiliation(s)
- Jung-Eun Shin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Adam J Riesselman
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- insitro, South San Francisco, CA, USA
| | - Aaron W Kollasch
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Conor McMahon
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Vertex Pharmaceuticals, Boston, MA, USA
| | - Elana Simon
- Harvard College, Cambridge, MA, USA
- Reverie Labs, Cambridge, MA, USA
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Aashish Manglik
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
- Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA
| | - Andrew C Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA.
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| |
Collapse
|
41
|
DiPrimio DJ, Holland PL. Repurposing metalloproteins as mimics of natural metalloenzymes for small-molecule activation. J Inorg Biochem 2021; 219:111430. [PMID: 33873051 DOI: 10.1016/j.jinorgbio.2021.111430] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 03/11/2021] [Accepted: 03/15/2021] [Indexed: 12/12/2022]
Abstract
Artificial metalloenzymes (ArMs) consist of an unnatural metal or cofactor embedded in a protein scaffold, and are an excellent platform for applying the concepts of protein engineering to catalysis. In this Focused Review, we describe the application of ArMs as simple, tunable artificial models of the active sites of complex natural metalloenzymes for small-molecule activation. In this sense, ArMs expand the strategies of synthetic model chemistry to protein-based supporting ligands with potential for participation from the second coordination sphere. We focus specifically on ArMs that are structural, spectroscopic, and functional models of enzymes for activation of small molecules like CO, CO2, O2, N2, and NO, as well as production/consumption of H2. These ArMs give insight into the identities and roles of metalloenzyme structural features within and near the cofactor. We give examples of ArM work relevant to hydrogenases, acetyl-coenzyme A synthase, superoxide dismutase, heme oxygenases, nitric oxide reductase, methyl-coenzyme M reductase, copper-O2 enzymes, and nitrogenases.
Collapse
Affiliation(s)
- Daniel J DiPrimio
- Department of Chemistry, Yale University, New Haven, CT, 06520, United States
| | - Patrick L Holland
- Department of Chemistry, Yale University, New Haven, CT, 06520, United States.
| |
Collapse
|
42
|
Ferguson AL, Ranganathan R. 100th Anniversary of Macromolecular Science Viewpoint: Data-Driven Protein Design. ACS Macro Lett 2021; 10:327-340. [PMID: 35549066 DOI: 10.1021/acsmacrolett.0c00885] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The design of synthetic proteins with the desired function is a long-standing goal in biomolecular science, with broad applications in biochemical engineering, agriculture, medicine, and public health. Rational de novo design and experimental directed evolution have achieved remarkable successes but are challenged by the requirement to find functional "needles" in the vast "haystack" of protein sequence space. Data-driven models for fitness landscapes provide a predictive map between protein sequence and function and can prospectively identify functional candidates for experimental testing to greatly improve the efficiency of this search. This Viewpoint reviews the applications of machine learning and, in particular, deep learning as part of data-driven protein engineering platforms. We highlight recent successes, review promising computational methodologies, and provide an outlook on future challenges and opportunities. The article is written for a broad audience comprising both polymer and protein scientists and computer and data scientists interested in an up-to-date review of recent innovations and opportunities in this rapidly evolving field.
Collapse
Affiliation(s)
- Andrew L. Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Rama Ranganathan
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
- Center for Physics of Evolving Systems, University of Chicago, Chicago, Illinois 60637, United States
- Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois 60637, United States
| |
Collapse
|
43
|
Norn C, Wicky BIM, Juergens D, Liu S, Kim D, Tischer D, Koepnick B, Anishchenko I, Baker D, Ovchinnikov S. Protein sequence design by conformational landscape optimization. Proc Natl Acad Sci U S A 2021; 118:e2017228118. [PMID: 33712545 PMCID: PMC7980421 DOI: 10.1073/pnas.2017228118] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowest energy state. As this calculation involves not only all possible amino acid sequences but also, all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest-energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest-energy conformation for the designed sequence, and typically discarding a large fraction of designed sequences for which this is not the case. Here, we show that by backpropagating gradients through the transform-restrained Rosetta (trRosetta) structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino acid sequences and all possible structures in a single calculation. We find that trRosetta calculations, which consider the full conformational landscape, can be more effective than Rosetta single-point energy estimations in predicting folding and stability of de novo designed proteins. We compare sequence design by conformational landscape optimization with the standard energy-based sequence design methodology in Rosetta and show that the former can result in energy landscapes with fewer alternative energy minima. We show further that more funneled energy landscapes can be designed by combining the strengths of the two approaches: the low-resolution trRosetta model serves to disfavor alternative states, and the high-resolution Rosetta model serves to create a deep energy minimum at the design target structure.
