Predicting Mutation-Induced Allosteric Changes in Structures and Conformational Ensembles of the ABL Kinase Using AlphaFold2 Adaptations with Alanine Sequence Scanning DOI Open Access
Nishank Raisinghani, Mohammed Alshahrani,

Grace Gupta

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(18), P. 10082 - 10082

Published: Sept. 19, 2024

Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations multiple functional conformations allosteric proteins and have been challenged to accurately capture effects point mutations that induced significant structural changes. We examined several implementations predict conformational ensembles for state-switching mutants ABL kinase. The results revealed a combination randomized alanine sequence masking with shallow alignment subsampling can significantly expand diversity predicted shifts populations active inactive states. Consistent NMR experiments, M309L/L320I M309L/H415P perturb regulatory spine networks featured increased population fully closed state. proposed adaptation AlphaFold reproduce experimentally observed mutation-induced redistributions relative states on rearrangements kinase domain. ensemble-based network analysis complemented predictions by revealing hotspots correspond mutational sites which may explain global effect changes between This study suggested attention-based learning long-range dependencies positions homologous folds deciphering patterns interactions further augment predictive abilities modeling alternative sates, transformations.

Language: Английский

Modeling conformational states of proteins with AlphaFold DOI Creative Commons
Davide Sala, Felipe Engelberger, Hassane S. Mchaourab

et al.

Current Opinion in Structural Biology, Journal Year: 2023, Volume and Issue: 81, P. 102645 - 102645

Published: June 29, 2023

Language: Английский

Citations

110
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 drug development for membrane protein targets DOI
Haijian Li, Xiaolin Sun, Wenqiang Cui

et al.

Nature Biotechnology, Journal Year: 2024, Volume and Issue: 42(2), P. 229 - 242

Published: Feb. 1, 2024

Language: Английский

Citations

17
AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility DOI
Juliette Martin

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(5), P. 1473 - 1480

Published: Feb. 19, 2024

Predicting whether two proteins physically interact is one of the holy grails computational biology, galvanized by rapid advancements in deep learning. AlphaFold2, although not developed with this goal, promising respect. Here, I test prediction capability AlphaFold2 on a very challenging data set, where are structurally compatible, even when they do interact. achieves high discrimination between interacting and non-interacting proteins, cases misclassifications can either be rescued revisiting input sequences or suggest false positives negatives set. thus impaired compatibility protein structures has potential to applied large scale.

Language: Английский

Citations

9
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

23
Representing structures of the multiple conformational states of proteins DOI Creative Commons
Theresa A. Ramelot, Roberto Tejero, G.T. Montelione

et al.

Current Opinion in Structural Biology, Journal Year: 2023, Volume and Issue: 83, P. 102703 - 102703

Published: Sept. 28, 2023

Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. also address the challenges associated with representing single- multiple-state in data archives, a particular focus on NMR structures. Establishing standardized representations annotations will facilitate effective communication understanding these complex to broader scientific community.

Language: Английский

Citations

18
Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures DOI Creative Commons

Carmen Al-Masri,

Francesco Trozzi, Shu-Hang Lin

et al.

Bioinformatics Advances, Journal Year: 2023, Volume and Issue: 3(1)

Published: Jan. 1, 2023

Protein kinases are a family of signaling proteins, crucial for maintaining cellular homeostasis. When dysregulated, drive the pathogenesis several diseases, and thus one largest target categories drug discovery. Kinase activity is tightly controlled by switching through active inactive conformations in their catalytic domain. inhibitors have been designed to engage specific conformational states, where each conformation presents unique physico-chemical environment therapeutic intervention. Thus, modeling across can enable design novel optimally selective kinase drugs. Due recent success AlphaFold2 accurately predicting 3D structure proteins based on sequence, we investigated landscape protein as modeled AlphaFold2. We observed that able model kinome, however, certain only families. Furthermore, show per residue predicted local distance difference test capture information describing structural flexibility kinases. Finally, evaluated docking performance structures enriching known ligands. Taken together, see an opportunity leverage models structure-based discovery against pharmacologically relevant states.

Language: Английский

Citations

17
Identification of flavor peptides based on virtual screening and molecular docking from Hypsizygus marmoreuss DOI
Wenting Wang, Hongbo Li, Zhenbin Liu

et al.

Food Chemistry, Journal Year: 2024, Volume and Issue: 448, P. 139071 - 139071

Published: March 19, 2024

Language: Английский

Citations

7
Predicting Functional Conformational Ensembles and Binding Mechanisms of Convergent Evolution for SARS-CoV-2 Spike Omicron Variants Using AlphaFold2 Sequence Scanning Adaptations and Molecular Dynamics Simulations DOI Open Access
Nishank Raisinghani, Mohammed Alshahrani,

Grace Gupta

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 3, 2024

Abstract In this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles binding mechanisms convergent evolution the SARS-CoV-2 Spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 BQ.1.1. We employed validated several different adaptations AlphaFold methodology including introduced randomized full sequence scanning manipulation variations systematically explore dynamics complexes with ACE2 receptor. Microsecond dynamic provide a detailed characterization landscapes thermodynamic stability variant complexes. By integrating predictions from applying statistical confidence metrics can expand identify functional conformations that determine equilibrium ACE2. Conformational RBD-ACE2 obtained using are accurate comparative prediction energetics revealing an excellent agreement experimental data. particular, results demonstrated AlphaFold-generated extended produce energies The study suggested complementarities potential synergies between showing information both methods potentially yield more adequate This provides insights in interplay binding, through acquisition mutational sites may leverage adaptability couplings key energy hotspots optimize affinity enable immune evasion.

Language: Английский

Citations

7
Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase DOI
Nishank Raisinghani, Mohammed Alshahrani,

Grace Gupta

et al.

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(12), P. 5317 - 5336

Published: June 12, 2024

Despite the success of AlphaFold methods in predicting single protein structures, these showed intrinsic limitations characterization multiple functional conformations allosteric proteins. The recent NMR-based structural determination unbound ABL kinase active state and discovery inactive low-populated that are unique for present an ideal challenge AlphaFold2 approaches. In current study, we employ several adaptations methodology to predict conformational ensembles states including randomized alanine sequence scanning combined with alignment subsampling proposed this study. We show new adaptation local frustration profiling enables accurate prediction structures ensembles, also offering a robust approach interpretable predictions detection hidden states. found large high residue clusters uniquely characteristic low-populated, fully form can define energetically frustrated cracking sites transitions, presenting difficult targets AlphaFold2. results study uncovered previously unappreciated fundamental connections between profiles ability This integration landscape-based analysis allows atomistic providing physical basis successes detecting play significant role regulation.

Language: Английский

Citations

7