1
|
Hayakawa D, Watanabe Y, Gouda H. Molecular Interaction Fields Describing Halogen Bond Formable Areas on Protein Surfaces. J Chem Inf Model 2024; 64:6003-6013. [PMID: 39012240 PMCID: PMC11323840 DOI: 10.1021/acs.jcim.4c00896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/22/2024] [Accepted: 07/08/2024] [Indexed: 07/17/2024]
Abstract
Molecular interaction fields (MIFs) are three-dimensional interaction maps that describe the intermolecular interactions expected to be formed around target molecules. In this paper, a method for the fast computation of MIFs using the approximation functions of quantum mechanics-level MIFs of small model molecules is proposed. MIF functions of N-methylacetamide with chlorobenzene, bromobenzene, and iodobenzene probes were precisely approximated and used to calculate the MIFs on protein surfaces. This method appropriately reproduced halogen-bond-formable areas around the ligand-binding sites of proteins, where halogen bond formation was suggested in a previous study.
Collapse
Affiliation(s)
- Daichi Hayakawa
- Division of Biophysical
Chemistry,
Department of Pharmaceutical Sciences, Graduate School of Pharmacy, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8555, Japan
| | - Yurie Watanabe
- Division of Biophysical
Chemistry,
Department of Pharmaceutical Sciences, Graduate School of Pharmacy, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8555, Japan
| | - Hiroaki Gouda
- Division of Biophysical
Chemistry,
Department of Pharmaceutical Sciences, Graduate School of Pharmacy, Showa University, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8555, Japan
| |
Collapse
|
2
|
Khan O, Jones G, Lazou M, Joseph-McCarthy D, Kozakov D, Beglov D, Vajda S. Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites. J Chem Inf Model 2024; 64:2084-2100. [PMID: 38456842 DOI: 10.1021/acs.jcim.3c01969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.
Collapse
Affiliation(s)
- Omeir Khan
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States
| | - Maria Lazou
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Diane Joseph-McCarthy
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
- Acpharis Inc., Holliston, Massachusetts 01746, United States
| | - Sandor Vajda
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
| |
Collapse
|
3
|
Zou Y, Wang R, Du M, Wang X, Xu D. Identifying Protein-Ligand Interactions via a Novel Distance Self-Feedback Biomolecular Interaction Network. J Phys Chem B 2023; 127:899-911. [PMID: 36657025 DOI: 10.1021/acs.jpcb.2c07592] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Efficient and accurate characterizations of protein-ligand interactions are key to understanding biology at the molecular level. They are particularly useful in pharmaceutical industry applications. They are usually computationally demanding for those widely applied dynamics-based methods in identifying important residues or calculating ligand binding free energy. In this work, we proposed a graph deep learning (DL) framework, namely, the distance self-feedback biomolecular interaction network (DSBIN), in which the relationship between the complex structure and binding affinity can be established by means of a carefully designed distance self-feedback module and interaction layer. Our model can directly provide a quantitative evaluation of inhibitor binding affinities (pKd). More importantly, the DSBIN model efficiently identifies key interactions for inhibitor binding and thus intrinsically bears the interpretability. Its generalization performance was further verified using 1405 unseen structures. The predicted binding free energies' deviations were calculated to be less than 1.37 kcal/mol for more than 55% structures. Moreover, we also compared the DSBIN model with a commonly used theoretical method in calculating the substrate binding free energy, MM/GBSA. Our results show that the current DL model has generally better performance in predicting the binding free energy. For a specific complex system, mannopentaose/TmCBM27, the DSBIN predicted binding free energy is -8.21 kcal/mol, which is very close to experimentally measured -7.76 kcal/mol and MM/GBSA calculated -7.16 kcal/mol. Meanwhile, all important aromatic residues around the binding pocket can be identified by our DL model. Considering the accuracy and efficiency of the newly developed DL model, it may be very helpful in the field of drug design and molecular recognition.
Collapse
Affiliation(s)
- Yurong Zou
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Ruihan Wang
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Meng Du
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Xin Wang
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China
| | - Dingguo Xu
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan610064, PR China.,Research Center for Materials Genome Engineering, Sichuan University, Chengdu, Sichuan610065, PR China
| |
Collapse
|
4
|
Samways M, Bruce Macdonald HE, Taylor RD, Essex JW. Water Networks in Complexes between Proteins and FDA-Approved Drugs. J Chem Inf Model 2023; 63:387-396. [PMID: 36469670 PMCID: PMC9832485 DOI: 10.1021/acs.jcim.2c01225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Water molecules at protein-ligand interfaces are often of significant pharmaceutical interest, owing in part to the entropy which can be released upon the displacement of an ordered water by a therapeutic compound. Protein structures may not, however, completely resolve all critical bound water molecules, or there may be no experimental data available. As such, predicting the location of water molecules in the absence of a crystal structure is important in the context of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational technique that is gaining popularity for the simulation of buried water sites. In this work, we assess the ability of GCMC to accurately predict water binding locations, using a dataset that we have curated, containing 108 unique structures of complexes between proteins and Food and Drug Administration (FDA)-approved small-molecule drugs. We show that GCMC correctly predicts 81.4% of nonbulk crystallographic water sites to within 1.4 Å. However, our analysis demonstrates that the reported performance of water prediction methods is highly sensitive to the way in which the performance is measured. We also find that crystallographic water sites with more protein/ligand hydrogen bonds and stronger electron density are more reliably predicted by GCMC. An analysis of water networks revealed that more than half of the structures contain at least one ligand-contacting water network. In these cases, displacement of a water site by a ligand modification might yield unexpected results if the larger network is destabilized. Cooperative effects between waters should therefore be explicitly considered in structure-based drug design.
Collapse
Affiliation(s)
- Marley
L. Samways
- School
of Chemistry, University of Southampton, Southampton SO17 1BJ, U.K.
| | - Hannah E. Bruce Macdonald
- Computational
and Systems Biology Program, Memorial Sloan
Kettering Cancer Center, New York, New York 10065, United States
| | | | - Jonathan W. Essex
- School
of Chemistry, University of Southampton, Southampton SO17 1BJ, U.K.,
| |
Collapse
|
5
|
Smith ST, Shub L, Meiler J. PlaceWaters: Real-time, explicit interface water sampling during Rosetta ligand docking. PLoS One 2022; 17:e0269072. [PMID: 35639743 PMCID: PMC9154094 DOI: 10.1371/journal.pone.0269072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/13/2022] [Indexed: 01/29/2023] Open
Abstract
Water molecules at the protein-small molecule interface often form hydrogen bonds with both the small molecule ligand and the protein, affecting the structural integrity and energetics of a binding event. The inclusion of these 'bridging waters' has been shown to improve the accuracy of predicted docked structures; however, due to increased computational costs, this step is typically omitted in ligand docking simulations. In this study, we introduce a resource-efficient, Rosetta-based protocol named "PlaceWaters" to predict the location of explicit interface bridging waters during a ligand docking simulation. In contrast to other explicit water methods, this protocol is independent of knowledge of number and location of crystallographic waters in homologous structures. We test this method on a diverse protein-small molecule benchmark set in comparison to other Rosetta-based protocols. Our results suggest that this coarse-grained, structure-based approach quickly and accurately predicts the location of bridging waters, improving our ability to computationally screen drug candidates.
Collapse
Affiliation(s)
- Shannon T. Smith
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Laura Shub
- Biomedical Informatics Program, University of California San Francisco, San Francisco, California, United States of America
- Institute for Neurodegenerative Diseases, University of California San Francisco, San Francisco, California, United States of America
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Departments of Chemistry, Pharmacology, and Biomedical Informatics, Center for Structural Biology and Institute of Chemical Biology, Nashville, Tennessee, United States of America
- Institute for Drug Discovery, Leipzig University Medical School, SAC, Leipzig, Germany
- * E-mail:
| |
Collapse
|
6
|
Hadfield TE, Imrie F, Merritt A, Birchall K, Deane CM. Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration. J Chem Inf Model 2022; 62:2280-2292. [PMID: 35499971 PMCID: PMC9131447 DOI: 10.1021/acs.jcim.1c01311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Despite recent interest in deep generative models for scaffold elaboration, their applicability to fragment-to-lead campaigns has so far been limited. This is primarily due to their inability to account for local protein structure or a user's design hypothesis. We propose a novel method for fragment elaboration, STRIFE, that overcomes these issues. STRIFE takes as input fragment hotspot maps (FHMs) extracted from a protein target and processes them to provide meaningful and interpretable structural information to its generative model, which in turn is able to rapidly generate elaborations with complementary pharmacophores to the protein. In a large-scale evaluation, STRIFE outperforms existing, structure-unaware, fragment elaboration methods in proposing highly ligand-efficient elaborations. In addition to automatically extracting pharmacophoric information from a protein target's FHM, STRIFE optionally allows the user to specify their own design hypotheses.
