Systematic Modeling, Prediction, and Comparison of Domain–Peptide Affinities: Does it Work Effectively With the Peptide QSAR Methodology?

The protein–protein association in cellular signaling networks (CSNs) often acts as weak, transient, and reversible domain–peptide interaction (DPI), in which a flexible peptide segment on the surface of one protein is recognized and bound by a rigid peptide-recognition domain from another. Reliable modeling and accurate prediction of DPI binding affinities would help to ascertain the diverse biological events involved in CSNs and benefit our understanding of various biological implications underlying DPIs. Traditionally, peptide quantitative structure-activity relationship (pQSAR) has been widely used to model and predict the biological activity of oligopeptides, which employs amino acid descriptors (AADs) to characterize peptide structures at sequence level and then statistically correlate the resulting descriptor vector with observed activity data via regression. However, the QSAR has not yet been widely applied to treat the direct binding behavior of large-scale peptide ligands to their protein receptors. In this work, we attempted to clarify whether the pQSAR methodology can work effectively for modeling and predicting DPI affinities in a high-throughput manner? Over twenty thousand short linear motif (SLiM)-containing peptide segments involved in SH3, PDZ and 14-3-3 domain-medicated CSNs were compiled to define a comprehensive sequence-based data set of DPI affinities, which were represented by the Boehringer light units (BLUs) derived from previous arbitrary light intensity assays following SPOT peptide synthesis. Four sophisticated MLMs (MLMs) were then utilized to perform pQSAR modeling on the set described with different AADs to systematically create a variety of linear and nonlinear predictors, and then verified by rigorous statistical test. It is revealed that the genome-wide DPI events can only be modeled qualitatively or semiquantitatively with traditional pQSAR strategy due to the intrinsic disorder of peptide conformation and the potential interplay between different peptide residues. In addition, the arbitrary BLUs used to characterize DPI affinity values were measured via an indirect approach, which may not very reliable and may involve strong noise, thus leading to a considerable bias in the modeling. The Rprd2 = 0.7 can be considered as the upper limit of external generalization ability of the pQSAR methodology working on large-scale DPI affinity data.

Curcumins and its derivatives as potential inhibitors of New Coronavirus (COVID-19) main protease: an in silico strategy

Coronavirus (COVID-19) disease outbreak caused a worldwide pandemic with a powerful lethal potential and still, there is no specific treatment to it. Natural bioactive molecules like curcumins were investigated in this work aiming to block the active site of COVID-19 Main protease (Mpro), since they present several biological activities, being more suitable in terms of fewer side effects, once this disease overloads the immune system of patients. Hereby, curcumin and several derivatives were screened for their ability to react with Mpro receptors (PDB: 6LU7). N3, Azithromycin (AZT), and Baracitinib (BRT) were evaluated as positive controls and in combined therapeutics possibilities with curcumins. N3, AZT, and BRT bound to different protein receptors, and also it was observed that N3 bound in the same site as hexahydrocurcumin and curcumin glucuronide bound at the AZT’s site and bisdemethoxycurcumin, curcumin, curcumin sulfate, cyclocurcumin, demethoxycurcumin, dihydrocurcumin and hexahydrocurcuminol bound at BRT’s site. All molecules analyzed have high force interaction fields. Once the viral activity is mainly intracellular, these compounds also were evaluated for their hydropathic abilities. All molecules were classified and considered capable of membrane cell invading. These results suggest that the therapeutic approach of the curcumin derivatives associated with AZT and the antiviral inhibitor N3 is promissory for future evaluation of their synergism in in vitro and in vivo tests to define their additional viability in the treatment of COVID-19.

Exploration of Luteolin as Potential Anti-COVID19 agent: Molecular Docking, Molecular Dynamic Simulation, ADMET and DFT analysis

Background: Recently, coronavirus disease-2019 (COVID-19) has become a pandemic disease of the respiratory tract having mild to severe symptoms of pneumonia. No clinical antiviral agent is available so far, however, several repurposing drugs and vaccine are being given to individuals or in clinical trials against SARS-CoV-2 Objective: The aim of this study is to uncover the potential effects of Luteolin (Lut) as an inhibitor of SARS-CoV2 encoded proteins via utilizing computational tools Method: Molecular modelling to unfold the anti-SARS-CoV2 potential of Lut along with reference drugs namely remdesivir and nafamostat was performed by the use of molecular docking, molecular dynamic (MD) simulation, absorption, distribution, metabolism, excretion, toxicity (ADMET) and density functional theory (DFT) methods against the five different SARS-CoV-2 encoded key proteins and one human receptor protein. The chemical reactivity of Luteolin is done through prediction of HOMO-LUMO gap energy and other chemical descriptors analysis Results: In the present study, Lut binds effectively in the binding pockets of spike glycoprotein (6VSB), ADP phosphatase of NSP3 (6W02), and RNA dependent RNA polymerase (7AAP) protein receptors with significant values of docking scores -7.00, -7.25, and -6.46 respectively as compared to reference drugs remdesivir and nafamostat. Conclusion:: Thus, Lut can act as therapeutic agent and is orally safe for human consumption as predicted by molecular modelling against SARS-CoV-2 in the treatment of COVID-19

