1
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de Carvalho GA, Tambwe PM, Nascimento LRC, Campos BKP, Chiareli RA, Junior GPN, Menegatti R, Gomez RS, Pinto MCX. In silico evidence of bitopertin's broad interactions within the SLC6 transporter family. J Pharm Pharmacol 2024:rgae051. [PMID: 38982944 DOI: 10.1093/jpp/rgae051] [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: 01/12/2024] [Accepted: 04/16/2024] [Indexed: 07/11/2024]
Abstract
The Glycine Transporter Type 1 (GlyT1) significantly impacts central nervous system functions, influencing glycinergic and glutamatergic neurotransmission. Bitopertin, the first GlyT1 inhibitor in clinical trials, was developed for schizophrenia treatment but showed limited efficacy. Despite this, bitopertin's repositioning could advance treating various pathologies. This study aims to understand bitopertin's mechanism of action using computational methods, exploring off-target effects, and providing a comprehensive pharmacological profile. Similarity Ensemble Approach (SEA) and SwissTargetPrediction initially predicted targets, followed by molecular modeling on SWISS-MODEL and GalaxyWeb servers. Binding sites were identified using PrankWeb, and molecular docking was performed with DockThor and GOLD software. Molecular dynamics analyses were conducted on the Visual Dynamics platform. Reverse screening on SEA and SwissTargetPrediction identified GlyT1 (SLC6A9), GlyT2 (SLC6A5), PROT (SLC6A7), and DAT (SLC6A3) as potential bitopertin targets. Homology modeling on SwissModel generated high-resolution models, optimized further on GalaxyWeb. PrankWeb identified similar binding sites in GlyT1, GlyT2, PROT, and DAT, indicating potential interaction. Docking studies suggested bitopertin's interaction with GlyT1 and proximity to GlyT2 and PROT. Molecular dynamics confirmed docking results, highlighting bitopertin's target stability beyond GlyT1. The study concludes that bitopertin potentially interacts with multiple SLC6 family targets, indicating a broader pharmacological property.
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Affiliation(s)
- Gustavo Almeida de Carvalho
- Departamento de Farmacologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, CEP 74690-900, Goiânia-GO, Brazil
| | - Paul Magogo Tambwe
- Departamento de Farmacologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, CEP 74690-900, Goiânia-GO, Brazil
| | - Lucas Rodrigues Couto Nascimento
- Departamento de Farmacologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, CEP 74690-900, Goiânia-GO, Brazil
| | - Bruna Kelly Pedrosa Campos
- Departamento de Farmacologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, CEP 74690-900, Goiânia-GO, Brazil
| | - Raphaela Almeida Chiareli
- Departamento de Farmacologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, CEP 74690-900, Goiânia-GO, Brazil
| | - Guilhermino Pereira Nunes Junior
- Departamento de Farmacologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, CEP 74690-900, Goiânia-GO, Brazil
| | - Ricardo Menegatti
- Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Setor Leste Universitário, 74605170 - Goiânia, GO, Brazil
| | - Renato Santiago Gomez
- Departamento de Cirurgia, Faculdade de Medicina, Universidade Federal de Minas Gerais, Av. Alfredo Balena, 190, 30130-100, Belo Horizonte-MG, Brazil
| | - Mauro Cunha Xavier Pinto
- Departamento de Farmacologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, CEP 74690-900, Goiânia-GO, Brazil
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2
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Kashyap D, Roy R, Kar P, Jha HC. Plant-derived active compounds as a potential nucleocapsid protein inhibitor of SARS-CoV-2: an in-silico study. J Biomol Struct Dyn 2022:1-16. [PMID: 35532092 DOI: 10.1080/07391102.2022.2072951] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2. This virus has a high mismatch repair proofreading ability due to its unique exonuclease activity, making it knotty to treat. The nucleocapsid protein can serve as a potential antiviral drug target, as this protein is responsible for multiple captious functions during the viral life cycle. Herein, we have investigated the potential to repurpose active antiviral compounds of plant origins for treating the SARS-CoV-2 infection. In the present study, we followed the molecular docking methodology to screen druggable natural plants' active compounds against the nucleocapsid protein of SARS-CoV-2. The virtual screening of all 68 compounds revealed that the top seven active compounds, such as withanolide D, hypericin, silymarin, oxyacanthine, withaferin A, Acetyl aleuritolic acid, and rhein, exhibit good binding affinity with druggable ADME properties, toxicity, and Pass prediction. The stability of the docked complexes was studied by conducting molecular simulations of 100 ns. MM-GBSA calculated the binding free energy uncovered that withanolide D, hypericin, and silymarin result in highly stable binding conformations in three different sites of the nucleocapsid protein. However, further investigation is needed in order to validate the candidacy of these inhibitors for clinical trials. HighlightsNatural plants' active compounds may aid in the inhibition of SARS-CoV-2 replication and COVID-19 therapeutics.Hypericin, silymarin, withanolide D, oxyacanthine, withaferin A, Acetyl aleuritolic acid, and rhein are effective against SARS-CoV-2 N protein.Studied natural plants' active compounds could be useful against COVID-19 and its associated organs comorbidities.ADMET properties of selected compounds favor these compounds as druggable candidates.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Dharmendra Kashyap
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Madhya Pradesh, India
| | - Rajarshi Roy
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Madhya Pradesh, India
| | - Parimal Kar
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Madhya Pradesh, India
| | - Hem Chandra Jha
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology, Indore, Madhya Pradesh, India
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3
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Ganguly R, Mylliemngap BJ, Bhattacharjee A. Discovery of a novel inhibitor against urokinase-type plasminogen activator, a potential enzyme with a role in atherosclerotic plaque instability. J Biomol Struct Dyn 2022; 41:3485-3495. [PMID: 35362361 DOI: 10.1080/07391102.2022.2051742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The buildup of lipids, cholesterol, and other substances in and on the walls of the arteries is known as atherosclerosis and deposition is known as atherosclerotic plaque. Urokinase-type plasminogen activator (uPA) has multiple roles in the atherosclerotic plaque formation and even work simultaneously in making the atherosclerotic plaque unstable. Extracellular matrix plays a major role in the plaque remodeling and rapture. In this study, we have accessed that a higher interaction was observed in the molecular interaction score for uPA with ZINC380065722 having a GOLD fitness score of about 67.60, which is much higher as compared to the known standard inhibitor UK 122 which has reported an interaction score of 59.14. Ser217 and Asp192 are found to be the key amino acid residues in almost all the interactions. Protein frustration analysis has shown that these amino acid residues play a crucial role in the retention of the active pocket conformation and any mutation of these two residues can causes serious decrease in the overall function of the protein. It was observed that the molecule ZINC380065722 remained bound to the protein till 100 ns of simulation time. The average SASA for the apo-uPA and uPA-ligand complex was found to be stable. The network of hydrogen bonds for the intramolecular protein secondary structure and with the solvent system for the apo-protein and the uPA-ligand complex was found to be consistent.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rik Ganguly
- Department of Biotechnology and Bioinformatics, North-Eastern Hill University, Shillong, India
| | | | - Atanu Bhattacharjee
- Department of Biotechnology and Bioinformatics, North-Eastern Hill University, Shillong, India
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4
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Detailed Analyses of Molecular Interactions between Favipiravir and RNA Viruses In Silico. Viruses 2022; 14:v14020338. [PMID: 35215932 PMCID: PMC8879546 DOI: 10.3390/v14020338] [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: 12/24/2021] [Revised: 02/01/2022] [Accepted: 02/05/2022] [Indexed: 02/05/2023] Open
Abstract
There are currently no antiviral agents for human metapneumovirus (HMPV), respiratory syncytial virus (RSV), mumps virus (MuV), or measles virus (MeV). Favipiravir has been developed as an anti-influenza agent, and this agent may be effective against these viruses in vitro. However, the molecular mechanisms through which the agent affects virus replication remain to be fully elucidated. Thus, to clarify the detailed molecular interactions between favipiravir and the RNA-dependent RNA polymerase (RdRp) of HMPV, RSV, MuV, MeV, and influenza virus, we performed in silico studies using authentic bioinformatics technologies. As a result, we found that the active form of favipiravir (favipiravir ribofuranosyl-5′-triphosphate [F-RTP]) can bind to the RdRp active sites of HMPV, RSV, MuV, and MeV. The aspartic acid residue of RdRp active sites was involved in the interaction. Moreover, F-RTP was incorporated into the growing viral RNA chain in the presence of nucleotide triphosphate and magnesium ions. The results suggested that favipiravir shows two distinct mechanisms in various viruses: RdRp active site inhibition and/or genome replication inhibition.
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5
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A geometric deep learning approach to predict binding conformations of bioactive molecules. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00409-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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6
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Arefin A, Ismail Ema T, Islam T, Hossen S, Islam T, Al Azad S, Uddin Badal N, Islam A, Biswas P, Alam NU, Islam E, Anjum M, Masud A, Kamran S, Rahman A, Kumar Paul P. Target specificity of selective bioactive compounds in blocking α-dystroglycan receptor to suppress Lassa virus infection: an in silico approach. J Biomed Res 2021; 35:459-473. [PMID: 34857680 PMCID: PMC8637655 DOI: 10.7555/jbr.35.20210111] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Lassa hemorrhagic fever, caused by Lassa mammarenavirus (LASV) infection, accumulates up to 5000 deaths every year. Currently, there is no vaccine available to combat this disease. In this study, a library of 200 bioactive compounds was virtually screened to study their drug-likeness with the capacity to block the α-dystroglycan (α-DG) receptor and prevent LASV influx. Following rigorous absorption, distribution, metabolism, and excretion (ADME) and quantitative structure-activity relationship (QSAR) profiling, molecular docking was conducted with the top ligands against the α-DG receptor. The compounds chrysin, reticuline, and 3-caffeoylshikimic acid emerged as the top three ligands in terms of binding affinity. Post-docking analysis revealed that interactions with Arg76, Asn224, Ser259, and Lys302 amino acid residues of the receptor protein were important for the optimum binding affinity of ligands. Molecular dynamics simulation was performed comprehensively to study the stability of the protein-ligand complexes. In-depth assessment of root-mean-square deviation (RMSD), root mean square fluctuation (RMSF), polar surface area (PSA), B-Factor, radius of gyration (Rg), solvent accessible surface area (SASA), and molecular surface area (MolSA) values of the protein-ligand complexes affirmed that the candidates with the best binding affinity formed the most stable protein-ligand complexes. To authenticate the potentialities of the ligands as target-specific drugs, an in vivo study is underway in real time as the continuation of the research.