Collapse
Affiliation(s)
- Thomas E Hadfield
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom
| | - Fergus Imrie
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom
| | - Andy Merritt
- LifeArc, SBC Open Innovation Campus, Stevenage SG1 2FX, United Kingdom
| | - Kristian Birchall
- LifeArc, SBC Open Innovation Campus, Stevenage SG1 2FX, United Kingdom
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom
| |
Collapse
|
7
|
Wang H, Mulgaonkar N, Pérez LM, Fernando S. ELIXIR-A: An Interactive Visualization Tool for Multi-Target Pharmacophore Refinement. ACS OMEGA 2022; 7:12707-12715. [PMID: 35474832 PMCID: PMC9025992 DOI: 10.1021/acsomega.1c07144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 03/24/2022] [Indexed: 06/01/2023]
Abstract
Pharmacophore modeling is an important step in computer-aided drug design for identifying interaction points between the receptor and ligand complex. Pharmacophore-based models can be used for de novo drug design, lead identification, and optimization in virtual screening as well as for multi-target drug design. There is a need to develop a user-friendly interface to filter the pharmacophore points resulting from multiple ligand conformations. Here, we present ELIXIR-A, a Python-based pharmacophore refinement tool, to help refine the pharmacophores between multiple ligands from multiple receptors. Furthermore, the output can be easily used in virtual pharmacophore-based screening platforms, thereby contributing to the development of drug discovery.
Collapse
Affiliation(s)
- Haoqi Wang
- Biological
and Agricultural Engineering Department, Texas A&M University, College Station, Texas 77843, United States
| | - Nirmitee Mulgaonkar
- Biological
and Agricultural Engineering Department, Texas A&M University, College Station, Texas 77843, United States
| | - Lisa M. Pérez
- High
Performance Research Computing, Texas A&M
University, College
Station, Texas 77843, United States
| | - Sandun Fernando
- Biological
and Agricultural Engineering Department, Texas A&M University, College Station, Texas 77843, United States
| |
Collapse
|
8
|
Heidecker AA, Bohn M, Pöthig A. Crystal structure of a hexacationic Ag(I)-pillarplex-dodecyl-diammonium pseudo-rotaxane as terephthalate salt. Z KRIST-CRYST MATER 2022. [DOI: 10.1515/zkri-2021-2076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
A new pseudo-rotaxane, consisting of a tubular, organometallic Ag-pillarplex ring and dodecyldiammonium axle component, is introduced and investigated towards potential non-covalent interactions by Full Interaction Maps (FIMs). FIMs predict regions of probable supramolecular interactions solely at the organic ligands, namely the rim and the aromatic rings of the pillarplex. The results were compared to structural parameters experimentally obtained by single-crystal X-ray diffraction. The pseudo-rotaxane was crystallized as a hydrated terephthalate salt, and the molecular and the crystal structure are discussed. The experimentally observed interactions are quantified using Hirshfeld surface analysis. In contrast to the FIMs prediction, four different interaction modes can be experimentally observed in the solid-state: encapsulation of a guest molecule, hydrogen bonding, π- and metal interactions.
Collapse
Affiliation(s)
- Alexandra A. Heidecker
- Department of Chemistry & Catalysis Research Center , Technische Universität München , Ernst-Otto-Fischer-Straße 1, D-85748 Garching , Germany
| | - Moritz Bohn
- Department of Chemistry & Catalysis Research Center , Technische Universität München , Ernst-Otto-Fischer-Straße 1, D-85748 Garching , Germany
| | - Alexander Pöthig
- Department of Chemistry & Catalysis Research Center , Technische Universität München , Ernst-Otto-Fischer-Straße 1, D-85748 Garching , Germany
| |
Collapse
|
9
|
Kumar P, Mohanty D. Development of a Novel Pharmacophore Model Guided by the Ensemble of Waters and Small Molecule Fragments Bound to SARS-CoV-2 Main Protease. Mol Inform 2022; 41:e2100178. [PMID: 34633768 PMCID: PMC8646684 DOI: 10.1002/minf.202100178] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/20/2021] [Indexed: 11/17/2022]
Abstract
Recent fragment-based drug design efforts have generated huge amounts of information on water and small molecule fragment binding sites on SARS-CoV-2 Mpro and preference of the sites for various types of chemical moieties. However, this information has not been effectively utilized to develop automated tools for in silico drug discovery which are routinely used for screening large compound libraries. Utilization of this information in the development of pharmacophore models can help in bridging this gap. In this study, information on water and small molecule fragments bound to Mpro has been utilized to develop a novel Water Pharmacophore (Waterphore) model. The Waterphore model can also implicitly represent the conformational flexibilities of binding pockets in terms of pharmacophore features. The Waterphore model derived from 173 apo- or small molecule fragment-bound structures of Mpro has been validated by using a dataset of 68 known bioactive inhibitors and 78 crystal structure bound inhibitors of SARS-CoV-2 Mpro . It is encouraging to note that, even though no inhibitor data has been used in developing the Waterphore model, it could successfully identify the known inhibitors from a library of decoys with a ROC-AUC of 0.81 and active hit rate (AHR) of 70 %. The Waterphore model is also general enough for potential applications for other drug targets.
Collapse
Affiliation(s)
- Pawan Kumar
- National Institute of ImmunologyAruna Asaf Ali MargNew Delhi110067India
| | - Debasisa Mohanty
- National Institute of ImmunologyAruna Asaf Ali MargNew Delhi110067India
| |
Collapse
|
10
|
Samways ML, Taylor RD, Bruce Macdonald HE, Essex JW. Water molecules at protein-drug interfaces: computational prediction and analysis methods. Chem Soc Rev 2021; 50:9104-9120. [PMID: 34184009 DOI: 10.1039/d0cs00151a] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.
Collapse
Affiliation(s)
- Marley L Samways
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, UK.
| | | | | | | |
Collapse
|
11
|
Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. PROGRESS IN MEDICINAL CHEMISTRY 2021; 60:273-343. [PMID: 34147204 DOI: 10.1016/bs.pmch.2021.01.004] [Citation(s) in RCA: 135] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.
Collapse
Affiliation(s)
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
| |
Collapse
|
12
|
Privat C, Granadino-Roldán JM, Bonet J, Santos Tomas M, Perez JJ, Rubio-Martinez J. Fragment dissolved molecular dynamics: a systematic and efficient method to locate binding sites. Phys Chem Chem Phys 2021; 23:3123-3134. [PMID: 33491698 DOI: 10.1039/d0cp05471b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Diverse computational methods to support fragment-based drug discovery (FBDD) are available in the literature. Despite their demonstrated efficacy in supporting FBDD campaigns, they exhibit some drawbacks such as protein denaturation or ligand aggregation that have not yet been clearly overcome in the framework of biomolecular simulations. In the present work, we discuss a systematic semi-automatic novel computational procedure, designed to surpass these difficulties. The method, named fragment dissolved Molecular Dynamics (fdMD), utilizes simulation boxes of solvated small fragments, adding a repulsive Lennard-Jones potential term to avoid aggregation, which can be easily used to solvate the targets of interest. This method has the advantage of solvating the target with a low number of ligands, thus preventing the denaturation of the target, while simultaneously generating a database of ligand-solvated boxes that can be used in further studies. A number of scripts are made available to analyze the results and obtain the descriptors proposed as a means to trustfully discard spurious binding sites. To test our method, four test cases of different complexity have been solvated with ligand boxes and four molecular dynamics runs of 200 ns length have been run for each system, which have been extended up to 1 μs when needed. The reported results point out that the selected number of replicas are enough to identify the correct binding sites irrespective of the initial structure, even in the case of proteins having several close binding sites for the same ligand. We also propose a set of descriptors to analyze the results, among which the average MMGBSA and the average KDEEP energies have emerged as the most robust ones.
