Abstract 199: Blossom AI: A novel drug discovery app for the prediction of hotspots on multiplex protein protein interaction complexes using random forest algorithms

Abstract

Protein protein interactions (PPIs) form the backbone of signal transduction pathways in diverse physiological processes, mediating the transmission and regulation of oncogenic signals essential to cellular proliferation and survival, thus representing a potential new class of drug targets for anticancer therapeutic discovery. However, several challenges face the targeting of PPIs, including large PPI interface areas, a lack of deep pockets, the presence of noncontiguous binding sites, and a general lack of natural ligands. The presence of hot spots (small subsets of amino acid residues that contribute significantly to free binding energy) makes PPIs amenable to small molecule perturbations, playing essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein protein complexes form the hot spots is critical for understanding the principles of protein interactions and has broad application prospects in protein design and drug development. This project presents Blossom AI, a novel, user friendly mobile app developed in XCode and CoreML that uses random forest decision tree algorithms (RF) to computationally predict the presence of hotspots on protein complexes within seconds, aiding the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anticancer therapy. Leveraging features such as solvent accessible surface area (ASA), blocks substitution matrix, physicochemical properties (hydrophobicity, polarity, polarizability, propensities), position specific scoring matrix (PSSM) and solvent exposure, the RF is trained through a dataset of 313 mutated interface residues (133 hotspot residues and 180 non hotspot residues) from over 60 protein complexes to produce a training accuracy of 88.75%, validation accuracy of 92.86%, specificity of 87.18%, sensitivity of 75.38%, PPV 94.23%, NPV 86.61%. Blossom is high speed, low cost, and user friendly with significantly improved accuracy over the standard of alanine scanning mutagenesis. Citation Format: Stephanie Zhang, Minsoo Kang. Blossom AI: A novel drug discovery app for the prediction of hotspots on multiplex protein protein interaction complexes using random forest algorithms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 199.