1
|
Ghiandoni GM, Flanagan SR, Bodkin MJ, Nizi MG, Galera-Prat A, Brai A, Chen B, Wallace JEA, Hristozov D, Webster J, Manfroni G, Lehtiö L, Tabarrini O, Gillet VJ. Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors. Mol Inform 2024; 43:e202300183. [PMID: 38258328 DOI: 10.1002/minf.202300183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 01/24/2024]
Abstract
De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge. Reaction-based de novo design takes a conceptually simpler approach and aims to address synthesisability directly by mimicking synthetic chemistry and driving structural transformations by known reactions that are applied in a stepwise manner. However, the use of a small number of hand-coded transformations restricts the chemical space that can be accessed and there are few examples in the literature where molecules and their synthetic routes have been designed and executed successfully. Here we describe the application of reaction-based de novo design to the design of synthetically accessible and biologically active compounds as proof-of-concept of our reaction vector-based software. Reaction vectors are derived automatically from known reactions and allow access to a wide region of synthetically accessible chemical space. The design was aimed at producing molecules that are active against PARP1 and which have improved brain penetration properties compared to existing PARP1 inhibitors. We synthesised a selection of the designed molecules according to the provided synthetic routes and tested them experimentally. The results demonstrate that reaction vectors can be applied to the design of novel molecules of biological relevance that are also synthetically accessible.
Collapse
Affiliation(s)
- Gian Marco Ghiandoni
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Stuart R Flanagan
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - Michael J Bodkin
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - Maria Giulia Nizi
- Department of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy
| | - Albert Galera-Prat
- Faculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Oulu, FI-90014, Finland
| | - Annalaura Brai
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100, Siena, Italy
| | - Beining Chen
- Department of Chemistry, University of Sheffield, Dainton Building, Brook Hill, Sheffield, S3 7HF, UK
| | - James E A Wallace
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - Dimitar Hristozov
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - James Webster
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Giuseppe Manfroni
- Department of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy
| | - Lari Lehtiö
- Faculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Oulu, FI-90014, Finland
| | - Oriana Tabarrini
- Department of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy
| | - Valerie J Gillet
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| |
Collapse
|
2
|
Wilson C, Gardner JMF, Gray DW, Baragana B, Wyatt PG, Cookson A, Thompson S, Mendoza-Martinez C, Bodkin MJ, Gilbert IH, Tarver GJ. Design of the Global Health chemical diversity library v2 for screening against infectious diseases. PLoS Negl Trop Dis 2023; 17:e0011799. [PMID: 38150490 PMCID: PMC10752525 DOI: 10.1371/journal.pntd.0011799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/16/2023] [Indexed: 12/29/2023] Open
Abstract
There is a need for novel chemical matter for phenotypic and target-based screens to find starting points for drug discovery programmes in neglected infectious diseases and non-hormonal contraceptives that disproportionately affect Low- and Middle-Income Countries (LMICs). In some disease areas multiple screens of corporate and other libraries have been carried out, giving rise to some valuable starting points and leading to preclinical candidates. Whilst in other disease areas, little screening has been carried out. Much screening against pathogens has been conducted phenotypically as there are few robustly validated protein targets. However, many of the active compound series identified share the same molecular targets. To address the need for new chemical material, in this article we describe the design of a new library, designed for screening in drug discovery programmes for neglected infectious diseases. The compounds have been selected from the Enamine REAL (REadily AccessibLe) library, a virtual library which contains approximately 4.5 billion molecules. The molecules theoretically can be synthesized quickly using commercially available intermediates and building blocks. The vast majority of these have not been prepared before, so this is a source of novel compounds. In this paper we describe the design of a diverse library of 30,000 compounds from this collection (graphical abstract). The new library will be made available to laboratories working in neglected infectious diseases, subject to a review process. The project has been supported by the Bill & Melinda Gates Foundation and the Wellcome Trust (Wellcome).
Collapse
Affiliation(s)
- Caroline Wilson
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| | - J. Mark F. Gardner
- AMG Consultants Ltd, Discovery Park House, Ramsgate Road, Sandwich, Kent, United Kingdom
| | - David W. Gray
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| | - Beatriz Baragana
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| | - Paul G. Wyatt
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| | - Alex Cookson
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| | - Stephen Thompson
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| | - Cesar Mendoza-Martinez
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| | - Michael J. Bodkin
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| | - Ian H. Gilbert
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| | - Gary J. Tarver
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| |
Collapse
|
3
|
Ghiandoni GM, Bodkin MJ, Chen B, Hristozov D, Wallace JEA, Webster J, Gillet VJ. RENATE: A Pseudo-retrosynthetic Tool for Synthetically Accessible de Novo Design. Mol Inform 2021; 41:e2100207. [PMID: 34750989 PMCID: PMC9285524 DOI: 10.1002/minf.202100207] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 10/23/2021] [Indexed: 11/09/2022]
Abstract
Reaction‐based de novo design refers to the generation of synthetically accessible molecules using transformation rules extracted from known reactions in the literature. In this context, we have previously described the extraction of reaction vectors from a reactions database and their coupling with a structure generation algorithm for the generation of novel molecules from a starting material. An issue when designing molecules from a starting material is the combinatorial explosion of possible product molecules that can be generated, especially for multistep syntheses. Here, we present the development of RENATE, a reaction‐based de novo design tool, which is based on a pseudo‐retrosynthetic fragmentation of a reference ligand and an inside‐out approach to de novo design. The reference ligand is fragmented; each fragment is used to search for similar fragments as building blocks; the building blocks are combined into products using reaction vectors; and a synthetic route is suggested for each product molecule. The RENATE methodology is presented followed by a retrospective validation to recreate a set of approved drugs. Results show that RENATE can generate very similar or even identical structures to the corresponding input drugs, hence validating the fragmentation, search, and design heuristics implemented in the tool.