Collapse
Affiliation(s)
- Cristian Privat
- Departament de Ciència dels Materials i Química Física, Universitat de Barcelona (UB) and the Institut de Quimica Teorica i Computacional (IQTCUB), Martí i Franqués 1, 08028 Barcelona, Spain.
| | - José M Granadino-Roldán
- Departamento de Química Física y Analítica, Facultad de Ciencias Experimentales, Universidad de Jaén, Campus "Las Lagunillas" s/n, 23071, Jaén, Spain
| | - Jordi Bonet
- Departament de Ciència dels Materials i Química Física, Universitat de Barcelona (UB) and the Institut de Quimica Teorica i Computacional (IQTCUB), Martí i Franqués 1, 08028 Barcelona, Spain.
| | - Maria Santos Tomas
- Department of Architecture Technology, Universitat Politecnica de Catalunya, Av. Diagonal 649, 08028 Barcelona, Spain
| | - Juan J Perez
- Deparment of Chemical Engineering, Universitat Politecnica de Catalunya, Av. Diagonal 647, 08028 Barcelona, Spain
| | - Jaime Rubio-Martinez
- Departament de Ciència dels Materials i Química Física, Universitat de Barcelona (UB) and the Institut de Quimica Teorica i Computacional (IQTCUB), Martí i Franqués 1, 08028 Barcelona, Spain.
| |
Collapse
|
13
|
Castillo A, Ceballos P, Santos P, Cerón M, Venkatesan P, Pérez-Gutiérrez E, Sosa-Rivadeneyra M, Thamotharan S, Siegler MA, Percino MJ. Solution and Solid-State Photophysical Properties of Positional Isomeric Acrylonitrile Derivatives with Core Pyridine and Phenyl Moieties: Experimental and DFT Studies. Molecules 2021; 26:1500. [PMID: 33801942 PMCID: PMC8001298 DOI: 10.3390/molecules26061500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 02/26/2021] [Accepted: 03/04/2021] [Indexed: 11/17/2022] Open
Abstract
The compounds I (Z)-2-(phenyl)-3-(2,4,5-trimethoxyphenyl)acrylonitrile with one side (2,4,5-MeO-), one symmetrical (2Z,2'Z)-2,2'-(1,4-phenylene)bis(3-(2,4,5-trimethoxyphenyl)acrylonitrile), II (both sides with (2,4,5-MeO-), and three positional isomers with pyridine (Z)-2-(pyridin-2- 3, or 4-yl)-3-(2,4,5-trimethoxyphenyl)acrylonitrile, III-V were synthetized and characterized by UV-Vis, fluorescence, IR, H1-NMR, and EI mass spectrometry as well as single crystal X-ray diffraction (SCXRD). The optical properties were strongly influenced by the solvent (hyperchromic and hypochromic shift), which were compared with the solid state. According to the solvatochromism theory, the excited-state (μe) and ground-state (μg) dipole moments were calculated based on the variation of Stokes shift with the solvent's relative permittivity, refractive index, and polarity parameters. SCXRD analyses revealed that the compounds I and II crystallized in the monoclinic system with the space group, P21/n and P21/c, respectively, and with Z = 4 and 2. III, IV, and V crystallized in space groups: orthorhombic, Pbca; triclinic, P-1; and monoclinic, P21 with Z = 1, 2, and 2, respectively. The intermolecular interactions for compounds I-V were investigated using the CCDC Mercury software and their energies were quantified using PIXEL. The density of states (DOS), molecular electrostatic potential surfaces (MEPS), and natural bond orbitals (NBO) of the compounds were determined to evaluate the photophysical properties.
Collapse
Affiliation(s)
- Armando Castillo
- Unidad de Polímeros y Electrónica Orgánica, Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla, Val3-Ecocampus Valsequillo, Independencia O2 Sur 50, San Pedro Zacachimalpa 72960, Mexico; (A.C.); (P.C.); (P.S.); (M.C.); (P.V.); (E.P.-G.)
| | - Paulina Ceballos
- Unidad de Polímeros y Electrónica Orgánica, Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla, Val3-Ecocampus Valsequillo, Independencia O2 Sur 50, San Pedro Zacachimalpa 72960, Mexico; (A.C.); (P.C.); (P.S.); (M.C.); (P.V.); (E.P.-G.)
| | - Pilar Santos
- Unidad de Polímeros y Electrónica Orgánica, Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla, Val3-Ecocampus Valsequillo, Independencia O2 Sur 50, San Pedro Zacachimalpa 72960, Mexico; (A.C.); (P.C.); (P.S.); (M.C.); (P.V.); (E.P.-G.)
| | - Margarita Cerón
- Unidad de Polímeros y Electrónica Orgánica, Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla, Val3-Ecocampus Valsequillo, Independencia O2 Sur 50, San Pedro Zacachimalpa 72960, Mexico; (A.C.); (P.C.); (P.S.); (M.C.); (P.V.); (E.P.-G.)
| | - Perumal Venkatesan
- Unidad de Polímeros y Electrónica Orgánica, Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla, Val3-Ecocampus Valsequillo, Independencia O2 Sur 50, San Pedro Zacachimalpa 72960, Mexico; (A.C.); (P.C.); (P.S.); (M.C.); (P.V.); (E.P.-G.)
| | - Enrique Pérez-Gutiérrez
- Unidad de Polímeros y Electrónica Orgánica, Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla, Val3-Ecocampus Valsequillo, Independencia O2 Sur 50, San Pedro Zacachimalpa 72960, Mexico; (A.C.); (P.C.); (P.S.); (M.C.); (P.V.); (E.P.-G.)
| | - Martha Sosa-Rivadeneyra
- Facultad de Ciencias Químicas, Benemérita Universidad Autónoma de Puebla (BUAP), 14 Sur Esquina San Claudio, San Manuel, Puebla 72570, Mexico;
| | - Subbiah Thamotharan
- Biomolecular Crystallography Laboratory, School of Chemical & Biotechnology, Department of Bioinformatics, SASTRA Deemed University, Thanjavur 613401, India;
| | - Maxime A. Siegler
- Department of Chemistry, Johns Hopkins University, New Chemistry Building, 3400 N. Charles St., Baltimore, MD 21218, USA;
| | - María Judith Percino
- Unidad de Polímeros y Electrónica Orgánica, Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla, Val3-Ecocampus Valsequillo, Independencia O2 Sur 50, San Pedro Zacachimalpa 72960, Mexico; (A.C.); (P.C.); (P.S.); (M.C.); (P.V.); (E.P.-G.)
| |
Collapse
|
14
|
|
15
|
Curran PR, Radoux CJ, Smilova MD, Sykes RA, Higueruelo AP, Bradley AR, Marsden BD, Spring DR, Blundell TL, Leach AR, Pitt WR, Cole JC. Hotspots API: A Python Package for the Detection of Small Molecule Binding Hotspots and Application to Structure-Based Drug Design. J Chem Inf Model 2020; 60:1911-1916. [DOI: 10.1021/acs.jcim.9b00996] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Peter R. Curran
- The Cambridge Crystallographic Data Centre (CCDC), 12 Union Road, Cambridge, CB2 1EZ, U.K
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, U.K
| | | | - Mihaela D. Smilova
- Structural Genomic Consortium (SGC), Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7DQ, U.K
| | - Richard A. Sykes
- The Cambridge Crystallographic Data Centre (CCDC), 12 Union Road, Cambridge, CB2 1EZ, U.K
| | - Alicia P. Higueruelo
- The Cambridge Crystallographic Data Centre (CCDC), 12 Union Road, Cambridge, CB2 1EZ, U.K
| | | | - Brian D. Marsden
- Structural Genomic Consortium (SGC), Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Headington, Oxford, OX3 7DQ, U.K
- Kennedy Institute of Rheumatology, NDORMS, University of Oxford, Headington, Oxford, OX3 7FY, U.K
| | - David R. Spring
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, U.K
| | - Tom L. Blundell
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, U.K
| | - Andrew R. Leach
- Chemogenomics Team, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, U.K
| | | | - Jason C. Cole
- The Cambridge Crystallographic Data Centre (CCDC), 12 Union Road, Cambridge, CB2 1EZ, U.K
| |
Collapse
|
16
|
Schmalhorst PS, Bergner A. A Grid Map Based Approach to Identify Nonobvious Ligand Design Opportunities in 3D Protein Structure Ensembles. J Chem Inf Model 2020; 60:2178-2188. [DOI: 10.1021/acs.jcim.0c00051] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Philipp S. Schmalhorst
- Department of Medicinal Chemistry, Boehringer Ingelheim RCV GmbH & Co KG, Dr.-Boehringer-Gasse 5-11, 1121 Vienna, Austria
| | - Andreas Bergner
- Department of Medicinal Chemistry, Boehringer Ingelheim RCV GmbH & Co KG, Dr.-Boehringer-Gasse 5-11, 1121 Vienna, Austria
| |
Collapse
|
17
|
Hu X, Maffucci I, Contini A. Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations. Curr Med Chem 2020; 26:7598-7622. [DOI: 10.2174/0929867325666180514110824] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 02/26/2018] [Accepted: 04/18/2018] [Indexed: 12/30/2022]
Abstract
Background:
The inclusion of direct effects mediated by water during the ligandreceptor
recognition is a hot-topic of modern computational chemistry applied to drug discovery
and development. Docking or virtual screening with explicit hydration is still debatable,
despite the successful cases that have been presented in the last years. Indeed, how to select
the water molecules that will be included in the docking process or how the included waters
should be treated remain open questions.
