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1.
ACS Synth Biol ; 11(3): 1030-1039, 2022 03 18.
Article in English | MEDLINE | ID: covidwho-1795844

ABSTRACT

In addition to its biological function, the stability of a protein is a major determinant for its applicability. Unfortunately, engineering proteins for improved functionality usually results in destabilization of the protein. This so-called stability-function trade-off can be explained by the simple fact that the generation of a novel protein function─or the improvement of an existing one─necessitates the insertion of mutations, i.e., deviations from the evolutionarily optimized wild-type sequence. In fact, it was demonstrated that gain-of-function mutations are not more destabilizing than other random mutations. The stability-function trade-off is a universal phenomenon during protein evolution that has been observed with completely different types of proteins, including enzymes, antibodies, and engineered binding scaffolds. In this review, we discuss three types of strategies that have been successfully deployed to overcome this omnipresent obstacle in protein engineering approaches: (i) using highly stable parental proteins, (ii) minimizing the extent of destabilization during functional engineering (by library optimization and/or coselection for stability and function), and (iii) repairing damaged mutants through stability engineering. The implementation of these strategies in protein engineering campaigns will facilitate the efficient generation of protein variants that are not only functional but also stable and therefore better-suited for subsequent applications.


Subject(s)
Protein Engineering , Proteins , Gene Library , Mutant Proteins , Mutation , Protein Engineering/methods , Proteins/genetics
2.
Cells ; 10(12)2021 12 08.
Article in English | MEDLINE | ID: covidwho-1598446

ABSTRACT

Organ-specific proteins (OSPs) possess great medical potential both in clinics and in biomedical research. Applications of them-such as alanine transaminase, aspartate transaminase, and troponins-in clinics have raised certain concerns of their organ specificity. The dynamics and diversity of protein expression in heterogeneous human populations are well known, yet their effects on OSPs are less addressed. Here, we used mice as a model and implemented a breadth study to examine the panorgan proteome for potential variations in organ specificity in different genetic backgrounds. Using reasonable resources, we generated panorgan proteomes of four in-bred mouse strains. The results revealed a large diversity that was more profound among OSPs than among proteomes overall. We defined a robustness score to quantify such variation and derived three sets of OSPs with different stringencies. In the meantime, we found that the enriched biological functions of OSPs are also organ-specific and are sensitive and useful to assess the quality of OSPs. We hope our breadth study can open doors to explore the molecular diversity and dynamics of organ specificity at the protein level.


Subject(s)
Organ Specificity/genetics , Proteins/genetics , Proteome/genetics , Proteomics , Animals , Genetic Variation/genetics , Humans , Mice
3.
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
4.
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
5.
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
6.
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
7.
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
8.
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
10.
J Mol Graph Model ; 106: 107906, 2021 07.
Article in English | MEDLINE | ID: covidwho-1284228

ABSTRACT

Homologous proteins are often compared by pairwise sequence alignment, and structure superposition if the atomic coordinates are available. Unification of sequence and structure data is an important task in structural biology. Here, we present the Sequence Similarity 3D (SS3D) method of integrating sequence and structure information. SS3D is a distance and substitution matrix-based method for straightforward visualization of regions of similarity and difference between homologous proteins. This work details the SS3D approach, and demonstrates its utility through case studies comparing members of several protein families. The examples show that SS3D can effectively highlight biologically important regions of similarity and dissimilarity. We anticipate that the method will be useful for numerous structural biology applications, including, but not limited to, studies of binding specificity, structure-function relationships, and evolutionary pathways. SS3D is available with a manual and tutorial at https://github.com/0x462e41/SS3D/.


Subject(s)
Algorithms , Proteins , Humans , Proteins/genetics , Sequence Alignment
11.
RNA ; 27(9): 1025-1045, 2021 09.
Article in English | MEDLINE | ID: covidwho-1269913

ABSTRACT

Viruses rely on the host translation machinery to synthesize their own proteins. Consequently, they have evolved varied mechanisms to co-opt host translation for their survival. SARS-CoV-2 relies on a nonstructural protein, Nsp1, for shutting down host translation. However, it is currently unknown how viral proteins and host factors critical for viral replication can escape a global shutdown of host translation. Here, using a novel FACS-based assay called MeTAFlow, we report a dose-dependent reduction in both nascent protein synthesis and mRNA abundance in cells expressing Nsp1. We perform RNA-seq and matched ribosome profiling experiments to identify gene-specific changes both at the mRNA expression and translation levels. We discover that a functionally coherent subset of human genes is preferentially translated in the context of Nsp1 expression. These genes include the translation machinery components, RNA binding proteins, and others important for viral pathogenicity. Importantly, we uncovered a remarkable enrichment of 5' terminal oligo-pyrimidine (TOP) tracts among preferentially translated genes. Using reporter assays, we validated that 5' UTRs from TOP transcripts can drive preferential expression in the presence of Nsp1. Finally, we found that LARP1, a key effector protein in the mTOR pathway, may contribute to preferential translation of TOP transcripts in response to Nsp1 expression. Collectively, our study suggests fine-tuning of host gene expression and translation by Nsp1 despite its global repressive effect on host protein synthesis.