Collapse
Affiliation(s)
- Gian Marco Ghiandoni
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | | | - Beining Chen
- Chemistry Department, University of Sheffield, Dainton Building, Brook Hill, Sheffield, S3 7HF, UK
| | | | | | - James Webster
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Valerie J Gillet
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| |
Collapse
|
4
|
Kellici TF, Pilka ES, Bodkin MJ. Therapeutic Potential of Targeting Plasminogen Activator Inhibitor-1 in COVID-19. Trends Pharmacol Sci 2021; 42:431-433. [PMID: 33867130 PMCID: PMC7997307 DOI: 10.1016/j.tips.2021.03.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 01/19/2021] [Accepted: 03/22/2021] [Indexed: 02/07/2023]
Abstract
Latest research shows that SERPINE1 overexpression has an important role in Coronavirus 2019 (COVID-19)-associated coagulopathy leading to acute respiratory distress syndrome (ARDS). However, ways to target this protein remain elusive. In this forum, we discuss recent evidence linking SERPINE1 with COVID-19-related ARDS and summarize the available data on inhibitors of this target.
Collapse
|
5
|
Kellici TF, Pilka ES, Bodkin MJ. Small-molecule modulators of serine protease inhibitor proteins (serpins). Drug Discov Today 2020; 26:442-454. [PMID: 33259801 DOI: 10.1016/j.drudis.2020.11.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 10/11/2020] [Accepted: 11/10/2020] [Indexed: 02/06/2023]
Abstract
Serine protease inhibitors (serpins) are a large family of proteins that regulate and control crucial physiological processes, such as inflammation, coagulation, thrombosis and thrombolysis, and immune responses. The extraordinary impact that these proteins have on numerous crucial pathways makes them an attractive target for drug discovery. In this review, we discuss recent advances in research on small-molecule modulators of serpins, examine their mode of action, analyse the structural data from crystallised protein-ligand complexes, and highlight the potential obstacles and possible therapeutic perspectives. The application of in silico methods for rational drug discovery is also summarised. In addition, we stress the need for continued research in this field.
Collapse
|
6
|
Heifetz A, Morao I, Babu MM, James T, Southey MWY, Fedorov DG, Aldeghi M, Bodkin MJ, Townsend-Nicholson A. Characterizing Interhelical Interactions of G-Protein Coupled Receptors with the Fragment Molecular Orbital Method. J Chem Theory Comput 2020; 16:2814-2824. [PMID: 32096994 PMCID: PMC7161079 DOI: 10.1021/acs.jctc.9b01136] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
G-protein coupled receptors (GPCRs) are the largest superfamily of membrane proteins, regulating almost every aspect of cellular activity and serving as key targets for drug discovery. We have identified an accurate and reliable computational method to characterize the strength and chemical nature of the interhelical interactions between the residues of transmembrane (TM) domains during different receptor activation states, something that cannot be characterized solely by visual inspection of structural information. Using the fragment molecular orbital (FMO) quantum mechanics method to analyze 35 crystal structures representing different branches of the class A GPCR family, we have identified 69 topologically equivalent TM residues that form a consensus network of 51 inter-TM interactions, providing novel results that are consistent with and help to rationalize experimental data. This discovery establishes a comprehensive picture of how defined molecular forces govern specific interhelical interactions which, in turn, support the structural stability, ligand binding, and activation of GPCRs.
