CoMoDo: Identifying Dynamic Protein Domains Based on Covariances of Motion

Wieninger, Silke A.; Ullmann, G. Matthias

doi:10.1021/acs.jctc.5b00150

Cited by 8 publications

(7 citation statements)

References 76 publications

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“…Covariance-based methods delineating modes from ENM have been used to identify domains within a macromolecule that cluster structural features with concerted structural fluctuations. [19][20][21] While this is functionally relevant information, [22,23] ENM-based methods suffer from two major limitations. First, by either treating the macromolecule in isolation, or using implicit solvent schemes to screen interatomic interactions, the methods likely fail to account for configurational entropy of the surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

“…Normal modes/collective coordinates are then derived from this simplistic harmonic approximation of the potential energy function near equilibrium. Covariance‐based methods delineating modes from ENM have been used to identify domains within a macromolecule that cluster structural features with concerted structural fluctuations . While this is functionally relevant information, ENM‐based methods suffer from two major limitations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DIRECT‐ID: An automated method to identify and quantify conformational variations—application to β₂‐adrenergic GPCR

et al. 2015

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The conformational dynamics of a macromolecule can be modulated by a number of factors, including changes in environment, ligand binding and interactions with other macromolecules, among others. We present a method that quantifies the differences in macromolecular conformational dynamics and automatically extracts the structural features responsible for these changes. Given a set of molecular dynamics (MD) simulations of a macromolecule, the norms of the differences in covariance matrices are calculated for each pair of trajectories. A matrix of these norms thus quantifies the differences in conformational dynamics across the set of simulations. For each pair of trajectories, covariance difference matrices are parsed to extract structural elements that undergo changes in conformational properties. As a demonstration of its applicability to biomacromolecular systems, the method, referred to as DIRECT-ID, was used to identify relevant ligand-modulated structural variations in the β2-adrenergic (β2AR) G-protein coupled receptor. Micro-second MD simulations of the β2AR in an explicit lipid bilayer were run in the apo state and complexed with the ligands: BI-167107 (agonist), epinephrine (agonist), salbutamol (long-acting partial agonist) or carazolol (inverse agonist). Each ligand modulated the conformational dynamics of β2AR differently and DIRECT-ID analysis of the inverse-agonist vs. agonist-modulated β2AR identified residues known through previous studies to selectively propagate deactivation/activation information, along with some previously unidentified ligand-specific microswitches across the GPCR. This study demonstrates the utility of DIRECT-ID to rapidly extract functionally relevant conformational dynamics information from extended MD simulations of large and complex macromolecular systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DIRECT‐ID: An automated method to identify and quantify conformational variations—application to β₂‐adrenergic GPCR

et al. 2015

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“…As shown in the previous section, the method presented in this work is based on information derived from the time decays of correlation functions, therefore on grounds that are clearly different from those of various structure-based analyses customarily used to detect correlated motions and quasi-rigid domains in proteins. ,,− It may be therefore instructive to compare results from both kinds of approaches to assess their potential complementarity. A very illustrative example is given in Figure , where the normalized covariance map of HP35 (or dynamic cross-correlation mapDCCM), is shown along with the cross-correlation-time map (CCTM).…”

Section: Resultsmentioning

confidence: 99%

Decomposition of Proteins into Dynamic Units from Atomic Cross-Correlation Functions

Calligari

Gerolin

Abergel

et al. 2016

J. Chem. Theory Comput.

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In this article, we present a clustering method of atoms in proteins based on the analysis of the correlation times of interatomic distance correlation functions computed from MD simulations. The goal is to provide a coarse-grained description of the protein in terms of fewer elements that can be treated as dynamically independent subunits. Importantly, this domain decomposition method does not take into account structural properties of the protein. Instead, the clustering of protein residues in terms of networks of dynamically correlated domains is defined on the basis of the effective correlation times of the pair distance correlation functions. For these properties, our method stands as a complementary analysis to the customary protein decomposition in terms of quasi-rigid, structure-based domains. Results obtained for a prototypal protein structure illustrate the approach proposed.

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“…However, protein evolution does not always discretize dynamics over these domains (23,27). Here, we consider rigid structural regions, termed dynamic domains, that behave in a quasi-independent manner around stabilized points, or hinges, and experience characteristic motion (17,(38)(39)(40)(41). The concretization of dynamic domains can highlight the functional roles of a localized area.…”

Section: Introductionmentioning

confidence: 99%

Modeling coronavirus spike protein dynamics: implications for immunogenicity and immune escape

Kunkel

Madani

White

et al. 2021

Biophysical Journal

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The ongoing COVID-19 pandemic is a global public health emergency requiring urgent development of efficacious vaccines. While concentrated research efforts have focused primarily on antibody-based vaccines that neutralize SARS-CoV-2, and several first-generation vaccines have either been approved or received emergency use authorization, it is forecasted that COVID-19 will become an endemic disease requiring updated second-generation vaccines. The SARS-CoV-2 surface spike (S) glycoprotein represents a prime target for vaccine development because antibodies that block viral attachment and entry, i.e., neutralizing antibodies, bind almost exclusively to the receptor-binding domain. Here, we develop computational models for a large subset of S proteins associated with SARS-CoV-2, implemented through coarse-grained elastic network models and normal mode analysis. We then analyze local protein domain dynamics of the S protein systems and their thermal stability to characterize structural and dynamical variability among them. These results are compared against existing experimental data and used to elucidate the impact and mechanisms of SARS-CoV-2 S protein mutations and their associated antibody binding behavior. We construct a SARS-CoV-2 antigenic map and offer predictions about the neutralization capabilities of antibody and S mutant combinations based on protein dynamic signatures. We then compare SARS-CoV-2 S protein dynamics to SARS-CoV and MERS-CoV S proteins to investigate differing antibody binding and cellular fusion mechanisms that may explain the high transmissibility of SARS-CoV-2. The outbreaks associated with SARS-CoV, MERS-CoV, and SARS-CoV-2 over the last two decades suggest that the threat presented by coronaviruses is ever-changing and long term. Our results provide insights into the dynamics-driven mechanisms of immunogenicity associated with coronavirus S proteins and present a new, to our knowledge, approach to characterize and screen potential mutant candidates for immunogen design, as well as to characterize emerging natural variants that may escape vaccine-induced antibody responses.

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CoMoDo: Identifying Dynamic Protein Domains Based on Covariances of Motion

Cited by 8 publications

References 76 publications

DIRECT‐ID: An automated method to identify and quantify conformational variations—application to β₂‐adrenergic GPCR

DIRECT‐ID: An automated method to identify and quantify conformational variations—application to β₂‐adrenergic GPCR

Decomposition of Proteins into Dynamic Units from Atomic Cross-Correlation Functions

Modeling coronavirus spike protein dynamics: implications for immunogenicity and immune escape

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CoMoDo: Identifying Dynamic Protein Domains Based on Covariances of Motion

Cited by 8 publications

References 76 publications

DIRECT‐ID: An automated method to identify and quantify conformational variations—application to β2‐adrenergic GPCR

DIRECT‐ID: An automated method to identify and quantify conformational variations—application to β2‐adrenergic GPCR

Decomposition of Proteins into Dynamic Units from Atomic Cross-Correlation Functions

Modeling coronavirus spike protein dynamics: implications for immunogenicity and immune escape

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DIRECT‐ID: An automated method to identify and quantify conformational variations—application to β₂‐adrenergic GPCR

DIRECT‐ID: An automated method to identify and quantify conformational variations—application to β₂‐adrenergic GPCR