Software - Structural Bioinformatics of Protein Interactions

  • StructMAn (Structural Mutation Annotation server): a web server for structural annotations of non-synonymous single-nucleotide variations (nsSNVs) that alter protein sequence relative to other proteins, nucleic acids and low molecular-weight ligands. It makes use of all experimentally available three-dimensional structures of query proteins, and also, unlike other tools in the field, of structures of proteins with detectable sequence identity to them, which allows provide a structural context for around 20% of all nsSNVs in a typical human sequencing sample, for up to 60% of nsSNVs in genes related to human diseases, and for around 35% of nsSNVs in a typical bacterial sample. Each nsSNV can be visualized and inspected by the user in the corresponding three-dimensional structure of a protein or protein complex. A manuscript describing this tool is currently under review in Nucleic Acids Res.
  • viral-ppi-pred: a Python package for identification of compact clusters of extremely conserved and extremely variable amino acid residues on protein surface, based on the tight clustering algorithm. In viral proteins, these clusters have been demonstrated to correlate well with protein interactions sites with other protein, nucleic acids or ligands, as well as with antigenic sites. A manuscript describing this method is currently under review in Bioinformatics.
  • svm-agp: a Python package for identification of potential events of horizontal gene transfer into a family of related species (e.g. a viral family, or a set of bacterial strains). It is based on the k-mer (word) statistics of the family, and identifies gene that have such statistics significantly different from the rest of the family using a one-class SVM. For details, please see our publication.

Software developed by Olga before MPII (and still used in our research):

  • SDPpred: a tool for prediction of amino acid residues that determine differences in functional specificity of homologous proteins. Given a multiple sequence alignment of a family divided into specificity groups, SDPpred predicts a set of alignment positions (SDP, Specificity-Determining Positions) that determine differences in the functional specificity.
  • SDPsite: prediction of protein active and specific recognition sites from sequence and structure. Identification of protein active and other functional sites, based on spatial clustering of SDPs with conserved positions.
  • SDPfox: the fast tool for the prediction of functional specificity groups and amino acid residues that determine the specificity. A novel phylogeny-independent method for prediction of specificity-determining positions (SDPs) and grouping sequences into functional sub-groups.
  • ProtChemSI: the database of Protein-Chemical Structural Interactions includes all existing 3D structures of complexes of proteins with low molecular weight ligands. When one consideres the proteins and chemical vertices of a graph, all these interactions form a network. Biological networks are powerful tools for predicting undocumented relationships between molecules. The underlying principle is that existing interactions between molecules can be used to predict new interactions. For pairs of proteins sharing a common ligand, we use protein and chemical superimpositions combined with fast structural compatibility screens to predict whether additional compounds bound by one protein would bind the other.