protein interfaces
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2022 ◽  
Vol 72 ◽  
pp. 219-225
Author(s):  
Dapeng Xiong ◽  
Dongjin Lee ◽  
Le Li ◽  
Qiuye Zhao ◽  
Haiyuan Yu
Keyword(s):  

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Nam Hyeong Kim ◽  
Hojae Choi ◽  
Zafar Muhammad Shahzad ◽  
Heesoo Ki ◽  
Jaekyoung Lee ◽  
...  

AbstractSeveral phenomena occurring throughout the life of living things start and end with proteins. Various proteins form one complex structure to control detailed reactions. In contrast, one protein forms various structures and implements other biological phenomena depending on the situation. The basic principle that forms these hierarchical structures is protein self-assembly. A single building block is sufficient to create homogeneous structures with complex shapes, such as rings, filaments, or containers. These assemblies are widely used in biology as they enable multivalent binding, ultra-sensitive regulation, and compartmentalization. Moreover, with advances in the computational design of protein folding and protein–protein interfaces, considerable progress has recently been made in the de novo design of protein assemblies. Our review presents a description of the components of supramolecular protein assembly and their application in understanding biological phenomena to therapeutics.


2021 ◽  
Author(s):  
Joseph H. Lubin ◽  
Christopher Markosian ◽  
D. Balamurugan ◽  
Renata Pasqualini ◽  
Wadih Arap ◽  
...  

There is enormous ongoing interest in characterizing the binding properties of the SARS-CoV-2 Omicron Variant of Concern (VOC) (B.1.1.529), which continues to spread towards potential dominance worldwide. To aid these studies, based on the wealth of available structural information about several SARS-CoV-2 variants in the Protein Data Bank (PDB) and a modeling pipeline we have previously developed for tracking the ongoing global evolution of SARS-CoV-2 proteins, we provide a set of computed structural models (henceforth models) of the Omicron VOC receptor-binding domain (omRBD) bound to its corresponding receptor Angiotensin-Converting Enzyme (ACE2) and a variety of therapeutic entities, including neutralizing and therapeutic antibodies targeting previously-detected viral strains. We generated bound omRBD models using both experimentally-determined structures in the PDB as well as machine learning-based structure predictions as starting points. Examination of ACE2-bound omRBD models reveals an interdigitated multi-residue interaction network formed by omRBD-specific substituted residues (R493, S496, Y501, R498) and ACE2 residues at the interface, which was not present in the original Wuhan-Hu-1 RBD-ACE2 complex. Emergence of this interaction network suggests optimization of a key region of the binding interface, and positive cooperativity among various sites of residue substitutions in omRBD mediating ACE2 binding. Examination of neutralizing antibody complexes for Barnes Class 1 and Class 2 antibodies modeled with omRBD highlights an overall loss of interfacial interactions (with gain of new interactions in rare cases) mediated by substituted residues. Many of these substitutions have previously been found to independently dampen or even ablate antibody binding, and perhaps mediate antibody-mediated neutralization escape (e.g., K417N). We observe little compensation of corresponding interaction loss at interfaces when potential escape substitutions occur in combination. A few selected antibodies (e.g., Barnes Class 3 S309), however, feature largely unaltered or modestly affected protein-protein interfaces. While we stress that only qualitative insights can be obtained directly from our models at this time, we anticipate that they can provide starting points for more detailed and quantitative computational characterization, and, if needed, redesign of monoclonal antibodies for targeting the Omicron VOC Spike protein. In the broader context, the computational pipeline we developed provides a framework for rapidly and efficiently generating retrospective and prospective models for other novel variants of SARS-CoV-2 bound to entities of virological and therapeutic interest, in the setting of a global pandemic.


2021 ◽  
Author(s):  
Manon Réau ◽  
Nicolas Renaud ◽  
Li C. Xue ◽  
Alexandre M.J.J Bonvin

Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein-protein interfaces using Convolutional Neural Network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein-protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations. We have developed DeepRank-GNN, a framework that converts protein-protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized, and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN's performance for scoring docking models using a dedicated graph interaction neural network (GINet). We show that this graph-based model performs better than DeepRank, DOVE and HADDOCK scores and competes with iScore on the CAPRI score set. We show a significant gain in speed and storage requirement using DeepRank-GNN as compared to DeepRank. DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN.


