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Xia Q, Fu Q, Shen C, Brenk R, Huang N. Assessing small molecule conformational sampling methods in molecular docking. J Comput Chem 2025; 46:e27516. [PMID: 39476310 DOI: 10.1002/jcc.27516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 09/05/2024] [Accepted: 10/13/2024] [Indexed: 01/01/2025]
Abstract
Small molecule conformational sampling plays a pivotal role in molecular docking. Recent advancements have led to the emergence of various conformational sampling methods, each employing distinct algorithms. This study investigates the impact of different small molecule conformational sampling methods in molecular docking using UCSF DOCK 3.7. Specifically, six traditional sampling methods (Omega, BCL::Conf, CCDC Conformer Generator, ConfGenX, Conformator, RDKit ETKDGv3) and a deep learning-based model (Torsional Diffusion) for generating conformational ensembles are evaluated. These ensembles are subsequently docked against the Platinum Diverse Dataset, the PoseBusters dataset and the DUDE-Z dataset to assess binding pose reproducibility and screening power. Notably, different sampling methods exhibit varying performance due to their unique preferences, such as dihedral angle sampling ranges on rotatable bonds. Combining complementary methods may lead to further improvements in docking performance.
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Affiliation(s)
- Qiancheng Xia
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing, China
- National Institute of Biological Sciences, Beijing, China
| | - Qiuyu Fu
- National Institute of Biological Sciences, Beijing, China
| | - Cheng Shen
- National Institute of Biological Sciences, Beijing, China
| | - Ruth Brenk
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Niu Huang
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing, China
- National Institute of Biological Sciences, Beijing, China
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2
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Folmsbee D, Koes DR, Hutchison GR. Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences. J Chem Inf Model 2023; 63:7401-7411. [PMID: 38000780 PMCID: PMC10716907 DOI: 10.1021/acs.jcim.3c01278] [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: 08/11/2023] [Revised: 11/07/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023]
Abstract
We performed exhaustive torsion sampling on more than 3 million compounds using the GFN2-xTB method and performed a comparison of experimental crystallographic and gas-phase conformers. Many conformer sampling methods derive torsional angle distributions from experimental crystallographic data, limiting the torsion preferences to molecules that must be stable, synthetically accessible, and able to be crystallized. In this work, we evaluate the differences in torsional preferences of experimental crystallographic geometries and gas-phase computed conformers from a broad selection of compounds to determine whether torsional angle distributions obtained from semiempirical methods are suitable priors for conformer sampling. We find that differences in torsion preferences can be mostly attributed to a lack of available experimental crystallographic data with small deviations derived from gas-phase geometry differences. GFN2 demonstrates the ability to provide accurate and reliable torsional preferences that can provide a basis for new methods free from the limitations of experimental data collection. We provide Gaussian-based fits and sampling distributions suitable for torsion sampling and propose an alternative to the widely used "experimental torsion and knowledge distance geometry" (ETKDG) method using quantum torsion-derived distance geometry (QTDG) methods.
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Affiliation(s)
- Dakota
L. Folmsbee
- Department
of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States
- Department
of Anesthesiology & Perioperative Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - David R. Koes
- Department
of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Geoffrey R. Hutchison
- Department
of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemical & Petroleum Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, United States
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Foloppe N, Chen IJ. The reorganization energy of compounds upon binding to proteins, from dynamic and solvated bound and unbound states. Bioorg Med Chem 2021; 51:116464. [PMID: 34798378 DOI: 10.1016/j.bmc.2021.116464] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/26/2021] [Accepted: 10/01/2021] [Indexed: 11/30/2022]
Abstract
The intramolecular reorganization energy (ΔEReorg) of compounds upon binding to proteins is a component of the binding free energy, which has long received particular attention, for fundamental and practical reasons. Understanding ΔEReorg would benefit the science of molecular recognition and drug design. For instance, the tolerable strain energy of compounds upon binding has been elusive. Prior studies found some large ΔEReorg values (e.g. > 10 kcal/mol), received with skepticism since they imply excessive opposition to binding. Indeed, estimating ΔEReorg is technically difficult. Typically, ΔEReorg has been approached by taking two energy-minimized conformers representing the bound and unbound states, and subtracting their conformational energy. This is a drastic oversimplification, liable to conformational collapse of the unbound conformer. Instead, the present work applies extensive molecular dynamics (MD) and the modern OPLS3 force-field to simulate compounds bound and unbound states, in explicit solvent under physically relevant conditions. The thermalized unbound compounds populate multiple conformations, not reducible to one or a few energy-minimized conformers. The intramolecular energies in the bound and unbound states were averaged over pertinent conformational ensembles, and the reorganization enthalpy upon binding (ΔHReorg) deduced by subtraction. This was applied to 76 systems, including 43 approved drugs, carefully selected for i) the quality of the bioactive X-ray structures and ii) the diversity of the chemotypes, their properties and protein targets. It yielded comparatively low ΔHReorg values (median = 1.4 kcal/mol, mean = 3.0 kcal/mol). A new finding is the observation of negative ΔHReorg values. Indeed, reorganization energies do not have to oppose binding, e.g. when intramolecular interactions stabilize preferentially the bound state. Conversely, even with competing water molecules, intramolecular interactions can occur predominantly in the unbound compound, and be replaced by intermolecular counterparts upon protein binding. Such disruption of intramolecular interactions upon binding gives rise to occasional larger ΔHReorg values. Such counterintuitive larger ΔHReorg values may be rationalized as a redistribution of interactions upon binding, qualitatively compatible with binding.
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Affiliation(s)
- Nicolas Foloppe
- Vernalis (R&D) Ltd, Granta Park, Abington, Cambridge CB21 6GB, UK.
| | - I-Jen Chen
- Vernalis (R&D) Ltd, Granta Park, Abington, Cambridge CB21 6GB, UK.
