Designing better drugs
Project LeaderDr David Ascher
Location: Bio21 Institute, 30 Flemington Rd
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.
Dr David Ascher, Group Leader
Dr Douglas Pires (Fiocruz-Minas)
Carlos Henrique Miranda Rodrigues, Masters Student (UFMG / CPqRR, Brazil), CPNq, co-supervised with Douglas Pires
Professor Sir Tom Blundell, University of Cambridge
Dr Lisa Kaminskas, University of Queensland
Professor Véronique Dartois, Public Health Research Institute Center
Newton Fund/MRC: "Understanding Antimicrobial Resistance Mutations in Tuberculosis: Towards Personalised Treatment to Combat Multi‐drug Resistance."
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.
- Jubb HC, Higuerueloa AP, Ochoa-Montañoa 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.
- Park Y, Pacitto A, Bayliss T, Cleghorn LAT, Wang Z, Hartman T, Arora K, Ioerger TR, Sacchettini J, Rizzi M, Donini S, Blundell TL, Ascher DB, Rhee K, Breda A, Zhou N, Dartois V, Jonnala SR, Via LE, Mizrahi V, Epemolu O, Stojanovski L, Simeons F, Osuna-Cabello M, Ellis L, MacKenzie CJ, Smith ARC, Davis SH, Murugesan D, Buchanan KI, Turner PA, Huggett M, Zuccotto F, Rebollo-Lopez MJ, Lafuente-Monasterio MJ, Sanz O, Diaz GS, Lelièvre J, Ballell L, Selenski C, Axtman M, Ghidelli-Disse S, Pflaumer H, Bösche M, Drewes G, Freiberg GM, Kurnick MD, Srikumaran M, Kempf DJ, Green SR, Ray PC, Read K, Wyatt P, Barry III CE , Boshoff HI. Essential but not vulnerable: indazole sulfonamides targeting inosine monophosphate dehydrogenase as potential leads against Mycobacterium tuberculosis. ACS Infectious Diseases 2016; In Press.
- Pires DEV, Ascher DB. CSM-lig: a web server for assessing and comparing protein-small molecule affinities. Nucleic Acids Research 2016; 44: W557-561.
- Singh V, Donini S, Pacitto A, Sala C, Hartkoorn RC, Dhar N, Keri G, Ascher DB, Mondésert G, Vocat A, Lupien A, Sommer R, Vermet H, Lagrange S, Buechler J, Warner DF, McKinney JD, Pato J, Cole ST, Blundell TL, Rizzi M, Mizrahi V. The inosine monophosphate dehydrogenase, GuaB2, is a vulnerable new bactericidal drug target for tuberculosis. ACS Infectious Diseases 2016; In Press.
- Pires DEV, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic properties using graph-based signatures. Journal of Medicinal Chemistry 2015; 58(9): 4066-4072.
- Sigurdardottir AG, Winter A, Sobkowicz A, Fragai M, Chirgadze D, Ascher DB, Blundell TL, Gherardi E. Exploring the chemical space of the lysine-binding pocket of the first kringle domain of hepatocyte growth factor/scatter factor (HGF/SF) yields a new class of inhibitors of HGF/SF-MET binding. Chemical Science 2015; 6(11): 6147-6157.
Faculty Research Themes
School Research Themes
For further information about this research, please contact the research group leader.