Dr. Olga Kalinina

Dr. Olga Kalinina

Max-Planck-Institut für Informatik
Structural Bioinformatics of Protein Interactions

HIPS Campus E8 1
66123 Saarbrücken, Germany
email:   kalinina@mpi-inf.mpg.de
phone:   0681 98806 3600
fax:   0681 98806 3009
room:   127 (Building E8.1)


  1. Knops E, Sierra S, Kalaghatgi P, Heger E, Kaiser R, Kalinina OV (2018) Epistatic Interactions in NS5A of Hepatitis C Virus Suggest Drug Resistance Mechanisms. Genes, accepted.

  2. Bastys T, Gapsys V, Doncheva NT, Kaiser R, de Groot BL, Kalinina (2018) Consistent prediction of mutation effect on drug binding in HIV-1 protease using alchemical calculations. Journal Chem Theory Comput, accepted.

  3. Gress A, Ramensky V, Kalinina OV (2017) Spatial distribution of disease-associated variants in three-dimensional structures of protein complexes. Oncogenesis, 6:e380.

  4. Ahmad M, Helms V, Kalinina OV, Lengauer T (2017) Elucidating the Energetic Contributions to the Binding Free Energy. J Chem Phys, 146(1):014105.

  5. Voitenko OS, Dhroso A, Feldmann A, Korkin D, Kalinina OV (2016) Patterns of amino acid conservation in human and animal immunodeficiency viruses. In Proceedings of ECCB 2016 Bioinformatics, 32(17): i685-i692.

  6. Gress A, Ramensky V, Büch J, Keller A, Kalinina OV (2016) StructMAn: annotation of single- nucleotide polymorphisms in the structural context. Nucleic Acids Res, 44(W1): W463-W468.

  7. Ahmad M, Helms V, Kalinina OV, Lengauer T (2016) The role of conformational changes in molecular recognition. J Phys Chem B, 120(9): 2138-2144.

  8. Mueller SC, Backes C, Gress A, Baumgarten N, Kalinina OV, Moll A, Kohlbacher O, Meese E, Keller A. (2016) BALL-SNPgp – from genetic variants towards computational diagnostics. Bioinformatics, 32(12): 1888-1890.

  9. Zeke A, Bastys T, Alexa A, Garai Á, Mészáros B, Kirsch K, Dosztányi Z, Kalinina OV, Reményi A (2015) Systematic discovery of linear binding motifs targeting an ancient protein interaction surface on MAP kinases. Mol Syst Biol, 11: 837

  10. Caprari S, Metzler S, Lengauer T, Kalinina OV (2015) Sequence and structure analysis of distantly related viruses reveals extensive gene transfer between viruses and hosts and among viruses. Viruses, 7(10): 5388-5409

  11. Dietzen M, Kalinina OV, Taskova K, Kneissl B, Hildebrandt A-K, Jaenicke E, Decker H, Lengauer T, Hildebrandt A (2015) Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces. PROTEINS: Structure, Function, and Bioinformatics, 83: 1887-1899

  12. Mueller SC, Backes C, Kalinina OV, Meder B, Stöckel D, Lenhof H-P, Meese E, Keller A (2015) BALL-SNP: Combining genetic and structural information to identify candidate non-synonymous single nucleotide polymorphisms. Genome Medicine, 7:65

  13. Ahmad M, Helms V, Lengauer T, Kalinina OV (2015) How molecular conformational changes affect changes in free energy. Journal Chem Theory Comput, 11: 2945–2957

  14. Ahmad M, Helms V, Lengauer T, Kalinina OV (2015) Enthalpy-entropy compensation upon molecular conformational changes. Journal Chem Theory Comput, 11: 1410-1418

  15. Metzler S, Kalinina OV (2014) Detection of atypical genes in virus families using a one-class SVM. BMC Genomics 15: 913

  16. Kalinina OV, Lengauer T (2014) Protein Structure Prediction and Databases. In: eLS. John Wiley & Sons Ltd, Chichester

  17. Ahmad M, Kalinina OV, Lengauer T (2014) Entropy gain due to water release upon ligand binding. Journal of Cheminformatics 6(Suppl 1):P35

