1
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Kumar N, Acharya V. Advances in machine intelligence-driven virtual screening approaches for big-data. Med Res Rev 2024; 44:939-974. [PMID: 38129992 DOI: 10.1002/med.21995] [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: 09/12/2022] [Revised: 07/15/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
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Affiliation(s)
- Neeraj Kumar
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Vishal Acharya
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
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2
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Cleves AE, Jain AN, Demeter DA, Buchan ZA, Wilmot J, Hancock EN. From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product. J Comput Aided Mol Des 2024; 38:19. [PMID: 38630341 DOI: 10.1007/s10822-024-00555-3] [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: 01/02/2024] [Accepted: 02/26/2024] [Indexed: 04/19/2024]
Abstract
Scaffold replacement as part of an optimization process that requires maintenance of potency, desirable biodistribution, metabolic stability, and considerations of synthesis at very large scale is a complex challenge. Here, we consider a set of over 1000 time-stamped compounds, beginning with a macrocyclic natural-product lead and ending with a broad-spectrum crop anti-fungal. We demonstrate the application of the QuanSA 3D-QSAR method employing an active learning procedure that combines two types of molecular selection. The first identifies compounds predicted to be most active of those most likely to be well-covered by the model. The second identifies compounds predicted to be most informative based on exhibiting low predicted activity but showing high 3D similarity to a highly active nearest-neighbor training molecule. Beginning with just 100 compounds, using a deterministic and automatic procedure, five rounds of 20-compound selection and model refinement identifies the binding metabolic form of florylpicoxamid. We show how iterative refinement broadens the domain of applicability of the successive models while also enhancing predictive accuracy. We also demonstrate how a simple method requiring very sparse data can be used to generate relevant ideas for synthetic candidates.
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Affiliation(s)
- Ann E Cleves
- BioPharmics Division, Optibrium Limited, Cambridge, CB25 9GL, UK.
| | - Ajay N Jain
- BioPharmics Division, Optibrium Limited, Cambridge, CB25 9GL, UK
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3
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Vázquez J, García R, Llinares P, Luque FJ, Herrero E. On the relevance of query definition in the performance of 3D ligand-based virtual screening. J Comput Aided Mol Des 2024; 38:18. [PMID: 38573547 PMCID: PMC10995064 DOI: 10.1007/s10822-024-00561-5] [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: 03/01/2024] [Accepted: 03/26/2024] [Indexed: 04/05/2024]
Abstract
Ligand-based virtual screening (LBVS) methods are widely used to explore the vast chemical space in the search of novel compounds resorting to a variety of properties encoded in 1D, 2D or 3D descriptors. The success of 3D-LBVS is affected by the overlay of molecular pairs, thus making selection of the template compound, search of accessible conformational space and choice of the query conformation to be potential factors that modulate the successful retrieval of actives. This study examines the impact of adopting different choices for the query conformation of the template, paying also attention to the influence exerted by the structural similarity between templates and actives. The analysis is performed using PharmScreen, a 3D LBVS tool that relies on similarity measurements of the hydrophobic/philic pattern of molecules, and Phase Shape, which is based on the alignment of atom triplets followed by refinement of the volume overlap. The study is performed for the original DUD-E+ database and a Morgan Fingerprint filtered version (denoted DUD-E+-Diverse; available in https://github.com/Pharmacelera/Query-models-to-3DLBVS ), which was prepared to minimize the 2D resemblance between template and actives. Although in most cases the query conformation exhibits a mild influence on the overall performance, a critical analysis is made to disclose factors, such as the content of structural features between template and actives and the induction of conformational strain in the template, that underlie the drastic impact of the query definition in the recovery of actives for certain targets. The findings of this research also provide valuable guidance for assisting the selection of the query definition in 3D LBVS campaigns.
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Affiliation(s)
- Javier Vázquez
- Pharmacelera, Parc Científic de Barcelona (PCB), C/ Baldiri Reixac 4-8, Barcelona, 08028, Spain.
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Facultat de Farmàcia i Ciències de l'Alimentació, Institut de Química Teòrica I Computacional (IQTC-UB), Institut de Biomedicina (IBUB), University of Barcelona, Av. Prat de la Riba 171 , Santa Coloma de Gramenet, -08921, Spain.
| | - Ricardo García
- Pharmacelera, Parc Científic de Barcelona (PCB), C/ Baldiri Reixac 4-8, Barcelona, 08028, Spain
| | - Paula Llinares
- Pharmacelera, Parc Científic de Barcelona (PCB), C/ Baldiri Reixac 4-8, Barcelona, 08028, Spain
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Facultat de Farmàcia i Ciències de l'Alimentació, Institut de Química Teòrica I Computacional (IQTC-UB), Institut de Biomedicina (IBUB), University of Barcelona, Av. Prat de la Riba 171 , Santa Coloma de Gramenet, -08921, Spain
| | - F Javier Luque
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Facultat de Farmàcia i Ciències de l'Alimentació, Institut de Química Teòrica I Computacional (IQTC-UB), Institut de Biomedicina (IBUB), University of Barcelona, Av. Prat de la Riba 171 , Santa Coloma de Gramenet, -08921, Spain
| | - Enric Herrero
- Pharmacelera, Parc Científic de Barcelona (PCB), C/ Baldiri Reixac 4-8, Barcelona, 08028, Spain
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4
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Pisoni LA, Semple SJ, Liu S, Sykes MJ, Venter H. Combined Structure- and Ligand-Based Approach for the Identification of Inhibitors of AcrAB-TolC in Escherichia coli. ACS Infect Dis 2023; 9:2504-2522. [PMID: 37888944 DOI: 10.1021/acsinfecdis.3c00350] [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] [Indexed: 10/28/2023]
Abstract
The inhibition of efflux pumps is a promising approach to combating multidrug-resistant bacteria. We have developed a combined structure- and ligand-based model, using OpenEye software, for the identification of inhibitors of AcrB, the inner membrane protein component of the AcrAB-TolC efflux pump in Escherichia coli. From a database of 1391 FDA-approved drugs, 23 compounds were selected to test for efflux inhibition in E. coli. Seven compounds, including ivacaftor (25), butenafine (19), naftifine (27), pimozide (30), thioridazine (35), trifluoperazine (37), and meloxicam (26), enhanced the activity of at least one antimicrobial substrate and inhibited the efflux pump-mediated removal of the substrate Nile Red from cells. Ivacaftor (25) inhibited efflux dose dependently, had no effect on an E. coli strain with genomic deletion of the gene encoding AcrB, and did not damage the bacterial outer membrane. In the presence of a sub-minimum inhibitory concentration (MIC) of the outer membrane permeabilizer colistin, ivacaftor at 1 μg/mL reduced the MICs of erythromycin and minocycline by 4- to 8-fold. The identification of seven potential AcrB inhibitors shows the merits of a combined structure- and ligand-based approach to virtual screening.
