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Zhao M, Yu W, MacKerell AD. Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms. J Phys Chem B 2024; 128:7362-7375. [PMID: 39031121 PMCID: PMC11294009 DOI: 10.1021/acs.jpcb.4c03045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]
Abstract
In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.
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Affiliation(s)
- Mingtian Zhao
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
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Saini RS, Binduhayyim RIH, Gurumurthy V, Alshadidi AAF, Bavabeedu SS, Vyas R, Dermawan D, Naseef PP, Mosaddad SA, Heboyan A. In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations. JOURNAL OF MATERIALS SCIENCE. MATERIALS IN MEDICINE 2024; 35:28. [PMID: 38833196 DOI: 10.1007/s10856-024-06799-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 05/09/2024] [Indexed: 06/06/2024]
Abstract
AIM This study aimed to comprehensively assess the biocompatibility and toxicity profiles of poly(methyl methacrylate) (PMMA) and its monomeric unit, methyl methacrylate (MMA), crucial components in dental materials for interim prosthetic restorations. METHODOLOGY Molecular docking was employed to predict the binding affinities, energetics, and steric features of MMA and PMMA with selected receptors involved in bone metabolism and tissue development, including RANKL, Fibronectin, BMP9, NOTCH2, and other related receptors. The HADDOCK standalone version was utilized for docking calculations, employing a Lamarckian genetic algorithm to explore the conformational space of ligand-receptor interactions. Furthermore, molecular dynamics (MD) simulations over 100 nanoseconds were conducted using the GROMACS package to evaluate dynamic actions and structural stability. The LigandScout was utilized for pharmacophore modeling, which employs a shape-based screening approach to identify potential ligand binding sites on protein targets. RESULTS The molecular docking studies elucidated promising interactions between PMMA and MMA with key biomolecular targets relevant to dental applications. MD simulation results provided strong evidence supporting the structural stability of PMMA complexes over time. Pharmacophore modeling highlighted the significance of carbonyl and hydroxyl groups as pharmacophoric features, indicating compounds with favorable biocompatibility profiles. CONCLUSION This study underscores the potential of PMMA in dental applications, emphasizing its structural stability, molecular interactions, and safety considerations. These findings lay a foundation for future advancements in dental biomaterials, guiding the design and optimization of materials for enhanced biocompatibility. Future directions include experimental validation of computational findings and the development of PMMA-based dental materials with improved biocompatibility and clinical performance.
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Affiliation(s)
- Ravinder S Saini
- Department of Dental Technology, COAMS, King Khalid University, Abha, Saudi Arabia
| | | | | | | | - Shashit Shetty Bavabeedu
- Department of Restorative Dentistry, College of Dentistry, King Khalid University, Abha, Saudi Arabia
| | - Rajesh Vyas
- Department of Dental Technology, COAMS, King Khalid University, Abha, Saudi Arabia
| | - Doni Dermawan
- Department of Chemistry, Warsaw University of Technology, Warsaw, Poland
| | | | - Seyed Ali Mosaddad
- Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
- Student Research Committee, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Artak Heboyan
- Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
- Department of Prosthodontics, Faculty of Stomatology, Yerevan State Medical University after Mkhitar Heratsi, Yerevan, Armenia.
- Department of Prosthodontics, School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran.
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Wu Z, Chen S, Wang Y, Li F, Xu H, Li M, Zeng Y, Wu Z, Gao Y. Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis. Int J Surg 2024; 110:3848-3878. [PMID: 38502850 PMCID: PMC11175770 DOI: 10.1097/js9.0000000000001289] [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: 11/08/2023] [Accepted: 02/22/2024] [Indexed: 03/21/2024]
Abstract
AIM Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics. METHODS A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace. RESULTS A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications. CONCLUSIONS Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.
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Affiliation(s)
- Zhenhui Wu
- School of Pharmacy, Jiangxi University of Chinese Medicine
- School of Clinical Medicine, Jiangxi University of Chinese Medicine, Nanchang
- Beijing Institute of Radiation Medicine, Academy of Military Sciences, Beijing, People’s Republic of China
| | - Shupeng Chen
- School of Clinical Medicine, Jiangxi University of Chinese Medicine, Nanchang
| | - Yihao Wang
- Beijing Institute of Radiation Medicine, Academy of Military Sciences, Beijing, People’s Republic of China
| | - Fangyang Li
- Beijing Institute of Radiation Medicine, Academy of Military Sciences, Beijing, People’s Republic of China
| | - Huanhua Xu
- School of Pharmacy, Jiangxi University of Chinese Medicine
| | - Maoxing Li
- Beijing Institute of Radiation Medicine, Academy of Military Sciences, Beijing, People’s Republic of China
| | - Yingjian Zeng
- School of Clinical Medicine, Jiangxi University of Chinese Medicine, Nanchang
| | - Zhenfeng Wu
- School of Pharmacy, Jiangxi University of Chinese Medicine
| | - Yue Gao
- School of Pharmacy, Jiangxi University of Chinese Medicine
- Beijing Institute of Radiation Medicine, Academy of Military Sciences, Beijing, People’s Republic of China
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4
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Andreev G, Kovalenko M, Bozdaganyan ME, Orekhov PS. Colabind: A Cloud-Based Approach for Prediction of Binding Sites Using Coarse-Grained Simulations with Molecular Probes. J Phys Chem B 2024; 128:3211-3219. [PMID: 38514440 DOI: 10.1021/acs.jpcb.3c07853] [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: 03/23/2024]
Abstract
Binding site prediction is a crucial step in understanding protein-ligand and protein-protein interactions (PPIs) with broad implications in drug discovery and bioinformatics. This study introduces Colabind, a robust, versatile, and user-friendly cloud-based approach that employs coarse-grained molecular dynamics simulations in the presence of molecular probes, mimicking fragments of drug-like compounds. Our method has demonstrated high effectiveness when validated across a diverse range of biological targets spanning various protein classes, successfully identifying orthosteric binding sites, as well as known druggable allosteric or PPI sites, in both experimentally determined and AI-predicted protein structures, consistently placing them among the top-ranked sites. Furthermore, we suggest that careful inspection of the identified regions with a high affinity for specific probes can provide valuable insights for the development of pharmacophore hypotheses. The approach is available at https://github.com/porekhov/CG_probeMD.
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Affiliation(s)
- Georgy Andreev
- Insilico Medicine AI Ltd., Masdar City 145748, United Arab Emirates
| | - Max Kovalenko
- Division of Scientific Computing, Department of Information Technology, Uppsala University, Uppsala 752 37, Sweden
| | | | - Philipp S Orekhov
- Faculty of Biology, Shenzhen MSU-BIT University, Shenzhen 518172, China
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5
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Cao C, Wang H, Yang JR, Chen Q, Guo YM, Chen JZ. MCPNET: Development of an interpretable deep learning model based on multiple conformations of the compound for predicting developmental toxicity. Comput Biol Med 2024; 171:108037. [PMID: 38377716 DOI: 10.1016/j.compbiomed.2024.108037] [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: 09/09/2023] [Revised: 12/21/2023] [Accepted: 01/26/2024] [Indexed: 02/22/2024]
Abstract
The development of deep learning models for predicting toxicological endpoints has shown great promise, but one of the challenges in the field is the accuracy and interpretability of these models. The bioactive conformation of a compound plays a critical role for it to bind in the target. It is a big issue to figure out the bioactive conformation in deep learning without the co-crystal structure or highly precise molecular simulations. In this study, we developed a deep learning framework of Multi-Conformation Point Network (MCPNET) to construct classification and regression models, respectively, based on electrostatic potential distributions on vdW surfaces around multiple conformations of the compound using a dataset of compounds with developmental toxicity in zebrafish embryo. MCPNET applied 3D multi-conformational surface point cloud to extract the molecular features for model training, which may be critical for capturing the structural diversity of compounds. The models achieved an accuracy of 85 % on the classification task and R2 of 0.66 on the regression task, outperforming traditional machine learning models and other deep learning models. The key feature of our model is its interpretability with the component visualization to identify the factors contributing to the prediction and to understand the compound action mechanism. MCPNET may predict the conformation quietly close to the bioactive conformation of a compound by attention-based multi-conformation pooling mechanism. Our results demonstrated the potential of deep learning based on 3D molecular representations in accurately predicting developmental toxicity. The source code is publicly available at https://github.com/Superlit-CC/MCPNET.
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Affiliation(s)
- Cheng Cao
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China; Polytechnic Institute, Zhejiang University, 269 Shixiang Rd, Hangzhou, Zhejiang, 310015, China
| | - Hao Wang
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China
| | - Jin-Rong Yang
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China; Polytechnic Institute, Zhejiang University, 269 Shixiang Rd, Hangzhou, Zhejiang, 310015, China
| | - Qiang Chen
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China
| | - Ya-Min Guo
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China
| | - Jian-Zhong Chen
- College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd., Hangzhou, Zhejiang, 310058, China.
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6
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Beyens O, De Winter H. Preventing lipophilic aggregation in cosolvent molecular dynamics simulations with hydrophobic probes using Plumed Automatic Restraining Tool (PART). J Cheminform 2024; 16:23. [PMID: 38414037 PMCID: PMC10898161 DOI: 10.1186/s13321-024-00819-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/23/2024] [Indexed: 02/29/2024] Open
Abstract
Cosolvent molecular dynamics (MD) simulations are molecular dynamics simulations used to identify preferable locations of small organic fragments on a protein target. Most cosolvent molecular dynamics workflows make use of only water-soluble fragments, as hydrophobic fragments would cause lipophilic aggregation. To date the two approaches that allow usage of hydrophobic cosolvent molecules are to use a low (0.2 M) concentration of hydrophobic probes, with the disadvantage of a lower sampling speed, or to use force field modifications, with the disadvantage of a difficult and inflexible setup procedure. Here we present a third alternative, that does not suffer from low sampling speed nor from cumbersome preparation procedures. We have built an easy-to-use open source command line tool PART (Plumed Automatic Restraining Tool) to generate a PLUMED file handling all intermolecular restraints to prevent lipophilic aggregation. We have compared restrained and unrestrained cosolvent MD simulations, showing that restraints are necessary to prevent lipophilic aggregation at hydrophobic probe concentrations of 0.5 M. Furthermore, we benchmarked PART generated restraints on a test set of four proteins (Factor-Xa, HIV protease, P38 MAP kinase and RNase A), showing that cosolvent MD with PART generated restraints qualitatively reproduces binding features of cocrystallised ligands.
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Affiliation(s)
- Olivier Beyens
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Hans De Winter
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium.
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Jiang W, Wijerathne TD, Zhang H, Lin YC, Jo S, Im W, Lacroix JJ, Luo YL. Structural and thermodynamic framework for PIEZO1 modulation by small molecules. Proc Natl Acad Sci U S A 2023; 120:e2310933120. [PMID: 38060566 PMCID: PMC10723123 DOI: 10.1073/pnas.2310933120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/12/2023] [Indexed: 12/17/2023] Open
Abstract
Mechanosensitive PIEZO channels constitute potential pharmacological targets for multiple clinical conditions, spurring the search for potent chemical PIEZO modulators. Among them is Yoda1, a widely used synthetic small molecule PIEZO1 activator discovered through cell-based high-throughput screening. Yoda1 is thought to bind to PIEZO1's mechanosensory arm domain, sandwiched between two transmembrane regions near the channel pore. However, how the binding of Yoda1 to this region promotes channel activation remains elusive. Here, we first demonstrate that cross-linking PIEZO1 repeats A and B with disulfide bridges reduces the effects of Yoda1 in a redox-dependent manner, suggesting that Yoda1 acts by perturbing the contact between these repeats. Using molecular dynamics-based absolute binding free energy simulations, we next show that Yoda1 preferentially occupies a deeper, amphipathic binding site with higher affinity in PIEZO1 open state. Using Yoda1's binding poses in open and closed states, relative binding free energy simulations were conducted in the membrane environment, recapitulating structure-activity relationships of known Yoda1 analogs. Through virtual screening of an 8 million-compound library using computed fragment maps of the Yoda1 binding site, we subsequently identified two chemical scaffolds with agonist activity toward PIEZO1. This study supports a pharmacological model in which Yoda1 activates PIEZO1 by wedging repeats A and B, providing a structural and thermodynamic framework for the rational design of PIEZO1 modulators. Beyond PIEZO channels, the three orthogonal computational approaches employed here represent a promising path toward drug discovery in highly heterogeneous membrane protein systems.
