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Yang ZJ, Shao Q, Jiang Y, Jurich C, Ran X, Juarez RJ, Yan B, Stull SL, Gollu A, Ding N. Mutexa: A Computational Ecosystem for Intelligent Protein Engineering. J Chem Theory Comput 2023; 19:7459-7477. [PMID: 37828731 PMCID: PMC10653112 DOI: 10.1021/acs.jctc.3c00602] [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: 06/06/2023] [Indexed: 10/14/2023]
Abstract
Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.
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Affiliation(s)
- Zhongyue J. Yang
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt
Institute of Chemical Biology, Vanderbilt
University, Nashville, Tennessee 37235, United States
- Department
of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235, United States
- Data
Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Qianzhen Shao
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yaoyukun Jiang
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Christopher Jurich
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt
Institute of Chemical Biology, Vanderbilt
University, Nashville, Tennessee 37235, United States
| | - Xinchun Ran
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Reecan J. Juarez
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37235, United States
| | - Bailu Yan
- Department
of Biostatistics, Vanderbilt University, Nashville, Tennessee 37205, United States
| | - Sebastian L. Stull
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Anvita Gollu
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Ning Ding
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
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Ran X, Jiang Y, Shao Q, Yang ZJ. EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution. Chem Sci 2023; 14:12073-12082. [PMID: 37969577 PMCID: PMC10631226 DOI: 10.1039/d3sc02752j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/16/2023] [Indexed: 11/17/2023] Open
Abstract
Hydrolase-catalyzed kinetic resolution is a well-established biocatalytic process. However, the computational tools that predict favorable enzyme scaffolds for separating a racemic substrate mixture are underdeveloped. To address this challenge, we trained a deep learning framework, EnzyKR, to automate the selection of hydrolases for stereoselective biocatalysis. EnzyKR adopts a classifier-regressor architecture that first identifies the reactive binding conformer of a substrate-hydrolase complex, and then predicts its activation free energy. A structure-based encoding strategy was used to depict the chiral interactions between hydrolases and enantiomers. Different from existing models trained on protein sequences and substrate SMILES strings, EnzyKR was trained using 204 substrate-hydrolase complexes, which were constructed by docking. EnzyKR was tested using a held-out dataset of 20 complexes on the task of predicting activation free energy. EnzyKR achieved a Pearson correlation coefficient (R) of 0.72, a Spearman rank correlation coefficient (Spearman R) of 0.72, and a mean absolute error (MAE) of 1.54 kcal mol-1 in this task. Furthermore, EnzyKR was tested on the task of predicting enantiomeric excess ratios for 28 hydrolytic kinetic resolution reactions catalyzed by fluoroacetate dehalogenase RPA1163, halohydrin HheC, A. mediolanus epoxide hydrolase, and P. fluorescens esterase. The performance of EnzyKR was compared against that of a recently developed kinetic predictor, DLKcat. EnzyKR correctly predicts the favored enantiomer and outperforms DLKcat in 18 out of 28 reactions, occupying 64% of the test cases. These results demonstrate EnzyKR to be a new approach for prediction of enantiomeric outcomes in hydrolase-catalyzed kinetic resolution reactions.
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Affiliation(s)
- Xinchun Ran
- Department of Chemistry, Vanderbilt University Nashville Tennessee 37235 USA +1-343-9849
| | - Yaoyukun Jiang
- Department of Chemistry, Vanderbilt University Nashville Tennessee 37235 USA +1-343-9849
| | - Qianzhen Shao
- Department of Chemistry, Vanderbilt University Nashville Tennessee 37235 USA +1-343-9849
| | - Zhongyue J Yang
- Department of Chemistry, Vanderbilt University Nashville Tennessee 37235 USA +1-343-9849
- Center for Structural Biology, Vanderbilt University Nashville Tennessee 37235 USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University Nashville Tennessee 37235 USA
- Data Science Institute, Vanderbilt University Nashville Tennessee 37235 USA
- Department of Chemical and Biomolecular Engineering, Vanderbilt University Nashville Tennessee 37235 USA
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3
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Jurich C, Yang ZJ. High-throughput computational investigation of protein electrostatics and cavity for SAM-dependent methyltransferases. Protein Sci 2023; 32:e4690. [PMID: 37278582 PMCID: PMC10273352 DOI: 10.1002/pro.4690] [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: 02/06/2023] [Revised: 04/25/2023] [Accepted: 05/29/2023] [Indexed: 06/07/2023]
Abstract
S-adenosyl methionine (SAM)-dependent methyl transferases (MTases) are a ubiquitous class of enzymes catalyzing dozens of essential life processes. Despite targeting a large space of substrates with diverse intrinsic reactivity, SAM MTases have similar catalytic efficiency. While understanding of MTase mechanism has grown tremendously through the integration of structural characterization, kinetic assays, and multiscale simulations, it remains elusive how these enzymes have evolved to fit the diverse chemical needs of their respective substrates. In this work, we performed a high-throughput molecular modeling analysis of 91 SAM MTases to better understand how their properties (i.e., electric field [EF] strength and active site volumes) help achieve similar catalytic efficiency toward substrates of different reactivity. We found that EF strengths have largely adjusted to make the target atom a better methyl acceptor. For MTases that target RNA/DNA and histone proteins, our results suggest that EF strength accommodates formal hybridization state and variation in cavity volume trends with diversity of substrate classes. Metal ions in SAM MTases contribute negatively to EF strength for methyl donation and enzyme scaffolds tend to offset these contributions.