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Affiliation(s)
- Lily A Pisoni
- Health and Biomedical Innovation, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia 5000, Australia
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia 5000, Australia
| | - Susan J Semple
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia 5000, Australia
| | - Sida Liu
- Health and Biomedical Innovation, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia 5000, Australia
| | - Matthew J Sykes
- Health and Biomedical Innovation, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia 5000, Australia
| | - Henrietta Venter
- Health and Biomedical Innovation, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia 5000, Australia
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5
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Zhang Q, Han J, Zhu Y, Yu F, Hu X, Tong HHY, Liu H. Discovery of novel and potent InhA direct inhibitors by ensemble docking-based virtual screening and biological assays. J Comput Aided Mol Des 2023; 37:695-706. [PMID: 37642861 DOI: 10.1007/s10822-023-00530-4] [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: 05/09/2023] [Accepted: 08/15/2023] [Indexed: 08/31/2023]
Abstract
Multidrug-resistant tuberculosis (MDR-TB) continues to spread worldwide and remains one of the leading causes of death among infectious diseases. The enoyl-acyl carrier protein reductase (InhA) belongs to FAS-II family and is essential for the formation of the Mycobacterium tuberculosis cell wall. Recent years, InhA direct inhibitors have been extensively studied to overcome MDR-TB. However, there are still no inhibitors that have entered clinical research. Here, the ensemble docking-based virtual screening along with biological assay were used to identify potent InhA direct inhibitors from Chembridge, Chemdiv, and Specs. Ultimately, 34 compounds were purchased and first assayed for the binding affinity, of which four compounds can bind InhA well with KD values ranging from 48.4 to 56.2 µM. Among them, compound 9,222,034 has the best inhibitory activity against InhA enzyme with an IC50 value of 18.05 µM. In addition, the molecular dynamic simulation and binding free energy calculation indicate that the identified compounds bind to InhA with "extended" conformation. Residue energy decomposition shows that residues such as Tyr158, Met161, and Met191 have higher energy contributions in the binding of compounds. By analyzing the binding modes, we found that these compounds can bind to a hydrophobic sub-pocket formed by residues Tyr158, Phe149, Ile215, Leu218, etc., resulting in extensive van der Waals interactions. In summary, this study proposed an efficient strategy for discovering InhA direct inhibitors through ensemble docking-based virtual screening, and finally identified four active compounds with new skeletons, which can provide valuable information for the discovery and optimization of InhA direct inhibitors.
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Affiliation(s)
- Qianqian Zhang
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, SAR, China
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Jianting Han
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, China
| | - Yongchang Zhu
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, China
| | - Fansen Yu
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Xiaopeng Hu
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, SAR, China
| | - Henry H Y Tong
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, SAR, China
| | - Huanxiang Liu
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, SAR, China.
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6
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Cheng Z, Hwang SS, Bhave M, Rahman T, Chee Wezen X. Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ. J Chem Inf Model 2023; 63:6912-6924. [PMID: 37883148 DOI: 10.1021/acs.jcim.3c01252] [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: 10/27/2023]
Abstract
Polo-like kinase 1 (PLK1) and p38γ mitogen-activated protein kinase (p38γ) play important roles in cancer pathogenesis by controlling cell cycle progression and are therefore attractive cancer targets. The design of multitarget inhibitors may offer synergistic inhibition of distinct targets and reduce the risk of drug-drug interactions to improve the balance between therapeutic efficacy and safety. We combined deep-learning-based quantitative structure-activity relationship (QSAR) modeling and hybrid-based consensus scoring to screen for inhibitors with potential activity against the targeted proteins. Using this combination strategy, we identified a potent PLK1 inhibitor (compound 4) that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Next, we deployed both our QSAR models for PLK1 and p38γ on the Enamine compound library to identify dual-targeting inhibitors against PLK1 and p38γ. Likewise, the identified hits were subsequently subjected to hybrid-based consensus scoring. Using this method, we identified a promising compound (compound 14) that could inhibit both PLK1 and p38γ activities. At nanomolar concentrations, compound 14 inhibited the growth of human hepatocellular carcinoma and hepatoblastoma cells in vitro. This study demonstrates the combined screening strategy to identify novel potential inhibitors for existing targets.
