1
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Nazarshodeh E, Marashi SA, Gharaghani S. Structural systems pharmacology: A framework for integrating metabolic network and structure-based virtual screening for drug discovery against bacteria. PLoS One 2021; 16:e0261267. [PMID: 34905555 PMCID: PMC8670682 DOI: 10.1371/journal.pone.0261267] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/26/2021] [Indexed: 12/05/2022] Open
Abstract
Advances in genome-scale metabolic models (GEMs) and computational drug discovery have caused the identification of drug targets at the system-level and inhibitors to combat bacterial infection and drug resistance. Here we report a structural systems pharmacology framework that integrates the GEM and structure-based virtual screening (SBVS) method to identify drugs effective for Escherichia coli infection. The most complete genome-scale metabolic reconstruction integrated with protein structures (GEM-PRO) of E. coli, iML1515_GP, and FDA-approved drugs have been used. FBA was performed to predict drug targets in silico. The 195 essential genes were predicted in the rich medium. The subsystems in which a significant number of these genes are involved are cofactor, lipopolysaccharide (LPS) biosynthesis that are necessary for cell growth. Therefore, some proteins encoded by these genes are responsible for the biosynthesis and transport of LPS which is the first line of defense against threats. So, these proteins can be potential drug targets. The enzymes with experimental structure and cognate ligands were selected as final drug targets for performing the SBVS method. Finally, we have suggested those drugs that have good interaction with the selected proteins as drug repositioning cases. Also, the suggested molecules could be promising lead compounds. This framework may be helpful to fill the gap between genomics and drug discovery. Results show this framework suggests novel antibacterials that can be subjected to experimental testing soon and it can be suitable for other pathogens.
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Affiliation(s)
- Elmira Nazarshodeh
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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2
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Miryala SK, Basu S, Naha A, Debroy R, Ramaiah S, Anbarasu A, Natarajan S. Identification of bioactive natural compounds as efficient inhibitors against Mycobacterium tuberculosis protein-targets: A molecular docking and molecular dynamics simulation study. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117340] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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3
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Implementation of pharmacophore-based 3D QSAR model and scaffold analysis in order to excavate pristine ALK inhibitors. Med Chem Res 2019. [DOI: 10.1007/s00044-019-02410-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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4
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Guedes IA, Pereira FSS, Dardenne LE. Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges. Front Pharmacol 2018; 9:1089. [PMID: 30319422 PMCID: PMC6165880 DOI: 10.3389/fphar.2018.01089] [Citation(s) in RCA: 144] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Accepted: 09/07/2018] [Indexed: 12/19/2022] Open
Abstract
Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.
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Affiliation(s)
- Isabella A Guedes
- Grupo de Modelagem Molecular em Sistemas Biológicos, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Felipe S S Pereira
- Grupo de Modelagem Molecular em Sistemas Biológicos, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Laurent E Dardenne
- Grupo de Modelagem Molecular em Sistemas Biológicos, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
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5
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Ozalp L, Sağ Erdem S, Yüce-Dursun B, Mutlu Ö, Özbil M. Computational insight into the phthalocyanine-DNA binding via docking and molecular dynamics simulations. Comput Biol Chem 2018; 77:87-96. [PMID: 30245350 DOI: 10.1016/j.compbiolchem.2018.09.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 09/09/2018] [Accepted: 09/12/2018] [Indexed: 10/28/2022]
Abstract
Phthalocyanines are considered as good DNA binders, which makes them promising anti-tumor drug leads. The purpose of this study is to investigate the interactions between DNA and quaternary metallophthalocyanine derivatives (Q-MPc) possessing varying metals (M = Zn, Ni, Cu, Fe, Mg and Ca) by molecular docking since there seems to be a lack of information in the literature regarding this issue. In this direction, Autodock Vina and Molegro Virtual Docker programs were employed. Autodock Vina results reveal that each Q-MPc derivative binds to DNA strongly with similar binding energies and almost identical binding modes. They bind to the grooves of DNA by constituting favorable interactions between phosphate groups of DNA and Q-MPcs. Although changing the metal has no significant effect on binding, presence of quaternary amine substituents increases the binding constant Kb by about 2-fold comparing to the core Pc (ZnPc). Contrary to Autodock Vina, the calculated Molegro Virtual Docker binding scores have been more diverse indicating that the scoring function of Molegro is better in differentiating these metals. Despite the fact that Molegro is superior to Autodock Vina in terms of metal characterization, Autodock Vina and Molegro exhibit similar binding sites for the studied metallophthalocyanines. We propose that Q-MPc derivatives designed in this study are promising anti-tumor lead compounds since they tightly bind to DNA with considerably high Kb values. Cationic substituents and presence of metal have both positive effects on DNA binding which is critical for designing DNA-active drugs. Additional calculations employing molecular dynamics (MD) simulations verified the stability of Q-MPc-DNA complexes which remained in contact after 20 ns via attractive interactions mainly between DNA backbone and the Pc metal center.