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Affiliation(s)
- Adittya Arefin
- Wolfson Institute for Biomedical Research, Division of Medicine, University College London, London WC1E6AE, UK
| | - Tanzila Ismail Ema
- Department of Biochemistry and Microbiology, North South University, Dhaka 1229, Bangladesh
| | - Tamnia Islam
- Wolfson Institute for Biomedical Research, Division of Medicine, University College London, London WC1E6AE, UK
| | - Saddam Hossen
- Faculty of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
| | - Tariqul Islam
- Faculty of Pharmacy, International Islamic University Malaysia, Kuantan, Pahang 25200, Malaysia
| | - Salauddin Al Azad
- School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Nasir Uddin Badal
- Department of Biomedical Technology, Tampere University, Tampere 33014, Finland
| | - Aminul Islam
- Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore 7408, Bangladesh
| | - Partha Biswas
- Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore 7408, Bangladesh
| | - Nafee Ul Alam
- Faculty of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
| | - Enayetul Islam
- Department of Genetic Engineering and Biotechnology, University of Chittagong, Chittagong 4331, Bangladesh
| | - Maliha Anjum
- Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore 7408, Bangladesh
| | - Afsana Masud
- Department of Biochemistry and Microbiology, North South University, Dhaka 1229, Bangladesh
| | - Shaikh Kamran
- Applied Statistics and Data Science, Jahangirnagar University, Dhaka 1342, Bangladesh
| | - Ahsab Rahman
- Department of Mathematics and Natural Sciences, Brac University, Dhaka 1212, Bangladesh
| | - Parag Kumar Paul
- Department of Electrical and Electronic Engineering, United International University, Dhaka 1212, Bangladesh
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7
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Gu S, Smith MS, Yang Y, Irwin JJ, Shoichet BK. Ligand Strain Energy in Large Library Docking. J Chem Inf Model 2021; 61:4331-4341. [PMID: 34467754 DOI: 10.1021/acs.jcim.1c00368] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
While small molecule internal strain is crucial to molecular docking, using it in evaluating ligand scores has remained elusive. Here, we investigate a technique that calculates strain using relative torsional populations in the Cambridge Structural Database, enabling fast precalculation of these energies. In retrospective studies of large docking screens of the dopamine D4 receptor and of AmpC β-lactamase, where close to 600 docking hits were tested experimentally, including such strain energies improved hit rates by preferentially reducing the ranks of strained high-scoring decoy molecules. In a 40-target subset of the DUD-E benchmark, we found two thresholds that usefully distinguished between ligands and decoys: one based on the total strain energy of the small molecules and another based on the maximum strain allowed for any given torsion within them. Using these criteria, about 75% of the benchmark targets had improved enrichment after strain filtering. Relying on precalculated population distributions, this approach is rapid, taking less than 0.04 s to evaluate a conformation on a standard core, making it pragmatic for precalculating strain in even ultralarge libraries. Since it is scoring function agnostic, it may be useful to multiple docking approaches; it is openly available at http://tldr.docking.org.
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Affiliation(s)
- Shuo Gu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States
| | - Matthew S Smith
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States.,Program of Biophysics, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States
| | - Ying Yang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 Fourth Street, San Francisco, California 94143-2550, United States
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8
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Gusmão AS, Abreu LS, Tavares JF, de Freitas HF, Silva da Rocha Pita S, Dos Santos EG, Caldas IS, Vieira AA, Silva EO. Computer-Guided Trypanocidal Activity of Natural Lactones Produced by Endophytic Fungus of Euphorbia umbellata. Chem Biodivers 2021; 18:e2100493. [PMID: 34403573 DOI: 10.1002/cbdv.202100493] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 08/17/2021] [Indexed: 11/11/2022]
Abstract
Hundreds of millions of people worldwide are affected by Chagas' disease caused by Trypanosoma cruzi. Since the current treatment lack efficacy, specificity, and suffers from several side-effects, novel therapeutics are mandatory. Natural products from endophytic fungi have been useful sources of lead compounds. In this study, three lactones isolated from an endophytic strain culture were in silico evaluated for rational guidance of their bioassay screening. All lactones displayed in vitro activity against T. cruzi epimastigote and trypomastigote forms. Notably, the IC50 values of (+)-phomolactone were lower than benznidazole (0.86 vs. 30.78 μM against epimastigotes and 0.41 vs. 4.88 μM against trypomastigotes). Target-based studies suggested that lactones displayed their trypanocidal activities due to T. cruzi glyceraldehyde-3-phosphate dehydrogenase (TcGAPDH) inhibition, and the binding free energy for all three TcGAPDH-lactone complexes suggested that (+)-phomolactone has a lower score value (-3.38), corroborating with IC50 assays. These results highlight the potential of these lactones for further anti-T. cruzi drug development.
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Affiliation(s)
- Amanda Santos Gusmão
- Organic Chemistry Department, Chemistry Institute, Federal University of Bahia, Barão de Jeremoabo 147, Salvador, 40170115, Bahia, Brazil
| | - Lucas Silva Abreu
- Institute for Research in Pharmaceuticals and Medications, Federal University of Paraíba, Campus I, João Pessoa, 58051900, Paraíba, Brazil
| | - Josean Fechine Tavares
- Institute for Research in Pharmaceuticals and Medications, Federal University of Paraíba, Campus I, João Pessoa, 58051900, Paraíba, Brazil
| | - Humberto Fonseca de Freitas
- Laboratory of Bioinformatics and Molecular Modeling (LaBiMM), Pharmacy College, Federal University of Bahia, Barão de Jeremoabo 147, Salvador, 40170115, Bahia, Brazil
| | - Samuel Silva da Rocha Pita
- Laboratory of Bioinformatics and Molecular Modeling (LaBiMM), Pharmacy College, Federal University of Bahia, Barão de Jeremoabo 147, Salvador, 40170115, Bahia, Brazil
| | - Elda Gonçalves Dos Santos
- Pathology and Parasitology Department, Institute of Biomedical Sciences, Federal University of Alfenas, Gabriel Monteiro da Silva 500, Alfenas, 37130001, Minas Gerais, Brazil
| | - Ivo Santana Caldas
- Pathology and Parasitology Department, Institute of Biomedical Sciences, Federal University of Alfenas, Gabriel Monteiro da Silva 500, Alfenas, 37130001, Minas Gerais, Brazil
| | - André Alexandre Vieira
- Organic Chemistry Department, Chemistry Institute, Federal University of Bahia, Barão de Jeremoabo 147, Salvador, 40170115, Bahia, Brazil
| | - Eliane Oliveira Silva
- Organic Chemistry Department, Chemistry Institute, Federal University of Bahia, Barão de Jeremoabo 147, Salvador, 40170115, Bahia, Brazil
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9
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Pathak S, Gokhroo A, Kumar Dubey A, Majumdar S, Gupta S, Almeida A, Mahajan GB, Kate A, Mishra P, Sharma R, Kumar S, Vishwakarma R, Balakrishnan A, Atreya H, Nandi D. 7-Hydroxy Frullanolide, a sesquiterpene lactone, increases intracellular calcium amounts, lowers CD4 + T cell and macrophage responses, and ameliorates DSS-induced colitis. Int Immunopharmacol 2021; 97:107655. [PMID: 33901737 DOI: 10.1016/j.intimp.2021.107655] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/19/2021] [Accepted: 04/03/2021] [Indexed: 12/16/2022]
Abstract
Sesquiterpene lactones are a class of anti-inflammatory molecules obtained from plants belonging to the Asteraceae family. In this study, the effects of 7-hydroxy frullanolide (7HF), a sesquiterpene lactone, in inhibiting CD4+ T cell and peritoneal macrophage responses were investigated. 7HF, in a dose dependent manner, lowers CD69 upregulation, IL2 production and CD4+ T cell cycling upon activation with the combination of anti-CD3 and anti-CD28. Further mechanistic studies demonstrated that 7HF, at early time points, increases intracellular Ca2+ amounts, over and above the levels induced upon activation. The functional relevance of 7HF-induced Ca2+ increase was confirmed using sub-optimal amounts of BAPTA, an intracellular Ca2+ chelator, which lowers lactate and rescues CD4+ T cell cycling. In addition, 7HF lowers T cell cycling with the combination of PMA and Ionomycin. However, 7HF increases CD4+ T cell cycling with sub-optimal activating signals: only PMA or anti-CD3. Furthermore, LPS-induced nitrite and IL6 production by peritoneal macrophages is inhibited by 7HF in a Ca2+-dependent manner. Studies with Ca2+ channel inhibitors, Ruthenium Red and 2-Aminoethoxydiphenyl borate, lowers the inhibitory effects of 7HF on CD4+ T cell and macrophage responses. In silico studies demonstrated that 7HF binds to Ca2+ channels, TRPV1, IP3R and SERCA, which is mechanistically important. Finally, intraperitoneal administration of 7HF lowers serum inflammatory cytokines, IFNγ and IL6, and reduces the effects of DSS-induced colitis with respect to colon length and colon damage. Overall, this study sheds mechanistic light on the anti-inflammatory potential of 7HF, a natural plant compound, in lowering immune responses.
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Affiliation(s)
- Sanmoy Pathak
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, India
| | - Abhijeet Gokhroo
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, India
| | - Ashim Kumar Dubey
- Undergraduate Program, Indian Institute of Science, Bangalore 560012, India
| | - Shamik Majumdar
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, India
| | - Souradeep Gupta
- NMR Research Facility, Indian Institute of Science, Bangalore 560012, India
| | - Asha Almeida
- High Throughput Department, Piramal Research Center,1 Nirlon Complex, Off Western Express Highway, Goregaon East, Mumbai 400063, India
| | - Girish B Mahajan
- High Throughput Department, Piramal Research Center,1 Nirlon Complex, Off Western Express Highway, Goregaon East, Mumbai 400063, India
| | - Abhijeet Kate
- High Throughput Department, Piramal Research Center,1 Nirlon Complex, Off Western Express Highway, Goregaon East, Mumbai 400063, India
| | - Prabhu Mishra
- High Throughput Department, Piramal Research Center,1 Nirlon Complex, Off Western Express Highway, Goregaon East, Mumbai 400063, India
| | - Rajiv Sharma
- High Throughput Department, Piramal Research Center,1 Nirlon Complex, Off Western Express Highway, Goregaon East, Mumbai 400063, India
| | - Sanjay Kumar
- High Throughput Department, Piramal Research Center,1 Nirlon Complex, Off Western Express Highway, Goregaon East, Mumbai 400063, India
| | - Ram Vishwakarma
- High Throughput Department, Piramal Research Center,1 Nirlon Complex, Off Western Express Highway, Goregaon East, Mumbai 400063, India
| | - Arun Balakrishnan
- High Throughput Department, Piramal Research Center,1 Nirlon Complex, Off Western Express Highway, Goregaon East, Mumbai 400063, India
| | - Hanudatta Atreya
- NMR Research Facility, Indian Institute of Science, Bangalore 560012, India
| | - Dipankar Nandi
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, India.
