Olga Kalinina combines genomic-, modeling-, and biophysics-based methods to assess the mutations observed in given sequences of viral proteins and infer their potential influence on the function of the protein. This enables suggestions for drug treatment of viral diseases.
Protein structure and interactions
Proteins as biological machines
Proteins are biological molecules that facilitate life’s processes: they are molecular engines that synthesize and decompose molecular building blocks such as nucleic and amino acids; convert energy; and facilitate regulation and communication inside cells. They can do so because they are made up of a chain of amino acids that folds into a complicated 3-dimensional structure, thus translating information from 1D to 3D. The specific way in which each individual protein folds depends on the physical and chemical properties of the amino acids that comprise the protein. Structural biology has accumulated extensive data on protein 3D structures that can be analyzed using computational methods.
We extend this paradigm to molecular complexes. Proteins do not act by themselves, but rather are part of the conveyor belt that performs a single action towards a common larger goal. That is why, on many occasions, proteins assemble into complexes to perform their task. These complexes can comprise hundred of proteins. Prediction and analysis of properties of protein complexes is one of the major tasks in structural bioinformatics.
Structural bioinformatics of viral systems
Viruses invade cells of higher organisms and use them to replicate and multiply. They are small systems, typically containing 10-20 proteins. Using this limited toolkit, they engage in a complex network of interactions with the host cell and recruit host cell proteins to facilitate the virus life cycle. Simultaneously, they adapt very quickly to the persistent pressure exerted by the host immune system and possible drug treatment. Impressive amounts of clinical and laboratory data on the variation of protein sequence in viruses are available. This makes viral systems unique targets for bioinformatics studies.
Protein sequences from virus strains isolated from various patients provide valuable information for patient treatment, as demonstrated by the geno2pheno approach . Including structural information in this analysis will allow us to understand how the variations characteristic of a sequence under consideration influence the function of the protein and its interactions with other molecules in its environment. For example, one mutation in the protein’s active site can ruin the protein’s catalytic ability, whereas a different mutation nearby will merely diminish its affinity to an inhibitor. If a mutation is located in an interaction interface with a host protein, this may destroy the binding, and the mutated virus will not survive.
A pipeline that combines sequence and structural analysis to predict effect of sequence alterations
We analyze the sequences of viral proteins that have specific properties, e.g. are resistant to certain drugs. We extract the sequence features that are responsible for this phenotype and analyze them in a structural context. We have analyzed multiple sequences of the viral protein that forms the capsid of the human immunodeficiency virus (HIV) and predicted several pairs of mutations that are likely to interact functionally. One of those point mutations restores the viral infectivity that would otherwise be hampered by the other mutation of the pair. Using the structural analysis, we demonstrated that this pair of amino acids could be responsible for keeping individual protein subunits in the ordered capsid structure.
We combine genomic-, modeling- and biophysics-based methods to assess the alterations observed in a given sequence and predict their potential influence on protein function. We also analyze the binding affinity to inhibitors and can suggest possible drug treatment.
 Altmann A, Däumer M, Beerenwinkel N, Peres Y, Schülter E, Büch J, Rhee SY, Sönnerborg A, Fessel WJ, Shafer RW, Zazzi M, Kaiser R, Lengauer T. (2009) Predicting the response to combination antiretroviral therapy: retrospective validation of geno2pheno-THEO on a large clinical database. J Infect Dis. 199: 999-1006.
About the author:
Olga Kalinina, since 2011Senior Scientist at the Dept. 3, Computational Biology and Applied Algorithmics, with Professor Lengauer. After her Master's Degree in Mathematics, she finished 2006 her Ph.D. in molecular biology at the Institute for Molecular Biology RAS in Moscow. From 2007 on she stayed for two PostDoc employments in Heidelberg, Germany, at EMBL-Heidelberg and Heidelberg University, respectively.
Contact: kalinina (at) mpi-inf.mpg.de
|Created by:||Bertram Somieski/MPI-INF, 05/14/2012 09:09 AM||Last modified by:||Uwe Brahm/MPII/DE, 05/13/2013 05:30 PM|