Objective:
In this review, we will discuss some of the most recent methods that can be used in
computational drug discovery and drug development when the effect of a single water, or of a
small network of interacting waters, needs to be explicitly considered.
Results:
Here, we analyse the software to aid the selection, or to predict the position, of water
molecules that are going to be explicitly considered in later docking studies. We also present
software and protocols able to efficiently treat flexible water molecules during docking, including
examples of applications. Finally, we discuss methods based on molecular dynamics
simulations that can be used to integrate docking studies or to reliably and efficiently compute
binding energies of ligands in presence of interfacial or bridging water molecules.
Conclusions:
Software applications aiding the design of new drugs that exploit water molecules,
either as displaceable residues or as bridges to the receptor, are constantly being developed.
Although further validation is needed, workflows that explicitly consider water will
probably become a standard for computational drug discovery soon.
Collapse
Affiliation(s)
- Xiao Hu
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
| | - Irene Maffucci
- Pasteur, Département de Chimie, École Normale Supérieure, PSL Research University, Sorbonne Universités, UPMC Univ. Paris 06, CNRS, 75005 Paris, France
| | - Alessandro Contini
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
| |
Collapse
|
18
|
Lee JY, Krieger JM, Li H, Bahar I. Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations. Protein Sci 2019; 29:76-86. [PMID: 31576621 PMCID: PMC6933858 DOI: 10.1002/pro.3732] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 12/14/2022]
Abstract
Recent years have seen progress in druggability simulations, that is, molecular dynamics simulations of target proteins in solutions containing drug‐like probe molecules to characterize their drug‐binding abilities, if any. An important consecutive step is to analyze the trajectories to construct pharmacophore models (PMs) to use for virtual screening of libraries of small molecules. While considerable success has been observed in this type of computer‐aided drug discovery, a systematic tool encompassing multiple steps from druggability simulations to pharmacophore modeling, to identifying hits by virtual screening of libraries of compounds, has been lacking. We address this need here by developing a new tool, Pharmmaker, building on the DruGUI module of our ProDy application programming interface. Pharmmaker is composed of a suite of steps: (Step 1) identification of high affinity residues for each probe molecule type; (Step 2) selecting high affinity residues and hot spots in the vicinity of sites identified by DruGUI; (Step 3) ranking of the interactions between high affinity residues and specific probes; (Step 4) obtaining probe binding poses and corresponding protein conformations by collecting top‐ranked snapshots; and (Step 5) using those snapshots for constructing PMs. The PMs are then used as filters for identifying hits in structure‐based virtual screening. Pharmmaker, accessible online at http://prody.csb.pitt.edu/pharmmaker/, can be used in conjunction with other tools available in ProDy.
Collapse
Affiliation(s)
- Ji Young Lee
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - James M Krieger
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Hongchun Li
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| |
Collapse
|
19
|
Taylor R, Wood PA. A Million Crystal Structures: The Whole Is Greater than the Sum of Its Parts. Chem Rev 2019; 119:9427-9477. [PMID: 31244003 DOI: 10.1021/acs.chemrev.9b00155] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The founding in 1965 of what is now called the Cambridge Structural Database (CSD) has reaped dividends in numerous and diverse areas of chemical research. Each of the million or so crystal structures in the database was solved for its own particular reason, but collected together, the structures can be reused to address a multitude of new problems. In this Review, which is focused mainly on the last 10 years, we chronicle the contribution of the CSD to research into molecular geometries, molecular interactions, and molecular assemblies and demonstrate its value in the design of biologically active molecules and the solid forms in which they are delivered. Its potential in other commercially relevant areas is described, including gas storage and delivery, thin films, and (opto)electronics. The CSD also aids the solution of new crystal structures. Because no scientific instrument is without shortcomings, the limitations of CSD research are assessed. We emphasize the importance of maintaining database quality: notwithstanding the arrival of big data and machine learning, it remains perilous to ignore the principle of garbage in, garbage out. Finally, we explain why the CSD must evolve with the world around it to ensure it remains fit for purpose in the years ahead.
Collapse
Affiliation(s)
- Robin Taylor
- Cambridge Crystallographic Data Centre , 12 Union Road , Cambridge CB2 1EZ , United Kingdom
| | - Peter A Wood
- Cambridge Crystallographic Data Centre , 12 Union Road , Cambridge CB2 1EZ , United Kingdom
| |
Collapse
|
20
|
Pradhan MR, Nguyen MN, Kannan S, Fox SJ, Kwoh CK, Lane DP, Verma CS. Characterization of Hydration Properties in Structural Ensembles of Biomolecules. J Chem Inf Model 2019; 59:3316-3329. [DOI: 10.1021/acs.jcim.8b00453] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Mohan R. Pradhan
- Bioinformatics Institute, A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671
- School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
| | - Minh N. Nguyen
- Bioinformatics Institute, A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671
| | - Srinivasaraghavan Kannan
- Bioinformatics Institute, A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671
| | - Stephen J. Fox
- Bioinformatics Institute, A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671
| | - Chee Keong Kwoh
- School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
| | - David P. Lane
- p53 Laboratory, A*STAR (Agency for Science, Technology and Research), 8A Biomedical Grove, #06-04/05, Neuros/Immunos, Singapore 138648
| | - Chandra S. Verma
- Bioinformatics Institute, A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671
- School of Biological Sciences, Nanyang Technological University, 50 Nanyang Drive, Singapore 637551
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543
| |
Collapse
|
21
|
O'Reilly M, Cleasby A, Davies TG, Hall RJ, Ludlow RF, Murray CW, Tisi D, Jhoti H. Crystallographic screening using ultra-low-molecular-weight ligands to guide drug design. Drug Discov Today 2019; 24:1081-1086. [PMID: 30878562 DOI: 10.1016/j.drudis.2019.03.009] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 02/06/2019] [Accepted: 03/08/2019] [Indexed: 02/06/2023]
Abstract
We present a novel crystallographic screening methodology (MiniFrags) that employs high-concentration aqueous soaks with a chemically diverse and ultra-low-molecular-weight library (heavy atom count 5-7) to identify ligand-binding hot and warm spots on proteins. We propose that MiniFrag screening represents a highly effective method for guiding optimisation of fragment-derived lead compounds or chemical tools and that the high screening hit rates reflect enhanced sampling of chemical space.