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Affiliation(s)
| | - Zhongyue J. Yang
- Department of ChemistryVanderbilt UniversityNashvilleTennesseeUSA
- Center for Structural BiologyVanderbilt UniversityNashvilleTennesseeUSA
- Vanderbilt Institute of Chemical Biology, Vanderbilt UniversityNashvilleTennesseeUSA
- Data Science InstituteVanderbilt UniversityNashvilleTennesseeUSA
- Department of Chemical and Biomolecular EngineeringVanderbilt UniversityNashvilleTennesseeUSA
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Gu J, Xu Y, Nie Y. Role of distal sites in enzyme engineering. Biotechnol Adv 2023; 63:108094. [PMID: 36621725 DOI: 10.1016/j.biotechadv.2023.108094] [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: 08/02/2022] [Revised: 11/15/2022] [Accepted: 01/01/2023] [Indexed: 01/06/2023]
Abstract
The limitations associated with natural enzyme catalysis have triggered the rise of the field of protein engineering. Traditional rational design was based on the analysis of protein structural information and catalytic mechanisms to identify key active sites or ligand binding sites to reshape the substrate pocket. The role and significance of functional sites in the active center have been studied extensively. With a deeper understanding of the structure-catalysis relationship map, the entire protein molecule can be filled with residues that play a substantial role in its structure and function. However, the catalytic mechanism underlying distal mutations remains unclear. The aim of this review was to highlight the criticality of the distal site in enzyme engineering based on the following three aspects: What can distal mutations exert on function from mutability landscape? How do distal sites influence enzyme function? How to predict and design distal mutations? This review provides insights into the catalytic mechanism of enzymes from the global interaction network, knowledge from sequence-structure-dynamics-function relationships, and strategies for distal mutation-based protein engineering.
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Affiliation(s)
- Jie Gu
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yan Xu
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China; State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Yao Nie
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China; Suqian Industrial Technology Research Institute of Jiangnan University, Suqian 223814, China.
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Jiang Y, Ran X, Yang ZJ. Data-driven enzyme engineering to identify function-enhancing enzymes. Protein Eng Des Sel 2023; 36:gzac009. [PMID: 36214500 PMCID: PMC10365845 DOI: 10.1093/protein/gzac009] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/08/2022] [Accepted: 09/28/2022] [Indexed: 01/22/2023] Open
Abstract
Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of plastics and other pollutants, and medical treatment of food allergies. Data-driven strategies, including statistical modeling, machine learning, and deep learning, have largely advanced the understanding of the sequence-structure-function relationships for enzymes. They have also enhanced the capability of predicting and designing new enzymes and enzyme variants for catalyzing the transformation of new-to-nature reactions. Here, we reviewed the recent progresses of data-driven models that were applied in identifying efficiency-enhancing mutants for catalytic reactions. We also discussed existing challenges and obstacles faced by the community. Although the review is by no means comprehensive, we hope that the discussion can inform the readers about the state-of-the-art in data-driven enzyme engineering, inspiring more joint experimental-computational efforts to develop and apply data-driven modeling to innovate biocatalysts for synthetic and pharmaceutical applications.
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Affiliation(s)
- Yaoyukun Jiang
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Xinchun Ran
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Zhongyue J Yang
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN 37235, USA
- Data Science Institute, Vanderbilt University, Nashville, TN 37235, USA
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN 37235, USA
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6
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Kari J, Schaller K, Molina GA, Borch K, Westh P. The Sabatier principle as a tool for discovery and engineering of industrial enzymes. Curr Opin Biotechnol 2022; 78:102843. [PMID: 36375405 DOI: 10.1016/j.copbio.2022.102843] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 10/16/2022] [Indexed: 11/13/2022]
Abstract
The recent breakthrough in all-atom, protein structure prediction opens new avenues for a range of computational approaches in enzyme design. These new approaches could become instrumental for the development of technical biocatalysts, and hence our transition toward more sustainable industries. Here, we discuss one approach, which is well-known within inorganic catalysis, but essentially unexploited in biotechnology. Specifically, we review examples of linear free-energy relationships (LFERs) for enzyme reactions and discuss how LFERs and the associated Sabatier Principle may be implemented in algorithms that estimate kinetic parameters and enzyme performance based on model structures.