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Affiliation(s)
- Lalehan Ozalp
- Chemistry Department, Faculty of Arts and Sciences, Marmara University, Goztepe, Istanbul, Turkey.
| | - Safiye Sağ Erdem
- Chemistry Department, Faculty of Arts and Sciences, Marmara University, Goztepe, Istanbul, Turkey.
| | - Başak Yüce-Dursun
- Chemistry Department, Faculty of Arts and Sciences, Marmara University, Goztepe, Istanbul, Turkey.
| | - Özal Mutlu
- Biology Department, Faculty of Arts and Sciences, Marmara University, Goztepe, Istanbul, Turkey.
| | - Mehmet Özbil
- Department of Molecular Biology and Genetics, Faculty of Arts and Sciences, Istanbul Arel University, Buyukcekmece, Istanbul, Turkey.
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6
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Che X, Zhang J, Quan H, Yang L, Gao YQ. CDNs-STING Interaction Mechanism Investigations and Instructions on Design of CDN-Derivatives. J Phys Chem B 2018; 122:1862-1868. [PMID: 29361230 DOI: 10.1021/acs.jpcb.7b12276] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Cyclic dinucleotides (CDNs) present thousand-fold differences of dissociation constants to STING, a pivotal protein in cytosolic dsDNA immunity. To understand how subtle chemical changes in CDNs lead to these substantial variances, a precise ranking of binding affinity is needed. However, the large size and flexibility of CDNs elevate the entropic effect and pose a challenge for this precise prediction. Therefore, in this paper, we developed a new protocol, a combination of selective-integrated tempering sampling of ligands and molecular docking, to take into account the entropic effects originating from extensive ligand configurational space and solvation on binding affinity evaluations. The calculated ranking orders of CDNs and CDN-derivatives to wild type STING and R232H mutant are in agreement with experimental measurements. Further molecular dynamics analysis revealed that the interaction between phosphonate groups and 232R differentiates the binding affinities. The 2'-5' linked phosphonate groups have a larger tendency to form hydrogen bonds with 232R than those with 3'-5' linkages. Moreover, the new protocol identified structural features that enhanced CDNs-STING binding, such as anti-glycosidic bonds and large pro-R distances, which explains the high binding affinity of dithio-RpRp-2'3'-CDA to STING and is expected to provide valuable guidance in the lead-drug optimization.