Collapse
Affiliation(s)
- Marc O'Reilly
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Anne Cleasby
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Thomas G Davies
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Richard J Hall
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - R Frederick Ludlow
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Christopher W Murray
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Dominic Tisi
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Harren Jhoti
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK.
| |
Collapse
|
22
|
Nittinger E, Gibbons P, Eigenbrot C, Davies DR, Maurer B, Yu CL, Kiefer JR, Kuglstatter A, Murray J, Ortwine DF, Tang Y, Tsui V. Water molecules in protein–ligand interfaces. Evaluation of software tools and SAR comparison. J Comput Aided Mol Des 2019; 33:307-330. [DOI: 10.1007/s10822-019-00187-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 01/24/2019] [Indexed: 01/08/2023]
|
23
|
Tran-Nguyen VK, Da Silva F, Bret G, Rognan D. All in One: Cavity Detection, Druggability Estimate, Cavity-Based Pharmacophore Perception, and Virtual Screening. J Chem Inf Model 2019; 59:573-585. [PMID: 30563339 DOI: 10.1021/acs.jcim.8b00684] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Discovering the very first ligands of pharmacologically important targets in a fast and cost-efficient manner is an important issue in drug discovery. In the absence of structural information on either endogenous or synthetic ligands, computational chemists classically identify the very first hits by docking compound libraries to a binding site of interest, with well-known biases arising from the usage of scoring functions. We herewith propose a novel computational method tailored to ligand-free protein structures and consisting in the generation of simple cavity-based pharmacophores to which potential ligands could be aligned by the use of a smooth Gaussian function. The method, embedded in the IChem toolkit, automatically detects ligand-binding cavities, then predicts their structural druggability, and last creates a structure-based pharmacophore for predicted druggable binding sites. A companion tool (Shaper2) was designed to align ligands to cavity-derived pharmacophoric features. The proposed method is as efficient as state-of-the-art virtual screening methods (ROCS, Surflex-Dock) in both posing and virtual screening challenges. Interestingly, IChem-Shaper2 is clearly orthogonal to these latter methods in retrieving unique chemotypes from high-throughput virtual screening data.
Collapse
Affiliation(s)
- Viet-Khoa Tran-Nguyen
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| | - Franck Da Silva
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| | - Guillaume Bret
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| |
Collapse
|
24
|
Seidel T, Schuetz DA, Garon A, Langer T. The Pharmacophore Concept and Its Applications in Computer-Aided Drug Design. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 2019; 110:99-141. [PMID: 31621012 DOI: 10.1007/978-3-030-14632-0_4] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Pharmacophore-based techniques currently are an integral part of many computer-aided drug design workflows and have been successfully and extensively applied for tasks such as virtual screening, de novo design, and lead optimization. Pharmacophore models can be derived both in a receptor-based and in a ligand-based manner, and provide an abstract description of essential non-bonded interactions that typically occur between small-molecule ligands and macromolecular targets. Due to their simplistic and abstract nature, pharmacophores are both perfectly suited for efficient computer processing and easy to comprehend by life and physical scientists. As a consequence, they have also proven to be a valuable tool for communicating between computational and medicinal chemists.This chapter aims to provide a short overview of the pharmacophore concept and its applications in modern computer-aided drug design. The chapter is divided into three distinct parts. The first section contains a brief introduction to the pharmacophore concept. The second section provides a description of the most common nonbonded interaction types and their representation as pharmacophoric features. Furthermore, it gives an overview of the various methods for pharmacophore generation and important pharmacophore-based techniques in drug design. This part concludes with examples for recent pharmacophore concept-related research and development. The last section is dedicated to a review of research in the field of natural product chemistry as carried out by employing pharmacophore-based drug design methods.
Collapse
Affiliation(s)
- Thomas Seidel
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria.
| | - Doris A Schuetz
- InteLigand GmbH, IRIC-Institut de Recherche en Immunologie et en Cancérologie, Université de Montréal, Montréal, QC, Canada
| | - Arthur Garon
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| |
Collapse
|
25
|
Marchand JR, Caflisch A. In silico fragment-based drug design with SEED. Eur J Med Chem 2018; 156:907-917. [PMID: 30064119 DOI: 10.1016/j.ejmech.2018.07.042] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 07/11/2018] [Accepted: 07/15/2018] [Indexed: 12/13/2022]
Abstract
We report on two fragment-based drug design protocols, SEED2XR and ALTA, which start by high-throughput docking. SEED2XR is a two-stage protocol for fragment-based drug design. The first stage is in silico and consists of the automatic docking of 103-104 fragments using SEED, which requires about 1 s per fragment. SEED is a docking software developed specifically for fragment docking and binding energy evaluation by a force field with implicit solvent. In the second stage of SEED2XR, the 10-102 fragments with the most favorable predicted binding energies are validated by protein X-ray crystallography. The recent applications of SEED2XR to bromodomains demonstrate that the whole SEED2XR protocol can be carried out in about a week of working time, with hit rates ranging from 10% to 40%. Information on fragment-target interactions generated by the SEED2XR protocol or directly from SEED docking has been used for the discovery of hundreds of hits. ALTA is a computational protocol for screening which identifies candidate ligands that preserve the interactions between the optimal SEED fragments and the protein target. Medicinal chemistry optimization of ligands predicted by ALTA has resulted in pre-clinical candidates for protein kinases and bromodomains. The high-throughput, very low cost, sustainability, and high hit rate of the SEED-based protocols, unreachable by purely experimental techniques, make them perfectly suitable for both academic and industrial drug discovery research.
Collapse
Affiliation(s)
- Jean-Rémy Marchand
- Department of Biochemistry, University of Zürich, CH-8057, Zürich, Switzerland
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zürich, CH-8057, Zürich, Switzerland.
| |
Collapse
|
26
|
Identification of Antifungal Targets Based on Computer Modeling. J Fungi (Basel) 2018; 4:jof4030081. [PMID: 29973534 PMCID: PMC6162656 DOI: 10.3390/jof4030081] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 06/24/2018] [Accepted: 06/29/2018] [Indexed: 01/07/2023] Open
Abstract
Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host⁻pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.
Collapse
|
27
|
Chevillard F, Rimmer H, Betti C, Pardon E, Ballet S, van Hilten N, Steyaert J, Diederich WE, Kolb P. Binding-Site Compatible Fragment Growing Applied to the Design of β 2-Adrenergic Receptor Ligands. J Med Chem 2018; 61:1118-1129. [PMID: 29364664 DOI: 10.1021/acs.jmedchem.7b01558] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Fragment-based drug discovery is intimately linked to fragment extension approaches that can be accelerated using software for de novo design. Although computers allow for the facile generation of millions of suggestions, synthetic feasibility is however often neglected. In this study we computationally extended, chemically synthesized, and experimentally assayed new ligands for the β2-adrenergic receptor (β2AR) by growing fragment-sized ligands. In order to address the synthetic tractability issue, our in silico workflow aims at derivatized products based on robust organic reactions. The study started from the predicted binding modes of five fragments. We suggested a total of eight diverse extensions that were easily synthesized, and further assays showed that four products had an improved affinity (up to 40-fold) compared to their respective initial fragment. The described workflow, which we call "growing via merging" and for which the key tools are available online, can improve early fragment-based drug discovery projects, making it a useful creative tool for medicinal chemists during structure-activity relationship (SAR) studies.
Collapse
Affiliation(s)
- Florent Chevillard
- Department of Pharmaceutical Chemistry, Philipps-University Marburg , Marbacher Weg 6, 35032 Marburg, Germany
| | - Helena Rimmer
- Department of Pharmaceutical Chemistry and Center for Tumor Biology and Immunology, Philipps-University Marburg , Hans-Meerwein-Straße 3, 35032 Marburg, Germany
| | - Cecilia Betti
- Research Group of Organic Chemistry, Departments of Chemistry and Bio-Engineering Sciences, Vrije Universiteit Brussel , Pleinlaan 2, 1050 Brussels, Belgium
| | - Els Pardon
- VIB-VUB Center for Structural Biology, VIB , 1050 Brussels, Belgium.,Structural Biology Brussels, Vrije Universiteit Brussel , 1050 Brussels, Belgium
| | - Steven Ballet
- Research Group of Organic Chemistry, Departments of Chemistry and Bio-Engineering Sciences, Vrije Universiteit Brussel , Pleinlaan 2, 1050 Brussels, Belgium
| | - Niek van Hilten
- Department of Pharmaceutical Chemistry, Philipps-University Marburg , Marbacher Weg 6, 35032 Marburg, Germany
| | - Jan Steyaert
- VIB-VUB Center for Structural Biology, VIB , 1050 Brussels, Belgium.,Structural Biology Brussels, Vrije Universiteit Brussel , 1050 Brussels, Belgium
| | - Wibke E Diederich
- Department of Pharmaceutical Chemistry and Center for Tumor Biology and Immunology, Philipps-University Marburg , Hans-Meerwein-Straße 3, 35032 Marburg, Germany
| | - Peter Kolb
- Department of Pharmaceutical Chemistry, Philipps-University Marburg , Marbacher Weg 6, 35032 Marburg, Germany
| |
Collapse
|
28
|
Ferreira de Freitas R, Schapira M. A systematic analysis of atomic protein-ligand interactions in the PDB. MEDCHEMCOMM 2017; 8:1970-1981. [PMID: 29308120 PMCID: PMC5708362 DOI: 10.1039/c7md00381a] [Citation(s) in RCA: 258] [Impact Index Per Article: 36.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 09/15/2017] [Indexed: 12/20/2022]
Abstract
As the protein databank (PDB) recently passed the cap of 123 456 structures, it stands more than ever as an important resource not only to analyze structural features of specific biological systems, but also to study the prevalence of structural patterns observed in a large body of unrelated structures, that may reflect rules governing protein folding or molecular recognition. Here, we compiled a list of 11 016 unique structures of small-molecule ligands bound to proteins - 6444 of which have experimental binding affinity - representing 750 873 protein-ligand atomic interactions, and analyzed the frequency, geometry and impact of each interaction type. We find that hydrophobic interactions are generally enriched in high-efficiency ligands, but polar interactions are over-represented in fragment inhibitors. While most observations extracted from the PDB will be familiar to seasoned medicinal chemists, less expected findings, such as the high number of C-H···O hydrogen bonds or the relatively frequent amide-π stacking between the backbone amide of proteins and aromatic rings of ligands, uncover underused ligand design strategies.
