1
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Jang YJ, Qin QQ, Huang SY, Peter ATJ, Ding XM, Kornmann B. Accurate prediction of protein function using statistics-informed graph networks. Nat Commun 2024; 15:6601. [PMID: 39097570 PMCID: PMC11297950 DOI: 10.1038/s41467-024-50955-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/15/2024] [Indexed: 08/05/2024] Open
Abstract
Understanding protein function is pivotal in comprehending the intricate mechanisms that underlie many crucial biological activities, with far-reaching implications in the fields of medicine, biotechnology, and drug development. However, more than 200 million proteins remain uncharacterized, and computational efforts heavily rely on protein structural information to predict annotations of varying quality. Here, we present a method that utilizes statistics-informed graph networks to predict protein functions solely from its sequence. Our method inherently characterizes evolutionary signatures, allowing for a quantitative assessment of the significance of residues that carry out specific functions. PhiGnet not only demonstrates superior performance compared to alternative approaches but also narrows the sequence-function gap, even in the absence of structural information. Our findings indicate that applying deep learning to evolutionary data can highlight functional sites at the residue level, providing valuable support for interpreting both existing properties and new functionalities of proteins in research and biomedicine.
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Affiliation(s)
- Yaan J Jang
- Department of Biochemistry, University of Oxford, Oxford, UK.
- AmoAi Technologies, Oxford, UK.
| | - Qi-Qi Qin
- AmoAi Technologies, Oxford, UK
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Si-Yu Huang
- AmoAi Technologies, Oxford, UK
- Oxford Martin School, University of Oxford, Oxford, UK
- School of Systems Science, Beijing Normal University, Beijing, China
| | | | - Xue-Ming Ding
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Benoît Kornmann
- Department of Biochemistry, University of Oxford, Oxford, UK.
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2
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Freschlin CR, Fahlberg SA, Heinzelman P, Romero PA. Neural network extrapolation to distant regions of the protein fitness landscape. Nat Commun 2024; 15:6405. [PMID: 39080282 PMCID: PMC11289474 DOI: 10.1038/s41467-024-50712-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 07/13/2024] [Indexed: 08/02/2024] Open
Abstract
Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-function information, to accurately predict distant fitness peaks. In this work, we evaluate neural networks' capacity to extrapolate beyond their training data. We perform model-guided design using a panel of neural network architectures trained on protein G (GB1)-Immunoglobulin G (IgG) binding data and experimentally test thousands of GB1 designs to systematically evaluate the models' extrapolation. We find each model architecture infers markedly different landscapes from the same data, which give rise to unique design preferences. We find simpler models excel in local extrapolation to design high fitness proteins, while more sophisticated convolutional models can venture deep into sequence space to design proteins that fold but are no longer functional. We also find that implementing a simple ensemble of convolutional neural networks enables robust design of high-performing variants in the local landscape. Our findings highlight how each architecture's inductive biases prime them to learn different aspects of the protein fitness landscape and how a simple ensembling approach makes protein engineering more robust.
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Affiliation(s)
- Chase R Freschlin
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Sarah A Fahlberg
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Pete Heinzelman
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Philip A Romero
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.
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3
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Ding K, Chin M, Zhao Y, Huang W, Mai BK, Wang H, Liu P, Yang Y, Luo Y. Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering. Nat Commun 2024; 15:6392. [PMID: 39080249 PMCID: PMC11289365 DOI: 10.1038/s41467-024-50698-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Accepted: 07/19/2024] [Indexed: 08/02/2024] Open
Abstract
The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochrome c to achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY's potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution.
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Affiliation(s)
- Kerr Ding
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Michael Chin
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93106, USA
| | - Yunlong Zhao
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93106, USA
| | - Wei Huang
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93106, USA
| | - Binh Khanh Mai
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Huanan Wang
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93106, USA
| | - Peng Liu
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
| | - Yang Yang
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93106, USA.
- Biomolecular Science and Engineering (BMSE) Program, University of California, Santa Barbara, CA, 93106, USA.
| | - Yunan Luo
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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4
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Vornholt T, Mutný M, Schmidt GW, Schellhaas C, Tachibana R, Panke S, Ward TR, Krause A, Jeschek M. Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning. ACS CENTRAL SCIENCE 2024; 10:1357-1370. [PMID: 39071060 PMCID: PMC11273458 DOI: 10.1021/acscentsci.4c00258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/22/2024] [Accepted: 05/02/2024] [Indexed: 07/30/2024]
Abstract
Tailored enzymes are crucial for the transition to a sustainable bioeconomy. However, enzyme engineering is laborious and failure-prone due to its reliance on serendipity. The efficiency and success rates of engineering campaigns may be improved by applying machine learning to map the sequence-activity landscape based on small experimental data sets. Yet, it often proves challenging to reliably model large sequence spaces while keeping the experimental effort tractable. To address this challenge, we present an integrated pipeline combining large-scale screening with active machine learning, which we applied to engineer an artificial metalloenzyme (ArM) catalyzing a new-to-nature hydroamination reaction. Combining lab automation and next-generation sequencing, we acquired sequence-activity data for several thousand ArM variants. We then used Gaussian process regression to model the activity landscape and guide further screening rounds. Critical characteristics of our pipeline include the cost-effective generation of information-rich data sets, the integration of an explorative round to improve the model's performance, and the inclusion of experimental noise. Our approach led to an order-of-magnitude boost in the hit rate while making efficient use of experimental resources. Search strategies like this should find broad utility in enzyme engineering and accelerate the development of novel biocatalysts.
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Affiliation(s)
- Tobias Vornholt
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
- National
Centre of Competence in Research (NCCR) Molecular Systems Engineering, 4056 Basel,Switzerland
| | - Mojmír Mutný
- Department
of Computer Science, ETH Zurich, Andreasstrasse 5, 8092 Zurich, Switzerland
| | - Gregor W. Schmidt
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Christian Schellhaas
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Ryo Tachibana
- Department
of Chemistry, University of Basel, Mattenstrasse 24a, 4058 Basel, Switzerland
| | - Sven Panke
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
- National
Centre of Competence in Research (NCCR) Molecular Systems Engineering, 4056 Basel,Switzerland
| | - Thomas R. Ward
- National
Centre of Competence in Research (NCCR) Molecular Systems Engineering, 4056 Basel,Switzerland
- Department
of Chemistry, University of Basel, Mattenstrasse 24a, 4058 Basel, Switzerland
| | - Andreas Krause
- Department
of Computer Science, ETH Zurich, Andreasstrasse 5, 8092 Zurich, Switzerland
| | - Markus Jeschek
- Department
of Biosystems Science and Engineering, ETH
Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
- Institute
of Microbiology, University of Regensburg, Universitätsstraße 31, 93053 Regensburg, Germany
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5
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Hilvert D. Spiers Memorial Lecture: Engineering biocatalysts. Faraday Discuss 2024. [PMID: 39046423 DOI: 10.1039/d4fd00139g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
Enzymes are being engineered to catalyze chemical reactions for many practical applications in chemistry and biotechnology. The approaches used are surveyed in this short review, emphasizing methods for accessing reactivities not expressed by native protein scaffolds. The successful generation of completely de novo enzymes that rival the rates and selectivities of their natural counterparts highlights the potential role that designer enzymes may play in the coming years in research, industry, and medicine. Some challenges that need to be addressed to realize this ambitious dream are considered together with possible solutions.
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Affiliation(s)
- Donald Hilvert
- Laboratory of Organic Chemistry, ETH Zürich, 8093 Zürich, Switzerland.
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6
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Ozkan S, Padilla N, de la Cruz X. QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning. Hum Genet 2024:10.1007/s00439-024-02692-z. [PMID: 39048855 DOI: 10.1007/s00439-024-02692-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/14/2024] [Indexed: 07/27/2024]
Abstract
Next-generation sequencing (NGS) has revolutionized genetic diagnostics, yet its application in precision medicine remains incomplete, despite significant advances in computational tools for variant annotation. Many variants remain unannotated, and existing tools often fail to accurately predict the range of impacts that variants have on protein function. This limitation restricts their utility in relevant applications such as predicting disease severity and onset age. In response to these challenges, a new generation of computational models is emerging, aimed at producing quantitative predictions of genetic variant impacts. However, the field is still in its early stages, and several issues need to be addressed, including improved performance and better interpretability. This study introduces QAFI, a novel methodology that integrates protein-specific regression models within an ensemble learning framework, utilizing conservation-based and structure-related features derived from AlphaFold models. Our findings indicate that QAFI significantly enhances the accuracy of quantitative predictions across various proteins. The approach has been rigorously validated through its application in the CAGI6 contest, focusing on ARSA protein variants, and further tested on a comprehensive set of clinically labeled variants, demonstrating its generalizability and robust predictive power. The straightforward nature of our models may also contribute to better interpretability of the results.
