Predicting Pathological Effect of Mutation and Identifying Cancer Driver Event Based on Protein Structure

Identifying cancer driver mutations is critical for understanding molecular mechanisms triggering tumorigenesis, and for designing targeted treatments in precision oncology. The majority of existing in-silico methods focus on predicting cancer driver genes, but are limited as they cannot identify specific sites where mutations drive tumorigenesis from a noisy mutational background landscape. Among the 2.94 millions missense mutations from COSMIC cancer samples, over 94% have low recurrence (<3) and over 50% are predicted to exhibit pathogenic effects by various bioinformatics methods. As no further information is provided, it is challenging to determine which mutations are driving tumorigenesis using current methods that are frequency-based or mutation effect-based approaches. In this study, we develop a new method called Structure-CAncer-Relationship-on-Pathogenicity (SCARP) to identify cancer driver mutations by systematically integrating mutation effects, co-clustering effects of spatial regions near the mutation sites, as well as mutation recurrence. First, we use our novel Structure-Pathogenicity Relationship Identifier (SPRI) method to estimate the likelihood of pathogenicity of a specified mutation, as SPRI captures essential biological properties from structural, biophysical, and evolutionary features, and exhibits favorable performance to identify deleterious mutations on the ground truth of Mendelian disease-type mutations compared with multiple state-of-the-art methods. Furthermore, it demonstrates great transferability in distinguishing cancer driver mutations from passenger mutations. Second, we quantify the influence of co-clustering mutations in the structural neighborhood regions of the mutation site, as biological functions often require specific structural arrangements of residues. Third, we utilize mutation recurrence collected from pan-cancer or tissue-specific cancer cohorts. We show our method can effectively identify cancer driver genes and provides detailed rankings of pathogenicity of the mutation sites. Our results show that accurate recognition of co-clustering mutational effects is important for predicting site-specific cancer driver events

Folding-Degradation Relationship of a Membrane Protein Mediated by the Universally Conserved ATP-Dependent Protease FtsH

ATP-dependent protein degradation mediated by AAA+ proteases is one of the major cellular pathways for protein quality control and regulation of functional networks. While a majority of studies of protein degradation have focused on water-soluble proteins, it is not well understood how membrane proteins with abnormal conformation are selectively degraded. The knowledge gap stems from the lack of an in vitro system in which detailed molecular mechanisms can be studied as well as difficulties in studying membrane protein folding in lipid bilayers. To quantitatively define the folding-degradation relationship of membrane proteins, we reconstituted the degradation using the conserved membrane-integrated AAA+ protease FtsH as a model degradation machine and the stable helical-bundle membrane protein GlpG as a model substrate in the lipid bilayer environment. We demonstrate that FtsH possesses a substantial ability to actively unfold GlpG, and the degradation significantly depends on the stability and hydrophobicity near the degradation marker. We find that FtsH hydrolyzes 380–550 ATP molecules to degrade one copy of GlpG. Remarkably, FtsH overcomes the dual-energetic burden of substrate unfolding and membrane dislocation with the ATP cost comparable to that for water-soluble substrates by robust ClpAP/XP proteases. The physical principles elucidated in this study provide general insights into membrane protein degradation mediated by ATP-dependent proteolytic systems