Chiral CuxCoyS‐CuzS Nanoflowers with Bioinspired Enantioselective Catalytic Performances DOI

Qingrui Si,

Fang Wang, Qi Ding

et al.

Small, Journal Year: 2024, Volume and Issue: 20(24)

Published: Jan. 9, 2024

Abstract Nanomaterials with biomimetic catalytic abilities have attracted significant attention. However, the stereoselectivity of natural enzymes determined by their unique configurations is difficult to imitate. In this work, a kind chiral Cu x Co y S‐Cu z S nanoflowers ( L / D ‐Pen‐NFs) developed, using porous nanoparticles (NPs) as stamens, sheets petals, and penicillamine surface stabilizers. Compared laccase enzyme, ‐Pen‐NFs exhibit advantages in efficiency, stability against harsh environments, recyclability, convenience construction. Most importantly, they display high enantioselectivity toward neurotransmitters, which proved ‐ ‐Pen‐NFs’ different efficiencies enantiomers. are more efficient catalyzing oxidation ‐epinephrine ‐dopamine compared ‐Pen‐NFs. efficiency oxidizing ‐norepinephrine ‐DOPA lower than that The reason for difference distinct binding affinities between nano‐enantiomers molecules. This work can spur development nanostructures functions.

Language: Английский

Machine Learning-Guided Protein Engineering DOI Creative Commons
Petr Kouba, Pavel Kohout, Faraneh Haddadi

et al.

ACS Catalysis, Journal Year: 2023, Volume and Issue: 13(21), P. 13863 - 13895

Published: Oct. 13, 2023

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid the discovery annotation of enzymes, as well suggesting beneficial mutations for improving known targets. The field protein is gathering steam, driven by recent success stories notable other areas. It already encompasses ambitious tasks such understanding predicting structure function, catalytic efficiency, enantioselectivity, dynamics, stability, solubility, aggregation, more. Nonetheless, still evolving, with many challenges overcome questions address. In this Perspective, we provide an overview ongoing trends domain, highlight case studies, examine current limitations learning-based We emphasize crucial importance thorough validation emerging models before their use rational design. present our opinions on fundamental problems outline potential directions future research.

Language: Английский

Citations

90
Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering DOI Creative Commons
Jason Yang, Francesca-Zhoufan Li, Frances H. Arnold

et al.

ACS Central Science, Journal Year: 2024, Volume and Issue: 10(2), P. 226 - 241

Published: Feb. 5, 2024

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 unlock new activities not found in nature. Because search space possible proteins is vast, enzyme engineering usually involves discovering an starting point that has some desired activity followed by directed evolution improve its "fitness" for a application. Recently, machine learning (ML) emerged powerful tool complement this empirical process. ML models contribute (1) discovery functional annotation known protein or generating novel with functions (2) navigating fitness landscapes optimization mappings between associated values. In Outlook, we explain how complements discuss future potential improved outcomes.

Language: Английский

Citations

70
Self-driving laboratories to autonomously navigate the protein fitness landscape DOI Creative Commons
Jacob Rapp,

Bennett J. Bremer,

Philip A. Romero

et al.

Nature Chemical Engineering, Journal Year: 2024, Volume and Issue: 1(1), P. 97 - 107

Published: Jan. 11, 2024

Abstract Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive inefficient. Here we present the Self-driving Autonomous Machines for Landscape Exploration (SAMPLE) platform fully autonomous protein engineering. SAMPLE is driven by an intelligent agent that learns sequence–function relationships, designs sends to a automated robotic system experimentally tests designed provides feedback improve agent’s understanding of system. We deploy four agents goal glycoside hydrolase enzymes enhanced thermal tolerance. Despite showing individual differences in their search behavior, all quickly converge on thermostable enzymes. laboratories automate accelerate scientific discovery process hold great potential fields synthetic biology.

Language: Английский

Citations

61
Opportunities and challenges in design and optimization of protein function DOI
Dina Listov, Casper A. Goverde, Bruno E. Correia

et al.