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Affiliation(s)
- Xing Che
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, and Biodynamic Optical Imaging Center, Peking University , Beijing 100871, China
| | - Jun Zhang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, and Biodynamic Optical Imaging Center, Peking University , Beijing 100871, China
| | - Hui Quan
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, and Biodynamic Optical Imaging Center, Peking University , Beijing 100871, China
| | - Lijiang Yang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, and Biodynamic Optical Imaging Center, Peking University , Beijing 100871, China
| | - Yi Qin Gao
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, and Biodynamic Optical Imaging Center, Peking University , Beijing 100871, China
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7
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Güette-Fernández JR, Meléndez E, Maldonado-Rojas W, Ortega-Zúñiga C, Olivero-Verbel J, Parés-Matos EI. A molecular docking study of the interactions between human transferrin and seven metallocene dichlorides. J Mol Graph Model 2017; 75:250-265. [PMID: 28609757 DOI: 10.1016/j.jmgm.2017.05.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2016] [Revised: 05/08/2017] [Accepted: 05/09/2017] [Indexed: 02/08/2023]
Abstract
Human Transferrin (hTf) is a metal-binding protein found in blood plasma and is well known for its role in iron delivery. With only a 30% of its capacity for Fe+3 binding, this protein has the potential ability to transport other metal ions or organometallic compounds from the blood stream to all cell tissues. In this perspective, recent studies have described seven metallocene dichlorides (Cp2M(IV)Cl2, M(IV)=V, Mo, W, Nb, Ti, Zr, Hf) suitable as anticancer drugs and less secondary effects than cisplatin. However, these studies have not provided enough data to clearly explain how hTf binds and transports these organometallic compounds into the cells. Thus, a computational docking study with native apo-hTf using Sybyl-X 2.0 program was conducted to explore the binding modes of these seven Cp2M(IV)Cl2 after their optimization and minimization using Gaussian 09. Our model showed that the first three Cp2M(IV)Cl2 (M(IV)=V, Mo, W) can interact with apo-hTf on a common binding site with the amino acid residues Leu-46, Ile-49, Arg-50, Leu-66, Asp-69, Ala-70, Leu-72, Ala-73, Pro-74 and Asn-75, while the next four Cp2M(IV)Cl2 (M(IV)=Nb, Ti, Zr, Hf) showed different binding sites, unknown until now. A decreasing order in the total score (equal to -log Kd) was observed from these docking studies: W (5.4356), Mo (5.2692), Nb (5.1672), V (4.5973), Ti (3.6529), Zr (2.0054) and Hf (1.8811). High and significant correlation between the affinity of these seven ligands (metallocenes) for apo-hTf and their bond angles CpMCp (r=0.94, p<0.01) and Cl-M-Cl (r=0.95, p<0.01) were observed, thus indicating the important role that these bond angles can play in ligand-protein interactions. Fluorescence spectra of apo-hTf, measured at pH 7.4, had a decrease in the fluorescence emission spectrum with increasing concentration of Cp2M(IV)Cl2. Experimental data has a good correlation between KA (r=0.84, p=0.027) and Kd (r=0.94, p=0.0014) values and the calculated total scores obtained from our docking experiments. In conclusion, these results suggest that the seven Cp2M(IV)Cl2 used for this study can interact with apo-hTf, and their affinity was directly and inversely proportional to their bond angles CpMCp and ClMCl, respectively. Our docking studies also suggest that the binding of the first three Cp2M(IV)Cl2 (M(IV)=V, Mo, W) to hTf could abrogate the formation of the hTf-receptor complex, and as a consequence the metallocene-hTf complex might require another transport mechanism in order to get into the cell.
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Affiliation(s)
- Jorge R Güette-Fernández
- Department of Chemistry at Mayagüez, University of Puerto Rico, Mayagüez, PR 00681; Environmental and Computational Chemistry Group, School of Pharmaceutical Sciences, Zaragocilla Campus, University of Cartagena, 130014, Cartagena, Colombia
| | - Enrique Meléndez
- Department of Chemistry at Mayagüez, University of Puerto Rico, Mayagüez, PR 00681
| | - Wilson Maldonado-Rojas
- Environmental and Computational Chemistry Group, School of Pharmaceutical Sciences, Zaragocilla Campus, University of Cartagena, 130014, Cartagena, Colombia
| | - Carlos Ortega-Zúñiga
- Department of Chemistry at Mayagüez, University of Puerto Rico, Mayagüez, PR 00681; Environmental and Computational Chemistry Group, School of Pharmaceutical Sciences, Zaragocilla Campus, University of Cartagena, 130014, Cartagena, Colombia
| | - Jesus Olivero-Verbel
- Environmental and Computational Chemistry Group, School of Pharmaceutical Sciences, Zaragocilla Campus, University of Cartagena, 130014, Cartagena, Colombia
| | - Elsie I Parés-Matos
- Department of Chemistry at Mayagüez, University of Puerto Rico, Mayagüez, PR 00681.