Collapse
Affiliation(s)
- Alexander Heifetz
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
- Institute
of Structural & Molecular Biology, Research Department of Structural
& Molecular Biology, Division of Biosciences, University College London, London, WC1E 6BT, United Kingdom
- E-mail: (A.H.)
| | - Inaki Morao
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
- E-mail: (I.M.)
| | - M. Madan Babu
- MRC
Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Tim James
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
| | | | - Dmitri G. Fedorov
- CD-FMat,
National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
| | - Matteo Aldeghi
- Department
of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany
| | - Michael J. Bodkin
- Evotec
(U.K.) Ltd., 114 Milton Park, Abingdon, Oxfordshire OX14 4SA, United Kingdom
| | - Andrea Townsend-Nicholson
- Institute
of Structural & Molecular Biology, Research Department of Structural
& Molecular Biology, Division of Biosciences, University College London, London, WC1E 6BT, United Kingdom
| |
Collapse
|
7
|
Ghiandoni GM, Bodkin MJ, Chen B, Hristozov D, Wallace JEA, Webster J, Gillet VJ. Enhancing reaction-based de novo design using a multi-label reaction class recommender. J Comput Aided Mol Des 2020; 34:783-803. [PMID: 32112286 PMCID: PMC7293200 DOI: 10.1007/s10822-020-00300-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 02/13/2020] [Indexed: 12/31/2022]
Abstract
Reaction-based de novo design refers to the in-silico generation of novel chemical structures by combining reagents using structural transformations derived from known reactions. The driver for using reaction-based transformations is to increase the likelihood of the designed molecules being synthetically accessible. We have previously described a reaction-based de novo design method based on reaction vectors which are transformation rules that are encoded automatically from reaction databases. A limitation of reaction vectors is that they account for structural changes that occur at the core of a reaction only, and they do not consider the presence of competing functionalities that can compromise the reaction outcome. Here, we present the development of a Reaction Class Recommender to enhance the reaction vector framework. The recommender is intended to be used as a filter on the reaction vectors that are applied during de novo design to reduce the combinatorial explosion of in-silico molecules produced while limiting the generated structures to those which are most likely to be synthesisable. The recommender has been validated using an external data set extracted from the recent medicinal chemistry literature and in two simulated de novo design experiments. Results suggest that the use of the recommender drastically reduces the number of solutions explored by the algorithm while preserving the chance of finding relevant solutions and increasing the global synthetic accessibility of the designed molecules.
Collapse
Affiliation(s)
- Gian Marco Ghiandoni
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Michael J Bodkin
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - Beining Chen
- Chemistry Department, University of Sheffield, Dainton Building, Brook Hill, Sheffield, S3 7HF, UK
| | - Dimitar Hristozov
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - James E A Wallace
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - James Webster
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Valerie J Gillet
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK.
| |
Collapse
|
8
|
Ghiandoni GM, Bodkin MJ, Chen B, Hristozov D, Wallace JEA, Webster J, Gillet VJ. Development and Application of a Data-Driven Reaction Classification Model: Comparison of an Electronic Lab Notebook and Medicinal Chemistry Literature. J Chem Inf Model 2019; 59:4167-4187. [PMID: 31529948 DOI: 10.1021/acs.jcim.9b00537] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Reaction classification has often been considered an important task for many different applications, and has traditionally been accomplished using hand-coded rule-based approaches. However, the availability of large collections of reactions enables data-driven approaches to be developed. We present the development and validation of a 336-class machine learning-based classification model integrated within a Conformal Prediction (CP) framework to associate reaction class predictions with confidence estimations. We also propose a data-driven approach for "dynamic" reaction fingerprinting to maximize the effectiveness of reaction encoding, as well as developing a novel reaction classification system that organizes labels into four hierarchical levels (SHREC: Sheffield Hierarchical REaction Classification). We show that the performance of the CP augmented model can be improved by defining confidence thresholds to detect predictions that are less likely to be false. For example, the external validation of the model reports 95% of predictions as correct by filtering out less than 15% of the uncertain classifications. The application of the model is demonstrated by classifying two reaction data sets: one extracted from an industrial ELN and the other from the medicinal chemistry literature. We show how confidence estimations and class compositions across different levels of information can be used to gain immediate insights on the nature of reaction collections and hidden relationships between reaction classes.
Collapse
Affiliation(s)
- Gian Marco Ghiandoni
- Information School , University of Sheffield , Regent Court, 211 Portobello , Sheffield S1 4DP , United Kingdom
| | - Michael J Bodkin
- Evotec (U.K.) Ltd. , 114 Innovation Drive , Milton Park, Abingdon OX14 4RZ , United Kingdom
| | - Beining Chen
- Chemistry Department , University of Sheffield , Dainton Building , Brook Hill, Sheffield S3 7HF , United Kingdom
| | - Dimitar Hristozov
- Evotec (U.K.) Ltd. , 114 Innovation Drive , Milton Park, Abingdon OX14 4RZ , United Kingdom
| | - James E A Wallace
- Evotec (U.K.) Ltd. , 114 Innovation Drive , Milton Park, Abingdon OX14 4RZ , United Kingdom
| | - James Webster
- Information School , University of Sheffield , Regent Court, 211 Portobello , Sheffield S1 4DP , United Kingdom
| | - Valerie J Gillet
- Information School , University of Sheffield , Regent Court, 211 Portobello , Sheffield S1 4DP , United Kingdom
| |
Collapse
|
9
|
Aldeghi M, Ross GA, Bodkin MJ, Essex JW, Knapp S, Biggin PC. Large-scale analysis of water stability in bromodomain binding pockets with grand canonical Monte Carlo. Commun Chem 2018; 1:19. [PMID: 29863194 PMCID: PMC5978690 DOI: 10.1038/s42004-018-0019-x] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 03/01/2018] [Indexed: 12/17/2022] Open
Abstract
Conserved water molecules are of interest in drug design, as displacement of such waters can lead to higher affinity ligands and in some cases, contribute towards selectivity. Bromodomains, small protein domains involved in the epigenetic regulation of gene transcription, display a network of four conserved water molecules in their binding pockets and have recently been the focus of intense medicinal chemistry efforts. Understanding why certain bromodomains have displaceable water molecules and others do not is extremely challenging, and it remains unclear which water molecules in a given bromodomain can be targeted for displacement. Here we estimate the stability of the conserved water molecules in 35 bromodomains via binding free energy calculations using all-atom grand canonical Monte Carlo simulations. Encouraging quantitative agreement to the available experimental evidence is found. We thus discuss the expected ease of water displacement in different bromodomains and the implications for ligand selectivity.
