An integrative approach to protein sequence design through multiobjective optimization DOI Creative Commons
Lu Hong, Tanja Kortemme

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(7), P. e1011953 - e1011953

Published: July 11, 2024

With recent methodological advances in the field of computational protein design, particular those based on deep learning, there is an increasing need for frameworks that allow coherent, direct integration different models and objective functions into generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such approach. established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as framework, use AlphaFold2 ProteinMPNN confidence metrics define space, a mutation operator composed ESM-1v rank then redesign least favorable positions. Using two-state problem foldswitching RfaH in-depth case study, PapD calmodulin examples higher-dimensional problems, show approach leads significant reduction bias variance native sequence recovery, compared application ProteinMPNN. We suggest this improvement due three factors: (i) informative accelerates space exploration, (ii) parallel, iterative process inherent genetic algorithm improves upon autoregressive decoding scheme, (iii) explicit approximation Pareto front optimal candidates representing diverse tradeoff conditions. anticipate readily adaptable broadly relevant tasks with complex specifications.

Language: Английский

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences DOI Creative Commons
Mihály Váradi,

Damian Bertoni,

Paulyna Magaña

et al.

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 52(D1), P. D368 - D375

Published: Nov. 2, 2023

The AlphaFold Database Protein Structure (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled groundbreaking AlphaFold2 artificial intelligence (AI) system, predictions archived DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, a host of curated datasets. We detail access mechanisms direct file via FTP to advanced queries using Google Cloud Public Datasets programmatic endpoints database. also discuss improvements services added since its release, including Predicted Aligned Error viewer, customisation options for 3D search engine DB.

Language: Английский

Citations

663
AlphaFold2 and its applications in the fields of biology and medicine DOI Creative Commons
Zhenyu Yang, Xiaoxi Zeng, Yi Zhao

et al.

Signal Transduction and Targeted Therapy, Journal Year: 2023, Volume and Issue: 8(1)

Published: March 14, 2023

Abstract AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction one the most challenging problems in computational biology and chemistry, has puzzled scientists for 50 years. The advent AF2 presents unprecedented progress protein attracted much attention. Subsequent release more than 200 million predicted further aroused great enthusiasm science community, especially fields medicine. thought to have a significant impact on structural research areas need information, such as drug discovery, design, function, et al. Though time not long since was developed, there are already quite few application studies medicine, many them having preliminarily proved potential AF2. To better understand promote its applications, we will this article summarize principle architecture well recipe success, particularly focus reviewing applications Limitations current also be discussed.

Language: Английский

Citations

272
AlphaFold2 structures guide prospective ligand discovery DOI
Jiankun Lyu, Nicholas J. Kapolka, Ryan H. Gumpper

et al.

Science, Journal Year: 2024, Volume and Issue: 384(6702)

Published: May 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

Language: Английский

Citations

58
De novo protein design by inversion of the AlphaFold structure prediction network DOI Creative Commons
Casper A. Goverde, Benedict Wolf, Hamed Khakzad

et al.

Protein Science, Journal Year: 2023, Volume and Issue: 32(6)

Published: May 11, 2023

De novo protein design enhances our understanding of the principles that govern folding and interactions, has potential to revolutionize biotechnology through engineering novel functionalities. Despite recent progress in computational strategies, de structures remains challenging, given vast size sequence-structure space. AlphaFold2 (AF2), a state-of-the-art neural network architecture, achieved remarkable accuracy predicting from amino acid sequences. This raises question whether AF2 learned sufficiently for design. Here, we sought answer this by inverting network, using prediction weight set loss function bias generated sequences adopt target fold. Initial trials resulted designs with an overrepresentation hydrophobic residues on surface compared their natural family, requiring additional optimization. In silico validation showed correct fold, hydrophilic densely packed core. vitro 7 out 39 were folded stable solution high melting temperatures. summary, workflow solely based does not seem fully capture basic design, as observed surface's vs. patterning. However, minimal post-design intervention, these pipelines viable assessed experimental characterization. Thus, such show contribute solving outstanding challenges

Language: Английский

Citations

51
A new age in protein design empowered by deep learning DOI Creative Commons
Hamed Khakzad, Ilia Igashov, Arne Schneuing

et al.