Subject(s)
Host-Pathogen Interactions/genetics , Protein Biosynthesis , Proteins/chemistry , Proteins/genetics , Viral Nonstructural Proteins/genetics , 5' Untranslated Regions , Autoantigens/genetics , Autoantigens/metabolism , Gene Expression Regulation , HEK293 Cells , Humans , Protein Folding , Pyrimidines , RNA, Messenger/genetics , Ribonucleoproteins/genetics , Ribonucleoproteins/metabolism , Ribosomes/genetics , Ribosomes/virology , TOR Serine-Threonine Kinases/genetics , TOR Serine-Threonine Kinases/metabolism , Viral Nonstructural Proteins/metabolism
12.
Emerg Top Life Sci ; 5(1): 1-12, 2021 05 14.
Article in English | MEDLINE | ID: covidwho-1254002

ABSTRACT

With millions of signalling events occurring simultaneously, cells process a continuous flux of information. The genesis, processing, and regulation of information are dictated by a huge network of protein interactions. This is proven by the fact that alterations in the levels of proteins, single amino acid changes, post-translational modifications, protein products arising out of gene fusions alter the interaction landscape leading to diseases such as congenital disorders, deleterious syndromes like cancer, and crippling diseases like the neurodegenerative disorders which are often fatal. Needless to say, there is an immense effort to understand the biophysical basis of such direct interactions between any two proteins, the structure, domains, and sequence motifs involved in tethering them, their spatio-temporal regulation in cells, the structure of the network, and their eventual manipulation for intervention in diseases. In this chapter, we will deliberate on a few techniques that allow us to dissect the thermodynamic and kinetic aspects of protein interaction, how innovation has rendered some of the traditional techniques applicable for rapid analysis of multiple samples using small amounts of material. These advances coupled with automation are catching up with the genome-wide or proteome-wide studies aimed at identifying new therapeutic targets. The chapter will also summarize how some of these techniques are suited either in the standalone mode or in combination with other biophysical techniques for the drug discovery process.


Subject(s)
Drug Discovery , Proteins , Biophysics , Kinetics , Proteins/genetics , Thermodynamics
13.
Infect Genet Evol ; 93: 104921, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1230672

ABSTRACT

The development of therapeutic targets for COVID-19 relies on understanding the molecular mechanism of pathogenesis. Identifying genes or proteins involved in the infection mechanism is the key to shedding light on the complex molecular mechanisms. The combined effort of many laboratories distributed throughout the world has produced protein and genetic interactions. We integrated available results and obtained a host protein-protein interaction network composed of 1432 human proteins. Next, we performed network centrality analysis to identify critical proteins in the derived network. Finally, we performed a functional enrichment analysis of central proteins. We observed that the identified proteins are primarily associated with several crucial pathways, including cellular process, signaling transduction, neurodegenerative diseases. We focused on the proteins that are involved in human respiratory tract diseases. We highlighted many potential therapeutic targets, including RBX1, HSPA5, ITCH, RAB7A, RAB5A, RAB8A, PSMC5, CAPZB, CANX, IGF2R, and HSPA1A, which are central and also associated with multiple diseases.


Subject(s)
COVID-19/metabolism , Host-Pathogen Interactions/physiology , Protein Interaction Maps , SARS-CoV-2/pathogenicity , Gene Ontology , Humans , Protein Interaction Maps/genetics , Proteins/genetics , Proteins/metabolism , Viral Proteins/metabolism
14.
Braz J Microbiol ; 52(3): 1151-1159, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1217507

ABSTRACT

Infection by SARS-CoV-2, the causative agent of COVID-19, is critically connected with host metabolism. Through functional enrichment analysis, the present study aims to evaluate the biological processes involving host proteins interfered by SARS-CoV-2 to verify the potential metabolic impact of the infection. Furthermore, tissue enrichment analyses and differential gene expression of host proteins were applied to understand the interference by SARS-CoV-2 on tissue levels. Results based on functional and tissue-specific enrichment analyses, presented in this study, suggest that SARS-CoV-2, mediated interference on host proteins, can affect the metabolism and catabolism of molecular building blocks and control intracellular mechanisms, including gene expression in metabolism-related organs, to support viral demands. Thus, SARS-CoV-2 can broadly affect the host metabolism and catabolism at tissue and physiological levels contributing to a more severe disease.


