Atomic context-conditioned protein sequence design using LigandMPNN DOI Creative Commons
Justas Dauparas, Gyu Rie Lee, Robert Pecoraro

и другие.

Nature Methods, Год журнала: 2025, Номер unknown

Опубликована: Март 28, 2025

Protein sequence design in the context of small molecules, nucleotides and metals is critical to enzyme small-molecule binder sensor design, but current state-of-the-art deep-learning-based methods are unable model nonprotein atoms molecules. Here we describe a protein method called LigandMPNN that explicitly models all components biomolecular systems. significantly outperforms Rosetta ProteinMPNN on native backbone recovery for residues interacting with molecules (63.3% versus 50.4% 50.5%), (50.5% 35.2% 34.0%) (77.5% 36.0% 40.6%). generates not only sequences also sidechain conformations allow detailed evaluation binding interactions. has been used over 100 experimentally validated DNA-binding proteins high affinity structural accuracy (as indicated by four X-ray crystal structures), redesign designs increased as much 100-fold. We anticipate will be widely useful designing new proteins, sensors enzymes.

Язык: Английский

Accurate structure prediction of biomolecular interactions with AlphaFold 3 DOI Creative Commons
Josh Abramson, Jonas Adler,

Jack Dunger

и другие.

Nature, Год журнала: 2024, Номер 630(8016), С. 493 - 500

Опубликована: Май 8, 2024

Abstract The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure proteins and their interactions, enabling huge range applications protein design 2–6 . Here we describe our 3 model with substantially updated diffusion-based architecture that is capable predicting joint complexes including proteins, nucleic acids, small molecules, ions modified residues. new demonstrates improved accuracy over many previous specialized tools: far greater for protein–ligand interactions compared state-of-the-art docking tools, much higher protein–nucleic acid nucleic-acid-specific predictors antibody–antigen prediction AlphaFold-Multimer v.2.3 7,8 Together, these results show high-accuracy across biomolecular space possible within single unified deep-learning framework.

Язык: Английский

Процитировано

3719
Generalized biomolecular modeling and design with RoseTTAFold All-Atom DOI
Rohith Krishna, Jue Wang, Woody Ahern

и другие.

Science, Год журнала: 2024, Номер 384(6693)

Опубликована: Март 7, 2024

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids DNA bases with an atomic all other groups model assemblies that contain proteins, nucleic acids, small molecules, metals, covalent modifications, given their sequences chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion (RFdiffusionAA), builds structures around molecules. Starting from random distributions acid residues surrounding target designed experimentally validated, through crystallography binding measurements, proteins bind the cardiac disease therapeutic digoxigenin, enzymatic cofactor heme, light-harvesting molecule bilin.

Язык: Английский

Процитировано

355
De novo protein design—From new structures to programmable functions DOI Creative Commons
Tanja Kortemme

Cell, Год журнала: 2024, Номер 187(3), С. 526 - 544

Опубликована: Фев. 1, 2024

Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes molecular functions de novo, without starting found in nature. In this Perspective, I will discuss the state field novo protein design at juncture physics-based modeling approaches AI. New folds higher-order assemblies be designed considerable experimental success rates, difficult problems requiring tunable control over conformations precise shape complementarity for recognition are coming into reach. Emerging incorporate engineering principles-tunability, controllability, modularity-into process beginning. Exciting frontiers lie deconstructing cellular and, conversely, constructing synthetic signaling ground up. As methods improve, many more challenges unsolved.

Язык: Английский

Процитировано

99
Atomically accurate de novo design of single-domain antibodies DOI Creative Commons
Nathaniel R. Bennett, Joseph L. Watson, Robert J. Ragotte

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Март 18, 2024

Despite the central role that antibodies play in modern medicine, there is currently no way to rationally design novel bind a specific epitope on target. Instead, antibody discovery involves time-consuming immunization of an animal or library screening approaches. Here we demonstrate fine-tuned RFdiffusion network capable designing de novo variable heavy chains (VHH's) user-specified epitopes. We experimentally confirm binders four disease-relevant epitopes, and cryo-EM structure designed VHH bound influenza hemagglutinin nearly identical model both configuration CDR loops overall binding pose.

Язык: Английский

Процитировано

80
Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering DOI Creative Commons
Jason Yang, Francesca-Zhoufan Li, Frances H. Arnold

и другие.

ACS Central Science, Год журнала: 2024, Номер 10(2), С. 226 - 241

Опубликована: Фев. 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.

Язык: Английский

Процитировано

78
AlphaFold2 structures guide prospective ligand discovery DOI
Jiankun Lyu, Nicholas J. Kapolka, Ryan H. Gumpper

и другие.

Science, Год журнала: 2024, Номер 384(6702)

Опубликована: Май 16, 2024

AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 of the σ

Язык: Английский

Процитировано

58
Opportunities and challenges in design and optimization of protein function DOI
Dina Listov, Casper A. Goverde, Bruno E. Correia

и другие.

Nature Reviews Molecular Cell Biology, Год журнала: 2024, Номер 25(8), С. 639 - 653

Опубликована: Апрель 2, 2024

Язык: Английский

Процитировано

53
Sparks of function by de novo protein design DOI
Alexander E. Chu, Tianyu Lu, Po‐Ssu Huang

и другие.

Nature Biotechnology, Год журнала: 2024, Номер 42(2), С. 203 - 215

Опубликована: Фев. 1, 2024

Язык: Английский

Процитировано

34
Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development DOI Creative Commons
Xinru Qiu, H. Li, Greg Ver Steeg

и другие.

Biomolecules, Год журнала: 2024, Номер 14(3), С. 339 - 339

Опубликована: Март 12, 2024

Recent advancements in AI-driven technologies, particularly protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on question how these technological breakthroughs, exemplified by AlphaFold2, revolutionizing our understanding function changes underlying cancer improve approaches to counter them. By enhancing precision speed at which targets identified candidates can be designed optimized, technologies streamlining entire development process. We explore use AlphaFold2 development, scrutinizing its efficacy, limitations, potential challenges. also compare with other algorithms like ESMFold, explaining diverse methodologies employed this field practical effects differences for application specific algorithms. Additionally, we discuss broader applications including prediction complex structures generative design novel proteins.

Язык: Английский

Процитировано

32
Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities DOI Creative Commons
Connor D. Flynn, Dingran Chang

Diagnostics, Год журнала: 2024, Номер 14(11), С. 1100 - 1100

Опубликована: Май 25, 2024

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at patient level. This review paper explores transformative impact AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, future prospects in field. We provide an overview core their use POC, highlighting issues challenges that may be solved with AI. follow can applied including machine learning algorithms, neural networks, data processing frameworks facilitate real-time analytical decision-making. explore applications each stage biosensor development process, diverse opportunities beyond simple analysis procedures. include a thorough outstanding field AI-assisted focusing technical ethical regarding widespread adoption these technologies, such as security, algorithmic bias, regulatory compliance. Through this review, we aim emphasize role advancing inform researchers, clinicians, policymakers about reshaping global healthcare landscapes.

Язык: Английский

Процитировано

32