ACE2-containing defensosomes serve as decoys to inhibit SARS-CoV-2 infection

Extracellular vesicles of endosomal origin, exosomes, mediate intercellular communication by transporting substrates with a variety of functions related to tissue homeostasis and disease. Their diagnostic and therapeutic potential has been recognized for diseases such as cancer in which signaling defects are prominent. However, it is unclear to what extent exosomes and their cargo inform the progression of infectious diseases. We recently defined a subset of exosomes termed defensosomes that are mobilized during bacterial infection in a manner dependent on autophagy proteins. Through incorporating protein receptors on their surface, defensosomes mediated host defense by binding and inhibiting pore-forming toxins secreted by bacterial pathogens. Given this capacity to serve as decoys that interfere with surface protein interactions, we investigated the role of defensosomes during infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of COVID-19. Consistent with a protective function, exosomes containing high levels of the viral receptor ACE2 in bronchioalveolar lavage fluid from critically ill COVID-19 patients was associated with reduced ICU and hospitalization times. We found ACE2+ exosomes were induced by SARS-CoV-2 infection and activation of viral sensors in cell culture, which required the autophagy protein ATG16L1, defining these as defensosomes. We further demonstrate that ACE2+ defensosomes directly bind and block viral entry. These findings suggest that defensosomes may contribute to the antiviral response against SARS-CoV-2 and expand our knowledge on the regulation and effects of extracellular vesicles during infection.

Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals

Adaptive immunity's success relies on the extraordinary diversity of protein receptors on B and T cell membranes. Despite this diversity, the existence of public receptors shared by many individuals gives hope for developing population wide vaccines and therapeutics. Yet many of these public receptors are shared by chance. We present a statistical approach, defined in terms of a probabilistic V(D)J recombination model enhanced by a selection factor, that describes repertoire diversity and predicts with high accuracy the spectrum of repertoire overlap in healthy individuals. The model underestimates sharing between repertoires of individuals infected with SARS-CoV-2, suggesting strong antigen-driven convergent selection. We exploit this discrepancy to identify COVID-associated receptors, which we validate against datasets of receptors with known viral specificity. We study their properties in terms of sequence features and network organization, and use them to design an accurate diagnosis tool for predicting SARS-CoV-2 status from repertoire data.

Link prediction of viral spike proteins and cell receptors using structural perturbation method

Many protein receptors for animal and human viruses have been discovered in decades of studies. The main determinant of virus entry is the binding of the viral spike protein to host cell receptors, which mediates membrane fusion.In this work, a bilayer network is constructed by integrating the similarity network of the viral spike proteins, the similarity network of host receptors, and the association network between viruses and receptors. The structural perturbation method (SPM) is used to predict possible emerging infection of a virus in potential new host organisms. The reliability of this method is based on the hypothesis that the major barrier to virus infection is the differences in the compatibility of spike proteins and cell receptors, which is determined by the amino acid sequences among species.

InterPepScore: A Deep Learning Score for Improving the FlexPepDock Refinement Protocol

Motivation: Interactions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine the structural details of. As such, many computational methods have been developed to aid in peptide-protein docking or structure prediction. One such method is Rosetta FlexPepDock which consistently refines coarse peptide-protein models into sub-Ångström precision using Monte-Carlo simulations and statistical potentials. Deep learning has recently seen increased use in protein structure prediction, with graph neural network seeing use in protein model quality assessment. Results: Here, we introduce a graph neural network, InterPepScore, as an additional scoring term to complement and improve the Rosetta FlexPepDock refinement protocol. InterPepScore is trained on simulation trajectories from FlexPepDock refinement starting from thousands of peptide-protein complexes generated by a wide variety of docking schemes. The addition of InterPepScore into the refinement protocol consistently improves the quality of models created, and on an independent benchmark on 109 peptide-protein complexes its inclusion results in an increase in the number of complexes for which the top-scoring model had a DockQ-score of 0.49 (Medium quality) or better from 14.8% to 26.1%.

