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Rather MA, Agarwal D, Bhat TA, Khan IA, Zafar I, Kumar S, Amin A, Sundaray JK, Qadri T. Bioinformatics approaches and big data analytics opportunities in improving fisheries and aquaculture. Int J Biol Macromol 2023; 233:123549. [PMID: 36740117 DOI: 10.1016/j.ijbiomac.2023.123549] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023]
Abstract
Aquaculture has witnessed an excellent growth rate during the last two decades and offers huge potential to provide nutritional as well as livelihood security. Genomic research has contributed significantly toward the development of beneficial technologies for aquaculture. The existing high throughput technologies like next-generation technologies generate oceanic data which requires extensive analysis using appropriate tools. Bioinformatics is a rapidly evolving science that involves integrating gene based information and computational technology to produce new knowledge for the benefit of aquaculture. Bioinformatics provides new opportunities as well as challenges for information and data processing in new generation aquaculture. Rapid technical advancements have opened up a world of possibilities for using current genomics to improve aquaculture performance. Understanding the genes that govern economically relevant characteristics, necessitates a significant amount of additional research. The various dimensions of data sources includes next-generation DNA sequencing, protein sequencing, RNA sequencing gene expression profiles, metabolic pathways, molecular markers, and so on. Appropriate bioinformatics tools are developed to mine the biologically relevant and commercially useful results. The purpose of this scoping review is to present various arms of diverse bioinformatics tools with special emphasis on practical translation to the aquaculture industry.
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Affiliation(s)
- Mohd Ashraf Rather
- Division of Fish Genetics and Biotechnology, Faculty of Fisheries Ganderbal, Sher-e- Kashmir University of Agricultural Science and Technology, Kashmir, India.
| | - Deepak Agarwal
- Institute of Fisheries Post Graduation Studies OMR Campus, Vaniyanchavadi, Chennai, India
| | | | - Irfan Ahamd Khan
- Division of Fish Genetics and Biotechnology, Faculty of Fisheries Ganderbal, Sher-e- Kashmir University of Agricultural Science and Technology, Kashmir, India
| | - Imran Zafar
- Department of Bioinformatics and Computational Biology, Virtual University Punjab, Pakistan
| | - Sujit Kumar
- Department of Bioinformatics and Computational Biology, Virtual University Punjab, Pakistan
| | - Adnan Amin
- Postgraduate Institute of Fisheries Education and Research Kamdhenu University, Gandhinagar-India University of Kurasthra, India; Department of Aquatic Environmental Management, Faculty of Fisheries Rangil- Ganderbel -SKUAST-K, India
| | - Jitendra Kumar Sundaray
- ICAR-Central Institute of Freshwater Aquaculture, Kausalyaganga, Bhubaneswar, Odisha 751002, India
| | - Tahiya Qadri
- Division of Food Science and Technology, SKUAST-K, Shalimar, India
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Wang J, Mao J, Wang M, Le X, Wang Y. Explore drug-like space with deep generative models. Methods 2023; 210:52-59. [PMID: 36682423 DOI: 10.1016/j.ymeth.2023.01.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/05/2023] [Accepted: 01/17/2023] [Indexed: 01/20/2023] Open
Abstract
The process of design/discovery of drugs involves the identification and design of novel molecules that have the desired properties and bind well to a given disease-relevant target. One of the main challenges to effectively identify potential drug candidates is to explore the vast drug-like chemical space to find novel chemical structures with desired physicochemical properties and biological characteristics. Moreover, the chemical space of currently available molecular libraries is only a small fraction of the total possible drug-like chemical space. Deep molecular generative models have received much attention and provide an alternative approach to the design and discovery of molecules. To efficiently explore the drug-like space, we first constructed the drug-like dataset and then performed the generative design of drug-like molecules using a Conditional Randomized Transformer approach with the molecular access system (MACCS) fingerprint as a condition and compared it with previously published molecular generative models. The results show that the deep molecular generative model explores the wider drug-like chemical space. The generated drug-like molecules share the chemical space with known drugs, and the drug-like space captured by the combination of quantitative estimation of drug-likeness (QED) and quantitative estimate of protein-protein interaction targeting drug-likeness (QEPPI) can cover a larger drug-like space. Finally, we show the potential application of the model in design of inhibitors of MDM2-p53 protein-protein interaction. Our results demonstrate the potential application of deep molecular generative models for guided exploration in drug-like chemical space and molecular design.
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Affiliation(s)
- Jianmin Wang
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Korea
| | - Jiashun Mao
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Korea
| | - Meng Wang
- Department of Biostatistics, School of Public Health, Harbin Medical University
| | - Xiangyang Le
- Department of Medicinal Chemistry, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, China
| | - Yunyun Wang
- School of Pharmacy and Jiangsu Province Key Laboratory for Inflammation and Molecular Drug Target, Nantong University, Nantong 226001, China
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3
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Sohraby F, Javaheri Moghadam M, Aliyar M, Aryapour H. Complete reconstruction of dasatinib unbinding pathway from c-Src kinase by supervised molecular dynamics simulation method; assessing efficiency and trustworthiness of the method. J Biomol Struct Dyn 2022; 40:12535-12545. [PMID: 34472425 DOI: 10.1080/07391102.2021.1972839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Over the past years, rational drug design has gained lots of attention since employing it gave the world targeted therapy and more effective treatment solutions. Structure-based drug design (SBDD) is an excellent tool in rational drug design that takes advantage of accurate methods such as unbiased molecular dynamics (UMD) simulation for designing and optimizing molecular entities by understanding the binding and unbinding pathways of the binders. Supervised molecular dynamics (SuMD) simulation is a branch of UMD in which long-duration simulations are turned into short simulations, called replica, and a specific parameter is monitored throughout the simulation. In this work, we utilized this strategy to reconstruct the unbinding pathway of the anticancer drug dasatinib from its target protein, the c-Src kinase. Several unbinding events with valuable details were achieved. Then, to assess the efficiency and trustworthiness of the SuMD method, the unbinding pathway was also reconstructed by conventional UMD simulation, which uncovered some of the limitations of this method, such as limited sampling of the active site and finding the metastable states in the unbinding pathway. Furthermore, in times like these, when the world is desperate to find treatments for the Covid-19 disease, we think these methods are of exceptional value.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Farzin Sohraby
- Department of Biology, Faculty of Science, Golestan University, Gorgan, Iran
| | | | - Masoud Aliyar
- Department of Biology, Faculty of Science, Golestan University, Gorgan, Iran
| | - Hassan Aryapour
- Department of Biology, Faculty of Science, Golestan University, Gorgan, Iran
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Qazi S, Jit BP, Das A, Karthikeyan M, Saxena A, Ray M, Singh AR, Raza K, Jayaram B, Sharma A. BESFA: bioinformatics based evolutionary, structural & functional analysis of prostrate, Placenta, Ovary, Testis, and Embryo (POTE) paralogs. Heliyon 2022; 8:e10476. [PMID: 36132183 PMCID: PMC9483601 DOI: 10.1016/j.heliyon.2022.e10476] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/25/2022] [Accepted: 08/23/2022] [Indexed: 11/30/2022] Open
Abstract
The POTE family comprises 14 paralogues and is primarily expressed in Prostrate, Placenta, Ovary, Testis, Embryo (POTE), and cancerous cells. The prospective function of the POTE protein family under physiological conditions is less understood. We systematically analyzed their cellular localization and molecular docking analysis to elucidate POTE proteins' structure, function, and Adaptive Divergence. Our results suggest that group three POTE paralogs (POTEE, POTEF, POTEI, POTEJ, and POTEKP (a pseudogene)) exhibits significant variation among other members could be because of their Adaptive Divergence. Furthermore, our molecular docking studies on POTE protein revealed the highest binding affinity with NCI-approved anticancer compounds. Additionally, POTEE, POTEF, POTEI, and POTEJ were subject to an explicit molecular dynamic simulation for 50ns. MM-GBSA and other essential electrostatics were calculated that showcased that only POTEE and POTEF have absolute binding affinities with minimum energy exploitation. Thus, this study’s outcomes are expected to drive cancer research to successful utilization of POTE genes family as a new biomarker, which could pave the way for the discovery of new therapies.