Collapse
Affiliation(s)
- Matteo Aldeghi
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, United Kingdom
| | - Gregory A. Ross
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Michael J. Bodkin
- Evotec (U.K.) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, United Kingdom
| | - Jonathan W. Essex
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Stefan Knapp
- Structural Genomics Consortium, Nuffield Department of Clinical Medicine, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
- Target Discovery Institute, Nuffield Department of Clinical Medicine, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, United Kingdom
| | - Philip C. Biggin
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, United Kingdom
| |
Collapse
|
10
|
Aldeghi M, Bodkin MJ, Knapp S, Biggin PC. Statistical Analysis on the Performance of Molecular Mechanics Poisson-Boltzmann Surface Area versus Absolute Binding Free Energy Calculations: Bromodomains as a Case Study. J Chem Inf Model 2017; 57:2203-2221. [PMID: 28786670 PMCID: PMC5615372 DOI: 10.1021/acs.jcim.7b00347] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Binding free energy calculations that make use of alchemical pathways are becoming increasingly feasible thanks to advances in hardware and algorithms. Although relative binding free energy (RBFE) calculations are starting to find widespread use, absolute binding free energy (ABFE) calculations are still being explored mainly in academic settings due to the high computational requirements and still uncertain predictive value. However, in some drug design scenarios, RBFE calculations are not applicable and ABFE calculations could provide an alternative. Computationally cheaper end-point calculations in implicit solvent, such as molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations, could too be used if one is primarily interested in a relative ranking of affinities. Here, we compare MMPBSA calculations to previously performed absolute alchemical free energy calculations in their ability to correlate with experimental binding free energies for three sets of bromodomain-inhibitor pairs. Different MMPBSA approaches have been considered, including a standard single-trajectory protocol, a protocol that includes a binding entropy estimate, and protocols that take into account the ligand hydration shell. Despite the improvements observed with the latter two MMPBSA approaches, ABFE calculations were found to be overall superior in obtaining correlation with experimental affinities for the test cases considered. A difference in weighted average Pearson ([Formula: see text]) and Spearman ([Formula: see text]) correlations of 0.25 and 0.31 was observed when using a standard single-trajectory MMPBSA setup ([Formula: see text] = 0.64 and [Formula: see text] = 0.66 for ABFE; [Formula: see text] = 0.39 and [Formula: see text] = 0.35 for MMPBSA). The best performing MMPBSA protocols returned weighted average Pearson and Spearman correlations that were about 0.1 inferior to ABFE calculations: [Formula: see text] = 0.55 and [Formula: see text] = 0.56 when including an entropy estimate, and [Formula: see text] = 0.53 and [Formula: see text] = 0.55 when including explicit water molecules. Overall, the study suggests that ABFE calculations are indeed the more accurate approach, yet there is also value in MMPBSA calculations considering the lower compute requirements, and if agreement to experimental affinities in absolute terms is not of interest. Moreover, for the specific protein-ligand systems considered in this study, we find that including an explicit ligand hydration shell or a binding entropy estimate in the MMPBSA calculations resulted in significant performance improvements at a negligible computational cost.