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Affiliation(s)
- Selen Ozkan
- Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Natàlia Padilla
- Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xavier de la Cruz
- Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
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7
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Cheng P, Mao C, Tang J, Yang S, Cheng Y, Wang W, Gu Q, Han W, Chen H, Li S, Chen Y, Zhou J, Li W, Pan A, Zhao S, Huang X, Zhu S, Zhang J, Shu W, Wang S. Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering. Cell Res 2024:10.1038/s41422-024-00989-2. [PMID: 38969803 DOI: 10.1038/s41422-024-00989-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 06/03/2024] [Indexed: 07/07/2024] Open
Abstract
Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we present Protein Mutational Effect Predictor (ProMEP), a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects. A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from ~160 million proteins. ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering. Specifically, ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA, and successfully guides the development of high-performance gene-editing tools with their engineered variants. The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-to-G conversion frequency of up to 77.27% (vs 69.80% for ABE8e, a previous TadA-based adenine base editor) with significantly reduced bystander and off-target effects compared to ABE8e. ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering. Therefore, ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins, thereby advancing studies in biomedicine and synthetic biology.
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Affiliation(s)
- Peng Cheng
- Bioinformatics Center of AMMS, Beijing, China
| | - Cong Mao
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jin Tang
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Sen Yang
- Bioinformatics Center of AMMS, Beijing, China
| | - Yu Cheng
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wuke Wang
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Qiuxi Gu
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wei Han
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Hao Chen
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Sihan Li
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | | | | | - Wuju Li
- Bioinformatics Center of AMMS, Beijing, China
| | - Aimin Pan
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Suwen Zhao
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Xingxu Huang
- Zhejiang Lab, Hangzhou, Zhejiang, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | | | - Jun Zhang
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Wenjie Shu
- Bioinformatics Center of AMMS, Beijing, China.
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8
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Zhou Z, Zhang L, Yu Y, Wu B, Li M, Hong L, Tan P. Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning. Nat Commun 2024; 15:5566. [PMID: 38956442 PMCID: PMC11219809 DOI: 10.1038/s41467-024-49798-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 06/11/2024] [Indexed: 07/04/2024] Open
Abstract
Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.
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Affiliation(s)
- Ziyi Zhou
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China
- Shanghai National Center for Applied Mathematics (SJTU Center) & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Liang Zhang
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yuanxi Yu
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Banghao Wu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Mingchen Li
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Liang Hong
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Shanghai National Center for Applied Mathematics (SJTU Center) & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
- Zhang Jiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 201203, China.
| | - Pan Tan
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Shanghai National Center for Applied Mathematics (SJTU Center) & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
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9
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Nestl BM, Nebel BA, Resch V, Schürmann M, Tischler D. The Development and Opportunities of Predictive Biotechnology. Chembiochem 2024; 25:e202300863. [PMID: 38713151 DOI: 10.1002/cbic.202300863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/05/2024] [Indexed: 05/08/2024]
Abstract
Recent advances in bioeconomy allow a holistic view of existing and new process chains and enable novel production routines continuously advanced by academia and industry. All this progress benefits from a growing number of prediction tools that have found their way into the field. For example, automated genome annotations, tools for building model structures of proteins, and structural protein prediction methods such as AlphaFold2TM or RoseTTAFold have gained popularity in recent years. Recently, it has become apparent that more and more AI-based tools are being developed and used for biocatalysis and biotechnology. This is an excellent opportunity for academia and industry to accelerate advancements in the field further. Biotechnology, as a rapidly growing interdisciplinary field, stands to benefit greatly from these developments.
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Affiliation(s)
- Bettina M Nestl
- Joint working group on biotransformations of the Association for General and Applied Microbiology VAAM, the Society for Chemical Engineering, Biotechnology DECHEMA, Theodor-Heuss-Allee 25, 60486, Frankfurt, Germany
- Innophore GmbH, Am Eisernen Tor 3, 8010, Graz, Austria
| | - Bernd A Nebel
- Innophore GmbH, Am Eisernen Tor 3, 8010, Graz, Austria
| | - Verena Resch
- Innophore GmbH, Am Eisernen Tor 3, 8010, Graz, Austria
| | - Martin Schürmann
- Joint working group on biotransformations of the Association for General and Applied Microbiology VAAM, the Society for Chemical Engineering, Biotechnology DECHEMA, Theodor-Heuss-Allee 25, 60486, Frankfurt, Germany
- InnoSyn B. V., Urmonderbaan 22, 6167 RD, Geleen, The Netherlands
- SynSilico B. V., Urmonderbaan 22, 6167 RD, Geleen, The Netherlands
| | - Dirk Tischler
- Joint working group on biotransformations of the Association for General and Applied Microbiology VAAM, the Society for Chemical Engineering, Biotechnology DECHEMA, Theodor-Heuss-Allee 25, 60486, Frankfurt, Germany
- Microbial Biotechnology, Ruhr University Bochum, Universitätsstrasse 150, 44780, Bochum, Germany
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10
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Wirnsberger G, Pritišanac I, Oberdorfer G, Gruber K. Flattening the curve-How to get better results with small deep-mutational-scanning datasets. Proteins 2024; 92:886-902. [PMID: 38501649 DOI: 10.1002/prot.26686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/24/2024] [Accepted: 03/07/2024] [Indexed: 03/20/2024]
Abstract
Proteins are used in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino-acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the effects of these exchanges on protein function. DMS data can then inform the training of a neural network to predict the impact of mutations. Most approaches use some representation of the protein sequence for training and prediction. As proteins are characterized by complex structures and intricate residue interaction networks, directly providing structural information as input reduces the need to learn these features from the data. We introduce a method for encoding protein structures as stacked 2D contact maps, which capture residue interactions, their evolutionary conservation, and mutation-induced interaction changes. Furthermore, we explored techniques to augment neural network training performance on smaller DMS datasets. To validate our approach, we trained three neural network architectures originally used for image analysis on three DMS datasets, and we compared their performances with networks trained solely on protein sequences. The results confirm the effectiveness of the protein structure encoding in machine learning efforts on DMS data. Using structural representations as direct input to the networks, along with data augmentation and pretraining, significantly reduced demands on training data size and improved prediction performance, especially on smaller datasets, while performance on large datasets was on par with state-of-the-art sequence convolutional neural networks. The methods presented here have the potential to provide the same workflow as DMS without the experimental and financial burden of testing thousands of mutants. Additionally, we present an open-source, user-friendly software tool to make these data analysis techniques accessible, particularly to biotechnology and protein engineering researchers who wish to apply them to their mutagenesis data.
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Affiliation(s)
| | - Iva Pritišanac
- Institute of Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Gustav Oberdorfer
- BioTechMed-Graz, Graz, Austria
- Institute of Biochemistry, Graz University of Technology, Graz, Austria
| | - Karl Gruber
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- Field of Excellence BioHealth, University of Graz, Graz, Austria
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11
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Nishikawa KK, Chen J, Acheson JF, Harbaugh SV, Huss P, Frenkel M, Novy N, Sieren HR, Lodewyk EC, Lee DH, Chávez JL, Fox BG, Raman S. Highly multiplexed design of an allosteric transcription factor to sense novel ligands. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.07.583947. [PMID: 38496486 PMCID: PMC10942455 DOI: 10.1101/2024.03.07.583947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Allosteric transcription factors (aTF), widely used as biosensors, have proven challenging to design for detecting novel molecules because mutation of ligand-binding residues often disrupts allostery. We developed Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screened a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identified novel biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design showed shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we developed cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid, scalable design of new biosensors, overcoming constraints of natural biosensors.