Collapse
Affiliation(s)
- Christoffer Norn
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
| | - Basile I M Wicky
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
| | - David Juergens
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
- Graduate Program in Molecular Engineering, University of Washington, Seattle, WA 98105
| | - Sirui Liu
- Faculty of Arts and Sciences, Division of Science, Harvard University, Cambridge, MA 02138
| | - David Kim
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
| | - Doug Tischer
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
| | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98105;
- Institute for Protein Design, University of Washington, Seattle, WA 98105
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105
| | - Sergey Ovchinnikov
- Faculty of Arts and Sciences, Division of Science, Harvard University, Cambridge, MA 02138;
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA 02138
| |
Collapse
|
44
|
Kopf A, Claassen M. Latent representation learning in biology and translational medicine. PATTERNS (NEW YORK, N.Y.) 2021; 2:100198. [PMID: 33748792 PMCID: PMC7961186 DOI: 10.1016/j.patter.2021.100198] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Current data generation capabilities in the life sciences render scientists in an apparently contradicting situation. While it is possible to simultaneously measure an ever-increasing number of systems parameters, the resulting data are becoming increasingly difficult to interpret. Latent variable modeling allows for such interpretation by learning non-measurable hidden variables from observations. This review gives an overview over the different formal approaches to latent variable modeling, as well as applications at different scales of biological systems, such as molecular structures, intra- and intercellular regulatory up to physiological networks. The focus is on demonstrating how these approaches have enabled interpretable representations and ultimately insights in each of these domains. We anticipate that a wider dissemination of latent variable modeling in the life sciences will enable a more effective and productive interpretation of studies based on heterogeneous and high-dimensional data modalities.
Collapse
Affiliation(s)
- Andreas Kopf
- Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland
| | - Manfred Claassen
- Division of Clinical Bioinformatics, Department of Internal Medicine I, University Hospital Tübingen, 72076 Tübingen, Germany
- Computer Science Department, Eberhard Karls University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence Machine Learning (EXC 2064), Eberhard Karls University of Tübingen, 72076 Tübingen, Germany
| |
Collapse
|
45
|
Repecka D, Jauniskis V, Karpus L, Rembeza E, Rokaitis I, Zrimec J, Poviloniene S, Laurynenas A, Viknander S, Abuajwa W, Savolainen O, Meskys R, Engqvist MKM, Zelezniak A. Expanding functional protein sequence spaces using generative adversarial networks. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00310-5] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
|
46
|
Narayanan H, Dingfelder F, Butté A, Lorenzen N, Sokolov M, Arosio P. Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation. Trends Pharmacol Sci 2021; 42:151-165. [DOI: 10.1016/j.tips.2020.12.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 12/10/2020] [Accepted: 12/16/2020] [Indexed: 12/19/2022]
|
47
|
Wittmann BJ, Johnston KE, Wu Z, Arnold FH. Advances in machine learning for directed evolution. Curr Opin Struct Biol 2021; 69:11-18. [PMID: 33647531 DOI: 10.1016/j.sbi.2021.01.008] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/09/2021] [Accepted: 01/26/2021] [Indexed: 01/11/2023]
Abstract
Machine learning (ML) can expedite directed evolution by allowing researchers to move expensive experimental screens in silico. Gathering sequence-function data for training ML models, however, can still be costly. In contrast, raw protein sequence data is widely available. Recent advances in ML approaches use protein sequences to augment limited sequence-function data for directed evolution. We highlight contributions in a growing effort to use sequences to reduce or eliminate the amount of sequence-function data needed for effective in silico screening. We also highlight approaches that use ML models trained on sequences to generate new functional sequence diversity, focusing on strategies that use these generative models to efficiently explore vast regions of protein space.
Collapse
Affiliation(s)
- Bruce J Wittmann
- Division of Biology and Biological Engineering, California Institute of Technology, MC 210-41, 1200 E. California Boulevard, Pasadena, CA 91125, USA
| | - Kadina E Johnston
- Division of Biology and Biological Engineering, California Institute of Technology, MC 210-41, 1200 E. California Boulevard, Pasadena, CA 91125, USA
| | - Zachary Wu
- Division of Chemistry and Chemical Engineering, California Institute of Technology, MC 210-41, 1200 E. California Boulevard, Pasadena, CA 91125, USA; Present address: Google DeepMind, 6 Pancras Square, Kings Cross, London, N1C 4AG, UK
| | - Frances H Arnold
- Division of Biology and Biological Engineering, California Institute of Technology, MC 210-41, 1200 E. California Boulevard, Pasadena, CA 91125, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, MC 210-41, 1200 E. California Boulevard, Pasadena, CA 91125, USA.