Collapse
Affiliation(s)
- Matteo Aldeghi
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford , South Parks Road, Oxford OX1 3QU, United Kingdom
| | - Michael J Bodkin
- Evotec (U.K.) Ltd. , 114 Innovation Drive, Milton Park, Abingdon OX14 4RZ, United Kingdom
| | - Stefan Knapp
- Structural Genomics Consortium, Nuffield Department of Clinical Medicine, University of Oxford , Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, United Kingdom.,Institute for Pharmaceutical Chemistry and Buchmann Institute for Life Sciences, Johann Wolfgang Goethe-University , D-60438 Frankfurt am Main, Germany
| | - Philip C Biggin
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford , South Parks Road, Oxford OX1 3QU, United Kingdom
| |
Collapse
|
11
|
Drakakis G, Wafford KA, Brewerton SC, Bodkin MJ, Evans DA, Bender A. Polypharmacological in Silico Bioactivity Profiling and Experimental Validation Uncovers Sedative-Hypnotic Effects of Approved and Experimental Drugs in Rat. ACS Chem Biol 2017; 12:1593-1602. [PMID: 28414209 DOI: 10.1021/acschembio.7b00209] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In this work, we describe the computational ("in silico") mode-of-action analysis of CNS-active drugs, which is taking both multiple simultaneous hypotheses as well as sets of protein targets for each mode-of-action into account, and which was followed by successful prospective in vitro and in vivo validation. Using sleep-related phenotypic readouts describing both efficacy and side effects for 491 compounds tested in rat, we defined an "optimal" (desirable) sleeping pattern. Compounds were subjected to in silico target prediction (which was experimentally confirmed for 21 out of 28 cases), followed by the utilization of decision trees for deriving polypharmacological bioactivity profiles. We demonstrated that predicted bioactivities improved classification performance compared to using only structural information. Moreover, DrugBank molecules were processed via the same pipeline, and compounds in many cases not annotated as sedative-hypnotic (alcaftadine, benzatropine, palonosetron, ecopipam, cyproheptadine, sertindole, and clopenthixol) were prospectively validated in vivo. Alcaftadine, ecopipam cyproheptadine, and clopenthixol were found to promote sleep as predicted, benzatropine showed only a small increase in NREM sleep, whereas sertindole promoted wakefulness. To our knowledge, the sedative-hypnotic effects of alcaftadine and ecopipam have not been previously discussed in the literature. The method described extends previous single-target, single-mode-of-action models and is applicable across disease areas.
Collapse
Affiliation(s)
- Georgios Drakakis
- Centre
for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Keith A. Wafford
- Eli Lilly U.K., Erl Wood Manor, Windlesham, Surrey GU206PH, United Kingdom
| | | | - Michael J. Bodkin
- Eli Lilly U.K., Erl Wood Manor, Windlesham, Surrey GU206PH, United Kingdom
| | - David A. Evans
- Eli Lilly U.K., Erl Wood Manor, Windlesham, Surrey GU206PH, United Kingdom
| | - Andreas Bender
- Centre
for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| |
Collapse
|
12
|
Abstract
Binding selectivity is a requirement for the development of a safe drug, and it is a critical property for chemical probes used in preclinical target validation. Engineering selectivity adds considerable complexity to the rational design of new drugs, as it involves the optimization of multiple binding affinities. Computationally, the prediction of binding selectivity is a challenge, and generally applicable methodologies are still not available to the computational and medicinal chemistry communities. Absolute binding free energy calculations based on alchemical pathways provide a rigorous framework for affinity predictions and could thus offer a general approach to the problem. We evaluated the performance of free energy calculations based on molecular dynamics for the prediction of selectivity by estimating the affinity profile of three bromodomain inhibitors across multiple bromodomain families, and by comparing the results to isothermal titration calorimetry data. Two case studies were considered. In the first one, the affinities of two similar ligands for seven bromodomains were calculated and returned excellent agreement with experiment (mean unsigned error of 0.81 kcal/mol and Pearson correlation of 0.75). In this test case, we also show how the preferred binding orientation of a ligand for different proteins can be estimated via free energy calculations. In the second case, the affinities of a broad-spectrum inhibitor for 22 bromodomains were calculated and returned a more modest accuracy (mean unsigned error of 1.76 kcal/mol and Pearson correlation of 0.48); however, the reparametrization of a sulfonamide moiety improved the agreement with experiment.
Collapse
Affiliation(s)
- Matteo Aldeghi
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford , South Parks Road, Oxford OX1 3QU, U.K
| | - Alexander Heifetz
- Evotec (U.K.) Ltd. , 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, U.K
| | - Michael J Bodkin
- Evotec (U.K.) Ltd. , 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, U.K
| | - Stefan Knapp
- Structural Genomics Consortium, Nuffield Department of Clinical Medicine, University of Oxford , Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, U.K.,Target Discovery Institute, Nuffield Department of Clinical Medicine, University of Oxford , Roosevelt Drive, Oxford OX3 7BN, U.K.,Institute for Pharmaceutical Chemistry, Goethe University Frankfurt , 60438 Frankfurt, Germany
| | - Philip C Biggin
- Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford , South Parks Road, Oxford OX1 3QU, U.K
| |
Collapse
|
13
|
Heifetz A, James T, Morao I, Bodkin MJ, Biggin PC. Guiding lead optimization with GPCR structure modeling and molecular dynamics. Curr Opin Pharmacol 2016; 30:14-21. [DOI: 10.1016/j.coph.2016.06.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 06/16/2016] [Accepted: 06/17/2016] [Indexed: 01/04/2023]
|
14
|
Aldeghi M, Heifetz A, Bodkin MJ, Knapp S, Biggin PC. Accurate calculation of the absolute free energy of binding for drug molecules. Chem Sci 2016; 7:207-218. [PMID: 26798447 PMCID: PMC4700411 DOI: 10.1039/c5sc02678d] [Citation(s) in RCA: 207] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Accepted: 09/24/2015] [Indexed: 12/13/2022] Open
Abstract
Accurate prediction of binding affinities has been a central goal of computational chemistry for decades, yet remains elusive. Despite good progress, the required accuracy for use in a drug-discovery context has not been consistently achieved for drug-like molecules. Here, we perform absolute free energy calculations based on a thermodynamic cycle for a set of diverse inhibitors binding to bromodomain-containing protein 4 (BRD4) and demonstrate that a mean absolute error of 0.6 kcal mol-1 can be achieved. We also show a similar level of accuracy (1.0 kcal mol-1) can be achieved in pseudo prospective approach. Bromodomains are epigenetic mark readers that recognize acetylation motifs and regulate gene transcription, and are currently being investigated as therapeutic targets for cancer and inflammation. The unprecedented accuracy offers the exciting prospect that the binding free energy of drug-like compounds can be predicted for pharmacologically relevant targets.
