Computationally designing peptides to interfere with p53-MDM2 and p53-sirtuin interaction

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Project Summary With tunable binding affinity, solubility, and specificity characteristics, peptide inhibitors can interrupt biological processes from cell signaling to viral infection vectors. Unfortunately, it is an unsolved challenge to design a peptide to possess specifically sought interaction characteristics. Leveraging current capabilities in structural bioinformatics, we aim to develop a general design platform for peptides that will bind appreciably only to a specific binding site on one target protein. To this end, we rank order candidate peptides by employing a combination of data-mining, molecular docking, and molecular dynamics simulation in a serial-style pipeline. The verification stage is to experimentally measure the binding characteristics of the top candidates. Successive experiments will be performed as the ranked ordered list is traversed. As the results are compiled, supervised machine learning will be iteratively applied to re-rank the candidate list to identify peptides with binding characteristics that are sought. In this project, the p53 protein and its MDM2 and SIRT1 binding partners serve as a model system where a strategic set of systematic experiments will be performed. Importantly, because p53 is a critical hub protein in humans that modulates cellular function, transcription, and proliferation, there is considerable published data that will be used to establish controls regarding this tumor suppressor, as well as its biomedically important partners MDM2 and SIRT1. The format of the experimental design affords testing of a multivariate binding objective involving more than one binding partner. Compared to rational design strategies for small molecules, the potential for the development of a peptide-based lead compound is considerably higher. An outcome of this project will be two separate public-domain software tools. The first, called pepStream, will generate a candidate list of peptides ordered by propensity to bind to a target site using open repositories of sequence and structural data. The second, is a supervised machine learning tool that is integrated with the results from experimental measurements for successively re-ranking the candidate list to enhance success rates.