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Affiliation(s)
- Kyle K Nishikawa
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jackie Chen
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Justin F Acheson
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Svetlana V Harbaugh
- 711th Human Performance Wing, Air Force Research Laboratory Wright Patterson Air Force Base, OH, USA
| | - Phil Huss
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Max Frenkel
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Nathan Novy
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Hailey R Sieren
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ella C Lodewyk
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Daniel H Lee
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jorge L Chávez
- 711th Human Performance Wing, Air Force Research Laboratory Wright Patterson Air Force Base, OH, USA
| | - Brian G Fox
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
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12
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Gelman S, Johnson B, Freschlin C, D'Costa S, Gitter A, Romero PA. Biophysics-based protein language models for protein engineering. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585128. [PMID: 38559182 PMCID: PMC10980077 DOI: 10.1101/2024.03.15.585128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure, and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose Mutational Effect Transfer Learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure, and energetics. We finetune METL on experimental sequence-function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity, and fluorescence. METL excels in challenging protein engineering tasks like generalizing from small training sets and position extrapolation, although existing methods that train on evolutionary signals remain powerful for many types of experimental assays. We demonstrate METL's ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering.
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Affiliation(s)
- Sam Gelman
- Department of Computer Sciences, University of Wisconsin-Madison
- Morgridge Institute for Research
| | - Bryce Johnson
- Department of Computer Sciences, University of Wisconsin-Madison
- Morgridge Institute for Research
| | | | - Sameer D'Costa
- Department of Biochemistry, University of Wisconsin-Madison
| | - Anthony Gitter
- Department of Computer Sciences, University of Wisconsin-Madison
- Morgridge Institute for Research
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
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13
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Goshisht MK. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges. ACS OMEGA 2024; 9:9921-9945. [PMID: 38463314 PMCID: PMC10918679 DOI: 10.1021/acsomega.3c05913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 03/12/2024]
Abstract
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.
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Affiliation(s)
- Manoj Kumar Goshisht
- Department of Chemistry, Natural and
Applied Sciences, University of Wisconsin—Green
Bay, Green
Bay, Wisconsin 54311-7001, United States
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14
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Martinusen SG, Denard CA. Leveraging yeast sequestration to study and engineer posttranslational modification enzymes. Biotechnol Bioeng 2024; 121:903-914. [PMID: 38079116 PMCID: PMC11229454 DOI: 10.1002/bit.28621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/04/2023] [Accepted: 11/27/2023] [Indexed: 02/20/2024]
Abstract
Enzymes that catalyze posttranslational modifications (PTMs) of peptides and proteins (PTM-enzymes)-proteases, protein ligases, oxidoreductases, kinases, and other transferases-are foundational to our understanding of health and disease and empower applications in chemical biology, synthetic biology, and biomedicine. To fully harness the potential of PTM-enzymes, there is a critical need to decipher their enzymatic and biological mechanisms, develop molecules that can probe and modulate them, and endow them with improved and novel functions. These objectives are contingent upon implementation of high-throughput functional screens and selections that interrogate large sequence libraries to isolate desired PTM-enzyme properties. This review discusses the principles of Saccharomyces cerevisiae organelle sequestration to study and engineer PTM-enzymes. These include outer membrane sequestration, specifically methods that modify yeast surface display, and cytoplasmic sequestration based on enzyme-mediated transcription activation. Furthermore, we present a detailed discussion of yeast endoplasmic reticulum sequestration for the first time. Where appropriate, we highlight the major features and limitations of different systems, specifically how they can measure and control enzyme catalytic efficiencies. Taken together, yeast-based high-throughput sequestration approaches significantly lower the barrier to understanding how PTM-enzymes function and how to reprogram them.
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Affiliation(s)
- Samantha G Martinusen
- Department of Chemical Engineering, University of Florida, Gainesville, Florida, USA
| | - Carl A Denard
- Department of Chemical Engineering, University of Florida, Gainesville, Florida, USA
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15
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Yang J, Li FZ, Arnold FH. Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering. ACS CENTRAL SCIENCE 2024; 10:226-241. [PMID: 38435522 PMCID: PMC10906252 DOI: 10.1021/acscentsci.3c01275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/26/2023] [Accepted: 01/16/2024] [Indexed: 03/05/2024]
Abstract
Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.
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Affiliation(s)
- Jason Yang
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Francesca-Zhoufan Li
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Frances H. Arnold
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
- Division
of Biology and Biological Engineering, California
Institute of Technology, Pasadena, California 91125, United States
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16
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Zeng M, Sarker B, Rondthaler SN, Vu V, Andrews LB. Identifying LasR Quorum Sensors with Improved Signal Specificity by Mapping the Sequence-Function Landscape. ACS Synth Biol 2024; 13:568-589. [PMID: 38206199 DOI: 10.1021/acssynbio.3c00543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Programmable intercellular signaling using components of naturally occurring quorum sensing can allow for coordinated functions to be engineered in microbial consortia. LuxR-type transcriptional regulators are widely used for this purpose and are activated by homoserine lactone (HSL) signals. However, they often suffer from imperfect molecular discrimination of structurally similar HSLs, causing misregulation within engineered consortia containing multiple HSL signals. Here, we studied one such example, the regulator LasR from Pseudomonas aeruginosa. We elucidated its sequence-function relationship for ligand specificity using targeted protein engineering and multiplexed high-throughput biosensor screening. A pooled combinatorial saturation mutagenesis library (9,486 LasR DNA sequences) was created by mutating six residues in LasR's β5 sheet with single, double, or triple amino acid substitutions. Sort-seq assays were performed in parallel using cognate and noncognate HSLs to quantify each corresponding sensor's response to each HSL signal, which identified hundreds of highly specific variants. Sensor variants identified were individually assayed and exhibited up to 60.6-fold (p = 0.0013) improved relative activation by the cognate signal compared to the wildtype. Interestingly, we uncovered prevalent mutational epistasis and previously unidentified residues contributing to signal specificity. The resulting sensors with negligible signal crosstalk could be broadly applied to engineer bacteria consortia.
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Affiliation(s)
- Min Zeng
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Biprodev Sarker
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Stephen N Rondthaler
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Vanessa Vu
- Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Lauren B Andrews
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Molecular and Cellular Biology Graduate Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Biotechnology Training Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
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17
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Ao YF, Dörr M, Menke MJ, Born S, Heuson E, Bornscheuer UT. Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity. Chembiochem 2024; 25:e202300754. [PMID: 38029350 DOI: 10.1002/cbic.202300754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/01/2023]
Abstract
Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.
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Affiliation(s)
- Yu-Fei Ao
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Zhongguancun North First Street 2, Beijing, 100190, China
- University of Chinese Academy of Sciences, Yuquan Road 19(A), Beijing, 100049, China
| | - Mark Dörr
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
| | - Marian J Menke
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
| | - Stefan Born
- Technische Universität Berlin, Chair of Bioprocess Engineering, Ackerstraße 76, 13355, Berlin, Germany
| | - Egon Heuson
- Univ. Lille, CNRS, Centrale Lille, Univ. Artois, UMR 8181 UCCS, Unité de Catalyse et Chimie du Solide, 59000, Lille, France
| | - Uwe T Bornscheuer
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
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18
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Zhu D, Brookes DH, Busia A, Carneiro A, Fannjiang C, Popova G, Shin D, Donohue KC, Lin LF, Miller ZM, Williams ER, Chang EF, Nowakowski TJ, Listgarten J, Schaffer DV. Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy. SCIENCE ADVANCES 2024; 10:eadj3786. [PMID: 38266077 PMCID: PMC10807795 DOI: 10.1126/sciadv.adj3786] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 12/22/2023] [Indexed: 01/26/2024]
Abstract
Adeno-associated viruses (AAVs) hold tremendous promise as delivery vectors for gene therapies. AAVs have been successfully engineered-for instance, for more efficient and/or cell-specific delivery to numerous tissues-by creating large, diverse starting libraries and selecting for desired properties. However, these starting libraries often contain a high proportion of variants unable to assemble or package their genomes, a prerequisite for any gene delivery goal. Here, we present and showcase a machine learning (ML) method for designing AAV peptide insertion libraries that achieve fivefold higher packaging fitness than the standard NNK library with negligible reduction in diversity. To demonstrate our ML-designed library's utility for downstream engineering goals, we show that it yields approximately 10-fold more successful variants than the NNK library after selection for infection of human brain tissue, leading to a promising glial-specific variant. Moreover, our design approach can be applied to other types of libraries for AAV and beyond.
