Optimizing enzyme thermostability by combining multiple mutations using protein language model

mLife, Год журнала: 2024, Номер 3(4), С. 492 - 504

Опубликована: Дек. 1, 2024

Optimizing enzyme thermostability is essential for advancements in protein science and industrial applications. Currently, (semi-)rational design random mutagenesis methods can accurately identify single-point mutations that enhance thermostability. However, complex epistatic interactions often arise when multiple mutation sites are combined, leading to the complete inactivation of combinatorial mutants. As a result, constructing an optimized requires repeated rounds incrementally incorporate single sites, which highly time-consuming. In this study, we developed AI-aided strategy engineering efficiently facilitates recombination beneficial mutations. We utilized data from creatinase, including 18 mutants, 22 double-point 21 triple-point 12 quadruple-point Using these as inputs, used temperature-guided language model, Pro-PRIME, learn features After two design, obtained 50 mutants with superior thermostability, achieving success rate 100%. The best mutant, 13M4, contained 13 maintained nearly full catalytic activity compared wild-type. It showed 10.19°C increase melting temperature ~655-fold half-life at 58°C. Additionally, model successfully captured epistasis high-order sign (K351E) synergistic (D17V/I149V). elucidated mechanism long-range detail using dynamics cross-correlation matrix method. Our work provides efficient framework designing studying effects protein-directed evolution.

Язык: Английский

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iDesignGPT: large language model agentic workflows boost engineering design

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Янв. 7, 2025

Engineering design, a cornerstone of technological innovation, faces persistent challenges from the rigidity traditional methods and insufficient responsiveness emerging AI tools to fully address its inherently complex, dynamic, creativity-driven demands. Here we introduce iDesignGPT, novel framework that integrates large language model with established design methodologies enable dynamic multi-agent collaboration for problem refinement, information gathering, space exploration, iterative optimization. By incorporating metrics such as coverage, diversity, novelty, iDesignGPT provides decision-enabling, data-driven insights conceptual engineering evaluation. Our results reveal surpasses benchmark models in generating innovative, modular, rational solutions, particularly exploratory, open-ended scenarios prioritizing creativity adaptability. User studies, involving both students experienced engineers, validate ability uncover hidden requirements, foster creativity, enhance workflow transparency. Collectively, these findings position scalable platform lowers expertise barrier, fosters interdisciplinary collaboration, expands transformative potential AI-assisted design.

Язык: Английский

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AI-enabled Alkaline-resistant Evolution of Protein to Apply in Mass Production

Опубликована: Фев. 7, 2025

Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it assist protein design improve efficiency engineering and reduce manufacturing cost. However, industrial settings, are often required work extreme environments where they relatively scarce or even non-existent nature. Since such almost absent training datasets, is uncertain whether AI model possesses capability evolving adapt conditions. Antibodies crucial components affinity chromatography, hoped remain active at most cannot tolerate. In this study, we applied an advanced large language (LLM), Pro-PRIME model, alkali resistance a representative antibody, VHH antibody capable binding growth hormone. Through two rounds design, ensured that selected mutant has enhanced functionality, including higher thermal stability, pH stronger affinity, thereby validating generalized LLM meeting specific demands. To best our knowledge, first LLM-designed product, which successfully mass production.

Язык: Английский

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AI-enabled alkaline-resistant evolution of protein to apply in mass production

eLife, Год журнала: 2025, Номер 13

Опубликована: Фев. 19, 2025

Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it assist protein design improve efficiency engineering and reduce manufacturing cost. However, industrial settings, are often required work extreme environments where they relatively scarce or even non-existent nature. Since such almost absent training datasets, is uncertain whether AI model possesses capability evolving adapt conditions. Antibodies crucial components affinity chromatography, hoped remain active at most cannot tolerate. In this study, we applied an advanced large language (LLM), Pro-PRIME model, alkali resistance a representative antibody, VHH antibody capable binding growth hormone. Through two rounds design, ensured that selected mutant has enhanced functionality, including higher thermal stability, pH resistance, stronger affinity, thereby validating generalized LLM meeting specific demands. To best our knowledge, first LLM-designed product, which successfully mass production.