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Affiliation(s)
- Sahar Qazi
- Department of Biochemistry, All India Institute of Medical Sciences, Delhi 110029, India
- Department of Computer Science, Jamia Millia Islamia, New Delhi 110025, India
| | - Bimal Prasad Jit
- Department of Biochemistry, All India Institute of Medical Sciences, Delhi 110029, India
| | - Abhishek Das
- Department of Biochemistry, All India Institute of Medical Sciences, Delhi 110029, India
| | - Muthukumarasamy Karthikeyan
- National Chemical Laboratory, Council of Scientific and Industrial Research (NCL-CSIR), Pune, Maharashtra, India
| | - Amit Saxena
- Centre for Development of Advanced Computing, Pune, Maharashtra, India
| | - M.D. Ray
- Dr. B.R.A Institute-Rotary Cancer Hospital, All India Institute of Medical Sciences, Delhi 110029, India
| | - Angel Rajan Singh
- Dr. B.R.A Institute-Rotary Cancer Hospital, All India Institute of Medical Sciences, Delhi 110029, India
| | - Khalid Raza
- Department of Computer Science, Jamia Millia Islamia, New Delhi 110025, India
| | - B. Jayaram
- Supercomputing Facility for Bioinformatics & Computational Biology, Indian Institute of Technology, Delhi, India
| | - Ashok Sharma
- Department of Biochemistry, All India Institute of Medical Sciences, Delhi 110029, India
- Corresponding author.
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5
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Türker D, Üstün E, Günal S, Yıldız H, D Düşünceli S, Özdemir İ. Cyanopropyl functionalized benzimidazolium salts and their silver N-heterocyclic carbene complexes: Synthesis, antimicrobial activity, and theoretical analysis. Arch Pharm (Weinheim) 2022; 355:e2200041. [PMID: 35352839 DOI: 10.1002/ardp.202200041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/02/2022] [Accepted: 03/04/2022] [Indexed: 11/08/2022]
Abstract
The reaction of N-substituted benzimidazole with 4-bromobutyronitrile gives the corresponding benzimidazolium salts as N-heterocyclic carbene (NHC) precursors. Silver(I) carbene complexes are synthesized by the reaction of the corresponding benzimidazolium salts with Ag2 O in dichloromethane. These new NHC precursors and Ag-NHC complexes were characterized by spectroscopy techniques and also screened for their antibacterial activities against the standard bacterial strains Escherichia coli, Pseudomonas aeruginosa, Acinetobacter baumannii, Klebsiella pneumoniae, Staphylococcus aureus, methicillin-resistant Staphylococcus aureus, and Enterococcus faecalis, and the standard fungal strains Candida albicans and Candida glabrata, and promising results were achieved. The compounds were also analyzed by density functional theory (DFT)/time-dependent DFT and docking methods.
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Affiliation(s)
- Dilek Türker
- Inorganic Chemistry, Catalysis Research and Application Center, İnönü University, Malatya, Turkey
| | - Elvan Üstün
- Inorganic Chemistry, Department of Chemistry, Faculty of Science and Art, Ordu University, Ordu, Turkey
| | - Selami Günal
- Pharmaceutical Chemistry, Department of Microbiology, Faculty of Medicine, İnonu University, Malatya, Turkey
| | - Hatice Yıldız
- Pharmaceutical Chemistry, Department of Microbiology, Faculty of Medicine, İnonu University, Malatya, Turkey
| | - Serpil D Düşünceli
- Inorganic Chemistry, Catalysis Research and Application Center, İnönü University, Malatya, Turkey.,Inorganic Chemistry, Department of Chemistry, Faculty of Science and Arts, İnönü University, Malatya, Turkey.,Drug Application and Research Center, İnönü University, Malatya, Turkey
| | - İsmail Özdemir
- Inorganic Chemistry, Catalysis Research and Application Center, İnönü University, Malatya, Turkey.,Inorganic Chemistry, Department of Chemistry, Faculty of Science and Arts, İnönü University, Malatya, Turkey.,Drug Application and Research Center, İnönü University, Malatya, Turkey
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6
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Chelliah R, Banan-MwineDaliri E, Khan I, Wei S, Elahi F, Yeon SJ, Selvakumar V, Ofosu FK, Rubab M, Ju HH, Rallabandi HR, Madar IH, Sultan G, Oh DH. A review on the application of bioinformatics tools in food microbiome studies. Brief Bioinform 2022; 23:6533500. [PMID: 35189636 DOI: 10.1093/bib/bbac007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/20/2021] [Accepted: 01/05/2022] [Indexed: 12/12/2022] Open
Abstract
There is currently a transformed interest toward understanding the impact of fermentation on functional food development due to growing consumer interest on modified health benefits of sustainable foods. In this review, we attempt to summarize recent findings regarding the impact of Next-generation sequencing and other bioinformatics methods in the food microbiome and use prediction software to understand the critical role of microbes in producing fermented foods. Traditionally, fermentation methods and starter culture development were considered conventional methods needing optimization to eliminate errors in technique and were influenced by technical knowledge of fermentation. Recent advances in high-output omics innovations permit the implementation of additional logical tactics for developing fermentation methods. Further, the review describes the multiple functions of the predictions based on docking studies and the correlation of genomic and metabolomic analysis to develop trends to understand the potential food microbiome interactions and associated products to become a part of a healthy diet.
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Affiliation(s)
- Ramachandran Chelliah
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea
| | - Eric Banan-MwineDaliri
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea
| | - Imran Khan
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea.,Department of Biotechnology, University of Malakand, Khyber Pakhtunkhwa Pakistan
| | - Shuai Wei
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea.,Guangdong Provincial Key Laboratory of Aquatic Product Processing and Safety, College of Food Science and Technology, Guangdong Ocean University, Zhanjiang 524088, China
| | - Fazle Elahi
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea
| | - Su-Jung Yeon
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea
| | - Vijayalakshmi Selvakumar
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea
| | - Fred Kwame Ofosu
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea
| | - Momna Rubab
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea
| | - Hum Hun Ju
- Department of Biological Environment, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea
| | - Harikrishna Reddy Rallabandi
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea
| | - Inamul Hasan Madar
- Department of Biochemistry, School of Life Science, Bharathidasan, University, Thiruchirappalli, Tamilnadu, India
| | - Ghazala Sultan
- Department of Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India
| | - Deog Hwan Oh
- Department of Food Science and Biotechnology, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do 24341, Korea
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7
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Wang J, Zhang Y, Nie W, Luo Y, Deng L. Computational anti-COVID-19 drug design: progress and challenges. Brief Bioinform 2022; 23:bbab484. [PMID: 34850817 PMCID: PMC8690229 DOI: 10.1093/bib/bbab484] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 12/14/2022] Open
Abstract
Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.
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Affiliation(s)
- Jinxian Wang
- School of Computer Science and Engineering, Central South University,410075, Changsha, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, 150001, Harbin, China
| | - Wenjuan Nie
- School of Computer Science and Engineering, Central South University,410075, Changsha, China
| | - Yi Luo
- School of Science, The University of Auckland,Auckland 1010, Auckland, New Zealand
| | - Lei Deng
- School of Computer Science and Engineering, Central South University,410075, Changsha, China
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8
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Fundamental considerations in drug design. COMPUTER AIDED DRUG DESIGN (CADD): FROM LIGAND-BASED METHODS TO STRUCTURE-BASED APPROACHES 2022:17-55. [PMCID: PMC9212230 DOI: 10.1016/b978-0-323-90608-1.00005-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.
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9
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Renaud N, Geng C, Georgievska S, Ambrosetti F, Ridder L, Marzella DF, Réau MF, Bonvin AMJJ, Xue LC. DeepRank: a deep learning framework for data mining 3D protein-protein interfaces. Nat Commun 2021; 12:7068. [PMID: 34862392 PMCID: PMC8642403 DOI: 10.1038/s41467-021-27396-0] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 11/12/2021] [Indexed: 11/08/2022] Open
Abstract
Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.
