Personalising treatments for genetic diseases

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 Douglas Pires (Fiocruz-Minas)

Carlos Henrique Miranda Rodrigues, Masters Student (UFMG / CPqRR, Brazil), CNPq, co-supervised with Douglas Pires

Collaborators

Professor Eamonn Maher, University of Cambridge

Dr Andrea Zatkova, Slovak Academy of Sciences

Professor Lakshminarayan Ranganath, University of Liverpool

Professor Sergio Penna, UFMG, Brazil

Dr Lisa Kaminskas, University of Queensland

Dr Ottavia Spiga, Università degli Studi Siena

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. 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.
  2. Jubb HC, Pandurangan A, Turner MA, Ochoa-Montaño B, Blundell TL, Ascher DB. Mutations at protein-protein interfaces: Small changes over big surfaces have large impacts on human health. Progress in Biophysics and Molecular Biology 2016; In Press.
  3. Nemethova M, Radvanszky J, Kadasi L, Ascher DB, Pires DEV, Blundell TL, Porfirio B, Manoni A, Santucci A, Milucci L, Sestini S, Biolcati G, Sorge F, Aurizi C, Aquaron R, Alsbou M, Lourenço CM, Ramadevi K, Ranganath LR, Gallagher JA, Kan V, Hall AK, Junestrand C, Sireau N, Ayoob H, Timmis OG, Quan Sang K, Genovese F, Imrich R, Rovensky J, Zatkova A.  Twelve novel HGD gene variants identified in 99 alkaptonuria patients: focus on 'black bone disease' in Italy. European Journal of Human Genetics 2016; 24(1): 66-72.
  4. Jafri M, Wake NC, Ascher DB, Pires DEV, Gentle D, Morris MR, Rattenberry E, Simpson MA, Trembath RC, Weber A, Woodward ER, Donaldson A, Blundell TL, Latif F, Maher ER.  Germline Mutations in the CDKN2B tumor suppressor gene predispose to renal cell carcinoma. Cancer Discovery 2015); 5(7): 723-729.
  5. Usher JL, Ascher DB, Pires DEV, Milan AM, Blundell TL, Ranganath LR. Analysis of HGD gene mutations in patients with Alkaptonuria from the United Kingdom: Identification of novel mutations. Journal of Inherited Metabolic Disease 2015; 24: 3-11.

Research Group

Ascher laboratory: Structural biology and bioinformatics



Faculty Research Themes

Cancer

School Research Themes

Cancer, Molecular Mechanisms of Disease, Therapeutics & Translation



Key Contact

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

Department / Centre

Biochemistry and Molecular Biology