Язык: Английский

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Entropy-driven zero-shot deep learning model selection for viral proteins

Physical Review Research, Год журнала: 2025, Номер 7(1)

Опубликована: Фев. 28, 2025

Predicting the fitness of viral proteins is fundamental to understanding evolution and developing antiviral strategies. This study introduces Venus-EEM, an entropy-driven ensemble model, aimed at improving performance zero-shot predictions for protein across diverse datasets. We demonstrate that entropy serves as effective criterion selecting optimal models, enabling adaptive model selection different prediction tasks. By incorporating entropy-weighted learning from multiple language Venus-EEM achieves superior compared existing methods. validate model's effectiveness through comprehensive evaluation on datasets a detailed case T7 RNA polymerase (T7 RNAP) activity. Our findings provide approach predicting mutations based entropy, bridging physics principles with practical biological challenges. Published by American Physical Society 2025

Язык: Английский

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Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis

mLife, Год журнала: 2025, Номер 4(2), С. 107 - 125

Опубликована: Март 28, 2025

Abstract Biosynthesis—a process utilizing biological systems to synthesize chemical compounds—has emerged as a revolutionary solution 21st‐century challenges due its environmental sustainability, scalability, and high stereoselectivity regioselectivity. Recent advancements in artificial intelligence (AI) are accelerating biosynthesis by enabling intelligent design, construction, optimization of enzymatic reactions systems. We first introduce the molecular retrosynthesis route planning biochemical pathway including single‐step algorithms AI‐based design tools. highlight advantages large language models addressing sparsity data. Furthermore, we review enzyme discovery methods based on sequence structure alignment techniques. Breakthroughs structural prediction expected significantly improve accuracy discovery. also summarize for de novo generation nonnatural or orphan reactions, focusing functional annotation techniques reaction small molecule similarity. Turning engineering, discuss strategies thermostability, solubility, activity, well applications AI these fields. The shift from traditional experiment‐driven data‐driven computationally driven is already underway. Finally, present potential provide perspective future research directions. envision expanded biocatalysis drug development, green chemistry, complex synthesis.

Язык: Английский

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Recent Advancements for Enhanced Biocatalyst and Biotransformation

Molecular Biotechnology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 9, 2025

Язык: Английский

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PRIME, a temperature-guided language model revolutionizes protein engineering

Acta Pharmaceutica Sinica B, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

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Enriching stabilizing mutations through automated analysis of molecular dynamics simulations using BoostMut

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Май 3, 2025

Abstract Thermostability is a critical goal in protein engineering for applications of biocatalysts and biomedicines. Despite striking advances biomolecular predictive modeling, reliably identifying stabilizing mutations remains challenging. Previously, molecular dynamics (MD) simulations visual inspection have been used as secondary filter to improve the success rate pre-selected by thermostability algorithms. However, this approach suffers from low throughput subjectivity. Here, we introduce BoostMut (Biophysical Overview Optimal Stabilizing Mutations), computational tool that standardizes automates mutation filtering analyzing dynamic structural features MD. formalizes principles guiding manual verification, providing consistent reproducible stability assessment. Rigorous benchmarking across multiple datasets showed integrating BoostMut’s biophysical analysis improves prediction regardless initial predictor. Given modest amount existing mutant data, performance can be further enhanced with lightweight machine learning model. Upon experimentally validating predictions on enzyme limonene-epoxide hydrolase, identified previously overlooked inspection, achieved higher overall rate. We foresee being filtering, an integrated step workflows, labelling data train future predictors.

Язык: Английский

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Deep learning methods for protein representation and function prediction: A comprehensive overview

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 155, С. 110977 - 110977

Опубликована: Май 14, 2025

Язык: Английский

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