Collapse
Affiliation(s)
| | - Matthieu Schapira
- Structural Genomics Consortium , University of Toronto , Toronto , ON M5G 1L7 , Canada .
- Department of Pharmacology and Toxicology , University of Toronto , Toronto , ON M5S 1A8 , Canada
| |
Collapse
|
29
|
Xiao W, He Z, Sun M, Li S, Li H. Statistical Analysis, Investigation, and Prediction of the Water Positions in the Binding Sites of Proteins. J Chem Inf Model 2017; 57:1517-1528. [DOI: 10.1021/acs.jcim.6b00620] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Wei Xiao
- School
of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
- Shanghai
Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zenghui He
- Shanghai
Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Meijian Sun
- Shanghai
Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Shiliang Li
- Shanghai
Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Honglin Li
- School
of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
- Shanghai
Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| |
Collapse
|
30
|
Rathi PC, Ludlow RF, Hall RJ, Murray CW, Mortenson PN, Verdonk ML. Predicting “Hot” and “Warm” Spots for Fragment Binding. J Med Chem 2017; 60:4036-4046. [DOI: 10.1021/acs.jmedchem.7b00366] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Prakash Chandra Rathi
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - R. Frederick Ludlow
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Richard J. Hall
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Christopher W. Murray
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Paul N. Mortenson
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Marcel L. Verdonk
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| |
Collapse
|
31
|
Cole JC, Giangreco I, Groom CR. Using more than 801 296 small-molecule crystal structures to aid in protein structure refinement and analysis. Acta Crystallogr D Struct Biol 2017; 73:234-239. [PMID: 28291758 PMCID: PMC5349435 DOI: 10.1107/s2059798316014352] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 09/09/2016] [Indexed: 01/20/2023] Open
Abstract
The Cambridge Structural Database (CSD) is the worldwide resource for the dissemination of all published three-dimensional structures of small-molecule organic and metal-organic compounds. This paper briefly describes how this collection of crystal structures can be used en masse in the context of macromolecular crystallography. Examples highlight how the CSD and associated software aid protein-ligand complex validation, and show how the CSD could be further used in the generation of geometrical restraints for protein structure refinement.
Collapse
Affiliation(s)
- Jason C. Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, England
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, England
| | - Colin R. Groom
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, England
| |
Collapse
|
32
|
Groom CR, Cole JC. The use of small-molecule structures to complement protein-ligand crystal structures in drug discovery. Acta Crystallogr D Struct Biol 2017; 73:240-245. [PMID: 28291759 PMCID: PMC5349436 DOI: 10.1107/s2059798317000675] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 01/13/2017] [Indexed: 11/10/2022] Open
Abstract
Many ligand-discovery stories tell of the use of structures of protein-ligand complexes, but the contribution of structural chemistry is such a core part of finding and improving ligands that it is often overlooked. More than 800 000 crystal structures are available to the community through the Cambridge Structural Database (CSD). Individually, these structures can be of tremendous value and the collection of crystal structures is even more helpful. This article provides examples of how small-molecule crystal structures have been used to complement those of protein-ligand complexes to address challenges ranging from affinity, selectivity and bioavailability though to solubility.
Collapse
Affiliation(s)
- Colin R. Groom
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, England
| | - Jason C. Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, England
| |
Collapse
|
33
|
Sridhar A, Ross GA, Biggin PC. Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin. PLoS One 2017; 12:e0172743. [PMID: 28235019 PMCID: PMC5325533 DOI: 10.1371/journal.pone.0172743] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2016] [Accepted: 02/08/2017] [Indexed: 12/30/2022] Open
Abstract
Water is often found to mediate interactions between a ligand and a protein. It can play a significant role in orientating the ligand within a binding pocket and contribute to the free energy of binding. It would thus be extremely useful to be able to accurately predict the position and orientation of water molecules within a binding pocket. Recently, we developed the WaterDock protocol that was able to predict 97% of the water molecules in a test set. However, this approach generated false positives at a rate of over 20% in most cases and whilst this might be acceptable for some applications, in high throughput scenarios this is not desirable. Here we tackle this problem via the inclusion of knowledge regarding the solvation structure of ligand functional groups. We call this new protocol WaterDock2 and demonstrate that this protocol maintains a similar true positive rate to the original implementation but is capable of reducing the false-positive rate by over 50%. To improve the usability of the method, we have also developed a plugin for the popular graphics program PyMOL. The plugin also contains an implementation of the original WaterDock.
Collapse
Affiliation(s)
- Akshay Sridhar
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Gregory A. Ross
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Philip C. Biggin
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
34
|
Bartuzi D, Kaczor AA, Targowska-Duda KM, Matosiuk D. Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors. Molecules 2017; 22:molecules22020340. [PMID: 28241450 PMCID: PMC6155844 DOI: 10.3390/molecules22020340] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 01/27/2017] [Accepted: 02/15/2017] [Indexed: 12/16/2022] Open
Abstract
The growing number of studies on G protein-coupled receptors (GPCRs) family are a source of noticeable improvement in our understanding of the functioning of these proteins. GPCRs are responsible for a vast part of signaling in vertebrates and, as such, invariably remain in the spotlight of medicinal chemistry. A deeper insight into the underlying mechanisms of interesting phenomena observed in GPCRs, such as biased signaling or allosteric modulation, can be gained with experimental and computational studies. The latter play an important role in this process, since they allow for observations on scales inaccessible for most other methods. One of the key steps in such studies is proper computational reconstruction of actual ligand-receptor or protein-protein interactions, a process called molecular docking. A number of improvements and innovative applications of this method were documented recently. In this review, we focus particularly on innovations in docking to GPCRs.
Collapse
Affiliation(s)
- Damian Bartuzi
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, 4A Chodźki Str., PL20093 Lublin, Poland.
| | - Agnieszka A Kaczor
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, 4A Chodźki Str., PL20093 Lublin, Poland.
- School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, P.O. Box 1627, FI-70211 Kuopio, Finland.
| | | | - Dariusz Matosiuk
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, 4A Chodźki Str., PL20093 Lublin, Poland.
| |
Collapse
|
35
|
Saito R, Hoshi M, Kato A, Ishikawa C, Komatsu T. Green fluorescent protein chromophore derivatives as a new class of aldose reductase inhibitors. Eur J Med Chem 2017; 125:965-974. [DOI: 10.1016/j.ejmech.2016.10.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 09/14/2016] [Accepted: 10/07/2016] [Indexed: 10/20/2022]
|
36
|
AutoDock-GIST: Incorporating Thermodynamics of Active-Site Water into Scoring Function for Accurate Protein-Ligand Docking. Molecules 2016; 21:molecules21111604. [PMID: 27886114 PMCID: PMC6274120 DOI: 10.3390/molecules21111604] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 11/15/2016] [Accepted: 11/16/2016] [Indexed: 11/27/2022] Open
Abstract
Water plays a significant role in the binding process between protein and ligand. However, the thermodynamics of water molecules are often underestimated, or even ignored, in protein-ligand docking. Usually, the free energies of active-site water molecules are substantially different from those of waters in the bulk region. The binding of a ligand to a protein causes a displacement of these waters from an active site to bulk, and this displacement process substantially contributes to the free energy change of protein-ligand binding. The free energy of active-site water molecules can be calculated by grid inhomogeneous solvation theory (GIST), using molecular dynamics (MD) and the trajectory of a target protein and water molecules. Here, we show a case study of the combination of GIST and a docking program and discuss the effectiveness of the displacing gain of unfavorable water in protein-ligand docking. We combined the GIST-based desolvation function with the scoring function of AutoDock4, which is called AutoDock-GIST. The proposed scoring function was assessed employing 51 ligands of coagulation factor Xa (FXa), and results showed that both scoring accuracy and docking success rate were improved. We also evaluated virtual screening performance of AutoDock-GIST using FXa ligands in the directory of useful decoys-enhanced (DUD-E), thus finding that the displacing gain of unfavorable water is effective for a successful docking campaign.
