Mutations to cytoskeletal proteins are associated with a wide range of human genetic disorders and cancers. However, the molecular mechanisms underlying pathogenesis can be profoundly different for cytoskeletal proteins compared to other disease-associated proteins. In particular, they are often associated with dominant inheritance related to the misassembly of mutant proteins into cytoskeletal filaments or associated molecular motors. For example, dominant-negative mutations that interfere with assembly have been seen in α-tubulin (causing amyotrophic lateral sclerosis or lissencephaly), kinesin (causing mental retardation) and dynein (causing spinal muscular atrophy). Moreover, our own analyses have revealed that current computational approaches are markedly worse at predicting dominant disease-causing mutations in cytoskeletal proteins.
This project will seek to integrate computational and experimental strategies in order to: 1) investigate the molecular mechanisms underlying dominant pathogenic mutations of cytoskeletal proteins; and 2) develop models to predict them.
First, the student will perform a systematic computational analysis of mutations in cytoskeletal proteins, taking advantage of the structural bioinformatics and protein modelling expertise of the Marsh lab. The student will also prioritise pathogenic mutations that are not correctly predicted by current computational approaches for further experimental characterisation.
This offers the student the opportunity to learn and develop computational methodology in bioinformatics and protein modelling
Next, the student will test these pathogenic mutations experimentally, focusing on candidates such as tubulin and microtubule motor proteins for which there are excellent reagents and expertise developed by the Welburn group. 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
This powerful approach will allow the student to dissect the molecular mechanism of the mutants in vitro on microtubule dynamics and motor complex properties. Ultimately, the goal is to relate the molecular properties of mutants across scales, from computational analyses to mechanisms, phenotypes and then disease. This can only be done using an integrated approach between informatics, cell biology and super-resolution microscopy.
Finally, building upon our previous experience in modelling the effects of dominant mutations and protein assembly (McEntagart et al, 2016), and in understanding the molecular mechanisms of cytoskeletal motors (Talapatra et al, 2015, Legal et al, 2016), the student will develop a new model for predicting pathogenic mutations. They will use machine-learning methods to integrate the molecular, evolutionary properties of mutations with the mechanistic insights underlying the experimental and disease phenotypes, in order to develop gene-family specific models for predicting pathogenesis.
This offers the student the opportunity to learn machine-learning methods and to gain experience in integrating both experimental and computational approaches for identifying damaging genetic variants.
Legal et al (2016) Molecular architecture of the Dam1 complex-microtubule interaction. Open Biol 6:150237
McEntagart M et al (2016) A restricted repertoire of de novo mutations in ITPR1 cause Gillespie syndrome with evidence for dominant-negative effect. Am J Hum Genet 98:981-992
Talapatra SK, Harker B & Welburn J (2015) The C-terminal region of the motor protein MCAK controls its structure and activity through a conformational switch. Elife 4:e06421