Protein engineering

Project Details

On-going technological advancements have led to dramatic increases in the amounts of biological data being generated. Along with the evolution of high performance computing and computational tools, this has provided us with a wealth of information, analytical power and the opportunity to investigate fundamental health and biotechnological problems of a different magnitude and kind, complementary to and able to guide conventional approaches. Our group is interested in developing and experimentally validating novel computational methods to exploit this data, enhancing the impact of genome sequencing, structural genomics, and functional genomics on biology and medicine.

One of our main areas of interest is in the development of predictive and analytical tools and databases to investigate and understand the relationship between protein sequence, structure and function and phenotype, allowing us to gain unique insights into:

  • The molecular basis of genetic diseases, including cancer;
  • Understanding the molecular mechanisms behind drug resistance, to guide personalized patient treatment and the development of resistance resistant drugs;
  • Evolutionary insights derived from the analysis of protein structure and function;
  • Small molecule activity and toxicity as an aid to the design of novel drugs.

Keywords: Machine learning, databases, mutations, genetic disease, drug resistance, cancer, molecular mechanism, homology modelling, protein structure and function, small molecules, drug development.

We have developed and host a wide range of widely used and freely-available tools, including:

  • Arpeggio: Calculation and visualisation of all molecular interactions.
  • mCSM-Stability: Predicting effects of mutations on protein stability.
  • DUET: An integrated method for predicting effects of mutations on protein stability.
  • mCSM-PPI (http://bleoberis.bioc.cam.ac.uk/m: Predicting effects of mutations on the affinity of protein-protein interactions.
  • mCSM-AB: Predicting effects of mutations on antibody-antigen binding affinity.
  • mCSM-NA: Predicting effects of mutations on the affinity of protein-nucleic acid interactions.
  • mCSM-lig: Predicting effects of protein mutations on affinity for small molecules.
  • CSM-lig: Predicting the protein binding affinity of small molecules.
  • KAMP: Identification of protein kinase activating mutations.
  • pkCSM: Predicting small molecule pharmacokinetic and toxicity properties.
  • Platinum DB: Structural database of experimentally measured effects of missense mutations on protein-ligand complexes.
  • TROMBONE DB: Optimisation of Botulinum and Tetnus neurotoxins for medicinal purposes.
  • Symphony DB: Classification of VHL missense mutations according to risk of clear cell Renal carcinoma.

Researchers

Dr David Ascher, Group Leader

Dr Douglas Pires (Fiocruz-Minas)

Liviu Copoiu, PhD Student (Cambridge, UK), co-supervised by Professor Sir Tom Blundell

Collaborators

Professor Sir Tom Blundell, University of Cambridge

Dr Lisa Kaminskas, University of Queensland

Professor Chris Smith, University of Cambridge

Funding

Jack Brockhoff Foundation Grant: “Understanding the Molecular Mechanisms of Complex Mutations”.

Research Opportunities

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.

Research Publications

  1. Jubb HC, Higuerueloa AP, Ochoa-Monta├▒oa B, Pittb, WR, Ascher DB, Blundell TL.  Arpeggio: a web server for calculating and visualising interatomic interactions in protein structures. Journal of Molecular Biology 2016; In Press.
  2. 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.
  3. 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.
  4. Pires DEV, Chen J, Blundell TL, Ascher DB.  In silico functional dissection of saturation mutagenesis: Interpreting the relationship between phenotypes and changes in protein stability, interactions and activity. Scientific Reports 2016; 6: 19848.
  5. Chan LJ, Ascher DB, Yadav R, Bulitta JB, Landersdorfer CB, Porter CJ, Williams CC, Kaminskas LM. Conjugation of 10 kDa linear PEG onto trastuzumab Fab is sufficient to significantly enhance lymphatic exposure while preserving in vitro biological activity. Molecular Pharmaceutics 2016; 13(4): 1229-1241.

Research Group

Ascher laboratory: Structural biology and bioinformatics



Faculty Research Themes

Infection and Immunology, Cancer

School Research Themes

Cancer in Biomedicine, Infection & Immunity, Therapeutics & Translation, Cellular Imaging & Structural Biology



Key Contact

For further information about this research, please contact the research group leader.

Department / Centre

Biochemistry and Molecular Biology