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Affiliation(s)
- Wenjuan Jiang
- Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA91766
| | - Tharaka D. Wijerathne
- Department of Basic Medical Sciences, Western University of Health Sciences, Pomona, CA91766
| | - Han Zhang
- Department of Biological Sciences, Lehigh University, Bethlehem, PA18015
- Department of Chemistry, Lehigh University, Bethlehem, PA18015
- Department of Bioengineering, Lehigh University, Bethlehem, PA18015
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA18015
| | - Yi-Chun Lin
- Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA91766
| | - Sunhwan Jo
- Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD21201
| | - Wonpil Im
- Department of Biological Sciences, Lehigh University, Bethlehem, PA18015
- Department of Chemistry, Lehigh University, Bethlehem, PA18015
- Department of Bioengineering, Lehigh University, Bethlehem, PA18015
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA18015
| | - Jerome J. Lacroix
- Department of Basic Medical Sciences, Western University of Health Sciences, Pomona, CA91766
| | - Yun L. Luo
- Department of Biotechnology and Pharmaceutical Sciences, Western University of Health Sciences, Pomona, CA91766
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8
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Olson KM, Devereaux AL, Chatterjee P, Saldaña-Shumaker SL, Shafer A, Plotkin A, Kandasamy R, MacKerell AD, Traynor JR, Cunningham CW. Nitro-benzylideneoxymorphone, a bifunctional mu and delta opioid receptor ligand with high mu opioid receptor efficacy. Front Pharmacol 2023; 14:1230053. [PMID: 37469877 PMCID: PMC10352325 DOI: 10.3389/fphar.2023.1230053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023] Open
Abstract
Introduction: There is a major societal need for analgesics with less tolerance, dependence, and abuse liability. Preclinical rodent studies suggest that bifunctional ligands with both mu (MOPr) and delta (DOPr) opioid peptide receptor activity may produce analgesia with reduced tolerance and other side effects. This study explores the structure-activity relationships (SAR) of our previously reported MOPr/DOPr lead, benzylideneoxymorphone (BOM) with C7-methylene-substituted analogs. Methods: Analogs were synthesized and tested in vitro for opioid receptor binding and efficacy. One compound, nitro-BOM (NBOM, 12) was evaluated for antinociceptive effects in the warm water tail withdrawal assay in C57BL/6 mice. Acute and chronic antinociception was determined, as was toxicologic effects on chronic administration. Molecular modeling experiments were performed using the Site Identification by Ligand Competitive Saturation (SILCS) method. Results: NBOM was found to be a potent MOPr agonist/DOPr partial agonist that produces high-efficacy antinociception. Antinociceptive tolerance was observed, as was weight loss; this toxicity was only observed with NBOM and not with BOM. Modeling supports the hypothesis that the increased MOPr efficacy of NBOM is due to the substituted benzylidene ring occupying a nonpolar region within the MOPr agonist state. Discussion: Though antinociceptive tolerance and non-specific toxicity was observed on repeated administration, NBOM provides an important new tool for understanding MOPr/DOPr pharmacology.
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Affiliation(s)
- Keith M. Olson
- Department of Pharmacology and Edward F. Domino Research Center, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Andrea L. Devereaux
- Department of Pharmaceutical Sciences, Concordia University Wisconsin School of Pharmacy, Mequon, WI, United States
| | - Payal Chatterjee
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, United States
| | - Savanah L. Saldaña-Shumaker
- Department of Pharmaceutical Sciences, Concordia University Wisconsin School of Pharmacy, Mequon, WI, United States
| | - Amanda Shafer
- Department of Pharmacology and Edward F. Domino Research Center, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Adam Plotkin
- Department of Pharmaceutical Sciences, Concordia University Wisconsin School of Pharmacy, Mequon, WI, United States
| | - Ram Kandasamy
- Department of Pharmacology and Edward F. Domino Research Center, University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Psychology, California State University, East Bay, Hayward, CA, United States
| | - Alexander D. MacKerell
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, United States
| | - John R. Traynor
- Department of Pharmacology and Edward F. Domino Research Center, University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, MI, United States
| | - Christopher W. Cunningham
- Department of Pharmaceutical Sciences, Concordia University Wisconsin School of Pharmacy, Mequon, WI, United States
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Coburn KM, Roth B, Varney KM, Carrier F, Weber DJ. 1H, 13C, and 15N assignments of the mRNA binding protein hnRNP A18. BIOMOLECULAR NMR ASSIGNMENTS 2023; 17:37-41. [PMID: 36539586 DOI: 10.1007/s12104-022-10117-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 12/08/2022] [Indexed: 06/02/2023]
Abstract
Heterogeneous ribonuclear protein A18 (hnRNP A18) is an RNA binding protein (RBP) involved in the hypoxic cellular stress response and regulation of cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) expression in melanoma, breast cancer, prostate cancer, and colon cancer solid tumors. hnRNP A18 is comprised of an N-terminal structured RNA recognition motif (RMM) and a C-terminal intrinsically disordered domain (IDD). Upon cellar stressors, such as UV and hypoxia, hnRNP A18 is phosphorylated by casein kinase 2 (CK2) and glycogen synthase kinase 3β (GSK-3β). After phosphorylation, hnRNP A18 translocates from the nucleus to the cytosol where it interacts with pro-survival mRNA transcripts for proteins such as hypoxia inducible factor 1α and CTLA-4. Both the hypoxic cellular response and modulation of immune checkpoints by cancer cells promote chemoradiation resistance and metastasis. In this study, the 1 H, 13 C, and 15 N backbone and sidechain resonances of the 172 amino acid hnRNP A18 were assigned sequence-specifically and provide a framework for future NMR-based drug discovery studies toward targeting hnRNP A18. These data will also enable the investigation of the dynamic structural changes within the IDD of hnRNP A18 upon phosphorylation by CK2 and GSK-3β to provide critical insight into the structure and function of IDDs.
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Affiliation(s)
- Katherine M Coburn
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, 108 N. Greene Street, 21201, Baltimore, MD, USA
| | - Braden Roth
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, 108 N. Greene Street, 21201, Baltimore, MD, USA
| | - Kristen M Varney
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, 108 N. Greene Street, 21201, Baltimore, MD, USA
- Center for Biomolecular Therapeutics (CBT), Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, 108 N. Greene St, 21201, Baltimore Maryland, USA
| | - France Carrier
- Department of Radiation Oncology, University of Maryland School of Medicine, 655 West Baltimore, Street, 21201, Baltimore, MD, USA
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, 21201, Baltimore, MD, USA
| | - David J Weber
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, 108 N. Greene Street, 21201, Baltimore, MD, USA.
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, 21201, Baltimore, MD, USA.
- Center for Biomolecular Therapeutics (CBT), Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, 108 N. Greene St, 21201, Baltimore Maryland, USA.
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10
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Yu W, Weber DJ, MacKerell AD. Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation. J Chem Theory Comput 2023; 19:3007-3021. [PMID: 37115781 PMCID: PMC10205696 DOI: 10.1021/acs.jctc.3c00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Covalent drug design is an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium, while irreversible covalent drugs increase the drug-target interaction duration by forming a covalent bond with targeted residues and thus may offer a more effective therapeutic approach. To facilitate the design of this class of ligands, computational methods can be used to help identify reactive nucleophilic residues, frequently cysteines, on a target protein for covalent binding, to test various warhead groups for their potential reactivities, and to predict noncovalent contributions to binding that can facilitate drug-target interactions that are important for binding specificity. To further aid covalent drug design, we extended a functional group mapping approach based on explicit solvent all-atom molecular simulations (SILCS: site identification by ligand competitive saturation) that intrinsically considers protein flexibility, functional group, and protein desolvation along with functional group-protein interactions. Through docking of a library of representative warhead fragments using SILCS-Monte Carlo (SILCS-MC), reactive cysteines can be correctly identified for proteins being tested. Furthermore, a machine learning model was trained to quantify the effectiveness of various warhead groups for proteins using metrics from SILCS-MC as well as experimental model compound warhead reactivity data. The ability to rank covalent molecular binders with similar warheads using SILCS ligand grid free energy (LGFE) ranking was also tested for several proteins. Based on these tools, an integrated SILCS-based workflow was developed, named SILCS-Covalent, which can both qualitatively and quantitatively inform covalent drug discovery.
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Affiliation(s)
- Wenbo Yu
- Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, Maryland 20850, United States
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - David J. Weber
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, Maryland 20850, United States
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
| | - Alexander D. MacKerell
- Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, Maryland 20850, United States
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland Baltimore, Baltimore, Maryland 21201, United States
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Orr AA, Tao A, Guvench O, MacKerell AD. Site Identification by Ligand Competitive Saturation-Biologics Approach for Structure-Based Protein Charge Prediction. Mol Pharm 2023; 20:2600-2611. [PMID: 37017675 PMCID: PMC10159941 DOI: 10.1021/acs.molpharmaceut.3c00064] [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] [Indexed: 04/06/2023]
Abstract
Protein-based therapeutics typically require high concentrations of the active protein, which can lead to protein aggregation and high solution viscosity. Such solution behaviors can limit the stability, bioavailability, and manufacturability of protein-based therapeutics and are directly influenced by the charge of a protein. Protein charge is a system property affected by its environment, including the buffer composition, pH, and temperature. Thus, the charge calculated by summing the charges of each residue in a protein, as is commonly done in computational methods, may significantly differ from the effective charge of the protein as these calculations do not account for contributions from bound ions. Here, we present an extension of the structure-based approach termed site identification by ligand competitive saturation-biologics (SILCS-Biologics) to predict the effective charge of proteins. The SILCS-Biologics approach was applied on a range of protein targets in different salt environments for which membrane-confined electrophoresis-determined charges were previously reported. SILCS-Biologics maps the 3D distribution and predicted occupancy of ions, buffer molecules, and excipient molecules bound to the protein surface in a given salt environment. Using this information, the effective charge of the protein is predicted such that the concentrations of the ions and the presence of excipients or buffers are accounted for. Additionally, SILCS-Biologics also produces 3D structures of the binding sites of ions on the proteins, which enable further analyses such as the characterization of protein surface charge distribution and dipole moments in different environments. Notable is the capability of the method to account for competition between salts, excipients, and buffers on the calculated electrostatic properties in different protein formulations. Our study demonstrates the ability of the SILCS-Biologics approach to predict the effective charge of proteins and its applicability in uncovering protein-ion interactions and their contributions to protein solubility and function.