| |
Collapse
|
48
|
Hawkins-Hooker A, Depardieu F, Baur S, Couairon G, Chen A, Bikard D. Generating functional protein variants with variational autoencoders. PLoS Comput Biol 2021; 17:e1008736. [PMID: 33635868 PMCID: PMC7946179 DOI: 10.1371/journal.pcbi.1008736] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 03/10/2021] [Accepted: 01/25/2021] [Indexed: 11/20/2022] Open
Abstract
The vast expansion of protein sequence databases provides an opportunity for new protein design approaches which seek to learn the sequence-function relationship directly from natural sequence variation. Deep generative models trained on protein sequence data have been shown to learn biologically meaningful representations helpful for a variety of downstream tasks, but their potential for direct use in the design of novel proteins remains largely unexplored. Here we show that variational autoencoders trained on a dataset of almost 70000 luciferase-like oxidoreductases can be used to generate novel, functional variants of the luxA bacterial luciferase. We propose separate VAE models to work with aligned sequence input (MSA VAE) and raw sequence input (AR-VAE), and offer evidence that while both are able to reproduce patterns of amino acid usage characteristic of the family, the MSA VAE is better able to capture long-distance dependencies reflecting the influence of 3D structure. To confirm the practical utility of the models, we used them to generate variants of luxA whose luminescence activity was validated experimentally. We further showed that conditional variants of both models could be used to increase the solubility of luxA without disrupting function. Altogether 6/12 of the variants generated using the unconditional AR-VAE and 9/11 generated using the unconditional MSA VAE retained measurable luminescence, together with all 23 of the less distant variants generated by conditional versions of the models; the most distant functional variant contained 35 differences relative to the nearest training set sequence. These results demonstrate the feasibility of using deep generative models to explore the space of possible protein sequences and generate useful variants, providing a method complementary to rational design and directed evolution approaches.
Collapse
Affiliation(s)
- Alex Hawkins-Hooker
- Synthetic Biology Group, Microbiology Department, Institut Pasteur, Paris, France
| | - Florence Depardieu
- Synthetic Biology Group, Microbiology Department, Institut Pasteur, Paris, France
| | - Sebastien Baur
- Synthetic Biology Group, Microbiology Department, Institut Pasteur, Paris, France
| | - Guillaume Couairon
- Synthetic Biology Group, Microbiology Department, Institut Pasteur, Paris, France
| | - Arthur Chen
- Synthetic Biology Group, Microbiology Department, Institut Pasteur, Paris, France
| | - David Bikard
- Synthetic Biology Group, Microbiology Department, Institut Pasteur, Paris, France
| |
Collapse
|
49
|
Karimi M, Zhu S, Cao Y, Shen Y. De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks. J Chem Inf Model 2020; 60:5667-5681. [PMID: 32945673 PMCID: PMC7775287 DOI: 10.1021/acs.jcim.0c00593] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Although massive data is quickly accumulating on protein sequence and structure, there is a small and limited number of protein architectural types (or structural folds). This study is addressing the following question: how well could one reveal underlying sequence-structure relationships and design protein sequences for an arbitrary, potentially novel, structural fold? In response to the question, we have developed novel deep generative models, namely, semisupervised gcWGAN (guided, conditional, Wasserstein Generative Adversarial Networks). To overcome training difficulties and improve design qualities, we build our models on conditional Wasserstein GAN (WGAN) that uses Wasserstein distance in the loss function. Our major contributions include (1) constructing a low-dimensional and generalizable representation of the fold space for the conditional input, (2) developing an ultrafast sequence-to-fold predictor (or oracle) and incorporating its feedback into WGAN as a loss to guide model training, and (3) exploiting sequence data with and without paired structures to enable a semisupervised training strategy. Assessed by the oracle over 100 novel folds not in the training set, gcWGAN generates more successful designs and covers 3.5 times more target folds compared to a competing data-driven method (cVAE). Assessed by sequence- and structure-based predictors, gcWGAN designs are physically and biologically sound. Assessed by a structure predictor over representative novel folds, including one not even part of basis folds, gcWGAN designs have comparable or better fold accuracy yet much more sequence diversity and novelty than cVAE. The ultrafast data-driven model is further shown to boost the success of a principle-driven de novo method (RosettaDesign), through generating design seeds and tailoring design space. In conclusion, gcWGAN explores uncharted sequence space to design proteins by learning generalizable principles from current sequence-structure data. Data, source codes, and trained models are available at https://github.com/Shen-Lab/gcWGAN.
Collapse
Affiliation(s)
- Mostafa Karimi
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
- TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas 77840, United States
| | - Shaowen Zhu
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Yue Cao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
- TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas 77840, United States
| |
Collapse
|
50
|
Gao W, Mahajan SP, Sulam J, Gray JJ. Deep Learning in Protein Structural Modeling and Design. PATTERNS (NEW YORK, N.Y.) 2020; 1:100142. [PMID: 33336200 PMCID: PMC7733882 DOI: 10.1016/j.patter.2020.100142] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the "sequence → structure → function" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.
Collapse
Affiliation(s)
- Wenhao Gao
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| |
Collapse
|