Assessing Structures and Conformational Ensembles of Apo and Holo Protein States Using Randomized Alanine Sequence Scanning Combined with Shallow Subsampling in AlphaFold2 : Insights and Lessons from Predictions of Functional Allosteric Conformations DOI Open Access
Nishank Raisinghani, Victoria N. Parikh, Brian Foley

и другие.

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

Опубликована: Ноя. 6, 2024

Abstract Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study these particularly apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, interactions. In current study, we use AlphaFold2 that combines randomized alanine sequence masking with shallow alignment subsampling to expand diversity predicted structural ensembles capture changes between forms. Using several well-established datasets structurally diverse apo-holo pairs, proposed approach enables robust predictions structures ensembles, while also displaying notably similar dynamics distributions. These observations are consistent view intrinsic allosteric proteins defined topology fold favors conserved motions driven soft modes. Our findings support notion approaches can yield reasonable accuracy predicting minor adjustments especially moderate localized upon ligand binding. However, large, hinge-like domain movements, tends predict most stable orientation which typically form rather than full range functional conformations characteristic ensemble. results indicate modeling states may require more accurate characterization flexible regions detection high energy conformations. By incorporating a wider variety training including both model learn recognize occur

Язык: Английский

Protein painting for structural and binding site analysis via intracellular lysine reactivity profiling with o-phthalaldehyde DOI Creative Commons
Zhenxiang Zheng,

Ya Zeng,

Kunjia Lai

и другие.

Chemical Science, Год журнала: 2024, Номер 15(16), С. 6064 - 6075

Опубликована: Янв. 1, 2024

We developed an intracellular chemical covalent labeling method based on lysine reactive shift coupled with a new data analysis strategy RAPID to analyze the conformational changes of proteins and ligand-binding sites proteome scale.

Язык: Английский

Процитировано

4
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

и другие.

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(18), С. 10082 - 10082

Опубликована: Сен. 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.

Язык: Английский

Процитировано

3
AlphaFold and what is next: bridging functional, systems and structural biology DOI Creative Commons
Kacper Szczepski, Łukasz Jaremko

Expert Review of Proteomics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

The DeepMind's AlphaFold (AF) has revolutionized biomedical research by providing both experts and non-experts with an invaluable tool for predicting protein structures. However, while AF is highly effective structures of rigid globular proteins, it not able to fully capture the dynamics, conformational variability, interactions proteins ligands other biomacromolecules. In this review, we present a comprehensive overview latest advancements in 3D model predictions biomacromolecules using AF. We also provide detailed analysis its strengths limitations, explore more recent iterations, modifications, practical applications strategy. Moreover, map path forward expanding landscape toward every peptide proteome most physiologically relevant form. This discussion based on extensive literature search performed PubMed Google Scholar. While significant progress been made enhance AF's modeling capabilities, argue that combined approach integrating various silico vitro methods will be beneficial future structural biology, bridging gaps between static dynamic features their functions.

Язык: Английский

Процитировано

0
MAST Kinases’ Function and Regulation: Insights from Structural Modeling and Disease Mutations DOI Creative Commons
Michael C. Lemke,

Nithin R. Avala,

Mildred Rader

и другие.

Biomedicines, Год журнала: 2025, Номер 13(4), С. 925 - 925

Опубликована: Апрель 9, 2025

Background/Objectives: The MAST kinases are ancient AGC associated with many human diseases, such as cancer, diabetes, and neurodevelopmental disorders. We set out to describe the origins diversification of from a structural bioinformatic perspective inform future research directions. Methods: investigated MAST-lineage using database sequence analysis. also estimate functional consequences disease point mutations on protein stability by integrating predictive algorithms AlphaFold. Results: Higher-order organisms often have multiple MASTs single MASTL kinase. proteins conserve an kinase domain, domain unknown function 1908 (DUF), PDZ binding domain. D. discoideum contains kinase-like that exhibit characteristic insertion within T-loop but do not DUF or domains. While is conserved in plants, not. four mammalian demonstrate tissue expression heterogeneity mRNA protein. MAST1-4 likely regulated 14-3-3 based interactome data silico predictions. Comparative ΔΔG estimation identified MAST1-L232P G522E destabilizing. Conclusions: conclude diverged primordial MAST, which operated both biological niches. number paralogs then expanded heterogeneous subfamily seen mammals all interaction. reported pathogenic primarily represent alterations post-translational modification topology Our report outlines computational basis for work regulation drug discovery.