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Affiliation(s)
- Asuka A. Orr
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, MD, USA
| | - Aoxiang Tao
- SilcsBio LLC, 1100 Wicomico Street, Suite 323, Baltimore, MD, USA
| | - Olgun Guvench
- SilcsBio LLC, 1100 Wicomico Street, Suite 323, Baltimore, MD, USA
| | - Alexander D. MacKerell
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland Baltimore, Baltimore, MD, USA
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12
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Zhao M, Kognole AA, Jo S, Tao A, Hazel A, MacKerell AD. GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations. J Comput Chem 2023. [PMID: 37093676 DOI: 10.1002/jcc.27121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/22/2023] [Accepted: 04/12/2023] [Indexed: 04/25/2023]
Abstract
The Grand Canonical Monte Carlo (GCMC) ensemble defined by the excess chemical potential, μex , volume, and temperature, in the context of molecular simulations allows for variations in the number of particles in the system. In practice, GCMC simulations have been widely applied for the sampling of rare gasses and water, but limited in the context of larger molecules. To overcome this limitation, the oscillating μex GCMC method was introduced and shown to be of utility for sampling small solutes, such as formamide, propane, and benzene, as well as for ionic species such as monocations, acetate, and methylammonium. However, the acceptance of GCMC insertions is low, and the method is computationally demanding. In the present study, we improved the sampling efficiency of the GCMC method using known cavity-bias and configurational-bias algorithms in the context of GPU architecture. Specifically, for GCMC simulations of aqueous solution systems, the configurational-bias algorithm was extended by applying system partitioning in conjunction with a random interval extraction algorithm, thereby improving the efficiency in a highly parallel computing environment. The method is parallelized on the GPU using CUDA and OpenCL, allowing for the code to run on both Nvidia and AMD GPUs, respectively. Notably, the method is particularly well suited for GPU computing as the large number of threads allows for simultaneous sampling of a large number of configurations during insertion attempts without additional computational overhead. In addition, the partitioning scheme allows for simultaneous insertion attempts for large systems, offering considerable efficiency. Calculations on the BK Channel, a transporter, including a lipid bilayer with over 760,000 atoms, show a speed up of ~53-fold through the use of system partitioning. The improved algorithm is then combined with an enhanced μex oscillation protocol and shown to be of utility in the context of the site-identification by ligand competitive saturation (SILCS) co-solvent sampling approach as illustrated through application to the protein CDK2.
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Affiliation(s)
- Mingtian Zhao
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
| | | | | | | | - Anthony Hazel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
| | - Alexander D MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
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13
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Yu W, Weber DJ, MacKerell AD. Computer-Aided Drug Design: An Update. Methods Mol Biol 2023; 2601:123-152. [PMID: 36445582 PMCID: PMC9838881 DOI: 10.1007/978-1-0716-2855-3_7] [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] [Indexed: 11/30/2022]
Abstract
Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5 years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.
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Affiliation(s)
- Wenbo Yu
- Department of Pharmaceutical Sciences, Computer-Aided Drug Design Center, School of Pharmacy, University of Maryland, Baltimore, MD, USA.
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA.
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA.
| | - David J Weber
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Alexander D MacKerell
- Department of Pharmaceutical Sciences, Computer-Aided Drug Design Center, School of Pharmacy, University of Maryland, Baltimore, MD, USA.
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA.
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA.
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14
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In silico identification of a β 2-adrenoceptor allosteric site that selectively augments canonical β 2AR-Gs signaling and function. Proc Natl Acad Sci U S A 2022; 119:e2214024119. [PMID: 36449547 PMCID: PMC9894167 DOI: 10.1073/pnas.2214024119] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Activation of β2-adrenoceptors (β2ARs) causes airway smooth muscle (ASM) relaxation and bronchodilation, and β2AR agonists (β-agonists) are front-line treatments for asthma and other obstructive lung diseases. However, the therapeutic efficacy of β-agonists is limited by agonist-induced β2AR desensitization and noncanonical β2AR signaling involving β-arrestin that is shown to promote asthma pathophysiology. Accordingly, we undertook the identification of an allosteric site on β2AR that could modulate the activity of β-agonists to overcome these limitations. We employed the site identification by ligand competitive saturation (SILCS) computational method to comprehensively map the entire 3D structure of in silico-generated β2AR intermediate conformations and identified a putative allosteric binding site. Subsequent database screening using SILCS identified drug-like molecules with the potential to bind to the site. Experimental assays in HEK293 cells (expressing recombinant wild-type human β2AR) and human ASM cells (expressing endogenous β2AR) identified positive and negative allosteric modulators (PAMs and NAMs) of β2AR as assessed by regulation of β-agonist-stimulation of cyclic AMP generation. PAMs/NAMs had no effect on β-agonist-induced recruitment of β-arrestin to β2AR- or β-agonist-induced loss of cell surface expression in HEK293 cells expressing β2AR. Mutagenesis analysis of β2AR confirmed the SILCS identified site based on mutants of amino acids R131, Y219, and F282. Finally, functional studies revealed augmentation of β-agonist-induced relaxation of contracted human ASM cells and bronchodilation of contracted airways. These findings identify a allosteric binding site on the β2AR, whose activation selectively augments β-agonist-induced Gs signaling, and increases relaxation of ASM cells, the principal therapeutic effect of β-agonists.
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Antimicrobial Activity of Rhenium Di- and Tricarbonyl Diimine Complexes: Insights on Membrane-Bound S. aureus Protein Binding. Pharmaceuticals (Basel) 2022; 15:ph15091107. [PMID: 36145328 PMCID: PMC9501577 DOI: 10.3390/ph15091107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 08/31/2022] [Accepted: 08/31/2022] [Indexed: 11/16/2022] Open
Abstract
Antimicrobial resistance is one of the major human health threats, with significant impacts on the global economy. Antibiotics are becoming increasingly ineffective as drug-resistance spreads, imposing an urgent need for new and innovative antimicrobial agents. Metal complexes are an untapped source of antimicrobial potential. Rhenium complexes, amongst others, are particularly attractive due to their low in vivo toxicity and high antimicrobial activity, but little is known about their targets and mechanism of action. In this study, a series of rhenium di- and tricarbonyl diimine complexes were prepared and evaluated for their antimicrobial potential against eight different microorganisms comprising Gram-negative and -positive bacteria. Our data showed that none of the Re dicarbonyl or neutral tricarbonyl species have either bactericidal or bacteriostatic potential. In order to identify possible targets of the molecules, and thus possibly understand the observed differences in the antimicrobial efficacy of the molecules, we computationally evaluated the binding affinity of active and inactive complexes against structurally characterized membrane-bound S. aureus proteins. The computational analysis indicates two possible major targets for this class of compounds, namely lipoteichoic acids flippase (LtaA) and lipoprotein signal peptidase II (LspA). Our results, consistent with the published in vitro studies, will be useful for the future design of rhenium tricarbonyl diimine-based antibiotics.
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16
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Goel H, Yu W, MacKerell AD. hERG Blockade Prediction by Combining Site Identification by Ligand Competitive Saturation and Physicochemical Properties. CHEMISTRY (BASEL, SWITZERLAND) 2022; 4:630-646. [PMID: 36712295 PMCID: PMC9881610 DOI: 10.3390/chemistry4030045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Human ether-a-go-go-related gene (hERG) potassium channel is well-known contributor to drug-induced cardiotoxicity and therefore an extremely important target when performing safety assessments of drug candidates. Ligand-based approaches in connection with quantitative structure active relationships (QSAR) analyses have been developed to predict hERG toxicity. Availability of the recent published cryogenic electron microscopy (cryo-EM) structure for the hERG channel opened the prospect for using structure-based simulation and docking approaches for hERG drug liability predictions. In recent time, the idea of combining structure- and ligand-based approaches for modeling hERG drug liability has gained momentum offering improvements in predictability when compared to ligand-based QSAR practices alone. The present article demonstrates uniting the structure-based SILCS (site-identification by ligand competitive saturation) approach in conjunction with physicochemical properties to develop predictive models for hERG blockade. This combination leads to improved model predictability based on Pearson's R and percent correct (represents rank-ordering of ligands) metric for different validation sets of hERG blockers involving diverse chemical scaffold and wide range of pIC50 values. The inclusion of the SILCS structure-based approach allows determination of the hERG region to which compounds bind and the contribution of different chemical moieties in the compounds to blockade, thereby facilitating the rational ligand design to minimize hERG liability.
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Affiliation(s)
- Himanshu Goel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St. Baltimore, MD 21201, United States
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St. Baltimore, MD 21201, United States
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St. Baltimore, MD 21201, United States
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17
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Wakefield AE, Kozakov D, Vajda S. Mapping the binding sites of challenging drug targets. Curr Opin Struct Biol 2022; 75:102396. [PMID: 35636004 PMCID: PMC9790766 DOI: 10.1016/j.sbi.2022.102396] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/20/2022] [Accepted: 04/25/2022] [Indexed: 02/03/2023]
Abstract
An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein-protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.
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Affiliation(s)
- Amanda E. Wakefield
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215,Department of Chemistry, Boston University, Boston, Massachusetts 02215
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA NY, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215,Department of Chemistry, Boston University, Boston, Massachusetts 02215
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18
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Wang H, Mulgaonkar N, Pérez LM, Fernando S. ELIXIR-A: An Interactive Visualization Tool for Multi-Target Pharmacophore Refinement. ACS OMEGA 2022; 7:12707-12715. [PMID: 35474832 PMCID: PMC9025992 DOI: 10.1021/acsomega.1c07144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 03/24/2022] [Indexed: 06/01/2023]
Abstract
Pharmacophore modeling is an important step in computer-aided drug design for identifying interaction points between the receptor and ligand complex. Pharmacophore-based models can be used for de novo drug design, lead identification, and optimization in virtual screening as well as for multi-target drug design. There is a need to develop a user-friendly interface to filter the pharmacophore points resulting from multiple ligand conformations. Here, we present ELIXIR-A, a Python-based pharmacophore refinement tool, to help refine the pharmacophores between multiple ligands from multiple receptors. Furthermore, the output can be easily used in virtual pharmacophore-based screening platforms, thereby contributing to the development of drug discovery.
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Affiliation(s)
- Haoqi Wang
- Biological
and Agricultural Engineering Department, Texas A&M University, College Station, Texas 77843, United States
| | - Nirmitee Mulgaonkar
- Biological
and Agricultural Engineering Department, Texas A&M University, College Station, Texas 77843, United States
| | - Lisa M. Pérez
- High
Performance Research Computing, Texas A&M
University, College
Station, Texas 77843, United States
| | - Sandun Fernando
- Biological
and Agricultural Engineering Department, Texas A&M University, College Station, Texas 77843, United States
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19
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Wang J, Bhattarai A, Do HN, Akhter S, Miao Y. Molecular Simulations and Drug Discovery of Adenosine Receptors. Molecules 2022; 27:2054. [PMID: 35408454 PMCID: PMC9000248 DOI: 10.3390/molecules27072054] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/18/2022] [Accepted: 03/20/2022] [Indexed: 02/02/2023] Open
Abstract
G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins. Four subtypes of adenosine receptors (ARs), the A1AR, A2AAR, A2BAR and A3AR, each with a unique pharmacological profile and distribution within the tissues in the human body, mediate many physiological functions and serve as critical drug targets for treating numerous human diseases including cancer, neuropathic pain, cardiac ischemia, stroke and diabetes. The A1AR and A3AR preferentially couple to the Gi/o proteins, while the A2AAR and A2BAR prefer coupling to the Gs proteins. Adenosine receptors were the first subclass of GPCRs that had experimental structures determined in complex with distinct G proteins. Here, we will review recent studies in molecular simulations and computer-aided drug discovery of the adenosine receptors and also highlight their future research opportunities.
