1
|
Das S, Merz KM. Molecular Gas-Phase Conformational Ensembles. J Chem Inf Model 2024; 64:749-760. [PMID: 38206321 DOI: 10.1021/acs.jcim.3c01309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.
Collapse
Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| |
Collapse
|
2
|
Baillif B, Cole J, Giangreco I, McCabe P, Bender A. Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations. J Cheminform 2023; 15:124. [PMID: 38129933 PMCID: PMC10740246 DOI: 10.1186/s13321-023-00794-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023] Open
Abstract
Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.
Collapse
Affiliation(s)
- Benoit Baillif
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge, CB2 1EW, UK
| | - Jason Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
- Exscientia plc, The Schrödinger Building, Oxford Science Park, Oxford, OX4 4GE, UK
| | - Patrick McCabe
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
| | - Andreas Bender
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge, CB2 1EW, UK.
| |
Collapse
|
3
|
Stylianakis I, Zervos N, Lii JH, Pantazis DA, Kolocouris A. Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory. J Comput Aided Mol Des 2023; 37:607-656. [PMID: 37597063 PMCID: PMC10618395 DOI: 10.1007/s10822-023-00513-5] [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: 05/09/2023] [Accepted: 06/03/2023] [Indexed: 08/21/2023]
Abstract
We selected 145 reference organic molecules that include model fragments used in computer-aided drug design. We calculated 158 conformational energies and barriers using force fields, with wide applicability in commercial and free softwares and extensive application on the calculation of conformational energies of organic molecules, e.g. the UFF and DREIDING force fields, the Allinger's force fields MM3-96, MM3-00, MM4-8, the MM2-91 clones MMX and MM+, the MMFF94 force field, MM4, ab initio Hartree-Fock (HF) theory with different basis sets, the standard density functional theory B3LYP, the second-order post-HF MP2 theory and the Domain-based Local Pair Natural Orbital Coupled Cluster DLPNO-CCSD(T) theory, with the latter used for accurate reference values. The data set of the organic molecules includes hydrocarbons, haloalkanes, conjugated compounds, and oxygen-, nitrogen-, phosphorus- and sulphur-containing compounds. We reviewed in detail the conformational aspects of these model organic molecules providing the current understanding of the steric and electronic factors that determine the stability of low energy conformers and the literature including previous experimental observations and calculated findings. While progress on the computer hardware allows the calculations of thousands of conformations for later use in drug design projects, this study is an update from previous classical studies that used, as reference values, experimental ones using a variety of methods and different environments. The lowest mean error against the DLPNO-CCSD(T) reference was calculated for MP2 (0.35 kcal mol-1), followed by B3LYP (0.69 kcal mol-1) and the HF theories (0.81-1.0 kcal mol-1). As regards the force fields, the lowest errors were observed for the Allinger's force fields MM3-00 (1.28 kcal mol-1), ΜΜ3-96 (1.40 kcal mol-1) and the Halgren's MMFF94 force field (1.30 kcal mol-1) and then for the MM2-91 clones MMX (1.77 kcal mol-1) and MM+ (2.01 kcal mol-1) and MM4 (2.05 kcal mol-1). The DREIDING (3.63 kcal mol-1) and UFF (3.77 kcal mol-1) force fields have the lowest performance. These model organic molecules we used are often present as fragments in drug-like molecules. The values calculated using DLPNO-CCSD(T) make up a valuable data set for further comparisons and for improved force field parameterization.
Collapse
Affiliation(s)
- Ioannis Stylianakis
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece
| | - Nikolaos Zervos
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece
| | - Jenn-Huei Lii
- Department of Chemistry, National Changhua University of Education, Changhua City, Taiwan
| | - Dimitrios A Pantazis
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470, Mülheim an der Ruhr, Germany
| | - Antonios Kolocouris
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece.
- Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou, 15771, Athens, Greece.
| |
Collapse
|
4
|
Zhang Y, Song J, Zhou Y, Jia H, Zhou T, Sun Y, Gao Q, Zhao Y, Pan Y, Sun Z, Chu P. Discovery of selective and potent USP22 inhibitors via structure-based virtual screening and bioassays exerting anti-tumor activity. Bioorg Chem 2023; 141:106842. [PMID: 37769523 DOI: 10.1016/j.bioorg.2023.106842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/20/2023] [Accepted: 09/05/2023] [Indexed: 10/03/2023]
Abstract
Ubiquitin-specific protease 22 (USP22) plays a prominent role in tumor development, invasion, metastasis and immune reprogramming, which has been proposed as a potential therapeutic target for cancer. Herein, we employed a structure-based discovery and biological evaluation and discovered that Rottlerin (IC50 = 2.53 μM) and Morusin (IC50 = 8.29 μM) and as selective and potent USP22 inhibitors. Treatment of HCT116 cells and A375 cells with each of the two compounds resulted in increased monoubiquitination of histones H2A and H2B, as well as reduced protein expression levels of Sirt1 and PD-L1, all of which are known as USP22 substrates. Additionally, our study demonstrated that the administration of Rottlerin or Morusin resulted in an increase H2Bub levels, while simultaneously reducing the expression of Sirt1 and PD-L1 in a manner dependent on USP22. Furthermore, Rottlerin and Morusin were found to enhance the degradation of PD-L1 and Sirt1, as well as increase the polyubiquitination of endogenous PD-L1 and Sirt1 in HCT116 cells. Moreover, in an in vivo syngeneic tumor model, Rottlerin and Morusin exhibited potent antitumor activity, which was accompanied by an enhanced infiltration of T cells into the tumor tissues. Using in-depth molecular dynamics (MD) and binding free energy calculation, conserved residue Leu475 and non-conserved residue Arg419 were proven to be crucial for the binding affinity and inhibitory function of USP22 inhibitors. In summary, our study established a highly efficient approach for USP22-specific inhibitor discovery, which lead to identification of two selective and potent USP22 inhibitors as potential drugs in anticancer therapy.
Collapse
Affiliation(s)
- Yue Zhang
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
| | - Jiankun Song
- Department of Biochemistry and Molecular Biology, Dalian Medical University, Dalian 116044, China
| | - Yuanzhang Zhou
- Department of Biochemistry and Molecular Biology, Dalian Medical University, Dalian 116044, China
| | - Huijun Jia
- Department of Biochemistry and Molecular Biology, Dalian Medical University, Dalian 116044, China
| | - Tianyu Zhou
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
| | - Yingbo Sun
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
| | - Qiong Gao
- Department of Biochemistry and Molecular Biology, Dalian Medical University, Dalian 116044, China
| | - Yue Zhao
- Department of Biochemistry and Molecular Biology, Dalian Medical University, Dalian 116044, China
| | - Yujie Pan
- College of Pharmacy, Dalian Medical University, Dalian 116044, China
| | - Zhaolin Sun
- College of Pharmacy, Dalian Medical University, Dalian 116044, China; Department of Biochemistry and Molecular Biology, Dalian Medical University, Dalian 116044, China.
| | - Peng Chu
- College of Pharmacy, Dalian Medical University, Dalian 116044, China.
| |
Collapse
|
5
|
Jain AN, Brueckner AC, Jorge C, Cleves AE, Khandelwal P, Cortes JC, Mueller L. Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design. J Comput Aided Mol Des 2023; 37:519-535. [PMID: 37535171 PMCID: PMC10505130 DOI: 10.1007/s10822-023-00524-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/20/2023] [Indexed: 08/04/2023]
Abstract
Systematic optimization of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead-optimization using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate. We show how conformational restraints can be derived by exploiting NMR data to identify low-energy solution ensembles of a lead compound. Such restraints can be used to focus conformational search for analogs in order to accurately predict bound ligand poses through molecular docking and thereby estimate ligand strain and protein-ligand intermolecular binding energy. We also describe an analogous ligand-based approach that employs molecular similarity optimization to predict bound poses. Both approaches are shown to be effective for prioritizing lead-compound analogs. Surprisingly, relatively small ligand modifications, which may have minimal effects on predicted bound pose or intermolecular interactions, often lead to large changes in estimated strain that have dominating effects on overall binding energy estimates. Effective macrocyclic conformational search is crucial, whether in the context of NMR-based restraints, X-ray ligand refinement, partial torsional restraint for docking/ligand-similarity calculations or agnostic search for nominal global minima. Lead optimization for peptidic macrocycles can be made more productive using a multi-disciplinary approach that combines biophysical data with practical and efficient computational methods.