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Affiliation(s)
- Danqing Zhu
- California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA 94720, USA
| | - David H. Brookes
- Biophysics Graduate Group, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Akosua Busia
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Ana Carneiro
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
| | | | - Galina Popova
- Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Psychiatry and Behavioural Sciences, University of California San Francisco, San Francisco, CA 94143, USA
- Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA
| | - David Shin
- Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Psychiatry and Behavioural Sciences, University of California San Francisco, San Francisco, CA 94143, USA
- Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA
| | - Kevin C. Donohue
- Department of Psychiatry and Behavioural Sciences, University of California San Francisco, San Francisco, CA 94143, USA
- School of Medicine, University of California San Francisco, San Francisco, CA, USA. 94143
- Kavli Institute of Fundamental Neuroscience, University of California San Francisco, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143, USA
| | - Li F. Lin
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Zachary M. Miller
- Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Evan R. Williams
- Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Edward F. Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
| | - Tomasz J. Nowakowski
- Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Psychiatry and Behavioural Sciences, University of California San Francisco, San Francisco, CA 94143, USA
- Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
| | - Jennifer Listgarten
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - David V. Schaffer
- California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
- Innovative Genomics Institute (IGI), University of California, Berkeley, Berkeley, CA 94720, USA
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19
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Xing H, Cai P, Liu D, Han M, Liu J, Le Y, Zhang D, Hu QN. High-throughput prediction of enzyme promiscuity based on substrate-product pairs. Brief Bioinform 2024; 25:bbae089. [PMID: 38487850 PMCID: PMC10940840 DOI: 10.1093/bib/bbae089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/20/2024] [Accepted: 02/03/2024] [Indexed: 03/18/2024] Open
Abstract
The screening of enzymes for catalyzing specific substrate-product pairs is often constrained in the realms of metabolic engineering and synthetic biology. Existing tools based on substrate and reaction similarity predominantly rely on prior knowledge, demonstrating limited extrapolative capabilities and an inability to incorporate custom candidate-enzyme libraries. Addressing these limitations, we have developed the Substrate-product Pair-based Enzyme Promiscuity Prediction (SPEPP) model. This innovative approach utilizes transfer learning and transformer architecture to predict enzyme promiscuity, thereby elucidating the intricate interplay between enzymes and substrate-product pairs. SPEPP exhibited robust predictive ability, eliminating the need for prior knowledge of reactions and allowing users to define their own candidate-enzyme libraries. It can be seamlessly integrated into various applications, including metabolic engineering, de novo pathway design, and hazardous material degradation. To better assist metabolic engineers in designing and refining biochemical pathways, particularly those without programming skills, we also designed EnzyPick, an easy-to-use web server for enzyme screening based on SPEPP. EnzyPick is accessible at http://www.biosynther.com/enzypick/.
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Affiliation(s)
- Huadong Xing
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Pengli Cai
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Dongliang Liu
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengying Han
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Juan Liu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Yingying Le
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Dachuan Zhang
- Institute of Environmental Engineering, ETH Zurich, Laura-Hezner-Weg 7, 8093 Zurich, Switzerland
| | - Qian-Nan Hu
- CAS Key Laboratory of Computational Biology, CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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20
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Xi C, Diao J, Moon TS. Advances in ligand-specific biosensing for structurally similar molecules. Cell Syst 2023; 14:1024-1043. [PMID: 38128482 PMCID: PMC10751988 DOI: 10.1016/j.cels.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/23/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.
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Affiliation(s)
- Chenggang Xi
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jinjin Diao
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA.
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21
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Xie WJ, Warshel A. Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering. Natl Sci Rev 2023; 10:nwad331. [PMID: 38299119 PMCID: PMC10829072 DOI: 10.1093/nsr/nwad331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/27/2023] [Accepted: 10/13/2023] [Indexed: 02/02/2024] Open
Abstract
Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novo enzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.
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Affiliation(s)
- Wen Jun Xie
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, Genetics Institute, University of Florida, Gainesville, FL 32610, USA
| | - Arieh Warshel
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA
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22
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Buller R, Lutz S, Kazlauskas RJ, Snajdrova R, Moore JC, Bornscheuer UT. From nature to industry: Harnessing enzymes for biocatalysis. Science 2023; 382:eadh8615. [PMID: 37995253 DOI: 10.1126/science.adh8615] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/17/2023] [Indexed: 11/25/2023]
Abstract
Biocatalysis harnesses enzymes to make valuable products. This green technology is used in countless applications from bench scale to industrial production and allows practitioners to access complex organic molecules, often with fewer synthetic steps and reduced waste. The last decade has seen an explosion in the development of experimental and computational tools to tailor enzymatic properties, equipping enzyme engineers with the ability to create biocatalysts that perform reactions not present in nature. By using (chemo)-enzymatic synthesis routes or orchestrating intricate enzyme cascades, scientists can synthesize elaborate targets ranging from DNA and complex pharmaceuticals to starch made in vitro from CO2-derived methanol. In addition, new chemistries have emerged through the combination of biocatalysis with transition metal catalysis, photocatalysis, and electrocatalysis. This review highlights recent key developments, identifies current limitations, and provides a future prospect for this rapidly developing technology.
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Affiliation(s)
- R Buller
- Competence Center for Biocatalysis, Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, 8820 Wädenswil, Switzerland
| | - S Lutz
- Codexis Incorporated, Redwood City, CA 94063, USA
| | - R J Kazlauskas
- Department of Biochemistry, Molecular Biology and Biophysics, Biotechnology Institute, University of Minnesota, Saint Paul, MN 55108, USA
| | - R Snajdrova
- Novartis Institutes for BioMedical Research, Global Discovery Chemistry, 4056 Basel, Switzerland
| | - J C Moore
- MRL, Merck & Co., Rahway, NJ 07065, USA
| | - U T Bornscheuer
- Institute of Biochemistry, Dept. of Biotechnology and Enzyme Catalysis, Greifswald University, Greifswald, Germany
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23
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Nisonoff H, Wang Y, Listgarten J. Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction. ACS Synth Biol 2023; 12:3242-3251. [PMID: 37888887 DOI: 10.1021/acssynbio.3c00217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Predicting properties of proteins is of interest for basic biological understanding and protein engineering alike. Increasingly, machine learning (ML) approaches are being used for this task. However, the accuracy of such ML models typically degrades as test proteins stray further from the training data distribution. On the other hand, models that are more data-free, such as biophysics-based models, are typically uniformly accurate over all of the protein space, even if inferior for test points close to the training distribution. Consequently, being able to cohesively blend these two types of information within one model, as appropriate in different parts of the protein space, will improve overall importance. Herein, we tackle just this problem to yield a simple, practical, and scalable approach that can be easily implemented. In particular, we use a Bayesian formulation to integrate biophysical knowledge into neural networks. However, in doing so, a technical challenge arises: Bayesian neural networks (BNNs) enable the user to specify prior information only on the neural network weight parameters, rather than on the function values given to us from a typical biophysics-based model. Consequently, we devise a principled probabilistic method to overcome this challenge. Our approach yields intuitively pleasing results: predictions rely more heavily on the biophysical prior information when the BNN epistemic uncertainty─uncertainty arising from a lack of training data rather than sensor noise─is large and more heavily on the neural network when the epistemic uncertainty is small. We demonstrate this approach on an illustrative synthetic example, on two examples of protein property prediction (fluorescence and binding), and for generality on one small molecule property prediction problem.
