Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE DOI Open Access
Xinyu Gu, Akashnathan Aranganathan, Pratyush Tiwary

et al.

Published: Aug. 14, 2024

Small molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2’s strides in protein native structure prediction, its focus apo structures overlooks ligands and associated holo Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application AlphaFold2 models virtual screening dis-covery remains tentative. Here, we demonstrate an based framework combined with all-atom enhanced sampling molecular dynamics induced fit docking, named AF2RAVE-Glide, to conduct computational model small binding kinase conformations, initiated sequences. We AF2RAVE-Glide workflow three different kinases their type I II inhibitors, special emphasis known inhibitors which target classical DFG-out state. These states are not easy sample AlphaFold2. Here how AF2RAVE these conformations can be sampled for high enough ac- curacy enable subsequent docking more than 50% success rates across calculations. believe protocol should deployable other proteins generally.

Language: Английский

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

et al.

Science, Journal Year: 2024, Volume and Issue: 384(6693)

Published: March 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.

Language: Английский

Citations

349
Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2 DOI Creative Commons
T. Reid Alderson, Iva Pritišanac, Đesika Kolarić

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2023, Volume and Issue: 120(44)

Published: Oct. 25, 2023

The AlphaFold Protein Structure Database contains predicted structures for millions of proteins. For the majority human proteins that contain intrinsically disordered regions (IDRs), which do not adopt a stable structure, it is generally assumed these have low AlphaFold2 confidence scores reflect low-confidence structural predictions. Here, we show assigns confident to nearly 15% IDRs. By comparison experimental NMR data subset IDRs are known conditionally fold (i.e., upon binding or under other specific conditions), find often predicts structure folded state. Based on databases fold, estimate can identify folding at precision as high 88% 10% false positive rate, remarkable considering IDR were minimally represented in its training data. We disease mutations fivefold enriched over general and up 80% prokaryotes compared less than 20% eukaryotic These results indicate large proteomes eukaryotes function absence conditional folding, but acquire folds more sensitive mutations. emphasize predictions reveal functionally relevant plasticity within cannot offer realistic ensemble representations

Language: Английский

Citations

128
The Art and Science of Molecular Docking DOI
Joseph M. Paggi, Ayush Pandit, Ron O. Dror

et al.

Annual Review of Biochemistry, Journal Year: 2024, Volume and Issue: 93(1), P. 389 - 410

Published: April 10, 2024

Molecular docking has become an essential part of a structural biologist's and medicinal chemist's toolkits. Given chemical compound the three-dimensional structure molecular target—for example, protein—docking methods fit into target, predicting compound's bound binding energy. Docking can be used to discover novel ligands for target by screening large virtual libraries. also provide useful starting point structure-based ligand optimization or investigating ligand's mechanism action. Advances in computational methods, including both physics-based machine learning approaches, as well complementary experimental techniques, are making even more powerful tool. We review how works it drive drug discovery biological research. describe its current limitations ongoing efforts overcome them.

Language: Английский

Citations

83
Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom DOI Creative Commons
Rohith Krishna, Jue Wang, Woody Ahern

et al.

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

Published: Oct. 9, 2023

Abstract Although AlphaFold2 (AF2) and RoseTTAFold (RF) have transformed structural biology by enabling high-accuracy protein structure modeling, they are unable to model covalent modifications or interactions with small molecules other non-protein that can play key roles in biological function. Here, we describe All-Atom (RFAA), a deep network capable of modeling full assemblies containing proteins, nucleic acids, molecules, metals, given the sequences polymers atomic bonded geometry modifications. Following training on structures Protein Data Bank (PDB), RFAA has comparable prediction accuracy AF2, excellent performance CAMEO for flexible backbone molecule docking, reasonable proteins multiple acid chains which, our knowledge, no existing method simultaneously. By fine-tuning diffusive denoising tasks, develop RFdiffusion (RFdiffusionAA ) , which generates binding pockets directly building around molecules. Starting from random distributions amino residues surrounding target design experimentally validate bind cardiac disease therapeutic digoxigenin, enzymatic cofactor heme, optically active bilin potential expanding range wavelengths captured photosynthesis. We anticipate RFdiffusionAA will be widely useful designing complex biomolecular systems.

