Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

ACS Omega, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 8, 2024

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey field computational enzymology, highlighting key principles governing and discussing ongoing challenges promising advances. Over years, computer simulations have become indispensable in study mechanisms, with integration experimental exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies demonstrated power characterizing reaction pathways, transition states, substrate selectivity, product distribution, dynamic conformational changes various enzymes. Nevertheless, significant remain investigating multistep reactions, large-scale changes, allosteric regulation. Beyond mechanistic studies, modeling has emerged an tool computer-aided design rational discovery covalent drugs targeted therapies. Overall, design/engineering drug development can greatly benefit from our understanding detailed enzymes, such protein dynamics, entropy contributions, allostery, revealed by studies. Such convergence different research approaches expected continue, creating synergies research. This outlining ever-expanding research, aims provide guidance future directions facilitate new developments important evolving field.

Language: Английский

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Simultaneous Engineering of the Thermostability and Activity of a Novel Aldehyde Dehydrogenase

ACS Catalysis, Journal Year: 2025, Volume and Issue: 15(3), P. 1841 - 1853

Published: Jan. 17, 2025

Acetaldehyde is a toxic pollutant that can be detoxified by acetaldehyde dehydrogenases (ADAs) through its conversion to acetyl-CoA. This study developed an integrated approach combining virtual screening, rational design, and dual scoring mechanism identify engineer hyperactive ADA variants. A library of 5000 Dickeya parazeae (DpADA) homologues was created protein BLAST, deep learning tools predicted their Kcat values. The top 100 candidates were selected based on binding affinity, evaluated molecular docking phylogenetic analysis. Among these, ADA6 from Buttiauxella sp. S04-F03 exhibited the highest activity, converting 57.6% acetyl-CoA, which 14.1 times higher than DpADA. To improve ADA6's thermostability, folding engineering applied, resulting in P443C variant with 80.7% increase residual activity after heat treatment. Molecular dynamics simulation pinpointed I440 as bottleneck substrate tunnel, guiding design dual-scoring system integrates structural adjustments electronic optimization evaluate mutations for improved exposure activity. final optimized variant, P443C-I440T, achieved efficiency 93.2%. demonstrates effectiveness computational mutagenesis enhance enzyme stability engineering.

Language: Английский

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Practical Machine Learning-Assisted Design Protocol for Protein Engineering: Transaminase Engineering for the Conversion of Bulky Substrates

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(9), P. 6462 - 6469

Published: April 12, 2024

Protein engineering is essential for improving the catalytic performance of enzymes applications in biocatalysis, which machine learning provides an emerging approach variant design. Transaminases are powerful biocatalysts stereoselective synthesis chiral amines but one major challenge their limited substrate scope. We present a general and practical design protocol protein to combine advantages three strategies, including directed evolution, rational design, learning, demonstrate application transaminases with higher activity toward bulky substrates. A high-quality data set was obtained by selected key positions, then applied create model transaminase activity. This data-assisted optimized variants, showed improved (up 3-fold over parent) substrates, maintaining enantioselectivity starting enzyme scaffold as well enantiomeric excess >99%ee).

Language: Английский

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Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 24, 2024

Abstract Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization often hindered by rugged, expansive protein search space and costly experiments. In this work, we present TeleProt, an ML framework that blends evolutionary experimental data design diverse variant libraries, employ it improve the catalytic activity nuclease enzyme degrades biofilms accumulate on chronic wounds. After multiple rounds high-throughput experiments using both TeleProt standard directed evolution (DE) approaches parallel, find our approach found significantly better top-performing than DE, had hit rate at finding diverse, high-activity variants, was even able high-performance initial library no prior data. We have released dataset 55K one most extensive genotype-phenotype landscapes date, drive further progress ML-guided design.

Language: Английский

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Enhancing Lipase Immobilization via Physical Adsorption: Advancements in Stability, Reusability, and Industrial Applications for Sustainable Biotechnological Processes

ACS Omega, Journal Year: 2024, Volume and Issue: 9(47), P. 46698 - 46732

Published: Nov. 14, 2024

Immobilization of lipases by physical adsorption improves their stability, recovery, and reusability in biotechnological processes. The present review provides an advanced bibliometric analysis a comprehensive overview research progress this field. By searching Web Science, 39,575 publications were analyzed, 325 relevant articles selected. Key journals, countries, institutions, authors identified. most cited focus on biofuel production industrial applications. revealed four themes with the biofuel. method is effective when appropriate support used. Despite decrease patent applications, interest remains high. Future studies should optimizing materials exploring new applications technique. detailed understanding immobilization adsorption.