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10
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Jedwabny W, Dyguda-Kazimierowicz E, Pernal K, Szalewicz K, Patkowski K. Extension of an Atom-Atom Dispersion Function to Halogen Bonds and Its Use for Rational Design of Drugs and Biocatalysts. J Phys Chem A 2021; 125:1787-1799. [PMID: 33620223 PMCID: PMC8028329 DOI: 10.1021/acs.jpca.0c11347] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 02/10/2021] [Indexed: 12/17/2022]
Abstract
A dispersion function Das in the form of a damped atom-atom asymptotic expansion fitted to ab initio dispersion energies from symmetry-adapted perturbation theory was improved and extended to systems containing heavier halogen atoms. To illustrate its performance, the revised Das function was implemented in the multipole first-order electrostatic and second-order dispersion (MED) scoring model. The extension has allowed applications to a much larger set of biocomplexes than it was possible with the original Das. A reasonable correlation between MED and experimentally determined inhibitory activities was achieved in a number of test cases, including structures featuring nonphysically shortened intermonomer distances, which constitute a particular challenge for binding strength predictions. Since the MED model is also computationally efficient, it can be used for reliable and rapid assessment of the ligand affinity or multidimensional scanning of amino acid side-chain conformations in the process of rational design of novel drugs or biocatalysts.
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Affiliation(s)
- Wiktoria Jedwabny
- Department
of Chemistry, Wrocław University of
Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
| | - Edyta Dyguda-Kazimierowicz
- Department
of Chemistry, Wrocław University of
Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
| | - Katarzyna Pernal
- Institute
of Physics, Łódź University
of Technology, Wólczańska
219, 90-924 Łódź, Poland
| | - Krzysztof Szalewicz
- Department
of Physics and Astronomy, University of
Delaware, Newark, Delaware 19716, United
States
| | - Konrad Patkowski
- Department
of Chemistry and Biochemistry, Auburn University, Auburn, Alabama 36849, United States
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11
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Xu S, Wang L, Pan X. An evaluation of combined strategies for improving the performance of molecular docking. J Bioinform Comput Biol 2021; 19:2150003. [PMID: 33641636 DOI: 10.1142/s0219720021500037] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Molecular docking is a fast and efficient computational method for the prediction of the binding mode and binding affinity between a ligand and a target protein at the atomic level. However, the performance of current docking programs is less than satisfactory. Herein, with a focus on free programs and scoring functions, the performances of LeDock and three standalone scoring functions were tested by 195 high-quality protein-ligand complexes. Results showed that the success rate for the best pose of the free available docking program LeDock achieved 89.20%, indicative of a strong sampling power. Based on the poses generated by LeDock, a comparative evaluation on other three non-commercial scoring functions, including DSX (DrugScore X), PoseScore and X-score was performed. Among all the evaluated scoring functions, DSX and X-score exhibited the best scoring power and ranking power, respectively. The performances of LeDock, DSX and X-score were similar in docking power test, which was much better than the PoseScore. Accordingly, it was suggested that the combination of pose sampling by LeDock with rescoring by DSX or X-score could improve the prediction accuracy of molecular docking and applied in the lead discovery.
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Affiliation(s)
- Siqi Xu
- Department of Medicinal Chemistry, College of Pharmacy, Southwest Medical University, Luzhou, P. R. China.,Department of Nuclear Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, P. R. China.,Nuclear Medicine and Molecular Imaging, Key Laboratory of Sichuan Province, Luzhou, P. R. China
| | - Li Wang
- Department of Nuclear Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, P. R. China.,Nuclear Medicine and Molecular Imaging, Key Laboratory of Sichuan Province, Luzhou, P. R. China
| | - Xianchao Pan
- Department of Medicinal Chemistry, College of Pharmacy, Southwest Medical University, Luzhou, P. R. China
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12
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Erguven M, Karakulak T, Diril MK, Karaca E. How Far Are We from the Rapid Prediction of Drug Resistance Arising Due to Kinase Mutations? ACS OMEGA 2021; 6:1254-1265. [PMID: 33490784 PMCID: PMC7818309 DOI: 10.1021/acsomega.0c04672] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 12/11/2020] [Indexed: 06/12/2023]
Abstract
In all living organisms, protein kinases regulate various cell signaling events through phosphorylation. The phosphorylation occurs upon transferring an ATP's terminal phosphate to a target residue. Because of the central role of protein kinases in several proliferative pathways, point mutations occurring within the kinase's ATP-binding site can lead to a constitutively active enzyme, and ultimately, to cancer. A select set of these point mutations can also make the enzyme drug resistant toward the available kinase inhibitors. Because of technical and economical limitations, rapid experimental exploration of the impact of these mutations remains to be a challenge. This underscores the importance of kinase-ligand binding affinity prediction tools that are poised to measure the efficacy of inhibitors in the presence of kinase mutations. To this end, here, we compare the performances of six web-based scoring tools (DSX-ONLINE, KDEEP, HADDOCK2.2, PDBePISA, Pose&Rank, and PRODIGY-LIG) in assessing the impact of kinase mutations on their interactions with their inhibitors. This assessment is carried out on a new structure-based BINDKIN benchmark we compiled. BINDKIN contains wild-type and mutant structure pairs of kinase-inhibitor complexes, together with their corresponding experimental binding affinities (in the form of IC50, K d, and K i). The performance of various web servers over BINDKIN shows that they cannot predict the binding affinities (ΔGs) of wild-type and mutant cases directly. Still, they could catch whether a mutation improves or worsens the ligand binding (ΔΔGs) where the highest Pearson's R correlation coefficient is reached by DSX-ONLINE over the K i dataset. When homology models are used instead of K i-associated crystal structures, DSX-ONLINE loses its predictive capacity. These results highlight that there is room to improve the available scoring functions to estimate the impact of protein kinase point mutations on inhibitor binding. The BINDKIN benchmark with all related results is freely accessible online (https://github.com/CSB-KaracaLab/BINDKIN).
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Affiliation(s)
- Mehmet Erguven
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
| | - Tülay Karakulak
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
| | - M. Kasim Diril
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
| | - Ezgi Karaca
- Izmir
Biomedicine and Genome Center, 35330 Izmir, Turkey
- Izmir
International Biomedicine and Genome Institute, Dokuz Eylul University, 35340 Izmir, Turkey
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13
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Roda S, Santiago G, Guallar V. Mapping enzyme-substrate interactions: its potential to study the mechanism of enzymes. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 122:1-31. [PMID: 32951809 DOI: 10.1016/bs.apcsb.2020.06.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
With the increase of the need to use more sustainable processes for the industry in our society, the modeling of enzymes has become crucial to fully comprehend their mechanism of action and use this knowledge to enhance and design their properties. A lot of methods to study enzymes computationally exist and they have been classified on sequence-based, structure-based, and the more new artificial intelligence-based ones. Albeit the abundance of methods to help predict the function of an enzyme, molecular modeling is crucial when trying to understand the enzyme mechanism, as they aim to correlate atomistic information with experimental data. Among them, methods that simulate the system dynamics at a molecular mechanics level of theory (classical force fields) have shown to offer a comprehensive study. In this book chapter, we will analyze these techniques, emphasizing the importance of precise modeling of enzyme-substrate interactions. In the end, a brief explanation of the transference of the information from research studies to the industry is given accompanied with two examples of family enzymes where their modeling has helped their exploitation.
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Affiliation(s)
- Sergi Roda
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | | | - Victor Guallar
- Barcelona Supercomputing Center (BSC), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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14
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Saikia S, Bordoloi M. Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective. Curr Drug Targets 2020; 20:501-521. [PMID: 30360733 DOI: 10.2174/1389450119666181022153016] [Citation(s) in RCA: 203] [Impact Index Per Article: 50.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 06/08/2018] [Accepted: 08/28/2018] [Indexed: 01/21/2023]
Abstract
Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.
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Affiliation(s)
- Surovi Saikia
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
| | - Manobjyoti Bordoloi
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
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15
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Wang Z, Wang X, Li Y, Lei T, Wang E, Li D, Kang Y, Zhu F, Hou T. farPPI: a webserver for accurate prediction of protein-ligand binding structures for small-molecule PPI inhibitors by MM/PB(GB)SA methods. Bioinformatics 2020; 35:1777-1779. [PMID: 30329012 DOI: 10.1093/bioinformatics/bty879] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 09/20/2018] [Accepted: 10/15/2018] [Indexed: 12/31/2022] Open
Abstract
SUMMARY Protein-protein interactions (PPIs) have been regarded as an attractive emerging class of therapeutic targets for the development of new treatments. Computational approaches, especially molecular docking, have been extensively employed to predict the binding structures of PPI-inhibitors or discover novel small molecule PPI inhibitors. However, due to the relatively 'undruggable' features of PPI interfaces, accurate predictions of the binding structures for ligands towards PPI targets are quite challenging for most docking algorithms. Here, we constructed a non-redundant pose ranking benchmark dataset for small-molecule PPI inhibitors, which contains 900 binding poses for 184 protein-ligand complexes. Then, we evaluated the performance of MM/PB(GB)SA approaches to identify the correct binding poses for PPI inhibitors, including two Prime MM/GBSA procedures from the Schrödinger suite and seven different MM/PB(GB)SA procedures from the Amber package. Our results showed that MM/PBSA outperformed the Glide SP scoring function (success rate of 58.6%) and MM/GBSA in most cases, especially the PB3 procedure which could achieve an overall success rate of ∼74%. Moreover, the GB6 procedure (success rate of 68.9%) performed much better than the other MM/GBSA procedures, highlighting the excellent potential of the GBNSR6 implicit solvation model for pose ranking. Finally, we developed the webserver of Fast Amber Rescoring for PPI Inhibitors (farPPI), which offers a freely available service to rescore the docking poses for PPI inhibitors by using the MM/PB(GB)SA methods. AVAILABILITY AND IMPLEMENTATION farPPI web server is freely available at http://cadd.zju.edu.cn/farppi/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhe Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xuwen Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu, China
| | - Tailong Lei
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ercheng Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, China
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16
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Vallat B, Webb B, Westbrook J, Sali A, Berman HM. Archiving and disseminating integrative structure models. JOURNAL OF BIOMOLECULAR NMR 2019; 73:385-398. [PMID: 31278630 PMCID: PMC6692293 DOI: 10.1007/s10858-019-00264-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 06/25/2019] [Indexed: 05/04/2023]
Abstract
Limitations in the applicability, accuracy, and precision of individual structure characterization methods can sometimes be overcome via an integrative modeling approach that relies on information from all available sources, including all available experimental data and prior models. The open-source Integrative Modeling Platform (IMP) is one piece of software that implements all computational aspects of integrative modeling. To maximize the impact of integrative structures, the coordinates should be made publicly available, as is already the case for structures based on X-ray crystallography, NMR spectroscopy, and electron microscopy. Moreover, the associated experimental data and modeling protocols should also be archived, such that the original results can easily be reproduced. Finally, it is essential that the integrative structures are validated as part of their publication and deposition. A number of research groups have already developed software to implement integrative modeling and have generated a number of structures, prompting the formation of an Integrative/Hybrid Methods Task Force. Following the recommendations of this task force, the existing PDBx/mmCIF data representation used for atomic PDB structures has been extended to address the requirements for archiving integrative structural models. This IHM-dictionary adds a flexible model representation, including coarse graining, models in multiple states and/or related by time or other order, and multiple input experimental information sources. A prototype archiving system called PDB-Dev ( https://pdb-dev.wwpdb.org ) has also been created to archive integrative structural models, together with a Python library to facilitate handling of integrative models in PDBx/mmCIF format.
