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1.
Cytokine ; 153: 155849, 2022 May.
Article in English | MEDLINE | ID: covidwho-1783275

ABSTRACT

As a member of JAK family of non-receptor tyrosine kinases, TYK2 has a crucial role in regulation of immune responses. This protein has a crucial role in constant expression of IFNAR1 on surface of cells and initiation of type I IFN signaling. In the current study, we measured expression of IFNAR1 and TYK2 levels in venous blood samples of COVID-19 patients and matched controls. TYK2 was significantly down-regulated in male patients compared with male controls (RME = 0.34, P value = 0.03). Though, levels of TYK2 were not different between female cases and female controls, or between ICU-admitted and non-ICU-admitted cases. Expression of IFNAR1 was not different either between COVID-19 cases and controls or between patients required ICU admission and non-ICU-admitted cases. However, none of these transcripts can properly diffrentiate COVID-19 cases from controls or separate patients based on disease severity. The current study proposes down-regulation of TYK2 as a molecular mechanism for incapacity of SARS-CoV-2 in induction of a competent IFN response.


Subject(s)
COVID-19 , Female , Humans , Male , Proteins/metabolism , Receptor, Interferon alpha-beta/genetics , Receptor, Interferon alpha-beta/metabolism , SARS-CoV-2 , TYK2 Kinase/genetics , TYK2 Kinase/metabolism
2.
Sci Rep ; 12(1): 5867, 2022 Apr 07.
Article in English | MEDLINE | ID: covidwho-1778626

ABSTRACT

SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein-protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.


Subject(s)
COVID-19 , Influenza A Virus, H1N1 Subtype , Influenza, Human , Humans , Influenza A Virus, H1N1 Subtype/metabolism , Protein Interaction Maps , Proteins/metabolism , SARS-CoV-2
3.
Int J Mol Sci ; 23(6)2022 Mar 13.
Article in English | MEDLINE | ID: covidwho-1745035

ABSTRACT

Cerebrovascular events, notably acute ischemic strokes (AIS), have been reported in the setting of novel coronavirus disease (COVID-19) infection. Commonly regarded as cryptogenic, to date, the etiology is thought to be multifactorial and remains obscure; it is linked either to a direct viral invasion or to an indirect virus-induced prothrombotic state, with or without the presence of conventional cerebrovascular risk factors. In addition, patients are at a greater risk of developing long-term negative sequelae, i.e., long-COVID-related neurological problems, when compared to non-COVID-19 stroke patients. Central to the underlying neurobiology of stroke recovery in the context of COVID-19 infection is reduced angiotensin-converting enzyme 2 (ACE2) expression, which is known to lead to thrombo-inflammation and ACE2/angiotensin-(1-7)/mitochondrial assembly receptor (MasR) (ACE2/Ang-(1-7)/MasR) axis inhibition. Moreover, after AIS, the activated nucleotide-binding oligomerization domain (NOD)-like receptor (NLR) family pyrin domain-containing 3 (NLRP3) inflammasome may heighten the production of numerous proinflammatory cytokines, mediating neuro-glial cell dysfunction, ultimately leading to nerve-cell death. Therefore, potential neuroprotective therapies targeting the molecular mechanisms of the aforementioned mediators may help to inform rehabilitation strategies to improve brain reorganization (i.e., neuro-gliogenesis and synaptogenesis) and secondary prevention among AIS patients with or without COVID-19. Therefore, this narrative review aims to evaluate the mediating role of the ACE2/Ang- (1-7)/MasR axis and NLRP3 inflammasome in COVID-19-mediated AIS, as well as the prospects of these neuroinflammation mediators for brain repair and in secondary prevention strategies against AIS in stroke rehabilitation.


Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , COVID-19/metabolism , Inflammasomes/metabolism , Ischemic Stroke/metabolism , Proteins/metabolism , Angiotensin I/metabolism , COVID-19/complications , COVID-19/virology , Humans , Ischemic Stroke/complications , NLR Family, Pyrin Domain-Containing 3 Protein/metabolism , Peptide Fragments/metabolism , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Signal Transduction
4.
Biophys Chem ; 285: 106780, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1693833

ABSTRACT

Messenger RNAs (mRNAs) serve as blueprints for protein synthesis by the molecular machine the ribosome. The ribosome relies on hydrogen bonding interactions between adaptor aminoacyl-transfer RNA molecules and mRNAs to ensure the rapid and faithful translation of the genetic code into protein. There is a growing body of evidence suggesting that chemical modifications to mRNA nucleosides impact the speed and accuracy of protein synthesis by the ribosome. Modulations in translation rates have downstream effects beyond protein production, influencing protein folding and mRNA stability. Given the prevalence of such modifications in mRNA coding regions, it is imperative to understand the consequences of individual modifications on translation. In this review we present the current state of our knowledge regarding how individual mRNA modifications influence ribosome function. Our comprehensive comparison of the impacts of 16 different mRNA modifications on translation reveals that most modifications can alter the elongation step in the protein synthesis pathway. Additionally, we discuss the context dependence of these effects, highlighting the necessity of further study to uncover the rules that govern how any given chemical modification in an mRNA codon is read by the ribosome.


Subject(s)
Peptide Chain Elongation, Translational , Protein Biosynthesis , Codon/analysis , Codon/metabolism , Proteins/metabolism , RNA Stability , RNA, Messenger/chemistry , RNA, Messenger/genetics , RNA, Messenger/metabolism , Ribosomes/chemistry , Ribosomes/genetics , Ribosomes/metabolism
5.
Gigascience ; 10(12)2021 12 29.
Article in English | MEDLINE | ID: covidwho-1595199

ABSTRACT

BACKGROUND: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. RESULTS: We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. CONCLUSIONS: We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.


Subject(s)
COVID-19 , SARS-CoV-2 , Algorithms , Humans , Protein Interaction Maps , Proteins/metabolism
6.
Science ; 374(6569): eabj1541, 2021 Nov 12.
Article in English | MEDLINE | ID: covidwho-1526448

ABSTRACT

Characterization of the genetic regulation of proteins is essential for understanding disease etiology and developing therapies. We identified 10,674 genetic associations for 3892 plasma proteins to create a cis-anchored gene-protein-disease map of 1859 connections that highlights strong cross-disease biological convergence. This proteo-genomic map provides a framework to connect etiologically related diseases, to provide biological context for new or emerging disorders, and to integrate different biological domains to establish mechanisms for known gene-disease links. Our results identify proteo-genomic connections within and between diseases and establish the value of cis-protein variants for annotation of likely causal disease genes at loci identified in genome-wide association studies, thereby addressing a major barrier to experimental validation and clinical translation of genetic discoveries.


Subject(s)
Blood Proteins/genetics , Disease/genetics , Genome, Human , Genomics , Proteins/genetics , Proteome , Aging , Alternative Splicing , Blood Proteins/metabolism , COVID-19/genetics , Connective Tissue Diseases/genetics , Disease/etiology , Drug Development , Female , Gallstones/genetics , Genetic Association Studies , Genetic Variation , Genome-Wide Association Study , Humans , Internet , Male , Phenotype , Proteins/metabolism , Quantitative Trait Loci , Sex Characteristics
7.
Viruses ; 13(11)2021 11 15.
Article in English | MEDLINE | ID: covidwho-1524171

ABSTRACT

Angiotensin-converting enzyme 2 (ACE2) is a main receptor for SARS-CoV-2 entry to the host cell. Indeed, the first step in viral entry is the binding of the viral trimeric spike (S) protein to ACE2. Abundantly present in human epithelial cells of many organs, ACE2 is also expressed in the human brain. ACE2 is a type I membrane protein with an extracellular N-terminal peptidase domain and a C-terminal collectrin-like domain that ends with a single transmembrane helix and an intracellular 44-residue segment. This C-terminal segment contains a PDZ-binding motif (PBM) targeting protein-interacting domains called PSD-95/Dlg/ZO-1 (PDZ). Here, we identified the human PDZ specificity profile of the ACE2 PBM using the high-throughput holdup assay and measuring the binding intensities of the PBM of ACE2 against the full human PDZome. We discovered 14 human PDZ binders of ACE2 showing significant binding with dissociation constants' values ranging from 3 to 81 µM. NHERF, SHANK, and SNX27 proteins found in this study are involved in protein trafficking. The PDZ/PBM interactions with ACE2 could play a role in ACE2 internalization and recycling that could be of benefit for the virus entry. Interestingly, most of the ACE2 partners we identified are expressed in neuronal cells, such as SHANK and MAST families, and modifications of the interactions between ACE2 and these neuronal proteins may be involved in the neurological symptoms of COVID-19.


