Proteins for Hyperelastic Materials DOI Open Access

Rui Su,

Chao Ma, Bing Han

и другие.

Small, Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Meticulous engineering and the yielded hyperelastic performance of structural proteins represent a new frontier in developing next-generation functional biomaterials. These materials exhibit outstanding programmable mechanical properties, including elasticity, resilience, toughness, active biological characteristics, such as degradability tissue repairability, compared with their chemically synthetic counterparts. However, there are several critical issues regarding preparation approaches protein-based materials: limited natural sequence modules, non-hierarchical assembly, imbalance between compressive tensile leading to unmet demands. Therefore, it is pivotal develop an alternative strategy for biofabricating materials. Herein, molecular design, engineering, property regulation overviewed. First, methodologies deeper exploration machine learning-aided de novo random mutations sequences, multiblock fusion techniques, actively introduced. facilitate generation elastomeric protein modules demonstrate enhanced versatility. Subsequently, assembly tactics (i.e., physical modulation, genetic adaptations, chemical modifications) reviewed, yielding hierarchically ordered structures. Finally, advances biophysical techniques more nuanced characterization ensembles discussed, unveiling tuning mechanisms elasticity across scales. Future developments biomaterials also envisioned.

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

349

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

97

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

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

75

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

51

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

33

Artificial intelligence in drug development DOI
Kang Zhang, Xin Yang, Yifei Wang

и другие.

Nature Medicine, Год журнала: 2025, Номер 31(1), С. 45 - 59

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

32

Structure prediction of protein-ligand complexes from sequence information with Umol DOI Creative Commons
Patrick Bryant, Atharva Kelkar, Andrea Guljas

и другие.

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Май 28, 2024

Abstract Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure required often the treated as fully or partially rigid. Here we develop AI system that can predict flexible all-atom of protein-ligand complexes directly from sequence information. We find classical methods are still superior, but depend upon having crystal structures target protein. In addition predicting structures, predicted confidence metrics (plDDT) be used select accurate predictions well distinguish between strong weak binders. The advances presented here suggest goal AI-based one step closer, there way go grasp complexity interactions fully. Umol available at: https://github.com/patrickbryant1/Umol .

Язык: Английский

Процитировано

31