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Affiliation(s)
- Zixuan Cheng
- School of Engineering and Science, Swinburne University of Technology Sarawak, 93350 Kuching, Malaysia
| | - Siaw San Hwang
- School of Engineering and Science, Swinburne University of Technology Sarawak, 93350 Kuching, Malaysia
| | - Mrinal Bhave
- Department of Chemistry and Biotechnology, Swinburne University of Technology, Melbourne 3122, Victoria, Australia
| | - Taufiq Rahman
- Department of Pharmacology, University of Cambridge, Cambridge CB2 1PD, U.K
| | - Xavier Chee Wezen
- School of Engineering and Science, Swinburne University of Technology Sarawak, 93350 Kuching, Malaysia
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7
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Khan M, Kandwal S, Fayne D. DataPype: A Fully Automated Unified Software Platform for Computer-Aided Drug Design. ACS OMEGA 2023; 8:39468-39480. [PMID: 37901539 PMCID: PMC10601415 DOI: 10.1021/acsomega.3c05207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 09/26/2023] [Indexed: 10/31/2023]
Abstract
With the advent of computer-aided drug design (CADD), traditional physical testing of thousands of molecules has now been replaced by target-focused drug discovery, where potentially bioactive molecules are predicted by computer software before their physical synthesis. However, despite being a significant breakthrough, CADD still faces various limitations and challenges. The increasing availability of data on small molecules has created a need to streamline the sourcing of data from different databases and automate the processing and cleaning of data into a form that can be used by multiple CADD software applications. Several standalone software packages are available to aid the drug designer, each with its own specific application, requiring specialized knowledge and expertise for optimal use. These applications require their own input and output files, making it a challenge for nonexpert users or multidisciplinary discovery teams. Here, we have developed a new software platform called DataPype, which wraps around these different software packages. It provides a unified automated workflow to search for hit compounds using specialist software. Additionally, multiple virtual screening packages can be used in the one workflow, and if different ways of looking at potential hit compounds all predict the same set of molecules, we have higher confidence that we should make or purchase and test the molecules. Importantly, DataPype can run on computer servers, speeding up the virtual screening for new compounds. Combining access to multiple CADD tools within one interface will enhance the early stage of drug discovery, increase usability, and enable the use of parallel computing.
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Affiliation(s)
- Mohemmed
Faraz Khan
- Molecular
Design Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
- Department
of Pharmaceutical Chemistry, Faculty of Pharmacy, Integral University, Lucknow U.P., 226026, India
| | - Shubhangi Kandwal
- Molecular
Design Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
| | - Darren Fayne
- Molecular
Design Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
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8
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Scarano N, Abbotto E, Musumeci F, Salis A, Brullo C, Fossa P, Schenone S, Bruzzone S, Cichero E. Virtual Screening Combined with Enzymatic Assays to Guide the Discovery of Novel SIRT2 Inhibitors. Int J Mol Sci 2023; 24:ijms24119363. [PMID: 37298312 DOI: 10.3390/ijms24119363] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Sirtuin isoform 2 (SIRT2) is one of the seven sirtuin isoforms present in humans, being classified as class III histone deacetylases (HDACs). Based on the high sequence similarity among SIRTs, the identification of isoform selective modulators represents a challenging task, especially for the high conservation observed in the catalytic site. Efforts in rationalizing selectivity based on key residues belonging to the SIRT2 enzyme were accompanied in 2015 by the publication of the first X-ray crystallographic structure of the potent and selective SIRT2 inhibitor SirReal2. The subsequent studies led to different experimental data regarding this protein in complex with further different chemo-types as SIRT2 inhibitors. Herein, we reported preliminary Structure-Based Virtual Screening (SBVS) studies using a commercially available library of compounds to identify novel scaffolds for the design of new SIRT2 inhibitors. Biochemical assays involving five selected compounds allowed us to highlight the most effective chemical features supporting the observed SIRT2 inhibitory ability. This information guided the following in silico evaluation and in vitro testing of further compounds from in-house libraries of pyrazolo-pyrimidine derivatives towards novel SIRT2 inhibitors (1-5). The final results indicated the effectiveness of this scaffold for the design of promising and selective SIRT2 inhibitors, featuring the highest inhibition among the tested compounds, and validating the applied strategy.
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Affiliation(s)
- Naomi Scarano
- Department of Pharmacy, Section of Medicinal Chemistry, School of Medical and Pharmaceutical Sciences, University of Genoa, Viale Benedetto XV, 3, 16132 Genoa, Italy
| | - Elena Abbotto
- Department of Experimental Medicine, Section of Biochemistry, University of Genoa, Viale Benedetto XV 1, 16132 Genoa, Italy
| | - Francesca Musumeci
- Department of Pharmacy, Section of Medicinal Chemistry, School of Medical and Pharmaceutical Sciences, University of Genoa, Viale Benedetto XV, 3, 16132 Genoa, Italy
| | - Annalisa Salis
- Department of Experimental Medicine, Section of Biochemistry, University of Genoa, Viale Benedetto XV 1, 16132 Genoa, Italy
| | - Chiara Brullo
- Department of Pharmacy, Section of Medicinal Chemistry, School of Medical and Pharmaceutical Sciences, University of Genoa, Viale Benedetto XV, 3, 16132 Genoa, Italy
| | - Paola Fossa
- Department of Pharmacy, Section of Medicinal Chemistry, School of Medical and Pharmaceutical Sciences, University of Genoa, Viale Benedetto XV, 3, 16132 Genoa, Italy
| | - Silvia Schenone
- Department of Pharmacy, Section of Medicinal Chemistry, School of Medical and Pharmaceutical Sciences, University of Genoa, Viale Benedetto XV, 3, 16132 Genoa, Italy
| | - Santina Bruzzone
- Department of Experimental Medicine, Section of Biochemistry, University of Genoa, Viale Benedetto XV 1, 16132 Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy
| | - Elena Cichero
- Department of Pharmacy, Section of Medicinal Chemistry, School of Medical and Pharmaceutical Sciences, University of Genoa, Viale Benedetto XV, 3, 16132 Genoa, Italy
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Rogers DM, Agarwal R, Vermaas JV, Smith MD, Rajeshwar RT, Cooper C, Sedova A, Boehm S, Baker M, Glaser J, Smith JC. SARS-CoV2 billion-compound docking. Sci Data 2023; 10:173. [PMID: 36977690 PMCID: PMC10044124 DOI: 10.1038/s41597-023-01984-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 01/24/2023] [Indexed: 03/30/2023] Open
Abstract
This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens.