Collapse
Affiliation(s)
- Ajay N Jain
- Research and Development, BioPharmics LLC, Sonoma County, CA, USA.
| | | | | | - Ann E Cleves
- Research and Development, BioPharmics LLC, Sonoma County, CA, USA
| | | | | | | |
Collapse
|
6
|
Jain AN, Brueckner AC, Cleves AE, Reibarkh M, Sherer EC. A Distributional Model of Bound Ligand Conformational Strain: From Small Molecules up to Large Peptidic Macrocycles. J Med Chem 2023; 66:1955-1971. [PMID: 36701387 PMCID: PMC9923749 DOI: 10.1021/acs.jmedchem.2c01744] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The internal conformational strain incurred by ligands upon binding a target site has a critical impact on binding affinity, and expectations about the magnitude of ligand strain guide conformational search protocols. Estimates for bound ligand strain begin with modeled ligand atomic coordinates from X-ray co-crystal structures. By deriving low-energy conformational ensembles to fit X-ray diffraction data, calculated strain energies are substantially reduced compared with prior approaches. We show that the distribution of expected global strain energy values is dependent on molecular size in a superlinear manner. The distribution of strain energy follows a rectified normal distribution whose mean and variance are related to conformational complexity. The modeled strain distribution closely matches calculated strain values from experimental data comprising over 3000 protein-ligand complexes. The distributional model has direct implications for conformational search protocols as well as for directions in molecular design.
Collapse
Affiliation(s)
- Ajay N. Jain
- Research
& Development, BioPharmics LLC, Sonoma County, California95404, United States,
| | - Alexander C. Brueckner
- Molecular
Structure & Design, Bristol Myers Squibb, Princeton, New Jersey08543, United States
| | - Ann E. Cleves
- Research
& Development, BioPharmics LLC, Sonoma County, California95404, United States
| | - Mikhail Reibarkh
- Analytical
Research and Development, Merck & Co.
Inc., Rahway, New Jersey07065, United States
| | - Edward C. Sherer
- Analytical
Research and Development, Merck & Co.
Inc., Rahway, New Jersey07065, United States,
| |
Collapse
|
7
|
Suriñach A, Hospital A, Westermaier Y, Jordà L, Orozco-Ruiz S, Beltrán D, Colizzi F, Andrio P, Soliva R, Municoy M, Gelpí JL, Orozco M. High-Throughput Prediction of the Impact of Genetic Variability on Drug Sensitivity and Resistance Patterns for Clinically Relevant Epidermal Growth Factor Receptor Mutations from Atomistic Simulations. J Chem Inf Model 2023; 63:321-334. [PMID: 36576351 DOI: 10.1021/acs.jcim.2c01344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to "state-of-the-art" atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these "state-of-the-art" methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.
Collapse
Affiliation(s)
- Aristarc Suriñach
- Nostrum Biodiscovery, Av. Josep Tarradellas 8-10, 08029 Barcelona, Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain
| | - Yvonne Westermaier
- Nostrum Biodiscovery, Av. Josep Tarradellas 8-10, 08029 Barcelona, Spain
| | - Luis Jordà
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain
| | - Sergi Orozco-Ruiz
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain
| | - Daniel Beltrán
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain
| | - Francesco Colizzi
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain
| | - Pau Andrio
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain
| | - Robert Soliva
- Nostrum Biodiscovery, Av. Josep Tarradellas 8-10, 08029 Barcelona, Spain
| | - Martí Municoy
- Nostrum Biodiscovery, Av. Josep Tarradellas 8-10, 08029 Barcelona, Spain
| | - Josep Lluís Gelpí
- Barcelona Supercomputing Center (BSC), Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain.,Department Biochemistry and Molecular Biomedicine, University of Barcelona, Barcelona 08029, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona 08028, Spain.,Department Biochemistry and Molecular Biomedicine, University of Barcelona, Barcelona 08029, Spain
| |
Collapse
|
8
|
Fraga GG, Colasurdo DD, Santiago CC, Ponzinibbio A, Sasiambarrena LD. Rotamerization equilibrium in novel N,N-disubstituted chloroacetamides: An NMR spectroscopic study. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.132892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
9
|
Zhou Y, Jiang Y, Chen SJ. RNA-ligand molecular docking: advances and challenges. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2022; 12:e1571. [PMID: 37293430 PMCID: PMC10250017 DOI: 10.1002/wcms.1571] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 07/20/2021] [Indexed: 12/16/2022]
Abstract
With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.
