Cortes-Cabrera A.,University of Alcalá |
Morris G.M.,InhibOx Ltd |
Morris G.M.,Crysalin |
Finn P.W.,InhibOx Ltd |
And 2 more authors.
British Journal of Pharmacology | Year: 2013
Background and Purpose Some existing computational methods are used to infer protein targets of small molecules and can therefore be used to find new targets for existing drugs, with the goals of re-directing the molecule towards a different therapeutic purpose or explaining off-target effects due to multiple targeting. Inherent limitations, however, arise from the fact that chemical analogy is calculated on the basis of common frameworks or scaffolds and also because target information is neglected. The method we present addresses these issues by taking into account 3D information from both the ligand and the target. Experimental Approach ElectroShape is an established method for ultra-fast comparison of the shapes and charge distributions of ligands that is validated here for prediction of on-target activities, off-target profiles and adverse effects of drugs and drug-like molecules taken from the DrugBank database. Key Results The method is shown to predict polypharmacology profiles and relate targets from two complementary viewpoints (ligand- and target-based networks). Conclusions and Implications The open-access web tool presented here (http://ub.cbm.uam.es/chemogenomics/) allows interactive navigation in a unified 'pharmacological space' from the viewpoints of both ligands and targets. It also enables prediction of pharmacological profiles, including likely side effects, for new compounds. We hope this web interface will help many pharmacologists to become aware of this new paradigm (up to now mostly used in the realm of the so-called 'chemical biology') and encourage its use with a view to revealing 'hidden' relationships between new and existing compounds and pharmacologically relevant targets. © 2013 The British Pharmacological Society.
Agency: European Commission | Branch: FP7 | Program: MC-ITN | Phase: FP7-PEOPLE-ITN-2008 | Award Amount: 2.08M | Year: 2009
Researchers who aspire to work in drug discovery need to adapt to constantly changing technology and be able to harness new tools both to ask and to answer pertinent scientific questions. Structural biology was going to rationalize drug design. Next, combinatorial chemistry was to be the industrial panacea, only to be superseded by high-throughput screening of chemical libraries. Technologies such as molecular cell biology, in silico modelling, genetic engineering and NMR, are now also part of an ever-evolving set of tools and approach that researchers need to keep pace. This changing technological landscape is also affecting employment. Technological advances are also making some skills less marketable, while creating demands for other skills that have not traditionally been associated with drug development. Scientists that can communicate across disciplines are increasingly in demand and those with multidisciplinary skills are at a premium. This aim of this initial training network is to improve the career perspectives of early stage researchers by providing multidisciplinary training in a network of industrial and academic partners involved in the discovery, development and commercialization of novel antibacterial and anti-infective drugs.
Bryant D.H.,Rice University |
Moll M.,Rice University |
Finn P.W.,InhibOx Ltd |
Kavraki L.E.,Rice University
PLoS Computational Biology | Year: 2013
The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (CCORPS) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, CCORPS is applied to the problem of identifying structural features of the kinase ATP binding site that are informative of inhibitor binding. CCORPS is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, CCORPS is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors. © 2013 Bryant et al.
Kher S.S.,Latvian Institute of Organic Synthesis |
Penzo M.,UK National Institute for Medical Research |
Fulle S.,InhibOx Ltd. |
Finn P.W.,InhibOx Ltd. |
And 2 more authors.
Bioorganic and Medicinal Chemistry Letters | Year: 2014
Peptidic α-ketoamides have been developed as inhibitors of the malarial protease PfSUB1. The design of inhibitors was based on the best known endogenous PfSUB1 substrate sequence, leading to compounds with low micromolar to submicromolar inhibitory activity. SAR studies were performed indicating the requirement of an aspartate mimicking the P1′ substituent and optimal P1-P4length of the non-prime part. The importance of each of the P1-P4amino acid side chains was investigated, revealing crucial interactions and size limitations. © 2014 Elsevier Ltd. All rights reserved.