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Affiliation(s)
- David M Rogers
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
| | - Rupesh Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA
| | - Josh V Vermaas
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA
| | - Micholas Dean Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA
| | - Rajitha T Rajeshwar
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA
| | - Connor Cooper
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Biological Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Ada Sedova
- Biological Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Swen Boehm
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Matthew Baker
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Jens Glaser
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Jeremy C Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA.
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10
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Stillson NJ, Anderson KE, Reich NO. In silico study of selective inhibition mechanism of S-adenosyl-L-methionine analogs for human DNA methyltransferase 3A. Comput Biol Chem 2023; 102:107796. [PMID: 36495748 DOI: 10.1016/j.compbiolchem.2022.107796] [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: 07/08/2022] [Revised: 11/19/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022]
Abstract
Epigenetic mechanisms leading to transcriptional regulation, including DNA methylation, are frequently dysregulated in diverse cancers. Interfering with aberrant DNA methylation performed by DNA cytosine methyltransferases (DNMTs) is a clinically validated approach. In particular, the selective inhibition of the de novo DNMT3A and DNMT3B enzymes, whose expression is limited to early embryogenesis, adult stem cells, and in cancers, is particularly attractive; such selectivity is likely to attenuate the dose limiting toxicity shown by current, non-selective DNMT inhibitors. We use molecular dynamics (MD) based computational analysis to study known small molecule binders of DNMT3A, then propose reversible, tight binding, and selective inhibitors that exploit the Asn1192/Arg688 difference between the maintenance DNMT1 and DNMT3A near the active site. A similar strategy exploiting the presence of a unique active site cysteine Cys666 is used to propose DNMT3A-selective irreversible inhibitors. We report our results of relative binding energies of the known and proposed compounds estimated using MM/GBSA and umbrella sampling (US) techniques, and our evaluation of other end-point binding free energy calculation methods for these receptors. These calculations offer insight into the potential for small molecules to selectively target the active site of DNMT3A.
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Affiliation(s)
- Nathaniel J Stillson
- The Department of Chemistry and Biochemistry University of California, Santa Barbara 93106-9510, USA
| | - Kyle E Anderson
- The Department of Chemistry and Biochemistry University of California, Santa Barbara 93106-9510, USA
| | - Norbert O Reich
- The Department of Chemistry and Biochemistry University of California, Santa Barbara 93106-9510, USA.
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11
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Wang G, Bai Y, Cui J, Zong Z, Gao Y, Zheng Z. Computer-Aided Drug Design Boosts RAS Inhibitor Discovery. Molecules 2022; 27:molecules27175710. [PMID: 36080477 PMCID: PMC9457765 DOI: 10.3390/molecules27175710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/13/2022] [Accepted: 08/26/2022] [Indexed: 11/16/2022] Open
Abstract
The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.
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Affiliation(s)
- Ge Wang
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Yuhao Bai
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Jiarui Cui
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Zirui Zong
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Yuan Gao
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai 200120, China
| | - Zhen Zheng
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Correspondence:
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12
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fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions. PLoS Comput Biol 2022; 18:e1009783. [PMID: 35653385 PMCID: PMC9197077 DOI: 10.1371/journal.pcbi.1009783] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/14/2022] [Accepted: 05/06/2022] [Indexed: 11/19/2022] Open
Abstract
Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.
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13
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Ruiz Puentes P, Rueda-Gensini L, Valderrama N, Hernández I, González C, Daza L, Muñoz-Camargo C, Cruz JC, Arbeláez P. Predicting target-ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery. Sci Rep 2022; 12:8434. [PMID: 35589824 PMCID: PMC9119967 DOI: 10.1038/s41598-022-12180-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 05/05/2022] [Indexed: 02/08/2023] Open
Abstract
Drug Discovery is an active research area that demands great investments and generates low returns due to its inherent complexity and great costs. To identify potential therapeutic candidates more effectively, we propose protein–ligand with adversarial augmentations network (PLA-Net), a deep learning-based approach to predict target–ligand interactions. PLA-Net consists of a two-module deep graph convolutional network that considers ligands’ and targets’ most relevant chemical information, successfully combining them to find their binding capability. Moreover, we generate adversarial data augmentations that preserve relevant biological backgrounds and improve the interpretability of our model, highlighting the relevant substructures of the ligands reported to interact with the protein targets. Our experiments demonstrate that the joint ligand–target information and the adversarial augmentations significantly increase the interaction prediction performance. PLA-Net achieves 86.52% in mean average precision for 102 target proteins with perfect performance for 30 of them, in a curated version of actives as decoys dataset. Lastly, we accurately predict pharmacologically-relevant molecules when screening the ligands of ChEMBL and drug repurposing Hub datasets with the perfect-scoring targets.
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Affiliation(s)
- Paola Ruiz Puentes
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Laura Rueda-Gensini
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Natalia Valderrama
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Isabela Hernández
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Cristina González
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Laura Daza
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia.,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Carolina Muñoz-Camargo
- Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Juan C Cruz
- Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia
| | - Pablo Arbeláez
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, 111711, Colombia. .,Department of Biomedical Engineering, Universidad de los Andes, Bogotá, 111711, Colombia.
