1
|
Mukherjee RP, Yow GY, Sarakbi S, Menegatti S, Gurgel PV, Carbonell RG, Bobay BG. Integrated in silico and experimental discovery of trimeric peptide ligands targeting Butyrylcholinesterase. Comput Biol Chem 2023; 102:107797. [PMID: 36463785 DOI: 10.1016/j.compbiolchem.2022.107797] [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: 08/11/2022] [Revised: 11/09/2022] [Accepted: 11/25/2022] [Indexed: 12/02/2022]
Abstract
Butyrylcholinesterase (BChE) is recognized as a high value biotherapeutic in the treatment of Alzheimer's disease and drug addiction. This study presents the rational design and screening of an in-silico library of trimeric peptides against BChE and the experimental characterization of peptide ligands for purification. The selected peptides consistently afforded high BChE recovery (> 90 %) and purity, yielding up to a 1000-fold purification factor. This study revealed a marked anti-correlated conformational movement governed by the ionic strength and pH of the aqueous environment, which ultimately controls BChE binding and release during chromatographic purification; and highlighted the role of residues within and allosteric to the catalytic triad of BChE in determining biorecognition, thus providing useful guidance for ligand design and affinity maturation.
Collapse
Affiliation(s)
- Rudra Palash Mukherjee
- Biomanufacturing Training and Education Center (BTEC), North Carolina State University, Raleigh, NC 27606, USA; Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27606, USA
| | | | | | - Stefano Menegatti
- Biomanufacturing Training and Education Center (BTEC), North Carolina State University, Raleigh, NC 27606, USA; Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27606, USA
| | - Patrick V Gurgel
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27606, USA; Prometic Bioseparations Ltd, Cambridge CB23 7AJ, UK
| | - Ruben G Carbonell
- Biomanufacturing Training and Education Center (BTEC), North Carolina State University, Raleigh, NC 27606, USA; Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27606, USA; William R. Kenan, Jr. Institute for Engineering, Technology and Science North Carolina State University, Raleigh, NC 27606, USA.
| | - Benjamin G Bobay
- Duke University NMR Center, Duke University Medical Center, Durham, NC 27710, USA; Department of Biochemistry, Duke University, Durham, NC 27710, USA; Department of Radiology, Duke University, Durham, NC 27710, USA.
| |
Collapse
|
2
|
Abstract
Membrane transporter proteins are divided into channels/pores and carriers and constitute protein families of physiological and pharmacological importance. Several presently used therapeutic compounds elucidate their effects by targeting membrane transporter proteins, including anti-arrhythmic, anesthetic, antidepressant, anxiolytic and diuretic drugs. The lack of three-dimensional structures of human transporters hampers experimental studies and drug discovery. In this chapter, the use of homology modeling for generating structural models of membrane transporter proteins is reviewed. The increasing number of atomic resolution structures available as templates, together with improvements in methods and algorithms for sequence alignments, secondary structure predictions, and model generation, in addition to the increase in computational power have increased the applicability of homology modeling for generating structural models of transporter proteins. Different pitfalls and hints for template selection, multiple-sequence alignments, generation and optimization, validation of the models, and the use of transporter homology models for structure-based virtual ligand screening are discussed.
Collapse
Affiliation(s)
- Ingebrigt Sylte
- Molecular Pharmacology and Toxicology, Department of Medical Biology, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
| | - Mari Gabrielsen
- Molecular Pharmacology and Toxicology, Department of Medical Biology, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| | - Kurt Kristiansen
- Molecular Pharmacology and Toxicology, Department of Medical Biology, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| |
Collapse
|
3
|
Natural Products as Mcl-1 Inhibitors: A Comparative Study of Experimental and Computational Modelling Data. CHEMISTRY 2022. [DOI: 10.3390/chemistry4030067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The human myeloid leukemia cell differentiation protein (hMcl-1) is an anti-apoptotic multi-partner protein, belonging to the B-cell lymphoma-2 (Bcl-2) family of proteins. Studies have linked hMcl-1 alleviated expression with resistance to hemopoietic chemotherapeutics, which makes it a key drug target in blood cancers. However, most of the developed small- to medium-sized hMcl-1 inhibitors have typical off-target activity towards other members of the Bcl-2 family. To improve the hMcl-1 inhibitor design, especially exploring a suitable scaffold with pharmacophoric features, we focused on natural hMcl-1 inhibitors. To date, seven classes of natural compounds have been isolated, which display a low micromolar affinity for hMcl-1 and have limited biophysical studies. We screened hMcl-1 co-crystal structures, and identified nine co-crystal structures of hMcl-1 protein, which were later evaluated by multiple receptor conformations (which indicates that the differences between hMcl-1 in crystal structures are low (RMSD values between 0.52 and 1.13 Å, average RMSD of 0.638–0.888 Å, with a standard deviation of 0.102–0.185Å)), and multiple ligand conformations (which led to the selection of the PDB structure, 3WIX (RMSD value = 0.879 Å, standard deviation 0.116 Å), to accommodate various Mcl-1 ligands from a range of co-crystal PDB files) methods. Later, the three adopted docking methods were assessed for their ability to reproduce the conformation bound to the crystal as well as predict trends in Ki values based on calculated RMSD and docking energies. Iterative docking and clustering of the docked pose within ≤1.0 Å was used to evaluate the reproducibility of the adopted docking methods and compared with their experimentally determined hMcl-1 affinity data.
Collapse
|
4
|
Alanazi KM, Farah MA, Hor YY. Multi-Targeted Approaches and Drug Repurposing Reveal Possible SARS-CoV-2 Inhibitors. Vaccines (Basel) 2021; 10:vaccines10010024. [PMID: 35062685 PMCID: PMC8781363 DOI: 10.3390/vaccines10010024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 12/03/2021] [Accepted: 12/16/2021] [Indexed: 01/09/2023] Open
Abstract
The COVID-19 pandemic caused by SARS-CoV-2 is unprecedented in recent memory owing to the non-stop escalation in number of infections and deaths in almost every country of the world. The lack of treatment options further worsens the scenario, thereby necessitating the exploration of already existing US FDA-approved drugs for their effectiveness against COVID-19. In the present study, we have performed virtual screening of nutraceuticals available from DrugBank against 14 SARS-CoV-2 proteins. Molecular docking identified several inhibitors, two of which, rutin and NADH, displayed strong binding affinities and inhibitory potential against SARS-CoV-2 proteins. Further normal model-based simulations were performed to gain insights into the conformational transitions in proteins induced by the drugs. The computational analysis in the present study paves the way for experimental validation and development of multi-target guided inhibitors to fight COVID-19.
Collapse
Affiliation(s)
- Khalid Mashay Alanazi
- Department of Zoology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia; (K.M.A.); (M.A.F.)
| | - Mohammad Abul Farah
- Department of Zoology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia; (K.M.A.); (M.A.F.)
| | - Yan-Yan Hor
- Department of Biotechnology, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Gyeongbuk-do, Korea
- Correspondence:
| |
Collapse
|
5
|
Leveraging Fungal and Human Calcineurin-Inhibitor Structures, Biophysical Data, and Dynamics To Design Selective and Nonimmunosuppressive FK506 Analogs. mBio 2021; 12:e0300021. [PMID: 34809463 PMCID: PMC8609367 DOI: 10.1128/mbio.03000-21] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Calcineurin is a critical enzyme in fungal pathogenesis and antifungal drug tolerance and, therefore, an attractive antifungal target. Current clinically accessible calcineurin inhibitors, such as FK506, are immunosuppressive to humans, so exploiting calcineurin inhibition as an antifungal strategy necessitates fungal specificity in order to avoid inhibiting the human pathway. Harnessing fungal calcineurin-inhibitor crystal structures, we recently developed a less immunosuppressive FK506 analog, APX879, with broad-spectrum antifungal activity and demonstrable efficacy in a murine model of invasive fungal infection. Our overarching goal is to better understand, at a molecular level, the interaction determinants of the human and fungal FK506-binding proteins (FKBP12) required for calcineurin inhibition in order to guide the design of fungus-selective, nonimmunosuppressive FK506 analogs. To this end, we characterized high-resolution structures of the Mucor circinelloides FKBP12 bound to FK506 and of the Aspergillus fumigatus, M. circinelloides, and human FKBP12 proteins bound to the FK506 analog APX879, which exhibits enhanced selectivity for fungal pathogens. Combining structural, genetic, and biophysical methodologies with molecular dynamics simulations, we identify critical variations in these structurally similar FKBP12-ligand complexes. The work presented here, aimed at the rational design of more effective calcineurin inhibitors, indeed suggests that modifications to the APX879 scaffold centered around the C15, C16, C18, C36, and C37 positions provide the potential to significantly enhance fungal selectivity. IMPORTANCE Invasive fungal infections are a leading cause of death in the immunocompromised patient population. The rise in drug resistance to current antifungals highlights the urgent need to develop more efficacious and highly selective agents. Numerous investigations of major fungal pathogens have confirmed the critical role of the calcineurin pathway for fungal virulence, making it an attractive target for antifungal development. Although FK506 inhibits calcineurin, it is immunosuppressive in humans and cannot be used as an antifungal. By combining structural, genetic, biophysical, and in silico methodologies, we pinpoint regions of the FK506 scaffold and a less immunosuppressive analog, APX879, centered around the C15 to C18 and C36 to C37 positions that could be altered with selective extensions and/or deletions to enhance fungal selectivity. This work represents a significant advancement toward realizing calcineurin as a viable target for antifungal drug discovery.
