Biology subjects

Sedova, M.

Publications and source records attributed to Sedova, M..

6 recordsLinked to original sources

bioRxiv2024

Global atlas of predicted functional domains in Legionella pneumophila Dot/Icm translocated effectors

Legionella pneumophila utilizes the Dot/Icm type IVB secretion system to deliver hundreds of effector proteins inside eukaryotic cells to ensure intracellular replication. Our understanding of the molecular functions of this largest pathogenic arsenal known to the bacterial world remains incomplete. By leveraging advancements in 3D protein structure prediction, we provide a comprehensive structural analysis of 368 L. pneumophila effectors, representing a global atlas of predicted functional domains summarized in a database (https://pathogens3d.org/legionella-pneumophila). Our analysis identified 157 types of diverse functional domains in 287 effectors, including 159 effectors with no prior functional annotations. Furthermore, we identified 35 unique domains in 30 effector models that have no similarity with experimentally structurally characterized proteins, thus, hinting at novel functionalities. Using this analysis, we demonstrate the activity of thirteen domains, including three unique folds, predicted in L. pneumophila effectors to cause growth defects in the Saccharomyces cerevisiae model system. This illustrates an emerging strategy of exploring synergies between predictions and targeted experimental approaches in elucidating novel effector activities involved in infection.

microbiology↗

bioRxiv2022

Monitoring for SARS-CoV-2 drug resistance mutations in broad viral populations

The search for drugs against COVID-19 and other diseases caused by coronaviruses focuses on the most conserved and essential proteins, mainly the main (Mpro) and the papain-like (PLpro) proteases and the RNA-dependent RNA polymerase (RdRp). Nirmatrelvir, an inhibitor for Mpro, was recently approved by FDA as a part of a two-drug combination, Paxlovid, and many more drugs are in various stages of development. Multiple candidates for the PLpro inhibitors are being studied, but none have yet progressed to clinical trials. Several repurposed inhibitors of RdRp are already in use. We can expect that once anti-COVID-19 drugs become widely used, resistant variants of SARS-CoV-2 will emerge, and we already see that for the drugs targeting SARS-CoV-2 RdRp. We hypothesize that emergence of such variants can be anticipated by identifying possible escape mutations already present in the existing populations of viruses. Our group previously developed the coronavirus3D server (https://coronavirus3d.org), tracking the evolution of SARS-CoV-2 in the context of the three-dimensional structures of its proteins. Here we introduce dedicated pages tracking the emergence of potential drug resistant mutations to Mpro and PLpro, showing that such mutations are already circulating in the SARS-CoV-2 viral population. With regular updates, the drug resistance tracker provides an easy way to monitor and potentially predict the emergence of drug resistance-conferring mutations in the SARS-CoV-2 virus.

bioinformatics↗

bioRxiv2020

The interplay of SARS-CoV-2 evolution and constraints imposed by the structure and functionality of its proteins

Fast evolution of the SARS-CoV-2 virus provides us with unique information about the patterns of genetic changes in a single pathogen in the timescale of months. This data is used extensively to track the phylodynamic of the pandemics spread and its split into distinct clades. Here we show that the patterns of SARS-CoV-2 virus mutations along its genome are closely correlated with the structural features of the coded proteins. We show that the foldability of proteins 3D structures and conservation of their functions are the universal factors driving evolutionary selection in protein-coding genes. Insights from the analysis of mutation distribution in the context of the SARS-CoV-2 proteins structures and functions have practical implications including evaluating potential antigen epitopes or selection of primers for PCR-based COVID-19 tests.

evolutionary biology↗

bioRxiv2020

Large scale analysis of smoking-induced changes in the tumor immune microenvironment

Tobacco smoke is a known carcinogen, mostly due to its genotoxicity, but its effects on the host immune system are also playing an important role. Here, we leveraged recent results on the immune landscape of cancer based on The Cancer Genomic Atlas (TCGA) data analysis and compared the proportions of major classes of tumor-infiltrating immune cells (TIICs) between smokers and never smokers in ten TCGA cancer types. We show that statistically significant changes can be identified in all ten cancers, with increased plasma cell populations and the modified ratio of activated to resting TIICs being the most consistent features distinguishing smokers and never-smokers across different cancers, with both being correlated with survival outcomes. Analysis of existing single-cell RNA-seq data further showed that smoking differentially affects the gene expression profile of cancer patients based on the immune cell type. The smoking-induced changes in the patterns of immune cells and their correlations to survival outcomes are stronger in female smokers.

cancer biology↗

bioRxiv2019

Difference contact maps: from what to why in the analysis of the conformational flexibility of proteins

Protein structures, usually visualized in various highly idealized forms focusing on the three-dimensional arrangements of secondary structure elements, can also be described as lists of interacting residues or atoms and visualized as two-dimensional distance or contact maps. We show that contact maps provide an ideal tool to describe and analyze differences between structures of proteins in different conformations. Expanding functionality of the PDBFlex server and database developed previously in our group, we describe how analysis of difference contact maps (DCMs) can be used to identify critical interactions stabilizing alternative protein conformations, recognize residues and positions controlling protein functions and build hypotheses as to molecular mechanisms of disease mutations.\n\nAuthor SummaryProtein folding is driven, and the three-dimensional structures of proteins are defined, by the non-local interactions between amino acid chains. However, the usual visualizations of protein structures focus on the overall chain topology and do not provide much information about the interactions defining them. Here we explore contact map visualization of protein structures, which directly focuses on interactions stabilizing the structure and thus can be easily used to understand effects of mutations on protein structure. We show that contact map visualization is particularly useful for comparing alternative conformations of proteins and identifying critical positions controlling functional flexibility.

bioinformatics↗

bioRxiv2018

Cancer3D 2.0: interactive analysis of 3D patterns of cancer mutations in cancer subsets

Our knowledge of cancer genomics exploded in last several years, providing us with detailed knowledge of genetic alterations in almost all cancer types. Analysis of this data gave us new insights into molecular aspects of cancer, most important being the amazing diversity of molecular abnormalities in individual cancers. The most important question in cancer research today is how to classify this diversity to identify subtypes that are most relevant for treatment and outcome prediction for individual patients. The Cancer3D database at http://www.cancer3d.org gives an open and user-friendly way to analyze cancer missense mutations in the context of structures of proteins they are found in and in relation to patients clinical data. This approach allows users to find novel candidate driver regions for specific subgroups, that often cannot be found when similar analyses are done on the whole gene level and for large, diverse cohorts. Interactive interface allows user to visualize the distribution of mutations is subgroups defined by cancer type and stage, gender and age brackets, patients ethnicity, or vice versa find dominant cancer type, gender or age groups for specific three-dimensional mutation patterns.

bioinformatics↗