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14
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Stafford KA, Anderson BM, Sorenson J, van den Bedem H. AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens. J Chem Inf Model 2022; 62:1178-1189. [PMID: 35235748 PMCID: PMC8924924 DOI: 10.1021/acs.jcim.1c01250] [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] [Indexed: 12/17/2022]
Abstract
Structure-based, virtual High-Throughput Screening (vHTS) methods for predicting ligand activity in drug discovery are important when there are no or relatively few known compounds that interact with a therapeutic target of interest. State-of-the-art computational vHTS necessarily relies on effective methods for pose sampling and docking and generating an accurate affinity score from the docked poses. However, proteins are dynamic; in vivo ligands bind to a conformational ensemble. In silico docking to the single conformation represented by a crystal structure can adversely affect the pose quality. Here, we introduce AtomNet PoseRanker (ANPR), a graph convolutional network trained to identify and rerank crystal-like ligand poses from a sampled ensemble of protein conformations and ligand poses. In contrast to conventional vHTS methods that incorporate receptor flexibility, a deep learning approach can internalize valid cognate and noncognate binding modes corresponding to distinct receptor conformations, thereby learning to infer and account for receptor flexibility even on single conformations. ANPR significantly enriched pose quality in docking to cognate and noncognate receptors of the PDBbind v2019 data set. Improved pose rankings that better represent experimentally observed ligand binding modes improve hit rates in vHTS campaigns and thereby advance computational drug discovery, especially for novel therapeutic targets or novel binding sites.
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Affiliation(s)
- Kate A Stafford
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103, United States
| | - Brandon M Anderson
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103, United States
| | - Jon Sorenson
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103, United States
| | - Henry van den Bedem
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103, United States.,Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94158, United States
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15
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Giangreco I, Mukhopadhyay A, Cole JC. Validation of a Field-Based Ligand Screener Using a Novel Benchmarking Data Set for Assessing 3D-Based Virtual Screening Methods. J Chem Inf Model 2021; 61:5841-5852. [PMID: 34792345 DOI: 10.1021/acs.jcim.1c00866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Ligand-based methods play a crucial role in virtual screening when the 3D structure of the target is not available. This study discusses the results of a validation study of the CSD field-based ligand screener using a novel benchmarking data set containing 56 targets. The data set was created starting from the target UniProt IDs in a previously published data set (i.e., the AZ data set), by mining ChEMBL to find known active molecules for these targets and by using DUD-E to generate property-matched decoys of the identified actives. Several experiments were performed to assess the virtual screening performance of the new method. One of its strengths is that it can use an overlay of multiple flexible ligands as a query without the need to run several parallel calculations with one ligand at a time. Here, we discuss how changes to different parameter settings or adoption of different query models can influence the final performance compared to the performance when using the experimentally observed overlay of ligands. We have also generated the enrichment scores based on three external benchmark data sets to enable the comparison with existing methods previously validated using these data sets. Here, we present results for the standard DUD-E data set, the DUD-E+ data set, as well as the DUD_Lib_VS_1.0 data set which was designed for ligand-based virtual screening validation and hence is more suitable for this type of methods.
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Affiliation(s)
- Ilenia Giangreco
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Abhik Mukhopadhyay
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
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16
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Schwarz D, Georges G, Kelm S, Shi J, Vangone A, Deane CM. Co-evolutionary distance predictions contain flexibility information. Bioinformatics 2021; 38:65-72. [PMID: 34383892 DOI: 10.1093/bioinformatics/btab562] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 06/19/2021] [Accepted: 08/10/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue-residue distance predictions to be informative of protein flexibility rather than simply static structure. RESULTS We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dominik Schwarz
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
| | - Guy Georges
- Department of Computational Engineering and Data Science, Large Molecule Research, Penzberg 82377, Germany
| | - Sebastian Kelm
- Computer-Aided Drug Design, UCB Pharma, Slough SL1 3WE, UK
| | - Jiye Shi
- Computer-Aided Drug Design, UCB Pharma, Slough SL1 3WE, UK
| | - Anna Vangone
- Department of Computational Engineering and Data Science, Large Molecule Research, Penzberg 82377, Germany
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17
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Spiegel J, Senderowitz H. A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening. Int J Mol Sci 2021; 23:43. [PMID: 35008467 PMCID: PMC8744642 DOI: 10.3390/ijms23010043] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/18/2021] [Accepted: 12/19/2021] [Indexed: 12/30/2022] Open
Abstract
Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set (e.g.,QCV2) or the test set (e.g., specificity, selectivity or QF1/F2/F32). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.
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Affiliation(s)
| | - Hanoch Senderowitz
- Department of Chemistry, Bar-Ilan University, Ramat-Gan 5290002, Israel;
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18
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Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes. Proc Natl Acad Sci U S A 2021; 118:2112621118. [PMID: 34921117 PMCID: PMC8713799 DOI: 10.1073/pnas.2112621118] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/15/2021] [Indexed: 01/02/2023] Open
Abstract
Structure-based drug design depends on the ability to predict both the three-dimensional structures of candidate molecules bound to their targets and the associated binding affinities. We demonstrate that one can substantially improve the accuracy of these predictions using easily obtained data about completely different molecules that bind to the same target without requiring any target-bound structures of these molecules. The approach we developed to integrate physical and data-driven modeling may find a variety of applications in the rapidly growing field of artificial intelligence for drug discovery. Over the past five decades, tremendous effort has been devoted to computational methods for predicting properties of ligands—i.e., molecules that bind macromolecular targets. Such methods, which are critical to rational drug design, fall into two categories: physics-based methods, which directly model ligand interactions with the target given the target’s three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here, we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand’s pose—the 3D structure of the ligand bound to its target—that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves pose prediction accuracy across all major families of drug targets. Using the same framework, we develop a method for virtual screening of drug candidates, which outperforms standard physics-based and ligand-based virtual screening methods. Our results suggest broad opportunities to improve prediction of various ligand properties by combining diverse sources of information through customized machine-learning approaches.
