GWYRE: A Resource for Mapping Variants onto Experimental and Modeled Structures of Human Protein Complexes

Malladi, S. B.; Powell, Harold R.; David, Alessia; Islam, Suhail A.; Copeland, Matthew M.; Kundrotas, Petras J.; Sternberg, Michael J.E.; Vakser, Ilya A.

doi:10.1016/j.jmb.2022.167608

Cited by 5 publications

(3 citation statements)

References 47 publications

(54 reference statements)

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“…In this study, the Phyre2 and Al-phaFold Protein Structure databases were employed to generate 3D models of the proteins of interest. Both tools are open access, facilitating exploration of bioinformatics analysis of different proteins with satisfactory results [119][120][121]. Nevertheless, the use of other, more powerful modeling tools should be considered for the validation of structural predictions.…”

Section: Discussionmentioning

confidence: 99%

In Silico Analysis of Protein–Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in Schizosaccharomyces pombe

González-Esparragoza,

Carrasco-Carballo,

Rosas-Murrieta

et al. 2024

CIMB

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Ermp1 is a putative metalloprotease from Schizosaccharomyces pombe and a member of the Fxna peptidases. Although their function is unknown, orthologous proteins from rats and humans have been associated with the maturation of ovarian follicles and increased ER stress. This study focuses on proposing the first prediction of PPI by comparison of the interologues between humans and yeasts, as well as the molecular docking and dynamics of the M28 domain of Ermp1 with possible target proteins. As results, 45 proteins are proposed that could interact with the metalloprotease. Most of these proteins are related to the transport of Ca2+ and the metabolism of amino acids and proteins. Docking and molecular dynamics suggest that the M28 domain of Ermp1 could hydrolyze leucine and methionine residues of Amk2, Ypt5 and Pex12. These results could support future experimental investigations of other Fxna peptidases, such as human ERMP1.

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Section: Discussionmentioning

confidence: 99%

In Silico Analysis of Protein–Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in Schizosaccharomyces pombe

González-Esparragoza,

Carrasco-Carballo,

Rosas-Murrieta

et al. 2024

CIMB

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“…We have developed Missense3D-PPI to specifically address the problem of predicting the effect of missense variants occurring on the surface of a protein in a PPI site. Missense3D-PPI requires the 3D coordinates of a protein complex, experimentally determined or modelled through algorithms, such as AlphaFold [12] or GWIDD [39], the latter available from the GWYRE database [9]. Moreover, additional precomputed 3D models of protein complexes are available from databases, such as Interactome3D [8] and PrePPI [10].…”

Section: Discussionmentioning

confidence: 99%

“…In recent years there has been an increase in the availability of three-dimensional structures of protein complexes, both experimentally solved and obtained from protein docking and homology modelling [6]. These 3D coordinates are publicly available from databases, such as PDB [7], Interactome3D [8],GWYRE [9] and PrePPI [10]. Although at present the coverage of the protein interactome remains limited, we can expect an exponential increase in 3D coordinates of PPI complexes in the coming years as a result of the recent breakthrough in protein modelling achieved by AlphaFold and similar approaches which use deep learning [11] [12].…”

Section: Introductionmentioning

confidence: 99%

A new web resource to predict the impact of missense variants at protein interfaces using 3D structural data: Missense3D-PPI

Pennica

Hanna

Islam

et al. 2023

Preprint

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In 2019, we released Missense3D which identifies stereochemical features that are disrupted by a missense variant, such as introducing a buried charge. Missense3D analyses the effect of a missense variant on a single structure and thus may fail to identify as damaging surface variants disrupting a protein interface i.e., a protein-protein interaction (PPI) site. Here we present Missense3D-PPI designed to predict missense variants at PPI interfaces. Our development dataset comprised of 1,279 missense variants (pathogenic n=733, benign n=546) in 434 proteins and 545 experimental structures of PPI complexes. Benchmarking of Missense3D-PPI was performed after dividing the dataset in training (320 benign and 320 pathogenic variants) and testing (226 benign and 413 pathogenic). Structural features affecting PPI, such as disruption of interchain bonds and introduction of unbalanced charged interface residues, were analysed to assess the impact of the variant at PPI. The performance of Missense3D-PPI was superior to that of Missense3D: sensitivity 42% versus 8% and accuracy 58% versus 40%, p=4.23x10-16. However, the specificity of Missense3D-PPI was slightly lower compared to Missense3D (84% versus 98%). On our dataset, Missense3D-PPI was superior to BeAtMuSiC (p=2.3x10-5), mCSM-PPI2 (p=3.2x10-12) and MutaBind2 (p=0.003). Missense3D-PPI represents a valuable tool for predicting the structural effect of missense variants on biological protein networks and is available at the Missense3D web portal (http://missense3d.bc.ic.ac.uk/missense3d/indexppi.html).

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Computational Resources for Molecular Biology 2022

Casadio

Mathews

Sternberg

2022

Journal of Molecular Biology

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GWYRE: A Resource for Mapping Variants onto Experimental and Modeled Structures of Human Protein Complexes

Cited by 5 publications

References 47 publications

In Silico Analysis of Protein–Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in Schizosaccharomyces pombe

In Silico Analysis of Protein–Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in Schizosaccharomyces pombe

A new web resource to predict the impact of missense variants at protein interfaces using 3D structural data: Missense3D-PPI

Computational Resources for Molecular Biology 2022

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