Protein Receptors Evolved from Homologous Cohesion Modules That Self-Associated and Are Encoded by Interactive Networked Genes

Previously, it was proposed that protein receptors evolved from self-binding peptides that were encoded by self-interacting gene segments (inverted repeats) widely dispersed in the genome. In addition, self-association of the peptides was thought to be mediated by regions of amino acid sequence similarity. To extend these ideas, special features of receptors have been explored, such as their degree of homology to other proteins, and the arrangement of their genes for clues about their evolutionary origins and dynamics in the genome. As predicted, BLASTP searches for homologous proteins detected a greater number of unique hits for queries with receptor sequences than for sequences of randomly-selected, non-receptor proteins. This suggested that the building blocks (cohesion modules) for receptors were duplicated, dispersed, and maintained in the genome, due to structure/function relationships discussed here. Furthermore, the genes coding for a representative panel of receptors participated in a larger number of gene–gene interactions than for randomly-selected genes. This could conceivably reflect a greater evolutionary conservation of the receptor genes, with their more extensive integration into networks along with inherent properties of the genes themselves. In support of the latter possibility, some receptor genes were located in active areas of adaptive gene relocation/amalgamation to form functional blocks of related genes. It is suggested that adaptive relocation might allow for their joint regulation by common promoters and enhancers, and affect local chromatin structural domains to facilitate or repress gene expression. Speculation is included about the nature of the coordinated communication between receptors and the genes that encode them.

The SARS-CoV-2 Spike protein disrupts human cardiac pericytes function through CD147-receptor-mediated signalling: a potential non-infective mechanism of COVID-19 microvascular disease

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a broad range of clinical responses including prominent microvascular damage. The capacity of SARS-CoV-2 to infect vascular cells is still debated. Additionally, the SARS-CoV-2 Spike (S) protein may act as a ligand to induce non-infective cellular stress. We tested this hypothesis in pericytes (PCs), which are reportedly reduced in the heart of patients with severe coronavirus disease-2019 (COVID-19). Here we newly show that the in vitro exposure of primary human cardiac PCs to the SARS-CoV-2 wild type strain or the Alpha and Delta variants caused rare infection events. Exposure to the recombinant S protein alone elicited signalling and functional alterations, including: (1) increased migration, (2) reduced ability to support endothelial cell (EC) network formation on Matrigel, (3) secretion of pro-inflammatory molecules typically involved in the cytokine storm, and (4) production of pro-apoptotic factors causing EC death. Next, adopting a blocking strategy against the S protein receptors angiotensin-converting enzyme 2 (ACE2) and CD147, we discovered that the S protein stimulates the phosphorylation/activation of the extracellular signal-regulated kinase 1/2 (ERK1/2) through the CD147 receptor, but not ACE2, in PCs. The neutralisation of CD147, either using a blocking antibody or mRNA silencing, reduced ERK1/2 activation, and rescued PC function in the presence of the S protein. Immunoreactive S protein was detected in the peripheral blood of infected patients. In conclusion, our findings suggest that the S protein may prompt PC dysfunction, potentially contributing to microvascular injury. This mechanism may have clinical and therapeutic implications.

Transcriptomics, Epigenetics, and Metabolomics of Primary Aldosteronism

Introduction: Primary aldosteronism (PA) is the most common cause of endocrine hypertension, mainly caused by aldosterone-producing adenomas or hyperplasia; understanding its pathophysiological background is important in order to provide ameliorative treatment strategies. Over the past several years, significant progress has been documented in this field, in particular in the clarification of the genetic and molecular mechanisms responsible for the pathogenesis of aldosterone-producing adenomas (APAs). Methods: Systematic searches of the PubMed and Cochrane databases were performed for all human studies applying transcriptomic, epigenetic or metabolomic analyses to PA subjects. Studies involving serial analysis of gene expression and microarray, epigenetic studies with methylome analyses and micro-RNA expression profiles, and metabolomic studies focused on improving understanding of the regulation of autonomous aldosterone production in PA were all included. Results: In this review we summarize the main findings in this area and analyze the interplay between primary aldosteronism and several signaling pathways with differential regulation of the RNA and protein expression of several factors involved in, among others, steroidogenesis, calcium signaling, and nuclear, membrane and G-coupled protein receptors. Distinct transcriptomic and metabolomic patterns are also presented herein, depending on the mutational status of APAs. In particular, two partially opposite transcriptional and steroidogenic profiles appear to distinguish APAs carrying a KCNJ5 mutation from all other APAs, which carry different mutations. Conclusions: These findings can substantially contribute to the development of personalized treatment in patients with PA.