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19
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Pihan E, Kotev M, Rabal O, Beato C, Diaz Gonzalez C. Fine tuning for success in structure-based virtual screening. J Comput Aided Mol Des 2021; 35:1195-1206. [PMID: 34799816 DOI: 10.1007/s10822-021-00431-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 11/08/2021] [Indexed: 11/30/2022]
Abstract
Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.
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Affiliation(s)
- Emilie Pihan
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France.
| | - Martin Kotev
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France
| | - Obdulia Rabal
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France
| | - Claudia Beato
- Aptuit (Verona) Srl, an Evotec Company, Via Alessandro Fleming, 4, 37135, Verona, Italy
| | - Constantino Diaz Gonzalez
- Computational Drug Discovery, Evotec (France) SAS, Campus Curie, 195 Route d'Espagne, 31036, Toulouse, France
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20
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Nunes-Alves A, Ormersbach F, Wade RC. Prediction of the Drug-Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis. J Chem Inf Model 2021; 61:3708-3721. [PMID: 34197096 DOI: 10.1021/acs.jcim.1c00639] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
There is growing consensus that the optimization of the kinetic parameters for drug-protein binding leads to improved drug efficacy. Therefore, computational methods have been developed to predict kinetic rates and to derive quantitative structure-kinetic relationships (QSKRs). Many of these methods are based on crystal structures of ligand-protein complexes. However, a drawback is that each ligand-protein complex is usually treated as having a single structure. Here, we present a modification of COMparative BINding Energy (COMBINE) analysis, which uses the structures of ligand-protein complexes to predict binding parameters. We introduce the option of using multiple structures to describe each ligand-protein complex in COMBINE analysis and apply this to study the effects of protein flexibility on the derivation of dissociation rate constants (koff) for inhibitors of p38 mitogen-activated protein (MAP) kinase, which has a flexible binding site. Multiple structures were obtained for each ligand-protein complex by performing docking to an ensemble of protein configurations obtained from molecular dynamics simulations. Coefficients to scale ligand-protein interaction energies determined from energy-minimized structures of ligand-protein complexes were obtained by partial least squares regression, and they allowed for the computation of koff values. The QSKR model obtained using single, energy-minimized crystal structures for each ligand-protein complex had higher predictive power than the QSKR model obtained with multiple structures from ensemble docking. However, incorporation of ligand-protein flexibility helped to highlight additional ligand-protein interactions that lead to longer residence times, such as interactions with residues Arg67 and Asp168, which are close to the ligand in many crystal structures. These results show that COMBINE analysis is a promising method to guide the design of compounds that bind to flexible proteins with improved binding kinetics.
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Affiliation(s)
- Ariane Nunes-Alves
- Heidelberg Institute for Theoretical Studies (HITS), Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.,Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
| | - Fabian Ormersbach
- Heidelberg Institute for Theoretical Studies (HITS), Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Rebecca C Wade
- Heidelberg Institute for Theoretical Studies (HITS), Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.,Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
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21
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Affiliation(s)
- W Patrick Walters
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - Renxiao Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
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22
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Gossen J, Albani S, Hanke A, Joseph BP, Bergh C, Kuzikov M, Costanzi E, Manelfi C, Storici P, Gribbon P, Beccari AR, Talarico C, Spyrakis F, Lindahl E, Zaliani A, Carloni P, Wade RC, Musiani F, Kokh DB, Rossetti G. A Blueprint for High Affinity SARS-CoV-2 Mpro Inhibitors from Activity-Based Compound Library Screening Guided by Analysis of Protein Dynamics. ACS Pharmacol Transl Sci 2021; 4:1079-1095. [PMID: 34136757 PMCID: PMC8009102 DOI: 10.1021/acsptsci.0c00215] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Indexed: 12/27/2022]
Abstract
The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ∼30 000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ∼200 virtual screenings of compound libraries on selected protein structures, we redefine the protein's druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.
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Affiliation(s)
- Jonas Gossen
- Institute
for Neuroscience and Medicine (INM-9), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Institute
for Advanced Simulations (IAS-5) “Computational biomedicine”, Forschungszentrum Jülich, Jülich, 52425, Germany
- Faculty of
Mathematics, Computer Science and Natural Sciences, RWTH Aachen, Aachen, 52062, Germany
| | - Simone Albani
- Institute
for Neuroscience and Medicine (INM-9), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Institute
for Advanced Simulations (IAS-5) “Computational biomedicine”, Forschungszentrum Jülich, Jülich, 52425, Germany
- Faculty of
Mathematics, Computer Science and Natural Sciences, RWTH Aachen, Aachen, 52062, Germany
| | - Anton Hanke
- Molecular
and Cellular Modeling Group, Heidelberg
Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, Heidelberg, 69118, Germany
- Institute
of Pharmacy and Molecular Biotechnology (IPMB), Heidelberg University, Im Neuenheimer Feld 364, Heidelberg, 69120, Germany
| | - Benjamin P. Joseph
- Institute
for Neuroscience and Medicine (INM-9), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Institute
for Advanced Simulations (IAS-5) “Computational biomedicine”, Forschungszentrum Jülich, Jülich, 52425, Germany
- Faculty of