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Affiliation(s)
- Jeppe Kari
- Roskilde University, Dept. Science and Environment, Universitetsvej 1, DK-4000 Roskilde, Denmark
| | - Kay Schaller
- Technical University of Denmark, Dept. of Biotechnology and Biomedicine, Sølvtofts Plads 224, DK-2800, Kgs. Lyngby, Denmark; University of Copenhagen, Dept. of Drug Design and Pharmacology, Universitetsparken 2, DK-2100 Copenhagen, Denmark
| | - Gustavo A Molina
- Technical University of Denmark, Dept. of Biotechnology and Biomedicine, Sølvtofts Plads 224, DK-2800, Kgs. Lyngby, Denmark; Technical University of Denmark, The Novo Nordisk Foundation Center for Biosustainability, Build. 220, Kemitorvet, DK-2800, Kgs. Lyngby, Denmark
| | - Kim Borch
- Novozymes A/S, Biologiens vej 2, DK-2800, Kgs. Lyngby, Denmark
| | - Peter Westh
- Technical University of Denmark, Dept. of Biotechnology and Biomedicine, Sølvtofts Plads 224, DK-2800, Kgs. Lyngby, Denmark.
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7
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Yan B, Ran X, Gollu A, Cheng Z, Zhou X, Chen Y, Yang ZJ. IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database. J Chem Inf Model 2022; 62:5841-5848. [PMID: 36286319 DOI: 10.1021/acs.jcim.2c01139] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Data-driven modeling has emerged as a new paradigm for biocatalyst design and discovery. Biocatalytic databases that integrate enzyme structure and function data are in urgent need. Here we describe IntEnzyDB as an integrated structure-kinetics database for facile statistical modeling and machine learning. IntEnzyDB employs a relational database architecture with a flattened data structure, which allows rapid data operation. This architecture also makes it easy for IntEnzyDB to incorporate more types of enzyme function data. IntEnzyDB contains enzyme kinetics and structure data from six enzyme commission classes. Using 1050 enzyme structure-kinetics pairs, we investigated the efficiency-perturbing propensities of mutations that are close or distal to the active site. The statistical results show that efficiency-enhancing mutations are globally encoded and that deleterious mutations are much more likely to occur in close mutations than in distal mutations. Finally, we describe a web interface that allows public users to access enzymology data stored in IntEnzyDB. IntEnzyDB will provide a computational facility for data-driven modeling in biocatalysis and molecular evolution.
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Affiliation(s)
- Bailu Yan
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.,Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37205, United States
| | - Xinchun Ran
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Anvita Gollu
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Zihao Cheng
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Xiang Zhou
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yiwen Chen
- Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Zhongyue J Yang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, United States.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States.,Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States.,Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37205, United States
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8
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Derat E, Kamerlin SCL. Computational Advances in Protein Engineering and Enzyme Design. J Phys Chem B 2022; 126:2449-2451. [PMID: 35387452 DOI: 10.1021/acs.jpcb.2c01198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Etienne Derat
- Institut Parisien de Chimie Moléculaire, UMR 8232 CNRS, Sorbonne Université, 75005 Paris, France
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9
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Jiang Y, Yan B, Chen Y, Juarez RJ, Yang ZJ. Molecular Dynamics-Derived Descriptor Informs the Impact of Mutation on the Catalytic Turnover Number in Lactonase Across Substrates. J Phys Chem B 2022; 126:2486-2495. [PMID: 35324218 DOI: 10.1021/acs.jpcb.2c00142] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers remain largely unexplored. In this work, we report the identification of molecular modeling-derived descriptors to predict mutation effect on the turnover number of lactonase SsoPox with both native and non-native substrates. The study consists of 10 enzyme-substrate complexes resulting from a combination of five enzyme variants with two substrates. For each complex, we derived 15 descriptors from molecular dynamics simulations and applied principal component analysis to rank the predictive capability of the descriptors. A top-ranked descriptor was identified, which is the solvent-accessible surface area (SASA) ratio of the substrate to the active site pocket. A uniform volcano-shaped plot was observed in the distribution of experimental activation free energy against the SASA ratio. To achieve efficient lactonase hydrolysis, a non-native substrate-bound enzyme variant needs to involve a similar range of the SASA ratio to the native substrate-bound wild-type enzyme. The descriptor reflects how well the enzyme active site pocket accommodates a substrate for reaction, which has the potential of guiding optimization of enzyme reaction turnover for non-native chemical transformations.
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Affiliation(s)
- Yaoyukun Jiang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Bailu Yan
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yu Chen
- School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, Jiangsu 210023, China
| | - Reecan J Juarez
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Zhongyue J Yang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, United States.,Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States.,Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
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10
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Shao Q, Jiang Y, Yang ZJ. EnzyHTP: A High-Throughput Computational Platform for Enzyme Modeling. J Chem Inf Model 2022; 62:647-655. [DOI: 10.1021/acs.jcim.1c01424] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Qianzhen Shao
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Yaoyukun Jiang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Zhongyue J. Yang
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
- Data Science Institute, Vanderbilt University, Nashville, Tennessee 37235, United States
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