1
|
Mersmann S, Strömich L, Song FJ, Wu N, Vianello F, Barahona M, Yaliraki S. ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules. Nucleic Acids Res 2021; 49:W551-W558. [PMID: 33978752 PMCID: PMC8661402 DOI: 10.1093/nar/gkab350] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/16/2021] [Accepted: 04/22/2021] [Indexed: 11/28/2022] Open
Abstract
The investigation of allosteric effects in biomolecular structures is of great current interest in diverse areas, from fundamental biological enquiry to drug discovery. Here we present ProteinLens, a user-friendly and interactive web application for the investigation of allosteric signalling based on atomistic graph-theoretical methods. Starting from the PDB file of a biomolecule (or a biomolecular complex) ProteinLens obtains an atomistic, energy-weighted graph description of the structure of the biomolecule, and subsequently provides a systematic analysis of allosteric signalling and communication across the structure using two computationally efficient methods: Markov Transients and bond-to-bond propensities. ProteinLens scores and ranks every bond and residue according to the speed and magnitude of the propagation of fluctuations emanating from any site of choice (e.g. the active site). The results are presented through statistical quantile scores visualised with interactive plots and adjustable 3D structure viewers, which can also be downloaded. ProteinLens thus allows the investigation of signalling in biomolecular structures of interest to aid the detection of allosteric sites and pathways. ProteinLens is implemented in Python/SQL and freely available to use at: www.proteinlens.io.
Collapse
Affiliation(s)
- Sophia F Mersmann
- Department of Mathematics, Imperial College London, Huxley Building, 180 Queen’s Gate, London SW7 2AZ, UK
| | - Léonie Strömich
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Florian J Song
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Nan Wu
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Francesca Vianello
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, Huxley Building, 180 Queen’s Gate, London SW7 2AZ, UK
| | - Sophia N Yaliraki
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| |
Collapse
|
2
|
Fowler PW. How quickly can we predict trimethoprim resistance using alchemical free energy methods? Interface Focus 2020; 10:20190141. [PMID: 33178416 PMCID: PMC7653339 DOI: 10.1098/rsfs.2019.0141] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2020] [Indexed: 12/15/2022] Open
Abstract
The emergence of antimicrobial resistance threatens modern medicine and necessitates more personalized treatment of bacterial infections. Sequencing the whole genome of the pathogen(s) in a clinical sample offers one way to improve clinical microbiology diagnostic services, and has already been adopted for tuberculosis in some countries. A key weakness of a genetics clinical microbiology is it cannot return a result for rare or novel genetic variants and therefore predictive methods are required. Non-synonymous mutations in the S. aureus dfrB gene can be successfully classified as either conferring resistance (or not) by calculating their effect on the binding free energy of the antibiotic, trimethoprim. The underlying approach, alchemical free energy methods, requires large numbers of molecular dynamics simulations to be run. We show that a large number (N = 15) of binding free energies calculated from a series of very short (50 ps) molecular dynamics simulations are able to satisfactorily classify all seven mutations in our clinically derived testset. A result for a single mutation could therefore be returned in less than an hour, thereby demonstrating that this or similar methods are now sufficiently fast and reproducible for clinical use.
Collapse
Affiliation(s)
- Philip W. Fowler
- Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
- National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| |
Collapse
|
3
|
Subramanian G, Vairagoundar R, Bowen SJ, Roush N, Zachary T, Javens C, Williams T, Janssen A, Gonzales A. Synthetic inhibitor leads of human tropomyosin receptor kinase A ( hTrkA). RSC Med Chem 2020; 11:370-377. [PMID: 33479642 DOI: 10.1039/c9md00554d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 12/22/2019] [Indexed: 11/21/2022] Open
Abstract
In silico virtual screening followed by in vitro biochemical, biophysical, and cellular screening resulted in the identification of distinctly different hTrkA kinase domain inhibitor scaffolds. X-ray structural analysis of representative inhibitors bound to hTrkA kinase domain defined the binding mode and rationalized the mechanism of action. Preliminary assessment of the sub-type selectivity against the closest hTrkB isoform, and early ADME guided the progression of select inhibitor leads in the screening cascade. The possibility of the actives sustaining to known hTrkA resistance mutations assessed in silico offers initial guidance into the required multiparametric lead optimization to arrive at a clinical candidate.
Collapse
Affiliation(s)
- Govindan Subramanian
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Rajendran Vairagoundar
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Scott J Bowen
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Nicole Roush
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Theresa Zachary
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Christopher Javens
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Tracey Williams
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Ann Janssen
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| | - Andrea Gonzales
- Veterinary Medicine Research & Development , Zoetis , 333 Portage Street , Kalamazoo , MI 49007 , USA .
| |
Collapse
|