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Affiliation(s)
- Nicolas Renaud
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
| | - Cunliang Geng
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands
| | - Sonja Georgievska
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
| | - Francesco Ambrosetti
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands
| | - Lars Ridder
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
| | - Dario F Marzella
- Center for Molecular and Biomolecular Informatics, Radboudumc, Greet Grooteplein 26-28, 6525, Nijmegen, GA, The Netherlands
| | - Manon F Réau
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands.
| | - Li C Xue
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands.
- Center for Molecular and Biomolecular Informatics, Radboudumc, Greet Grooteplein 26-28, 6525, Nijmegen, GA, The Netherlands.
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Bitter taste in silico: A review on virtual ligand screening and characterization methods for TAS2R-bitterant interactions. Int J Pharm 2021; 600:120486. [PMID: 33744445 DOI: 10.1016/j.ijpharm.2021.120486] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 02/21/2021] [Accepted: 03/09/2021] [Indexed: 11/21/2022]
Abstract
The growing pharmaceutical interest in the human bitter taste receptors (hTAS2Rs) has two dimensions; i) evaluation of the bitterness of active pharmaceutical compounds, in order to develop strategies for improving patients' adherence to medication, and ii) application of ligands for extra-cellular hTAS2Rs for potential preventive therapeutic achievements. The result is an increasing demand on robust tools for bitterness assessment and screening the receptor-ligand affinity. In silico tools are useful for aiding experimental-screening, as well as to elucide ligand-receptor interactions. In this review, the ligand-based and structure-based approaches are described as the two main in silico tools for bitter taste analysis. The strengths and weaknesses of each approach are discussed. Both approaches provide key tools for understanding and exploiting bitter taste for human health applications.
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11
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Della Corte D, Della Corte KA. The Data-Centric Lab: A Pharmaceutical Perspective. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2021. [DOI: 10.1007/978-3-030-73103-8_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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12
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Combinations of Legume Protein Hydrolysates Synergistically Inhibit Biological Markers Associated with Adipogenesis. Foods 2020; 9:foods9111678. [PMID: 33212815 PMCID: PMC7696775 DOI: 10.3390/foods9111678] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/10/2020] [Accepted: 11/12/2020] [Indexed: 12/20/2022] Open
Abstract
The objective was to investigate the anti-adipogenesis potential of selected legume protein hydrolysates (LPH) and combinations using biochemical assays and in silico predictions. Black bean, green pea, chickpea, lentil and fava bean protein isolates were hydrolyzed using alcalase (A) or pepsin/pancreatin (PP). The degree of hydrolysis ranged from 15.5% to 35.5% for A-LPH and PP-LPH, respectively. Antioxidant capacities ranged for ABTS•+ IC50 from 0.3 to 0.9 Trolox equivalents (TE) mg/mL, DPPH• IC50 from 0.7 to 13.5 TE mg/mL and nitric oxide (NO) inhibition IC50 from 0.3 to 1.3 mg/mL. LPH from PP–green pea, A–green pea and A–black bean inhibited pancreatic lipase (PL) (IC50 = 0.9 mg/mL, 2.2 mg/mL and 1.2 mg/mL, respectively) (p < 0.05). For HMG-CoA reductase (HMGR) inhibition, the LPH from A–chickpea (0.15 mg/mL), PP–lentil (1.2 mg/mL), A–green pea (1.4 mg/mL) and PP–green pea (1.5 mg/mL) were potent inhibitors. Combinations of PP–green pea + A–black bean (IC50 = 0.4 mg/mL), A–green pea + PP–green pea (IC50 = 0.9 mg/mL) and A–black bean + A–green pea (IC50 = 0.6 mg/mL) presented synergistic effects to inhibit PL. A–chickpea + PP–lentil (IC50 = 0.8 mg/mL) and PP–lentil + A–green pea (IC50 = 1.3 mg/mL) interacted additively to inhibit HMGR and synergistically in the combination of A–chickpea + PP–black bean (IC50 = 1.3 mg/mL) to block HMGR. Peptides FEDGLV and PYGVPVGVR inhibited PL and HMGR in silico, showing predicted binding energy interactions of −7.6 and −8.8 kcal/mol, respectively. Combinations of LPH from different legume protein sources could increase synergistically their anti-adipogenic potential.
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Prieto-Martínez FD, Medina-Franco JL. Current advances on the development of BET inhibitors: insights from computational methods. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 122:127-180. [PMID: 32951810 DOI: 10.1016/bs.apcsb.2020.06.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Epigenetics was coined almost 70 years ago for the description of heritable phenotype without altering DNA sequences. Research on the field has uncovered significant roles of such mechanisms, that account for the biogenesis of several diseases. Further studies have led the way for drug development which targets epi-enzymes, mainly for cancer treatment. Of the numerous epi-targets involved with histone acetylation, bromodomains have captured the spotlight of drug discovery focused on novel therapies. However, due to high sequence identity, the development of potent and selective inhibitors poses a significant challenge. Herein, we discuss recent computational developments on BET inhibitors and other methods that may be applied for drug discovery in general. As a proof-of-concept, we discuss a virtual screening to identify novel BET inhibitors based on coumarin derivatives. From public data, we identified putative structure-activity relationships of coumarin scaffold and propose R-group modifications for BET selectivity. Results showed that the optimization and design of novel coumarins could be further explored.
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Affiliation(s)
- Fernando D Prieto-Martínez
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
| | - José L Medina-Franco
- Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
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14
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Ghadermarzi S, Li X, Li M, Kurgan L. Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins. Front Genet 2019; 10:1075. [PMID: 31803227 PMCID: PMC6872670 DOI: 10.3389/fgene.2019.01075] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 10/09/2019] [Indexed: 12/16/2022] Open
Abstract
Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug–protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.
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Affiliation(s)
- Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Xingyi Li
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
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15
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Hu G, Wang K, Song J, Uversky VN, Kurgan L. Taxonomic Landscape of the Dark Proteomes: Whole-Proteome Scale Interplay Between Structural Darkness, Intrinsic Disorder, and Crystallization Propensity. Proteomics 2018; 18:e1800243. [PMID: 30198635 DOI: 10.1002/pmic.201800243] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 08/30/2018] [Indexed: 12/14/2022]
Abstract
Growth rate of the protein sequence universe dramatically exceeds the speed of expansion for the protein structure universe, generating an immense dark proteome that includes proteins with unknown structure. A whole-proteome scale analysis of 5.4 million proteins from 987 proteomes in the three domains of life and viruses to systematically dissect an interplay between structural coverage, degree of putative intrinsic disorder, and predicted propensity for structure determination is performed. It has been found that Archaean and Bacterial proteomes have relatively high structural coverage and low amounts of disorder, whereas Eukaryotic and Viral proteomes are characterized by a broad spread of structural coverage and higher disorder levels. The analysis reveals that dark proteomes (i.e., proteomes containing high fractions of proteins with unknown structure) have significantly elevated amounts of intrinsic disorder and are predicted to be difficult to solve structurally. Although the majority of dark proteomes are of viral origin, many dark viral proteomes have at least modest crystallization propensity and only a handful of them are enriched in the intrinsic disorder. The disorder, structural coverage, and propensity are mapped for structural determination onto a novel proteome-level sequence similarity network to analyze the interplay of these characteristics in the taxonomic landscape.
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Affiliation(s)
- Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, P. R. China
| | - Kui Wang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, P. R. China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Vladimir N Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, 33612, USA.,Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, 142290, Russia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
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16
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Meng F, Murray GF, Kurgan L, Donahue HJ. Functional and structural characterization of osteocytic MLO-Y4 cell proteins encoded by genes differentially expressed in response to mechanical signals in vitro. Sci Rep 2018; 8:6716. [PMID: 29712973 PMCID: PMC5928037 DOI: 10.1038/s41598-018-25113-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 04/09/2018] [Indexed: 12/29/2022] Open
Abstract
The anabolic response of bone to mechanical load is partially the result of osteocyte response to fluid flow-induced shear stress. Understanding signaling pathways activated in osteocytes exposed to fluid flow could identify novel signaling pathways involved in the response of bone to mechanical load. Bioinformatics allows for a unique perspective and provides key first steps in understanding these signaling pathways. We examined proteins encoded by genes differentially expressed in response to fluid flow in murine osteocytic MLO-Y4 cells. We considered structural and functional characteristics including putative intrinsic disorder, evolutionary conservation, interconnectedness in protein-protein interaction networks, and cellular localization. Our analysis suggests that proteins encoded by fluid flow activated genes have lower than expected conservation, are depleted in intrinsic disorder, maintain typical levels of connectivity for the murine proteome, and are found in the cytoplasm and extracellular space. Pathway analyses reveal that these proteins are associated with cellular response to stress, chemokine and cytokine activity, enzyme binding, and osteoclast differentiation. The lower than expected disorder of proteins encoded by flow activated genes suggests they are relatively specialized.