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Affiliation(s)
- Brinda Vallat
- Institute for Quantitative Biomedicine, Piscataway, USA
| | - Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA, 94143, USA
| | - John Westbrook
- Institute for Quantitative Biomedicine, Piscataway, USA
- RCSB Protein Data Bank, Piscataway, USA
| | - Andrej Sali
- RCSB Protein Data Bank, Piscataway, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA, 94143, USA.
- Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, CA, 94143, USA.
- Lead Contacts, San Francisco, USA.
| | - Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
- Lead Contacts, Piscataway, USA.
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17
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Zheng Z, Pei J, Bansal N, Liu H, Song LF, Merz KM. Generation of Pairwise Potentials Using Multidimensional Data Mining. J Chem Theory Comput 2018; 14:5045-5067. [PMID: 30183299 DOI: 10.1021/acs.jctc.8b00516] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The rapid development of molecular structural databases provides the chemistry community access to an enormous array of experimental data that can be used to build and validate computational models. Using radial distribution functions collected from experimentally available X-ray and NMR structures, a number of so-called statistical potentials have been developed over the years using the structural data mining strategy. These potentials have been developed within the context of the two-particle Kirkwood equation by extending its original use for isotropic monatomic systems to anisotropic biomolecular systems. However, the accuracy and the unclear physical meaning of statistical potentials have long formed the central arguments against such methods. In this work, we present a new approach to generate molecular energy functions using structural data mining. Instead of employing the Kirkwood equation and introducing the "reference state" approximation, we model the multidimensional probability distributions of the molecular system using graphical models and generate the target pairwise Boltzmann probabilities using the Bayesian field theory. Different from the current statistical potentials that mimic the "knowledge-based" PMF based on the 2-particle Kirkwood equation, the graphical-model-based structure-derived potential developed in this study focuses on the generation of lower-dimensional Boltzmann distributions of atoms through reduction of dimensionality. We have named this new scoring function GARF, and in this work we focus on the mathematical derivation of our novel approach followed by validation studies on its ability to predict protein-ligand interactions.
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Affiliation(s)
- Zheng Zheng
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Jun Pei
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Nupur Bansal
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Hao Liu
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Lin Frank Song
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Kenneth M Merz
- Department of Chemistry , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
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18
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Webb B, Viswanath S, Bonomi M, Pellarin R, Greenberg CH, Saltzberg D, Sali A. Integrative structure modeling with the Integrative Modeling Platform. Protein Sci 2017; 27:245-258. [PMID: 28960548 DOI: 10.1002/pro.3311] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 09/23/2017] [Accepted: 09/25/2017] [Indexed: 11/06/2022]
Abstract
Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use.
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Affiliation(s)
- Benjamin Webb
- California Institute for Quantitative Biosciences, University of California, San Francisco, California, 94158
| | - Shruthi Viswanath
- California Institute for Quantitative Biosciences, University of California, San Francisco, California, 94158
| | | | - Riccardo Pellarin
- Structural Bioinformatics Unit, Institut Pasteur, CNRS UMR 3528, Paris, France
| | - Charles H Greenberg
- California Institute for Quantitative Biosciences, University of California, San Francisco, California, 94158
| | - Daniel Saltzberg
- California Institute for Quantitative Biosciences, University of California, San Francisco, California, 94158
| | - Andrej Sali
- California Institute for Quantitative Biosciences, University of California, San Francisco, California, 94158
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19
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Brylinski M. Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des 2017; 91:380-390. [PMID: 28816025 DOI: 10.1111/cbdd.13084] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Revised: 06/29/2017] [Accepted: 08/11/2017] [Indexed: 12/22/2022]
Abstract
The ability to design and fine-tune non-covalent interactions between organic ligands and proteins is indispensable to rational drug development. Aromatic stacking has long been recognized as one of the key constituents of ligand-protein interfaces. In this communication, we employ a two-parameter geometric model to conduct a large-scale statistical analysis of aromatic contacts in the experimental and computer-generated structures of ligand-protein complexes, considering various combinations of aromatic amino acid residues and ligand rings. The geometry of interfacial π-π stacking in crystal structures accords with experimental and theoretical data collected for simple systems, such as the benzene dimer. Many contemporary ligand docking programs implicitly treat aromatic stacking with van der Waals and Coulombic potentials. Although this approach generally provides a sufficient specificity to model aromatic interactions, the geometry of π-π contacts in high-scoring docking conformations could still be improved. The comprehensive analysis of aromatic geometries at ligand-protein interfaces lies the foundation for the development of type-specific statistical potentials to more accurately describe aromatic interactions in molecular docking. A Perl script to detect and calculate the geometric parameters of aromatic interactions in ligand-protein complexes is available at https://github.com/michal-brylinski/earomatic. The dataset comprising experimental complex structures and computer-generated models is available at https://osf.io/rztha/.
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Affiliation(s)
- Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.,Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, USA
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20
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Rodríguez-Guerra Pedregal J, Sciortino G, Guasp J, Municoy M, Maréchal JD. GaudiMM: A modular multi-objective platform for molecular modeling. J Comput Chem 2017; 38:2118-2126. [PMID: 28605037 DOI: 10.1002/jcc.24847] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Revised: 04/27/2017] [Accepted: 05/10/2017] [Indexed: 01/16/2023]
Abstract
GaudiMM (for Genetic Algorithms with Unrestricted Descriptors for Intuitive Molecular Modeling) is here presented as a modular platform for rapid 3D sketching of molecular systems. It combines a Multi-Objective Genetic Algorithm with diverse molecular descriptors to overcome the difficulty of generating candidate models for systems with scarce structural data. Its grounds consist in transforming any molecular descriptor (i.e. those generally used for analysis of data) as a guiding objective for PES explorations. The platform is written in Python with flexibility in mind: the user can choose which descriptors to use for each problem and is even encouraged to code custom ones. Illustrative cases of its potential applications are included to demonstrate the flexibility of this approach, including metal coordination of multidentate ligands, peptide folding, and protein-ligand docking. GaudiMM is available free of charge from https://github.com/insilichem/gaudi. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
| | - Giuseppe Sciortino
- Departament de Química, Universitat Autónoma de Barcelona, Cerdanyola del Vallès, Barcelona, 08193, Spain
| | - Jordi Guasp
- Departament de Química, Universitat Autónoma de Barcelona, Cerdanyola del Vallès, Barcelona, 08193, Spain
| | - Martí Municoy
- Departament de Química, Universitat Autónoma de Barcelona, Cerdanyola del Vallès, Barcelona, 08193, Spain
| | - Jean-Didier Maréchal
- Departament de Química, Universitat Autónoma de Barcelona, Cerdanyola del Vallès, Barcelona, 08193, Spain
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21
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Heinemann T, Klapp SHL. Coarse-graining strategy for molecular pair interactions: A reaction coordinate study for two- and three-dimensional systems. J Chem Phys 2017; 146:164107. [PMID: 28456203 DOI: 10.1063/1.4981207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
We investigate and provide optimal sets of reaction coordinates for mixed pairs of molecules displaying polar, uniaxial, or spherical symmetry in two and three dimensions. These coordinates are non-redundant, i.e., they implicitly involve the molecules' symmetries. By tabulating pair interactions in these coordinates, resulting tables are thus minimal in length and require a minimal memory space. The intended fields of application are computer simulations of large ensembles of molecules or colloids with rather complex interactions in a fluid or liquid crystalline phase at low densities. Using effective interactions directly in the form of tables can help bridging the time and length scales without introducing errors stemming from any modeling procedure. Finally, we outline an exemplary computational methodology for gaining an effective pair potential in these coordinates, based on the Boltzmann inversion principle, by providing a step-by-step recipe.
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Affiliation(s)
- Thomas Heinemann
- Department of Chemistry, Seoul National University, Seoul 08826, Korea
| | - Sabine H L Klapp
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstraße 36, 10623 Berlin, Germany
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22
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. Molecular Docking at a Glance. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.
