pyHVis3D: visualising molecular simulation deduced H-bond networks in 3D: application to T-cell receptor interactions

Knapp, Bernhard; Alcala, Marta; Zhang, Hao; West, Clare E.; Merwe, P. Anton van der; Deane, Charlotte M.

doi:10.1093/bioinformatics/btx842

Cited by 6 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally we chose the A6, JM22, and 1G4 systems as they have been investigated computationally before (e.g. [14,15,18,27]) and all three TCRs bind to HLA-A*02:01 while the LC13 TCR binds HLA-B*08:01. This allows us to investigate if there are conserved TCR reaction features across MHC types as well as within HLA-A*02:01.…”

Section: Methodsmentioning

confidence: 99%

“…Final production runs were carried out using Gromacs 4 [28] and the GROMOS 53a6 force field [30]. Parts of the LC13 simulations were taken from our previous work in [21] and [23] while parts of the A6, JM22, and 1G4 simulations were taken from [14] and [15].…”

Section: Methodsmentioning

confidence: 99%

“…Fortunately, computational methods such as Molecular Dynamics (MD) or Monte Carlo simulations can be used to explore this possibility (reviewed in [13]). Recent approaches include TCR/pMHC interface H-bond network analysis [14,15], binding free energy [16] and detachment [17,18] simulations, effects on the MHC [19], peptide [20], and on the TCR [21] or CDR loop characterisation [22]. In a previous large scale study we analysed the same TCR/MHC in combination with 172 different peptides of known experimental immunogenicity [23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MHC binding affects the dynamics of different T-cell receptors in different ways

et al. 2019

Self Cite

View full text Add to dashboard Cite

T cells use their T-cell receptors (TCRs) to scan other cells for antigenic peptides presented by MHC molecules (pMHC). If a TCR encounters a pMHC, it can trigger a signalling pathway that could lead to the activation of the T cell and the initiation of an immune response. It is currently not clear how the binding of pMHC to the TCR initiates signalling within the T cell. One hypothesis is that conformational changes in the TCR lead to further downstream signalling. Here we investigate four different TCRs in their free state as well as in their pMHC bound state using large scale molecular simulations totalling 26 000 ns. We find that the dynamical features within TCRs differ significantly between unbound TCR and TCR/pMHC simulations. However, apart from expected results such as reduced solvent accessibility and flexibility of the interface residues, these features are not conserved among different TCR types. The presence of a pMHC alone is not sufficient to cause cross-TCR-conserved dynamical features within a TCR. Our results argue against models of TCR triggering involving conserved allosteric conformational changes.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MHC binding affects the dynamics of different T-cell receptors in different ways

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on the cognate peptide, MHC and TCR structures in the aforementioned database, there have been a number of attempts to accurately predict peptide-MHC conformations, including docking algorithms (140,141), protein threading (142), all-atom molecular dynamics (MD) simulations (143)(144)(145), energy minimization (146) and hybrid of these approaches (147). Likewise, approaches to model pMHC-TCR include MD or Monte Carlo simulations, TCR:pMHC hydrogen bond network analysis (148,149), binding free energy simulation (150) and CDR loop characterization (130). Both rigid and flexible docking protocols have been proposed to assemble unbound structures (151).…”

Section: Features From Structural Modelingmentioning

confidence: 99%

Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors

et al. 2020

View full text Add to dashboard Cite

Adaptive immune recognition is mediated by specific interactions between heterodimeric T cell receptors (TCRs) and their cognate peptide-MHC (pMHC) ligands, and the methods to accurately predict TCR:pMHC interaction would have profound clinical, therapeutic and pharmaceutical applications. Herein, we review recent developments in predicting cross-reactivity and antigen specificity of TCR recognition. We discuss current experimental and computational approaches to investigate cross-reactivity and antigen-specificity of TCRs and highlight how integrating kinetic, biophysical and structural features may offer valuable insights in modeling immunogenicity. We further underscore the close interrelationship of these two interconnected notions and the need to investigate each in the light of the other for a better understanding of T cell responsiveness for the effective clinical applications.

show abstract

“…For example, by correlating average feature values with MSM eigenvectors along micro-state (Pérez-Hernández et al , 2013), one can thus extract features that represent best the slowest eigenvectors of an MSM. Alternatively, one can identify significant feature differences (SFDs) among pairs of simulated ensembles—e.g., meta-stable states (sets) of micro-states, even across different mutants—as implemented for non-covalent contact frequencies in (Farabella et al , 2014), or in the PIA (Stolzenberg, 2014; Stolzenberg et al , 2015, 2016), and pyHVis3D (Knapp et al , 2018) tools. In this paper, I have developed the object-oriented Python package PySFD (Significant Feature Differences analyzer for Python), a generalized and more powerful framework that efficiently detects and visualizes significant differences in any user-defined feature between many pairs or many groups of molecular simulation state ensembles.…”

Section: Introductionmentioning

confidence: 99%

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles

Stolzenberg

2018

Bioinformatics

View full text Add to dashboard Cite

Motivation Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this “curse of dimensionality”, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification. Results To recover such molecular details, I have developed PySFD (Significant Feature Differences analyzer for Python), a multi-processing software package that efficiently selects significantly different features of any user-defined feature type among potentially many different simulated state ensembles, such as meta-stable states of a Markov State Model (MSM). Applying PySFD on MSMs of an aggregate of 300 microseconds MD simulations recently performed on the major histocompatibility complex class II (MHCII) protein, I demonstrate how this toolkit can extract and visualize valuable mechanistic information from big MD simulation data, e.g., in form of networks of dynamic interaction changes connecting functionally relevant sites of a protein complex. Availability and implementation PySFD is freely available under the L-GPL license at https://github.com/markovmodel/PySFD . Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

pyHVis3D: visualising molecular simulation deduced H-bond networks in 3D: application to T-cell receptor interactions

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 6 publications

References 8 publications

MHC binding affects the dynamics of different T-cell receptors in different ways

MHC binding affects the dynamics of different T-cell receptors in different ways

Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles

Contact Info

Product

Resources

About