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Affiliation(s)
- Fanchi Meng
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Graeme F Murray
- Bone Engineering, Science and Technology (BEST) Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, United States of America.
| | - Henry J Donahue
- Bone Engineering, Science and Technology (BEST) Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, Virginia, United States of America.
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17
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Nakhaei E, Nowroozi A, Ravari F. The hydrogen-bonded complexes of the 5-fluorouracil with the DNA purine bases: a comprehensive quantum chemical study. Struct Chem 2018. [DOI: 10.1007/s11224-017-1001-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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18
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Feig M, Yu I, Wang PH, Nawrocki G, Sugita Y. Crowding in Cellular Environments at an Atomistic Level from Computer Simulations. J Phys Chem B 2017; 121:8009-8025. [PMID: 28666087 PMCID: PMC5582368 DOI: 10.1021/acs.jpcb.7b03570] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
![]()
The
effects of crowding in biological environments on biomolecular
structure, dynamics, and function remain not well understood. Computer
simulations of atomistic models of concentrated peptide and protein
systems at different levels of complexity are beginning to provide
new insights. Crowding, weak interactions with other macromolecules
and metabolites, and altered solvent properties within cellular environments
appear to remodel the energy landscape of peptides and proteins in
significant ways including the possibility of native state destabilization.
Crowding is also seen to affect dynamic properties, both conformational
dynamics and diffusional properties of macromolecules. Recent simulations
that address these questions are reviewed here and discussed in the
context of relevant experiments.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan, United States.,Quantitative Biology Center, RIKEN , Kobe, Japan
| | - Isseki Yu
- Theoretical Molecular Science Laboratory, RIKEN , Wako, Japan.,iTHES Research Group, RIKEN , Wako, Japan
| | - Po-Hung Wang
- Theoretical Molecular Science Laboratory, RIKEN , Wako, Japan
| | - Grzegorz Nawrocki
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan, United States
| | - Yuji Sugita
- Quantitative Biology Center, RIKEN , Kobe, Japan.,Theoretical Molecular Science Laboratory, RIKEN , Wako, Japan.,iTHES Research Group, RIKEN , Wako, Japan.,Advanced Institute for Computational Science, RIKEN , Kobe, Japan
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19
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De Vivo M, Cavalli A. Recent advances in dynamic docking for drug discovery. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2017. [DOI: 10.1002/wcms.1320] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Marco De Vivo
- Laboratory of Molecular Modeling and Drug DiscoveryIstituto Italiano di TecnologiaGenoaItaly
- IAS‐S/INM‐9 Computational BiomedicineForschungszentrum JülichJülichGermany
| | - Andrea Cavalli
- CompunetIstituto Italiano di TecnologiaGenoaItaly
- Department of Pharmacy and Biotechnology, Alma Mater StudiorumUniversity of BolognaBolognaItaly
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20
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Feig M. Computational protein structure refinement: Almost there, yet still so far to go. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2017; 7:e1307. [PMID: 30613211 PMCID: PMC6319934 DOI: 10.1002/wcms.1307] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Rd., Room 218 BCH, East Lansing, MI, USA, ; 517-432-7439
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21
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Bian J, Li X, Wang N, Wu X, You Q, Zhang X. Discovery of quinone-directed antitumor agents selectively bioactivated by NQO1 over CPR with improved safety profile. Eur J Med Chem 2017; 129:27-40. [DOI: 10.1016/j.ejmech.2017.02.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 02/03/2017] [Accepted: 02/04/2017] [Indexed: 12/11/2022]
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22
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Geometry optimization of steroid sulfatase inhibitors - the influence on the free binding energy with STS. Struct Chem 2017. [DOI: 10.1007/s11224-016-0903-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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23
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Abstract
Docking, a molecular modelling method, has wide applications in identification and optimization in modern drug discovery. This chapter addresses the recent advances in the docking methodologies like fragment docking, covalent docking, inverse docking, post processing, hybrid techniques, homology modeling etc. and its protocol like searching and scoring functions. Advances in scoring functions for e.g. consensus scoring, quantum mechanics methods, clustering and entropy based methods, fingerprinting, etc. are used to overcome the limitations of the commonly used force-field, empirical and knowledge based scoring functions. It will cover crucial necessities and different algorithms of docking and scoring. Further different aspects like protein flexibility, ligand sampling and flexibility, and the performance of scoring function will be discussed.
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Affiliation(s)
- Ashwani Kumar
- Guru Jambheshwar University of Science and Technology, India
| | - Ruchika Goyal
- Guru Jambheshwar University of Science and Technology, India
| | - Sandeep Jain
- Guru Jambheshwar University of Science and Technology, India
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24
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De Vivo M, Masetti M, Bottegoni G, Cavalli A. Role of Molecular Dynamics and Related Methods in Drug Discovery. J Med Chem 2016; 59:4035-61. [DOI: 10.1021/acs.jmedchem.5b01684] [Citation(s) in RCA: 538] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Marco De Vivo
- Laboratory
of Molecular Modeling and Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
- IAS-5/INM-9 Computational
Biomedicine Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
| | - Matteo Masetti
- Department
of Pharmacy and Biotechnology, University of Bologna, Via Belmeloro
6, I-40126 Bologna, Italy
| | - Giovanni Bottegoni
- CompuNet, Istituto
Italiano di Tecnologia, Via Morego
30, 16163 Genova, Italy
- BiKi Technologies
srl, Via XX Settembre 33/10, 16121 Genova, Italy
| | - Andrea Cavalli
- Department
of Pharmacy and Biotechnology, University of Bologna, Via Belmeloro
6, I-40126 Bologna, Italy
- CompuNet, Istituto
Italiano di Tecnologia, Via Morego
30, 16163 Genova, Italy
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25
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Al-Ali H. The evolution of drug discovery: from phenotypes to targets, and back. MEDCHEMCOMM 2016. [DOI: 10.1039/c6md00129g] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Cumulative scientific and technological advances over the past two centuries have transformed drug discovery from a largely serendipitous process into the high tech pipelines of today.
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Affiliation(s)
- Hassan Al-Ali
- Miami Project to Cure Paralysis
- University of Miami Miller School of Medicine
- Miami FL 33136
- USA
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26
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Zhao C, Caplan DA, Noskov SY. Evaluations of the Absolute and Relative Free Energies for Antidepressant Binding to the Amino Acid Membrane Transporter LeuT with Free Energy Simulations. J Chem Theory Comput 2015; 6:1900-14. [PMID: 26615849 DOI: 10.1021/ct9006597] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The binding of ligands to protein receptors with high affinity and specificity is central to many cellular processes. The quest for the development of computational models capable of accurately evaluating binding affinity remains one of the main goals of modern computational biophysics. In this work, free energy perturbation/molecular dynamics simulations were used to evaluate absolute and relative binding affinity for three different antidepressants to a sodium-dependent membrane transporter, LeuT, a bacterial homologue of human serotonin and dopamine transporters. Dysfunction of these membrane transporters in mammals has been implicated in multiple diseases of the nervous system, including bipolar disorder and depression. Furthermore, these proteins are key targets for antidepressants including fluoxetine (aka Prozac) and tricyclic antidepressants known to block transport activity. In addition to being clinically relevant, this system, where multiple crystal structures are readily available, represents an ideal testing ground for methods used to study the molecular mechanisms of ligand binding to membrane proteins. We discuss possible pitfalls and different levels of approximation required to evaluate binding affinity, such as the dependence of the computed affinities on the strength of constraints and the sensitivity of the computed affinities to the particular partial charges derived from restrained electrostatic potential fitting of quantum mechanics electrostatic potential. Finally, we compare the effects of different constraint schemes on the absolute and relative binding affinities obtained from free energy simulations.