Subject(s)
COVID-19/metabolism , COVID-19/immunology , Energy Metabolism , Gene Expression , Host-Pathogen Interactions , Humans , Protein Interaction Maps , Proteins/genetics , Proteins/metabolism , SARS-CoV-2/metabolism
15.
J Gene Med ; 23(3): e3318, 2021 03.
Article in English | MEDLINE | ID: covidwho-1084739

ABSTRACT

Pulmonary fibrosis is characterized by progressive and irreversible scarring in the lungs with poor prognosis and treatment. It is caused by various factors, including environmental and occupational exposures, and some rheumatic immune diseases. Even the rapid global spread of the COVID-19 pandemic can also cause pulmonary fibrosis with a high probability. Functions attributed to long non-coding RNAs (lncRNAs) make them highly attractive diagnostic and therapeutic targets in fibroproliferative diseases. Therefore, an understanding of the specific mechanisms by which lncRNAs regulate pulmonary fibrotic pathogenesis is urgently needed to identify new possibilities for therapy. In this review, we focus on the molecular mechanisms and implications of lncRNAs targeted protein-coding and non-coding genes during pulmonary fibrogenesis, and systematically analyze the communication of lncRNAs with various types of RNAs, including microRNA, circular RNA and mRNA. Finally, we propose the potential approach of lncRNA-based diagnosis and therapy for pulmonary fibrosis. We hope that understanding these interactions between protein-coding and non-coding genes will contribute to the development of lncRNA-based clinical applications for pulmonary fibrosis.


Subject(s)
Genetic Markers/genetics , Pulmonary Fibrosis/genetics , RNA, Long Noncoding/genetics , Gene Expression Regulation , Genetic Therapy/methods , Humans , MicroRNAs/genetics , Proteins/genetics , Pulmonary Fibrosis/diagnosis , Pulmonary Fibrosis/pathology , Pulmonary Fibrosis/therapy , RNA, Circular/genetics
16.
Nat Commun ; 11(1): 6397, 2020 12 16.
Article in English | MEDLINE | ID: covidwho-1023894

ABSTRACT

Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).


Subject(s)
COVID-19/genetics , COVID-19/virology , Host-Pathogen Interactions/genetics , Proteins/genetics , SARS-CoV-2/physiology , ABO Blood-Group System/metabolism , Aptamers, Peptide/blood , Aptamers, Peptide/metabolism , Blood Coagulation , Drug Delivery Systems , Female , Gene Expression Regulation , Host-Derived Cellular Factors/metabolism , Humans , Internet , Male , Middle Aged , Quantitative Trait Loci/genetics
17.
PLoS Pathog ; 17(1): e1009111, 2021 01.
Article in English | MEDLINE | ID: covidwho-1015956

ABSTRACT

Antiviral innate immune response to RNA virus infection is supported by Pattern-Recognition Receptors (PRR) including RIG-I-Like Receptors (RLR), which lead to type I interferons (IFNs) and IFN-stimulated genes (ISG) production. Upon sensing of viral RNA, the E3 ubiquitin ligase TNF Receptor-Associated Factor-3 (TRAF3) is recruited along with its substrate TANK-Binding Kinase (TBK1), to MAVS-containing subcellular compartments, including mitochondria, peroxisomes, and the mitochondria-associated endoplasmic reticulum membrane (MAM). However, the regulation of such events remains largely unresolved. Here, we identify TRK-Fused Gene (TFG), a protein involved in the transport of newly synthesized proteins to the endomembrane system via the Coat Protein complex II (COPII) transport vesicles, as a new TRAF3-interacting protein allowing the efficient recruitment of TRAF3 to MAVS and TBK1 following Sendai virus (SeV) infection. Using siRNA and shRNA approaches, we show that TFG is required for virus-induced TBK1 activation resulting in C-terminal IRF3 phosphorylation and dimerization. We further show that the ability of the TRAF3-TFG complex to engage mTOR following SeV infection allows TBK1 to phosphorylate mTOR on serine 2159, a post-translational modification shown to promote mTORC1 signaling. We demonstrate that the activation of mTORC1 signaling during SeV infection plays a positive role in the expression of Viperin, IRF7 and IFN-induced proteins with tetratricopeptide repeats (IFITs) proteins, and that depleting TFG resulted in a compromised antiviral state. Our study, therefore, identifies TFG as an essential component of the RLR-dependent type I IFN antiviral response.