Язык: Английский

Процитировано

0
AlphaFold2-Based Characterization of Apo and Holo Protein Structures and Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics and Ligand-Induced Conformational Changes DOI Open Access
Nishank Raisinghani, Victoria N. Parikh, Brian Foley

и другие.

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(23), С. 12968 - 12968

Опубликована: Дек. 2, 2024

Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study these particularly apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, interactions. In current study, we use AlphaFold2, which combines randomized alanine sequence masking with shallow alignment subsampling to expand diversity predicted structural ensembles capture changes between forms. Using several well-established datasets structurally diverse apo-holo pairs, proposed approach enables robust predictions structures ensembles, while also displaying notably similar dynamics distributions. These observations are consistent view that intrinsic allosteric proteins defined topology fold favor conserved motions driven soft modes. Our findings provide evidence AlphaFold2 combined can yield accurate results predicting moderate adjustments especially localized upon ligand binding. For large hinge-like domain movements, predict functional conformations characteristic both ligand-bound absence information. relevant using this AlphaFold adaptation probing selection mechanisms according adopt conformations, including those competent indicate modeling states may require more characterization flexible regions detection high-energy conformations. By incorporating a wider variety training datasets, model learn recognize occur

Язык: Английский

Процитировано

1
Leveraging Machine Learning and AlphaFold2 Steering to Discover State-Specific Inhibitors Across the Kinome DOI Creative Commons
Francesco Trozzi, Oanh Tran, Carmen Al Masri

и другие.

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

Опубликована: Авг. 19, 2024

Protein kinases are structurally dynamic proteins that control downstream signaling cascades by phosphorylating their substrates. regulate function adopting several conformational states in active site determined the movements of motifs such as αC-Helix, DFG residues and activation loop. Each state represents a distinct physicochemical environment accepts or precludes ligand binding. However, most kinome have not been crystalized across these possible states. It has shown shallow Multiple Sequence Alignments (MSA) can enable AlphaFold2 (AF2) to model alternative conformations. it is unclear if models be leveraged for structure-based drug discovery. Additionally, there machine learning tools predict protein-ligand interactions based on chemotype binding pocket properties, but cannot used identify ligands with clear specificity. Here, we first present an approach called Steering (AF2-Steering), systematic methodology direct AF2 sample inactive We use our protein precise demonstrate utility AF2-steered kinase employing them prospective virtual screening study integrates docking find specific inhibitors well-studied dark lack structures state. then experimentally validate hits, essential step often overlooked, later confirm conformation-specificity identified FLT3, currently lacks crystal structure. Against strict criterion at least 1μM Kd, modelled achieved overall hit rate 53%. also 4/7 FLT3 ligands, thus demonstrating value MSA-steered combined guide conformation-specific

Язык: Английский

Процитировано

0
Assessing Structures and Conformational Ensembles of Apo and Holo Protein States Using Randomized Alanine Sequence Scanning Combined with Shallow Subsampling in AlphaFold2 : Insights and Lessons from Predictions of Functional Allosteric Conformations DOI Open Access
Nishank Raisinghani, Victoria N. Parikh, Brian Foley

и другие.

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

Опубликована: Ноя. 6, 2024

Abstract Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study these particularly apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, interactions. In current study, we use AlphaFold2 that combines randomized alanine sequence masking with shallow alignment subsampling to expand diversity predicted structural ensembles capture changes between forms. Using several well-established datasets structurally diverse apo-holo pairs, proposed approach enables robust predictions structures ensembles, while also displaying notably similar dynamics distributions. These observations are consistent view intrinsic allosteric proteins defined topology fold favors conserved motions driven soft modes. Our findings support notion approaches can yield reasonable accuracy predicting minor adjustments especially moderate localized upon ligand binding. However, large, hinge-like domain movements, tends predict most stable orientation which typically form rather than full range functional conformations characteristic ensemble. results indicate modeling states may require more accurate characterization flexible regions detection high energy conformations. By incorporating a wider variety training including both model learn recognize occur

Язык: Английский

Процитировано

0