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Affiliation(s)
| | | | | | | | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA; (J.W.); (A.B.); (H.N.D.); (S.A.)
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20
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Goel H, Hazel A, Yu W, Jo S, MacKerell AD. Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design. NEW J CHEM 2022; 46:919-932. [PMID: 35210743 PMCID: PMC8863107 DOI: 10.1039/d1nj04028f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Site Identification by Ligand Competitive Saturation (SILCS) is a molecular simulation approach that uses diverse small solutes in aqueous solution to obtain functional group affinity patterns of a protein or other macromolecule. This involves employing a combined Grand Canonical Monte Carlo (GCMC)-molecular dynamics (MD) method to sample the full 3D space of the protein, including deep binding pockets and interior cavities from which functional group free energy maps (FragMaps) are obtained. The information content in the maps, which include contributions from protein flexibilty and both protein and functional group desolvation contributions, can be used in many aspects of the drug discovery process. These include identification of novel ligand binding pockets, including allosteric sites, pharmacophore modeling, prediction of relative protein-ligand binding affinities for database screening and lead optimization efforts, evaluation of protein-protein interactions as well as in the formulation of biologics-based drugs including monoclonal antibodies. The present article summarizes the various tools developed in the context of the SILCS methodology and their utility in computer-aided drug design (CADD) applications, showing how the SILCS toolset can improve the drug-development process on a number of fronts with respect to both accuracy and throughput representing a new avenue of CADD applications.
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Affiliation(s)
- Himanshu Goel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Anthony Hazel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Sunhwan Jo
- SilcsBio LLC, 1100 Wicomico St. Suite 323, Baltimore, MD, 21230, United States
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States., SilcsBio LLC, 1100 Wicomico St. Suite 323, Baltimore, MD, 21230, United States.,, Tel: 410-706-7442, Fax: 410-706-5017
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21
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Tyagi R, Singh A, Chaudhary KK, Yadav MK. Pharmacophore modeling and its applications. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00009-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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22
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Kumar P, Mohanty D. Development of a Novel Pharmacophore Model Guided by the Ensemble of Waters and Small Molecule Fragments Bound to SARS-CoV-2 Main Protease. Mol Inform 2021; 41:e2100178. [PMID: 34633768 PMCID: PMC8646684 DOI: 10.1002/minf.202100178] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/20/2021] [Indexed: 11/17/2022]
Abstract
Recent fragment‐based drug design efforts have generated huge amounts of information on water and small molecule fragment binding sites on SARS‐CoV‐2 Mpro and preference of the sites for various types of chemical moieties. However, this information has not been effectively utilized to develop automated tools for in silico drug discovery which are routinely used for screening large compound libraries. Utilization of this information in the development of pharmacophore models can help in bridging this gap. In this study, information on water and small molecule fragments bound to Mpro has been utilized to develop a novel Water Pharmacophore (Waterphore) model. The Waterphore model can also implicitly represent the conformational flexibilities of binding pockets in terms of pharmacophore features. The Waterphore model derived from 173 apo‐ or small molecule fragment‐bound structures of Mpro has been validated by using a dataset of 68 known bioactive inhibitors and 78 crystal structure bound inhibitors of SARS‐CoV‐2 Mpro. It is encouraging to note that, even though no inhibitor data has been used in developing the Waterphore model, it could successfully identify the known inhibitors from a library of decoys with a ROC‐AUC of 0.81 and active hit rate (AHR) of 70 %. The Waterphore model is also general enough for potential applications for other drug targets.
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Affiliation(s)
- Pawan Kumar
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Debasisa Mohanty
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi, 110067, India
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23
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Species-Specific Endotoxin Stimulus Determines Toll-Like Receptor 4- and Caspase 11-Mediated Pathway Activation Characteristics. mSystems 2021; 6:e0030621. [PMID: 34342534 PMCID: PMC8407122 DOI: 10.1128/msystems.00306-21] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The innate immune system is the body’s first line of defense against pathogens and its protection against infectious diseases. On the surface of host myeloid cells, Toll-like receptor 4 (TLR4) senses lipopolysaccharide (LPS), the major outer membrane component of Gram-negative bacteria. Intracellularly, LPS is recognized by caspase 11 through the noncanonical inflammasome to induce pyroptosis—an inflammatory form of lytic cell death. While TLR4-mediated signaling perturbations result in secretion of cytokines and chemokines that help clear infection and facilitate adaptive immunity, caspase 11-mediated pyroptosis leads to the release of damage-associated molecular patterns and inflammatory mediators. Although the core signaling events and many associated proteins in the TLR4 signaling pathway are known, the complex signaling events and protein networks within the noncanonical inflammasome pathway remain obscure. Moreover, there is mounting evidence for pathogen-specific innate immune tuning. We characterized the major LPS structures from two different pathogens, modeled their binding to the surface receptors, systematically examined macrophage inflammatory responses to these LPS molecules, and surveyed the temporal differences in global protein secretion resulting from TLR4 and caspase 11 activation in macrophages using mass spectrometry (MS)-based quantitative proteomics. This integrated strategy, spanning functional activity assays, top-down structural elucidation of endotoxins, and secretome analysis of stimulated macrophages, allowed us to identify crucial differences in TLR4- and caspase 11-mediated protein secretion in response to two Gram-negative bacterial endotoxins. IMPORTANCE Macrophages and monocytes are innate immune cells playing an important role in orchestrating the initial innate immune response to bacterial infection and the tissue damage. This response is facilitated by specific receptors on the cell surface and intracellularly. One of the bacterial molecules recognized is a Gram-negative bacteria cell wall component, lipopolysaccharide (LPS). The structure of LPS differs between different species. We have characterized the innate immune responses to the LPS molecules from two bacteria, Escherichia coli and Bordetella pertussis, administered either extracellularly or intracellularly, whose structures we first determined. We observed marked differences in the temporal dynamics and amounts of proteins secreted by the innate immune cells stimulated by any of these molecules and routes. This suggests that there is specificity in the first line of response to different Gram-negative bacteria that can be explored to tailor specific therapeutic interventions.
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Goel H, Hazel A, Ustach VD, Jo S, Yu W, MacKerell AD. Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation. Chem Sci 2021; 12:8844-8858. [PMID: 34257885 PMCID: PMC8246086 DOI: 10.1039/d1sc01781k] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 05/24/2021] [Indexed: 01/18/2023] Open
Abstract
Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The site identification by ligand competitive saturation (SILCS) methodology is based on functional group affinity patterns in the form of free energy maps that may be used to compute protein-ligand binding poses and affinities. Presented are results obtained from the SILCS methodology for a set of eight target proteins as reported originally in Wang et al. (J. Am. Chem. Soc., 2015, 137, 2695-2703) using free energy perturbation (FEP) methods in conjunction with enhanced sampling and cycle closure corrections. These eight targets have been subsequently studied by many other authors to compare the efficacy of their method while comparing with the outcomes of Wang et al. In this work, we present results for a total of 407 ligands on the eight targets and include specific analysis on the subset of 199 ligands considered previously. Using the SILCS methodology we can achieve an average accuracy of up to 77% and 74% when considering the eight targets with their 199 and 407 ligands, respectively, for rank-ordering ligand affinities as calculated by the percent correct metric. This accuracy increases to 82% and 80%, respectively, when the SILCS atomic free energy contributions are optimized using a Bayesian Markov-chain Monte Carlo approach. We also report other metrics including Pearson's correlation coefficient, Pearlman's predictive index, mean unsigned error, and root mean square error for both sets of ligands. The results obtained for the 199 ligands are compared with the outcomes of Wang et al. and other published works. Overall, the SILCS methodology yields similar or better-quality predictions without a priori need for known ligand orientations in terms of the different metrics when compared to current FEP approaches with significant computational savings while additionally offering quantitative estimates of individual atomic contributions to binding free energies. These results further validate the SILCS methodology as an accurate, computationally efficient tool to support lead optimization and drug discovery.
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Affiliation(s)
- Himanshu Goel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Anthony Hazel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Vincent D Ustach
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Sunhwan Jo
- SilcsBio LLC 8 Market Place, Suite 300 Baltimore Maryland 21201 USA
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
| | - Alexander D MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy 20, Penn St. Baltimore Maryland 21201 USA +1-410-706-5017 +1-410-706-7442
- SilcsBio LLC 8 Market Place, Suite 300 Baltimore Maryland 21201 USA
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Muñoz AM, Fragoso-Vázquez MJ, Martel BP, Chávez-Blanco A, Dueñas-González A, R García-Sánchez J, Bello M, Romero-Castro A, Correa-Basurto J. Targeting Breast Cancer Cells with G4 PAMAM Dendrimers and Valproic Acid Derivative Complexes. Anticancer Agents Med Chem 2021; 20:1857-1872. [PMID: 32324521 DOI: 10.2174/1871520620666200423073812] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 02/15/2020] [Accepted: 03/09/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Our research group has developed some Valproic Acid (VPA) derivatives employed as anti-proliferative compounds targeting the HDAC8 enzyme. However, some of these compounds are poorly soluble in water. OBJECTIVE Employed the four generations of Polyamidoamine (G4 PAMAM) dendrimers as drug carriers of these compounds to increase their water solubility for further in vitro evaluation. METHODS VPA derivatives were subjected to Docking and Molecular Dynamics (MD) simulations to evaluate their affinity on G4 PAMAM. Then, HPLC-UV/VIS, 1H NMR, MALDI-TOF and atomic force microscopy were employed to establish the formation of the drug-G4 PAMAM complexes. RESULTS The docking results showed that the amide groups of VPA derivatives make polar interactions with G4 PAMAM, whereas MD simulations corroborated the stability of the complexes. HPLC UV/VIS experiments showed an increase in the drug water solubility which was found to be directly proportional to the amount of G4 PAMAM. 1H NMR showed a disappearance of the proton amine group signals, correlating with docking results. MALDI-TOF and atomic force microscopy suggested the drug-G4 PAMAM dendrimer complexes formation. DISCUSSION In vitro studies showed that G4 PAMAM has toxicity in the micromolar concentration in MDAMB- 231, MCF7, and 3T3-L1 cell lines. VPA CF-G4 PAMAM dendrimer complex showed anti-proliferative properties in the micromolar concentration in MCF-7 and 3T3-L1, and in the milimolar concentration in MDAMB- 231, whereas VPA MF-G4 PAMAM dendrimer complex didn't show effects on the three cell lines employed. CONCLUSION These results demonstrate that G4 PAMAM dendrimers are capableof transporting poorly watersoluble aryl-VPA derivate compounds to increase its cytotoxic activity against neoplastic cell lines.
