scholarly journals Multilayer view of pathogenic SNVs in human interactome throughin-silicoedgetic profiling

2018 ◽  
Author(s):  
Hongzhu Cui ◽  
Nan Zhao ◽  
Dmitry Korkin
Keyword(s):  
Protein Interactions ◽  
In Silico ◽  
Molecular Mechanisms ◽  
Synergistic Effects ◽  
Patient Specific ◽  
Single Nucleotide Variants ◽  
Human Interactome ◽  
Frameshift Mutations ◽  
Next Generation Sequencing Technology ◽  
Synonymous Mutations

ABSTRACTNon-synonymous mutations linked to the complex diseases often have a global impact on a biological system, affecting large biomolecular networks and pathways. However, the magnitude of the mutation-driven effects on the macromolecular network is yet to be fully explored. In this work, we present an systematic multi-level characterization of human mutations associated with genetic disorders by determining their individual and combined interaction-rewiring, “edgetic”, effects on the human interactome. Ourin-silicoanalysis highlights the intrinsic differences and important similarities between the pathogenic single nucleotide variants (SNVs) and frameshift mutations. We show that pathogenic SNVs are more likely to cause gene pleiotropy than pathogenic frameshift mutations and are enriched on the protein interaction interfaces. Functional profiling of SNVs indicates widespread disruption of the protein-protein interactions and synergistic effects of SNVs. The coverage of our approach is several times greater than the recently published experimental study and has the minimal overlap with it, while the distributions of determined edgotypes between the two sets of profiled mutations are remarkably similar. Case studies reveal the central role of interaction-disrupting mutations in type 2 diabetes mellitus, and suggest the importance of studying mutations that abnormally strengthen the protein interactions in cancer. With the advancement of next-generation sequencing technology that drives precision medicine, there is an increasing demand in understanding the changes in molecular mechanisms caused by the patient-specific genetic variation. The current and futurein-silicoedgotyping tools present a cheap and fast solution to deal with the rapidly growing datasets of discovered mutations.

scholarly journals Integrated Computational Approaches and Tools for Allosteric Drug Discovery

2020 ◽  
Vol 21 (3) ◽  
pp. 847 ◽  
Author(s):  
Olivier Sheik Amamuddy ◽  
Wayde Veldman ◽  
Colleen Manyumwa ◽  
Afrah Khairallah ◽  
Steve Agajanian ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
In Silico ◽  
Molecular Mechanisms ◽  
Pathogen Resistance ◽  
Allosteric Modulators ◽  
Computational Approaches ◽  
Allosteric Sites ◽  
Computer Based ◽  
Allosteric Mechanisms

Understanding molecular mechanisms underlying the complexity of allosteric regulation in proteins has attracted considerable attention in drug discovery due to the benefits and versatility of allosteric modulators in providing desirable selectivity against protein targets while minimizing toxicity and other side effects. The proliferation of novel computational approaches for predicting ligand–protein interactions and binding using dynamic and network-centric perspectives has led to new insights into allosteric mechanisms and facilitated computer-based discovery of allosteric drugs. Although no absolute method of experimental and in silico allosteric drug/site discovery exists, current methods are still being improved. As such, the critical analysis and integration of established approaches into robust, reproducible, and customizable computational pipelines with experimental feedback could make allosteric drug discovery more efficient and reliable. In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen resistance and precision medicine. The emerging realization that allosteric modulators can exploit distinct regulatory mechanisms and can provide access to targeted modulation of protein activities could open opportunities for probing biological processes and in silico design of drug combinations with improved therapeutic indices and a broad range of activities.

scholarly journals PINA 3.0: mining cancer interactome

2020 ◽  
Vol 49 (D1) ◽  
pp. D1351-D1357
Author(s):  
Yang Du ◽  
Meng Cai ◽  
Xiaofang Xing ◽  
Jiafu Ji ◽  
Ence Yang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Type ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Specific Context ◽  
Human Interactome ◽  
Interactive Network ◽  
Cancer Types