Collapse
Affiliation(s)
- Matteo Aldeghi
- Structural Bioinformatics and Computational Biochemistry , Department of Biochemistry , University of Oxford , South Parks Road , Oxford , OX1 3QU , UK . ; ; Tel: +44 (0)1865 613305
| | - Alexander Heifetz
- Evotec (U.K.) Ltd , 114 Innovation Drive, Milton Park , Abingdon , Oxfordshire OX14 4RZ , UK
| | - Michael J Bodkin
- Evotec (U.K.) Ltd , 114 Innovation Drive, Milton Park , Abingdon , Oxfordshire OX14 4RZ , UK
| | - Stefan Knapp
- Structural Genomics Consortium , Nuffield Department of Clinical Medicine , University of Oxford , Old Road Campus Research Building, Roosevelt Drive , Oxford OX3 7DQ , UK ; Target Discovery Institute , Nuffield Department of Clinical Medicine , University of Oxford , Roosevelt Drive , Oxford OX3 7BN , UK
| | - Philip C Biggin
- Structural Bioinformatics and Computational Biochemistry , Department of Biochemistry , University of Oxford , South Parks Road , Oxford , OX1 3QU , UK . ; ; Tel: +44 (0)1865 613305
| |
Collapse
|
15
|
Abstract
Most of the previous content of this book has focused on obtaining the structures of membrane proteins. In this chapter we explore how those structures can be further used in two key ways. The first is their use in structure based drug design (SBDD) and the second is how they can be used to extend our understanding of their functional activity via the use of molecular dynamics. Both aspects now heavily rely on computations. This area is vast, and alas, too large to consider in depth in a single book chapter. Thus where appropriate we have referred the reader to recent reviews for deeper assessment of the field. We discuss progress via the use of examples from two main drug target areas; G-protein coupled receptors (GPCRs) and ion channels. We end with a discussion of some of the main challenges in the area.
Collapse
Affiliation(s)
- Philip C Biggin
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.
| | - Matteo Aldeghi
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Michael J Bodkin
- Evotec Ltd, 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| | - Alexander Heifetz
- Evotec Ltd, 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK
| |
Collapse
|
16
|
Drakakis G, Koutsoukas A, Brewerton SC, Bodkin MJ, Evans DA, Bender A. Comparing global and local likelihood score thresholds in multiclass laplacian-modified Naive Bayes protein target prediction. Comb Chem High Throughput Screen 2015; 18:323-30. [PMID: 25747441 DOI: 10.2174/1386207318666150305145012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Revised: 10/18/2014] [Accepted: 11/08/2014] [Indexed: 11/22/2022]
Abstract
The increase of publicly available bioactivity data has led to the extensive development and usage of in silico bioactivity prediction algorithms. A particularly popular approach for such analyses is the multiclass Naïve Bayes, whose output is commonly processed by applying empirically-derived likelihood score thresholds. In this work, we describe a systematic way for deriving score cut-offs on a per-protein target basis and compare their performance with global thresholds on a large scale using both 5-fold cross-validation (ChEMBL 14, 189k ligand-protein pairs over 477 protein targets) and external validation (WOMBAT, 63k pairs, 421 targets). The individual protein target cut-offs derived were compared to global cut-offs ranging from -10 to 40 in score bouts of 2.5. The results indicate that individual thresholds had equal or better performance in all comparisons with global thresholds, ranging from 95% of protein targets to 57.96%. It is shown that local thresholds behave differently for particular families of targets (CYPs, GPCRs, Kinases and TFs). Furthermore, we demonstrate the discrepancy in performance when we move away from the training dataset chemical space, using Tanimoto similarity as a metric (from 0 to 1 in steps of 0.2). Finally, the individual protein score cut-offs derived for the in silico bioactivity application used in this work are released, as well as the reproducible and transferable KNIME workflows used to carry out the analysis.