Subject(s)
Antiviral Agents/metabolism , Immunity, Innate/immunology , Interferon Type I/metabolism , Proteins/metabolism , Rhabdoviridae Infections/immunology , Secretory Pathway , Vesiculovirus/immunology , Adaptor Proteins, Signal Transducing/genetics , Adaptor Proteins, Signal Transducing/metabolism , HeLa Cells , Humans , /metabolism , Proteins/genetics , Rhabdoviridae Infections/metabolism , Rhabdoviridae Infections/virology , Signal Transduction , TNF Receptor-Associated Factor 3/genetics , TNF Receptor-Associated Factor 3/metabolism , Vesiculovirus/physiology
18.
PLoS One ; 15(12): e0244241, 2020.
Article in English | MEDLINE | ID: covidwho-992711

ABSTRACT

The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.


Subject(s)
COVID-19/genetics , Internet , Metabolic Networks and Pathways/genetics , SARS-CoV-2/genetics , Algorithms , COVID-19/metabolism , COVID-19/virology , Humans , Proteins/genetics , Proteins/metabolism , SARS-CoV-2/metabolism , SARS-CoV-2/pathogenicity
19.
J Proteome Res ; 19(11): 4553-4566, 2020 11 06.
Article in English | MEDLINE | ID: covidwho-974862

ABSTRACT

While the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein-protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK 293 cells published by Gordon et al. (Nature 2020, 583, 459-468) to reveal processes that are potentially affected by the virus and putative protein binding sites. Specifically, we performed a set of network-based functional and sequence motif enrichment analyses on SARS-CoV-2-interacting human proteins and on PPI networks generated by supplementing viral-host PPIs with known interactions. Using a novel implementation of our GoNet algorithm, we identified 329 Gene Ontology terms for which the SARS-CoV-2-interacting human proteins are significantly clustered in PPI networks. Furthermore, we present a novel protein sequence motif discovery approach, LESMoN-Pro, that identified 9 amino acid motifs for which the associated proteins are clustered in PPI networks. Together, these results provide insights into the processes and sequence motifs that are putatively implicated in SARS-CoV-2 infection and could lead to potential therapeutic targets.


Subject(s)
Betacoronavirus , Coronavirus Infections , Host-Pathogen Interactions/genetics , Pandemics , Pneumonia, Viral , Protein Interaction Maps , Algorithms , Amino Acid Motifs , Betacoronavirus/chemistry , Betacoronavirus/metabolism , Betacoronavirus/pathogenicity , COVID-19 , Cluster Analysis , Coronavirus Infections/metabolism , Coronavirus Infections/virology , Gene Ontology , HEK293 Cells , Humans , Molecular Sequence Annotation , Pneumonia, Viral/metabolism , Pneumonia, Viral/virology , Protein Binding , Protein Interaction Maps/genetics , Protein Interaction Maps/physiology , Proteins/chemistry , Proteins/classification , Proteins/genetics , Proteins/metabolism , SARS-CoV-2 , Viral Proteins/chemistry , Viral Proteins/genetics , Viral Proteins/metabolism
20.
J Bioinform Comput Biol ; 19(1): 2050043, 2021 02.
Article in English | MEDLINE | ID: covidwho-936942

ABSTRACT

This paper has developed and described a detailed method for selecting inhibitors based on modified natural peptides for the SARS-CoV BJ01 spike-glycoprotein. The selection of inhibitors is carried out by increasing the affinity of the peptide to the active center of the protein. This paper also provides a step-by-step algorithm for analyzing the affinity of protein interactions and presents an analysis of energy interactions between the active center of a protein and the wild-type peptide interacting with it, taking into account modifications of the latter. A description of the software package that implements the presented algorithm is given on the website https://binomlabs.com/covid19.


Subject(s)
Antiviral Agents/pharmacology , Drug Evaluation, Preclinical/methods , Spike Glycoprotein, Coronavirus/antagonists & inhibitors , Spike Glycoprotein, Coronavirus/chemistry , Algorithms , Amino Acid Substitution , Catalytic Domain , Entropy , Protein Interaction Domains and Motifs/genetics , Proteins/chemistry , Proteins/genetics , Proteins/metabolism , Proteins/pharmacology , Software , Spike Glycoprotein, Coronavirus/metabolism
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