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Affiliation(s)
- Alberto M Muñoz
- Laboratorio de Diseno y Desarrollo de Nuevos Farmacos e Innovacion Biotecnologica de la Escuela Superior de Medicina, Instituto Politecnico Nacional, Mexico. Plan de San Luis Y Diaz Miron S/N, Col. Casco de Santo Tomas, Mexico City, CP 11340, Mexico
| | - Manuel J Fragoso-Vázquez
- Departamento de Quimica Organica, Escuela Nacional de Ciencias, Biologicas, Instituto Politecnico Nacional, Prolongacion de Carpio y Plan de Ayala, Col. Casco de Santo Tomas, Mexico City, CP 11340, Mexico
| | - Berenice P Martel
- Laboratorio de Diseno y Desarrollo de Nuevos Farmacos e Innovacion Biotecnologica de la Escuela Superior de Medicina, Instituto Politecnico Nacional, Mexico. Plan de San Luis Y Diaz Miron S/N, Col. Casco de Santo Tomas, Mexico City, CP 11340, Mexico
| | - Alma Chávez-Blanco
- Division de Investigacion Basica, Subdireccion de Investigacion Basica, Instituto Nacional de Cancerologia, Tlalpan, Seccion XVI, Ciudad de Mexico, Mexico
| | - Alfonso Dueñas-González
- Instituto de Investigaciones Biomedicas, Universidad Nacional Autonoma de Mexico/Instituto Nacional de Cancerologia, Ciudad de Mexico, Mexico
| | - José R García-Sánchez
- Laboratorio de oncologia Molecular y estres oxidativo de la Escuela Superior de Medicina, Instituto Politecnico Nacional, Mexico. Plan de San Luis Y Diaz Miron S/N, Col. Casco de Santo Tomas, Mexico City, CP 11340, Mexico
| | - Martiniano Bello
- Laboratorio de Diseno y Desarrollo de Nuevos Farmacos e Innovacion Biotecnologica de la Escuela Superior de Medicina, Instituto Politecnico Nacional, Mexico. Plan de San Luis Y Diaz Miron S/N, Col. Casco de Santo Tomas, Mexico City, CP 11340, Mexico
| | - Aurelio Romero-Castro
- Division de Ciencias de la Salud, Universidad de Quintana Roo. Av. Erik Paolo Martinez S/N. Esquina Av. 4 de Marzo, Col. Magisterial, Chetumal, Quintana Roo, C.P. 77039, Mexico
| | - José Correa-Basurto
- Laboratorio de Diseno y Desarrollo de Nuevos Farmacos e Innovacion Biotecnologica de la Escuela Superior de Medicina, Instituto Politecnico Nacional, Mexico. Plan de San Luis Y Diaz Miron S/N, Col. Casco de Santo Tomas, Mexico City, CP 11340, Mexico
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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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27
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Solano-Gonzalez E, Coburn KM, Yu W, Wilson GM, Nurmemmedov E, Kesari S, Chang ET, MacKerell AD, Weber DJ, Carrier F. Small molecules inhibitors of the heterogeneous ribonuclear protein A18 (hnRNP A18): a regulator of protein translation and an immune checkpoint. Nucleic Acids Res 2021; 49:1235-1246. [PMID: 33398344 PMCID: PMC7897483 DOI: 10.1093/nar/gkaa1254] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 12/01/2022] Open
Abstract
We have identified chemical probes that simultaneously inhibit cancer cell progression and an immune checkpoint. Using the computational Site Identification by Ligand Competitive Saturation (SILCS) technology, structural biology and cell-based assays, we identify small molecules that directly and selectively bind to the RNA Recognition Motif (RRM) of hnRNP A18, a regulator of protein translation in cancer cells. hnRNP A18 recognizes a specific RNA signature motif in the 3′UTR of transcripts associated with cancer cell progression (Trx, VEGF, RPA) and, as shown here, a tumor immune checkpoint (CTLA-4). Post-transcriptional regulation of immune checkpoints is a potential therapeutic strategy that remains to be exploited. The probes target hnRNP A18 RRM in vitro and in cells as evaluated by cellular target engagement. As single agents, the probes specifically disrupt hnRNP A18–RNA interactions, downregulate Trx and CTLA-4 protein levels and inhibit proliferation of several cancer cell lines without affecting the viability of normal epithelial cells. These first-in-class chemical probes will greatly facilitate the elucidation of the underexplored biological function of RNA Binding Proteins (RBPs) in cancer cells, including their effects on proliferation and immune checkpoint activation.
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Affiliation(s)
- Eduardo Solano-Gonzalez
- University of Maryland, Baltimore, School of Medicine, Department of Radiation Oncology, 655 West Baltimore, Street, Baltimore, MD 21201, USA
| | - Katherine M Coburn
- University of Maryland, Baltimore, School of Medicine, Department of Biochemistry and Molecular Biology, 108 N. Greene Street, Baltimore, MD 21201, USA
| | - Wenbo Yu
- Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, 20 Penn Street, Baltimore MD 21201, USA.,Center for Biomolecular Therapeutics (CBT), University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Gerald M Wilson
- University of Maryland, Baltimore, School of Medicine, Department of Biochemistry and Molecular Biology, 108 N. Greene Street, Baltimore, MD 21201, USA
| | - Elmar Nurmemmedov
- John Wayne Cancer Institute, 2200 Santa Monica Blvd, Santa Monica, CA 90404, USA
| | - Santosh Kesari
- John Wayne Cancer Institute, 2200 Santa Monica Blvd, Santa Monica, CA 90404, USA
| | - Elizabeth T Chang
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD 21201, USA
| | - Alexander D MacKerell
- Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, 20 Penn Street, Baltimore MD 21201, USA.,Center for Biomolecular Therapeutics (CBT), University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - David J Weber
- University of Maryland, Baltimore, School of Medicine, Department of Biochemistry and Molecular Biology, 108 N. Greene Street, Baltimore, MD 21201, USA.,University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD 21201, USA.,Center for Biomolecular Therapeutics (CBT), University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - France Carrier
- University of Maryland, Baltimore, School of Medicine, Department of Radiation Oncology, 655 West Baltimore, Street, Baltimore, MD 21201, USA.,University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD 21201, USA
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28
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Parvaiz N, Ahmad F, Yu W, MacKerell AD, Azam SS. Discovery of beta-lactamase CMY-10 inhibitors for combination therapy against multi-drug resistant Enterobacteriaceae. PLoS One 2021; 16:e0244967. [PMID: 33449932 PMCID: PMC7810305 DOI: 10.1371/journal.pone.0244967] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 12/18/2020] [Indexed: 12/14/2022] Open
Abstract
β-lactam antibiotics are the most widely used antimicrobial agents since the discovery of benzylpenicillin in the 1920s. Unfortunately, these life-saving antibiotics are vulnerable to inactivation by continuously evolving β-lactamase enzymes that are primary resistance determinants in multi-drug resistant pathogens. The current study exploits the strategy of combination therapeutics and aims at identifying novel β-lactamase inhibitors that can inactivate the β-lactamase enzyme of the pathogen while allowing the β-lactam antibiotic to act against its penicillin-binding protein target. Inhibitor discovery applied the Site-Identification by Ligand Competitive Saturation (SILCS) technology to map the functional group requirements of the β-lactamase CMY-10 and generate pharmacophore models of active site. SILCS-MC, Ligand-grid Free Energy (LGFE) analysis and Machine-learning based random-forest (RF) scoring methods were then used to screen and filter a library of 700,000 compounds. From the computational screens 74 compounds were subjected to experimental validation in which β-lactamase activity assay, in vitro susceptibility testing, and Scanning Electron Microscope (SEM) analysis were conducted to explore their antibacterial potential. Eleven compounds were identified as enhancers while 7 compounds were recognized as inhibitors of CMY-10. Of these, compound 11 showed promising activity in β-lactamase activity assay, in vitro susceptibility testing against ATCC strains (E. coli, E. cloacae, E. agglomerans, E. alvei) and MDR clinical isolates (E. cloacae, E. alvei and E. agglomerans), with synergistic assay indicating its potential as a β-lactam enhancer and β-lactamase inhibitor. Structural similarity search against the active compound 11 yielded 28 more compounds. The majority of these compounds also exhibited β-lactamase inhibition potential and antibacterial activity. The non-β-lactam-based β-lactamase inhibitors identified in the current study have the potential to be used in combination therapy with lactam-based antibiotics against MDR clinical isolates that have been found resistant against last-line antibiotics.
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Affiliation(s)
- Nousheen Parvaiz
- Computational Biology Lab, National Center for Bioinformatics, Quaid-i-Azam University, Islamabad, Pakistan
| | - Faisal Ahmad
- Computational Biology Lab, National Center for Bioinformatics, Quaid-i-Azam University, Islamabad, Pakistan
| | - Wenbo Yu
- University of Maryland Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, United States of America
| | - Alexander D. MacKerell
- University of Maryland Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, United States of America
| | - Syed Sikander Azam
- Computational Biology Lab, National Center for Bioinformatics, Quaid-i-Azam University, Islamabad, Pakistan
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29
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Weston S, Baracco L, Keller C, Matthews K, McGrath ME, Logue J, Liang J, Dyall J, Holbrook MR, Hensley LE, Jahrling PB, Yu W, MacKerell AD, Frieman MB. The SKI complex is a broad-spectrum, host-directed antiviral drug target for coronaviruses, influenza, and filoviruses. Proc Natl Acad Sci U S A 2020; 117:30687-30698. [PMID: 33184176 PMCID: PMC7720140 DOI: 10.1073/pnas.2012939117] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The SARS-CoV-2 pandemic has made it clear that we have a desperate need for antivirals. We present work that the mammalian SKI complex is a broad-spectrum, host-directed, antiviral drug target. Yeast suppressor screening was utilized to find a functional genetic interaction between proteins from influenza A virus (IAV) and Middle East respiratory syndrome coronavirus (MERS-CoV) with eukaryotic proteins that may be potential host factors involved in replication. This screening identified the SKI complex as a potential host factor for both viruses. In mammalian systems siRNA-mediated knockdown of SKI genes inhibited replication of IAV and MERS-CoV. In silico modeling and database screening identified a binding pocket on the SKI complex and compounds predicted to bind. Experimental assays of those compounds identified three chemical structures that were antiviral against IAV and MERS-CoV along with the filoviruses Ebola and Marburg and two further coronaviruses, SARS-CoV and SARS-CoV-2. The mechanism of antiviral activity is through inhibition of viral RNA production. This work defines the mammalian SKI complex as a broad-spectrum antiviral drug target and identifies lead compounds for further development.
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Affiliation(s)
- Stuart Weston
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Lauren Baracco
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Chloe Keller
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Krystal Matthews
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Marisa E McGrath
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - James Logue
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Janie Liang
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
| | - Julie Dyall
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
| | - Michael R Holbrook
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
| | - Lisa E Hensley
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
| | - Peter B Jahrling
- Integrated Research Facility, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
- Emerging Viral Pathogens Section, National Institute of Allergy and Infectious Diseases, NIH, Frederick, MD 21702
| | - Wenbo Yu
- Center for Biomolecular Therapeutics, University of Maryland School of Medicine, Baltimore, MD 21201
- University of Maryland Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
| | - Alexander D MacKerell
- Center for Biomolecular Therapeutics, University of Maryland School of Medicine, Baltimore, MD 21201
- University of Maryland Computer-Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
| | - Matthew B Frieman
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201;
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30
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Jo S, Xu A, Curtis JE, Somani S, MacKerell AD. Computational Characterization of Antibody-Excipient Interactions for Rational Excipient Selection Using the Site Identification by Ligand Competitive Saturation-Biologics Approach. Mol Pharm 2020; 17:4323-4333. [PMID: 32965126 DOI: 10.1021/acs.molpharmaceut.0c00775] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Protein therapeutics typically require a concentrated protein formulation, which can lead to self-association and/or high viscosity due to protein-protein interaction (PPI). Excipients are often added to improve stability, bioavailability, and manufacturability of the protein therapeutics, but the selection of excipients often relies on trial and error. Therefore, understanding the excipient-protein interaction and its effect on non-specific PPI is important for rational selection of formulation development. In this study, we validate a general workflow based on the site identification by ligand competitive saturation (SILCS) technology, termed SILCS-Biologics, that can be applied to protein therapeutics for rational excipient selection. The National Institute of Standards and Technology monoclonal antibody (NISTmAb) reference along with the CNTO607 mAb is used as model antibody proteins to examine PPIs, and NISTmAb was used to further examine excipient-protein interactions, in silico. Metrics from SILCS include the distribution and predicted affinity of excipients, buffer interactions with the NISTmAb Fab, and the relation of the interactions to predicted PPI. Comparison with a range of experimental data showed multiple SILCS metrics to be predictive. Specifically, the number of favorable sites to which an excipient binds and the number of sites to which an excipient binds that are involved in predicted PPIs correlate with the experimentally determined viscosity. In addition, a combination of the number of binding sites and the predicted binding affinity is indicated to be predictive of relative protein stability. Comparison of arginine, trehalose, and sucrose, all of which give the highest viscosity in combination with analysis of B22 and kD and the SILCS metrics, indicates that higher viscosities are associated with a low number of predicted binding sites, with lower binding affinity of arginine leading to its anomalously high impact on viscosity. The present study indicates the potential for the SILCS-Biologics approach to be of utility in the rational design of excipients during biologics formulation.