  18. Kalinina OV, Pfeifer N, Lengauer T (2013) Modelling binding between CCR5 and CXCR4 receptors and their ligands suggests the surface electrostatic potential of the co-receptor to be a key player in the HIV-1 tropism. Retrovirology 10: 130

  19. Kalinina OV, Oberwinkler H, Glass B, Kraeusslich H-G, Russell RB, Briggs JAG (2012) Computational identification of novel amino-acid interactions in HIV Gag via correlated evolution. PLoS One 7(8): e42468

  20. Kalinina OV, Wichmann O, Apic G, Russell RB (2012) ProtChemSI: a network of protein-chemical structural interactions. Nucleic Acids Res. 40 (D1): D549-D553

  21. Kalinina OV, Wichmann O, Apic G, Russell RB (2011) Combinations of proteinchemical complex structures reveal new targets for established drugs. PLoS Comp Biol. 7(5): e1002043

  22. Mazin PV, Gelfand MS, Mironov AA, Rakhmaninova AB, Rubinov AR, Russell RB, Kalinina OV. (2010) An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies. Algorithms Mol Biol. 5: 29

  23. Yus E, Maier T, Michalodimitrakis K, van Noort V, Yamada T, Chen WH, Wodke JA, Güell M, Martíez S, Bourgeois R, Kühner S, Raineri E, Letunic I, Kalinina OV, Rode M, Herrmann R, Gutiérez-Gallego R, Russell RB, Gavin AC, Bork P, Serrano L. (2009) Impact of genome reduction on bacterial metabolism and its regulation. Science 26: 1263-1268

  24. Kalinina OV, Gelfand MS, Russell RB. (2009) Combining specificity determining and conserved residues improves functional site prediction. BMC Bioinformatics 10: 174

  25. Kalinina OV, Russell RB, Rakhmaninova AB, Gelfand MS. (2007) Computational method for prediction of protein functional sites using specificity determinants. Mol Biol. (Moscow) 41(1): 137-147

  26. Devos D, Kalinina OV, Russell RB. (2006) Harry Potter and the structural biologist's (Key)stone. Genome Biol. 7(12): 333

  27. Permina EA, Kazakov AE, Kalinina OV, Gelfand MS. (2006) Comparative genomics of regulation of heavy metal resistance in Eubacteria. BMC Microbiol. 6: 49

  28. Kalinina OV, Gelfand MS. (2006) Amino acid residues that determine functional specificity of NADP- and NAD-dependent isocitrate and isopropylmalate dehydrogenases. Proteins 64(4): 1001-1009

  29. Oparina NJ, Kalinina OV, Gelfand MS, Kisselev LL. (2005) Common and specific amino acid residues in the prokaryotic polypeptide release factors RF1 and RF2: possible functional implications. Nucleic Acids Res. 33(16): 5226-5234

  30. Rakhmaninova AB, Kalinina OV, Minin AA. (2004) Discriminative sites in the conserved core of various annexin subfamilies of vertebrates. Annexins 1(2): 137-142

  31. Kalinina OV, Novichkov PS, Mironov AA, Gelfand MS, Rakhmaninova AB. (2004) SDPpred: a tool for prediction of amino acid residues that determine differences in functional specificity of homologous proteins. Nucleic Acids Res. 32 (Web Server issue): W424-428

  32. Kalinina OV, Mironov AA, Gelfand MS, Rakhmaninova AB. (2004) Automated selection of positions determining functional specificity of proteins by comparative analysis of orthologous groups in protein families. Protein Sci. 13(2): 443-456

  33. Kalinina OV, Gelfand MS, Mironov AA, Rakhmaninova AB. (2003) Amino acid residues forming specific contacts between subunits in tetramers of the membrane channel GlpF. Biophysics (Moscow) Vol. 48, Suppl. 1: 141-145

  34. Kalinina OV, Makeev VJ, Sutormin RA, Gelfand MS, Rakhmaninova AB. (2003) The channel in transporters is formed by residues that are rare in transmembrane helices. In Silico Biol. 3(1-2): 197-204


  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.