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Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
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23
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Raschka S, Bemister-Buffington J, Kuhn LA. Detecting the native ligand orientation by interfacial rigidity: SiteInterlock. Proteins 2016; 84:1888-1901. [DOI: 10.1002/prot.25172] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 09/19/2016] [Accepted: 09/27/2016] [Indexed: 01/27/2023]
Affiliation(s)
- Sebastian Raschka
- Department of Biochemistry and Molecular Biology; Michigan State University; East Lansing Michigan 48824 USA
| | - Joseph Bemister-Buffington
- Department of Biochemistry and Molecular Biology; Michigan State University; East Lansing Michigan 48824 USA
| | - Leslie A. Kuhn
- Department of Biochemistry and Molecular Biology; Michigan State University; East Lansing Michigan 48824 USA
- Department of Computer Science and Engineering; Michigan State University; East Lansing Michigan 48824 USA
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24
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Piotto S, Di Biasi L, Fino R, Parisi R, Sessa L, Concilio S. Yada: a novel tool for molecular docking calculations. J Comput Aided Mol Des 2016; 30:753-759. [DOI: 10.1007/s10822-016-9953-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Accepted: 08/23/2016] [Indexed: 10/21/2022]
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25
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Topham CM, Barbe S, André I. An Atomistic Statistically Effective Energy Function for Computational Protein Design. J Chem Theory Comput 2016; 12:4146-68. [PMID: 27341125 DOI: 10.1021/acs.jctc.6b00090] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Shortcomings in the definition of effective free-energy surfaces of proteins are recognized to be a major contributory factor responsible for the low success rates of existing automated methods for computational protein design (CPD). The formulation of an atomistic statistically effective energy function (SEEF) suitable for a wide range of CPD applications and its derivation from structural data extracted from protein domains and protein-ligand complexes are described here. The proposed energy function comprises nonlocal atom-based and local residue-based SEEFs, which are coupled using a novel atom connectivity number factor to scale short-range, pairwise, nonbonded atomic interaction energies and a surface-area-dependent cavity energy term. This energy function was used to derive additional SEEFs describing the unfolded-state ensemble of any given residue sequence based on computed average energies for partially or fully solvent-exposed fragments in regions of irregular structure in native proteins. Relative thermal stabilities of 97 T4 bacteriophage lysozyme mutants were predicted from calculated energy differences for folded and unfolded states with an average unsigned error (AUE) of 0.84 kcal mol(-1) when compared to experiment. To demonstrate the utility of the energy function for CPD, further validation was carried out in tests of its capacity to recover cognate protein sequences and to discriminate native and near-native protein folds, loop conformers, and small-molecule ligand binding poses from non-native benchmark decoys. Experimental ligand binding free energies for a diverse set of 80 protein complexes could be predicted with an AUE of 2.4 kcal mol(-1) using an additional energy term to account for the loss in ligand configurational entropy upon binding. The atomistic SEEF is expected to improve the accuracy of residue-based coarse-grained SEEFs currently used in CPD and to extend the range of applications of extant atom-based protein statistical potentials.
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Affiliation(s)
- Christopher M Topham
- Université de Toulouse; INSA, UPS, INP; LISBP , 135 Avenue de Rangueil, F-31077 Toulouse, France.,CNRS, UMR5504 , F-31400 Toulouse, France.,INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés , F-31400 Toulouse, France
| | - Sophie Barbe
- Université de Toulouse; INSA, UPS, INP; LISBP , 135 Avenue de Rangueil, F-31077 Toulouse, France.,CNRS, UMR5504 , F-31400 Toulouse, France.,INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés , F-31400 Toulouse, France
| | - Isabelle André
- Université de Toulouse; INSA, UPS, INP; LISBP , 135 Avenue de Rangueil, F-31077 Toulouse, France.,CNRS, UMR5504 , F-31400 Toulouse, France.,INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés , F-31400 Toulouse, France
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26
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27
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Atasever-Arslan B, Yilancioglu K, Kalkan Z, Timucin AC, Gür H, Isik FB, Deniz E, Erman B, Cetiner S. Screening of new antileukemic agents from essential oils of algae extracts and computational modeling of their interactions with intracellular signaling nodes. Eur J Pharm Sci 2015; 83:120-31. [PMID: 26709080 DOI: 10.1016/j.ejps.2015.12.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Revised: 11/13/2015] [Accepted: 12/01/2015] [Indexed: 11/16/2022]
Abstract
Microalgae are very rich in bioactive compounds, minerals, polysaccharides, poly-unsaturated fatty acids and vitamins, and these rich constituents make microalgae an important resource for the discovery of new bioactive compounds with applications in biotechnology. In this study, we studied the antileukemic activity of several chosen microalgae species at the molecular level and assessed their potential for drug development. Here we identified Stichococcus bacillaris, Phaeodactylum tricornutum, Microcystis aeruginosa and Nannochloropsis oculata microalgae extracts with possible antileukemic agent potentials. Specifically we studied the effects of these extracts on intracellular signal nodes and apoptotic pathways. We characterized the composition of essential oils of these fifteen different algae extracts using gas chromatography-mass spectrometry (GC-MS). Finally, to identify potential molecular targets causing the phenotypic changes in leukemic cell lines, we docked a selected group of these essential oils to several key intracellular proteins. According to results of rank score algorithm, five of these essential oils analyzed might be considered as in silico plausible candidates to be used as antileukemic agents.
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Affiliation(s)
- Belkis Atasever-Arslan
- Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences, Üsküdar University, Istanbul, Turkey.
| | - Kaan Yilancioglu
- Department of Bioengineering, Faculty of Engineering and Natural Sciences, Üsküdar University, Istanbul, Turkey
| | - Zeynep Kalkan
- Department of Bioengineering, Faculty of Engineering and Natural Sciences, Üsküdar University, Istanbul, Turkey; Neuroscience Program, Health Sciences Institute, Uskudar University, Istanbul, Turkey
| | - Ahmet Can Timucin
- Molecular Biology, Genetics and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Hazal Gür
- Department of Bioengineering, Faculty of Engineering and Natural Sciences, Üsküdar University, Istanbul, Turkey
| | - Fatma Busra Isik
- Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences, Üsküdar University, Istanbul, Turkey
| | - Emre Deniz
- Molecular Biology, Genetics and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey; Sabanci University Nanotechnology Research and Application Center-SUNUM, Turkey
| | - Batu Erman
- Molecular Biology, Genetics and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey; Sabanci University Nanotechnology Research and Application Center-SUNUM, Turkey
| | - Selim Cetiner
- Molecular Biology, Genetics and Bioengineering Program, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
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28
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Ding Y, Fang Y, Feinstein WP, Ramanujam J, Koppelman DM, Moreno J, Brylinski M, Jarrell M. GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem 2015; 36:2013-26. [PMID: 26250822 DOI: 10.1002/jcc.24031] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Revised: 06/07/2015] [Accepted: 07/03/2015] [Indexed: 12/26/2022]
Abstract
Molecular docking is an important component of computer-aided drug discovery. In this communication, we describe GeauxDock, a new docking approach that builds on the ideas of ligand homology modeling. GeauxDock features a descriptor-based scoring function integrating evolutionary constraints with physics-based energy terms, a mixed-resolution molecular representation of protein-ligand complexes, and an efficient Monte Carlo sampling protocol. To drive docking simulations toward experimental conformations, the scoring function was carefully optimized to produce a correlation between the total pseudoenergy and the native-likeness of binding poses. Indeed, benchmarking calculations demonstrate that GeauxDock has a strong capacity to identify near-native conformations across docking trajectories with the area under receiver operating characteristics of 0.85. By excluding closely related templates, we show that GeauxDock maintains its accuracy at lower levels of homology through the increased contribution from physics-based energy terms compensating for weak evolutionary constraints. GeauxDock is available at http://www.institute.loni.org/lasigma/package/dock/.
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Affiliation(s)
- Yun Ding
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana, 70803
| | - Ye Fang
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, Louisiana, 70803.,Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, 70803
| | - Wei P Feinstein
- Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, 70803
| | - Jagannathan Ramanujam
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, Louisiana, 70803.,Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, 70803
| | - David M Koppelman
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, Louisiana, 70803
| | - Juana Moreno
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana, 70803.,Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, 70803
| | - Michal Brylinski
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, 70803.,Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, 70803
| | - Mark Jarrell
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana, 70803.,Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, 70803
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29
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Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules 2015; 20:13384-421. [PMID: 26205061 PMCID: PMC6332083 DOI: 10.3390/molecules200713384] [Citation(s) in RCA: 965] [Impact Index Per Article: 107.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Revised: 07/14/2015] [Accepted: 07/20/2015] [Indexed: 02/07/2023] Open
Abstract
Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.
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Affiliation(s)
- Leonardo G Ferreira
- Laboratório de Química Medicinal e Computacional, Centro de Pesquisa e Inovação em Biodiversidade e Fármacos, Instituto de Física de São Carlos, Universidade de São Paulo, Av. João Dagnone 1100, São Carlos-SP 13563-120, Brazil.
| | - Ricardo N Dos Santos
- Laboratório de Química Medicinal e Computacional, Centro de Pesquisa e Inovação em Biodiversidade e Fármacos, Instituto de Física de São Carlos, Universidade de São Paulo, Av. João Dagnone 1100, São Carlos-SP 13563-120, Brazil.
| | - Glaucius Oliva
- Laboratório de Química Medicinal e Computacional, Centro de Pesquisa e Inovação em Biodiversidade e Fármacos, Instituto de Física de São Carlos, Universidade de São Paulo, Av. João Dagnone 1100, São Carlos-SP 13563-120, Brazil.
| | - Adriano D Andricopulo
- Laboratório de Química Medicinal e Computacional, Centro de Pesquisa e Inovação em Biodiversidade e Fármacos, Instituto de Física de São Carlos, Universidade de São Paulo, Av. João Dagnone 1100, São Carlos-SP 13563-120, Brazil.
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30
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Gaudreault F, Najmanovich RJ. FlexAID: Revisiting Docking on Non-Native-Complex Structures. J Chem Inf Model 2015; 55:1323-36. [PMID: 26076070 DOI: 10.1021/acs.jcim.5b00078] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Small-molecule protein docking is an essential tool in drug design and to understand molecular recognition. In the present work we introduce FlexAID, a small-molecule docking algorithm that accounts for target side-chain flexibility and utilizes a soft scoring function, i.e. one that is not highly dependent on specific geometric criteria, based on surface complementarity. The pairwise energy parameters were derived from a large dataset of true positive poses and negative decoys from the PDBbind database through an iterative process using Monte Carlo simulations. The prediction of binding poses is tested using the widely used Astex dataset as well as the HAP2 dataset, while performance in virtual screening is evaluated using a subset of the DUD dataset. We compare FlexAID to AutoDock Vina, FlexX, and rDock in an extensive number of scenarios to understand the strengths and limitations of the different programs as well as to reported results for Glide, GOLD, and DOCK6 where applicable. The most relevant among these scenarios is that of docking on flexible non-native-complex structures where as is the case in reality, the target conformation in the bound form is not known a priori. We demonstrate that FlexAID, unlike other programs, is robust against increasing structural variability. FlexAID obtains equivalent sampling success as GOLD and performs better than AutoDock Vina or FlexX in all scenarios against non-native-complex structures. FlexAID is better than rDock when there is at least one critical side-chain movement required upon ligand binding. In virtual screening, FlexAID results are lower on average than those of AutoDock Vina and rDock. The higher accuracy in flexible targets where critical movements are required, intuitive PyMOL-integrated graphical user interface and free source code as well as precompiled executables for Windows, Linux, and Mac OS make FlexAID a welcome addition to the arsenal of existing small-molecule protein docking methods.