Collapse
|
6
|
Lee Y, Hou X, Lee JH, Nayak A, Alexander V, Sharma PK, Chang H, Phan K, Gao ZG, Jacobson KA, Choi S, Jeong LS. Subtle Chemical Changes Cross the Boundary between Agonist and Antagonist: New A 3 Adenosine Receptor Homology Models and Structural Network Analysis Can Predict This Boundary. J Med Chem 2021; 64:12525-12536. [PMID: 34435786 DOI: 10.1021/acs.jmedchem.1c00239] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Distinguishing compounds' agonistic or antagonistic behavior would be of great utility for the rational discovery of selective modulators. We synthesized truncated nucleoside derivatives and discovered 6c (Ki = 2.40 nM) as a potent human A3 adenosine receptor (hA3AR) agonist, and subtle chemical modification induced a shift from antagonist to agonist. We elucidated this shift by developing new hA3AR homology models that consider the pharmacological profiles of the ligands. Taken together with molecular dynamics (MD) simulation and three-dimensional (3D) structural network analysis of the receptor-ligand complex, the results indicated that the hydrogen bonding with Thr943.36 and His2727.43 could make a stable interaction between the 3'-amino group with TM3 and TM7, and the corresponding induced-fit effects may play important roles in rendering the agonistic effect. Our results provide a more precise understanding of the compounds' actions at the atomic level and a rationale for the design of new drugs with specific pharmacological profiles.
Collapse
Affiliation(s)
- Yoonji Lee
- Global AI Drug Discovery Center, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea.,College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Xiyan Hou
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea.,College of Life Science, Dalian Minzu University, Dalian 116600, People's Republic of China
| | - Jin Hee Lee
- Global AI Drug Discovery Center, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Akshata Nayak
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
| | - Varughese Alexander
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
| | - Pankaz K Sharma
- Global AI Drug Discovery Center, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Hyerim Chang
- Global AI Drug Discovery Center, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Khai Phan
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Health, Bethesda, Maryland 20892, United States
| | - Zhan-Guo Gao
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Health, Bethesda, Maryland 20892, United States
| | - Kenneth A Jacobson
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Health, Bethesda, Maryland 20892, United States
| | - Sun Choi
- Global AI Drug Discovery Center, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Lak Shin Jeong
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
| |
Collapse
|
7
|
Wei C, Huang J, Luo Y, Wang S, Wu S, Xing Z, Chen J. Novel amide derivatives containing an imidazo[1,2-a]pyridine moiety: Design, synthesis as potential nematicidal and antibacterial agents. PESTICIDE BIOCHEMISTRY AND PHYSIOLOGY 2021; 175:104857. [PMID: 33993975 DOI: 10.1016/j.pestbp.2021.104857] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/17/2021] [Accepted: 04/12/2021] [Indexed: 05/12/2023]
Abstract
To discover new nematicides, a series of novel amide derivatives containing an imidazo[1,2-a]pyridine moeity were designed and synthesized. Among the title compounds, compounds 3 and 27 exhibited good nematicidal activities against Aphelenchoides besseyi (rice white-tip nematode), with LC50 values against of 27.3 and 35.9 mg/L, respectively, which were superior to that of fosthiazate (45.4 mg/L). Meanwhile, the LC50 value of compound 27 against Caenorhabditis elegans was 5.7 mg/L, which was superior to that of fosthiazate (77.2 mg/L). Compound 27 not only binds well to acetylcholinesterase (AChE) of nematodes, but also has a good inhibitory activity against AChE. Thus, AChE may be a potential target of compound 27 against nematodes. Unexpectedly, compound 28 exhibited excellent antibacterial activities with EC50 values of 1.2 and 3.1 mg/L against Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc), respectively, which were superior to those of bismerthiazol (68.6 and 77.1 mg/L) and thiodiazole copper (80.8 and 96.6 mg/L). The curative and protective activities of compound 28 against bacterial leaf blight were 37.0% and 36.8% at 50 mg/L, respectively, which were higher than those of thiodiazole copper (16.1% and 15.5%). In addition, compound 28 may inhibit the growth of Xoo by affecting the production of cell membranes and extracellular polysaccharides. Amide derivatives containing an imidazo[1,2-a]pyridine moeity can be used as good lead-structures to discover new nematicidal and antibacterial agents in the future.
Collapse
Affiliation(s)
- Chengqian Wei
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Junjie Huang
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Yuqin Luo
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Shaobo Wang
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Sikai Wu
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Zhifu Xing
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Jixiang Chen
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China.
| |
Collapse
|
8
|
Jin H, Xia J, Liu Z, Wang XS, Zhang L. A unique ligand-steered strategy for CC chemokine receptor 2 homology modeling to facilitate structure-based virtual screening. Chem Biol Drug Des 2021; 97:944-961. [PMID: 33386704 PMCID: PMC8048943 DOI: 10.1111/cbdd.13820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/12/2020] [Accepted: 12/13/2020] [Indexed: 12/29/2022]
Abstract
CC chemokine receptor 2 (CCR2) antagonists that disrupt CCR2/MCP-1 interaction are expected to treat a variety of inflammatory and autoimmune diseases. The lack of CCR2 crystal structure limits the application of structure-based drug design (SBDD) to this target. Although a few three-dimensional theoretical models have been reported, their accuracy remains to be improved in terms of templates and modeling approaches. In this study, we developed a unique ligand-steered strategy for CCR2 homology modeling. It starts with an initial model based on the X-ray structure of the closest homolog so far, that is, CXCR4. Then, it uses Elastic Network Normal Mode Analysis (EN-NMA) and flexible docking (FD) by AutoDock Vina software to generate ligand-induced fit models. It selects optimal model(s) as well as scoring function(s) via extensive evaluation of model performance based on a unique benchmarking set constructed by our in-house tool, that is, MUBD-DecoyMaker. The model of 81_04 presents the optimal enrichment when combined with the scoring function of PMF04, and the proposed binding mode between CCR2 and Teijin lead by this model complies with the reported mutagenesis data. To highlight the advantage of our strategy, we compared it with the only reported ligand-steered strategy for CCR2 homology modeling, that is, Discovery Studio/Ligand Minimization. Lastly, we performed prospective virtual screening based on 81_04 and CCR2 antagonist bioassay. The identification of two hit compounds, that is, E859-1281 and MolPort-007-767-945, validated the efficacy of our model and the ligand-steered strategy.