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2013.2.3.1-1 | Award Amount: 5.39M | Year: 2013
The NABARSI consortium will develop a cutting-edge drug discovery project to increase the antibacterial pipeline. The main goal of NABARSI is to find new chemical entities (NCEs) with antibacterial efficacy in animal models of multi-drug resistant (MDR) bacterial infection and to exploit the results through obtaining a co-development with industry. The NABARSI consortium consists of 5 partners: Omnia Molecular (Omnia, SME; Spain). InhibOx (SME, UK). Latvian Institute of Organic Synthesis (LIOS, Latvia), Leeds University (Leeds, UK) and Erasmus Medical Centre (ErasmusMC, The Netherlands - Coordinator). Antibacterial activity will be achieved through inhibition of essential aminoacyl-tRNA synthetases (aaRS). Individual aaRS are highly conserved across bacteria, enabling the discovery of broad-spectrum antibacterials. To reduce the likelihood of resistance, NABARSI will look for NCEs with inhibitory activity against multiple aaRS enzymes. InhibOx and LIOS will design NCEs by rational and fragment based drug discovery methods followed by synthetic structure optimization. To increase chemical diversity, virtual screening of large (>100 M) compound libraries available at InhibOx will be performed. Limitations of previous aaRS inhibitors will be overcome by novel approaches such as the In Omnia assay: activity of the compounds on pathogenic aaRS enzyme is measured inside a human cell, allowing rejection of compounds acting through human aaRS and identifying compounds that cross biological membranes. The expertise of Leeds in mode of action studies will be used at an early stage. Activity of the NCEs on clinical isolates of MDR strains available at ErasmusMC will be assessed. Resistance appearance frequency and mechanisms will also be assessed early by selection and characterization of resistant mutants by ErasmusMC and Leeds. A co-development agreement with pharmaceutical companies will be intensively sought with the aim of exploiting the NCEs upon finalisation of NABARSI.
Fulle S.,InhibOx Ltd. |
Withers-Martinez C.,UK National Institute for Medical Research |
Blackman M.J.,UK National Institute for Medical Research |
Morris G.M.,InhibOx Ltd. |
Finn P.W.,InhibOx Ltd.
Journal of Chemical Information and Modeling | Year: 2013
PfSUB1, a subtilisin-like protease of the human malaria parasite Plasmodium falciparum, is known to play important roles during the life cycle of the parasite and has emerged as a promising antimalarial drug target. In order to provide a detailed understanding of the origin of binding determinants of PfSUB1 substrates, we performed molecular dynamics simulations in combination with MM-GBSA free energy calculations using a homology model of PfSUB1 in complex with different substrate peptides. Key interactions, as well as residues that potentially make a major contribution to the binding free energy, are identified at the prime and nonprime side of the scissile bond and comprise peptide residues P4 to P2′. This finding stresses the requirement for peptide substrates to interact with both prime and nonprime side residues of the PfSUB1 binding site. Analyzing the energetic contributions of individual amino acids within the peptide-PfSUB1 complexes indicated that van der Waals interactions and the nonpolar part of solvation energy dictate the binding strength of the peptides and that the most favorable interactions are formed by peptide residues P4 and P1. Hot spot residues identified in PfSUB1 are dispersed over the entire binding site, but clustered areas of hot spots also exist and suggest that either the S4-S2 or the S1-S2′ binding site should be exploited in efforts to design small molecule inhibitors. The results are discussed with respect to which binding determinants are specific to PfSUB1 and, therefore, might allow binding selectivity to be obtained. © 2013 American Chemical Society.