Abstract Protein–protein interactions (PPIs) are crucial to mediate biological functions, and understanding PPIs in cancer type-specific context could help decipher the underlying molecular mechanisms of tumorigenesis and identify potential therapeutic options. Therefore, we update the Protein Interaction Network Analysis (PINA) platform to version 3.0, to integrate the unified human interactome with RNA-seq transcriptomes and mass spectrometry-based proteomes across tens of cancer types. A number of new analytical utilities were developed to help characterize the cancer context for a PPI network, which includes inferring proteins with expression specificity and identifying candidate prognosis biomarkers, putative cancer drivers, and therapeutic targets for a specific cancer type; as well as identifying pairs of co-expressing interacting proteins across cancer types. Furthermore, a brand-new web interface has been designed to integrate these new utilities within an interactive network visualization environment, which allows users to quickly and comprehensively investigate the roles of human interacting proteins in a cancer type-specific context. PINA is freely available at https://omics.bjcancer.org/pina/.

scholarly journals A reference map of the human protein interactome

2019 ◽  
Author(s):  
Katja Luck ◽  
Dae-Kyum Kim ◽  
Luke Lambourne ◽  
Kerstin Spirohn ◽  
Bridget E. Begg ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Molecular Mechanisms ◽  
Human Genetics ◽  
Genomic Variation ◽  
Small Scale ◽  
Human Interactome ◽  
Tissue Specific ◽  
Cellular Context ◽  
Reference Map

AbstractGlobal insights into cellular organization and function require comprehensive understanding of interactome networks. Similar to how a reference genome sequence revolutionized human genetics, a reference map of the human interactome network is critical to fully understand genotype-phenotype relationships. Here we present the first human “all-by-all” binary reference interactome map, or “HuRI”. With ~53,000 high-quality protein-protein interactions (PPIs), HuRI is approximately four times larger than the information curated from small-scale studies available in the literature. Integrating HuRI with genome, transcriptome and proteome data enables the study of cellular function within essentially any physiological or pathological cellular context. We demonstrate the use of HuRI in identifying specific subcellular roles of PPIs and protein function modulation via splicing during brain development. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI thus represents an unprecedented, systematic reference linking genomic variation to phenotypic outcomes.

scholarly journals Assessment of community efforts to advance computational prediction of protein-protein interactions

2021 ◽  
Author(s):  
Xu-Wen Wang ◽  
Lorenzo Madeddu ◽  
Kerstin Spirohn ◽  
Leonardo Martini ◽  
Adriano Fazzone ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Computational Prediction ◽  
Superior Performance ◽  
Systematic Evaluation ◽  
Human Interactome ◽  
C Elegans ◽  
Protein Protein Interaction ◽  
Human Ppis ◽  
Underlying Network

AbstractComprehensive insights from the human protein-protein interaction (PPI) network, known as the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of new PPIs. Many such approaches have been proposed. However, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 24 representative network-based methods to predict PPIs across five different interactomes, including a synthetic interactome generated by the duplication-mutation-complementation model, and the interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. We selected the top-seven methods through a computational validation on the human interactome. We next experimentally validated their top-500 predicted PPIs (in total 3,276 predicted PPIs) using the yeast two-hybrid assay, finding 1,177 new human PPIs (involving 633 proteins). Our results indicate that task-tailored similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods. Through experimental validation, we confirmed that the top-ranking methods show promising performance externally. For example, from the top 500 PPIs predicted by an advanced similarity-base method [MPS(B&T)], 430 were successfully tested by Y2H with 376 testing positive, yielding a precision of 87.4%. These results establish advanced similarity-based methods as powerful tools for the prediction of human PPIs.

scholarly journals Mutational landscape and in silico structure models of SARS-CoV-2 Spike Receptor Binding Domain reveal key molecular determinants for virus-host interaction

2020 ◽  
Author(s):  
Shijulal Nelson-Sathi ◽  
PK Umasankar ◽  
E Sreekumar ◽  
R Radhakrishnan Nair ◽  
Iype Joseph ◽  
...  
Keyword(s):  
Amino Acid ◽  
Protein Interactions ◽  
In Silico ◽  
Neutralizing Antibodies ◽  
Rna Virus ◽  
Binding Energies ◽  
Bioinformatics Pipeline ◽  
Synonymous Mutations ◽  
Binding Interface ◽  
Molecular Determinants