Collapse
Affiliation(s)
- Hongwei Jin
- State Key Laboratory of Natural and Biomimetic DrugsSchool of Pharmaceutical SciencesPeking UniversityBeijingChina
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural MedicinesDepartment of New Drug Research and DevelopmentInstitute of Materia MedicaChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic DrugsSchool of Pharmaceutical SciencesPeking UniversityBeijingChina
| | - Xiang Simon Wang
- Molecular Modeling and Drug Discovery Core for District of Columbia Center for AIDS Research (DC CFAR)Laboratory of Cheminformatics and Drug DesignDepartment of Pharmaceutical SciencesCollege of PharmacyHoward UniversityWashingtonDCUSA
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic DrugsSchool of Pharmaceutical SciencesPeking UniversityBeijingChina
| |
Collapse
|
9
|
OMN6 a novel bioengineered peptide for the treatment of multidrug resistant Gram negative bacteria. Sci Rep 2021; 11:6603. [PMID: 33758343 PMCID: PMC7988117 DOI: 10.1038/s41598-021-86155-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 03/11/2021] [Indexed: 01/31/2023] Open
Abstract
New antimicrobial agents are urgently needed, especially to eliminate multidrug resistant Gram-negative bacteria that stand for most antibiotic-resistant threats. In the following study, we present superior properties of an engineered antimicrobial peptide, OMN6, a 40-amino acid cyclic peptide based on Cecropin A, that presents high efficacy against Gram-negative bacteria with a bactericidal mechanism of action. The target of OMN6 is assumed to be the bacterial membrane in contrast to small molecule-based agents which bind to a specific enzyme or bacterial site. Moreover, OMN6 mechanism of action is effective on Acinetobacter baumannii laboratory strains and clinical isolates, regardless of the bacteria genotype or resistance-phenotype, thus, is by orders-of-magnitude, less likely for mutation-driven development of resistance, recrudescence, or tolerance. OMN6 displays an increase in stability and a significant decrease in proteolytic degradation with full safety margin on erythrocytes and HEK293T cells. Taken together, these results strongly suggest that OMN6 is an efficient, stable, and non-toxic novel antimicrobial agent with the potential to become a therapy for humans.
Collapse
|
10
|
Meseguer A, Dominguez L, Bota PM, Aguirre‐Plans J, Bonet J, Fernandez‐Fuentes N, Oliva B. Using collections of structural models to predict changes of binding affinity caused by mutations in protein-protein interactions. Protein Sci 2020; 29:2112-2130. [PMID: 32797645 PMCID: PMC7513729 DOI: 10.1002/pro.3930] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 08/04/2020] [Accepted: 08/05/2020] [Indexed: 12/24/2022]
Abstract
Protein-protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state-of-the-art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state-of-art methods.
Collapse
Affiliation(s)
- Alberto Meseguer
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Lluis Dominguez
- Integrative Biomedical Informatics Group (GRIB‐IMIM). Department of Experimental and Life SciencesUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Patricia M. Bota
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
- Department of BiosciencesUniversitat de Vic‐Universitat Central de CatalunyaVicCataloniaSpain
| | - Joaquim Aguirre‐Plans
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Jaume Bonet
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Narcis Fernandez‐Fuentes
- Department of BiosciencesUniversitat de Vic‐Universitat Central de CatalunyaVicCataloniaSpain
- Institute of Biological, Environmental and Rural SciencesAberystwyth UniversityAberystwythUK
| | - Baldo Oliva
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
| |
Collapse
|
11
|
Slater O, Kontoyianni M. The compromise of virtual screening and its impact on drug discovery. Expert Opin Drug Discov 2019; 14:619-637. [PMID: 31025886 DOI: 10.1080/17460441.2019.1604677] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.
Collapse
Affiliation(s)
- Olivia Slater
- a Department of Pharmaceutical Sciences , Southern Illinois University Edwardsville , Edwardsville , IL , USA
| | - Maria Kontoyianni
- a Department of Pharmaceutical Sciences , Southern Illinois University Edwardsville , Edwardsville , IL , USA
| |
Collapse
|
12
|
Lee JY, Krieger J, Herguedas B, García-Nafría J, Dutta A, Shaikh SA, Greger IH, Bahar I. Druggability Simulations and X-Ray Crystallography Reveal a Ligand-Binding Site in the GluA3 AMPA Receptor N-Terminal Domain. Structure 2018; 27:241-252.e3. [PMID: 30528594 DOI: 10.1016/j.str.2018.10.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 07/25/2018] [Accepted: 10/18/2018] [Indexed: 11/19/2022]
Abstract
Ionotropic glutamate receptors (iGluRs) mediate the majority of excitatory neurotransmission in the brain. Their dysfunction is implicated in many neurological disorders, rendering iGluRs potential drug targets. Here, we performed a systematic analysis of the druggability of two major iGluR subfamilies, using molecular dynamics simulations in the presence of drug-like molecules. We demonstrate the applicability of druggability simulations by faithfully identifying known agonist and modulator sites on AMPA receptors (AMPARs) and NMDA receptors. Simulations produced the expected allosteric changes of the AMPAR ligand-binding domain in response to agonist. We also identified a novel ligand-binding site specific to the GluA3 AMPAR N-terminal domain (NTD), resulting from its unique conformational flexibility that we explored further with crystal structures trapped in vastly different states. In addition to providing an in-depth analysis into iGluR NTD dynamics, our approach identifies druggable sites and permits the determination of pharmacophoric features toward novel iGluR modulators.
Collapse
Affiliation(s)
- Ji Young Lee
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Avenue, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - James Krieger
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Avenue, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Beatriz Herguedas
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Javier García-Nafría
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Anindita Dutta
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Avenue, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Saher A Shaikh
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Ingo H Greger
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge CB2 0QH, UK.
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Avenue, Suite 3064 BST3, Pittsburgh, PA 15260, USA.
| |
Collapse
|
13
|
Fu DY, Meiler J. Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review. J Chem Inf Model 2018; 58:225-233. [PMID: 29286651 DOI: 10.1021/acs.jcim.7b00418] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Incorporating experimental restraints is a powerful method of increasing accuracy in computational protein small molecule docking simulations. Different algorithms integrate distinct forms of biochemical data during the docking and/or scoring stages. These so-called hybrid methods make use of receptor-based information such as nuclear magnetic resonance (NMR) restraints or small molecule-based information such as structure-activity relationships (SARs). A third class of methods directly interrogates contacts between the protein receptor and the small molecule. This work reviews the current state of using such restraints in docking simulations, evaluates their feasibility across broad systems, and identifies potential areas of algorithm development.
Collapse
Affiliation(s)
- Darwin Y Fu
- Department of Chemistry Vanderbilt University Nashville, Tennessee 37235, United States
| | - Jens Meiler
- Department of Chemistry Vanderbilt University Nashville, Tennessee 37235, United States
| |
Collapse
|
14
|
Toussi CA, Soheilifard R. A better prediction of conformational changes of proteins using minimally connected network models. Phys Biol 2017; 13:066013. [PMID: 28112101 DOI: 10.1088/1478-3975/13/6/066013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Elastic network models have recently been used for studying low-frequency collective motions of proteins. These models simplify the complexity that arises from normal mode analysis by considering a simplified potential involving a few parameters. Two common parameters in most of the elastic network models are cutoff radius and force constant. Although the latter has been studied extensively and even elaborate new models were introduced, for the former usually an ad-hoc cutoff radius is considered. Moreover, the quality of the network models is usually assessed by evaluating their prediction against experimental B-factors. In this work, we consider various common elastic network models with different cutoff radii and assess them by their ability to predict conformational changes of proteins in complexes from unbound to bound state. This prediction is performed by perturbing the unbound structure using a number of low-frequency normal modes of its network model to optimally fit the bound structure. We evaluated a dataset of 30 proteins with distinct unbound and bound structures using this criterion. The results showed that, opposed to the common calibration process based on B-factors, a meaningful relationship exists between the quality of the prediction and model parameters. It was shown that the cutoff radius has a major role in this prediction and minimally connected network models, which use the shortest cutoff radius for which the network is stable, give the best results. It was also shown that by considering the first ten normal modes, the conformational changes can be predicted by about 25 percent. Hence, the evaluation process was extended to the case of considering the contribution of all normal modes in the prediction. The results indicated that minimally connected network models are superior, despite their simplicity, when any number of modes are considered in the prediction.
