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 pandemic’s 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.

FATCAT 2.0 server (http://fatcat.godziklab.org/), provides access to a flexible protein structure alignment algorithm developed in our group. In such an alignment, rotations and translations between elements in the structure are allowed to minimize the overall root mean square deviation (RMSD) between the compared structures. This allows to effectively compare protein structures even if they underwent structural rearrangements in different functional forms, different crystallization conditions or as a result of mutations. The major update for the server introduces a new graphical interface, much faster database searches and several new options for visualization of the structural differences between proteins.

There is a wide, and continuously widening, gap between the number of proteins known only by their amino acid sequence versus those structurally characterized by direct experiment. To close this gap, we mostly rely on homology-based inference and modeling to reason about the structures of the uncharacterized proteins by using structures of homologous proteins as templates. With the rapidly growing size of the Protein Data Bank, there are often multiple choices of templates, including multiple sets of coordinates from the same protein. The substantial conformational differences observed between different experimental structures of the same protein often reflect function related structural flexibility. Thus, depending on the questions being asked, using distant homologs, or coordinate sets with lower resolution but solved in the appropriate functional form, as templates may be more informative. The ModFlex server (https://modflex.org/) addresses this seldom mentioned gap in the standard homology modeling approach by providing the user with an interface with multiple options and tools to select the most relevant template and explore the range of structural diversity in the available templates. ModFlex is closely integrated with a range of other programs and servers developed in our group for the analysis and visualization of protein structural flexibility and divergence.

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.

The unprecedented pace of the sequencing of the SARS-CoV-2 virus genomes provides us with unique information about the genetic changes in a single pathogen during ongoing pandemic. By the analysis of close to 200,000 genomes we show that the patterns of the SARS-CoV-2 virus mutations along its genome are closely correlated with the structural and functional features of the encoded proteins. Requirements of foldability of proteins' 3D structures and the conservation of their key functional regions, such as protein-protein interaction interfaces, are the dominant factors driving evolutionary selection in protein-coding genes. At the same time, avoidance of the host immunity leads to the abundance of mutations in other regions, resulting in high variability of the missense mutation rate along the genome. "Unexplained" peaks and valleys in the mutation rate provide hints on function for yet uncharacterized genomic regions and specific protein structural and functional features they code for. Some of these observations have immediate practical implications for the selection of target regions for PCR-based COVID-19 tests and for evaluating the risk of mutations in epitopes targeted by specific antibodies and vaccine design strategies.

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 in subgroups defined by cancer type and stage, gender and age brackets, patient's ethnicity or vice versa find dominant cancer type, gender or age groups for specific three-dimensional mutation patterns.