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University of Cambridge

Computer-Aided Design of Ligands at Multiple Protein Targets for Multifactorial Diseases

Abstract

dc:description.abstract

Today, drug discovery predominately focuses on the design of ligands with high selectivity towards a specific biological target. A significant limitation in the case of multi-factorial diseases (e.g. neurodegenerative disorders) is that effective therapy may require multi-target drugs addressing the complexity of multi-factorial pathologies. Here, single- and multi-target ligand design was investigated to discover novel compounds active at multiple proteins/multiple binding sites including allosteric ligands. Calpain-1, a challenging target, was selected to develop and evaluate computational approaches to the discovery of novel ligands. Current selective calpain-1 inhibitors are reported to bind to an allosteric site and their mode of action has remained elusive. To elucidate this, a structure-based virtual screening protocol was implemented to find chemically novel compounds with improved selectivity and a reduced side-effects profile. To develop methods for the discovery of multi-target ligands, a multi-target design approach, which could be beneficial in the treatment of Lung Carcinoma and Neurodegenerative diseases, was investigated. A novel ensemble of proteins was targeted to elevate intracellular cAMP, deemed to be beneficial in these diseases resulting in the discovery of ligands with high binding affinity at three targets, PDE10A, A1 and A2A receptors. In tandem, functional activity at the A2A receptor and PDE10A was investigated, resulting in the discovery of novel compounds, which exhibited anti-proliferative effects in lung carcinoma cell-lines correlating with the co-expression of the two targets and increased cAMP levels. Critically, the dynamics of one amino acid residue, Val84, was identified as a novel conformational descriptor of A2A receptor activation. Overall, novel single- and multi-target ligand design approaches are presented in this work, which could be applicable to a wide range of ligand design problems, across (multi-factorial) disease areas and target families. The findings may facilitate improved design of allosteric calpain-1 inhibitors using the PEF(S) domain, and encourage investigating the therapeutic benefits of dual ligands at the A2A receptor and PDE10A against lung cancer in vivo.

Degree

thesis:*
Name dc:type.qualificationname
Doctor of Philosophy (PhD)
Level dc:type.qualificationlevel
Doctoral
Grantor dc:publisher.institution
University of Cambridge
Year dc:date.issued
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Kalash, Leen
Advisor dc:contributor.advisor
  • Bender, Andreas

Subjects

dc:subject × 8

Rights

dc:rights
Language dc:language
en

Identifiers

dc:identifier.*
DOI dc:identifier.doi
https://doi.org/10.17863/CAM.38293
OAI identifier oai:identifier
oai:www.repository.cam.ac.uk:1810/291112

Chain of custody

source
Harvested from
Cambridge University
Base URL
api.repository.cam.ac.uk/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Kalash, Leen. Computer-Aided Design of Ligands at Multiple Protein Targets for Multifactorial Diseases. Doctoral thesis, University of Cambridge, 2019. https://doi.org/10.17863/CAM.38293