Multiscale Allostery: Basic Mechanisms and Versatility in Diagnostics and Drug Design

Berezovsky, Igor N.; Nussinov, Ruth

doi:10.1016/j.jmb.2022.167751

Cited by 13 publications

(18 citation statements)

References 65 publications

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“…The MD-based approach comes, however, at the expense of increased computing time, making it necessary, for practical purposes, to combine data obtained in atomistic simulations with the massive coarse-grained one presented in AlloMAPS. The high-throughput data would also be instrumental for validation and calibration of computational predictions in conjunction with experimental assays ( 2 , 7 ), such as deep mutational scanning and fragment-based screening. The capability of artificial intelligence (AI) algorithms ( 34–36 ) in performing feature extraction from low-level data representation with non-obvious hierarchical connectivity and to model complex nonlinear input-output relationships can be utilized on the basis of these data.…”

Section: Discussionmentioning

confidence: 99%

“…Despite constantly growing interest in biomedical implications of allostery in general ( 1 , 2 ) and in design of allosteric effectors in particular ( 3–6 ), there is only a handful number of clinically approved allosteric drugs most of which were discovered serendipitously ( 4 ). Moreover, it is increasingly recognized that well-established principles and protocols in the screening of ligand libraries against traditional drug targets and their binding sites are not optimal for the identification and design of allosterically acting medicines ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

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AlloMAPS 2: allosteric fingerprints of the AlphaFold and Pfam-trRosetta predicted structures for engineering and design

Tan

Tee

Guarnera

et al. 2022

Nucleic Acids Research

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AlloMAPS 2 is an update of the Allosteric Mutation Analysis and Polymorphism of Signalling database, which contains data on allosteric communication obtained for predicted structures in the AlphaFold database (AFDB) and trRosetta-predicted Pfam domains. The data update contains Allosteric Signalling Maps (ASMs) and Allosteric Probing Maps (APMs) quantifying allosteric effects of mutations and of small probe binding, respectively. To ensure quality of the ASMs and APMs, we performed careful and accurate selection of protein sets containing high-quality predicted structures in both databases for each organism/structure, and the data is available for browsing and download. The data for remaining structures are available for download and should be used at user's discretion and responsibility. We believe these massive data can facilitate both diagnostics and drug design within the precision medicine paradigm. Specifically, it can be instrumental in the analysis of allosteric effects of pathological and rescue mutations, providing starting points for fragment-based design of allosteric effectors. The exhaustive character of allosteric signalling and probing fingerprints will be also useful in future developments of corresponding machine learning applications. The database is freely available at: http://allomaps.bii.a-star.edu.sg.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AlloMAPS 2: allosteric fingerprints of the AlphaFold and Pfam-trRosetta predicted structures for engineering and design

Tan

Tee

Guarnera

et al. 2022

Nucleic Acids Research

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“…In particular, a change in protein dynamics, even when the average structure is by-and-large unaffected, might contribute to changes in reactivity and/or affinity for the ligand, resulting in allosteric effects without detectable conformational changes. [7][8][9] Accordingly, the concept of allostery has been extended to simpler proteins, which are thought to contain subsets of residues involved in the propagation or distribution of energy through the protein ('allosteric networks'). [10][11][12] In this Perspective, we first recapitulate some of the key aspects concerning classic protein allostery.…”

Section: Introductionmentioning

confidence: 99%

Allostery Frustrates the Experimentalist

Gianni

Jemth

2023

Journal of Molecular Biology

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“…Allosteric signaling in proteins occurs when an effector molecule binds at one site and confers a functional change at a topologically distinct location. − The phenomenon has been known for over half a century and pervades biology to the extent that it has been dubbed “The second secret of life.” Nevertheless, the mechanism by which it traverses the protein remains actively debated. Models broadly categorize mechanisms as pathways, − in which signals travel along spatially proximal residues connecting the two sites, or energy landscapes proposed by Cooper and Dryden acting via a reorganization of the energy landscape over a long range. − …”

Section: Introductionmentioning

confidence: 99%

Network Analysis of Molecular Dynamics Sectors in the p53 Protein

Fabry

Thayer

2022

ACS Omega

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Design of allosteric regulators is an emergent field in the area of drug discovery holding promise for currently untreated diseases. Allosteric regulators bind to a protein in one location and affect a distant site. The ubiquitous presence of allosteric effectors in biology and the success of serendipitously identified allosteric compounds point to the potential they hold. Although the mechanism of transmission of an allosteric signal is not unequivocally determined, one hypothesis suggests that groups of evolutionarily covarying residues within a protein, termed sectors, are conduits. A long-term goal of our lab is to allosterically modulate the activity of proteins by binding small molecules at points of allosteric control. However, methods to consistently identify such points remain unclear. Sector residues on the surfaces of proteins are a promising source of allosteric targets. Recently, we introduced molecular dynamics (MD)-based sectors; MD sectors capitalize on covariance of motion, in place of evolutionary covariance. By focusing on motional covariance, MD sectors tap into the framework of statistical mechanics afforded by the Boltzmann ensemble of structural conformations comprising the underlying data set. We hypothesized that the method of MD sectors can be used to identify a cohesive network of motionally covarying residues capable of transmitting an allosteric signal in a protein. While our initial qualitative results showed promise for the method to predict sectors, that a network of cohesively covarying residues had been produced remained an untested assumption. In this work, we apply network theory to rigorously analyze MD sectors, allowing us to quantitatively assess the biologically relevant property of network cohesiveness of sectors in the context of the tumor suppressor protein, p53. We revised the methodology for assessing and improving MD sectors. Specifically, we introduce a metric to calculate the cohesive properties of the network. Our new approach separates residues into two categories: sector residues and non-sector residues. The relatedness within each respective group is computed with a distance metric. Cohesive sector networks are identified as those that have high relatedness among the sector residues which exceeds the relatedness of the residues to the nonsector residues in terms of the correlation of motions. Our major finding was that the revised means of obtaining sectors was more efficacious than previous iterations, as evidenced by the greater cohesion of the networks. These results are discussed in the context of the development of allosteric regulators of p53 in particular and the expected applicability of the method to the drug design field in general.

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Multiscale Allostery: Basic Mechanisms and Versatility in Diagnostics and Drug Design

Cited by 13 publications

References 65 publications

AlloMAPS 2: allosteric fingerprints of the AlphaFold and Pfam-trRosetta predicted structures for engineering and design

AlloMAPS 2: allosteric fingerprints of the AlphaFold and Pfam-trRosetta predicted structures for engineering and design

Allostery Frustrates the Experimentalist

Network Analysis of Molecular Dynamics Sectors in the p53 Protein

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