Collapse
Affiliation(s)
- Cyrus Ahmadi Toussi
- Department of Mechanical Engineering, Hakim Sabzevari University, Sabzevar, Iran
| | | |
Collapse
|
15
|
Frappier V, Chartier M, Najmanovich R. Applications of Normal Mode Analysis Methods in Computational Protein Design. Methods Mol Biol 2017; 1529:203-214. [PMID: 27914052 DOI: 10.1007/978-1-4939-6637-0_9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Recent advances in coarse-grained normal mode analysis methods make possible the large-scale prediction of the effect of mutations on protein stability and dynamics as well as the generation of biologically relevant conformational ensembles. Given the interplay between flexibility and enzymatic activity, the combined analysis of stability and dynamics using the Elastic Network Contact Model (ENCoM) method has ample applications in protein engineering in industrial and medical applications such as in computational antibody design. Here, we present a detailed tutorial on how to perform such calculations using ENCoM.
Collapse
Affiliation(s)
- Vincent Frappier
- Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts avenue, Cambridge, MA, 02139, USA
- Faculty of Medicine and Health Sciences, Department of Biochemistry, University of Sherbrooke, 3001, 12 Av., NordSherbrooke, QCJ1H 5N4, Canada
| | - Matthieu Chartier
- Faculty of Medicine and Health Sciences, Department of Biochemistry, University of Sherbrooke, 3001, 12 Av., NordSherbrooke, QCJ1H 5N4, Canada
| | - Rafael Najmanovich
- Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montreal, Montreal, H3C 3J7, QC, Canada.
- Faculty of Medicine and Health Sciences, Department of Biochemistry, University of Sherbrooke, 3001, 12 Av., NordSherbrooke, QCJ1H 5N4, Canada.
| |
Collapse
|
16
|
Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. Molecular Docking at a Glance. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.
Collapse
Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
| |
Collapse
|
17
|
Shoombuatong W, Prathipati P, Owasirikul W, Worachartcheewan A, Simeon S, Anuwongcharoen N, Wikberg JES, Nantasenamat C. Towards the Revival of Interpretable QSAR Models. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2017. [DOI: 10.1007/978-3-319-56850-8_1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
|
18
|
Kurkcuoglu Z, Doruker P. Ligand Docking to Intermediate and Close-To-Bound Conformers Generated by an Elastic Network Model Based Algorithm for Highly Flexible Proteins. PLoS One 2016; 11:e0158063. [PMID: 27348230 PMCID: PMC4922591 DOI: 10.1371/journal.pone.0158063] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Accepted: 06/09/2016] [Indexed: 01/03/2023] Open
Abstract
Incorporating receptor flexibility in small ligand-protein docking still poses a challenge for proteins undergoing large conformational changes. In the absence of bound structures, sampling conformers that are accessible by apo state may facilitate docking and drug design studies. For this aim, we developed an unbiased conformational search algorithm, by integrating global modes from elastic network model, clustering and energy minimization with implicit solvation. Our dataset consists of five diverse proteins with apo to complex RMSDs 4.7-15 Å. Applying this iterative algorithm on apo structures, conformers close to the bound-state (RMSD 1.4-3.8 Å), as well as the intermediate states were generated. Dockings to a sequence of conformers consisting of a closed structure and its "parents" up to the apo were performed to compare binding poses on different states of the receptor. For two periplasmic binding proteins and biotin carboxylase that exhibit hinge-type closure of two dynamics domains, the best pose was obtained for the conformer closest to the bound structure (ligand RMSDs 1.5-2 Å). In contrast, the best pose for adenylate kinase corresponded to an intermediate state with partially closed LID domain and open NMP domain, in line with recent studies (ligand RMSD 2.9 Å). The docking of a helical peptide to calmodulin was the most challenging case due to the complexity of its 15 Å transition, for which a two-stage procedure was necessary. The technique was first applied on the extended calmodulin to generate intermediate conformers; then peptide docking and a second generation stage on the complex were performed, which in turn yielded a final peptide RMSD of 2.9 Å. Our algorithm is effective in producing conformational states based on the apo state. This study underlines the importance of such intermediate states for ligand docking to proteins undergoing large transitions.
Collapse
Affiliation(s)
- Zeynep Kurkcuoglu
- Department of Chemical Engineering and Polymer Research Center, Bogazici University, Bebek, Istanbul, 34342, Turkey
- * E-mail: (ZK); (PD)
| | - Pemra Doruker
- Department of Chemical Engineering and Polymer Research Center, Bogazici University, Bebek, Istanbul, 34342, Turkey
- * E-mail: (ZK); (PD)
| |
Collapse
|
19
|
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
| |
Collapse
|
20
|
Convertino M, Dokholyan NV. Computational Modeling of Small Molecule Ligand Binding Interactions and Affinities. Methods Mol Biol 2016; 1414:23-32. [PMID: 27094283 DOI: 10.1007/978-1-4939-3569-7_2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Understanding and controlling biological phenomena via structure-based drug screening efforts often critically rely on accurate description of protein-ligand interactions. However, most of the currently available computational techniques are affected by severe deficiencies in both protein and ligand conformational sampling as well as in the scoring of the obtained docking solutions. To overcome these limitations, we have recently developed MedusaDock, a novel docking methodology, which simultaneously models ligand and receptor flexibility. Coupled with MedusaScore, a physical force field-based scoring function that accounts for the protein-ligand interaction energy, MedusaDock, has reported the highest success rate in the CSAR 2011 exercise. Here, we present a standard computational protocol to evaluate the binding properties of the two enantiomers of the non-selective β-blocker propanolol in the β2 adrenergic receptor's binding site. We describe details of our protocol, which have been successfully applied to several other targets.
Collapse
Affiliation(s)
- Marino Convertino
- Department of Biochemistry and Biophysics, University of North Carolina, 120 Mason Farm Road, 27599, Chapel Hill, NC, USA
| | - Nikolay V Dokholyan
- Department of Biochemistry and Biophysics, University of North Carolina, 120 Mason Farm Road, 27599, Chapel Hill, NC, USA.
| |
Collapse
|
21
|
Prathipati P, Mizuguchi K. Integration of Ligand and Structure Based Approaches for CSAR-2014. J Chem Inf Model 2015; 56:974-87. [PMID: 26492437 DOI: 10.1021/acs.jcim.5b00477] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The prediction of binding poses and affinities is an area of active interest in computer-aided drug design (CADD). Given the documented limitations with either ligand or structure based approaches, we employed an integrated approach and developed a rapid protocol for binding mode and affinity predictions. This workflow was applied to the three protein targets of Community Structure-Activity Resource-2014 (CSAR-2014) exercise: Factor Xa (FXa), Spleen Tyrosine Kinase (SYK), and tRNA (guanine-N(1))-methyltransferase (TrmD). Our docking and scoring workflow incorporates compound clustering and ligand and protein structure based pharmacophore modeling, followed by local docking, minimization, and scoring. While the former part of the protocol ensures high-quality ligand alignments and mapping, the subsequent minimization and scoring provides the predicted binding modes and affinities. We made blind predictions of docking pose for 1, 5, and 14 ligands docked into 1, 2, and 12 crystal structures of FXa, SYK, and TrmD, respectively. The resulting 174 poses were compared with cocrystallized structures (1, 5, and 14 complexes) made available at the end of CSAR. Our predicted poses were related to the experimentally determined structures with a mean root-mean-square deviation value of 3.4 Å. Further, we were able to classify high and low affinity ligands with the area under the curve values of 0.47, 0.60, and 0.69 for FXa, SYK, and TrmD, respectively, indicating the validity of our approach in at least two of the three systems. Detailed critical analysis of the results and CSAR methodology ranking procedures suggested that a straightforward application of our workflow has limitations, as some of the performance measures do not reflect the actual utility of pose and affinity predictions in the biological context of individual systems.