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Affiliation(s)
- Sunhwan Jo
- SilcsBio, LLC, 8 Market Place, Suite 300, Baltimore, Maryland 21202, United States
| | - Amy Xu
- NIST Center for Neutron Research, National Institute of Standards and Technology, 100 Bureau Drive, Mail Stop 6102, Gaithersburg, Maryland 20899, United States
| | - Joseph E Curtis
- NIST Center for Neutron Research, National Institute of Standards and Technology, 100 Bureau Drive, Mail Stop 6102, Gaithersburg, Maryland 20899, United States
| | - Sandeep Somani
- Discovery Sciences, Janssen Research and Development (Janssen R&D), Spring House, Pennsylvania 19477, United States
| | - Alexander D MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, United States
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31
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Somani S, Jo S, Thirumangalathu R, Rodrigues D, Tanenbaum LM, Amin K, MacKerell AD, Thakkar SV. Toward Biotherapeutics Formulation Composition Engineering using Site-Identification by Ligand Competitive Saturation (SILCS). J Pharm Sci 2020; 110:1103-1110. [PMID: 33137372 DOI: 10.1016/j.xphs.2020.10.051] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/23/2020] [Accepted: 10/26/2020] [Indexed: 10/23/2022]
Abstract
Formulation of protein-based therapeutics employ advanced formulation and analytical technologies for screening various parameters such as buffer, pH, and excipients. At a molecular level, physico-chemical properties of a protein formulation depend on self-interaction between protein molecules, protein-solvent and protein-excipient interactions. This work describes a novel in silico approach, SILCS-Biologics, for structure-based modeling of protein formulations. SILCS Biologics is based on the Site-Identification by Ligand Competitive Saturation (SILCS) technology and enables modeling of interactions among different components of a formulation at an atomistic level while accounting for protein flexibility. It predicts potential hotspot regions on the protein surface for protein-protein and protein-excipient interactions. Here we apply SILCS-Biologics on a Fab domain of a monoclonal antibody (mAbN) to model Fab-Fab interactions and interactions with three amino acid excipients, namely, arginine HCl, proline and lysine HCl. Experiments on 100 mg/ml formulations of mAbN showed that arginine increased, lysine reduced, and proline did not impact viscosity. We use SILCS-Biologics modeling to explore a structure-based hypothesis for the viscosity modulating effect of these excipients. Current efforts are aimed at further validation of this novel computational framework and expanding the scope to model full mAb and other protein therapeutics.
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Affiliation(s)
- Sandeep Somani
- Discovery Sciences, Janssen Research and Development (Janssen R&D), Spring House, PA 19477, USA
| | | | - Renuka Thirumangalathu
- BioTherapeutics Drug Product Development (BioTD DPD), Janssen Research and Development (Janssen R&D), Malvern, PA 19355, USA
| | - Danika Rodrigues
- BioTherapeutics Drug Product Development (BioTD DPD), Janssen Research and Development (Janssen R&D), Malvern, PA 19355, USA
| | - Laura M Tanenbaum
- BioTherapeutics Drug Product Development (BioTD DPD), Janssen Research and Development (Janssen R&D), Malvern, PA 19355, USA
| | - Ketan Amin
- BioTherapeutics Drug Product Development (BioTD DPD), Janssen Research and Development (Janssen R&D), Malvern, PA 19355, USA
| | - Alexander D MacKerell
- SilcsBio LLC, Baltimore, MD 21202, USA; Computer-Aided Drug Design Center, School of Pharmacy, University of Maryland, Baltimore, MD 21201, USA.
| | - Santosh V Thakkar
- BioTherapeutics Drug Product Development (BioTD DPD), Janssen Research and Development (Janssen R&D), Malvern, PA 19355, USA; BioTherapeutics Cell and Developability Sciences (BioTD CDS), Janssen Research and Development (Janssen R&D), Spring House, PA 19477, USA.
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32
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Tan YS, Verma CS. Straightforward Incorporation of Multiple Ligand Types into Molecular Dynamics Simulations for Efficient Binding Site Detection and Characterization. J Chem Theory Comput 2020; 16:6633-6644. [PMID: 32810406 DOI: 10.1021/acs.jctc.0c00405] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Binding site identification and characterization is an important initial step in structure-based drug design. To account for the effects of protein flexibility and solvation, several cosolvent molecular dynamics (MD) simulation methods that incorporate small organic molecules into the protein's solvent box to probe for binding sites have been developed. However, most of these methods are highly inefficient, as they allow for the use of only one probe type at a time, which means that multiple sets of simulations have to be performed to map different types of binding sites. The high probe concentrations used in some of these methods also necessitate the use of artificial repulsive forces to prevent the probes from aggregating. Here, we present multiple-ligand-mapping MD (mLMMD), a method that incorporates multiple types of probes for simultaneous and efficient mapping of different types of binding sites without the need for introduction of artificial forces that may cause unintended mapping artifacts. We validate the method on a diverse set of 10 proteins and show that the mLMMD probes are able to reliably identify hydrophobic, hydrogen-bonding, charged, and cryptic binding sites in all of the test cases. Our results also highlight the potential utility of mLMMD for virtual screening and rational drug design.
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Affiliation(s)
- Yaw Sing Tan
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671
| | - Chandra S Verma
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671.,Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543.,School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551
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Jiang S, Feher M, Williams C, Cole B, Shaw DE. AutoPH4: An Automated Method for Generating Pharmacophore Models from Protein Binding Pockets. J Chem Inf Model 2020; 60:4326-4338. [PMID: 32639159 DOI: 10.1021/acs.jcim.0c00121] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Pharmacophore models are widely used in computational drug discovery (e.g., in the virtual screening of drug molecules) to capture essential information about interactions between ligands and a target protein. Generating pharmacophore models from protein structures is typically a manual process, but there has been growing interest in automated pharmacophore generation methods. Automation makes feasible the processing of large numbers of protein conformations, such as those generated by molecular dynamics (MD) simulations, and thus may help achieve the longstanding goal of incorporating protein flexibility into virtual screening workflows. Here, we present AutoPH4, a new automated method for generating pharmacophore models based on protein structures; we show that a virtual screening workflow incorporating AutoPH4 ranks compounds more accurately than any other pharmacophore-based virtual screening workflow for which results on a public benchmark have been reported. The strong performance of the virtual screening workflow indicates that the AutoPH4 component of the workflow generates high-quality pharmacophores, making AutoPH4 promising for use in future virtual screening workflows as well, such as ones that use conformations generated by MD simulations.
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Affiliation(s)
- Siduo Jiang
- D. E. Shaw Research, New York, New York 10036, United States
| | - Miklos Feher
- D. E. Shaw Research, New York, New York 10036, United States
| | - Chris Williams
- Chemical Computing Group, Montreal, Quebec H3A 2R7, Canada
| | - Brian Cole
- D. E. Shaw Research, New York, New York 10036, United States
| | - David E Shaw
- D. E. Shaw Research, New York, New York 10036, United States.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, United States
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Modeling Epac1 interactions with the allosteric inhibitor AM-001 by co-solvent molecular dynamics. J Comput Aided Mol Des 2020; 34:1171-1179. [PMID: 32700175 PMCID: PMC7533256 DOI: 10.1007/s10822-020-00332-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 07/13/2020] [Indexed: 12/13/2022]
Abstract
The exchange proteins activated by cAMP (EPAC) are implicated in a large variety of physiological processes and they are considered as promising targets for a wide range of therapeutic applications. Several recent reports provided evidence for the therapeutic effectiveness of the inhibiting EPAC1 activity cardiac diseases. In that context, we recently characterized a selective EPAC1 antagonist named AM-001. This compound was featured by a non-competitive mechanism of action but the localization of its allosteric site to EPAC1 structure has yet to be investigated. Therefore, we performed cosolvent molecular dynamics with the aim to identify a suitable allosteric binding site. Then, the docking and molecular dynamics were used to determine the binding of the AM-001 to the regions highlighted by cosolvent molecular dynamics for EPAC1. These analyses led us to the identification of a suitable allosteric AM-001 binding pocket at EPAC1. As a model validation, we also evaluated the binding poses of the available AM-001 analogues, with a different biological potency. Finally, the complex EPAC1 with AM-001 bound at the putative allosteric site was further refined by molecular dynamics. The principal component analysis led us to identify the protein motion that resulted in an inactive like conformation upon the allosteric inhibitor binding.
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Schaller D, Šribar D, Noonan T, Deng L, Nguyen TN, Pach S, Machalz D, Bermudez M, Wolber G. Next generation 3D pharmacophore modeling. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1468] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- David Schaller
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Dora Šribar
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Theresa Noonan
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Lihua Deng
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Trung Ngoc Nguyen
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Szymon Pach
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - David Machalz
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Marcel Bermudez
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
| | - Gerhard Wolber
- Pharmaceutical and Medicinal Chemistry Freie Universität Berlin Berlin Germany
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Allosteric Binding Sites On Nuclear Receptors: Focus On Drug Efficacy and Selectivity. Int J Mol Sci 2020; 21:ijms21020534. [PMID: 31947677 PMCID: PMC7014104 DOI: 10.3390/ijms21020534] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 12/29/2019] [Accepted: 01/10/2020] [Indexed: 02/07/2023] Open
Abstract
Nuclear receptors (NRs) are highly relevant drug targets in major indications such as oncologic, metabolic, reproductive, and immunologic diseases. However, currently, marketed drugs designed towards the orthosteric binding site of NRs often suffer from resistance mechanisms and poor selectivity. The identification of two superficial allosteric sites, activation function-2 (AF-2) and binding function-3 (BF-3), as novel drug targets sparked the development of inhibitors, while selectivity concerns due to a high conservation degree remained. To determine important pharmacophores and hydration sites among AF-2 and BF-3 of eight hormonal NRs, we systematically analyzed over 10 μ s of molecular dynamics simulations including simulations in explicit water and solvent mixtures. In addition, a library of over 300 allosteric inhibitors was evaluated by molecular docking. Based on our results, we suggest the BF-3 site to offer a higher potential for drug selectivity as opposed to the AF-2 site that is more conserved among the selected receptors. Detected similarities among the AF-2 sites of various NRs urge for a broader selectivity assessment in future studies. In combination with the Supplementary Material, this work provides a foundation to improve both selectivity and potency of allosteric inhibitors in a rational manner and increase the therapeutic applicability of this promising compound class.