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Affiliation(s)
- Hunter Nisonoff
- Center for Computational Biology, University of California, Berkeley, Berkeley, California 94720-3220, United States
| | - Yixin Wang
- Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109-1107, United States
| | - Jennifer Listgarten
- Center for Computational Biology, University of California, Berkeley, Berkeley, California 94720-3220, United States
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California 94720-1776, United States
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24
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Fahlberg SA, Freschlin CR, Heinzelman P, Romero PA. Neural network extrapolation to distant regions of the protein fitness landscape. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.08.566287. [PMID: 37987009 PMCID: PMC10659313 DOI: 10.1101/2023.11.08.566287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-function information, to accurately predict distant fitness peaks. In this work, we evaluate neural networks' capacity to extrapolate beyond their training data. We perform model-guided design using a panel of neural network architectures trained on protein G (GB1)-Immunoglobulin G (IgG) binding data and experimentally test thousands of GB1 designs to systematically evaluate the models' extrapolation. We find each model architecture infers markedly different landscapes from the same data, which give rise to unique design preferences. We find simpler models excel in local extrapolation to design high fitness proteins, while more sophisticated convolutional models can venture deep into sequence space to design proteins that fold but are no longer functional. Our findings highlight how each architecture's inductive biases prime them to learn different aspects of the protein fitness landscape.
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Affiliation(s)
- Sarah A Fahlberg
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Chase R Freschlin
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Pete Heinzelman
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Philip A Romero
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
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25
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Xie WJ, Warshel A. Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561808. [PMID: 37873334 PMCID: PMC10592750 DOI: 10.1101/2023.10.10.561808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. By applying generative models, we could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, activity, and stability, rationalizing the laboratory evolution of de novo enzymes, decoding protein sequence semantics, and its applications in enzyme engineering. Notably, the prediction of enzyme activity and stability using natural enzyme sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.
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Affiliation(s)
- Wen Jun Xie
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA
- Departmet of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development (CNPD3), Genetics Institute, University of Florida, Gainesville, FL, USA
| | - Arieh Warshel
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA
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26
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Busia A, Listgarten J. MBE: model-based enrichment estimation and prediction for differential sequencing data. Genome Biol 2023; 24:218. [PMID: 37784130 PMCID: PMC10544408 DOI: 10.1186/s13059-023-03058-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/14/2023] [Indexed: 10/04/2023] Open
Abstract
Characterizing differences in sequences between two conditions, such as with and without drug exposure, using high-throughput sequencing data is a prevalent problem involving quantifying changes in sequence abundances, and predicting such differences for unobserved sequences. A key shortcoming of current approaches is their extremely limited ability to share information across related but non-identical reads. Consequently, they cannot use sequencing data effectively, nor be directly applied in many settings of interest. We introduce model-based enrichment (MBE) to overcome this shortcoming. We evaluate MBE using both simulated and real data. Overall, MBE improves accuracy compared to current differential analysis methods.
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Affiliation(s)
- Akosua Busia
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, 94720, CA, USA.
| | - Jennifer Listgarten
- Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkeley, 94720, CA, USA.
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27
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Golinski AW, Schmitz ZD, Nielsen GH, Johnson B, Saha D, Appiah S, Hackel BJ, Martiniani S. Predicting and Interpreting Protein Developability Via Transfer of Convolutional Sequence Representation. ACS Synth Biol 2023; 12:2600-2615. [PMID: 37642646 PMCID: PMC10829850 DOI: 10.1021/acssynbio.3c00196] [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: 08/31/2023]
Abstract
Engineered proteins have emerged as novel diagnostics, therapeutics, and catalysts. Often, poor protein developability─quantified by expression, solubility, and stability─hinders utility. The ability to predict protein developability from amino acid sequence would reduce the experimental burden when selecting candidates. Recent advances in screening technologies enabled a high-throughput (HT) developability dataset for 105 of 1020 possible variants of protein ligand scaffold Gp2. In this work, we evaluate the ability of neural networks to learn a developability representation from a HT dataset and transfer this knowledge to predict recombinant expression beyond observed sequences. The model convolves learned amino acid properties to predict expression levels 44% closer to the experimental variance compared to a non-embedded control. Analysis of learned amino acid embeddings highlights the uniqueness of cysteine, the importance of hydrophobicity and charge, and the unimportance of aromaticity, when aiming to improve the developability of small proteins. We identify clusters of similar sequences with increased recombinant expression through nonlinear dimensionality reduction and we explore the inferred expression landscape via nested sampling. The analysis enables the first direct visualization of the fitness landscape and highlights the existence of evolutionary bottlenecks in sequence space giving rise to competing subpopulations of sequences with different developability. The work advances applied protein engineering efforts by predicting and interpreting protein scaffold expression from a limited dataset. Furthermore, our statistical mechanical treatment of the problem advances foundational efforts to characterize the structure of the protein fitness landscape and the amino acid characteristics that influence protein developability.
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Affiliation(s)
- Alexander W. Golinski
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Zachary D. Schmitz
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Gregory H. Nielsen
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Bryce Johnson
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Diya Saha
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Sandhya Appiah
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Benjamin J. Hackel
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
| | - Stefano Martiniani
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455
- Center for Soft Matter Research, Department of Physics, New York University, New York, NY 10003
- Simons Center for Computational Physical Chemistry, Departments of Chemistry, New York University, New York, NY 10003
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10003
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28
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Nordquist E, Zhang G, Barethiya S, Ji N, White KM, Han L, Jia Z, Shi J, Cui J, Chen J. Incorporating physics to overcome data scarcity in predictive modeling of protein function: A case study of BK channels. PLoS Comput Biol 2023; 19:e1011460. [PMID: 37713443 PMCID: PMC10529646 DOI: 10.1371/journal.pcbi.1011460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 09/27/2023] [Accepted: 08/24/2023] [Indexed: 09/17/2023] Open
Abstract
Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ∆V1/2, with a RMSE ~ 32 mV and correlation coefficient of R ~ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ∆V1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.
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Affiliation(s)
- Erik Nordquist
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Guohui Zhang
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Shrishti Barethiya
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Nathan Ji
- Department of Biology, Boston College, Chestnut Hill, Massachusetts, United States of America
| | - Kelli M. White
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Lu Han
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Zhiguang Jia
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
| | - Jingyi Shi
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Jianmin Cui
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Jianhan Chen
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
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29
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Chen L, Zhang Z, Li Z, Li R, Huo R, Chen L, Wang D, Luo X, Chen K, Liao C, Zheng M. Learning protein fitness landscapes with deep mutational scanning data from multiple sources. Cell Syst 2023; 14:706-721.e5. [PMID: 37591206 DOI: 10.1016/j.cels.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/30/2023] [Accepted: 07/18/2023] [Indexed: 08/19/2023]
Abstract
One of the key points of machine learning-assisted directed evolution (MLDE) is the accurate learning of the fitness landscape, a conceptual mapping from sequence variants to the desired function. Here, we describe a multi-protein training scheme that leverages the existing deep mutational scanning data from diverse proteins to aid in understanding the fitness landscape of a new protein. Proof-of-concept trials are designed to validate this training scheme in three aspects: random and positional extrapolation for single-variant effects, zero-shot fitness predictions for new proteins, and extrapolation for higher-order variant effects from single-variant effects. Moreover, our study identified previously overlooked strong baselines, and their unexpectedly good performance brings our attention to the pitfalls of MLDE. Overall, these results may improve our understanding of the association between different protein fitness profiles and shed light on developing better machine learning-assisted approaches to the directed evolution of proteins. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Lin Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zehong Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenghao Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Rui Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Ruifeng Huo
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Lifan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China; School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Cangsong Liao
- University of Chinese Academy of Sciences, Beijing 100049, China; Chemical Biology Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Science, Shanghai 201203, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China; School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China; School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China.
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30
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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.
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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.
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31
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Nordquist E, Zhang G, Barethiya S, Ji N, White KM, Han L, Jia Z, Shi J, Cui J, Chen J. Incorporating physics to overcome data scarcity in predictive modeling of protein function: a case study of BK channels. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.24.546384. [PMID: 37425916 PMCID: PMC10327070 DOI: 10.1101/2023.06.24.546384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ΔV 1/2 , with a RMSE ∼ 32 mV and correlation coefficient of R ∼ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V 1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ΔV 1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction. Author Summary Deep machine learning has brought many exciting breakthroughs in chemistry, physics and biology. These models require large amount of training data and struggle when the data is scarce. The latter is true for predictive modeling of the function of complex proteins such as ion channels, where only hundreds of mutational data may be available. Using the big potassium (BK) channel as a biologically important model system, we demonstrate that a reliable predictive model of its voltage gating property could be derived from only 473 mutational data by incorporating physics-derived features, which include dynamic properties from molecular dynamics simulations and energetic quantities from Rosetta mutation calculations. We show that the final random forest model captures key trends and hotspots in mutational effects of BK voltage gating, such as the important role of pore hydrophobicity. A particularly curious prediction is that mutations of two adjacent residues on the S5 helix would always have opposite effects on the gating voltage, which was confirmed by experimental characterization of four novel mutations. The current work demonstrates the importance and effectiveness of incorporating physics in predictive modeling of protein function with scarce data.