Collapse
Affiliation(s)
- Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition , 7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan
| | - Kenji Mizuguchi
- National Institutes of Biomedical Innovation, Health and Nutrition , 7-6-8 Saito-Asagi, Ibaraki City, Osaka 567-0085, Japan
| |
Collapse
|
22
|
Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis. Future Med Chem 2015; 7:2317-31. [DOI: 10.4155/fmc.15.150] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Aim: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. Materials & Methods: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. Results & discussion: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility.[Formula: see text]
Collapse
|
23
|
Antunes DA, Devaurs D, Kavraki LE. Understanding the challenges of protein flexibility in drug design. Expert Opin Drug Discov 2015; 10:1301-13. [DOI: 10.1517/17460441.2015.1094458] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|
24
|
Nedumpully-Govindan P, Jemec DB, Ding F. CSAR Benchmark of Flexible MedusaDock in Affinity Prediction and Nativelike Binding Pose Selection. J Chem Inf Model 2015; 56:1042-52. [PMID: 26252196 DOI: 10.1021/acs.jcim.5b00303] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
While molecular docking with both ligand and receptor flexibilities can help capture conformational changes upon binding, correct ranking of nativelike binding poses and accurate estimation of binding affinities remains a major challenge. In addition to the commonly used scoring approach with intermolecular interaction energies, we included the contribution of intramolecular energies changes upon binding in our flexible docking method, MedusaDock. In CSAR 2013-2014 binding prediction benchmark exercises, the new scoring function MScomplex was found to better recapitulate experimental binding affinities and correctly identify ligand-binding sequences from decoy receptors. Our further analysis with the DUD data sets indicates significant improvement of virtual screening enrichment using the new scoring function when compared to the previous intermolecular energy based scoring method. Our postanalysis also suggests a new approach to select nativelike poses in the clustering-based pose ranking approach by MedusaDock. Since the calculation of intramolecular energy changes and clustering-based pose ranking and selection are not MedusaDock specific, we expect a broad application in force-field based estimation of binding affinities and pose ranking using flexible ligand-receptor docking.
Collapse
Affiliation(s)
- Praveen Nedumpully-Govindan
- Department of Physics and Astronomy and ‡Department of Genetics and Biochemistry, Clemson University , Clemson, South Carolina 29634, United States
| | - Domen B Jemec
- Department of Physics and Astronomy and ‡Department of Genetics and Biochemistry, Clemson University , Clemson, South Carolina 29634, United States
| | - Feng Ding
- Department of Physics and Astronomy and ‡Department of Genetics and Biochemistry, Clemson University , Clemson, South Carolina 29634, United States
| |
Collapse
|
25
|
Frappier V, Chartier M, Najmanovich RJ. ENCoM server: exploring protein conformational space and the effect of mutations on protein function and stability. Nucleic Acids Res 2015; 43:W395-400. [PMID: 25883149 PMCID: PMC4489264 DOI: 10.1093/nar/gkv343] [Citation(s) in RCA: 118] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Revised: 03/27/2015] [Accepted: 04/06/2015] [Indexed: 11/24/2022] Open
Abstract
ENCoM is a coarse-grained normal mode analysis method recently introduced that unlike previous such methods is unique in that it accounts for the nature of amino acids. The inclusion of this layer of information was shown to improve conformational space sampling and apply for the first time a coarse-grained normal mode analysis method to predict the effect of single point mutations on protein dynamics and thermostability resulting from vibrational entropy changes. Here we present a web server that allows non-technical users to have access to ENCoM calculations to predict the effect of mutations on thermostability and dynamics as well as to generate geometrically realistic conformational ensembles. The server is accessible at: http://bcb.med.usherbrooke.ca/encom.
Collapse
Affiliation(s)
- Vincent Frappier
- Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Quebec, J1H 5N4, Canada
| | - Matthieu Chartier
- Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Quebec, J1H 5N4, Canada
| | - Rafael J Najmanovich
- Department of Biochemistry, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Quebec, J1H 5N4, Canada
| |
Collapse
|
26
|
Eyal E, Lum G, Bahar I. The anisotropic network model web server at 2015 (ANM 2.0). Bioinformatics 2015; 31:1487-9. [PMID: 25568280 PMCID: PMC4410662 DOI: 10.1093/bioinformatics/btu847] [Citation(s) in RCA: 123] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Revised: 12/11/2014] [Accepted: 12/21/2014] [Indexed: 11/12/2022] Open
Abstract
SUMMARY The anisotropic network model (ANM) is one of the simplest yet powerful tools for exploring protein dynamics. Its main utility is to predict and visualize the collective motions of large complexes and assemblies near their equilibrium structures. The ANM server, introduced by us in 2006 helped making this tool more accessible to non-sophisticated users. We now provide a new version (ANM 2.0), which allows inclusion of nucleic acids and ligands in the network model and thus enables the investigation of the collective motions of protein-DNA/RNA and -ligand systems. The new version offers the flexibility of defining the system nodes and the interaction types and cutoffs. It also includes extensive improvements in hardware, software and graphical interfaces. AVAILABILITY AND IMPLEMENTATION ANM 2.0 is available at http://anm.csb.pitt.edu CONTACT eran.eyal@sheba.health.gov.il, eyal.eran@gmail.com.
Collapse
Affiliation(s)
- Eran Eyal
- Cancer Research Institute, Sheba Medical Center, 2 Sheba Rd, Ramat Gan 52621, Israel and Department of Computational and System Biology, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15213, USA
| | - Gengkon Lum
- Cancer Research Institute, Sheba Medical Center, 2 Sheba Rd, Ramat Gan 52621, Israel and Department of Computational and System Biology, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15213, USA
| | - Ivet Bahar
- Cancer Research Institute, Sheba Medical Center, 2 Sheba Rd, Ramat Gan 52621, Israel and Department of Computational and System Biology, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15213, USA
| |
Collapse
|
27
|
Buonfiglio R, Recanatini M, Masetti M. Protein Flexibility in Drug Discovery: From Theory to Computation. ChemMedChem 2015; 10:1141-8. [PMID: 25891095 DOI: 10.1002/cmdc.201500086] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Indexed: 01/01/2023]
Abstract
Nowadays it is widely accepted that the mechanisms of biomolecular recognition are strongly coupled to the intrinsic dynamic of proteins. In past years, this evidence has prompted the development of theoretical models of recognition able to describe ligand binding assisted by protein conformational changes. On a different perspective, the need to take into account protein flexibility in structure-based drug discovery has stimulated the development of several and extremely diversified computational methods. Herein, on the basis of a parallel between the major recognition models and the simulation strategies used to account for protein flexibility in ligand binding, we sort out and describe the most innovative and promising implementations for structure-based drug discovery.
Collapse
Affiliation(s)
- Rosa Buonfiglio
- Computational Chemistry, Chemistry Innovation Centre, Discovery Sciences, AstraZeneca R&D Mölndal, 43183 Mölndal (Sweden)
| | - Maurizio Recanatini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, 40126 Bologna (Italy)
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, 40126 Bologna (Italy).
| |
Collapse
|
28
|
Fenollosa C, Otón M, Andrio P, Cortés J, Orozco M, Goñi JR. SEABED: Small molEcule activity scanner weB servicE baseD. ACTA ACUST UNITED AC 2014; 31:773-5. [PMID: 25348211 PMCID: PMC7297214 DOI: 10.1093/bioinformatics/btu709] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Motivation: The SEABED web server integrates a variety of docking and QSAR techniques in a user-friendly environment. SEABED goes beyond the basic docking and QSAR web tools and implements extended functionalities like receptor preparation, library editing, flexible ensemble docking, hybrid docking/QSAR experiments or virtual screening on protein mutants. SEABED is not a monolithic workflow tool but Software as a Service platform. Availability and implementation: SEABED is a free web server available athttp://www.bsc.es/SEABED. No registration is required. Contact:ramon.goni@bsc.es Supplementary information:Supplementary data are available atBioinformatics online.
Collapse
Affiliation(s)
- Carlos Fenollosa
- Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain
| | - Marcel Otón
- Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain
| | - Pau Andrio
- Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain
| | - Jorge Cortés
- Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain
| | - Modesto Orozco
- Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molec
| | - J Ramon Goñi
- Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain Department of Life Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain, Joint BSC-CRG-IRB Program in Computational Biology, Barcelona, Spain, Computational Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08034, Spain, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain, Department of Biochemistry and Molecular Biology, Biology Faculty, University of Barcelona, Barcelona 08028, Spain and Structural Bioinformatics Node, National Institute of Bioinformatics, Barcelona 08028, Spain
| |
Collapse
|
29
|
Role of 3D Structures in Understanding, Predicting, and Designing Molecular Interactions in the Chemokine Receptor Family. ACTA ACUST UNITED AC 2014. [DOI: 10.1007/7355_2014_77] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
|
30
|
Daga PR, Polgar WE, Zaveri NT. Structure-based virtual screening of the nociceptin receptor: hybrid docking and shape-based approaches for improved hit identification. J Chem Inf Model 2014; 54:2732-43. [PMID: 25148595 PMCID: PMC4210177 DOI: 10.1021/ci500291a] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
![]()
The
antagonist-bound crystal structure of the nociceptin receptor
(NOP), from the opioid receptor family, was recently reported along
with those of the other opioid receptors bound to opioid antagonists.