Subject(s)
Angiotensin-Converting Enzyme 2/chemistry , Angiotensin-Converting Enzyme 2/metabolism , PDZ Domains , Proteins/chemistry , Proteins/metabolism , Receptors, Coronavirus/metabolism , Humans , Microtubule-Associated Proteins/chemistry , Microtubule-Associated Proteins/metabolism , Nerve Tissue Proteins/chemistry , Nerve Tissue Proteins/metabolism , Neurons/metabolism , Phosphoproteins/chemistry , Phosphoproteins/metabolism , /metabolism , Protein Transport , Sodium-Hydrogen Exchangers/chemistry , Sodium-Hydrogen Exchangers/metabolism , Sorting Nexins/chemistry , Sorting Nexins/metabolism
8.
Nucleic Acids Res ; 50(D1): D687-D692, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1522256

ABSTRACT

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied ('dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.


Subject(s)
Antiviral Agents/pharmacology , Knowledge Bases , Proteins/metabolism , COVID-19/metabolism , Data Curation , Genome, Human , Host-Pathogen Interactions , Humans , Proteins/genetics , Signal Transduction , Software
9.
Commun Biol ; 4(1): 1240, 2021 10 29.
Article in English | MEDLINE | ID: covidwho-1493232

ABSTRACT

Circular tandem repeat proteins ('cTRPs') are de novo designed protein scaffolds (in this and prior studies, based on antiparallel two-helix bundles) that contain repeated protein sequences and structural motifs and form closed circular structures. They can display significant stability and solubility, a wide range of sizes, and are useful as protein display particles for biotechnology applications. However, cTRPs also demonstrate inefficient self-assembly from smaller subunits. In this study, we describe a new generation of cTRPs, with longer repeats and increased interaction surfaces, which enhanced the self-assembly of two significantly different sizes of homotrimeric constructs. Finally, we demonstrated functionalization of these constructs with (1) a hexameric array of peptide-binding SH2 domains, and (2) a trimeric array of anti-SARS CoV-2 VHH domains. The latter proved capable of sub-nanomolar binding affinities towards the viral receptor binding domain and potent viral neutralization function.


Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , COVID-19/metabolism , Protein Engineering/methods , Proteins/chemistry , Proteins/metabolism , SARS-CoV-2/metabolism , Tandem Repeat Sequences , Amino Acid Sequence , COVID-19/virology , Computer Simulation , Crystallization , HEK293 Cells , Humans , Models, Molecular , Neutralization Tests , Protein Binding , Protein Domains , Protein Folding , Protein Structure, Secondary , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/metabolism
10.
Protein Sci ; 31(1): 173-186, 2022 01.
Article in English | MEDLINE | ID: covidwho-1490887

ABSTRACT

Protein Data Bank Japan (PDBj), a founding member of the worldwide Protein Data Bank (wwPDB) has accepted, processed and distributed experimentally determined biological macromolecular structures for 20 years. During that time, we have continuously made major improvements to our query search interface of PDBj Mine 2, the BMRBj web interface, and EM Navigator for PDB/BMRB/EMDB entries. PDBj also serves PDB-related secondary database data, original web-based modeling services such as Homology modeling of complex structure (HOMCOS), visualization services and utility tools, which we have continuously enhanced and expanded throughout the years. In addition, we have recently developed several unique archives, BSM-Arc for computational structure models, and XRDa for raw X-ray diffraction images, both of which promote open science in the structural biology community. During the COVID-19 pandemic, PDBj has also started to provide feature pages for COVID-19 related entries across all available archives at PDBj from raw experimental data and PDB structural data to computationally predicted models, while also providing COVID-19 outreach content for high school students and teachers.