Collapse
|
37
|
Kasahara K, Kinoshita K. Landscape of protein-small ligand binding modes. Protein Sci 2016; 25:1659-71. [PMID: 27327045 PMCID: PMC5338237 DOI: 10.1002/pro.2971] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2015] [Revised: 06/04/2016] [Accepted: 06/15/2016] [Indexed: 11/15/2022]
Abstract
Elucidating the mechanisms of specific small-molecule (ligand) recognition by proteins is a long-standing conundrum. While the structures of these molecules, proteins and ligands, have been extensively studied, protein-ligand interactions, or binding modes, have not been comprehensively analyzed. Although methods for assessing similarities of binding site structures have been extensively developed, the methods for the computational treatment of binding modes have not been well established. Here, we developed a computational method for encoding the information about binding modes as graphs, and assessing their similarities. An all-against-all comparison of 20,040 protein-ligand complexes provided the landscape of the protein-ligand binding modes and its relationships with protein- and chemical spaces. While similar proteins in the same SCOP Family tend to bind relatively similar ligands with similar binding modes, the correlation between ligand and binding similarities was not very high (R(2) = 0.443). We found many pairs with novel relationships, in which two evolutionally distant proteins recognize dissimilar ligands by similar binding modes (757,474 pairs out of 200,790,780 pairs were categorized into this relationship, in our dataset). In addition, there were an abundance of pairs of homologous proteins binding to similar ligands with different binding modes (68,217 pairs). Our results showed that many interesting relationships between protein-ligand complexes are still hidden in the structure database, and our new method for assessing binding mode similarities is effective to find them.
Collapse
Affiliation(s)
- Kota Kasahara
- College of Life SciencesRitsumeikan UniversityKusatsuShiga525‐8577Japan
| | - Kengo Kinoshita
- Graduate School of Information SciencesTohoku UniversitySendaiMiyagi980‐8597Japan
- Tohoku Medical Megabank OrganizationTohoku UniversitySendaiMiyagi980‐8573Japan
- Institute of Development, Aging and Cancer, Tohoku UniversitySendaiMiyagi980‐8575Japan
| |
Collapse
|
38
|
Verdonk ML, Ludlow RF, Giangreco I, Rathi PC. Protein–Ligand Informatics Force Field (PLIff): Toward a Fully Knowledge Driven “Force Field” for Biomolecular Interactions. J Med Chem 2016; 59:6891-902. [DOI: 10.1021/acs.jmedchem.6b00716] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Marcel L. Verdonk
- Astex Pharmaceuticals, 436
Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - R. Frederick Ludlow
- Astex Pharmaceuticals, 436
Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Ilenia Giangreco
- Astex Pharmaceuticals, 436
Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
- Dipartimento
Farmaco-Chimico, University of Bari, Via Orabona 4, I-70125 Bari, Italy
| | - Prakash Chandra Rathi
- Astex Pharmaceuticals, 436
Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| |
Collapse
|
39
|
Bodnarchuk MS. Water, water, everywhere… It's time to stop and think. Drug Discov Today 2016; 21:1139-46. [DOI: 10.1016/j.drudis.2016.05.009] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Revised: 04/15/2016] [Accepted: 05/13/2016] [Indexed: 12/11/2022]
|
40
|
Radoux CJ, Olsson TSG, Pitt WR, Groom CR, Blundell TL. Identifying Interactions that Determine Fragment Binding at Protein Hotspots. J Med Chem 2016; 59:4314-25. [PMID: 27043011 DOI: 10.1021/acs.jmedchem.5b01980] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Locating a ligand-binding site is an important first step in structure-guided drug discovery, but current methods do little to suggest which interactions within a pocket are the most important for binding. Here we illustrate a method that samples atomic hotspots with simple molecular probes to produce fragment hotspot maps. These maps specifically highlight fragment-binding sites and their corresponding pharmacophores. For ligand-bound structures, they provide an intuitive visual guide within the binding site, directing medicinal chemists where to grow the molecule and alerting them to suboptimal interactions within the original hit. The fragment hotspot map calculation is validated using experimental binding positions of 21 fragments and subsequent lead molecules. The ligands are found in high scoring areas of the fragment hotspot maps, with fragment atoms having a median percentage rank of 97%. Protein kinase B and pantothenate synthetase are examined in detail. In each case, the fragment hotspot maps are able to rationalize a Free-Wilson analysis of SAR data from a fragment-based drug design project.
Collapse
Affiliation(s)
- Chris J Radoux
- Cambridge Crystallographic Data Centre , 12 Union Road, Cambridge, CB2 1EZ, United Kingdom.,Department of Biochemistry, University of Cambridge , Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, United Kingdom
| | - Tjelvar S G Olsson
- Cambridge Crystallographic Data Centre , 12 Union Road, Cambridge, CB2 1EZ, United Kingdom.,John Innes Centre, Norwich Research Park , Colney Lane, Norwich, Norfolk NR4 7UH, United Kingdom
| | - Will R Pitt
- UCB , 208 Bath Road, Slough, West Berkshire SL1 3WE, United Kingdom
| | - Colin R Groom
- Cambridge Crystallographic Data Centre , 12 Union Road, Cambridge, CB2 1EZ, United Kingdom
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge , Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, United Kingdom
| |
Collapse
|
41
|
Korb O, Kuhn B, Hert J, Taylor N, Cole J, Groom C, Stahl M. Interactive and Versatile Navigation of Structural Databases. J Med Chem 2016; 59:4257-66. [DOI: 10.1021/acs.jmedchem.5b01756] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Oliver Korb
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Bernd Kuhn
- Roche
Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
| | - Jérôme Hert
- Roche
Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
| | - Neil Taylor
- Desert Scientific Software Pty Ltd., Level 5 Nexus Building, Norwest Business Park, 4 Columbia Court, Baulkham Hills, NSW 2153, Australia
| | - Jason Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Colin Groom
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Martin Stahl
- Roche
Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070 Basel, Switzerland
| |
Collapse
|
42
|
Spyrakis F, Cavasotto CN. Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description. Arch Biochem Biophys 2015; 583:105-19. [DOI: 10.1016/j.abb.2015.08.002] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Revised: 08/03/2015] [Accepted: 08/03/2015] [Indexed: 01/05/2023]
|
43
|
Wasko MJ, Pellegrene KA, Madura JD, Surratt CK. A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets. Front Neurol 2015; 6:197. [PMID: 26441817 PMCID: PMC4566055 DOI: 10.3389/fneur.2015.00197] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 08/24/2015] [Indexed: 01/12/2023] Open
Abstract
Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for “growing” the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.
Collapse
Affiliation(s)
- Michael J Wasko
- Mylan School of Pharmacy, Graduate School of Pharmaceutical Sciences, Duquesne University , Pittsburgh, PA , USA
| | - Kendy A Pellegrene
- Mylan School of Pharmacy, Graduate School of Pharmaceutical Sciences, Duquesne University , Pittsburgh, PA , USA
| | - Jeffry D Madura
- Department of Chemistry and Biochemistry, Center for Computational Sciences, Bayer School of Natural and Environmental Sciences, Duquesne University , Pittsburgh, PA , USA
| | - Christopher K Surratt
- Mylan School of Pharmacy, Graduate School of Pharmaceutical Sciences, Duquesne University , Pittsburgh, PA , USA
| |
Collapse
|
44
|
Wall ID, Hann MM, Leach AR, Pickett SD. Current Status and Future Direction of Fragment-Based Drug Discovery: A Computational Chemistry Perspective. FRAGMENT-BASED DRUG DISCOVERY 2015. [DOI: 10.1039/9781782620938-00073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Fragment-based drug discovery (FBDD) has become a well-established and widely used approach for lead identification. The computational chemistry community has played a central role in developing the ideas behind this area of research and computational tools are important throughout FBDD campaigns. This article discusses the evolution of best practice, on-going areas of debate and gaps in current capabilities from a computational chemistry perspective. In particular, the contribution of computational methods to areas such as fragment library design, screening analysis, data handling and the role of structure- and ligand-based design is discussed. The potential to combine FBDD with other hit-identification methods such as high-throughput screening in a more integrated approach is also highlighted.