We recently reported the first homology model of the ‘active-state’
of the NOP receptor, which when docked with ‘agonist’
ligands showed differences in the TM helices and residues, consistent
with GPCR activation after agonist binding. In this study, we explored
the use of the active-state NOP homology model for structure-based
virtual screening to discover NOP ligands containing new chemical
scaffolds. Several NOP agonist and antagonist ligands previously reported
are based on a common piperidine scaffold. Given the structure–activity
relationships for known NOP ligands, we developed a hybrid method
that combines a structure-based and ligand-based approach, utilizing
the active-state NOP receptor as well as the pharmacophoric features
of known NOP ligands, to identify novel NOP binding scaffolds by virtual
screening. Multiple conformations of the NOP active site including
the flexible second extracellular loop (EL2) loop were generated by
simulated annealing and ranked using enrichment factor (EF) analysis
and a ligand–decoy dataset containing known NOP agonist ligands.
The enrichment factors were further improved by combining shape-based
screening of this ligand–decoy dataset and calculation of consensus
scores. This combined structure-based and ligand-based EF analysis
yielded higher enrichment factors than the individual methods, suggesting
the effectiveness of the hybrid approach. Virtual screening of the
CNS Permeable subset of the ZINC database was carried out using the
above-mentioned hybrid approach in a tiered fashion utilizing a ligand
pharmacophore-based filtering step, followed by structure-based virtual
screening using the refined NOP active-state models from the enrichment
analysis. Determination of the NOP receptor binding affinity of a
selected set of top-scoring hits resulted in identification of several
compounds with measurable binding affinity at the NOP receptor, one
of which had a new chemotype for NOP receptor binding. The hybrid
ligand-based and structure-based methodology demonstrates an effective
approach for virtual screening that leverages existing SAR and receptor
structure information for identifying novel hits for NOP receptor
binding. The refined active-state NOP homology models obtained from
the enrichment studies can be further used for structure-based optimization
of these new chemotypes to obtain potent and selective NOP receptor
ligands for therapeutic development.
Collapse
Affiliation(s)
- Pankaj R Daga
- Astraea Therapeutics, LLC. , 320 Logue Avenue, Mountain View, California 94043, United States
| | | | | |
Collapse
|
31
|
Rodríguez D, Ranganathan A, Carlsson J. Strategies for improved modeling of GPCR-drug complexes: blind predictions of serotonin receptors bound to ergotamine. J Chem Inf Model 2014; 54:2004-21. [PMID: 25030302 DOI: 10.1021/ci5002235] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
The recent increase in the number of atomic-resolution structures of G protein-coupled receptors (GPCRs) has contributed to a deeper understanding of ligand binding to several important drug targets. However, reliable modeling of GPCR-ligand complexes for the vast majority of receptors with unknown structure remains to be one of the most challenging goals for computer-aided drug design. The GPCR Dock 2013 assessment, in which researchers were challenged to predict the crystallographic structures of serotonin 5-HT(1B) and 5-HT(2B) receptors bound to ergotamine, provided an excellent opportunity to benchmark the current state of this field. Our contributions to GPCR Dock 2013 accurately predicted the binding mode of ergotamine with RMSDs below 1.8 Å for both receptors, which included the best submissions for the 5-HT(1B) complex. Our models also had the most accurate description of the binding sites and receptor-ligand contacts. These results were obtained using a ligand-guided homology modeling approach, which combines extensive molecular docking screening with incorporation of information from multiple crystal structures and experimentally derived restraints. In this work, we retrospectively analyzed thousands of structures that were generated during the assessment to evaluate our modeling strategies. Major contributors to accuracy were found to be improved modeling of extracellular loop two in combination with the use of molecular docking to optimize the binding site for ligand recognition. Our results suggest that modeling of GPCR-drug complexes has reached a level of accuracy at which structure-based drug design could be applied to a large number of pharmaceutically relevant targets.
Collapse
Affiliation(s)
- David Rodríguez
- Science for Life Laboratory, Stockholm University , Box 1031, SE-171 21 Solna, Sweden
| | | | | |
Collapse
|
32
|
Molecular dynamics, dynamic site mapping, and highthroughput virtual screening on leptin and the Ob receptor as anti-obesity target. J Mol Model 2014; 20:2247. [DOI: 10.1007/s00894-014-2247-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2014] [Accepted: 04/09/2014] [Indexed: 10/25/2022]
|
33
|
Costanzi S. Modeling G protein-coupled receptors in complex with biased agonists. Trends Pharmacol Sci 2014; 35:277-83. [PMID: 24793542 DOI: 10.1016/j.tips.2014.04.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Revised: 03/01/2014] [Accepted: 04/03/2014] [Indexed: 01/09/2023]
Abstract
The biological response to the activation of G protein-coupled receptors (GPCRs) typically originates from the simultaneous modulation of various signaling pathways that lead to distinct biological consequences. Hence, 'biased agonists' (i.e., compounds that selectively activate one of the pathways while blocking the others) are highly sought-after molecules to provide fine-tuned pharmacological interventions. This review describes strategies that can be deployed to model the conformation of GPCRs in complex with ligands endowed with specific signaling profiles useful for the generation of hypotheses on the structural requirements for the activation of different signaling pathways or for rational computer-aided ligand discovery campaigns. In particular, it focuses on strategies potentially applicable to model the global or local conformational states of GPCRs stabilized by specific ligands.
Collapse
Affiliation(s)
- Stefano Costanzi
- Department of Chemistry, American University, Washington, DC 20016, USA; Center for Behavioral Neuroscience, American University, Washington, DC 20016, USA.
| |
Collapse
|
34
|
Kufareva I, Chen YC, Ilatovskiy AV, Abagyan R. Compound activity prediction using models of binding pockets or ligand properties in 3D. Curr Top Med Chem 2014; 12:1869-82. [PMID: 23116466 DOI: 10.2174/156802612804547335] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2012] [Revised: 10/10/2012] [Accepted: 10/11/2012] [Indexed: 12/18/2022]
Abstract
Transient interactions of endogenous and exogenous small molecules with flexible binding sites in proteins or macromolecular assemblies play a critical role in all biological processes. Current advances in high-resolution protein structure determination, database development, and docking methodology make it possible to design three-dimensional models for prediction of such interactions with increasing accuracy and specificity. Using the data collected in the Pocketome encyclopedia, we here provide an overview of two types of the three-dimensional ligand activity models, pocketbased and ligand property-based, for two important classes of proteins, nuclear and G-protein coupled receptors. For half the targets, the pocket models discriminate actives from property matched decoys with acceptable accuracy (the area under ROC curve, AUC, exceeding 84%) and for about one fifth of the targets with high accuracy (AUC > 95%). The 3D ligand property field models performed better than 95% in half of the cases. The high performance models can already become a basis of activity predictions for new chemicals. Family-wide benchmarking of the models highlights strengths of both approaches and helps identify their inherent bottlenecks and challenges.
Collapse
Affiliation(s)
- Irina Kufareva
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | | | | | | |
Collapse
|
35
|
Chen YC, Totrov M, Abagyan R. Docking to multiple pockets or ligand fields for screening, activity prediction and scaffold hopping. Future Med Chem 2014; 6:1741-55. [PMID: 25407367 PMCID: PMC4285145 DOI: 10.4155/fmc.14.113] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Two recent technological advances dramatically reducing the rate of false-negatives in activity prediction by docking flexible 3D models of compounds include multi-conformational docking (mPockDock) and the docking of candidates to atomic property fields derived by co-crystallized ligands (mApfDock). RESULTS The mApfDock and mPockDock provide the AUC of 90.4 and 83.8%, respectively. The mApfDock gave better performance when compounds required large induced-fit pocket changes unseen in crystallography, whereas the mPockDock is superior when the co-crystallized ligands do not represent sufficient chemical and binding location diversity. CONCLUSION Both approaches proved to be efficient for scaffold hopping; they are complementary when the coverage of the co-crystallized complexes is poor but become convergent when the complexes are diverse enough.