Subject(s)
Databases, Protein , Proteins/chemistry , Animals , Anniversaries and Special Events , COVID-19/metabolism , Humans , Japan , Models, Molecular , Protein Conformation , Proteins/metabolism , SARS-CoV-2/chemistry , SARS-CoV-2/metabolism , Software , User-Computer Interface , Viral Proteins/chemistry , Viral Proteins/metabolism
11.
Nat Commun ; 12(1): 3023, 2021 05 21.
Article in English | MEDLINE | ID: covidwho-1454758

ABSTRACT

Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of ß-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding.


Subject(s)
Computational Biology/methods , Deep Learning , Proteins/chemistry , Proteins/metabolism , Algorithms , Computer Simulation , Molecular Dynamics Simulation , Myosins , Protein Conformation , Software
12.
Structure ; 30(1): 181-189.e5, 2022 01 06.
Article in English | MEDLINE | ID: covidwho-1454541

ABSTRACT

The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances, and active-site boundaries are used for the analysis of active sites. The algorithms derived provide model equations that can predict whether changes in distances, such as contraction or expansion, will result in improved binding affinity. The algorithm is confirmed using kinetic studies of dihydrofolate reductase (DHFR), together with two DHFR-TS crystal structures. Empirical analyses of 881 crystal structures involving 180 ligands are used to interpret protein-ligand binding affinities. MANORAA links to major biological databases for web-based analysis of drug design. The frequency of atoms inside the main protease structures, including those from SARS-CoV-2, shows how the rigid part of the ligand can be used as a probe for molecular design (http://manoraa.org).


Subject(s)
Computational Biology/methods , Databases, Protein , Machine Learning , Protein Domains , Proteins/chemistry , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Crystallography, X-Ray , Drug Design , Humans , Ligands , Models, Molecular , Pandemics , Protein Binding , Proteins/metabolism , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Tetrahydrofolate Dehydrogenase/chemistry , Tetrahydrofolate Dehydrogenase/metabolism , Trimethoprim/chemistry , Trimethoprim/metabolism
13.
Gene ; 808: 145963, 2022 Jan 15.
Article in English | MEDLINE | ID: covidwho-1415409

ABSTRACT

As of July 2021, the outbreak of coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has led to more than 200 million infections and more than 4.2 million deaths globally. Complications of severe COVID-19 include acute kidney injury, liver dysfunction, cardiomyopathy, and coagulation dysfunction. Thus, there is an urgent need to identify proteins and genetic factors associated with COVID-19 susceptibility and outcome. We comprehensively reviewed recent findings of host-SARS-CoV-2 interactome analyses. To identify genetic variants associated with COVID-19, we focused on the findings from genome and transcriptome wide association studies (GWAS and TWAS) and bioinformatics analysis. We described established human proteins including ACE2, TMPRSS2, 40S ribosomal subunit, ApoA1, TOM70, HLA-A, and PALS1 interacting with SARS-CoV-2 based on cryo-electron microscopy results. Furthermore, we described approximately 1000 human proteins showing evidence of interaction with SARS-CoV-2 and highlighted host cellular processes such as innate immune pathways affected by infection. We summarized the evidence on more than 20 identified candidate genes in COVID-19 severity. Predicted deleterious and disruptive genetic variants with possible effects on COVID-19 infectivity have been also summarized. These findings provide novel insights into SARS-CoV-2 biology and infection as well as potential strategies for development of novel COVID therapeutic targets and drug repurposing.


Subject(s)
COVID-19/metabolism , Host Microbial Interactions/genetics , SARS-CoV-2/metabolism , COVID-19/physiopathology , Computational Biology/methods , Cryoelectron Microscopy/methods , Crystallography, X-Ray/methods , Genome-Wide Association Study , Host Microbial Interactions/physiology , Host-Pathogen Interactions/genetics , Humans , Proteins/metabolism , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity
14.
Mol Syst Biol ; 17(3): e9923, 2021 03.
Article in English | MEDLINE | ID: covidwho-1389839

ABSTRACT

Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter- and intracellular signaling, as well as transcriptional and post-transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath's web service (https://omnipathdb.org/), a Cytoscape plug-in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell-cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications studying SARS-CoV-2 infection and ulcerative colitis.