AbstractProtein-protein interactions between virus and host are crucial for infection. SARS-CoV-2, the causative agent of COVID-19 pandemic is an RNA virus prone to mutations. Formation of a stable binding interface between the Spike (S) protein Receptor Binding Domain (RBD) of SARS-CoV-2 and Angiotensin-Converting Enzyme 2 (ACE2) of host actuates viral entry. Yet, how this binding interface evolves as virus acquires mutations during pandemic remains elusive. Here, using a high fidelity bioinformatics pipeline, we analysed 31,403 SARS-CoV-2 genomes across the globe, and identified 444 non-synonymous mutations that cause 49 distinct amino acid substitutions in the RBD. Molecular phylogenetic analysis suggested independent emergence of these RBD mutants during pandemic. In silico structure modelling of interfaces induced by mutations on residues which directly engage ACE2 or lie in the near vicinity revealed molecular rearrangements and binding energies unique to each RBD mutant. Comparative structure analysis using binding interface from mouse that prevents SARS-CoV-2 entry uncovered minimal molecular determinants in RBD necessary for the formation of stable interface. We identified that interfacial interaction involving amino acid residues N487 and G496 on either ends of the binding scaffold are indispensable to anchor RBD and are well conserved in all SARS-like corona viruses. All other interactions appear to be required to locally remodel binding interface with varying affinities and thus may decide extent of viral transmission and disease outcome. Together, our findings propose the modalities and variations in RBD-ACE2 interface formation exploited by SARS-CoV-2 for endurance.ImportanceCOVID-19, so far the worst hit pandemic to mankind, started in January 2020 and is still prevailing globally. Our study identified key molecular arrangements in RBD-ACE2 interface that help virus to tolerate mutations and prevail. In addition, RBD mutations identified in this study can serve as a molecular directory for experimental biologists to perform functional validation experiments. The minimal molecular requirements for the formation of RBD-ACE2 interface predicted using in silico structure models may help precisely design neutralizing antibodies, vaccines and therapeutics. Our study also proposes the significance of understanding evolution of protein interfaces during pandemic.

Discovery of New AKT1 Inhibitors by Combination of In silico Structure Based Virtual Screening Approaches and Biological Evaluations

2019 ◽  
Author(s):  
Filip Fratev ◽  
Denisse A. Gutierrez ◽  
Renato J. Aguilera ◽  
suman sirimulla
Keyword(s):  
Virtual Screening ◽  
Success Rate ◽  
Protein Interactions ◽  
In Silico ◽  
Biological Data ◽  
In Vitro Binding ◽  
Vitro Binding ◽  
Screening Protocol ◽  
Screening Approaches

AKT1 is emerging as a useful target for treating cancer. Herein, we discovered a new set of ligands that inhibit the AKT1, as shown by in vitro binding and cell line studies, using a newly designed virtual screening protocol that combines structure-based pharmacophore and docking screens. Taking together with the biological data, the combination of structure based pharamcophore and docking methods demonstrated reasonable success rate in identifying new inhibitors (60-70%) proving the success of aforementioned approach. A detail analysis of the ligand-protein interactions was performed explaining observed activities.<br>

scholarly journals 1456. Resistance Mechanisms of Tigecycline in Enterococcus faecalis

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S730-S731
Author(s):  
Bing Bai ◽  
Zewen Wen ◽  
Zhiwei Lin ◽  
Tam Vincent H ◽  
Zhijian Yu
Keyword(s):  
Enterococcus Faecalis ◽  
Molecular Mechanisms ◽  
Genetic Mutations ◽  
Dilution Method ◽  
Agar Dilution Method ◽  
Whole Genome ◽  
Synonymous Mutations ◽  
Target Sites ◽  
Parental Isolate ◽  
Resistant Strains

Abstract Background Enterococcus faecalis have been regarded as one of the leading causes of the nosocomial infections worldwide. Tigecycline (TGC) is considered as a choice of last resort for the treatment of infections caused by multidrug-resistant E. faecalis, however, the emergence of TGC non-susceptibility has posted the therapeutic challenge. Non-susceptibility in clinical strains could be due to resistance (MIC &gt;0.5 mg/l) or heteroresistance. Therefore, this study aimed to understand the underlying molecular mechanisms of TGC resistance and heteroresistance in E. faecalis. Methods In vitro induction experiments were carried out under TGC pressure with two TGC- sensitive E. faecalis strains. Heteroresistance was evaluated by population analysis profiling (PAP) in 270 clinical TGC- sensitive E. faecalis strains. TGC susceptibility was determined by the agar dilution method. Resistance and heteroresistance mechanisms were investigated by identifying genetic mutations in tetracycline (Tet) target sites and susceptibility testing in the presence of the efflux protein inhibitors phenylalanine-arginine-β-naphthylamide (PaβN) and carbonyl cyanide m chlorophenylhydrazine (CCCP). Comparison of single nucleotide polymorphism in the whole genome between the parental isolate and two TGC-resistant strains were investigated by next-generation sequencing. Results No mutations in Tet target sites in seven TGC heteroresistant strains were present, whereas the mutations in Tet target sites of seven TGC-resistant E. faecalis were frequently found (Table 1). TGC MICs in heteroresistant strains were reduced by CCCP (Table 2). Whole genome sequencing revealed the same non-synonymous mutations and transcoding deletions in the exons of several genes encoding for various enzymes or transfer systems (Table 3). Table 1. The characteristics of the antimicrobial susceptibility, resistance mechanism of TGC-induced resistant isolates Table 2. Characteristics of clinical heteroresistant mother E. faecalis strains and heteroresistance-derived E. faecalis clones Table 3. List of mutation-related genes, amino acids and proteins by comparison of whole genome between the parental isolate and the TGC-induced resistant strains Conclusion Our data indicated that the main mechanism of TGC heteroresistance in E. faecalis might be associated with the efflux pumps. TGC resistance in E. faecalis was associated with mutations in the 16SrRNA site or 30S ribosome protein S10. The genetic mutations in several enzymes and transfer systems might also participate in the resistance development to TGC in E. faecalis. Disclosures All Authors: No reported disclosures