Collapse
Affiliation(s)
- Yu-Chen Chen
- Bioinformatics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Max Totrov
- Molsoft LLC, 11199 Sorrento Valley Road, S209, San Diego, CA 92121, USA
| | - Ruben Abagyan
- Skaggs School of Pharmacy & Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, MC 0747, La Jolla, CA 92093-0747, USA
| |
Collapse
|
36
|
Abstract
Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein-ligand applications. We summarise the main topics and recent computational and methodological advances in protein-ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.
Collapse
|
37
|
Tarcsay A, Paragi G, Vass M, Jójárt B, Bogár F, Keserű GM. The impact of molecular dynamics sampling on the performance of virtual screening against GPCRs. J Chem Inf Model 2013; 53:2990-9. [PMID: 24116387 DOI: 10.1021/ci400087b] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The formation of ligand-protein complexes requires simultaneous adaptation of the binding partners. In structure based virtual screening, high throughput docking approaches typically consider the ligand flexibility, but the conformational freedom of the protein is usually taken into account in a limited way. The goal of this study is to elaborate a methodology for incorporating protein flexibility to improve the virtual screening enrichments on GPCRs. Explicit-solvated molecular dynamics simulations (MD) were carried out in lipid bilayers to generate an ensemble of protein conformations for the X-ray structures and homology models of both aminergic and peptidergic GPCRs including the chemokine CXCR4, dopamine D3, histamine H4, and serotonin 5HT6 holo receptor complexes. The quality of the receptor models was assessed by enrichment studies to compare X-ray structures, homology models, and snapshots from the MD trajectory. According to our results, selected frames from the MD trajectory can outperform X-ray structures and homology models in terms of enrichment factor and AUC values. Significant changes were observed considering EF1% values: comparing the original CXCR4, D3, and H4 targets and the additional 5HT6 initial models to that of the best MD frame resulted in 0 to 6.7, 0.32 to 3.5 (10×), 13.3 to 26.7 (2×), and 0 to 14.1 improvements, respectively. It is worth noting that rank-average based ensemble evaluation calculated for different ensemble sizes could not improve the results further. We propose here that MD simulation can capture protein conformations representing the key interacting points of the receptor but less biased toward one specific chemotype. These conformations are useful for the identification of a "consensus" binding site with improved performance in virtual screening.
Collapse
Affiliation(s)
- Akos Tarcsay
- Discovery Chemistry, Gedeon Richter plc. , 19-21 Gyömrői út, H-1103 Budapest, Hungary
| | | | | | | | | | | |
Collapse
|
38
|
Tian S, Sun H, Li Y, Pan P, Li D, Hou T. Development and Evaluation of an Integrated Virtual Screening Strategy by Combining Molecular Docking and Pharmacophore Searching Based on Multiple Protein Structures. J Chem Inf Model 2013; 53:2743-56. [DOI: 10.1021/ci400382r] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Sheng Tian
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Huiyong Sun
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Youyong Li
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Peichen Pan
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
| | - Dan Li
- College
of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Tingjun Hou
- Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China
- College
of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| |
Collapse
|
39
|
In silico mechanistic profiling to probe small molecule binding to sulfotransferases. PLoS One 2013; 8:e73587. [PMID: 24039991 PMCID: PMC3765257 DOI: 10.1371/journal.pone.0073587] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2013] [Accepted: 07/28/2013] [Indexed: 01/01/2023] Open
Abstract
Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug–drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands’ binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.
Collapse
|
40
|
Ding F, Dokholyan NV. Incorporating Backbone Flexibility in MedusaDock Improves Ligand-Binding Pose Prediction in the CSAR2011 Docking Benchmark. J Chem Inf Model 2012; 53:1871-9. [DOI: 10.1021/ci300478y] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Feng Ding
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634,
United States
- Department
of Biochemistry and
Biophysics, University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, North Carolina 27599, United States
| | - Nikolay V. Dokholyan
- Department
of Biochemistry and
Biophysics, University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, North Carolina 27599, United States
| |
Collapse
|
41
|
Abstract
The identification and application of druggable pockets of targets play a key role in in silico drug design, which is a fundamental step in structure-based drug design. Herein, some recent progresses and developments of the computational analysis of pockets have been covered. Also, the pockets at the protein-protein interfaces (PPI) have been considered to further explore the pocket space for drug discovery. We have presented two case studies targeting the kinetic pockets generated by normal mode analysis and molecular dynamics method, respectively, in which we focus upon incorporating the pocket flexibility into the two-dimensional virtual screening with both affinity and specificity. We applied the specificity and affinity (SPA) score to quantitatively estimate affinity and evaluate specificity using the intrinsic specificity ratio (ISR) as a quantitative criterion. In one of two cases, we also included some applications of pockets located at the dimer interfaces to emphasize the role of PPI in drug discovery. This review will attempt to summarize the current status of this pocket issue and will present some prospective avenues of further inquiry.
Collapse
Affiliation(s)
- Xiliang Zheng
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, 5625 Renmin Street, Changchun, Jilin, 130022, People's Republic of China
| | | | | | | |
Collapse
|
42
|
Kalid O, Toledo Warshaviak D, Shechter S, Sherman W, Shacham S. Consensus Induced Fit Docking (cIFD): methodology, validation, and application to the discovery of novel Crm1 inhibitors. J Comput Aided Mol Des 2012; 26:1217-28. [PMID: 23053738 DOI: 10.1007/s10822-012-9611-9] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2012] [Accepted: 09/20/2012] [Indexed: 11/29/2022]
Abstract
We present the Consensus Induced Fit Docking (cIFD) approach for adapting a protein binding site to accommodate multiple diverse ligands for virtual screening. This novel approach results in a single binding site structure that can bind diverse chemotypes and is thus highly useful for efficient structure-based virtual screening. We first describe the cIFD method and its validation on three targets that were previously shown to be challenging for docking programs (COX-2, estrogen receptor, and HIV reverse transcriptase). We then demonstrate the application of cIFD to the challenging discovery of irreversible Crm1 inhibitors. We report the identification of 33 novel Crm1 inhibitors, which resulted from the testing of 402 purchased compounds selected from a screening set containing 261,680 compounds. This corresponds to a hit rate of 8.2 %. The novel Crm1 inhibitors reveal diverse chemical structures, validating the utility of the cIFD method in a real-world drug discovery project. This approach offers a pragmatic way to implicitly account for protein flexibility without the additional computational costs of ensemble docking or including full protein flexibility during virtual screening.
Collapse
Affiliation(s)
- Ori Kalid
- Karyopharm Therapeutics, 2 Mercer Road, Natick, MA 01760, USA.
| | | | | | | | | |
Collapse
|
43
|
Rueda M, Totrov M, Abagyan R. ALiBERO: evolving a team of complementary pocket conformations rather than a single leader. J Chem Inf Model 2012; 52:2705-14. [PMID: 22947092 PMCID: PMC3478405 DOI: 10.1021/ci3001088] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Docking and virtual screening (VS) reach maximum potential when the receptor displays the structural changes needed for accurate ligand binding. Unfortunately, these conformational changes are often poorly represented in experimental structures or homology models, debilitating their docking performance. Recently, we have shown that receptors optimized with our LiBERO method (Ligand-guided Backbone Ensemble Receptor Optimization) were able to better discriminate active ligands from inactives in flexible-ligand VS docking experiments. The LiBERO method relies on the use of ligand information for selecting the best performing individual pockets from ensembles derived from normal-mode analysis or Monte Carlo. Here we present ALiBERO, a new computational tool that has expanded the pocket selection from single to multiple, allowing for automatic iteration of the sampling-selection procedure. The selection of pockets is performed by a dual method that uses exhaustive combinatorial search plus individual addition of pockets, selecting only those that maximize the discrimination of known actives compounds from decoys. The resulting optimized pockets showed increased VS performance when later used in much larger unrelated test sets consisting of biologically active and inactive ligands. In this paper we will describe the design and implementation of the algorithm, using as a reference the human estrogen receptor alpha.