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Affiliation(s)
- Chunfeng Zhao
- Institute for Biocomplexity and Informatics and Department of Biological Sciences, University of Calgary, 2500 University Drive, BI558, Calgary, AB, Canada T2N 1N4 and Molecular Structure and Function, Hospital for Sick Children and Department of Biochemistry, University of Toronto, Ontario, Canada
| | - David A Caplan
- Institute for Biocomplexity and Informatics and Department of Biological Sciences, University of Calgary, 2500 University Drive, BI558, Calgary, AB, Canada T2N 1N4 and Molecular Structure and Function, Hospital for Sick Children and Department of Biochemistry, University of Toronto, Ontario, Canada
| | - Sergei Yu Noskov
- Institute for Biocomplexity and Informatics and Department of Biological Sciences, University of Calgary, 2500 University Drive, BI558, Calgary, AB, Canada T2N 1N4 and Molecular Structure and Function, Hospital for Sick Children and Department of Biochemistry, University of Toronto, Ontario, Canada
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27
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Hokmabady L, Raissi H, Khanmohammadi A. Interactions of the 5-fluorouracil anticancer drug with DNA pyrimidine bases: a detailed computational approach. Struct Chem 2015. [DOI: 10.1007/s11224-015-0578-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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28
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Franchini GR, Pórfido JL, Ibáñez Shimabukuro M, Rey Burusco MF, Bélgamo JA, Smith BO, Kennedy MW, Córsico B. The unusual lipid binding proteins of parasitic helminths and their potential roles in parasitism and as therapeutic targets. Prostaglandins Leukot Essent Fatty Acids 2015; 93:31-6. [PMID: 25282399 DOI: 10.1016/j.plefa.2014.08.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2014] [Revised: 08/17/2014] [Accepted: 08/19/2014] [Indexed: 02/03/2023]
Abstract
In this review paper we aim at presenting the current knowledge on structural aspects of soluble lipid binding proteins (LBPs) found in parasitic helminths and to discuss their potential role as novel drug targets. Helminth parasites produce and secrete a great variety of LBPs that may participate in the acquisition of nutrients from their host, such as fatty acids and cholesterol. It is also postulated that LBPs might interfere in the regulation of the host׳s immune response by sequestering lipidic intermediates or delivering bioactive lipids. A detailed comprehension of the structure of these proteins, as well as their interactions with ligands and membranes, is important to understand host-parasite relationships that they may mediate. This information could also contribute to determining the role that these proteins may play in the biology of parasitic helminths and how they modulate the immune systems of their hosts, and also towards the development of new therapeutics and prevention of the diseases caused by these highly pathogenic parasites.
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Affiliation(s)
- Gisela R Franchini
- INIBIOLP, Facultad de Ciencias Médicas, Universidad Nacional de La Plata, Calle 60 y 120, 1900 La Plata, Argentina.
| | - Jorge L Pórfido
- INIBIOLP, Facultad de Ciencias Médicas, Universidad Nacional de La Plata, Calle 60 y 120, 1900 La Plata, Argentina
| | - Marina Ibáñez Shimabukuro
- INIBIOLP, Facultad de Ciencias Médicas, Universidad Nacional de La Plata, Calle 60 y 120, 1900 La Plata, Argentina
| | - María F Rey Burusco
- INIBIOLP, Facultad de Ciencias Médicas, Universidad Nacional de La Plata, Calle 60 y 120, 1900 La Plata, Argentina
| | - Julián A Bélgamo
- INIBIOLP, Facultad de Ciencias Médicas, Universidad Nacional de La Plata, Calle 60 y 120, 1900 La Plata, Argentina
| | - Brian O Smith
- Institute of Molecular, Cell and Systems Biology & School of Life Sciences, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow G12 8QQ, Scotland, UK
| | - Malcolm W Kennedy
- Institute of Molecular, Cell and Systems Biology & School of Life Sciences, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow G12 8QQ, Scotland, UK
| | - Betina Córsico
- INIBIOLP, Facultad de Ciencias Médicas, Universidad Nacional de La Plata, Calle 60 y 120, 1900 La Plata, Argentina
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29
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Rodrigues ARO, Carvalho MSD, Cardoso JAV, Calhelha RC, Queiroz MJR, Coutinho PJ, Castanheira EM. Benzothienoquinolines: New one-pot synthesis and fluorescence studies of their interaction with DNA and polynucleotides. J Photochem Photobiol A Chem 2014. [DOI: 10.1016/j.jphotochem.2014.08.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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30
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Oda A, Saijo K, Ishioka C, Narita K, Katoh T, Watanabe Y, Fukuyoshi S, Takahashi O. Predicting the structures of complexes between phosphoinositide 3-kinase (PI3K) and romidepsin-related compounds for the drug design of PI3K/histone deacetylase dual inhibitors using computational docking and the ligand-based drug design approach. J Mol Graph Model 2014; 54:46-53. [PMID: 25254927 DOI: 10.1016/j.jmgm.2014.08.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2014] [Revised: 08/09/2014] [Accepted: 08/30/2014] [Indexed: 01/07/2023]
Abstract
Predictions of the three-dimensional (3D) structures of the complexes between phosphoinositide 3-kinase (PI3K) and two inhibitors were conducted using computational docking and the ligand-based drug design approach. The obtained structures were refined by structural optimizations and molecular dynamics (MD) simulations. The ligands were located deep inside the ligand binding pocket of the p110α subunit of PI3K, and the hydrogen bond formations and hydrophobic effects of the surrounding amino acids were predicted. Although rough structures were obtained for the PI3K-inhibitor complexes before the MD simulations, the refinement of the structures by these simulations clarified the hydrogen bonding patterns of the complexes.
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Affiliation(s)
- Akifumi Oda
- Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Ishikawa, Japan; Faculty of Pharmaceutical Sciences, Tohoku Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai 981-8558, Miyagi, Japan; Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan.
| | - Ken Saijo
- Institute of Development, Aging and Cancer, Tohoku University, 4-1 Seiryo-machi, Aoba-ku Sendai 980-8575, Miyagi, Japan
| | - Chikashi Ishioka
- Institute of Development, Aging and Cancer, Tohoku University, 4-1 Seiryo-machi, Aoba-ku Sendai 980-8575, Miyagi, Japan
| | - Koichi Narita
- Faculty of Pharmaceutical Sciences, Tohoku Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai 981-8558, Miyagi, Japan
| | - Tadashi Katoh
- Faculty of Pharmaceutical Sciences, Tohoku Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai 981-8558, Miyagi, Japan
| | - Yurie Watanabe
- Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Ishikawa, Japan
| | - Shuichi Fukuyoshi
- Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Ishikawa, Japan
| | - Ohgi Takahashi
- Faculty of Pharmaceutical Sciences, Tohoku Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai 981-8558, Miyagi, Japan
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31
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Chu X, Wang J. Specificity and affinity quantification of flexible recognition from underlying energy landscape topography. PLoS Comput Biol 2014; 10:e1003782. [PMID: 25144525 PMCID: PMC4140643 DOI: 10.1371/journal.pcbi.1003782] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2014] [Accepted: 06/25/2014] [Indexed: 01/07/2023] Open
Abstract
Flexibility in biomolecular recognition is essential and critical for many cellular activities. Flexible recognition often leads to moderate affinity but high specificity, in contradiction with the conventional wisdom that high affinity and high specificity are coupled. Furthermore, quantitative understanding of the role of flexibility in biomolecular recognition is still challenging. Here, we meet the challenge by quantifying the intrinsic biomolecular recognition energy landscapes with and without flexibility through the underlying density of states. We quantified the thermodynamic intrinsic specificity by the topography of the intrinsic binding energy landscape and the kinetic specificity by association rate. We found that the thermodynamic and kinetic specificity are strongly correlated. Furthermore, we found that flexibility decreases binding affinity on one hand, but increases binding specificity on the other hand, and the decreasing or increasing proportion of affinity and specificity are strongly correlated with the degree of flexibility. This shows more (less) flexibility leads to weaker (stronger) coupling between affinity and specificity. Our work provides a theoretical foundation and quantitative explanation of the previous qualitative studies on the relationship among flexibility, affinity and specificity. In addition, we found that the folding energy landscapes are more funneled with binding, indicating that binding helps folding during the recognition. Finally, we demonstrated that the whole binding-folding energy landscapes can be integrated by the rigid binding and isolated folding energy landscapes under weak flexibility. Our results provide a novel way to quantify the affinity and specificity in flexible biomolecular recognition. Flexibility in biomolecular recognition is crucial for the function. Flexibility often leads to moderate binding affinity but high binding specificity, challenging the conventional wisdom that high specificity is guaranteed by high affinity. Currently, understanding of the relationship between affinity and specificity in flexible biomolecular recognition is still obscure, even in a qualitative way. By exploring the intrinsic biomolecular recognition energy landscapes, we provided a novel way to quantify the thermodynamic intrinsic specificity by energy landscape topography and kinetic specificity by association rate. We show quantitatively that flexibility decreases binding affinity while increases binding specificity, and the relative changes in affinity and specificity are strongly correlated with the degree of flexibility. Our results show that more (less) flexibility leads to weaker (stronger) coupling between affinity and specificity. Importantly, we demonstrated that flexibility modulates affinity and specificity through the underlying energy landscape. Our study establishes the quantitative relationship among flexibility, affinity and specificity, bridging the gap between theory and experiments.