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Affiliation(s)
- Erik Nordquist
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Guohui Zhang
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Shrishti Barethiya
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Nathan Ji
- Department of Biology, Boston College, Chestnut Hill, Massachusetts, USA
| | - Kelli M. White
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Lu Han
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Zhiguang Jia
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, USA
| | - Jingyi Shi
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Jianmin Cui
- Department of Biomedical Engineering, Center for the Investigation of Membrane Excitability Disorders, Cardiac Bioelectricity and Arrhythmia Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Jianhan Chen
- Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, USA
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32
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Valeri JA, Soenksen LR, Collins KM, Ramesh P, Cai G, Powers R, Angenent-Mari NM, Camacho DM, Wong F, Lu TK, Collins JJ. BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences. Cell Syst 2023; 14:525-542.e9. [PMID: 37348466 PMCID: PMC10700034 DOI: 10.1016/j.cels.2023.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 02/17/2023] [Accepted: 05/22/2023] [Indexed: 06/24/2023]
Abstract
The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) and cannot be easily compared with other AutoML algorithms. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automatically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation, and designs optimized synthetic biology components, revealing salient sequence characteristics. By automating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.
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Affiliation(s)
- Jacqueline A Valeri
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Luis R Soenksen
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Katherine M Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Department of Engineering, University of Cambridge, Trumpington St, Cambridge CB2 1PZ, UK
| | - Pradeep Ramesh
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - George Cai
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Rani Powers
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Pluto Biosciences, Golden, CO 80402, USA
| | - Nicolaas M Angenent-Mari
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Diogo M Camacho
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Felix Wong
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Timothy K Lu
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - James J Collins
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA; Abdul Latif Jameel Clinic for Machine Learning in Health, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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33
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Hui T, Descoteaux ML, Miao J, Lin YS. Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles. J Chem Theory Comput 2023. [PMID: 37236147 PMCID: PMC10373485 DOI: 10.1021/acs.jctc.3c00154] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Cyclic peptides have emerged as a promising class of therapeutics. However, their de novo design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide drugs, adopt multiple conformations in water. The ability to characterize cyclic peptide structural ensembles would greatly aid their rational design. In a previous pioneering study, our group demonstrated that using molecular dynamics results to train machine learning models can efficiently predict structural ensembles of cyclic pentapeptides. Using this method, which was termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), linear regression models were able to predict the structural ensembles for an independent test set with R2 = 0.94 between the predicted populations for specific structures and the observed populations in molecular dynamics simulations for cyclic pentapeptides. An underlying assumption in these StrEAMM models is that cyclic peptide structural preferences are predominantly influenced by neighboring interactions, namely, interactions between (1,2) and (1,3) residues. Here we demonstrate that for larger cyclic peptides such as cyclic hexapeptides, linear regression models including only (1,2) and (1,3) interactions fail to produce satisfactory predictions (R2 = 0.47); further inclusion of (1,4) interactions leads to moderate improvements (R2 = 0.75). We show that when using convolutional neural networks and graph neural networks to incorporate complex nonlinear interaction patterns, we can achieve R2 = 0.97 and R2 = 0.91 for cyclic pentapeptides and hexapeptides, respectively.
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Affiliation(s)
- Tiffani Hui
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Marc L Descoteaux
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Jiayuan Miao
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Yu-Shan Lin
- Department of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
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34
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Diaz-Colunga J, Skwara A, Gowda K, Diaz-Uriarte R, Tikhonov M, Bajic D, Sanchez A. Global epistasis on fitness landscapes. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220053. [PMID: 37004717 PMCID: PMC10067270 DOI: 10.1098/rstb.2022.0053] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Epistatic interactions between mutations add substantial complexity to adaptive landscapes and are often thought of as detrimental to our ability to predict evolution. Yet, patterns of global epistasis, in which the fitness effect of a mutation is well-predicted by the fitness of its genetic background, may actually be of help in our efforts to reconstruct fitness landscapes and infer adaptive trajectories. Microscopic interactions between mutations, or inherent nonlinearities in the fitness landscape, may cause global epistasis patterns to emerge. In this brief review, we provide a succinct overview of recent work about global epistasis, with an emphasis on building intuition about why it is often observed. To this end, we reconcile simple geometric reasoning with recent mathematical analyses, using these to explain why different mutations in an empirical landscape may exhibit different global epistasis patterns—ranging from diminishing to increasing returns. Finally, we highlight open questions and research directions. This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.
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Affiliation(s)
- Juan Diaz-Colunga
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Abigail Skwara
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Karna Gowda
- Department of Ecology & Evolution & Center for the Physics of Evolving Systems, The University of Chicago, Chicago, IL 60637, USA
| | - Ramon Diaz-Uriarte
- Department of Biochemistry, School of Medicine, Universidad Autónoma de Madrid, Madrid 28029, Spain
- Instituto de Investigaciones Biomédicas ‘Alberto Sols’ (UAM-CSIC), Madrid 28029, Spain
| | - Mikhail Tikhonov
- Department of Physics, Washington University of St Louis, St Louis, MO 63130, USA
| | - Djordje Bajic
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Alvaro Sanchez
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT 06511, USA
- Department of Microbial Biotechnology, Campus de Cantoblanco, CNB-CSIC, Madrid 28049, Spain
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35
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Chen Y, Hu R, Li K, Zhang Y, Fu L, Zhang J, Si T. Deep Mutational Scanning of an Oxygen-Independent Fluorescent Protein CreiLOV for Comprehensive Profiling of Mutational and Epistatic Effects. ACS Synth Biol 2023; 12:1461-1473. [PMID: 37066862 PMCID: PMC10204710 DOI: 10.1021/acssynbio.2c00662] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Indexed: 04/18/2023]
Abstract
Oxygen-independent, flavin mononucleotide-based fluorescent proteins (FbFPs) are promising alternatives to green fluorescent protein in anaerobic contexts. Deep mutational scanning performs systematic profiling of protein sequence-function relationships but has not been applied to FbFPs. Focusing on CreiLOV from Chlamydomonas reinhardtii, we created and analyzed two comprehensive mutant collections: (1) single-residue, site-saturation mutagenesis libraries covering all 118 residues; and (2) a full combinatorial metagenesis library among 20 mutations at 15 residues, where mutation and residue selection was based on single-site mutagenesis results. Notably, the second type of library is indispensable to study higher-order epistasis but underrepresented in the literature. Using optimized FACS-seq assays, 2,185 (>92.5%) out of 2,360 possible single-site mutants and 165,428 (>89.7%) out of 184,320 possible combinatorial mutants were reliably assigned with fitness values. We constructed statistical and machine-learning models to analyze the CreiLOV data set, enabling accurate fitness prediction of higher-order mutants using lower-order mutagenesis data. In addition, we successfully isolated CreiLOV variants with improved fluorescence quantum yield and thermostability. This work provides new empirical data and design rules to engineer combinatorial protein variants.
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Affiliation(s)
- Yongcan Chen
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruyun Hu
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Keyi Li
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yating Zhang
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lihao Fu
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianzhi Zhang
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tong Si
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- BGI-Shenzhen, Shenzhen 518083, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
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36
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Gantz M, Neun S, Medcalf EJ, van Vliet LD, Hollfelder F. Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments. Chem Rev 2023; 123:5571-5611. [PMID: 37126602 PMCID: PMC10176489 DOI: 10.1021/acs.chemrev.2c00910] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >107-fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >107 variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated.
