Computational analysis and prediction of mechanisms of intragenic compensation of human pathogenic and bacterial resistance-associated mutations

• Applicant: Professorin Dr. Olga Kalinina
• Subject Area: Bioinformatics and Theoretical Biology
• Term: from 2020 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 430158625

Single mutations that cause a change in the amino acid sequence of a protein are fundamental events in protein evolution. These events can be regarded as traversing a protein’s fitness landscape in its sequence space. Most such mutations entail adverse consequences for protein’s structure, function, stability, or affinity to interaction partners. Compensation for such a deleterious event is possible either by a reverse mutation -- another substitution at the same position back to the wildtype, -- or by a mutation at a different protein site. Such compensatory (trailing) or permissive (preceding) mutations enable protein evolution: otherwise all sequences would be extremely conserved, since most mutations are deleterious. In this project, we will explore the mechanisms of intragenic compensation on an unprecedented scale: we will use the largest available collection of experimentally confirmed compensatory pairs, supplement it with predicted compensatory mutations provided by a phylogenetic algorithm that will be specifically developed in the framework of this project, and put them into the context of protein structural, phylogenetic and biophysical properties. We will develop a novel machine-learning tool that will predict compensating effects in pairs of mutations, and as a consequence improve prediction of functional effect of individual mutations given the sequence context, in which they are embedded. This work builds upon our previous vast experience in predicting functional effect of genetic variants from structural context of the corresponding amino acid substitutions, and is unique in the field in that it considers all potential effect of mutation on the protein structure and interactions with other molecules. Our proposed predictor will simultaneously produce a hypothesis for molecular mechanism of a particular compensation event. We will apply the developed method to study potential compensating mechanisms in more than 1,500 exomes from human patients with varying ethnic origin and wide phenotypic spectrum including cardiovascular diseases, monogenic disorders and autism spectrum disorder. Discovering novel compensatory pairs will shed light on potential sources of incomplete penetrance of disease mutations. In order to elucidate these mechanisms, we will study the exomes in our collection for putative compensations on a case-by-case basis. Additionally, we will apply our tools to a collection of bacterial pathogenic strains with experimentally determined resistances against common and novel antibiotics. Analysis of these unique data may lead to discovering new resistance mechanisms. Thus we will gain a deeper understanding of mechanisms behind the genetic compensating changes, and potentially predict novel markers in biomedical domains where genetic diagnostics can be applied.

• DFG Programme: Research Grants
• International Connection: Russia
• Partner Organisation: Russian Foundation for Basic Research, until 3/2022
• Cooperation Partner: Vasely Ramensky, Ph.D., until 3/2022