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Affiliation(s)
- Francis Gaudreault
- Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Quebec J1H5N4, Canada
| | - Rafael J Najmanovich
- Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Quebec J1H5N4, Canada
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31
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Loviso CL, Lozada M, Guibert LM, Musumeci MA, Sarango Cardenas S, Kuin RV, Marcos MS, Dionisi HM. Metagenomics reveals the high polycyclic aromatic hydrocarbon-degradation potential of abundant uncultured bacteria from chronically polluted subantarctic and temperate coastal marine environments. J Appl Microbiol 2015; 119:411-24. [PMID: 25968322 DOI: 10.1111/jam.12843] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Revised: 04/22/2015] [Accepted: 04/30/2015] [Indexed: 01/18/2023]
Abstract
AIMS To investigate the potential to degrade polycyclic aromatic hydrocarbons (PAHs) of yet-to-be-cultured bacterial populations from chronically polluted intertidal sediments. METHODS AND RESULTS A gene variant encoding the alpha subunit of the catalytic component of an aromatic-ring-hydroxylating oxygenase (RHO) was abundant in intertidal sediments from chronically polluted subantarctic and temperate coastal environments, and its abundance increased after PAH amendment. Conversely, this marker gene was not detected in sediments from a nonimpacted site, even after a short-term PAH exposure. A metagenomic fragment carrying this gene variant was identified in a fosmid library of subantarctic sediments. This fragment contained five pairs of alpha and beta subunit genes and a lone alpha subunit gene of oxygenases, classified as belonging to three different RHO functional classes. In silico structural analysis suggested that two of these oxygenases contain large substrate-binding pockets, capable of accepting high molecular weight PAHs. CONCLUSIONS The identified uncultured micro-organism presents the potential to degrade aromatic hydrocarbons with various chemical structures, and could represent an important member of the PAH-degrading community in these polluted coastal environments. SIGNIFICANCE AND IMPACT OF THE STUDY This work provides valuable information for the design of environmental molecular diagnostic tools and for the biotechnological application of RHO enzymes.
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Affiliation(s)
- C L Loviso
- Laboratorio de Microbiología Ambiental, Centro para el Estudio de Sistemas Marinos (CESIMAR CENPAT-CONICET), Puerto Madryn, Chubut, Argentina
| | - M Lozada
- Laboratorio de Microbiología Ambiental, Centro para el Estudio de Sistemas Marinos (CESIMAR CENPAT-CONICET), Puerto Madryn, Chubut, Argentina
| | - L M Guibert
- Laboratorio de Microbiología Ambiental, Centro para el Estudio de Sistemas Marinos (CESIMAR CENPAT-CONICET), Puerto Madryn, Chubut, Argentina
| | - M A Musumeci
- Laboratorio de Microbiología Ambiental, Centro para el Estudio de Sistemas Marinos (CESIMAR CENPAT-CONICET), Puerto Madryn, Chubut, Argentina
| | - S Sarango Cardenas
- Laboratorio de Microbiología Ambiental, Centro para el Estudio de Sistemas Marinos (CESIMAR CENPAT-CONICET), Puerto Madryn, Chubut, Argentina
| | - R V Kuin
- Laboratorio de Microbiología Ambiental, Centro para el Estudio de Sistemas Marinos (CESIMAR CENPAT-CONICET), Puerto Madryn, Chubut, Argentina
| | - M S Marcos
- Laboratorio de Microbiología Ambiental, Centro para el Estudio de Sistemas Marinos (CESIMAR CENPAT-CONICET), Puerto Madryn, Chubut, Argentina
| | - H M Dionisi
- Laboratorio de Microbiología Ambiental, Centro para el Estudio de Sistemas Marinos (CESIMAR CENPAT-CONICET), Puerto Madryn, Chubut, Argentina
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32
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Ye J, Wen F, Xu Y, Zhao N, Long L, Sun H, Yang J, Cooley J, Todd Pharr G, Webby R, Wan XF. Error-prone pcr-based mutagenesis strategy for rapidly generating high-yield influenza vaccine candidates. Virology 2015; 482:234-43. [PMID: 25899178 DOI: 10.1016/j.virol.2015.03.051] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2014] [Revised: 12/25/2014] [Accepted: 03/27/2015] [Indexed: 01/07/2023]
Abstract
Vaccination is the primary strategy for the prevention and control of influenza outbreaks. However, the manufacture of influenza vaccine requires a high-yield seed strain, and the conventional methods for generating such strains are time consuming. In this study, we developed a novel method to rapidly generate high-yield candidate vaccine strains by integrating error-prone PCR, site-directed mutagenesis strategies, and reverse genetics. We used this method to generate seed strains for the influenza A(H1N1)pdm09 virus and produced six high-yield candidate strains. We used a mouse model to assess the efficacy of two of the six candidate strains as a vaccine seed virus: both strains provided complete protection in mice against lethal challenge, thus validating our method. Results confirmed that the efficacy of these candidate vaccine seed strains was not affected by the yield-optimization procedure.
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Affiliation(s)
- Jianqiang Ye
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, MS, USA
| | - Feng Wen
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, MS, USA
| | - Yifei Xu
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, MS, USA
| | - Nan Zhao
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, MS, USA
| | - Liping Long
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, MS, USA
| | - Hailiang Sun
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, MS, USA
| | - Jialiang Yang
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, MS, USA
| | - Jim Cooley
- Department of Pathobiology and Population Medicine, College of Veterinary Medicine, Mississippi State University, MS, USA
| | - G Todd Pharr
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, MS, USA
| | - Richard Webby
- Department of Infectious Diseases, St. Jude Children׳s Research Hospital, Memphis, TN, USA
| | - Xiu-Feng Wan
- Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, MS, USA.
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33
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Li H, Leung KS, Wong MH, Ballester PJ. Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets. Mol Inform 2015; 34:115-26. [PMID: 27490034 DOI: 10.1002/minf.201400132] [Citation(s) in RCA: 151] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Accepted: 12/06/2014] [Indexed: 12/28/2022]
Abstract
There is a growing body of evidence showing that machine learning regression results in more accurate structure-based prediction of protein-ligand binding affinity. Docking methods that aim at optimizing the affinity of ligands for a target rely on how accurate their predicted ranking is. However, despite their proven advantages, machine-learning scoring functions are still not widely applied. This seems to be due to insufficient understanding of their properties and the lack of user-friendly software implementing them. Here we present a study where the accuracy of AutoDock Vina, arguably the most commonly-used docking software, is strongly improved by following a machine learning approach. We also analyse the factors that are responsible for this improvement and their generality. Most importantly, with the help of a proposed benchmark, we demonstrate that this improvement will be larger as more data becomes available for training Random Forest models, as regression models implying additive functional forms do not improve with more training data. We discuss how the latter opens the door to new opportunities in scoring function development. In order to facilitate the translation of this advance to enhance structure-based molecular design, we provide software to directly re-score Vina-generated poses and thus strongly improve their predicted binding affinity. The software is available at http://istar.cse.cuhk.edu.hk/rf-score-3.tgz and http://crcm. marseille.inserm.fr/fileadmin/rf-score-3.tgz.
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Affiliation(s)
- Hongjian Li
- Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Man-Hon Wong
- Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Pedro J Ballester
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. .,Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France, Institut Paoli-Calmettes, F-13009 Marseille, France, Aix-Marseille Université, F-13284 Marseille, France, CNRS UMR7258, F-13009 Marseille, France.
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34
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Chen YC. Beware of docking! Trends Pharmacol Sci 2015; 36:78-95. [DOI: 10.1016/j.tips.2014.12.001] [Citation(s) in RCA: 344] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Revised: 11/23/2014] [Accepted: 12/02/2014] [Indexed: 12/16/2022]
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35
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Pucci F, Bernaerts K, Teheux F, Gilis D, Rooman M. Symmetry Principles in Optimization Problems: an application to Protein Stability Prediction. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.ifacol.2015.05.068] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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36
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Evaluation of 11 scoring functions performance on matrix metalloproteinases. INTERNATIONAL JOURNAL OF MEDICINAL CHEMISTRY 2014; 2014:162150. [PMID: 25610645 PMCID: PMC4291136 DOI: 10.1155/2014/162150] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Revised: 12/01/2014] [Accepted: 12/01/2014] [Indexed: 11/20/2022]
Abstract
Matrix metalloproteinases (MMPs) have distinctive roles in various physiological and pathological processes such as inflammatory diseases and cancer. This study explored the performance of eleven scoring functions (D-Score, G-Score, ChemScore, F-Score, PMF-Score, PoseScore, RankScore, DSX, and X-Score and scoring functions of AutoDock4.1 and AutoDockVina). Their performance was judged by calculation of their correlations to experimental binding affinities of 3D ligand-enzyme complexes of MMP family. Furthermore, they were evaluated for their ability in reranking virtual screening study results performed on a member of MMP family (MMP-12). Enrichment factor at different levels and receiver operating characteristics (ROC) curves were used to assess their performance. Finally, we have developed a PCA model from the best functions. Of the scoring functions evaluated, F-Score, DSX, and ChemScore were the best overall performers in prediction of MMPs-inhibitors binding affinities while ChemScore, Autodock, and DSX had the best discriminative power in virtual screening against the MMP-12 target. Consensus scorings did not show statistically significant superiority over the other scorings methods in correlation study while PCA model which consists of ChemScore, Autodock, and DSX improved overall enrichment. Outcome of this study could be useful for the setting up of a suitable scoring protocol, resulting in enrichment of MMPs inhibitors.
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37
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Dong G, Calhoun S, Fan H, Kalyanaraman C, Branch MC, Mashiyama ST, London N, Jacobson MP, Babbitt PC, Shoichet BK, Armstrong RN, Sali A. Prediction of substrates for glutathione transferases by covalent docking. J Chem Inf Model 2014; 54:1687-99. [PMID: 24802635 PMCID: PMC4068255 DOI: 10.1021/ci5001554] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Indexed: 01/07/2023]
Abstract
Enzymes in the glutathione transferase (GST) superfamily catalyze the conjugation of glutathione (GSH) to electrophilic substrates. As a consequence they are involved in a number of key biological processes, including protection of cells against chemical damage, steroid and prostaglandin biosynthesis, tyrosine catabolism, and cell apoptosis. Although virtual screening has been used widely to discover substrates by docking potential noncovalent ligands into active site clefts of enzymes, docking has been rarely constrained by a covalent bond between the enzyme and ligand. In this study, we investigate the accuracy of docking poses and substrate discovery in the GST superfamily, by docking 6738 potential ligands from the KEGG and MetaCyc compound libraries into 14 representative GST enzymes with known structures and substrates using the PLOP program [ Jacobson Proteins 2004 , 55 , 351 ]. For X-ray structures as receptors, one of the top 3 ranked models is within 3 Å all-atom root mean square deviation (RMSD) of the native complex in 11 of the 14 cases; the enrichment LogAUC value is better than random in all cases, and better than 25 in 7 of 11 cases. For comparative models as receptors, near-native ligand-enzyme configurations are often sampled but difficult to rank highly. For models based on templates with the highest sequence identity, the enrichment LogAUC is better than 25 in 5 of 11 cases, not significantly different from the crystal structures. In conclusion, we show that covalent docking can be a useful tool for substrate discovery and point out specific challenges for future method improvement.