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Tong J, Zhao S. Large-Scale Analysis of Bioactive Ligand Conformational Strain Energy by Ab Initio Calculation. J Chem Inf Model 2021; 61:1180-1192. [PMID: 33630603 DOI: 10.1021/acs.jcim.0c01197] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Ligand conformational strain energy (LCSE) plays an important role in virtual screening and lead optimization. While various studies have provided insights into LCSE for small-molecule ligands in the Protein Data Bank (PDB), conclusions are inconsistent mainly due to small datasets, poor quality control of crystal structures, and molecular mechanics (MM) or low-level quantum mechanics (QM) calculations. Here, we built a high-quality dataset (LigBoundConf) of 8145 ligand-bound conformations from PDB crystal structures and calculated LCSE at the M062X-D3/ma-TZVPP (SMD)//M062X-D3/def2-SVP(SMD) level for each case in the dataset. The mean/median LCSE is 4.6/3.7 kcal/mol for 6672 successfully calculated cases, which is significantly lower than the estimates based on molecular mechanics in many previous analyses. Especially, when removing ligands with nonaromatic ring(s) that are prone to have large LCSEs due to electron density overfitting, the mean/median LCSE was reduced to 3.3/2.5 kcal/mol. We further reveal that LCSE is correlated with several ligand properties, including formal atomic charge, molecular weight, number of rotatable bonds, and number of hydrogen-bond donors and acceptors. In addition, our results show that although summation of torsion strains is a good approximation of LCSE for most cases, for a small fraction (about 6%) of our dataset, it underestimates LCSEs if ligands could form nonlocal intramolecular interactions in the unbound state. Taken together, our work provides a comprehensive profile of LCSE for ligands in PDB, which could help ligand conformation generation, ligand docking pose evaluation, and lead optimization.
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Affiliation(s)
- Jiahui Tong
- iHuman Institute, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China.,School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China.,University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, China.,Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China
| | - Suwen Zhao
- iHuman Institute, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China.,School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
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Hassan M, Coutsias EA. Protein secondary structure motifs: A kinematic construction. J Comput Chem 2021; 42:271-292. [PMID: 33306852 DOI: 10.1002/jcc.26448] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 09/02/2020] [Accepted: 09/29/2020] [Indexed: 12/19/2022]
Abstract
The kinematic geometry of protein backbone structures, constrained by either single or multiple hydrogen bonds (H-bonds), possibly in a periodic array, is discussed. These structures include regular secondary structure elements α-helices and β-sheets but also include other short H-bond stabilized irregular structural elements like β-turns. The work here shows that the variations observed in such structures have simple geometrical correlations consistent with constrained motion kinematics. A new classification of the ideal helices is given, in terms of the parameter α, the angle at a Cα atom to its two neighboring Cα 's along the helix, and shown how it can be generalized to include nonideal helices. Specifically, we derive an analytical expression of the backbone dihedrals, (ϕ, ψ), in terms of the parameter α subject to the constraint that the peptide planes are parallel to the helical axis. Helices constructed in this way exhibit near-vertical alignment of the C = O and N - H units and are the canonical objects of this study. These expressions are easily modifiable to include perturbations of parameters relevant to nonplanar peptide units and noncanonical angles. The addition of a second parameter, ε0 , inclination of successive peptide planes along a helix with respect to the helical axis leads to a generalization of the previous expression and provides an efficient parametrization of such structures in terms of coordinates consistent with H-bond parameters. An analogs parametrization of β-turns, using inverse kinematic methods, is also given. Besides offering a unifying viewpoint, our results may find useful applications to protein and peptide design.
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Affiliation(s)
- Mosavverul Hassan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
| | - Evangelos A Coutsias
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
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Hawkins PCD, Wlodek S. Decisions with Confidence: Application to the Conformation Sampling of Molecules in the Solid State. J Chem Inf Model 2020; 60:3518-3533. [DOI: 10.1021/acs.jcim.0c00358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Paul C. D. Hawkins
- OpenEye Scientific, 9 Bisbee Court, Suite D, Santa Fe, New Mexico 87508, United States
| | - Stanislaw Wlodek
- OpenEye Scientific, 9 Bisbee Court, Suite D, Santa Fe, New Mexico 87508, United States
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Abstract
Estimating the range of three-dimensional structures (conformations) that are available to a molecule is a key component of computer-aided drug design. Quantum mechanical simulation offers improved accuracy over forcefield methods, but at a high computational cost. The question is whether this increased cost can be justified in a context in which high-throughput analysis of large numbers of molecules is often key. This chapter discusses the application of quantum mechanics to conformational searching, with a focus on three key challenges: (1) the generation of ensembles that include a good approximation to a molecule's bioactive conformation at as prominent a ranking as possible; (2) rational analysis and modification of a pre-established bioactive conformation in terms of its energetics; and (3) approximation of real solution-phase conformational ensembles in tandem with NMR data. The impact of QM on the high-throughput application (1) is debatable, meaning that for the moment its primary application is still lower-throughput applications such as (2) and (3). The optimal choice of QM method is also discussed. Rigorous benchmarking suggests that DFT methods are only acceptable when used with large basis sets, but a trickle of papers continue to obtain useful results with relatively low-cost methods, leading to a dilemma that the literature has yet to fully resolve.
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Modelling the binding mode of macrocycles: Docking and conformational sampling. Bioorg Med Chem 2019; 28:115143. [PMID: 31771798 DOI: 10.1016/j.bmc.2019.115143] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Revised: 09/12/2019] [Accepted: 09/25/2019] [Indexed: 11/21/2022]
Abstract
Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general.
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