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MacKerell AD, Jo S, Lakkaraju SK, Lind C, Yu W. Identification and characterization of fragment binding sites for allosteric ligand design using the site identification by ligand competitive saturation hotspots approach (SILCS-Hotspots). Biochim Biophys Acta Gen Subj 2020; 1864:129519. [PMID: 31911242 DOI: 10.1016/j.bbagen.2020.129519] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/21/2019] [Accepted: 12/31/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND Fragment-based ligand design is used for the development of novel ligands that target macromolecules, most notably proteins. Central to its success is the identification of fragment binding sites that are spatially adjacent such that fragments occupying those sites may be linked to create drug-like ligands. Current experimental and computational approaches that address this problem typically identify only a limited number of sites as well as use a limited number of fragment types. METHODS The site-identification by ligand competitive saturation (SILCS) approach is extended to the identification of fragment bindings sites, with the method termed SILCS-Hotspots. The approach involves precomputation of the SILCS FragMaps following which the identification of Hotspots, performed by identifying of all possible fragment binding sites on the full 3D structure of the protein followed by spatial clustering. RESULTS The SILCS-Hotspots approach identifies a large number of sites on the target protein, including many sites not accessible in experimental structures due to low binding affinities and binding sites on the protein interior. The identified sites are shown to recapitulate the location of known drug-like molecules in both allosteric and orthosteric binding sites on seven proteins including the androgen receptor, the CDK2 and Erk5 kinases, PTP1B phosphatase and three GPCRs; the β2-adrenergic, GPR40 fatty-acid binding and M2-muscarinic receptors. Analysis indicates the importance of considering all possible fragment binding sites, and not just those accessible to experimental methods, when identifying novel binding sites and performing ligand design versus just considering the most favorable sites. The approach is shown to identify a larger number of known binding sites of drug-like molecules versus the commonly used FTMap and Fpocket methods. GENERAL SIGNIFICANCE The present results indicate the potential utility of the SILCS-Hotspots approach for fragment-based rational design of ligands, including allosteric modulators.
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Affiliation(s)
- Alexander D MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201, United States of America.
| | - Sunhwan Jo
- SilcsBio, LLC, 8 Market Place, Suite 300, Baltimore, MD 21202, United States of America
| | | | - Christoffer Lind
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201, United States of America
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201, United States of America
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Lee JY, Krieger JM, Li H, Bahar I. Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations. Protein Sci 2019; 29:76-86. [PMID: 31576621 PMCID: PMC6933858 DOI: 10.1002/pro.3732] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 12/14/2022]
Abstract
Recent years have seen progress in druggability simulations, that is, molecular dynamics simulations of target proteins in solutions containing drug‐like probe molecules to characterize their drug‐binding abilities, if any. An important consecutive step is to analyze the trajectories to construct pharmacophore models (PMs) to use for virtual screening of libraries of small molecules. While considerable success has been observed in this type of computer‐aided drug discovery, a systematic tool encompassing multiple steps from druggability simulations to pharmacophore modeling, to identifying hits by virtual screening of libraries of compounds, has been lacking. We address this need here by developing a new tool, Pharmmaker, building on the DruGUI module of our ProDy application programming interface. Pharmmaker is composed of a suite of steps: (Step 1) identification of high affinity residues for each probe molecule type; (Step 2) selecting high affinity residues and hot spots in the vicinity of sites identified by DruGUI; (Step 3) ranking of the interactions between high affinity residues and specific probes; (Step 4) obtaining probe binding poses and corresponding protein conformations by collecting top‐ranked snapshots; and (Step 5) using those snapshots for constructing PMs. The PMs are then used as filters for identifying hits in structure‐based virtual screening. Pharmmaker, accessible online at http://prody.csb.pitt.edu/pharmmaker/, can be used in conjunction with other tools available in ProDy.
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Affiliation(s)
- Ji Young Lee
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - James M Krieger
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Hongchun Li
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
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39
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Donohue E, Khorsand S, Mercado G, Varney KM, Wilder PT, Yu W, MacKerell AD, Alexander P, Van QN, Moree B, Stephen AG, Weber DJ, Salafsky J, McCormick F. Second harmonic generation detection of Ras conformational changes and discovery of a small molecule binder. Proc Natl Acad Sci U S A 2019; 116:17290-17297. [PMID: 31399543 PMCID: PMC6717309 DOI: 10.1073/pnas.1905516116] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Second harmonic generation (SHG) is an emergent biophysical method that sensitively measures real-time conformational change of biomolecules in the presence of biological ligands and small molecules. This study describes the successful implementation of SHG as a primary screening platform to identify fragment ligands to oncogenic Kirsten rat sarcoma (KRas). KRas is the most frequently mutated driver of pancreatic, colon, and lung cancers; however, there are few well-characterized small molecule ligands due to a lack of deep binding pockets. Using SHG, we identified a fragment binder to KRasG12D and used 1H 15N transverse relaxation optimized spectroscopy (TROSY) heteronuclear single-quantum coherence (HSQC) NMR to characterize its binding site as a pocket adjacent to the switch 2 region. The unique sensitivity of SHG furthered our study by revealing distinct conformations induced by our hit fragment compared with 4,6-dichloro-2-methyl-3-aminoethyl-indole (DCAI), a Ras ligand previously described to bind the same pocket. This study highlights SHG as a high-throughput screening platform that reveals structural insights in addition to ligand binding.
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Affiliation(s)
- Elizabeth Donohue
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158
- Biodesy, Inc., South San Francisco, CA 94080
| | - Sina Khorsand
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158
- Biodesy, Inc., South San Francisco, CA 94080
| | | | - Kristen M Varney
- Center for Biomolecular Therapeutics, School of Medicine, University of Maryland, Baltimore, MD 21201
- Department of Biochemistry and Molecular Biology, School of Medicine, University of Maryland, Baltimore, MD 21201
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD 21201
| | - Paul T Wilder
- Center for Biomolecular Therapeutics, School of Medicine, University of Maryland, Baltimore, MD 21201
- Department of Biochemistry and Molecular Biology, School of Medicine, University of Maryland, Baltimore, MD 21201
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD 21201
| | - Wenbo Yu
- Center for Biomolecular Therapeutics, School of Medicine, University of Maryland, Baltimore, MD 21201
- Department of Biochemistry and Molecular Biology, School of Medicine, University of Maryland, Baltimore, MD 21201
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
| | - Alexander D MacKerell
- Center for Biomolecular Therapeutics, School of Medicine, University of Maryland, Baltimore, MD 21201
- Department of Biochemistry and Molecular Biology, School of Medicine, University of Maryland, Baltimore, MD 21201
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD 21201
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
| | - Patrick Alexander
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702
| | - Que N Van
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702
| | - Ben Moree
- Biodesy, Inc., South San Francisco, CA 94080
| | - Andrew G Stephen
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702
| | - David J Weber
- Center for Biomolecular Therapeutics, School of Medicine, University of Maryland, Baltimore, MD 21201
- Department of Biochemistry and Molecular Biology, School of Medicine, University of Maryland, Baltimore, MD 21201
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD 21201
| | - Joshua Salafsky
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
| | - Frank McCormick
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158;
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 21702
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Arcon JP, Defelipe LA, Lopez ED, Burastero O, Modenutti CP, Barril X, Marti MA, Turjanski AG. Cosolvent-Based Protein Pharmacophore for Ligand Enrichment in Virtual Screening. J Chem Inf Model 2019; 59:3572-3583. [DOI: 10.1021/acs.jcim.9b00371] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
| | | | | | | | | | - Xavier Barril
- Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain
- Faculty of Pharmacy and Institute of Biomedicine (IBUB), University of Barcelona, Av. Joan XXIII 27-31, Barcelona 08028, Spain
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Ustach VD, Lakkaraju SK, Jo S, Yu W, Jiang W, MacKerell AD. Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization. J Chem Inf Model 2019; 59:3018-3035. [PMID: 31034213 PMCID: PMC6597307 DOI: 10.1021/acs.jcim.9b00210] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Chemical fragment cosolvent sampling techniques have become a versatile tool in ligand-protein binding prediction. Site-identification by ligand competitive saturation (SILCS) is one such method that maps the distribution of chemical fragments on a protein as free energy fields called FragMaps. Ligands are then simulated via Monte Carlo techniques in the field of the FragMaps (SILCS-MC) to predict their binding conformations and relative affinities for the target protein. Application of SILCS-MC using a number of different scoring schemes and MC sampling protocols against multiple protein targets was undertaken to evaluate and optimize the predictive capability of the method. Seven protein targets and 551 ligands with broad chemical variability were used to evaluate and optimize the model to maximize Pearson's correlation coefficient, Pearlman's predictive index, correct relative binding affinity, and root-mean-square error versus the absolute experimental binding affinities. Across the protein-ligand sets, the relative affinities of the ligands were predicted correctly an average of 69% of the time for the highest overall SILCS protocol. Training the FragMap weighting factors using a Bayesian machine learning (ML) algorithm led to an increase to an average 75% relative correct affinity predictions. Furthermore, once the optimal protocol is identified for a specific protein-ligand system average predictabilities of 76% are achieved. The ML algorithm is successful with small training sets of data (30 or more compounds) due to the use of physically correct FragMap weights as priors. Notably, the 76% correct relative prediction rate is similar to or better than free energy perturbation methods that are significantly computationally more expensive than SILCS. The results further support the utility of SILCS as a powerful and computationally accessible tool to support lead optimization and development in drug discovery.
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Affiliation(s)
- Vincent D. Ustach
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201
| | | | - Sunhwan Jo
- SilcsBio, LLC, 8 Market Place, Suite 300, Baltimore, MD 21202
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201
| | - Wenjuan Jiang
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201
- SilcsBio, LLC, 8 Market Place, Suite 300, Baltimore, MD 21202
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Zhang H, Jiang W, Chatterjee P, Luo Y. Ranking Reversible Covalent Drugs: From Free Energy Perturbation to Fragment Docking. J Chem Inf Model 2019; 59:2093-2102. [PMID: 30763080 PMCID: PMC6610880 DOI: 10.1021/acs.jcim.8b00959] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Reversible covalent inhibitors have drawn increasing attention in drug design, as they are likely more potent than noncovalent inhibitors and less toxic than covalent inhibitors. Despite those advantages, the computational prediction of reversible covalent binding presents a formidable challenge because the binding process consists of multiple steps and quantum mechanics (QM) level calculation is needed to estimate the covalent binding free energy. It has been shown that the dissociation rates and the equilibrium dissociation constants vary significantly even with similar warheads, due to noncovalent interactions. We have previously used a simplistic two-state model for predicting the relative binding selectivity of reversible covalent inhibitors ( J. Am. Chem. Soc. 2017, 139 , 17945 ). Here we go beyond binding selectivity and demonstrate that it is possible to use free energy perturbation (FEP) molecular dynamics (MD) to calculate the overall reversible covalent binding using a specially designed thermodynamic cycle. We show that FEP can predict the varying binding free energies of the analogs sharing a common warhead. More importantly, our results revealed that the chemical modification away from warhead alters the binding affinity at both noncovalent and covalent binding states, and the computational prediction can be improved by considering the binding free energy of both states. Furthermore, we explored the possibility of using a more rapid computational method, site-identification by ligand competitive saturation (SILCS), to rank the same set of reversible covalent inhibitors. We found that the fragment docking to a set of precomputed fragment maps produces a reasonable ranking. In conclusion, two independent approaches provided consistent results that the covalent binding state is suitable for the initial ranking of the reversible covalent drug candidates. For lead-optimization, the FEP approach designed here can provide more rigorous and detailed information regarding how much the covalent and noncovalent binding states are contributing to the overall binding affinity, thus offering a new avenue for fine-tuning the noncovalent interactions for optimizing reversible covalent drugs.