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Affiliation(s)
- Guang
Qiang Dong
- Department
of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical
Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California at San Francisco, San Francisco, California 94158, United States
| | - Sara Calhoun
- Department
of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical
Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California at San Francisco, San Francisco, California 94158, United States
| | - Hao Fan
- Bioinformatics
Institute, Agency for Science, Technology
and Research (A*STAR), 30 Biopolis Street, Matrix No. 07-01, Singapore SG 1386715
| | - Chakrapani Kalyanaraman
- Department
Pharmaceutical Chemistry, California Institute for Quantitative Biosciences
(QB3), University of California at San Francisco, San Francisco, California 94158, United States
| | - Megan C. Branch
- Departments
of Biochemistry and Chemistry, Center in Molecular Toxicology, and
Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37232-0146, United States
| | - Susan T. Mashiyama
- Department
of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical
Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California at San Francisco, San Francisco, California 94158, United States
| | - Nir London
- Department
Pharmaceutical Chemistry, California Institute for Quantitative Biosciences
(QB3), University of California at San Francisco, San Francisco, California 94158, United States
| | - Matthew P. Jacobson
- Department
Pharmaceutical Chemistry, California Institute for Quantitative Biosciences
(QB3), University of California at San Francisco, San Francisco, California 94158, United States
| | - Patricia C. Babbitt
- Department
of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical
Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California at San Francisco, San Francisco, California 94158, United States
| | - Brian K. Shoichet
- Faculty
of Pharmacy, University of Toronto, 160 College Street, Toronto, Ontario, Canada M5S 3E1
| | - Richard N. Armstrong
- Departments
of Biochemistry and Chemistry, Center in Molecular Toxicology, and
Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37232-0146, United States
| | - Andrej Sali
- Department
of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical
Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California at San Francisco, San Francisco, California 94158, United States
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38
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Wang B, Shen W, Yang H, Shen J, Sun T. Targeting EGFR mutants with non-cognate kinase inhibitors in non-small cell lung cancer. Med Chem Res 2014. [DOI: 10.1007/s00044-014-1012-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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39
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Schlessinger A, Khuri N, Giacomini KM, Sali A. Molecular modeling and ligand docking for solute carrier (SLC) transporters. Curr Top Med Chem 2014; 13:843-56. [PMID: 23578028 DOI: 10.2174/1568026611313070007] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2012] [Revised: 01/29/2013] [Accepted: 02/01/2013] [Indexed: 12/21/2022]
Abstract
Solute Carrier (SLC) transporters are membrane proteins that transport solutes, such as ions, metabolites, peptides, and drugs, across biological membranes, using diverse energy coupling mechanisms. In human, there are 386 SLC transporters, many of which contribute to the absorption, distribution, metabolism, and excretion of drugs and/or can be targeted directly by therapeutics. Recent atomic structures of SLC transporters determined by X-ray crystallography and NMR spectroscopy have significantly expanded the applicability of structure-based prediction of SLC transporter ligands, by enabling both comparative modeling of additional SLC transporters and virtual screening of small molecules libraries against experimental structures as well as comparative models. In this review, we begin by describing computational tools, including sequence analysis, comparative modeling, and virtual screening, that are used to predict the structures and functions of membrane proteins such as SLC transporters. We then illustrate the applications of these tools to predicting ligand specificities of select SLC transporters, followed by experimental validation using uptake kinetic measurements and other assays. We conclude by discussing future directions in the discovery of the SLC transporter ligands.
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Affiliation(s)
- Avner Schlessinger
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94158, USA.
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40
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Grinter SZ, Zou X. A Bayesian statistical approach of improving knowledge-based scoring functions for protein-ligand interactions. J Comput Chem 2014; 35:932-43. [DOI: 10.1002/jcc.23579] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Revised: 01/29/2014] [Accepted: 02/11/2014] [Indexed: 01/06/2023]
Affiliation(s)
- Sam Z. Grinter
- Informatics Institute, University of Missouri; Columbia Missouri 65211
- Dalton Cardiovascular Research Center, University of Missouri; Columbia Missouri 65211
| | - Xiaoqin Zou
- Informatics Institute, University of Missouri; Columbia Missouri 65211
- Dalton Cardiovascular Research Center, University of Missouri; Columbia Missouri 65211
- Department of Physics and Astronomy; University of Missouri; Columbia Missouri 65211
- Department of Biochemistry; University of Missouri; Columbia Missouri 65211
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41
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Ballester PJ, Schreyer A, Blundell TL. Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity? J Chem Inf Model 2014; 54:944-55. [PMID: 24528282 PMCID: PMC3966527 DOI: 10.1021/ci500091r] [Citation(s) in RCA: 130] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
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Predicting
the binding affinities of large sets of diverse molecules against
a range of macromolecular targets is an extremely challenging task.
The scoring functions that attempt such computational prediction are
essential for exploiting and analyzing the outputs of docking, which
is in turn an important tool in problems such as structure-based drug
design. Classical scoring functions assume a predetermined theory-inspired
functional form for the relationship between the variables that describe
an experimentally determined or modeled structure of a protein–ligand
complex and its binding affinity. The inherent problem of this approach
is in the difficulty of explicitly modeling the various contributions
of intermolecular interactions to binding affinity. New scoring functions
based on machine-learning regression models, which are able to exploit
effectively much larger amounts of experimental data and circumvent
the need for a predetermined functional form, have already been shown
to outperform a broad range of state-of-the-art scoring functions
in a widely used benchmark. Here, we investigate the impact of the
chemical description of the complex on the predictive power of the
resulting scoring function using a systematic battery of numerical
experiments. The latter resulted in the most accurate scoring function
to date on the benchmark. Strikingly, we also found that a more precise
chemical description of the protein–ligand complex does not
generally lead to a more accurate prediction of binding affinity.
We discuss four factors that may contribute to this result: modeling
assumptions, codependence of representation and regression, data restricted
to the bound state, and conformational heterogeneity in data.
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Affiliation(s)
- Pedro J Ballester
- European Bioinformatics Institute , Wellcome Trust Genome Campus, Hinxton - CB10 1SD, United Kingdom
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42
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Webb B, Eswar N, Fan H, Khuri N, Pieper U, Dong G, Sali A. Comparative Modeling of Drug Target Proteins☆. REFERENCE MODULE IN CHEMISTRY, MOLECULAR SCIENCES AND CHEMICAL ENGINEERING 2014. [PMCID: PMC7157477 DOI: 10.1016/b978-0-12-409547-2.11133-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.
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43
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Dong GQ, Fan H, Schneidman-Duhovny D, Webb B, Sali A. Optimized atomic statistical potentials: assessment of protein interfaces and loops. Bioinformatics 2013; 29:3158-66. [PMID: 24078704 PMCID: PMC3842762 DOI: 10.1093/bioinformatics/btt560] [Citation(s) in RCA: 98] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2013] [Revised: 08/13/2013] [Accepted: 09/22/2013] [Indexed: 01/16/2023] Open
Abstract
MOTIVATION Statistical potentials have been widely used for modeling whole proteins and their parts (e.g. sidechains and loops) as well as interactions between proteins, nucleic acids and small molecules. Here, we formulate the statistical potentials entirely within a statistical framework, avoiding questionable statistical mechanical assumptions and approximations, including a definition of the reference state. RESULTS We derive a general Bayesian framework for inferring statistically optimized atomic potentials (SOAP) in which the reference state is replaced with data-driven 'recovery' functions. Moreover, we restrain the relative orientation between two covalent bonds instead of a simple distance between two atoms, in an effort to capture orientation-dependent interactions such as hydrogen bonds. To demonstrate this general approach, we computed statistical potentials for protein-protein docking (SOAP-PP) and loop modeling (SOAP-Loop). For docking, a near-native model is within the top 10 scoring models in 40% of the PatchDock benchmark cases, compared with 23 and 27% for the state-of-the-art ZDOCK and FireDock scoring functions, respectively. Similarly, for modeling 12-residue loops in the PLOP benchmark, the average main-chain root mean square deviation of the best scored conformations by SOAP-Loop is 1.5 Å, close to the average root mean square deviation of the best sampled conformations (1.2 Å) and significantly better than that selected by Rosetta (2.1 Å), DFIRE (2.3 Å), DOPE (2.5 Å) and PLOP scoring functions (3.0 Å). Our Bayesian framework may also result in more accurate statistical potentials for additional modeling applications, thus affording better leverage of the experimentally determined protein structures. AVAILABILITY AND IMPLEMENTATION SOAP-PP and SOAP-Loop are available as part of MODELLER (http://salilab.org/modeller).
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Affiliation(s)
- Guang Qiang Dong
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA 94158, USA
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44
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Zheng Z, Ucisik MN, Merz KM. The Movable Type Method Applied to Protein-Ligand Binding. J Chem Theory Comput 2013; 9:5526-5538. [PMID: 24535920 DOI: 10.1021/ct4005992] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Accurately computing the free energy for biological processes like protein folding or protein-ligand association remains a challenging problem. Both describing the complex intermolecular forces involved and sampling the requisite configuration space make understanding these processes innately difficult. Herein, we address the sampling problem using a novel methodology we term "movable type". Conceptually it can be understood by analogy with the evolution of printing and, hence, the name movable type. For example, a common approach to the study of protein-ligand complexation involves taking a database of intact drug-like molecules and exhaustively docking them into a binding pocket. This is reminiscent of early woodblock printing where each page had to be laboriously created prior to printing a book. However, printing evolved to an approach where a database of symbols (letters, numerals, etc.) was created and then assembled using a movable type system, which allowed for the creation of all possible combinations of symbols on a given page, thereby, revolutionizing the dissemination of knowledge. Our movable type (MT) method involves the identification of all atom pairs seen in protein-ligand complexes and then creating two databases: one with their associated pairwise distant dependent energies and another associated with the probability of how these pairs can combine in terms of bonds, angles, dihedrals and non-bonded interactions. Combining these two databases coupled with the principles of statistical mechanics allows us to accurately estimate binding free energies as well as the pose of a ligand in a receptor. This method, by its mathematical construction, samples all of configuration space of a selected region (the protein active site here) in one shot without resorting to brute force sampling schemes involving Monte Carlo, genetic algorithms or molecular dynamics simulations making the methodology extremely efficient. Importantly, this method explores the free energy surface eliminating the need to estimate the enthalpy and entropy components individually. Finally, low free energy structures can be obtained via a free energy minimization procedure yielding all low free energy poses on a given free energy surface. Besides revolutionizing the protein-ligand docking and scoring problem this approach can be utilized in a wide range of applications in computational biology which involve the computation of free energies for systems with extensive phase spaces including protein folding, protein-protein docking and protein design.