Collapse
Affiliation(s)
- Yuanzhe Zhou
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
| | - Yangwei Jiang
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
| | - Shi-Jie Chen
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
| |
Collapse
|
10
|
Gervasoni S, Malloci G, Bosin A, Vargiu AV, Zgurskaya HI, Ruggerone P. AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials. Sci Data 2022; 9:148. [PMID: 35365662 PMCID: PMC8976083 DOI: 10.1038/s41597-022-01261-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/11/2022] [Indexed: 12/13/2022] Open
Abstract
Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials. Measurement(s) | molecular physical property analysis objective | Technology Type(s) | Computer Modeling |
Collapse
Affiliation(s)
- Silvia Gervasoni
- University of Cagliari, Department of Physics, I-09042, Monserrato (Cagliari), Italy
| | - Giuliano Malloci
- University of Cagliari, Department of Physics, I-09042, Monserrato (Cagliari), Italy.
| | - Andrea Bosin
- University of Cagliari, Department of Physics, I-09042, Monserrato (Cagliari), Italy
| | - Attilio V Vargiu
- University of Cagliari, Department of Physics, I-09042, Monserrato (Cagliari), Italy
| | - Helen I Zgurskaya
- University of Oklahoma, Department of Chemistry and Biochemistry, Norman, OK, 73072, United States
| | - Paolo Ruggerone
- University of Cagliari, Department of Physics, I-09042, Monserrato (Cagliari), Italy
| |
Collapse
|
11
|
Neutral and charged forms of inubosin B in aqueous solutions at different pH and on the surface of Ag nanoparticles. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2021.131828] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
12
|
Morado J, Mortenson PN, Nissink JWM, Verdonk ML, Ward RA, Essex JW, Skylaris CK. Generation of Quantum Configurational Ensembles Using Approximate Potentials. J Chem Theory Comput 2021; 17:7021-7042. [PMID: 34644088 DOI: 10.1021/acs.jctc.1c00532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Conformational analysis is of paramount importance in drug design: it is crucial to determine pharmacological properties, understand molecular recognition processes, and characterize the conformations of ligands when unbound. Molecular Mechanics (MM) simulation methods, such as Monte Carlo (MC) and molecular dynamics (MD), are usually employed to generate ensembles of structures due to their ability to extensively sample the conformational space of molecules. The accuracy of these MM-based schemes strongly depends on the functional form of the force field (FF) and its parametrization, components that often hinder their performance. High-level methods, such as ab initio MD, provide reliable structural information but are still too computationally expensive to allow for extensive sampling. Therefore, to overcome these limitations, we present a multilevel MC method that is capable of generating quantum configurational ensembles while keeping the computational cost at a minimum. We show that FF reparametrization is an efficient route to generate FFs that reproduce QM results more closely, which, in turn, can be used as low-cost models to achieve the gold standard QM accuracy. We demonstrate that the MC acceptance rate is strongly correlated with various phase space overlap measurements and that it constitutes a robust metric to evaluate the similarity between the MM and QM levels of theory. As a more advanced application, we present a self-parametrizing version of the algorithm, which combines sampling and FF parametrization in one scheme, and apply the methodology to generate the QM/MM distribution of a ligand in aqueous solution.