Cell Systems, Journal Year: 2023, Volume and Issue: 14(11), P. 925 - 939

Published: Nov. 1, 2023

Language: Английский

Citations

49
Cyclic peptide structure prediction and design using AlphaFold DOI Creative Commons
Stephen Rettie, Katelyn V. Campbell, Asim K. Bera

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Feb. 26, 2023

ABSTRACT Deep learning networks offer considerable opportunities for accurate structure prediction and design of biomolecules. While cyclic peptides have gained significant traction as a therapeutic modality, developing deep methods designing such has been slow, mostly due to the small number available structures molecules in this size range. Here, we report approaches modify AlphaFold network peptides. Our results show approach can accurately predict native from single sequence, with 36 out 49 cases predicted high confidence (pLDDT > 0.85) matching root mean squared deviation (RMSD) less than 1.5 Å. Further extending our approach, describe computational sequences peptide backbones generated by other backbone sampling de novo new macrocyclic We extensively sampled structural diversity between 7–13 amino acids, identified around 10,000 unique candidates fold into designed confidence. X-ray crystal seven diverse sizes match very closely models (root < 1.0 Å), highlighting atomic level accuracy approach. The scaffolds developed here provide basis custom-designing targeted applications.

Language: Английский

Citations

47
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

33
The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins DOI
Vinayak Agarwal, Andrew C. McShan

Nature Chemical Biology, Journal Year: 2024, Volume and Issue: 20(8), P. 950 - 959

Published: June 21, 2024

Language: Английский

Citations

30
Computational design of soluble and functional membrane protein analogues DOI Creative Commons
Casper A. Goverde, Martin Pačesa, Nicolas Goldbach

et al.

Nature, Journal Year: 2024, Volume and Issue: 631(8020), P. 449 - 458

Published: June 19, 2024

Abstract De novo design of complex protein folds using solely computational means remains a substantial challenge 1 . Here we use robust deep learning pipeline to and soluble analogues integral membrane proteins. Unique topologies, such as those from G-protein-coupled receptors 2 , are not found in the proteome, demonstrate that their structural features can be recapitulated solution. Biophysical analyses high thermal stability designs, experimental structures show remarkable accuracy. The were functionalized with native motifs, proof concept for bringing functions potentially enabling new approaches drug discovery. In summary, have designed topologies enriched them functionalities proteins, success rates, leading de facto expansion functional fold space.

Language: Английский

Citations

29
Tuberculosis vaccines and therapeutic drug: challenges and future directions DOI Creative Commons

Yajing An,

Ruizi Ni,

Zhuang Li

et al.

Molecular Biomedicine, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 22, 2025

Abstract Tuberculosis (TB) remains a prominent global health challenge, with the World Health Organization documenting over 1 million annual fatalities. Despite deployment of Bacille Calmette-Guérin (BCG) vaccine and available therapeutic agents, escalation drug-resistant Mycobacterium tuberculosis strains underscores pressing need for more efficacious vaccines treatments. This review meticulously maps out contemporary landscape TB development, focus on antigen identification, clinical trial progress, obstacles future trajectories in research. We spotlight innovative approaches, such as multi-antigen mRNA technology platforms. Furthermore, delves into current therapeutics, particularly multidrug-resistant (MDR-TB), exploring promising agents like bedaquiline (BDQ) delamanid (DLM), well potential host-directed therapies. The hurdles development encompass overcoming diversity, enhancing effectiveness across diverse populations, advancing novel Future initiatives emphasize combinatorial strategies, anti-TB compounds targeting pathways, personalized medicine treatment prevention. notable advances, persistent challenges diagnostic failures protracted regimens continue to impede progress. work aims steer research endeavors toward groundbreaking providing crucial insights prevention strategies.

Language: Английский

Citations

4