Translation Kinetics and their Effects on Protein Structure and Function, mRNA half-lives, and Cellular Phenotype

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Project Summary Translation kinetics critically influences protein structure and function, the rate of mRNA degradation, and cellular phenotype. When synonymous codon mutations are incorporated into an mRNA molecule (which changes the rate at which codon positions are translated by the ribosome but not the amino acids they encode) the specific activity of enzymes changes for long time periods, mRNA degradation rates are altered when these mutations lead to ribosome traffic jams, and the ability of cells to migrate and maintain a circadian rhythm can be affected. These effects occur across species, from bacteria to fruit flies, from fungi to humans. What is missing in this field is a comprehensive, molecular understanding of how translation kinetics influences these processes. Utilizing both computational (theory, simulation, and big data) and experimental (Mass Spec, Cryo- EM, and NMR) methods, this proposal focuses on four fundamental questions: (i) Can the existence of a novel form of protein misfolding, which is suggested to link synonymous mutations and altered protein function, be experimentally demonstrated? (ii) Can gene expression be altered when such protein misfolding occurs in transcription factors? (iii) Is it possible to understand and predict how elongation kinetics give rise to different patterns of ribosome traffic, and how these patterns influence translation-dependent mRNA degradation? (iv) What molecular mechanisms connect synonymous mutations in humans to changes in growth phenotype? Preliminary data suggest clear hypotheses to these questions. For questions (i) and (ii), synonymous mutations are hypothesized to alter the kinetic partitioning of nascent protein molecules into subpopulations of misfolded, soluble, self-entangled states that have reduced functionality, which can affect catalysis in the case of enzymes or DNA promotor binding in the case of transcription factors. For question (iii), application of interpretable machine learning has suggested molecular factors that can alter both ribosome traffic and mRNA degradation – which will form the basis for follow up in silico and in vivo testing. And for question (iv), rigorous statistical methods the PI has used have identified synonymous cancer drivers which provide a unique opportunity to understand and connect synonymous mutations to cellular phenotype. These hypotheses will be tested using computational tools including multi-scale simulation techniques, bioinformatics, and machine learning. And experimentally tested using mass spectrometry, Cryo-EM, NMR, and enzymatic chemotaxis. This research will establish a unifying mechanism by which synonymous mutations can alter soluble protein structure and function over long time periods. It will provide a novel molecular basis by which synonymous mutations can affect gene expression at the transcriptional level. It will result in a predictive model connecting non-linear effects between translation kinetics, ribosome traffic, and translation-dependent mRNA degradation. And finally, it will establish the existence of synonymous cancer drivers affecting human cell growth phenotype and the molecular mechanisms by which this occurs.