Collapse
Affiliation(s)
- Manuel Rueda
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, USA
| | | | | |
Collapse
|
44
|
Grigoryan AV, Wang H, Cardozo TJ. Can the energy gap in the protein-ligand binding energy landscape be used as a descriptor in virtual ligand screening? PLoS One 2012; 7:e46532. [PMID: 23071584 PMCID: PMC3468575 DOI: 10.1371/journal.pone.0046532] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2012] [Accepted: 09/05/2012] [Indexed: 11/18/2022] Open
Abstract
The ranking of scores of individual chemicals within a large screening library is a crucial step in virtual screening (VS) for drug discovery. Previous studies showed that the quality of protein-ligand recognition can be improved using spectrum properties and the shape of the binding energy landscape. Here, we investigate whether the energy gap, defined as the difference between the lowest energy pose generated by a docking experiment and the average energy of all other generated poses and inferred to be a measure of the binding energy landscape sharpness, can improve the separation power between true binders and decoys with respect to the use of the best docking score. We performed retrospective single- and multiple-receptor conformation VS experiments in a diverse benchmark of 40 domains from 38 therapeutically relevant protein targets. Also, we tested the performance of the energy gap on 36 protein targets from the Directory of Useful Decoys (DUD). The results indicate that the energy gap outperforms the best docking score in its ability to discriminate between true binders and decoys, and true binders tend to have larger energy gaps than decoys. Furthermore, we used the energy gap as a descriptor to measure the height of the native binding phase and obtained a significant increase in the success rate of near native binding pose identification when the ligand binding conformations within the boundaries of the native binding phase were considered. The performance of the energy gap was also evaluated on an independent test case of VS-identified PKR-like ER-localized eIF2α kinase (PERK) inhibitors. We found that the energy gap was superior to the best docking score in its ability to more highly rank active compounds from inactive ones. These results suggest that the energy gap of the protein-ligand binding energy landscape is a valuable descriptor for use in VS.
Collapse
Affiliation(s)
- Arsen V Grigoryan
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, New York, United States of America.
| | | | | |
Collapse
|
45
|
Beier C, Zacharias M. Tackling the challenges posed by target flexibility in drug design. Expert Opin Drug Discov 2012; 5:347-59. [PMID: 22823087 DOI: 10.1517/17460441003713462] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
IMPORTANCE OF THE FIELD Current computational docking methods are often effective in predicting accurate drug-binding geometries in cases of relatively rigid target structures. However, binding of drug-like ligands to protein receptor molecules frequently involves or even requires conformational adaptation. Realistic prediction of ligand-receptor binding geometries and complex stability needs in many cases an appropriate inclusion of conformational changes, not only for the ligand, but also for the receptor molecule. AREAS COVERED IN THIS REVIEW Recent approaches to efficiently account for target receptor flexibility during docking simulations are reviewed. WHAT THE READER WILL GAIN The reader will gain insights into methods to efficiently treat protein side-chain flexibility and approaches for continuous adaptation of backbone conformations in pre-calculated essential or soft collective degrees of freedom. In addition, molecular dynamics or Monte Carlo based methods providing simultaneous inclusion of receptor and ligand flexibility are discussed as well as promising new developments to generate conformationally diverse ensembles of a protein structure. The large variety of possible conformational changes in proteins on ligand binding is illustrated for the enzyme reverse transcriptase of HIV-1, which is an important drug target. TAKE HOME MESSAGE If the backbone conformation of the binding site does not change, current docking programs can perform well by taking side-chain reorientations into account only. Future progress to account for full target flexibility in docking requires both accurate prediction of the essential modes of backbone motion and improvements in scoring to enhance selectivity. Thus, the scoring function should realistically cover energetic and particularly entropic contributions to binding, which would allow more realistic estimates of binding free energies.
Collapse
Affiliation(s)
- Christian Beier
- Jacobs University Bremen, School of Engineering and Science, Campus Ring 1, D-28759 Bremen, Germany
| | | |
Collapse
|
46
|
Flick J, Tristram F, Wenzel W. Modeling loop backbone flexibility in receptor-ligand docking simulations. J Comput Chem 2012; 33:2504-15. [DOI: 10.1002/jcc.23087] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2012] [Revised: 06/15/2012] [Accepted: 07/09/2012] [Indexed: 12/20/2022]
|
47
|
Abstract
Structure-based drug design has become an essential tool for rapid lead discovery and optimization. As available structural information has increased, researchers have become increasingly aware of the importance of protein flexibility for accurate description of the native state. Typical protein-ligand docking efforts still rely on a single rigid receptor, which is an incomplete representation of potential binding conformations of the protein. These rigid docking efforts typically show the best performance rates between 50 and 75%, while fully flexible docking methods can enhance pose prediction up to 80-95%. This review examines the current toolbox for flexible protein-ligand docking and receptor surface mapping. Present limitations and possibilities for future development are discussed.
Collapse
Affiliation(s)
- Katrina W. Lexa
- Department of Medicinal Chemistry, University of Michigan, 428 Church Street, Ann Arbor, MI 48109-1065, USA
| | - Heather A. Carlson
- Department of Medicinal Chemistry, University of Michigan, 428 Church Street, Ann Arbor, MI 48109-1065, USA
| |
Collapse
|
48
|
Bakan A, Nevins N, Lakdawala AS, Bahar I. Druggability Assessment of Allosteric Proteins by Dynamics Simulations in the Presence of Probe Molecules. J Chem Theory Comput 2012; 8:2435-2447. [PMID: 22798729 PMCID: PMC3392909 DOI: 10.1021/ct300117j] [Citation(s) in RCA: 118] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2012] [Indexed: 12/14/2022]
Abstract
Druggability assessment of a target protein has emerged
in recent
years as an important concept in hit-to-lead optimization. A reliable
and physically relevant measure of druggability would allow informed
decisions on the risk of investing in a particular target. Here, we
define “druggability” as a quantitative estimate of
binding sites and affinities for a potential drug acting on a specific
protein target. In the present study, we describe a new methodology
that successfully predicts the druggability and maximal binding affinity
for a series of challenging targets, including those that function
through allosteric mechanisms. Two distinguishing features of the
methodology are (i) simulation of the binding dynamics of a diversity
of probe molecules selected on the basis of an analysis of approved
drugs and (ii) identification of druggable sites and estimation of
corresponding binding affinities on the basis of an evaluation of
the geometry and energetics of bound probe clusters. The use of the
methodology for a variety of targets such as murine double mutant-2,
protein tyrosine phosphatase 1B (PTP1B), lymphocyte function-associated
antigen 1, vertebrate kinesin-5 (Eg5), and p38 mitogen-activated protein
kinase provides examples for which the method correctly captures the
location and binding affinities of known drugs. It also provides insights
into novel druggable sites and the target’s structural changes
that would accommodate, if not promote and stabilize, drug binding.
Notably, the ability to identify high affinity spots even in challenging
cases such as PTP1B or Eg5 shows promise as a rational tool for assessing
the druggability of protein targets and identifying allosteric or
novel sites for drug binding.
Collapse
|
49
|
Abstract
Receptor models generated by homology or even obtained by crystallography often have their binding pockets suboptimal for ligand docking and virtual screening applications due to insufficient accuracy or induced fit bias. Knowledge of previously discovered receptor ligands provides key information that can be used for improving docking and screening performance of the receptor. Here, we present a comprehensive ligand-guided receptor optimization (LiBERO) algorithm that exploits ligand information for selecting the best performing protein models from an ensemble. The energetically feasible protein conformers are generated through normal mode analysis and Monte Carlo conformational sampling. The algorithm allows iteration of the conformer generation and selection steps until convergence of a specially developed fitness function which quantifies the conformer's ability to select known ligands from decoys in a small-scale virtual screening test. Because of the requirement for a large number of computationally intensive docking calculations, the automated algorithm has been implemented to use Linux clusters allowing easy parallel scaling. Here, we will discuss the setup of LiBERO calculations, selection of parameters, and a range of possible uses of the algorithm which has already proven itself in several practical applications to binding pocket optimization and prospective virtual ligand screening.
Collapse
Affiliation(s)
- Vsevolod Katritch
- Department of Molecular Biology, The Scripps Research Institute, La Jolla, CA, USA.
| | | | | |
Collapse
|
50
|
Osguthorpe DJ, Sherman W, Hagler AT. Generation of Receptor Structural Ensembles for Virtual Screening Using Binding Site Shape Analysis and Clustering. Chem Biol Drug Des 2012; 80:182-93. [DOI: 10.1111/j.1747-0285.2012.01396.x] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|