Collapse
Affiliation(s)
| | | | | | | | | | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| |
Collapse
|
17
|
Aldeghi M, Knapp S, Heifetz A, Barker JJ, Bodkin MJ, Law RJ, Biggin PC. Absolute Binding Free Energy Calculations of Bromodomain Inhibitors. Biophys J 2015. [DOI: 10.1016/j.bpj.2014.11.1954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
|
18
|
Ravindranath AC, Perualila-Tan N, Kasim A, Drakakis G, Liggi S, Brewerton SC, Mason D, Bodkin MJ, Evans DA, Bhagwat A, Talloen W, Göhlmann HWH, Shkedy Z, Bender A. Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis. Mol Biosyst 2014; 11:86-96. [PMID: 25254964 DOI: 10.1039/c4mb00328d] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein-ligand binding. This paper spotlights the integration of gene expression data and target prediction scores, providing insight into mechanism of action (MoA). Compounds are clustered based upon the similarity of their predicted protein targets and each cluster is linked to gene sets using Linear Models for Microarray Data. MLP analysis is used to generate gene sets based upon their biological processes and a qualitative search is performed on the homogeneous target-based compound clusters to identify pathways. Genes and proteins were linked through pathways for 6 of the 8 MCF7 and 6 of the 11 PC3 clusters. Three compound clusters are studied; (i) the target-driven cluster involving HSP90 inhibitors, geldanamycin and tanespimycin induces differential expression for HSP90-related genes and overlap with pathway response to unfolded protein. Gene expression results are in agreement with target prediction and pathway annotations add information to enable understanding of MoA. (ii) The antipsychotic cluster shows differential expression for genes LDLR and INSIG-1 and is predicted to target CYP2D6. Pathway steroid metabolic process links the protein and respective genes, hypothesizing the MoA for antipsychotics. A sub-cluster (verepamil and dexverepamil), although sharing similar protein targets with the antipsychotic drug cluster, has a lower intensity of expression profile on related genes, indicating that this method distinguishes close sub-clusters and suggests differences in their MoA. Lastly, (iii) the thiazolidinediones drug cluster predicted peroxisome proliferator activated receptor (PPAR) PPAR-alpha, PPAR-gamma, acyl CoA desaturase and significant differential expression of genes ANGPTL4, FABP4 and PRKCD. The targets and genes are linked via PPAR signalling pathway and induction of apoptosis, generating a hypothesis for the MoA of thiazolidinediones. Our analysis show one or more underlying MoA for compounds and were well-substantiated with literature.
Collapse
Affiliation(s)
- Aakash Chavan Ravindranath
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
19
|
Drakakis G, Hendry AE, Hanson K, Brewerton SC, Bodkin MJ, Evans DA, Wheeler GN, Bender A. Comparative mode-of-action analysis following manual and automated phenotype detection in Xenopus laevis. Med Chem Commun 2014. [DOI: 10.1039/c3md00313b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Given the increasing utilization of phenotypic screens in drug discovery also the subsequent mechanism-of-action analysis gains increased attention.
Collapse
Affiliation(s)
- Georgios Drakakis
- Unilever Centre for Molecular Science Informatics
- Department of Chemistry
- University of Cambridge
- Cambridge CB2 1EW
- UK
| | - Adam E. Hendry
- School of Biological Sciences
- University of East Anglia
- Norwich
- UK
| | | | | | | | | | | | - Andreas Bender
- Unilever Centre for Molecular Science Informatics
- Department of Chemistry
- University of Cambridge
- Cambridge CB2 1EW
- UK
| |
Collapse
|
20
|
Liggi S, Drakakis G, Hendry AE, Hanson KM, Brewerton SC, Wheeler GN, Bodkin MJ, Evans DA, Bender A. Extensions to In Silico Bioactivity Predictions Using Pathway Annotations and Differential Pharmacology Analysis: Application toXenopus laevisPhenotypic Readouts. Mol Inform 2013; 32:1009-24. [DOI: 10.1002/minf.201300102] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Accepted: 08/06/2013] [Indexed: 12/20/2022]
|
21
|
Abstract
A knowledge-based approach to the de novo design of synthetically feasible molecules is described. The method is based on reaction vectors which represent the structural changes that take place at the reaction center along with the environment in which the reaction occurs. The reaction vectors are derived automatically from a database of reactions which is not restricted by size or reaction complexity. A structure generation algorithm has been developed whereby reaction vectors can be applied to previously unseen starting materials in order to suggest novel syntheses. The approach has been implemented in KNIME and is validated by reproducing known synthetic routes. We then present applications of the method in different drug design scenarios including lead optimization and library enumeration. The method offers great potential for capturing and using the growing body of data on reactions that is becoming available through electronic laboratory notebooks.