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Affiliation(s)
- Maximilian Gantz
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Rd, Cambridge CB2 1GA, U.K
| | - Stefanie Neun
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Rd, Cambridge CB2 1GA, U.K
| | - Elliot J Medcalf
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Rd, Cambridge CB2 1GA, U.K
| | - Liisa D van Vliet
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Rd, Cambridge CB2 1GA, U.K
| | - Florian Hollfelder
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Rd, Cambridge CB2 1GA, U.K
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37
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Verkhivker G, Alshahrani M, Gupta G, Xiao S, Tao P. From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches. Int J Mol Sci 2023; 24:7747. [PMID: 37175454 PMCID: PMC10178073 DOI: 10.3390/ijms24097747] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 04/22/2023] [Accepted: 04/23/2023] [Indexed: 05/15/2023] Open
Abstract
The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.
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Affiliation(s)
- Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75275, USA; (S.X.); (P.T.)
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75275, USA; (S.X.); (P.T.)
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38
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Recent progress in the synthesis of advanced biofuel and bioproducts. Curr Opin Biotechnol 2023; 80:102913. [PMID: 36854202 DOI: 10.1016/j.copbio.2023.102913] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/20/2023] [Accepted: 01/30/2023] [Indexed: 02/27/2023]
Abstract
Energy is one of the most complex fields of study and an issue that influences nearly every aspect of modern life. Over the past century, combustion of fossil fuels, particularly in the transportation sector, has been the dominant form of energy release. Refining of petroleum and natural gas into liquid transportation fuels is also the centerpiece of the modern chemical industry used to produce materials, solvents, and other consumer goods. In the face of global climate change, the world is searching for alternative, sustainable means of producing energy carriers and chemical building blocks. The use of biofuels in engines predates modern refinery optimization and today represents a small but significant fraction of liquid transportation fuels burnt each year. Similarly, white biotechnology has been used to produce many natural products through fermentation. The evolution of recombinant DNA technology into modern synthetic biology has expanded the scope of biofuels and bioproducts that can be made by biocatalysts. This opinion examines the current trends in this research space, highlighting the substantial growth in computational tools and the growing influence of renewable electricity in the design of metabolic engineering strategies. In short, advanced biofuel and bioproduct synthesis remains a vibrant and critically important field of study whose focus is shifting away from the conversion of lignocellulosic biomass toward a broader consideration of how to reduce carbon dioxide to fuels and chemical products.
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39
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An B, Wang Y, Huang Y, Wang X, Liu Y, Xun D, Church GM, Dai Z, Yi X, Tang TC, Zhong C. Engineered Living Materials For Sustainability. Chem Rev 2023; 123:2349-2419. [PMID: 36512650 DOI: 10.1021/acs.chemrev.2c00512] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Recent advances in synthetic biology and materials science have given rise to a new form of materials, namely engineered living materials (ELMs), which are composed of living matter or cell communities embedded in self-regenerating matrices of their own or artificial scaffolds. Like natural materials such as bone, wood, and skin, ELMs, which possess the functional capabilities of living organisms, can grow, self-organize, and self-repair when needed. They also spontaneously perform programmed biological functions upon sensing external cues. Currently, ELMs show promise for green energy production, bioremediation, disease treatment, and fabricating advanced smart materials. This review first introduces the dynamic features of natural living systems and their potential for developing novel materials. We then summarize the recent research progress on living materials and emerging design strategies from both synthetic biology and materials science perspectives. Finally, we discuss the positive impacts of living materials on promoting sustainability and key future research directions.
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Affiliation(s)
- Bolin An
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yanyi Wang
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yuanyuan Huang
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xinyu Wang
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yuzhu Liu
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Dongmin Xun
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - George M Church
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston 02115, Massachusetts United States.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston 02115, Massachusetts United States
| | - Zhuojun Dai
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiao Yi
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tzu-Chieh Tang
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston 02115, Massachusetts United States.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston 02115, Massachusetts United States
| | - Chao Zhong
- Center for Materials Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
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40
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Inferring protein fitness landscapes from laboratory evolution experiments. PLoS Comput Biol 2023; 19:e1010956. [PMID: 36857380 PMCID: PMC10010530 DOI: 10.1371/journal.pcbi.1010956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/13/2023] [Accepted: 02/16/2023] [Indexed: 03/02/2023] Open
Abstract
Directed laboratory evolution applies iterative rounds of mutation and selection to explore the protein fitness landscape and provides rich information regarding the underlying relationships between protein sequence, structure, and function. Laboratory evolution data consist of protein sequences sampled from evolving populations over multiple generations and this data type does not fit into established supervised and unsupervised machine learning approaches. We develop a statistical learning framework that models the evolutionary process and can infer the protein fitness landscape from multiple snapshots along an evolutionary trajectory. We apply our modeling approach to dihydrofolate reductase (DHFR) laboratory evolution data and the resulting landscape parameters capture important aspects of DHFR structure and function. We use the resulting model to understand the structure of the fitness landscape and find numerous examples of epistasis but an overall global peak that is evolutionarily accessible from most starting sequences. Finally, we use the model to perform an in silico extrapolation of the DHFR laboratory evolution trajectory and computationally design proteins from future evolutionary rounds.
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41
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Li M, Kang L, Xiong Y, Wang YG, Fan G, Tan P, Hong L. SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering. J Cheminform 2023; 15:12. [PMID: 36737798 PMCID: PMC9898993 DOI: 10.1186/s13321-023-00688-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 01/23/2023] [Indexed: 02/05/2023] Open
Abstract
Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (< 50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.
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Affiliation(s)
- Mingchen Li
- Shanghai National Center for Applied Mathematics (SJTU Center), & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200240, China
| | - Liqi Kang
- Shanghai National Center for Applied Mathematics (SJTU Center), & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
- School of Physics and Astronomy & School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yu Guang Wang
- Shanghai National Center for Applied Mathematics (SJTU Center), & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200240, China
| | - Guisheng Fan
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200240, China
| | - Pan Tan
- Shanghai National Center for Applied Mathematics (SJTU Center), & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200240, China.
| | - Liang Hong
- Shanghai National Center for Applied Mathematics (SJTU Center), & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200240, China.
- School of Physics and Astronomy & School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China.
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42
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Huss P, Chen J, Raman S. High-throughput approaches to understand and engineer bacteriophages. Trends Biochem Sci 2023; 48:187-197. [PMID: 36180320 PMCID: PMC9868059 DOI: 10.1016/j.tibs.2022.08.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 01/26/2023]
Abstract
Bacteriophage research has been vital to fundamental aspects of modern biology. Advances in metagenomics have revealed treasure troves of new and uncharacterized bacteriophages ('phages') that are not yet understood. However, our ability to find new phages has outpaced our understanding of how sequence encodes function in phages. Traditional approaches for characterizing phages are limited in scale and face hurdles in determining how changes in sequence drive function. We describe powerful emerging technologies that can be used to clarify sequence-function relationships in phages through high-throughput genome engineering. Using these approaches, up to 105 variants can be characterized through pooled selection experiments and deep sequencing. We describe caveats when using these tools and provide examples of basic science and engineering goals that are pursuable using these approaches.
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Affiliation(s)
- Phil Huss
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, USA; Microbiology Doctoral Training Program, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jackie Chen
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
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43
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Hu R, Fu L, Chen Y, Chen J, Qiao Y, Si T. Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments. Brief Bioinform 2023; 24:6958505. [PMID: 36562723 DOI: 10.1093/bib/bbac570] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/14/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022] Open
Abstract
Directed protein evolution applies repeated rounds of genetic mutagenesis and phenotypic screening and is often limited by experimental throughput. Through in silico prioritization of mutant sequences, machine learning has been applied to reduce wet lab burden to a level practical for human researchers. On the other hand, robotics permits large batches and rapid iterations for protein engineering cycles, but such capacities have not been well exploited in existing machine learning-assisted directed evolution approaches. Here, we report a scalable and batched method, Bayesian Optimization-guided EVOlutionary (BO-EVO) algorithm, to guide multiple rounds of robotic experiments to explore protein fitness landscapes of combinatorial mutagenesis libraries. We first examined various design specifications based on an empirical landscape of protein G domain B1. Then, BO-EVO was successfully generalized to another empirical landscape of an Escherichia coli kinase PhoQ, as well as simulated NK landscapes with up to moderate epistasis. This approach was then applied to guide robotic library creation and screening to engineer enzyme specificity of RhlA, a key biosynthetic enzyme for rhamnolipid biosurfactants. A 4.8-fold improvement in producing a target rhamnolipid congener was achieved after examining less than 1% of all possible mutants after four iterations. Overall, BO-EVO proves to be an efficient and general approach to guide combinatorial protein engineering without prior knowledge.