Mathematics, Computer Science and Natural Sciences, RWTH Aachen, Aachen, 52062, Germany
| | - Cathrine Bergh
- Science for
Life Laboratory & Swedish e-Science Research Center, Department
of Applied Physics, KTH Royal Institute
of Technology, Stockholm, 11428, Sweden
| | - Maria Kuzikov
- Department
of Screening Port, Fraunhofer Institute
for Translational Medicine and Pharmacology ITMP, Schnackenburgallee 114, Hamburg, 22525, Germany
| | - Elisa Costanzi
- Elettra-Sincrotrone
Trieste S.C.p.A., SS 14-km 163,5 in AREA Science Park, Basovizza,
Trieste, 34149, Italy
| | - Candida Manelfi
- Dompé
Farmaceutici SpA, Via Campo di Pile, L’Aquila, 67100, Italy
| | - Paola Storici
- Elettra-Sincrotrone
Trieste S.C.p.A., SS 14-km 163,5 in AREA Science Park, Basovizza,
Trieste, 34149, Italy
| | - Philip Gribbon
- Department
of Screening Port, Fraunhofer Institute
for Translational Medicine and Pharmacology ITMP, Schnackenburgallee 114, Hamburg, 22525, Germany
| | | | - Carmine Talarico
- Dompé
Farmaceutici SpA, Via Campo di Pile, L’Aquila, 67100, Italy
| | - Francesca Spyrakis
- Department
of Drug Science and Technology, University
of Turin, via Giuria
9, Turin, 10125, Italy
| | - Erik Lindahl
- Science for
Life Laboratory & Swedish e-Science Research Center, Department
of Applied Physics, KTH Royal Institute
of Technology, Stockholm, 11428, Sweden
- Science
for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, SE-106 91, Sweden
| | - Andrea Zaliani
- Department
of Screening Port, Fraunhofer Institute
for Translational Medicine and Pharmacology ITMP, Schnackenburgallee 114, Hamburg, 22525, Germany
| | - Paolo Carloni
- Institute
for Neuroscience and Medicine (INM-9), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Institute
for Molecular Neuroscience and Neuroimaging (INM-11), Forschungszentrum Jülich, Jülich, 52425, Germany
- Institute
for Advanced Simulations (IAS-5) “Computational biomedicine”, Forschungszentrum Jülich, Jülich, 52425, Germany
- Faculty of
Mathematics, Computer Science and Natural Sciences, RWTH Aachen, Aachen, 52062, Germany
| | - Rebecca C. Wade
- Molecular
and Cellular Modeling Group, Heidelberg
Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, Heidelberg, 69118, Germany
- Zentrum
für Molekulare Biologie der University Heidelberg, DKFZ-ZMBH
Alliance, INF 282, Heidelberg, 69120, Germany
- Interdisciplinary
Center for Scientific Computing (IWR), Heidelberg
University, INF 368, Heidelberg, 69120, Germany
| | - Francesco Musiani
- Laboratory
of Bioinorganic Chemistry, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, 40126, Italy
| | - Daria B. Kokh
- Molecular
and Cellular Modeling Group, Heidelberg
Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, Heidelberg, 69118, Germany
| | - Giulia Rossetti
- Institute
for Neuroscience and Medicine (INM-9), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Institute
for Advanced Simulations (IAS-5) “Computational biomedicine”, Forschungszentrum Jülich, Jülich, 52425, Germany
- Jülich
Supercomputing Center (JSC), Forschungszentrum
Jülich, Jülich, 52425, Germany
- Department
of Hematology, Oncology, Hemostaseology, and Stem Cell Transplantation, RWTH Aachen University, Aachen, 44517, Germany
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23
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Tran-Nguyen VK, Bret G, Rognan D. True Accuracy of Fast Scoring Functions to Predict High-Throughput Screening Data from Docking Poses: The Simpler the Better. J Chem Inf Model 2021; 61:2788-2797. [PMID: 34109796 DOI: 10.1021/acs.jcim.1c00292] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Hundreds of fast scoring functions have been developed over the last 20 years to predict binding free energies from three-dimensional structures of protein-ligand complexes. Despite numerous statistical promises, we believe that none of them has been properly validated for daily prospective high-throughput virtual screening studies, mostly because in silico screening challenges usually employ artificially built and biased datasets. We here carry out a fully unbiased evaluation of four scoring functions (Pafnucy, ΔvinaRF20, IFP, and GRIM) on an in-house developed data collection of experimental high-confidence screening data (LIT-PCBA) covering about 3 million data points on 15 diverse pharmaceutical targets. All four scoring functions were applied to rescore the docking poses of LIT-PCBA compounds in conditions mimicking exactly standard drug discovery scenarios and were compared in terms of propensity to enrich true binders in the top 1%-ranked hit lists. Interestingly, rescoring based on simple interaction fingerprints or interaction graphs outperforms state-of-the-art machine learning and deep learning scoring functions in most of the cases. The current study notably highlights the strong tendency of deep learning methods to predict affinity values within a very narrow range centered on the mean value of samples used for training. Moreover, it suggests that knowledge of pre-existing binding modes is the key to detecting the most potent binders.
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Affiliation(s)
- Viet-Khoa Tran-Nguyen
- 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
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24
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Sulimov VB, Kutov DC, Taschilova AS, Ilin IS, Tyrtyshnikov EE, Sulimov AV. Docking Paradigm in Drug Design. Curr Top Med Chem 2021; 21:507-546. [PMID: 33292135 DOI: 10.2174/1568026620666201207095626] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 09/28/2020] [Accepted: 10/16/2020] [Indexed: 11/22/2022]
Abstract
Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.
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Affiliation(s)
- Vladimir B Sulimov
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Danil C Kutov
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Anna S Taschilova
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Ivan S Ilin
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
| | - Eugene E Tyrtyshnikov
- Institute of Numerical Mathematics of Russian Academy of Sciences, Moscow, Russian Federation
| | - Alexey V Sulimov
- Research Computer Center of Lomonosov Moscow State University, Moscow, Russian Federation
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25
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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.