Nature Reviews Molecular Cell Biology, Journal Year: 2024, Volume and Issue: 25(8), P. 639 - 653

Published: April 2, 2024

Language: Английский

Citations

41
Sparks of function by de novo protein design DOI
Alexander E. Chu, Tianyu Lu, Po‐Ssu Huang

et al.

Nature Biotechnology, Journal Year: 2024, Volume and Issue: 42(2), P. 203 - 215

Published: Feb. 1, 2024

Language: Английский

Citations

31
Navigating the landscape of enzyme design: from molecular simulations to machine learning DOI Creative Commons
Jiahui Zhou, Meilan Huang

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: 53(16), P. 8202 - 8239

Published: Jan. 1, 2024

Global environmental issues and sustainable development call for new technologies fine chemical synthesis waste valorization. Biocatalysis has attracted great attention as the alternative to traditional organic synthesis. However, it is challenging navigate vast sequence space identify those proteins with admirable biocatalytic functions. The recent of deep-learning based structure prediction methods such AlphaFold2 reinforced by different computational simulations or multiscale calculations largely expanded 3D databases enabled structure-based design. While approaches shed light on site-specific enzyme engineering, they are not suitable large-scale screening potential biocatalysts. Effective utilization big data using machine learning techniques opens up a era accelerated predictions. Here, we review applications machine-learning guided We also provide our view challenges perspectives effectively employing design integrating molecular learning, importance database construction algorithm in attaining predictive ML models explore fitness landscape

Language: Английский

Citations

21
Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy DOI Creative Commons
Nan Zheng,

Yongchao Cai,

Zehua Zhang

et al.

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

Published: Jan. 11, 2025

Language: Английский

Citations

2
Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design DOI Creative Commons

Braun Markus,

Gruber Christian C,

Krassnigg Andreas

et al.

ACS Catalysis, Journal Year: 2023, Volume and Issue: 13(21), P. 14454 - 14469

Published: Oct. 26, 2023

Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines previously needed optimize an enzyme its more efficient variant. For over a decade, benefits of predictive algorithms have helped scientists engineers navigate complexity functional sequence space. More recently, spurred by dramatic advances in underlying tools, faster, cheaper, accurate identification, characterization, has catapulted terms such as artificial intelligence machine learning must-have vocabulary field. This Perspective aims showcase current status pharmaceutical industry also discuss celebrate innovative approaches science highlighting their potential selected recent developments offering thoughts on future opportunities biocatalysis. It critically assesses technology's limitations, unanswered questions, unmet challenges.

Language: Английский

Citations

33
Exploring new galaxies: Perspectives on the discovery of novel PET-degrading enzymes DOI
Jan Mičan, Da’san M. M. Jaradat, Weidong Liu

et al.

Applied Catalysis B Environment and Energy, Journal Year: 2023, Volume and Issue: 342, P. 123404 - 123404

Published: Oct. 23, 2023

Language: Английский

Citations

28
Designed active-site library reveals thousands of functional GFP variants DOI Creative Commons
Jonathan J. Weinstein, Carlos Martí‐Gómez, Rosalie Lipsh‐Sokolik

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: May 20, 2023

Abstract Mutations in a protein active site can lead to dramatic and useful changes activity. The site, however, is sensitive mutations due high density of molecular interactions, substantially reducing the likelihood obtaining functional multipoint mutants. We introduce an atomistic machine-learning-based approach, called high-throughput Functional Libraries (htFuncLib), that designs sequence space which form low-energy combinations mitigate risk incompatible interactions. apply htFuncLib GFP chromophore-binding pocket, and, using fluorescence readout, recover >16,000 unique encoding as many eight active-site mutations. Many exhibit substantial diversity thermostability (up 96 °C), lifetime, quantum yield. By eliminating mutations, generates large sequences. envision will be used one-shot optimization activity enzymes, binders, other proteins.

Language: Английский

Citations

25