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Affiliation(s)
- Xiakun Chu
- College of Physics, Jilin University, Changchun, Jilin, P. R. China
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, P. R. China
| | - Jin Wang
- College of Physics, Jilin University, Changchun, Jilin, P. R. China
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, P. R. China
- Department of Chemistry and Physics, State University of New York at Stony Brook, Stony Brook, New York, United States of America
- * E-mail:
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32
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Ono K, Takeuchi K, Ueda H, Morita Y, Tanimura R, Shimada I, Takahashi H. Structure-Based Approach To Improve a Small-Molecule Inhibitor by the Use of a Competitive Peptide Ligand. Angew Chem Int Ed Engl 2014; 53:2597-601. [DOI: 10.1002/anie.201310749] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Indexed: 11/08/2022]
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Ono K, Takeuchi K, Ueda H, Morita Y, Tanimura R, Shimada I, Takahashi H. Structure-Based Approach To Improve a Small-Molecule Inhibitor by the Use of a Competitive Peptide Ligand. Angew Chem Int Ed Engl 2014. [DOI: 10.1002/ange.201310749] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Computational Approaches and Resources in Single Amino Acid Substitutions Analysis Toward Clinical Research. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 94:365-423. [DOI: 10.1016/b978-0-12-800168-4.00010-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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35
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Jo S, Jiang W, Lee HS, Roux B, Im W. CHARMM-GUI Ligand Binder for absolute binding free energy calculations and its application. J Chem Inf Model 2012. [PMID: 23205773 DOI: 10.1021/ci300505n] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Advanced free energy perturbation molecular dynamics (FEP/MD) simulation methods are available to accurately calculate absolute binding free energies of protein-ligand complexes. However, these methods rely on several sophisticated command scripts implementing various biasing energy restraints to enhance the convergence of the FEP/MD calculations, which must all be handled properly to yield correct results. Here, we present a user-friendly Web interface, CHARMM-GUI Ligand Binder ( http://www.charmm-gui.org/input/gbinding ), to provide standardized CHARMM input files for calculations of absolute binding free energies using the FEP/MD simulations. A number of features are implemented to conveniently set up the FEP/MD simulations in highly customizable manners, thereby permitting an accelerated throughput of this important class of computations while decreasing the possibility of human errors. The interface and a series of input files generated by the interface are tested with illustrative calculations of absolute binding free energies of three nonpolar aromatic ligands to the L99A mutant of T4 lysozyme and three FK506-related ligands to FKBP12. Statistical errors within individual calculations are found to be small (~1 kcal/mol), and the calculated binding free energies generally agree well with the experimental measurements and the previous computational studies (within ~2 kcal/mol). Therefore, CHARMM-GUI Ligand Binder provides a convenient and reliable way to set up the ligand binding free energy calculations and can be applicable to pharmaceutically important protein-ligand systems.
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Affiliation(s)
- Sunhwan Jo
- Department of Molecular Biosciences and Center for Bioinformatics, The University of Kansas, 2030 Becker Drive, Lawrence, Kansas 66047, USA
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Aparoy P, Reddy KK, Reddanna P. Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr Med Chem 2012; 19:3763-78. [PMID: 22680930 PMCID: PMC3480706 DOI: 10.2174/092986712801661112] [Citation(s) in RCA: 88] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2011] [Revised: 01/30/2012] [Accepted: 02/07/2012] [Indexed: 12/26/2022]
Abstract
Lipoxygenases (LOXs) are non-heme iron containing dioxygenases involved in the oxygenation of polyunsaturated fatty acids (PUFAs) such as arachidonic acid (AA). Depending on the position of insertion of oxygen, LOXs are classified into 5-, 8-, 9-, 12- and 15-LOX. Among these, 5-LOX is the most predominant isoform associated with the formation of 5-hydroperoxyeicosatetraenoic acid (5-HpETE), the precursor of non-peptido (LTB4) and peptido (LTC4, LTD4, and LTE4) leukotrienes. LTs are involved in inflammatory and allergic diseases like asthma, ulcerative colitis, rhinitis and also in cancer. Consequently 5-LOX has become target for the development of therapeutic molecules for treatment of various inflammatory disorders. Zileuton is one such inhibitor of 5-LOX approved for the treatment of asthma. In the recent times, computer aided drug design (CADD) strategies have been applied successfully in drug development processes. A comprehensive review on structure based drug design strategies in the development of novel 5-LOX inhibitors is presented in this article. Since the crystal structure of 5-LOX has been recently solved, efforts to develop 5-LOX inhibitors have mostly relied on ligand based rational approaches. The present review provides a comprehensive survey on these strategies in the development of 5-LOX inhibitors.
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KOLB BRIAN, THONHAUSER T. MOLECULAR BIOLOGY AT THE QUANTUM LEVEL: CAN MODERN DENSITY FUNCTIONAL THEORY FORGE THE PATH? ACTA ACUST UNITED AC 2012. [DOI: 10.1142/s1793984412300063] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recent years have seen vast improvements in the ability of rigorous quantum-mechanical methods to treat systems of interest to molecular biology. In this review article, we survey common computational methods used to study such large, weakly bound systems, starting from classical simulations and reaching to quantum chemistry and density functional theory. We sketch their underlying frameworks and investigate their strengths and weaknesses when applied to potentially large biomolecules. In particular, density functional theory — a framework that can treat thousands of atoms on firm theoretical ground — can now accurately describe systems dominated by weak van der Waals interactions. This newfound ability has rekindled interest in using this tried-and-true approach to investigate biological systems of real importance. In this review, we focus on some new methods within the density functional theory that allow for accurate inclusion of the weak interactions that dominate binding in biological macromolecules. Recent work utilizing these methods to study biologically relevant systems will be highlighted, and a vision for the future of density functional theory within molecular biology will be discussed.
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Affiliation(s)
- BRIAN KOLB
- Department of Physics, Wake Forest University, 1834 Wake Forest Road, Winston-Salem, NC 27109, USA
| | - T. THONHAUSER
- Department of Physics, Wake Forest University, 1834 Wake Forest Road, Winston-Salem, NC 27109, USA
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RIAHI SIAVASH, EYNOLLAHI SOLMAZ, GANJALI MOHAMMADREZA. INTERACTION OF EMODIN WITH DNA BASES: A DENSITY FUNCTIONAL THEORY. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2011. [DOI: 10.1142/s0219633610006055] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this study, we present work on the physicochemical interaction between the anticancer drug molecule Emodin (ED) and DNA. Comprehending the physicochemical properties of this drug besides the mechanism by which it interacts with DNA should eventually permit the rational design of novel anticancer or antiviral drugs. The final purpose is the clarification of this novel class of drugs as potential pharmaceutical agents. The properties of the isolated intercalator ED and its stacking interactions with adenine⋯thymine (AT) and guanine⋯cytosine (GC) (nucleic acid base pairs) in face-to-face and face-to-back models were studied by means of the density functional tightbinding (DFTB) method. This method was an approximate version of the density functional theory (DFT) method and it includes London dispersion energy. The molecular modeling of the complex formed between ED and DNA indicated that this complex was capable of contributing to the formation of a constant intercalation site. The results exhibit that ED changes affect DNA structure with reference to bond lengths, bond angles, torsion angles, and charges.
