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Wang T, Jin X, Lu X, Min X, Ge S, Li S. Empirical validation of ProteinMPNN's efficiency in enhancing protein fitness. Front Genet 2024; 14:1347667. [PMID: 38274106 PMCID: PMC10808456 DOI: 10.3389/fgene.2023.1347667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction: Protein engineering, which aims to improve the properties and functions of proteins, holds great research significance and application value. However, current models that predict the effects of amino acid substitutions often perform poorly when evaluated for precision. Recent research has shown that ProteinMPNN, a large-scale pre-training sequence design model based on protein structure, performs exceptionally well. It is capable of designing mutants with structures similar to the original protein. When applied to the field of protein engineering, the diverse designs for mutation positions generated by this model can be viewed as a more precise mutation range. Methods: We collected three biological experimental datasets and compared the design results of ProteinMPNN for wild-type proteins with the experimental datasets to verify the ability of ProteinMPNN in improving protein fitness. Results: The validation on biological experimental datasets shows that ProteinMPNN has the ability to design mutation types with higher fitness in single and multi-point mutations. We have verified the high accuracy of ProteinMPNN in protein engineering tasks from both positive and negative perspectives. Discussion: Our research indicates that using large-scale pre trained models to design protein mutants provides a new approach for protein engineering, providing strong support for guiding biological experiments and applications in biotechnology.
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Affiliation(s)
- Tianshu Wang
- School of Informatics, Institute of Artificial Intelligence, Xiamen University, Xiamen, China
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
| | - Xiaocheng Jin
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
| | - Xiaoli Lu
- Information and Networking Center, Xiamen University, Xiamen, China
| | - Xiaoping Min
- School of Informatics, Institute of Artificial Intelligence, Xiamen University, Xiamen, China
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
| | - Shengxiang Ge
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
| | - Shaowei Li
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
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Sarfati H, Naftaly S, Papo N, Keasar C. Predicting mutant outcome by combining deep mutational scanning and machine learning. Proteins 2021; 90:45-57. [PMID: 34293212 DOI: 10.1002/prot.26184] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 06/01/2021] [Accepted: 07/11/2021] [Indexed: 02/02/2023]
Abstract
Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the experimental data to gain insights about unexplored regions of the mutational landscape is a major computational challenge. Such insights may facilitate further experimental work and accelerate the development of novel protein variants with beneficial therapeutic or industrially relevant properties. Here we present a novel, machine learning approach for the prediction of functional mutation outcome in the context of deep mutational screens. Using sequence (one-hot) features of variants with known properties, as well as structural features derived from models thereof, we train predictive statistical models to estimate the unknown properties of other variants. The utility of the new computational scheme is demonstrated using five sets of mutational scanning data, denoted "targets": (a) protease specificity of APPI (amyloid precursor protein inhibitor) variants; (b-d) three stability related properties of IGBPG (immunoglobulin G-binding β1 domain of streptococcal protein G) variants; and (e) fluorescence of GFP (green fluorescent protein) variants. Performance is measured by the overall correlation of the predicted and observed properties, and enrichment-the ability to predict the most potent variants and presumably guide further experiments. Despite the diversity of the targets the statistical models can generalize variant examples thereof and predict the properties of test variants with both single and multiple mutations.
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Affiliation(s)
- Hagit Sarfati
- Department of Computer Science, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Si Naftaly
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Niv Papo
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Chen Keasar
- Department of Computer Science, Ben-Gurion University of the Negev, Be'er Sheva, Israel
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