Elucidating the molecular determinants of dominant-negative mutations in protein complexes

With the explosion in human genome sequence data and the fact that many disease-associated mutations have never been observed previously, there is a pressing need for computational methods that can be used to prioritise genetic variants that are most likely to be pathogenic. An often-neglected factor that can influence the phenotypic impact of a mutation at a molecular level is the assembly of proteins into homomeric complexes [1]. In such complex, a heterozygous mutation can result in a "dominant-negative" effect, in which a single mutated protein disrupts the function of the entire complex or "poisons" the activity of the wild-type protein [2]. While the dominant-negative effect is well known by geneticists, and is believed to be responsible for many Mendelian genetic disorders and cancers in humans, it has never been systematically studied. This project will combine computational and experimental approaches in order to: 1) investigate the molecular mechanisms underlying dominant-negative mutations; and 2) develop models to predict them.

First, the student will perform systematic structural bioinformatics analyses of dominant-negative mutations, focusing in particular on cytoskeletal proteins, for which many examples are known and many 3D structures are available. They will investigate which types of proteins are most likely to be associated with a dominant-negative mechanism, and how this is related to their patterns of assembly and quaternary structure organisation. They will also study the locations of missense mutations within protein complexes, and use molecular modelling to predict the effects of mutations on individual protein subunits and complexes. This will allow the assessment of whether there is a general tendency for dominant-negative mutations to be less disruptive, and will facilitate the identification of structural features that can best discriminate between different types of mutations.

This offers the student the opportunity to learn and develop computational methodology in bioinformatics and protein modelling.

Next, the student will experimentally characterise specific pathogenic mutations identified from the computational analyses, focusing on candidates such as tubulin and microtubule motor proteins for which we have excellent reagents and expertise available3. To define how the cytoskeletal organization and dynamics, cell division and cargo transport are affected and result in the disease phenotype, the student will use cell biology, super-resolution microscopy tools, and quantitative analysis. The student will dissect the underlying molecular mechanism of the mutants, using in vitro reconstitution assays of individual microtubules and single microtubule motor complexes.

This offers the student the opportunity to learn biochemical and cell biology approaches and develop novel super-resolution microscopy methods.

Finally, the student will assess the ability of currently available phenotype predictors to identify dominant-negative mutations. They will then use machine-learning approaches to integrate the molecular properties of mutations with the mechanistic insights underlying the experimental and disease phenotypes, in order to develop general and gene-family specific models for predicting dominant-negative mutations.