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Affiliation(s)
- SIAVASH RIAHI
- Institute of Petroleum Engineering, Faculty of Engineering, University of Tehran, P. O. Box 11365-4563, Tehran, Iran
- Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, P. O. Box 14155-6455, Tehran, Iran
| | - SOLMAZ EYNOLLAHI
- Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, P. O. Box 14155-6455, Tehran, Iran
| | - MOHAMMAD REZA GANJALI
- Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, P. O. Box 14155-6455, Tehran, Iran
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Ivetac A, McCammon JA. Molecular recognition in the case of flexible targets. Curr Pharm Des 2011; 17:1663-71. [PMID: 21619526 DOI: 10.2174/138161211796355056] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2011] [Accepted: 05/05/2011] [Indexed: 11/22/2022]
Abstract
A protein's flexibility is well recognized to underlie its capacity to engage in critical functions, such as signal transduction, biomolecular transport and biochemical reactivity. Molecular recognition is also tightly linked to the dynamics of the binding partners, yet protein flexibility has largely been ignored by the growing field of structure-based drug design (SBDD). In combination with experimentally determined structures, a number of computational methods have been proposed to model protein movements, which may be important for small molecule binding. Such techniques have the ability to expose new binding site conformations, which may in turn recognize and lead to the discovery of more potent and selective drugs through molecular docking. In this article, we discuss various methods and focus on the Relaxed Complex Scheme (RCS), which uses Molecular Dynamics (MD) simulations to model full protein flexibility and enhance virtual screening programmes. We review practical applications of the RCS and use a recent study of the HIV-1 reverse transcriptase to illustrate the various phases of the scheme. We also discuss some encouraging developments, aimed at addressing current weaknesses of the RCS.
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Affiliation(s)
- Anthony Ivetac
- Department of Chemistry and Biochemistry University of California San Diego, La Jolla, CA 92093-0365, USA.
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40
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Shim J, MacKerell AD. Computational ligand-based rational design: Role of conformational sampling and force fields in model development. MEDCHEMCOMM 2011; 2:356-370. [PMID: 21716805 PMCID: PMC3123535 DOI: 10.1039/c1md00044f] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.
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Toyoda T. Methods for open innovation on a genome-design platform associating scientific, commercial, and educational communities in synthetic biology. Methods Enzymol 2011; 498:189-203. [PMID: 21601679 DOI: 10.1016/b978-0-12-385120-8.00009-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Synthetic biology requires both engineering efficiency and compliance with safety guidelines and ethics. Focusing on the rational construction of biological systems based on engineering principles, synthetic biology depends on a genome-design platform to explore the combinations of multiple biological components or BIO bricks for quickly producing innovative devices. This chapter explains the differences among various platform models and details a methodology for promoting open innovation within the scope of the statutory exemption of patent laws. The detailed platform adopts a centralized evaluation model (CEM), computer-aided design (CAD) bricks, and a freemium model. It is also important for the platform to support the legal aspects of copyrights as well as patent and safety guidelines because intellectual work including DNA sequences designed rationally by human intelligence is basically copyrightable. An informational platform with high traceability, transparency, auditability, and security is required for copyright proof, safety compliance, and incentive management for open innovation in synthetic biology. GenoCon, which we have organized and explained here, is a competition-styled, open-innovation method involving worldwide participants from scientific, commercial, and educational communities that aims to improve the designs of genomic sequences that confer a desired function on an organism. Using only a Web browser, a participating contributor proposes a design expressed with CAD bricks that generate a relevant DNA sequence, which is then experimentally and intensively evaluated by the GenoCon organizers. The CAD bricks that comprise programs and databases as a Semantic Web are developed, executed, shared, reused, and well stocked on the secure Semantic Web platform called the Scientists' Networking System or SciNetS/SciNeS, based on which a CEM research center for synthetic biology and open innovation should be established.
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Affiliation(s)
- Tetsuro Toyoda
- Bioinformatics and Systems Engineering division, RIKEN, Yokohama, Kanagawa, Japan
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42
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Xue M, Zheng M, Xiong B, Li Y, Jiang H, Shen J. Knowledge-based scoring functions in drug design. 1. Developing a target-specific method for kinase-ligand interactions. J Chem Inf Model 2010; 50:1378-86. [PMID: 20681607 DOI: 10.1021/ci100182c] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein kinases are attractive targets for therapeutic interventions in many diseases. Due to their importance in drug discovery, a kinase family-specific potential of mean force (PMF) scoring function, kinase-PMF, was developed to assess the binding of ATP-competitive kinase inhibitors. It is hypothesized that target-specific PMF scoring functions may achieve increased performance in scoring along with the growth of the PDB database. The kinase-PMF inherits the functions and atom types in PMF04 and uses a kinase data set of 872 complexes to derive the potentials. The performance of kinase-PMF was evaluated with an external test set containing 128 kinase crystal structures. We compared it with eight scoring functions commonly used in computer-aided drug design, either in terms of the retrieval rate of retrieving "right" conformations or a virtual screening study. The evaluation results clearly demonstrate that a target-specific scoring function is a promising way to improve prediction power in structure-based drug design compared with other general scoring functions. To provide this rescoring service for researchers, a publicly accessible Web site was established at http://202.127.30.184:8080/scoring/index.jsp .
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Affiliation(s)
- Mengzhu Xue
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Zhangjiang Hi-Tech Park, Pudong, Shanghai, China 201203
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43
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Amaro RE, Li WW. Emerging methods for ensemble-based virtual screening. Curr Top Med Chem 2010; 10:3-13. [PMID: 19929833 DOI: 10.2174/156802610790232279] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2009] [Accepted: 09/16/2009] [Indexed: 02/06/2023]
Abstract
Ensemble based virtual screening refers to the use of conformational ensembles from crystal structures, NMR studies or molecular dynamics simulations. It has gained greater acceptance as advances in the theoretical framework, computational algorithms, and software packages enable simulations at longer time scales. Here we focus on the use of computationally generated conformational ensembles and emerging methods that use these ensembles for discovery, such as the Relaxed Complex Scheme or Dynamic Pharmacophore Model. We also discuss the more rigorous physics-based computational techniques such as accelerated molecular dynamics and thermodynamic integration and their applications in improving conformational sampling or the ranking of virtual screening hits. Finally, technological advances that will help make virtual screening tools more accessible to a wider audience in computer aided drug design are discussed.
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Affiliation(s)
- Rommie E Amaro
- Department of Pharmaceutical Sciences and Department of Information and Computer Science, University of California, Irvine, CA 92697, USA.
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44
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van Dijk M, Bonvin AMJJ. Pushing the limits of what is achievable in protein-DNA docking: benchmarking HADDOCK's performance. Nucleic Acids Res 2010; 38:5634-47. [PMID: 20466807 PMCID: PMC2943626 DOI: 10.1093/nar/gkq222] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The intrinsic flexibility of DNA and the difficulty of identifying its interaction surface have long been challenges that prevented the development of efficient protein-DNA docking methods. We have demonstrated the ability our flexible data-driven docking method HADDOCK to deal with these before, by using custom-built DNA structural models. Here we put our method to the test on a set of 47 complexes from the protein-DNA docking benchmark. We show that HADDOCK is able to predict many of the specific DNA conformational changes required to assemble the interface(s). Our DNA analysis and modelling procedure captures the bend and twist motions occurring upon complex formation and uses these to generate custom-built DNA structural models, more closely resembling the bound form, for use in a second docking round. We achieve throughout the benchmark an overall success rate of 94% of one-star solutions or higher (interface root mean square deviation ≤4 A and fraction of native contacts >10%) according to CAPRI criteria. Our improved protocol successfully predicts even the challenging protein-DNA complexes in the benchmark. Finally, our method is the first to readily dock multiple molecules (N > 2) simultaneously, pushing the limits of what is currently achievable in the field of protein-DNA docking.