Subject(s)
COVID-19/metabolism , Colitis, Ulcerative/metabolism , Computational Biology/methods , Proteins/metabolism , Signal Transduction , Animals , Cell Communication , Colitis, Ulcerative/pathology , Databases, Factual , Enzymes/metabolism , Humans , Mice , Protein Processing, Post-Translational , Proteins/genetics , Rats , Single-Cell Analysis , Software , Workflow
15.
Nucleic Acids Res ; 49(D1): D266-D273, 2021 01 08.
Article in English | MEDLINE | ID: covidwho-1387962

ABSTRACT

CATH (https://www.cathdb.info) identifies domains in protein structures from wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and functional annotations. There are two levels: CATH-B, a daily snapshot of the latest domain structures and superfamily assignments, and CATH+, with additional derived data, such as predicted sequence domains, and functionally coherent sequence subsets (Functional Families or FunFams). The latest CATH+ release, version 4.3, significantly increases coverage of structural and sequence data, with an addition of 65,351 fully-classified domains structures (+15%), providing 500 238 structural domains, and 151 million predicted sequence domains (+59%) assigned to 5481 superfamilies. The FunFam generation pipeline has been re-engineered to cope with the increased influx of data. Three times more sequences are captured in FunFams, with a concomitant increase in functional purity, information content and structural coverage. FunFam expansion increases the structural annotations provided for experimental GO terms (+59%). We also present CATH-FunVar web-pages displaying variations in protein sequences and their proximity to known or predicted functional sites. We present two case studies (1) putative cancer drivers and (2) SARS-CoV-2 proteins. Finally, we have improved links to and from CATH including SCOP, InterPro, Aquaria and 2DProt.


Subject(s)
Computational Biology/statistics & numerical data , Databases, Protein/statistics & numerical data , Protein Domains , Proteins/chemistry , Amino Acid Sequence , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Computational Biology/methods , Epidemics , Humans , Internet , Molecular Sequence Annotation , Proteins/genetics , Proteins/metabolism , SARS-CoV-2/genetics , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Sequence Analysis, Protein/methods , Sequence Homology, Amino Acid , Viral Proteins/chemistry , Viral Proteins/genetics , Viral Proteins/metabolism
16.
Nucleic Acids Res ; 49(D1): D261-D265, 2021 01 08.
Article in English | MEDLINE | ID: covidwho-1387959

ABSTRACT

ADP-ribosylation is a protein modification responsible for biological processes such as DNA repair, RNA regulation, cell cycle and biomolecular condensate formation. Dysregulation of ADP-ribosylation is implicated in cancer, neurodegeneration and viral infection. We developed ADPriboDB (adpribodb.leunglab.org) to facilitate studies in uncovering insights into the mechanisms and biological significance of ADP-ribosylation. ADPriboDB 2.0 serves as a one-stop repository comprising 48 346 entries and 9097 ADP-ribosylated proteins, of which 6708 were newly identified since the original database release. In this updated version, we provide information regarding the sites of ADP-ribosylation in 32 946 entries. The wealth of information allows us to interrogate existing databases or newly available data. For example, we found that ADP-ribosylated substrates are significantly associated with the recently identified human protein interaction networks associated with SARS-CoV-2, which encodes a conserved protein domain called macrodomain that binds and removes ADP-ribosylation. In addition, we create a new interactive tool to visualize the local context of ADP-ribosylation, such as structural and functional features as well as other post-translational modifications (e.g. phosphorylation, methylation and ubiquitination). This information provides opportunities to explore the biology of ADP-ribosylation and generate new hypotheses for experimental testing.