Pathogens ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1599
Author(s):  
Nejat Düzgüneş ◽  
Narcis Fernandez-Fuentes ◽  
Krystyna Konopka

Fusion of lipid-enveloped viruses with the cellular plasma membrane or the endosome membrane is mediated by viral envelope proteins that undergo large conformational changes following binding to receptors. The HIV-1 fusion protein gp41 undergoes a transition into a “six-helix bundle” after binding of the surface protein gp120 to the CD4 receptor and a co-receptor. Synthetic peptides that mimic part of this structure interfere with the formation of the helix structure and inhibit membrane fusion. This approach also works with the S spike protein of SARS-CoV-2. Here we review the peptide inhibitors of membrane fusion involved in infection by influenza virus, HIV-1, MERS and SARS coronaviruses, hepatitis viruses, paramyxoviruses, flaviviruses, herpesviruses and filoviruses. We also describe recent computational methods used for the identification of peptide sequences that can interact strongly with protein interfaces, with special emphasis on SARS-CoV-2, using the PePI-Covid19 database.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nicolas Renaud ◽  
Cunliang Geng ◽  
Sonja Georgievska ◽  
Francesco Ambrosetti ◽  
Lars Ridder ◽  
...  

AbstractThree-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.


2021 ◽  
Author(s):  
Neeladri Sen ◽  
Ivan Anishchenko ◽  
Nicola Bordin ◽  
Ian Sillitoe ◽  
Sameer Velankar ◽  
...  

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques such as RoseTTAFold and AlphaFold, we can predict the structure of these proteins even in the absence of structural homologues. We modeled and extracted the domains from 553 disease-associated human proteins. We noticed that the model quality was higher and the RMSD lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces, conserved residues and destabilising effects caused by residue mutations in these predicted structures. We then explored whether the disease-associated mutations were in the proximity of these predicted functional sites or if they destabilized the protein structure based on ddG calculations. We could explain 80% of these disease-associated mutations based on proximity to functional sites or structural destabilization. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.


2021 ◽  
Vol 14 (3) ◽  
pp. 1613-1631
Author(s):  
Christina Nilofer ◽  
Arumugam Mohanapriya

The outbreak of COVID-19 and its mutant variants has become a life-threatening and fatal viral disease to mankind. Several studies have been carried out to identify an effective receptor against coronavirus using clinically driven samples distinguished as hematological, immunological and biochemical biomarkers. Simultaneously, protein interfaces are being researched to understand the structural and functional mechanism of action. Therefore, we characterized and examined the interfaces of corona viral proteins using a dataset consisting of 366 homomeric and 199 heteromeric protein interfaces. The interfaces were analyzed using six parameters including interface area, interface size, van der Waal, hydrogen bond, electrostatic and total stabilizing energies. We observed the interfaces of corona viral proteins (homomer and heteromer) to be alike. Therefore, we clustered the interfaces based on the percent contribution of vdW towards total stabilizing energy as vdW energy dominant (≥60%) and vdW energy subdominant (<60%). We found 91% of interfaces to have vdW energy in dominance with large interface size [146±29 (homomer) and 122±29 (heteromer)] and interface area [1690±683 (homomer) and 1306±355 (heteromer)]. However, we also observed 9% of interfaces to have vdW energy in sub-dominance with small interface size [60±12 (homomer) and 41±20 (heteromer)] and interface area [472±174 (homomer) and 310±199 (heteromer)]. We noticed the interface area of large interfaces to be four-fold more when compared to small interfaces in homomer and heteromer. It was interesting to observe that the small interfaces of homomers to be rich in electrostatics (r2=0.50) destitute of hydrogen bond energy (r2=0.04). However, the heteromeric interfaces were equally pronounced with hydrogen bond (r2=0.70) and electrostatic (r2=0.61) energies. Hence, our earlier findings stating that the small protein interfaces are rich in electrostatic energy remaintrue with the homomeric interfaces of corona viral proteins whereas not in heteromeric interfaces.


2021 ◽  
Author(s):  
Benjamin J Livesey ◽  
Joseph A Marsh

The assembly of proteins into complexes and interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in 3D space, also show a significant disease enrichment. Finally, we show that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors.


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