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Affiliation(s)
- Han Zhang
- Western University of Health Sciences, College of Pharmacy, Pomona CA 91766
| | - Wenjuan Jiang
- Western University of Health Sciences, College of Pharmacy, Pomona CA 91766
| | - Payal Chatterjee
- Western University of Health Sciences, College of Pharmacy, Pomona CA 91766
| | - Yun Luo
- Western University of Health Sciences, College of Pharmacy, Pomona CA 91766
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Yu W, Jo S, Lakkaraju SK, Weber DJ, MacKerell AD. Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology. Proteins 2019; 87:289-301. [PMID: 30582220 PMCID: PMC6408985 DOI: 10.1002/prot.25650] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 12/06/2018] [Accepted: 12/19/2018] [Indexed: 01/05/2023]
Abstract
Protein docking methods are powerful computational tools to study protein-protein interactions (PPI). While a significant number of docking algorithms have been developed, they are usually based on rigid protein models or with limited considerations of protein flexibility and the desolvation effect is rarely considered in docking energy functions, which may lower the accuracy of the predictions. To address these issues, we introduce a PPI energy function based on the site-identification by ligand competitive saturation (SILCS) framework and utilize the fast Fourier transform (FFT) correlation approach. The free energy content of the SILCS FragMaps represent an alternative to traditional energy grids and they can be efficiently utilized to guide FFT-based protein docking. Application of the approach to eight diverse test cases, including seven from Protein Docking Benchmark 5.0, showed the PPI prediction using SILCS approach (SILCS-PPI) to be competitive with several commonly used protein docking methods indicating that the method has the ability to both qualitatively and quantitatively inform the prediction of PPI. Results show the utility of the SILCS-PPI docking approach for determination of probability distributions of PPI interactions over the surface of both partner proteins, allowing for identification of alternate binding poses. Such binding poses are confirmed by experimental crystal contacts in our test cases. While more computationally demanding than available PPI docking technologies, we anticipate that the SILCS-PPI docking approach will offer an alternative methodology for improved evaluation of PPIs that could be used in a variety of fields from systems biology to excipient design for biologics-based drugs.
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Affiliation(s)
- Wenbo Yu
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD 20850
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD 21201
| | | | | | - David J. Weber
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD 20850
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD 21201
| | - Alexander D. MacKerell
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD 20850
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD 21201
- SilcsBio LLC, Baltimore, MD 21202
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Tran-Nguyen VK, Da Silva F, Bret G, Rognan D. All in One: Cavity Detection, Druggability Estimate, Cavity-Based Pharmacophore Perception, and Virtual Screening. J Chem Inf Model 2019; 59:573-585. [PMID: 30563339 DOI: 10.1021/acs.jcim.8b00684] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Discovering the very first ligands of pharmacologically important targets in a fast and cost-efficient manner is an important issue in drug discovery. In the absence of structural information on either endogenous or synthetic ligands, computational chemists classically identify the very first hits by docking compound libraries to a binding site of interest, with well-known biases arising from the usage of scoring functions. We herewith propose a novel computational method tailored to ligand-free protein structures and consisting in the generation of simple cavity-based pharmacophores to which potential ligands could be aligned by the use of a smooth Gaussian function. The method, embedded in the IChem toolkit, automatically detects ligand-binding cavities, then predicts their structural druggability, and last creates a structure-based pharmacophore for predicted druggable binding sites. A companion tool (Shaper2) was designed to align ligands to cavity-derived pharmacophoric features. The proposed method is as efficient as state-of-the-art virtual screening methods (ROCS, Surflex-Dock) in both posing and virtual screening challenges. Interestingly, IChem-Shaper2 is clearly orthogonal to these latter methods in retrieving unique chemotypes from high-throughput virtual screening data.
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Affiliation(s)
- Viet-Khoa Tran-Nguyen
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| | - Franck Da Silva
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| | - Guillaume Bret
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique , UMR 7200 CNRS-Université de Strasbourg , 67400 Illkirch , France
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Solvents to Fragments to Drugs: MD Applications in Drug Design. Molecules 2018; 23:molecules23123269. [PMID: 30544890 PMCID: PMC6321499 DOI: 10.3390/molecules23123269] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/02/2018] [Accepted: 12/03/2018] [Indexed: 01/24/2023] Open
Abstract
Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.
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Lamothe G, Malliavin TE. re-TAMD: exploring interactions between H3 peptide and YEATS domain using enhanced sampling. BMC STRUCTURAL BIOLOGY 2018; 18:4. [PMID: 29615024 PMCID: PMC5883362 DOI: 10.1186/s12900-018-0083-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 03/04/2018] [Indexed: 01/05/2023]
Abstract
BACKGROUND Analysis of preferred binding regions of a ligand on a protein is important for detecting cryptic binding pockets and improving the ligand selectivity. RESULT The enhanced sampling approach TAMD has been adapted to allow a ligand to unbind from its native binding site and explore the protein surface. This so-called re-TAMD procedure was then used to explore the interaction between the N terminal peptide of histone H3 and the YEATS domain. Depending on the length of the peptide, several regions of the protein surface were explored. The peptide conformations sampled during the re-TAMD correspond to peptide free diffusion around the protein surface. CONCLUSIONS The re-TAMD approach permitted to get information on the relative influence of different regions of the N terminal peptide of H3 on the interaction between H3 and YEATS.
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Affiliation(s)
- Gilles Lamothe
- Unité de Bioinformatique Structurale, UMR CNRS 3528 and Institut Pasteur, Paris, France.,Université Denis Diderot Paris 7, Paris, France
| | - Thérèse E Malliavin
- Unité de Bioinformatique Structurale, UMR CNRS 3528 and Institut Pasteur, Paris, France.
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Ghanakota P, Carlson HA. Comparing pharmacophore models derived from crystallography and NMR ensembles. J Comput Aided Mol Des 2017; 31:979-993. [PMID: 29047011 DOI: 10.1007/s10822-017-0077-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 10/12/2017] [Indexed: 10/18/2022]
Abstract
NMR and X-ray crystallography are the two most widely used methods for determining protein structures. Our previous study examining NMR versus X-Ray sources of protein conformations showed improved performance with NMR structures when used in our Multiple Protein Structures (MPS) method for receptor-based pharmacophores (Damm, Carlson, J Am Chem Soc 129:8225-8235, 2007). However, that work was based on a single test case, HIV-1 protease, because of the rich data available for that system. New data for more systems are available now, which calls for further examination of the effect of different sources of protein conformations. The MPS technique was applied to Growth factor receptor bound protein 2 (Grb2), Src SH2 homology domain (Src-SH2), FK506-binding protein 1A (FKBP12), and Peroxisome proliferator-activated receptor-γ (PPAR-γ). Pharmacophore models from both crystal and NMR ensembles were able to discriminate between high-affinity, low-affinity, and decoy molecules. As we found in our original study, NMR models showed optimal performance when all elements were used. The crystal models had more pharmacophore elements compared to their NMR counterparts. The crystal-based models exhibited optimum performance only when pharmacophore elements were dropped. This supports our assertion that the higher flexibility in NMR ensembles helps focus the models on the most essential interactions with the protein. Our studies suggest that the "extra" pharmacophore elements seen at the periphery in X-ray models arise as a result of decreased protein flexibility and make very little contribution to model performance.
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Affiliation(s)
- Phani Ghanakota
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, MI, 48109-1065, USA
| | - Heather A Carlson
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, MI, 48109-1065, USA.
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Raman EP, Lakkaraju SK, Denny RA, MacKerell AD. Estimation of relative free energies of binding using pre-computed ensembles based on the single-step free energy perturbation and the site-identification by Ligand competitive saturation approaches. J Comput Chem 2017; 38:1238-1251. [PMID: 27782307 PMCID: PMC5403604 DOI: 10.1002/jcc.24522] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 09/17/2016] [Accepted: 10/04/2016] [Indexed: 12/19/2022]
Abstract
Accurate and rapid estimation of relative binding affinities of ligand-protein complexes is a requirement of computational methods for their effective use in rational ligand design. Of the approaches commonly used, free energy perturbation (FEP) methods are considered one of the most accurate, although they require significant computational resources. Accordingly, it is desirable to have alternative methods of similar accuracy but greater computational efficiency to facilitate ligand design. In the present study relative free energies of binding are estimated for one or two non-hydrogen atom changes in compounds targeting the proteins ACK1 and p38 MAP kinase using three methods. The methods include standard FEP, single-step free energy perturbation (SSFEP) and the site-identification by ligand competitive saturation (SILCS) ligand grid free energy (LGFE) approach. Results show the SSFEP and SILCS LGFE methods to be competitive with or better than the FEP results for the studied systems, with SILCS LGFE giving the best agreement with experimental results. This is supported by additional comparisons with published FEP data on p38 MAP kinase inhibitors. While both the SSFEP and SILCS LGFE approaches require a significant upfront computational investment, they offer a 1000-fold computational savings over FEP for calculating the relative affinities of ligand modifications once those pre-computations are complete. An illustrative example of the potential application of these methods in the context of screening large numbers of transformations is presented. Thus, the SSFEP and SILCS LGFE approaches represent viable alternatives for actively driving ligand design during drug discovery and development. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- E. Prabhu Raman
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn Street HSF II, Baltimore MD 21201
| | - Sirish Kaushik Lakkaraju
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn Street HSF II, Baltimore MD 21201
| | - Rajiah Aldrin Denny
- Medicine Design, Worldwide Research & Development, Pfizer Inc, 610 Main Street, Cambridge, MA 02139, USA
| | - Alexander D. MacKerell
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn Street HSF II, Baltimore MD 21201
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Arcon JP, Defelipe LA, Modenutti CP, López ED, Alvarez-Garcia D, Barril X, Turjanski AG, Martí MA. Molecular Dynamics in Mixed Solvents Reveals Protein–Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions. J Chem Inf Model 2017; 57:846-863. [DOI: 10.1021/acs.jcim.6b00678] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Juan Pablo Arcon
- Departamento
de Química Biológica e IQUIBICEN-CONICET, Facultad de
Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, C1428EHA Ciudad de Buenos Aires, Argentina
| | - Lucas A. Defelipe
- Departamento
de Química Biológica e IQUIBICEN-CONICET, Facultad de
Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, C1428EHA Ciudad de Buenos Aires, Argentina
| | - Carlos P. Modenutti
- Departamento
de Química Biológica e IQUIBICEN-CONICET, Facultad de
Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, C1428EHA Ciudad de Buenos Aires, Argentina
| | - Elias D. López
- Departamento
de Química Biológica e IQUIBICEN-CONICET, Facultad de
Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, C1428EHA Ciudad de Buenos Aires, Argentina
| | | | - Xavier Barril
- Institut
de Biomedicina de la Universitat de Barcelona (IBUB) and Facultat
de Farmàcia, Universitat de Barcelona, Av. Joan XXIII s/n, 08028 Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain
| | - Adrián G. Turjanski
- Departamento
de Química Biológica e IQUIBICEN-CONICET, Facultad de
Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, C1428EHA Ciudad de Buenos Aires, Argentina
| | - Marcelo A. Martí
- Departamento
de Química Biológica e IQUIBICEN-CONICET, Facultad de
Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pabellón 2, C1428EHA Ciudad de Buenos Aires, Argentina
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Abstract
Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.
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