Subject(s)
Adenosine Diphosphate Ribose/metabolism , Computational Biology/statistics & numerical data , Databases, Protein/statistics & numerical data , Proteins/metabolism , ADP-Ribosylation , Binding Sites , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Computational Biology/methods , Humans , Protein Domains , Protein Processing, Post-Translational , Proteins/chemistry , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Viral Proteins/chemistry , Viral Proteins/metabolism
17.
Brief Bioinform ; 22(5)2021 09 02.
Article in English | MEDLINE | ID: covidwho-1387713

ABSTRACT

Small molecule modulators of protein-protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. We have utilized a large data set of experimentally validated PPI modulators and developed machine learning classifiers for prediction of new small molecule modulators of PPI. Our analysis reveals that using random forest (RF) classifier, general PPI Modulators independent of PPI family can be predicted with ROC-AUC higher than 0.9, when training and test sets are generated by random split. The performance of the classifier on data sets very different from those used in training has also been estimated by using different state of the art protocols for removing various types of bias in division of data into training and test sets. The family-specific PPIM predictors developed in this work for 11 clinically important PPI families also have prediction accuracies of above 90% in majority of the cases. All these ML-based predictors have been implemented in a freely available software named SMMPPI for prediction of small molecule modulators for clinically relevant PPIs like RBD:hACE2, Bromodomain_Histone, BCL2-Like_BAX/BAK, LEDGF_IN, LFA_ICAM, MDM2-Like_P53, RAS_SOS1, XIAP_Smac, WDR5_MLL1, KEAP1_NRF2 and CD4_gp120. We have identified novel chemical scaffolds as inhibitors for RBD_hACE PPI involved in host cell entry of SARS-CoV-2. Docking studies for some of the compounds reveal that they can inhibit RBD_hACE2 interaction by high affinity binding to interaction hotspots on RBD. Some of these new scaffolds have also been found in SARS-CoV-2 viral growth inhibitors reported recently; however, it is not known if these molecules inhibit the entry phase.


Subject(s)
Angiotensin-Converting Enzyme 2/antagonists & inhibitors , Machine Learning , Protein Interaction Maps , Proteins/metabolism , SARS-CoV-2/metabolism , Angiotensin-Converting Enzyme 2/metabolism , Humans
19.
Brief Bioinform ; 22(2): 832-844, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1343659

ABSTRACT

While leading to millions of people's deaths every year the treatment of viral infectious diseases remains a huge public health challenge.Therefore, an in-depth understanding of human-virus protein-protein interactions (PPIs) as the molecular interface between a virus and its host cell is of paramount importance to obtain new insights into the pathogenesis of viral infections and development of antiviral therapeutic treatments. However, current human-virus PPI database resources are incomplete, lack annotation and usually do not provide the opportunity to computationally predict human-virus PPIs. Here, we present the Human-Virus Interaction DataBase (HVIDB, http://zzdlab.com/hvidb/) that provides comprehensively annotated human-virus PPI data as well as seamlessly integrates online PPI prediction tools. Currently, HVIDB highlights 48 643 experimentally verified human-virus PPIs covering 35 virus families, 6633 virally targeted host complexes, 3572 host dependency/restriction factors as well as 911 experimentally verified/predicted 3D complex structures of human-virus PPIs. Furthermore, our database resource provides tissue-specific expression profiles of 6790 human genes that are targeted by viruses and 129 Gene Expression Omnibus series of differentially expressed genes post-viral infections. Based on these multifaceted and annotated data, our database allows the users to easily obtain reliable information about PPIs of various human viruses and conduct an in-depth analysis of their inherent biological significance. In particular, HVIDB also integrates well-performing machine learning models to predict interactions between the human host and viral proteins that are based on (i) sequence embedding techniques, (ii) interolog mapping and (iii) domain-domain interaction inference. We anticipate that HVIDB will serve as a one-stop knowledge base to further guide hypothesis-driven experimental efforts to investigate human-virus relationships.


Subject(s)
Databases, Protein , Protein Interaction Mapping/methods , Proteins/metabolism , Viral Proteins/metabolism , Gene Expression Profiling , Humans , Machine Learning , Protein Array Analysis , Protein Conformation , Proteins/chemistry , Proteins/genetics , Viral Proteins/chemistry , Viral Proteins/genetics
20.
PLoS Comput Biol ; 17(8): e1009284, 2021 08.
Article in English | MEDLINE | ID: covidwho-1341479

ABSTRACT

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.


Subject(s)
Amino Acid Substitution , Models, Chemical , Proteins/metabolism , Point Mutation , Protein Binding , Proteins/chemistry , Proteins/genetics
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