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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
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26
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Stein RM, Yang Y, Balius TE, O'Meara MJ, Lyu J, Young J, Tang K, Shoichet BK, Irwin JJ. Property-Unmatched Decoys in Docking Benchmarks. J Chem Inf Model 2021; 61:699-714. [PMID: 33494610 PMCID: PMC7913603 DOI: 10.1021/acs.jcim.0c00598] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.
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Affiliation(s)
- Reed M Stein
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Ying Yang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Trent E Balius
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., P.O. Box B, Frederick, Maryland 21702, United States
| | - Matt J O'Meara
- Department of Computational Medicine and Bioinformatics, University of Michigan, Palmer Commons, 100 Washtenaw Ave. #2017, Ann Arbor, Michigan 48109, United States
| | - Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Jennifer Young
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Khanh Tang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
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27
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Schultz KJ, Colby SM, Yesiltepe Y, Nuñez JR, McGrady MY, Renslow RS. Application and assessment of deep learning for the generation of potential NMDA receptor antagonists. Phys Chem Chem Phys 2021; 23:1197-1214. [PMID: 33355332 DOI: 10.1039/d0cp03620j] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. We present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.
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Affiliation(s)
| | - Sean M Colby
- Pacific Northwest National Laboratory, Richland, WA, USA.
| | | | - Jamie R Nuñez
- Pacific Northwest National Laboratory, Richland, WA, USA.
| | | | - Ryan S Renslow
- Pacific Northwest National Laboratory, Richland, WA, USA.
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28
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Guedes IA, Barreto AMS, Marinho D, Krempser E, Kuenemann MA, Sperandio O, Dardenne LE, Miteva MA. New machine learning and physics-based scoring functions for drug discovery. Sci Rep 2021; 11:3198. [PMID: 33542326 PMCID: PMC7862620 DOI: 10.1038/s41598-021-82410-1] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br.
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Affiliation(s)
- Isabella A Guedes
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil.,Inserm U973, Université Paris Diderot, Paris, France
| | - André M S Barreto
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil
| | - Diogo Marinho
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil
| | | | | | - Olivier Sperandio
- Inserm U973, Université Paris Diderot, Paris, France.,Structural Bioinformatics Unit, CNRS UMR3528, Institut Pasteur, 75015, Paris, France
| | - Laurent E Dardenne
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil.
| | - Maria A Miteva
- Inserm U973, Université Paris Diderot, Paris, France. .,Inserm U1268 "Medicinal Chemistry and Translational Research", CiTCoM, UMR 8038, CNRS, Université de Paris, 75006, Paris, France.
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29
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Vázquez J, López M, Gibert E, Herrero E, Luque FJ. Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches. Molecules 2020; 25:E4723. [PMID: 33076254 PMCID: PMC7587536 DOI: 10.3390/molecules25204723] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/06/2020] [Accepted: 10/11/2020] [Indexed: 12/20/2022] Open
Abstract
Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.
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Affiliation(s)
- Javier Vázquez
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
| | - Manel López
- AB Science, Parc Scientifique de Luminy, Zone Luminy Enterprise, Case 922, 163 Av. de Luminy, 13288 Marseille, France;
| | - Enric Gibert
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - Enric Herrero
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - F. Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
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30
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Jaladanki CK, He Y, Zhao LN, Maurer-Stroh S, Loo LH, Song H, Fan H. Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids. Arch Toxicol 2020; 95:355-374. [PMID: 32909075 PMCID: PMC7811525 DOI: 10.1007/s00204-020-02897-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 08/27/2020] [Indexed: 12/17/2022]
Abstract
Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100-250 μM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations.
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Affiliation(s)
- Chaitanya K Jaladanki
- Bioinformatics Institute (BII), Agency for Science, Technology, and Research (A*STAR), 30 Biopolis Street, Matrix No. 07-01, Singapore, 138671, Singapore
- Toxicity Mode-of-Action Discovery (ToxMAD) Platform, Innovations in Food and Chemical Safety Programme, Agency for Science, Technology, and Research (A*STAR), Singapore, 138671, Singapore
| | - Yang He
- Institute of Molecular and Cell Biology, 61 Biopolis Drive, Singapore, 138673, Singapore
| | - Li Na Zhao
- Bioinformatics Institute (BII), Agency for Science, Technology, and Research (A*STAR), 30 Biopolis Street, Matrix No. 07-01, Singapore, 138671, Singapore
| | - Sebastian Maurer-Stroh
- Bioinformatics Institute (BII), Agency for Science, Technology, and Research (A*STAR), 30 Biopolis Street, Matrix No. 07-01, Singapore, 138671, Singapore
- Toxicity Mode-of-Action Discovery (ToxMAD) Platform, Innovations in Food and Chemical Safety Programme, Agency for Science, Technology, and Research (A*STAR), Singapore, 138671, Singapore
| | - Lit-Hsin Loo
- Bioinformatics Institute (BII), Agency for Science, Technology, and Research (A*STAR), 30 Biopolis Street, Matrix No. 07-01, Singapore, 138671, Singapore
- Toxicity Mode-of-Action Discovery (ToxMAD) Platform, Innovations in Food and Chemical Safety Programme, Agency for Science, Technology, and Research (A*STAR), Singapore, 138671, Singapore
| | - Haiwei Song
- Institute of Molecular and Cell Biology, 61 Biopolis Drive, Singapore, 138673, Singapore.
| | - Hao Fan
- Bioinformatics Institute (BII), Agency for Science, Technology, and Research (A*STAR), 30 Biopolis Street, Matrix No. 07-01, Singapore, 138671, Singapore.
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31
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Singh N, Chaput L, Villoutreix BO. Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces. J Chem Inf Model 2020; 60:3910-3934. [PMID: 32786511 DOI: 10.1021/acs.jcim.0c00545] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.
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Affiliation(s)
- Natesh Singh
- Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France
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