Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

Banerjee, Anupam; Saha, Satyaki; Tvedt, Nathan C; Yang, Lee‐Wei; Bahar, İvet

doi:10.1016/j.sbi.2022.102517

Cited by 12 publications

(2 citation statements)

References 73 publications

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“…200 As previously discussed, the progress made in the computational characterization of physics-based structural dynamics and artificial intelligence methods, has been mutually beneficial for the generation of highly accurate tools designed to predict pathogenicity and uncover functional mechanisms. 201 In the near future, the continuous development of these methods promises to further elucidate the complex nature of biological systems and potentially aid in the design of therapeutic interventions.…”

Section: Summary and Future Directionsmentioning

confidence: 99%

Computational biophysics meets cryo‐EM revolution in the search for the functional dynamics of biomolecular systems

Costa,

Gur,

Krieger

et al. 2023

WIREs Comput Mol Sci

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There is a variety of experimental and computational techniques available to explore protein dynamics, each presenting advantages and limitations. One promising experimental technique that is driving the development of computational methods is cryo‐electron microscopy (cryo‐EM). Cryo‐EM provides molecular‐level structural data and first estimates of conformational landscape from single particle analysis but cannot track real‐time protein dynamics and may contain uncertainties in atomic positions especially at highly dynamic regions. Molecular simulations offer atomic‐level insights into protein dynamics; however, their computing time requirements limit the conformational sampling accuracy, and it is often hard, to assess by full‐atomic simulations the cooperative movements of biological interest for large assemblies such as those resolved by cryo‐EM. Coarse‐grained (CG) simulations permit us to explore such systems, but at the costs of lower resolution and potentially incomplete sampling of conformational space. On the other hand, analytical methods may circumvent sampling limitations. In particular, elastic network models‐based normal mode analyses (ENM‐NMA) provide unique solutions for the complete mode spectra near equilibrium states, even for systems of megadaltons, and may thus deliver information on mechanisms of motions relevant to biological function. Yet, they lack atomic resolution as well as temporal information for non‐equilibrium systems. Given the complementary nature of these methods, the integration of molecular simulations and ENM‐NMA into hybrid methodologies has gained traction. This review presents the current state‐of‐the‐art in structure‐based computations and how they are helping us gain a deeper understanding of biological mechanisms, with emphasis on the development of hybrid methods accompanying the advances in cryo‐EM.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics

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Section: Summary and Future Directionsmentioning

confidence: 99%

Computational biophysics meets cryo‐EM revolution in the search for the functional dynamics of biomolecular systems

Costa,

Gur,

Krieger

et al. 2023

WIREs Comput Mol Sci

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“…Intrinsic dynamic refers to the collective modes of molecular motions evolutionarily optimized and uniquely encoded by the native fold, which usually enable protein-protein interactions, allosteric signaling, or other activities [29][30][31]. The structure-based modeling of protein dynamics has been successfully incorporated into previous ML-based algorithms for inferring the mechanisms of protein function [32]. The efficient evaluation of intrinsic dynamics using elastic network models (ENMs) [33,34] also proved useful in the genome-scale characterization of biomolecular dynamics [34], the ensemble analysis of protein families [35], or the ML-based prediction of pathogenicity for single-amino-acid variants [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Structural Dynamics Predominantly Determine the Adaptability of Proteins to Amino Acid Deletions

Banerjee

Bahar

2023

IJMS

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The insertion or deletion (indel) of amino acids has a variety of effects on protein function, ranging from disease-forming changes to gaining new functions. Despite their importance, indels have not been systematically characterized towards protein engineering or modification goals. In the present work, we focus on deletions composed of multiple contiguous amino acids (mAA-dels) and their effects on the protein (mutant) folding ability. Our analysis reveals that the mutant retains the native fold when the mAA-del obeys well-defined structural dynamics properties: localization in intrinsically flexible regions, showing low resistance to mechanical stress, and separation from allosteric signaling paths. Motivated by the possibility of distinguishing the features that underlie the adaptability of proteins to mAA-dels, and by the rapid evaluation of these features using elastic network models, we developed a positive-unlabeled learning-based classifier that can be adopted for protein design purposes. Trained on a consolidated set of features, including those reflecting the intrinsic dynamics of the regions where the mAA-dels occur, the new classifier yields a high recall of 84.3% for identifying mAA-dels that are stably tolerated by the protein. The comparative examination of the relative contribution of different features to the prediction reveals the dominant role of structural dynamics in enabling the adaptation of the mutant to mAA-del without disrupting the native fold.

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Structural insights into the pathogenicity of point mutations in human acyl-CoA dehydrogenase homotetramers

Faraji,

Ebrahim-Habibi

2023

J Biol Phys

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Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

Cited by 12 publications

References 73 publications

Computational biophysics meets cryo‐EM revolution in the search for the functional dynamics of biomolecular systems

Computational biophysics meets cryo‐EM revolution in the search for the functional dynamics of biomolecular systems

Structural Dynamics Predominantly Determine the Adaptability of Proteins to Amino Acid Deletions

Structural insights into the pathogenicity of point mutations in human acyl-CoA dehydrogenase homotetramers

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