Finn P.W.,InhibOx Ltd |
Morris G.M.,InhibOx Ltd
Wiley Interdisciplinary Reviews: Computational Molecular Science | Year: 2013
Shape similarity is a key concept and requirement for molecular recognition. As a result, much research has been undertaken to develop methods to represent molecular shape and to quantify the shape similarity between molecules. A great variety of shape descriptions and similarity comparison approaches have been developed, ranging from explicit representations using intersecting atom-centered spheres and molecular superposition to abstract statistical representations that allow alignment-free comparisons. Several of these methods have sufficient computational performance to allow shape similarity searches over extremely large compound databases as a ligand-based virtual screening (VS) technique. As with other approaches to VS, the relative performance of shape similarity methods is dataset and problem specific, with each approach having its merits and limitations and no one approach showing a clear and consistent advantage over the others. Again, as with other VS approaches, most reports of performance are in the context of retrospective validation studies, which show competitive performance against target-based and two-dimensional methods. Prospective studies are rarer in the literature, but a number of successes have been reported. Intensive research continues in the search for improved representations of shape, partial shape matching, and approaches to address the challenges imposed by ligand and target flexibility. © 2012 John Wiley & Sons, Ltd.
Cooper R.I.,University of Oxford |
Cooper R.I.,Inhibox Ltd. |
Thompson A.L.,University of Oxford |
Watkin D.J.,University of Oxford
Journal of Applied Crystallography | Year: 2010
Because they scatter X-rays weakly, H atoms are often abused or neglected during structure refinement. The reasons why the H atoms should be included in the refinement and some of the consequences of mistreatment are discussed along with selected real examples demonstrating some of the features for hydrogen treatment that can be found in the software suite CRYSTALS. © 2010 International Union of Crystallography Printed in Singapore-all rights reserved.
Ebejer J.-P.,University of Oxford |
Ebejer J.-P.,InhibOx Ltd |
Morris G.M.,InhibOx Ltd |
Deane C.M.,University of Oxford
Journal of Chemical Information and Modeling | Year: 2012
Conformer generation has important implications in cheminformatics, particularly in computational drug discovery where the quality of conformer generation software may affect the outcome of a virtual screening exercise. We examine the performance of four freely available small molecule conformer generation tools (Balloon, Confab, Frog2, and RDKit) alongside a commercial tool (MOE). The aim of this study is 3-fold: (i) to identify which tools most accurately reproduce experimentally determined structures; (ii) to examine the diversity of the generated conformational set; and (iii) to benchmark the computational time expended. These aspects were tested using a set of 708 drug-like molecules assembled from the OMEGA validation set and the Astex Diverse Set. These molecules have varying physicochemical properties and at least one known X-ray crystal structure. We found that RDKit and Confab are statistically better than other methods at generating low rmsd conformers to the known structure. RDKit is particularly suited for less flexible molecules while Confab, with its systematic approach, is able to generate conformers which are geometrically closer to the experimentally determined structure for molecules with a large number of rotatable bonds (≥10). In our tests RDKit also resulted as the second fastest method after Frog2. In order to enhance the performance of RDKit, we developed a postprocessing algorithm to build a diverse and representative set of conformers which also contains a close conformer to the known structure. Our analysis indicates that, with postprocessing, RDKit is a valid free alternative to commercial, proprietary software. © 2012 American Chemical Society.
Ross G.A.,University of Oxford |
Morris G.M.,InhibOx Ltd. |
Biggin P.C.,University of Oxford
Journal of Chemical Theory and Computation | Year: 2013
A major goal in computational chemistry has been to discover the set of rules that can accurately predict the binding affinity of any protein-drug complex, using only a single snapshot of its three-dimensional structure. Despite the continual development of structure-based models, predictive accuracy remains low, and the fundamental factors that inhibit the inference of all-encompassing rules have yet to be fully explored. Using statistical learning theory and information theory, here we prove that even the very best generalized structure-based model is inherently limited in its accuracy, and protein-specific models are always likely to be better. Our results refute the prevailing assumption that large data sets and advanced machine learning techniques will yield accurate, universally applicable models. We anticipate that the results will aid the development of more robust virtual screening strategies and scoring function error estimations. © 2013 American Chemical Society.