scholarly journals Adhesion and Growth of Neuralized Mouse Embryonic Stem Cells on Parylene-C/SiO2 Substrates

Materials ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3174
Author(s):  
Alan F. Murray ◽  
Evangelos Delivopoulos
Keyword(s):  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Embryonic Stem ◽  
Mouse Embryonic Stem Cells ◽  
Primary Neurons ◽  
Tissue Engineering Scaffolds ◽  
Parylene C ◽  
Unexpected Outcome

Neuronal patterning on microfabricated architectures has developed rapidly over the past few years, together with the emergence of soft biocompatible materials and tissue engineering scaffolds. Previously, we introduced a patterning technique based on serum and the biopolymer parylene-C, achieving highly compliant growth of primary neurons and astrocytes on different geometries. Here, we expanded this technique and illustrated that neuralized cells derived from mouse embryonic stem cells (mESCs) followed stripes of variable widths with conformity equal to or higher than that of primary neurons and astrocytes. Our results indicate the presence of undifferentiated mESCs, which also conformed to the underlying patterns to a high degree. This is an exciting and unexpected outcome, as molecular mechanisms governing cell and ECM protein interactions are different in stem cells and primary cells. Our study enables further investigations into the development and electrophysiology of differentiating patterned neural stem cells.

scholarly journals IκBα targeting promotes oxidative stress-dependent cell death

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Giovanna Carrà ◽  
Giuseppe Ermondi ◽  
Chiara Riganti ◽  
Luisella Righi ◽  
Giulia Caron ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Cell Fate ◽  
Protein Interactions ◽  
In Silico ◽  
Inverse Correlation ◽  
Ros Production ◽  
Gene Encoding ◽  
Specific Expression ◽  
Novel Approach ◽  
Stress Dependent

Abstract Background Oxidative stress is a hallmark of many cancers. The increment in reactive oxygen species (ROS), resulting from an increased mitochondrial respiration, is the major cause of oxidative stress. Cell fate is known to be intricately linked to the amount of ROS produced. The direct generation of ROS is also one of the mechanisms exploited by common anticancer therapies, such as chemotherapy. Methods We assessed the role of NFKBIA with various approaches, including in silico analyses, RNA-silencing and xenotransplantation. Western blot analyses, immunohistochemistry and RT-qPCR were used to detect the expression of specific proteins and genes. Immunoprecipitation and pull-down experiments were used to evaluate protein-protein interactions. Results Here, by using an in silico approach, following the identification of NFKBIA (the gene encoding IκBα) amplification in various cancers, we described an inverse correlation between IκBα, oxidative metabolism, and ROS production in lung cancer. Furthermore, we showed that novel IκBα targeting compounds combined with cisplatin treatment promote an increase in ROS beyond the tolerated threshold, thus causing death by oxytosis. Conclusions NFKBIA amplification and IκBα overexpression identify a unique cancer subtype associated with specific expression profile and metabolic signatures. Through p65-NFKB regulation, IκBα overexpression favors metabolic rewiring of cancer cells and distinct susceptibility to cisplatin. Lastly, we have developed a novel approach to disrupt IκBα/p65 interaction, restoring p65-mediated apoptotic responses to cisplatin due to mitochondria deregulation and ROS-production.

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