Predicting and analyzing variation in cellular interactomes

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Project Summary Over the last two decades, significant experimental efforts have determined large sets of “reference” interactions for humans and other model organisms, along with substantial knowledge about the binding specificities of proteins, including for a large fraction of human transcription factors (TFs). The resulting data have proven to be an incredibly useful resource for understanding how cells function; nevertheless, they do not capture how molecular interactions and networks are different from the reference across individuals. Indeed, while human genomes in both healthy and disease populations are rapidly being sequenced, the corresponding individual-specific interaction networks remain largely unexamined; this represents a major gap in our knowledge, as mutations that alter molecular interactions underlie a wide range of human diseases. Further, the substantial amount of genetic variation across populations makes it infeasible in the near term to experimentally determine per-individual interaction networks. Thus our long-term goal is to develop computational methods to uncover whether and how mutations within coding and non-coding portions of the genome perturb cellular interactions and networks. Our specific aims are: (1) We will develop computational structure-based approaches to identify and catalog, at proteome-scale, variations within proteins that are likely to impact their ability to bind with DNA, RNA, small molecules, peptides or ions, thereby providing a comprehensive resource for analyzing protein interaction variation. (2) We will develop novel structure-based and probabilistic methods to predict how DNA-binding specificities are altered when a TF is mutated; since mutated TFs have been linked to numerous diseases, this will be a great aid in understanding disease networks and pathology. (3) We will develop new methods to uncover non-coding somatic mutations that alter human regulatory networks in cancer; this is a critical step towards ultimately uncovering patient-specific cancer networks. Overall by pursuing these aims—which integrate mutational information with existing knowledge about reference interactions, interfaces and specificities—we will develop novel computational methods that will significantly advance our understanding of molecular interactions perturbed in disease and healthy contexts.