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Affiliation(s)
- Ruyun Hu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lihao Fu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen 518055, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongcan Chen
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen 518055, China
| | - Junyu Chen
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yu Qiao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tong Si
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen 518055, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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44
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Wei H, Li X. Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes. Front Genet 2023; 14:1087267. [PMID: 36713072 PMCID: PMC9878224 DOI: 10.3389/fgene.2023.1087267] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/02/2023] [Indexed: 01/13/2023] Open
Abstract
Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.
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Affiliation(s)
- Huijin Wei
- Zhejiang University—University of Edinburgh Institute, Zhejiang University, Haining, Zhejiang, China
| | - Xianghua Li
- Zhejiang University—University of Edinburgh Institute, Zhejiang University, Haining, Zhejiang, China,Deanery of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom,The Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China,Biomedical and Health Translational Centre of Zhejiang Province, Haining, Zhejiang, China,*Correspondence: Xianghua Li,
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Ahmed B, Haque MA, Iquebal MA, Jaiswal S, Angadi UB, Kumar D, Rai A. DeepAProt: Deep learning based abiotic stress protein sequence classification and identification tool in cereals. FRONTIERS IN PLANT SCIENCE 2023; 13:1008756. [PMID: 36714750 PMCID: PMC9877618 DOI: 10.3389/fpls.2022.1008756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 11/14/2022] [Indexed: 06/18/2023]
Abstract
The impact of climate change has been alarming for the crop growth. The extreme weather conditions can stress the crops and reduce the yield of major crops belonging to Poaceae family too, that sustains 50% of the world's food calorie and 20% of protein intake. Computational approaches, such as artificial intelligence-based techniques have become the forefront of prediction-based data interpretation and plant stress responses. In this study, we proposed a novel activation function, namely, Gaussian Error Linear Unit with Sigmoid (SIELU) which was implemented in the development of a Deep Learning (DL) model along with other hyper parameters for classification of unknown abiotic stress protein sequences from crops of Poaceae family. To develop this models, data pertaining to four different abiotic stress (namely, cold, drought, heat and salinity) responsive proteins of the crops belonging to poaceae family were retrieved from public domain. It was observed that efficiency of the DL models with our proposed novel SIELU activation function outperformed the models as compared to GeLU activation function, SVM and RF with 95.11%, 80.78%, 94.97%, and 81.69% accuracy for cold, drought, heat and salinity, respectively. Also, a web-based tool, named DeepAProt (http://login1.cabgrid.res.in:5500/) was developed using flask API, along with its mobile app. This server/App will provide researchers a convenient tool, which is rapid and economical in identification of proteins for abiotic stress management in crops Poaceae family, in endeavour of higher production for food security and combating hunger, ensuring UN SDG goal 2.0.
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Affiliation(s)
- Bulbul Ahmed
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Md Ashraful Haque
- Division of Computer Application, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Mir Asif Iquebal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sarika Jaiswal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - U. B. Angadi
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Dinesh Kumar
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
- Department of Biotechnology, School of Interdisciplinary and Applied Sciences, Central University of Haryana, Mahendergarh, Haryana, India
| | - Anil Rai
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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Wang W, Peng Z, Yang J. Single-sequence protein structure prediction using supervised transformer protein language models. NATURE COMPUTATIONAL SCIENCE 2022; 2:804-814. [PMID: 38177395 DOI: 10.1038/s43588-022-00373-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 11/06/2022] [Indexed: 01/06/2024]
Abstract
Significant progress has been made in protein structure prediction in recent years. However, it remains challenging for AlphaFold2 and other deep learning-based methods to predict protein structure with single-sequence input. Here we introduce trRosettaX-Single, an automated algorithm for single-sequence protein structure prediction. It incorporates the sequence embedding from a supervised transformer protein language model into a multi-scale network enhanced by knowledge distillation to predict inter-residue two-dimensional geometry, which is then used to reconstruct three-dimensional structures via energy minimization. Benchmark tests show that trRosettaX-Single outperforms AlphaFold2 and RoseTTAFold on orphan proteins and works well on human-designed proteins (with an average template modeling score (TM-score) of 0.79). An experimental test shows that the full trRosettaX-Single pipeline is two times faster than AlphaFold2, using much fewer computing resources (<10%). On 2,000 designed proteins from network hallucination, trRosettaX-Single generates structure models with high confidence. As a demonstration, trRosettaX-Single is applied to missense mutation analysis. These data suggest that trRosettaX-Single may find potential applications in protein design and related studies.
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Affiliation(s)
- Wenkai Wang
- School of Mathematical Sciences, Nankai University, Tianjin, China
| | - Zhenling Peng
- Ministry of Education Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China
| | - Jianyi Yang
- Ministry of Education Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China.
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Leander M, Liu Z, Cui Q, Raman S. Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins. eLife 2022; 11:e79932. [PMID: 36226916 PMCID: PMC9662819 DOI: 10.7554/elife.79932] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/13/2022] [Indexed: 01/29/2023] Open
Abstract
A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTFs) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.
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Affiliation(s)
- Megan Leander
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Zhuang Liu
- Department of Physics, Boston UniversityBostonUnited States
| | - Qiang Cui
- Department of Physics, Boston UniversityBostonUnited States
- Department of Chemistry, Boston UniversityBostonUnited States
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
- Department of Bacteriology, University of Wisconsin-MadisonMadisonUnited States
- Department of Chemical and Biological Engineering, University of Wisconsin-MadisonMadisonUnited States
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48
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Azbukina N, Zharikova A, Ramensky V. Intragenic compensation through the lens of deep mutational scanning. Biophys Rev 2022; 14:1161-1182. [PMID: 36345285 PMCID: PMC9636336 DOI: 10.1007/s12551-022-01005-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/26/2022] [Indexed: 12/20/2022] Open
Abstract
A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-022-01005-w.
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Affiliation(s)
- Nadezhda Azbukina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
| | - Anastasia Zharikova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
| | - Vasily Ramensky
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
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Zhou Y, Tremmel R, Schaeffeler E, Schwab M, Lauschke VM. Challenges and opportunities associated with rare-variant pharmacogenomics. Trends Pharmacol Sci 2022; 43:852-865. [PMID: 36008164 DOI: 10.1016/j.tips.2022.07.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/15/2022] [Accepted: 07/29/2022] [Indexed: 12/26/2022]
Abstract
Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Roman Tremmel
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany
| | - Elke Schaeffeler
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC2180) Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany
| | - Matthias Schwab
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; Cluster of Excellence iFIT (EXC2180) Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany; Department of Clinical Pharmacology, and Department of Biochemistry and Pharmacy, University of Tübingen, Tübingen, Germany
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden; Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany.
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50
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Abstract
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AlphaFold has burst into our lives. A powerful algorithm
that underscores
the strength of biological sequence data and artificial intelligence
(AI). AlphaFold has appended projects and research directions. The
database it has been creating promises an untold number of applications
with vast potential impacts that are still difficult to surmise. AI
approaches can revolutionize personalized treatments and usher in
better-informed clinical trials. They promise to make giant leaps
toward reshaping and revamping drug discovery strategies, selecting
and prioritizing combinations of drug targets. Here, we briefly overview
AI in structural biology, including in molecular dynamics simulations
and prediction of microbiota–human protein–protein interactions.
We highlight the advancements accomplished by the deep-learning-powered
AlphaFold in protein structure prediction and their powerful impact
on the life sciences. At the same time, AlphaFold does not resolve
the decades-long protein folding challenge, nor does it identify the
folding pathways. The models that AlphaFold provides do not capture
conformational mechanisms like frustration and allostery, which are
rooted in ensembles, and controlled by their dynamic distributions.
Allostery and signaling are properties of populations. AlphaFold also
does not generate ensembles of intrinsically disordered proteins and
regions, instead describing them by their low structural probabilities.
Since AlphaFold generates single ranked structures, rather than conformational
ensembles, it cannot elucidate the mechanisms of allosteric activating
driver hotspot mutations nor of allosteric drug resistance. However,
by capturing key features, deep learning techniques can use the single
predicted conformation as the basis for generating a diverse ensemble.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
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