Collapse
Affiliation(s)
- João Morado
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Paul N Mortenson
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - J Willem M Nissink
- Medicinal Chemistry, Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Marcel L Verdonk
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Richard A Ward
- Medicinal Chemistry, Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Chris-Kriton Skylaris
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| |
Collapse
|
13
|
Yousuf M, Rafi S, Ishrat U, Shafiga A, Dashdamirova G, Leyla V, Iqbal H. Potential Biological Targets Prediction, ADME Profiling, & Molecular Docking studies of Novel Steroidal Products from Cunninghamella Blakesleana. Med Chem 2021; 18:288-305. [PMID: 34102986 DOI: 10.2174/1573406417666210608143128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/07/2021] [Accepted: 01/26/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND New potential biological targets prediction through inverse molecular docking technique is an another smart strategy to forecast the possibility of compounds being biologically active against various target receptors. OBJECTIVES In this case of designed study, we screened our recently obtained novel acetylinic steroidal biotransformed products [(1) 8-β-methyl-14-α-hydroxy∆4tibolone (2) 9-α-Hydroxy∆4 tibolone (3) 8-β-methyl-11-β-hydroxy∆4tibolone (4) 6-β-hydroxy∆4tibolone, (5) 6-β-9-α-dihydroxy∆4tibolone (6) 7-β-hydroxy∆4tibolone) ] from fungi Cunninghemella Blakesleana to predict their possible biological targets and profiling of ADME properties. METHOD The prediction of pharmacokinetics properties membrane permeability as well as bioavailability radar properties were carried out by using Swiss target prediction, and Swiss ADME tools, respectively these metabolites were also subjected to predict the possible mechanism of action along with associated biological network pathways by using Reactome data-base. RESULTS All the six screened compounds possess excellent drug ability criteria, and exhibited exceptionally excellent non inhibitory potential against all five isozymes of CYP450 enzyme complex, including (CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4) respectively. All the screened compounds are lying within the acceptable pink zone of bioavailability radar and showing excellent descriptive properties. Compounds [1-4 & 6] are showing high BBB (Blood Brain Barrier) permeation, while compound 5 is exhibiting high HIA (Human Intestinal Absorption) property of (Egan Egg). CONCLUSION In conclusion, the results of this study smartly reveals that in-silico based studies are considered to provide robustness towards a rational drug designing and development approach, therefore in this way it helps to avoid the possibility of failure of drug candidates in the later experimental stages of drug development phases.
Collapse
Affiliation(s)
- Maria Yousuf
- Dow College of Biotechnology, Department of Bioinformatics, Dow University of Health Sciences Karachi, Pakistan
| | - Sidra Rafi
- International Centre for Chemical and Biological Sciences, University of Karachi, Pakistan
| | - Urooj Ishrat
- Dow Research Institute of Biotechnology and Biomedical Sciences, Dow University of Health Sciences, Karachi, Pakistan
| | | | | | | | - Heydarov Iqbal
- Botany Institute of, Azerbaijan National Academy of Sciences, Azerbaijan
| |
Collapse
|
14
|
Brueckner AC, Deng Q, Cleves AE, Lesburg CA, Alvarez JC, Reibarkh MY, Sherer EC, Jain AN. Conformational Strain of Macrocyclic Peptides in Ligand-Receptor Complexes Based on Advanced Refinement of Bound-State Conformers. J Med Chem 2021; 64:3282-3298. [PMID: 33724820 DOI: 10.1021/acs.jmedchem.0c02159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Macrocyclic peptides are an important modality in drug discovery, but molecular design is limited due to the complexity of their conformational landscape. To better understand conformational propensities, global strain energies were estimated for 156 protein-macrocyclic peptide cocrystal structures. Unexpectedly large strain energies were observed when the bound-state conformations were modeled with positional restraints. Instead, low-energy conformer ensembles were generated using xGen that fit experimental X-ray electron density maps and gave reasonable strain energy estimates. The ensembles featured significant conformational adjustments while still fitting the electron density as well or better than the original coordinates. Strain estimates suggest the interaction energy in protein-ligand complexes can offset a greater amount of strain for macrocyclic peptides than for small molecules and non-peptidic macrocycles. Across all molecular classes, the approximate upper bound on global strain energies had the same relationship with molecular size, and bound-state ensembles from xGen yielded favorable binding energy estimates.