Collapse
Affiliation(s)
- Hina Patel
- Department of Information Studies, University of Sheffield, Regent Court, Sheffield S1 4DP, UK
| | | | | | | |
Collapse
|
22
|
Evans DA, Bodkin MJ, Baker SR, Sharman GJ. Janocchio--a Java applet for viewing 3D structures and calculating NMR couplings and NOEs. Magn Reson Chem 2007; 45:595-600. [PMID: 17534870 DOI: 10.1002/mrc.2016] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
We present a Java applet, based on the open source Jmol program, which allows the calculation of coupling constants and NOEs from a three-dimensional structure. The program has all the viewing features of Jmol, but adds the capability to calculate both H-H and H-C 3-bond couplings constants. In the case of H--H couplings, the Altona equation is used to perform this. The program also calculates NOEs using the full relaxation matrix approach. All these calculations are driven from a simple point and click interface. The program can calculate values for multi-structure files, and can produce input files for the conformational fitting program NAMFIS.
Collapse
Affiliation(s)
- David A Evans
- Eli Lilly and Company Ltd, Lilly Research Centre, Windlesham, Surrey, GU20 6PH, UK
| | | | | | | |
Collapse
|
23
|
Abstract
The programs Phase and Catalyst HypoGen are compared for their performance in determining three-dimensional quantitative structure-activity relationships. Eight sets of compounds with measured activity were collected from the public literature and partitioned into suitable training and test sets by an automated procedure. A range of models is built with each program, and suggested parameter variations are investigated. The models are assessed by their ability to predict the activity of compounds in the test set, and it is demonstrated that the performance of Phase is better than or equal to that of Catalyst HypoGen, with the data sets and parameters used here. Additionally, compounds in two of the data sets are overlaid on crystallographic structures of similar ligands in complex with the target receptor, in order to guide pharmacophore generation by the two programs, but the resulting models do not perform better.
Collapse
Affiliation(s)
- David A Evans
- Eli Lilly and Company Ltd., Lilly Research Centre, Erl Wood Manor, Sunninghill Road, Windlesham, Surrey, GU20 6PH, England
| | | | | | | |
Collapse
|
24
|
|
25
|
Abstract
The titration of an aqueous solution of a de novo designed peptide with trifluoroethanol (TFE) shows complete helix formation with the addition of only 30% TFE. A molecular simulation of the peptide, in which a single shell of TFE molecules initially surrounds the peptide, reveals preferred sites of solvent interaction. The TFE molecules show greater preference for the hydrophobic compared with hydrophilic side chains. The helix-enhancing ability of TFE in aqueous solution may be rationalized in terms of stabilizing the hydrophobic collapse of apolar side chains of the formed helix.
Collapse
Affiliation(s)
- M J Bodkin
- Department of Crystallography Birkbeck College, University of London, UK
| | | |
Collapse
|
26
|
Abstract
The stability of a 15-residue peptide has been investigated using CD spectroscopy and molecular simulation techniques. The sequence of the peptide was designed to include key features that are known to stabilize alpha-helices, including ion pairs, helix dipole capping, peptide bond capping, and aromatic interactions. The degree of helicity has been determined experimentally by CD in three solvents (aqueous buffer, methanol, and trifluoroethanol) and at two temperatures. Simulations of the peptide in the aqueous system have been performed over 500 ps at the same two temperatures using a fully explicit solvent model. Consistent with the CD data, the degree of helicity is decreased at the higher temperature. Our analysis of the simulation results has focused on competition between different side-chain/side-chain and side-chain/main-chain interactions, which can, in principle, stabilize the helix. The unfolding in aqueous solution occurs at the amino terminus because the side-chain interactions are insufficient to stabilize both the helix dipole and the peptide hydrogen bonds. Loss of capping of the peptide backbone leads to water insertion within the first peptide hydrogen bond and hence unfolding. In contrast, the carboxy terminus of the alpha-helix is stable in both simulations because the C-terminal lysine residue stabilizes the helix dipole, but at the expense of an ion pair.
Collapse
Affiliation(s)
- M J Bodkin
- Department of Crystallography, Birkbeck College, University of London, United Kingdom
| | | |
Collapse
|
27
|
Kalindjian SB, Bodkin MJ, Buck IM, Dunstone DJ, Low CM, McDonald IM, Pether MJ, Steel KI. A new class of non-peptidic cholecystokinin-B/gastrin receptor antagonists based on dibenzobicyclo[2.2.2]octane. J Med Chem 1994; 37:3671-3. [PMID: 7966125 DOI: 10.1021/jm00048a001] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
|
28
|
McDonald IM, Bodkin MJ, Broughton HB, Dunstone DJ, Kalindjian S, Low CM. 2-NAP: a selective, hydrophilic, non-peptide CCKA - receptor antagonist derived from the cholecystokinin C-terminal dipeptide. Bioorg Med Chem Lett 1993. [DOI: 10.1016/s0960-894x(00)80008-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|