Language: Английский

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Unraveling the molecular basis of substrate specificity and halogen activation in vanadium-dependent haloperoxidases

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 28, 2025

Abstract Vanadium-dependent haloperoxidases (VHPOs) are biotechnologically valuable and operationally versatile biocatalysts. VHPOs share remarkable active-site structural similarities yet display variable reactivity selectivity. The factors dictating substrate specificity and, thus, a general understanding of VHPO reaction control still need to be discovered. This work’s strategic single-point mutation in the cyanobacterial bromoperoxidase Am facilitates selectivity switch allow aryl chlorination. induces loop formation that interacts with neighboring protein monomer, creating tunnel active sites. Structural analysis substrate-R425S-mutant complex reveals substrate-binding site at interface two adjacent units. There, residues Glu139 Phe401 interact arenes, extending residence time close vanadate cofactor stabilizing intermediates. Our findings validate long-debated existence direct binding provide detailed mechanistic understanding. work will pave way for broader application diverse chemical processes.

Language: Английский

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Data‐Driven Protein Engineering for Improving Catalytic Activity and Selectivity

ChemBioChem, Journal Year: 2023, Volume and Issue: 25(3)

Published: Nov. 29, 2023

Abstract Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes applications in biocatalysis. However, traditional approaches, such as directed evolution rational design, encounter challenge dealing with experimental screening process a large protein mutation space. Machine learning methods allow approximation fitness landscapes identification patterns using limited data, thus providing new avenue to guide campaigns. In this concept article, we review machine models that have been developed assess enzyme‐substrate‐catalysis performance relationships aiming improve through data‐driven engineering. Furthermore, prospect future development field provide additional strategies tools achieving desired activities selectivities.

Language: Английский

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FAIR Data and Software: Improving Efficiency and Quality of Biocatalytic Science

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(4), P. 2709 - 2718

Published: Feb. 7, 2024

Biocatalysis is entering a promising era as data-driven science. High-throughput experimentation generates rapidly increasing stream of biocatalytic data, which the raw material for mechanistic and modeling to design improved biocatalysts bioprocesses. However, our laboratory routines scientific practice communicating results are insufficient ensure reproducibility scalability experiments, data management has become bottleneck progress in biocatalysis. In order take full advantage rapid experimental computational technologies, should be findable, accessible, interoperable, reusable (FAIR). FAIRification software achieved by developing standardized exchange formats ontologies, electronic lab notebooks acquisition documentation experimentation, collaborative platforms analyzing repositories publishing together with data. The EnzymeML platform provides extensible tools FAIR scalable digitalization biocatalysis expected improve efficiency research automation guarantee quality science reproducibility. Most all, they foster reasoning creating hypotheses enabling reanalysis previously published thus promote disruptive innovation.

Language: Английский

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Effective engineering of a ketoreductase for the biocatalytic synthesis of an ipatasertib precursor

Communications Chemistry, Journal Year: 2024, Volume and Issue: 7(1)

Published: Feb. 28, 2024

Abstract Semi-rational enzyme engineering is a powerful method to develop industrial biocatalysts. Profiting from advances in molecular biology and bioinformatics, semi-rational approaches can effectively accelerate campaigns. Here, we present the optimization of ketoreductase Sporidiobolus salmonicolor for chemo-enzymatic synthesis ipatasertib, potent protein kinase B inhibitor. Harnessing power mutational scanning structure-guided rational design, created 10-amino acid substituted variant exhibiting 64-fold higher apparent k cat improved robustness under process conditions compared wild-type enzyme. In addition, benefit algorithm-aided was studied derive correlations sequence-function data, it found that applied Gaussian processes allowed us reduce library size. The final scalable high performing biocatalytic yielded alcohol intermediate with ≥ 98% conversion diastereomeric excess 99.7% ( R , - trans ) 100 g L −1 ketone after 30 h. Modelling kinetic studies shed light on mechanistic factors governing reaction outcome, mutations T134V, A238K, M242W Q245S exerting most beneficial effect reduction activity towards target ketone.

Language: Английский

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Biohydrogen production for sustainable energy transition: A bibliometric and systematic review of the reaction mechanisms, challenges, knowledge gaps and emerging trends

Biomass and Bioenergy, Journal Year: 2024, Volume and Issue: 188, P. 107345 - 107345

Published: Aug. 14, 2024

Language: Английский

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