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Affiliation(s)
- Zheng Zheng
- Department of Chemistry and the Quantum Theory Project, 2328 New Physics Building, P.O. Box 118435, University of Florida, Gainesville, Florida 32611-8435
| | - Melek N Ucisik
- Department of Chemistry and the Quantum Theory Project, 2328 New Physics Building, P.O. Box 118435, University of Florida, Gainesville, Florida 32611-8435
| | - Kenneth M Merz
- Department of Chemistry and the Quantum Theory Project, 2328 New Physics Building, P.O. Box 118435, University of Florida, Gainesville, Florida 32611-8435
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Pieper U, Webb BM, Dong GQ, Schneidman-Duhovny D, Fan H, Kim SJ, Khuri N, Spill YG, Weinkam P, Hammel M, Tainer JA, Nilges M, Sali A. ModBase, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res 2013; 42:D336-46. [PMID: 24271400 PMCID: PMC3965011 DOI: 10.1093/nar/gkt1144] [Citation(s) in RCA: 219] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence-structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains almost 30 million reliable models for domains in 4.7 million unique protein sequences. ModBase allows users to compute or update comparative models on demand, through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the AllosMod server for modeling ligand-induced protein dynamics (http://salilab.org/allosmod), the AllosMod-FoXS server for predicting a structural ensemble that fits an SAXS profile (http://salilab.org/allosmod-foxs), the FoXSDock server for protein–protein docking filtered by an SAXS profile (http://salilab.org/foxsdock), the SAXS Merge server for automatic merging of SAXS profiles (http://salilab.org/saxsmerge) and the Pose & Rank server for scoring protein–ligand complexes (http://salilab.org/poseandrank). In this update, we also highlight two applications of ModBase: a PSI:Biology initiative to maximize the structural coverage of the human alpha-helical transmembrane proteome and a determination of structural determinants of human immunodeficiency virus-1 protease specificity.
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Affiliation(s)
- Ursula Pieper
- Department of Bioengineering and Therapeutic Sciences, California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, USA, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall at Mission Bay, Office 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, USA, Graduate Group in Biophysics, University of California at San Francisco, CA 94158, USA, Structural Bioinformatics Unit, Structural Biology and Chemistry department, Institut Pasteur, 25 rue du Docteur Roux, 75015 Paris, France, Université Paris Diderot-Paris 7, école doctorale iViv, Paris Rive Gauche, 5 rue Thomas Mann, 75013 Paris, France, Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA, Department of Molecular Biology, Skaggs Institute of Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA, Life Sciences Division, Department of Molecular Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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46
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Zheng Z, Merz KM. Development of the knowledge-based and empirical combined scoring algorithm (KECSA) to score protein-ligand interactions. J Chem Inf Model 2013; 53:1073-83. [PMID: 23560465 DOI: 10.1021/ci300619x] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
We describe a novel knowledge-based protein-ligand scoring function that employs a new definition for the reference state, allowing us to relate a statistical potential to a Lennard-Jones (LJ) potential. In this way, the LJ potential parameters were generated from protein-ligand complex structural data contained in the Protein Databank (PDB). Forty-nine (49) types of atomic pairwise interactions were derived using this method, which we call the knowledge-based and empirical combined scoring algorithm (KECSA). Two validation benchmarks were introduced to test the performance of KECSA. The first validation benchmark included two test sets that address the training set and enthalpy/entropy of KECSA. The second validation benchmark suite included two large-scale and five small-scale test sets, to compare the reproducibility of KECSA, with respect to two empirical score functions previously developed in our laboratory (LISA and LISA+), as well as to other well-known scoring methods. Validation results illustrate that KECSA shows improved performance in all test sets when compared with other scoring methods, especially in its ability to minimize the root mean square error (RMSE). LISA and LISA+ displayed similar performance using the correlation coefficient and Kendall τ as the metric of quality for some of the small test sets. Further pathways for improvement are discussed for which would allow KECSA to be more sensitive to subtle changes in ligand structure.
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Affiliation(s)
- Zheng Zheng
- Department of Chemistry and the Quantum Theory Project, University of Florida, Gainesville, Florida 32611-8435, United States
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47
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Wang F, Zhou M, Singh S, Yennamalli RM, Bingman CA, Thorson JS, Phillips GN. Crystal structure of SsfS6, the putative C-glycosyltransferase involved in SF2575 biosynthesis. Proteins 2013; 81:1277-82. [PMID: 23526584 DOI: 10.1002/prot.24289] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2013] [Revised: 02/28/2013] [Accepted: 03/06/2013] [Indexed: 12/16/2022]
Abstract
The molecule known as SF2575 from Streptomyces sp. is a tetracycline polyketide natural product that displays antitumor activity against murine leukemia P388 in vivo. In the SF2575 biosynthetic pathway, SsfS6 has been implicated as the crucial C-glycosyltransferase (C-GT) that forms the C-C glycosidic bond between the sugar and the SF2575 tetracycline-like scaffold. Here, we report the crystal structure of SsfS6 in the free form and in complex with TDP, both at 2.4 Å resolution. The structures reveal SsfS6 to adopt a GT-B fold wherein the TDP and docked putative aglycon are consistent with the overall C-glycosylation reaction. As one of only a few existing structures for C-glycosyltransferases, the structures described herein may serve as a guide to better understand and engineer C-glycosylation.
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Affiliation(s)
- Fengbin Wang
- Department of Biochemistry and Cell Biology, Rice University, Houston, Texas 77005, USA
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48
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Yuriev E, Ramsland PA. Latest developments in molecular docking: 2010-2011 in review. J Mol Recognit 2013; 26:215-39. [PMID: 23526775 DOI: 10.1002/jmr.2266] [Citation(s) in RCA: 193] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2012] [Revised: 01/16/2013] [Accepted: 01/19/2013] [Indexed: 12/28/2022]
Affiliation(s)
- Elizabeth Yuriev
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences; Monash University; Parkville; VIC; 3052; Australia
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49
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Røgen P, Koehl P. Extracting knowledge from protein structure geometry. Proteins 2013; 81:841-51. [PMID: 23280479 DOI: 10.1002/prot.24242] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2012] [Revised: 11/28/2012] [Accepted: 12/08/2012] [Indexed: 11/06/2022]
Abstract
Protein structure prediction techniques proceed in two steps, namely the generation of many structural models for the protein of interest, followed by an evaluation of all these models to identify those that are native-like. In theory, the second step is easy, as native structures correspond to minima of their free energy surfaces. It is well known however that the situation is more complicated as the current force fields used for molecular simulations fail to recognize native states from misfolded structures. In an attempt to solve this problem, we follow an alternate approach and derive a new potential from geometric knowledge extracted from native and misfolded conformers of protein structures. This new potential, Metric Protein Potential (MPP), has two main features that are key to its success. Firstly, it is composite in that it includes local and nonlocal geometric information on proteins. At the short range level, it captures and quantifies the mapping between the sequences and structures of short (7-mer) fragments of protein backbones through the introduction of a new local energy term. The local energy term is then augmented with a nonlocal residue-based pairwise potential, and a solvent potential. Secondly, it is optimized to yield a maximized correlation between the energy of a structural model and its root mean square (RMS) to the native structure of the corresponding protein. We have shown that MPP yields high correlation values between RMS and energy and that it is able to retrieve the native structure of a protein from a set of high-resolution decoys.
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Affiliation(s)
- Peter Røgen
- Department of Mathematics, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark.
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Rosado LA, Caceres RA, de Azevedo WF, Basso LA, Santos DS. Role of Serine140 in the mode of action of Mycobacterium tuberculosis β-ketoacyl-ACP Reductase (MabA). BMC Res Notes 2012; 5:526. [PMID: 23006410 PMCID: PMC3519566 DOI: 10.1186/1756-0500-5-526] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2012] [Accepted: 09/14/2012] [Indexed: 11/10/2022] Open
Abstract
Background Tuberculosis (TB) still remains one of the most deadly infectious diseases in the world. Mycobacterium tuberculosis β-ketoacyl-ACP Reductase (MabA) is a member of the fatty acid elongation system type II, providing precursors of mycolic acids that are essential to the bacterial cell growth and survival. MabA has been shown to be essential for M. tuberculosis survival and to play a role in intracellular signal transduction of bacilli. Findings Here we describe site-directed mutagenesis, recombinant protein expression and purification, steady-state kinetics, fluorescence spectroscopy, and molecular modeling for S140T and S140A mutant MabA enzymes. No enzyme activity could be detected for S140T and S140A. Although the S140T protein showed impaired NADPH binding, the S140A mutant could bind to NADPH. Computational predictions for NADPH binding affinity to WT, S140T and S140A MabA proteins were consistent with fluorescence spectroscopy data. Conclusions The results suggest that the main role of the S140 side chain of MabA is in catalysis. The S140 side chain appears to also play an indirect role in NADPH binding. Interestingly, NADPH titrations curves shifted from sigmoidal for WT to hyperbolic for S140A, suggesting that the S140 residue may play a role in displacing the pre-existing equilibrium between two forms of MabA in solution. The results here reported provide a better understanding of the mode of action of MabA that should be useful to guide the rational (function-based) design of inhibitors of MabA enzyme activity which, hopefully, could be used as lead compounds with anti-TB action.
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Affiliation(s)
- Leonardo A Rosado
- Centro de Pesquisas em Biologia Molecular e Funcional, Instituto Nacional de Ciência e Tecnologia em Tuberculose, Pontifícia Universidade Católica do Rio Grande do Sul, Av. Ipiranga 6681, Porto Alegre, RS 90619-900, Brazil
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