Understanding the molecular consequences of mutations
|Dr David Ascherfirstname.lastname@example.org|
We are continuously striving to expand and further develop our mutational analysis platform.
New computational models and learning algorithms
We are evaluating and validating new computational models for representing a mutation, as well as new machine learning algorithms, in particular bioinspired (genetic programming and neural networks/deep learning) to build robust, efficient and effective predictive models of the various consequences of protein mutations.
Sequence and evolutionary information
In addition the the structural information used in our predictive platform, we are also including sequence and evolutionary information in order to better analyse the effects of mutations that alter post-translational modification and localisation, and those mutations within disordered regions which, while often lack detailed structural information, play very important roles in protein interactions. Ultimately we would also like to be able to predict the effects of mutations based upon protein sequence.
Proteins are dynamic molecules, and we have shown previously that how mutations alter the equilibrium between different conformations is important for understanding their role in disease and drug resistance. To address this we are using a combination of structural ensembles and coarse grained molecular dynamics to present a picture of the mutational effects across its conformational states.
To date, ours and others methods have focussed on understanding the effects of single point missense mutations. However using a simulated thermodynamic cycle, we are developing methods able to predict the effects of more complex mutations including insertions, deletions, alternative splicing events, multiple point mutations and heterozygous mutations.
Our hypothesis and work is based on the combined theory of codon optimality and cotranslational folding, that the choice of codon directly affects the translation efficiency of the ribosome. Consequently, different codons give the nascent polypeptide chain varying amounts of time to explore the fold-space and as such the choice of codon directly affects the final structure of protein.
Dr David Ascher, Group Leader
Dr Douglas Pires
Carlos Rodrigues, PhD Student
Malancha Karmakar, PhD Student
Michael Silk, PhD Student
Stephanie Portelli, PhD Student
YooChan Myung, PhD Student
Liviu Copoiu, PhD Student
Amanda Albanaz, Masters Student
Willy Cornelissen, Masters Student
Vasishth Sidarala, Masters Student
Newton Fund/MRC: "Understanding Antimicrobial Resistance Mutations in Tuberculosis: Towards Personalised Treatment to Combat Multi‐drug Resistance."
Jack Brockhoff Foundation Grant: “Understanding the Molecular Mechanisms of Complex Mutations”.
This research project is available to PhD students to join as part of their thesis.
Please contact the Research Group Leader to discuss your options.
- Pires DEV, Ascher DB. CSM-lig: a web server for assessing and comparing protein-small molecule affinities. Nucleic Acids Research 2016; 44: W557-561.
- Pires DEV, Ascher DB. mCSM-AB: a web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures. Nucleic Acids Research 2016; 44: W469-473.
- Pires DEV, Blundell TL, Ascher DB. mCSM-lig: quantifying the effects of mutations on protein-ligand affinity in genetic disease and the emergence of drug resistance. Scientific Reports 2016; 6: 29575.
- Pires DEV, Blundell TL, Ascher DB. Platinum: a database of experimentally measured effects of mutations on structurally defined protein–ligand complexes. Nucleic Acids Research 2015; 43: D387-391.
- Pires DEV, Ascher DB, Blundell TL. mCSM: predicting the effect of mutations in proteins using graph-based signatures. Bioinformatics 2014; 30(3): 335-342.
Faculty Research Themes
School Research Themes
For further information about this research, please contact the research group leader.