Collapse
Affiliation(s)
- Alexander C Brueckner
- Computational & Structural Chemistry, Merck & Co Inc, 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Qiaolin Deng
- Computational & Structural Chemistry, Merck & Co Inc, 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Ann E Cleves
- Bioengineering and Therapeutic Sciences, University of California San Francisco, Box 0128, San Francisco, California 94158, United States
| | - Charles A Lesburg
- Computational and Structural Chemistry, Merck and Co Inc, 33 Avenue Louis Pasteur, Boston, Massachusetts 02115, United States
| | - Juan C Alvarez
- Computational and Structural Chemistry, Merck and Co Inc, 33 Avenue Louis Pasteur, Boston, Massachusetts 02115, United States
| | - Mikhail Y Reibarkh
- Analytical Research and Development, Merck & Co Inc, 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States
| | - Edward C Sherer
- Analytical Research and Development, Merck & Co Inc, 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States
| | - Ajay N Jain
- Bioengineering and Therapeutic Sciences, University of California San Francisco, Box 0128, San Francisco, California 94158, United States
| |
Collapse
|
15
|
Moreno D, Zivanovic S, Colizzi F, Hospital A, Aranda J, Soliva R, Orozco M. DFFR: A New Method for High-Throughput Recalibration of Automatic Force-Fields for Drugs. J Chem Theory Comput 2020; 16:6598-6608. [PMID: 32856910 DOI: 10.1021/acs.jctc.0c00306] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We present drug force-field recalibration (DFFR), a new method for refining of automatic force-fields used to represent small drugs in docking and molecular dynamics simulations. The method is based on fine-tuning of torsional terms to obtain ensembles that reproduce observables derived from reference data. DFFR is fast and flexible and can be easily automatized for a high-throughput regime, making it useful in drug-design projects. We tested the performance of the method in a few model systems and also in a variety of druglike molecules using reference data derived from: (i) density functional theory coupled to a self-consistent reaction field (DFT/SCRF) calculations on highly populated conformers and (ii) enhanced sampling quantum mechanical/molecular mechanics (QM/MM) where the drug is reproduced at the QM level, while the solvent is represented by classical force-fields. Extension of the method to include other sources of reference data is discussed.
Collapse
Affiliation(s)
- David Moreno
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Sanja Zivanovic
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Francesco Colizzi
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Juan Aranda
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Robert Soliva
- Nostrum Biodiscovery, Nexus II Building, Barcelona 08034, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Baldiri Reixac, 10, 08028 Barcelona, Spain.,Departament de Bioquímica i Biomedicina, Facultat de Biologia, Universitat de Barcelona, Barcelona E08028, Spain
| |
Collapse
|
16
|
Zivanovic S, Bayarri G, Colizzi F, Moreno D, Gelpí JL, Soliva R, Hospital A, Orozco M. Bioactive Conformational Ensemble Server and Database. A Public Framework to Speed Up In Silico Drug Discovery. J Chem Theory Comput 2020; 16:6586-6597. [PMID: 32786900 DOI: 10.1021/acs.jctc.0c00305] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Modern high-throughput structure-based drug discovery algorithms consider ligand flexibility, but typically with low accuracy, which results in a loss of performance in the derived models. Here we present the bioactive conformational ensemble (BCE) server and its associated database. The server creates conformational ensembles of drug-like ligands and stores them in the BCE database, where a variety of analyses are offered to the user. The workflow implemented in the BCE server combines enhanced sampling molecular dynamics with self-consistent reaction field quantum mechanics (SCRF/QM) calculations. The server automatizes all of the steps to transform one-dimensional (1D) or 2D representation of drugs into 3D molecules, which are then titrated, parametrized, hydrated, and optimized before being subjected to Hamiltonian replica-exchange (HREX) molecular dynamics simulations. Ensembles are collected and subjected to a clustering procedure to derive representative conformers, which are then analyzed at the SCRF/QM level of theory. All structural data are organized in a noSQL database accessible through a graphical interface and in a programmatic manner through a REST API. The server allows the user to define a private workspace and offers a deposition protocol as well as input files for "in house" calculations in those cases where confidentiality is a must. The database and the associated server are available at https://mmb.irbbarcelona.org/BCE.
Collapse
Affiliation(s)
- Sanja Zivanovic
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology (BIST). Barcelona 08028, Spain
| | - Genís Bayarri
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology (BIST). Barcelona 08028, Spain
| | - Francesco Colizzi
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology (BIST). Barcelona 08028, Spain
| | - David Moreno
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology (BIST). Barcelona 08028, Spain
| | - Josep Lluís Gelpí
- Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain.,Departament de Bioquímica i Biomedicina, Facultat de Biologia. Universitat de Barcelona, Barcelona E08028, Spain
| | - Robert Soliva
- Nostrum Biodiscovery, Nexus II Building, Barcelona 08034, Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology (BIST). Barcelona 08028, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology (BIST). Barcelona 08028, Spain.,Departament de Bioquímica i Biomedicina, Facultat de Biologia. Universitat de Barcelona, Barcelona E08028, Spain
| |
Collapse
|