Physics of biomolecular recognition and conformational dynamics

Chu, Wen-Ting; Yan, Zhiqiang; Chu, Xiakun; Zheng, Xiliang; Liu, Zuojia; Xu, Li; Zhang, Kun; Wang, Jin

doi:10.1088/1361-6633/ac3800

Cited by 7 publications

(5 citation statements)

References 485 publications

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“…It is important to point out that models based on elastic networks for determining the configurational frustration of protein systems allow predicting intrinsic changes in the local energy contribution of residues that are not easily detectable with classical molecular dynamics methods. Especially since it has been described that an evolved sequence of a protein system can provide a minimally frustrated energy landscape, which results in the folding of a protein on a relatively short time scale, typically microseconds to milliseconds. , …”

Section: Resultsmentioning

confidence: 99%

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Intrinsic Dynamics of the ClpXP Proteolytic Machine Using Elastic Network Models

et al. 2023

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ClpXP complex is an ATP-dependent mitochondrial matrix protease that binds, unfolds, translocates, and subsequently degrades specific protein substrates. Its mechanisms of operation are still being debated, and several have been proposed, including the sequential translocation of two residues (SC/2R), six residues (SC/6R), and even long-pass probabilistic models. Therefore, it has been suggested to employ biophysical–computational approaches that can determine the kinetics and thermodynamics of the translocation. In this sense, and based on the apparent inconsistency between structural and functional studies, we propose to apply biophysical approaches based on elastic network models (ENM) to study the intrinsic dynamics of the theoretically most probable hydrolysis mechanism. The proposed models ENM suggest that the ClpP region is decisive for the stabilization of the ClpXP complex, contributing to the flexibility of the residues adjacent to the pore, favoring the increase in pore size and, therefore, with the energy of interaction of its residues with a larger portion of the substrate. It is predicted that the complex may undergo a stable configurational change once assembled and that the deformability of the system once assembled is oriented, to increase the rigidity of the domains of each region (ClpP and ClpX) and to gain flexibility of the pore. Our predictions could suggest under the conditions of this study the mechanism of the interaction of the system, of which the substrate passes through the unfolding of the pore in parallel with a folding of the bottleneck. The variations in the distance calculated by molecular dynamics could allow the passage of a substrate with a size equivalent to ∼3 residues. The theoretical behavior of the pore and the stability and energy of binding to the substrate based on ENM models suggest that in this system, there are thermodynamic, structural, and configurational conditions that allow a possible translocation mechanism that is not strictly sequential.

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Section: Resultsmentioning

confidence: 99%

“…Especially since it has been described that an evolved sequence of a protein system can provide a minimally frustrated energy landscape, which results in the folding of a protein on a relatively short time scale, typically microseconds to milliseconds. 62 , 63 …”

Section: Resultsmentioning

confidence: 99%

Intrinsic Dynamics of the ClpXP Proteolytic Machine Using Elastic Network Models

et al. 2023

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“…Utilization of these data enables the training of a model for predicting the binding affinities of protein-DNA interactions. The very robustness of evolution [33][34][35][36] offers an approach to extract the sequence-structure relation embedded in these available complexes, which learns an interpretable binding energy landscape that governs the recognition processes of those DNA-binding proteins.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Protein-DNA Interactions Captured by Structure-Sequence Optimization

Zhang,

Silvernail,

Lin

et al. 2024

Preprint

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Sequence-specific DNA recognition underlies essential processes in gene regulation, yet predictive methods for simultaneous prediction of genome-wide DNA recognition sites and their binding affinity remain lacking. Here, we present IDEA, an interpretable residue-level biophysical model capable of predicting binding sites and strengths of DNA-binding proteins across the genome. By leveraging the sequence-structure relationship from known protein-DNA complexes, IDEA learns an energy model enabling direct interpretation of physicochemical interactions among individual amino acids and nucleotides. Using transcription factors as examples, we demonstrate that this energy model accurately predicts genomic DNA recognition sites and their binding strengths. Additionally, the IDEA model is integrated into a coarse-grained simulation framework that accurately captures the absolute protein-DNA binding free energies. Overall, IDEA provides an integrated computational platform alleviating experimental costs and biases in assessing DNA recognition and can be utilized for mechanistic studies of various DNA-recognition processes.

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“…In fact, the structural determinants of molecular recognition are best described considering an ensemble of conformational states of each (macro)molecule involved. 2 , 3 This is particularly critical for proteins, which represent the majority of interactors and span a very wide flexibility range (from side-chain reorientations to large-scale domain motions, possibly coupled to secondary structure variations). 2 The adaptability of targets is crucial in accommodating various ligands that establish diverse binding interactions, especially for multi-specific proteins, whereby even minor conformational changes can enable the binding of multiple compounds to different regions of the same broad binding site.…”

Section: Introductionmentioning

confidence: 99%

Predicting binding events in very flexible, allosteric, multi-domain proteins

Basciu,

Athar,

Kurt

et al. 2024

Preprint

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Knowledge of the structures formed by proteins and small ligands is of fundamental importance for understanding molecular principles of chemotherapy and for designing new and more effective drugs. Due to the still high costs and to the several limitations of experimental techniques, it is most often desirable to predict these ligand-protein complexesin silico, particularly when screening for new putative drugs from databases of millions of compounds. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology aimed at generating bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites. Validation was performed on the enzyme adenylate kinase (ADK), a paradigmatic example of proteins that undergo very large conformational changes upon ligand binding. By only exploiting the unbound structure and the putative binding sites of the protein, we generated a significant fraction of bound-like structures, which employed in ensemble-docking calculations allowed to find native-like poses of substrates, inhibitors, and catalytically incompetent binders. Our protocol provides a general framework for the generation of bound-like conformations of flexible proteins that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein’s activity. We foresee applications in virtual screening for difficult targets, prediction of the impact of amino acid mutations on structure and dynamics, and protein engineering.

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Physics of biomolecular recognition and conformational dynamics

Cited by 7 publications

References 485 publications

Intrinsic Dynamics of the ClpXP Proteolytic Machine Using Elastic Network Models

Intrinsic Dynamics of the ClpXP Proteolytic Machine Using Elastic Network Models

Interpretable Protein-DNA Interactions Captured by Structure-Sequence Optimization

Predicting binding events in very flexible, allosteric, multi-domain proteins

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