Collapse
Affiliation(s)
- Ian D. Wall
- GlaxoSmithKline Gunnels Wood Road Stevenage, Hertfordshire, SG1 2NY UK
| | - Michael M. Hann
- GlaxoSmithKline Gunnels Wood Road Stevenage, Hertfordshire, SG1 2NY UK
| | - Andrew R. Leach
- GlaxoSmithKline Gunnels Wood Road Stevenage, Hertfordshire, SG1 2NY UK
| | | |
Collapse
|
45
|
Huang W, Blinov N, Wishart DS, Kovalenko A. Role of water in ligand binding to maltose-binding protein: insight from a new docking protocol based on the 3D-RISM-KH molecular theory of solvation. J Chem Inf Model 2015; 55:317-28. [PMID: 25545470 DOI: 10.1021/ci500520q] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Maltose-binding protein is a periplasmic binding protein responsible for transport of maltooligosaccarides through the periplasmic space of Gram-negative bacteria, as a part of the ABC transport system. The molecular mechanisms of the initial ligand binding and induced large scale motion of the protein's domains still remain elusive. In this study, we use a new docking protocol that combines a recently proposed explicit water placement algorithm based on the 3D-RISM-KH molecular theory of solvation and conventional docking software (AutoDock Vina) to explain the mechanisms of maltotriose binding to the apo-open state of a maltose-binding protein. We confirm the predictions of previous NMR spectroscopic experiments on binding modes of the ligand. We provide the molecular details on the binding mode that was not previously observed in the X-ray experiments. We show that this mode, which is defined by the fine balance between the protein-ligand direct interactions and solvation effects, can trigger the protein's domain motion resulting in the holo-closed structure of the maltose-binding protein with the maltotriose ligand in excellent agreement with the experimental data. We also discuss the role of water in blocking unfavorable binding sites and water-mediated interactions contributing to the stability of observable binding modes of maltotriose.
Collapse
Affiliation(s)
- WenJuan Huang
- Department of Mechanical Engineering, University of Alberta , Edmonton, AB T6G 2G8, Canada
| | | | | | | |
Collapse
|
46
|
Taylor R, Allen FH, Cole JC. Quantifying the symmetry preferences of intermolecular interactions in organic crystal structures. CrystEngComm 2015. [DOI: 10.1039/c5ce00035a] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Many intermolecular interactions show preferences for particular crystallographic symmetries and the extent to which this is the case is quantifiable.
Collapse
Affiliation(s)
- Robin Taylor
- Cambridge Crystallographic Data Centre
- Cambridge CB2 1EZ, UK
| | - Frank H. Allen
- Cambridge Crystallographic Data Centre
- Cambridge CB2 1EZ, UK
| | - Jason C. Cole
- Cambridge Crystallographic Data Centre
- Cambridge CB2 1EZ, UK
| |
Collapse
|
47
|
Krotzky T, Rickmeyer T, Fober T, Klebe G. Extraction of protein binding pockets in close neighborhood of bound ligands makes comparisons simple due to inherent shape similarity. J Chem Inf Model 2014; 54:3229-37. [PMID: 25345905 DOI: 10.1021/ci500553a] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Methods for comparing protein binding sites are frequently validated on data sets of pockets that were obtained simply by extracting the protein area next to the bound ligands. With this strategy, any unoccupied pocket will remain unconsidered. Furthermore, a large amount of ligand-biased intrinsic shape information is predefined, inclining the subsequent comparisons as rather trivial even in data sets that hardly contain redundancies in sequence information. In this study, we present the results of a very simplistic and shape-biased comparison approach, which stress that unrestricted cavity extraction is essential to enable unexpected cross-reactivity predictions among proteins and function annotations of orphan proteins.
Collapse
Affiliation(s)
- Timo Krotzky
- Institute of Pharmaceutical Chemistry, University of Marburg , Marbacher Weg 6-10, 35032 Marburg, Germany
| | | | | | | |
Collapse
|
48
|
Yang Y, Hu B, Lill MA. Analysis of factors influencing hydration site prediction based on molecular dynamics simulations. J Chem Inf Model 2014; 54:2987-95. [PMID: 25252619 PMCID: PMC4210176 DOI: 10.1021/ci500426q] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
![]()
Water
contributes significantly to the binding of small molecules
to proteins in biochemical systems. Molecular dynamics (MD) simulation
based programs such as WaterMap and WATsite have been used to probe
the locations and thermodynamic properties of hydration sites at the
surface or in the binding site of proteins generating important information
for structure-based drug design. However, questions associated with
the influence of the simulation protocol on hydration site analysis
remain. In this study, we use WATsite to investigate the influence
of factors such as simulation length and variations in initial protein
conformations on hydration site prediction. We find that 4 ns MD simulation
is appropriate to obtain a reliable prediction of the locations and
thermodynamic properties of hydration sites. In addition, hydration
site prediction can be largely affected by the initial protein conformations
used for MD simulations. Here, we provide a first quantification of
this effect and further indicate that similar conformations of binding
site residues (RMSD < 0.5 Å) are required to obtain consistent
hydration site predictions.
Collapse
Affiliation(s)
- Ying Yang
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University , 575 Stadium Mall Drive, West Lafayette, Indiana 47907, United States
| | | | | |
Collapse
|
49
|
Sabbadin D, Ciancetta A, Moro S. Perturbation of fluid dynamics properties of water molecules during G protein-coupled receptor-ligand recognition: the human A2A adenosine receptor as a key study. J Chem Inf Model 2014; 54:2846-55. [PMID: 25245783 DOI: 10.1021/ci500397y] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Recent advances in structural biology revealed that water molecules play a crucial structural role in the protein architecture and ligand binding of G protein-coupled receptors. In this work, we present an alternative approach to monitor the time-dependent organization of water molecules during the final stage of the ligand-receptor recognition process by means of membrane molecular dynamics simulations. We inspect the variation of fluid dynamics properties of water molecules upon ligand binding with the aim to correlate the results with the binding affinities. The outcomes of this analysis are transferred into a bidimensional graph called water fluid dynamics maps, that allow a fast graphical identification of protein "hot-spots" characterized by peculiar shape and electrostatic properties that can play a critical role in ligand binding. We hopefully believe that the proposed approach might represent a valuable tool for structure-based drug discovery that can be extended to cases where crystal structures are not yet available, or have not been solved at high resolution.
Collapse
Affiliation(s)
- Davide Sabbadin
- Molecular Modeling Section (MMS), Dipartimento di Scienze del Farmaco, Università di Padova , via Marzolo 5, 35131 Padova, Italy
| | | | | |
Collapse
|
50
|
Bruno IJ, Groom CR. A crystallographic perspective on sharing data and knowledge. J Comput Aided Mol Des 2014; 28:1015-22. [PMID: 25091065 PMCID: PMC4196029 DOI: 10.1007/s10822-014-9780-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Accepted: 07/17/2014] [Indexed: 11/24/2022]
Abstract
The crystallographic community is in many ways an exemplar of the benefits and practices of sharing data. Since the inception of the technique, virtually every published crystal structure has been made available to others. This has been achieved through the establishment of several specialist data centres, including the Cambridge Crystallographic Data Centre, which produces the Cambridge Structural Database. Containing curated structures of small organic molecules, some containing a metal, the database has been produced for almost 50 years. This has required the development of complex informatics tools and an environment allowing expert human curation. As importantly, a financial model has evolved which has, to date, ensured the sustainability of the resource. However, the opportunities afforded by technological changes and changing attitudes to sharing data make it an opportune moment to review current practices.
Collapse
Affiliation(s)
- Ian J Bruno
- The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK,
| | | |
Collapse
|