Language: Английский

Citations

64
Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures DOI
Anna M. Díaz-Rovira, Helena Martín, Thijs Beuming

et al.

Journal of Chemical Information and Modeling, Journal Year: 2023, Volume and Issue: 63(6), P. 1668 - 1674

Published: March 9, 2023

Machine learning-based protein structure prediction algorithms, such as RosettaFold and AlphaFold2, have greatly impacted the structural biology field, arousing a fair amount of discussion around their potential role in drug discovery. While there are few preliminary studies addressing usage these models virtual screening, none them focus on prospect hit-finding real-world screen with model based low prior information. In order to address this, we developed an AlphaFold2 version where exclude all templates more than 30% sequence identity from model-building process. previous study, used those conjunction state-of-the-art free energy perturbation methods demonstrated that it is possible obtain quantitatively accurate results. this work, using structures rigid receptor-ligand docking studies. Our results indicate out-of-the-box Alphafold2 not ideal scenario for screening campaigns; fact, strongly recommend include some post-processing modeling drive binding site into realistic holo model.

Language: Английский

Citations

63
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
AlphaFold, allosteric, and orthosteric drug discovery: Ways forward DOI Creative Commons
Ruth Nussinov, Mingzhen Zhang, Yonglan Liu

et al.

Drug Discovery Today, Journal Year: 2023, Volume and Issue: 28(6), P. 103551 - 103551

Published: March 11, 2023

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version buttressed by an innovative machine-learning approach that integrates physical biological knowledge about protein structures, raised drug hopes unsurprisingly, have not come to bear. Even though accurate, models are rigid, including pockets. AlphaFold's mixed performance poses question how its power can be harnessed in discovery. Here we discuss possible ways going forward wielding strengths, while bearing mind what AlphaFold cannot do. For kinases receptors, input enriched active (ON) state better chance rational design success.

Language: Английский

Citations

57
Evaluation of AlphaFold2 structures as docking targets DOI Creative Commons
Matthew Holcomb, Ya‐Ting Chang, David S. Goodsell

et al.

Protein Science, Journal Year: 2022, Volume and Issue: 32(1)

Published: Dec. 7, 2022

AlphaFold2 is a promising new tool for researchers to predict protein structures and generate high-quality models, with low backbone global root-mean-square deviation (RMSD) when compared experimental structures. However, it unclear if the predicted by will be valuable targets of docking. To address this question, we redocked ligands in PDBbind datasets against co-crystallized receptor using AutoDock-GPU. We find that quality measure provided during structure prediction not good predictor docking performance, despite accurately reflecting alpha carbon alignment Removing low-confidence regions making side chains flexible improves outcomes. Overall, conformation, fine structural details limit naive application models as targets.

Language: Английский

Citations

64
From byte to bench to bedside: molecular dynamics simulations and drug discovery DOI Creative Commons
M.W. Ahmed, Alex M. Maldonado, Jacob D. Durrant

et al.

BMC Biology, Journal Year: 2023, Volume and Issue: 21(1)

Published: Dec. 29, 2023

Molecular dynamics (MD) simulations and computer-aided drug design (CADD) have advanced substantially over the past two decades, thanks to continuous computer hardware software improvements. Given these advancements, MD are poised become even more powerful tools for investigating dynamic interactions between potential small-molecule drugs their target proteins, with significant implications pharmacological research.

Language: Английский

Citations

24
Enhanced mapping of small-molecule binding sites in cells DOI
Jacob M. Wozniak, Weichao Li, Paolo Governa

et al.

Nature Chemical Biology, Journal Year: 2024, Volume and Issue: 20(7), P. 823 - 834

Published: Jan. 2, 2024

Language: Английский

Citations

16