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Affiliation(s)
- Marc van Dijk
- Bijvoet Center for Biomolecular Research, Science Faculty, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
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45
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Rolo-Naranjo A, Codorniu-Hernández E, Ferro N. Quantum Chemical Associations Ligand−Residue: Their Role to Predict Flavonoid Binding Sites in Proteins. J Chem Inf Model 2010; 50:924-33. [DOI: 10.1021/ci900358z] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Alberto Rolo-Naranjo
- Department of Molecular Design and Synthesis, Higher Institute of Technologies and Applied Sciences, Habana, Cuba, Department of Chemistry, University of Calgary, Calgary, Alberta, Canada, and Institute for Physical and Theoretical Chemistry, University of Bonn, Bonn, Germany
| | - Edelsys Codorniu-Hernández
- Department of Molecular Design and Synthesis, Higher Institute of Technologies and Applied Sciences, Habana, Cuba, Department of Chemistry, University of Calgary, Calgary, Alberta, Canada, and Institute for Physical and Theoretical Chemistry, University of Bonn, Bonn, Germany
| | - Noel Ferro
- Department of Molecular Design and Synthesis, Higher Institute of Technologies and Applied Sciences, Habana, Cuba, Department of Chemistry, University of Calgary, Calgary, Alberta, Canada, and Institute for Physical and Theoretical Chemistry, University of Bonn, Bonn, Germany
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46
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Weisel M, Kriegl JM, Schneider G. Architectural Repertoire of Ligand-Binding Pockets on Protein Surfaces. Chembiochem 2010; 11:556-63. [DOI: 10.1002/cbic.200900604] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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47
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Li X, Li Y, Cheng T, Liu Z, Wang R. Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes. J Comput Chem 2010; 31:2109-25. [DOI: 10.1002/jcc.21498] [Citation(s) in RCA: 231] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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48
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Diago LA, Moreno E. Evaluation of geometric complementarity between molecular surfaces using compactly supported radial basis functions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2009; 6:689-694. [PMID: 19875866 DOI: 10.1109/tcbb.2009.31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
One of the challenges faced by all molecular docking algorithms is that of being able to discriminate between correct results and false positives obtained in the simulations. The scoring or energetic function is the one that must fulfill this task. Several scoring functions have been developed and new methodologies are still under development. In this paper, we have employed the Compactly Supported Radial Basis Functions (CSRBF) to create analytical representations of molecular surfaces, which are then included as key components of a new scoring function for molecular docking. The method proposed here achieves a better ranking of the solutions produced by the program DOCK, as compared with the ranking done by its native contact scoring function. Our new analytical scoring function based on CSRBF can be easily included in different available docking programs as a reliable and quick filter in large-scale docking simulations.
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Affiliation(s)
- Luis A Diago
- Department of Mechanical Science and Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguroku, PO Box 152-8550, Tokyo, Japan.
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49
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Cheng T, Li X, Li Y, Liu Z, Wang R. Comparative assessment of scoring functions on a diverse test set. J Chem Inf Model 2009; 49:1079-93. [PMID: 19358517 DOI: 10.1021/ci9000053] [Citation(s) in RCA: 359] [Impact Index Per Article: 23.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Scoring functions are widely applied to the evaluation of protein-ligand binding in structure-based drug design. We have conducted a comparative assessment of 16 popular scoring functions implemented in main-stream commercial software or released by academic research groups. A set of 195 diverse protein-ligand complexes with high-resolution crystal structures and reliable binding constants were selected through a systematic nonredundant sampling of the PDBbind database and used as the primary test set in our study. All scoring functions were evaluated in three aspects, that is, "docking power", "ranking power", and "scoring power", and all evaluations were independent from the context of molecular docking or virtual screening. As for "docking power", six scoring functions, including GOLD::ASP, DS::PLP1, DrugScore(PDB), GlideScore-SP, DS::LigScore, and GOLD::ChemScore, achieved success rates over 70% when the acceptance cutoff was root-mean-square deviation < 2.0 A. Combining these scoring functions into consensus scoring schemes improved the success rates to 80% or even higher. As for "ranking power" and "scoring power", the top four scoring functions on the primary test set were X-Score, DrugScore(CSD), DS::PLP, and SYBYL::ChemScore. They were able to correctly rank the protein-ligand complexes containing the same type of protein with success rates around 50%. Correlation coefficients between the experimental binding constants and the binding scores computed by these scoring functions ranged from 0.545 to 0.644. Besides the primary test set, each scoring function was also tested on four additional test sets, each consisting of a certain number of protein-ligand complexes containing one particular type of protein. Our study serves as an updated benchmark for evaluating the general performance of today's scoring functions. Our results indicate that no single scoring function consistently outperforms others in all three aspects. Thus, it is important in practice to choose the appropriate scoring functions for different purposes.
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Affiliation(s)
- Tiejun Cheng
- State Key Laboratory of Bioorganic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, P. R. China
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50
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Hassani M, Cai W, Koelsch KH, Holley DC, Rose AS, Olang F, Lineswala JP, Holloway WG, Gerdes JM, Behforouz M, Beall HD. Lavendamycin Antitumor Agents: Structure-Based Design, Synthesis, and NAD(P)H:Quinone Oxidoreductase 1 (NQO1) Model Validation with Molecular Docking and Biological Studies. J Med Chem 2008; 51:3104-15. [DOI: 10.1021/jm701066a] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Mary Hassani
- Center for Environmental Health Sciences, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Molecular Computational Core Facility, Center for Structural and Functional Neuroscience, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Department of Chemistry, Ball State University, Muncie, Indiana 47306
| | - Wen Cai
- Center for Environmental Health Sciences, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Molecular Computational Core Facility, Center for Structural and Functional Neuroscience, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Department of Chemistry, Ball State University, Muncie, Indiana 47306
| | - Katherine H. Koelsch
- Center for Environmental Health Sciences, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Molecular Computational Core Facility, Center for Structural and Functional Neuroscience, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Department of Chemistry, Ball State University, Muncie, Indiana 47306
| | - David C. Holley
- Center for Environmental Health Sciences, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Molecular Computational Core Facility, Center for Structural and Functional Neuroscience, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Department of Chemistry, Ball State University, Muncie, Indiana 47306
| | - Anthony S. Rose
- Center for Environmental Health Sciences, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Molecular Computational Core Facility, Center for Structural and Functional Neuroscience, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Department of Chemistry, Ball State University, Muncie, Indiana 47306
| | - Fatemeh Olang
- Center for Environmental Health Sciences, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Molecular Computational Core Facility, Center for Structural and Functional Neuroscience, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Department of Chemistry, Ball State University, Muncie, Indiana 47306
| | - Jayana P. Lineswala
- Center for Environmental Health Sciences, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Molecular Computational Core Facility, Center for Structural and Functional Neuroscience, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Department of Chemistry, Ball State University, Muncie, Indiana 47306
| | - William G. Holloway
- Center for Environmental Health Sciences, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Molecular Computational Core Facility, Center for Structural and Functional Neuroscience, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Department of Chemistry, Ball State University, Muncie, Indiana 47306
| | - John M. Gerdes
- Center for Environmental Health Sciences, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Molecular Computational Core Facility, Center for Structural and Functional Neuroscience, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Department of Chemistry, Ball State University, Muncie, Indiana 47306
| | - Mohammad Behforouz
- Center for Environmental Health Sciences, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Molecular Computational Core Facility, Center for Structural and Functional Neuroscience, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Department of Chemistry, Ball State University, Muncie, Indiana 47306
| | - Howard D. Beall
- Center for Environmental Health Sciences, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Molecular Computational Core Facility, Center for Structural and Functional Neuroscience, Department of Biomedical and Pharmaceutical Sciences, The University of Montana, Missoula, Montana 59812, Department of Chemistry, Ball State University, Muncie, Indiana 47306
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