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8
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Uehara S, Tanaka S. Cosolvent-Based Molecular Dynamics for Ensemble Docking: Practical Method for Generating Druggable Protein Conformations. J Chem Inf Model 2017; 57:742-756. [PMID: 28388074 DOI: 10.1021/acs.jcim.6b00791] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Protein flexibility is a major hurdle in current structure-based virtual screening (VS). In spite of the recent advances in high-performance computing, protein-ligand docking methods still demand tremendous computational cost to take into account the full degree of protein flexibility. In this context, ensemble docking has proven its utility and efficiency for VS studies, but it still needs a rational and efficient method to select and/or generate multiple protein conformations. Molecular dynamics (MD) simulations are useful to produce distinct protein conformations without abundant experimental structures. In this study, we present a novel strategy that makes use of cosolvent-based molecular dynamics (CMD) simulations for ensemble docking. By mixing small organic molecules into a solvent, CMD can stimulate dynamic protein motions and induce partial conformational changes of binding pocket residues appropriate for the binding of diverse ligands. The present method has been applied to six diverse target proteins and assessed by VS experiments using many actives and decoys of DEKOIS 2.0. The simulation results have revealed that the CMD is beneficial for ensemble docking. Utilizing cosolvent simulation allows the generation of druggable protein conformations, improving the VS performance compared with the use of a single experimental structure or ensemble docking by standard MD with pure water as the solvent.
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Affiliation(s)
- Shota Uehara
- Department of Computational Science, Graduate School of System Informatics, Kobe University , 1-1 Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan
| | - Shigenori Tanaka
- Department of Computational Science, Graduate School of System Informatics, Kobe University , 1-1 Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan
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9
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Exploring the resistance mechanism of imipenem in carbapenem hydrolysing class D beta-lactamases OXA-143 and its variant OXA-231 (D224A) expressing Acinetobacter baumannii: An in-silico approach. Comput Biol Chem 2017; 67:1-8. [DOI: 10.1016/j.compbiolchem.2016.12.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 11/06/2016] [Accepted: 12/06/2016] [Indexed: 01/16/2023]
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10
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Shin WH, Christoffer CW, Wang J, Kihara D. PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation. J Chem Inf Model 2016; 56:1676-91. [PMID: 27500657 PMCID: PMC5037053 DOI: 10.1021/acs.jcim.6b00163] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Virtual screening has become an indispensable procedure in drug discovery. Virtual screening methods can be classified into two categories: ligand-based and structure-based. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict the binding affinity of a ligand in a target binding pocket, albeit with substantially lower speed than ligand-based methods. Here we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which is represented by three-dimensional Zernike descriptors (3DZDs). By means of 3DZDs, the shapes and physicochemical complementarities of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared with the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by the addition of two more features, atom-based hydrophobicity and hydrogen-bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo-form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times shorter than those of conventional structure-based methods. The PL-PatchSurfer2 program is available at http://www.kiharalab.org/plps2/ .
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Affiliation(s)
- Woong-Hee Shin
- Department of Biological Science, Purdue University, 249 S. Martin Jischke Street, West Lafayette, IN, USA
| | - Charles W. Christoffer
- Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, IN, USA
| | - Jibo Wang
- Discovery Chemistry Research and Technologies, Eli Lilly and Company, 893 S. Delaware Street, Indianapolis, IN, USA
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, 249 S. Martin Jischke Street, West Lafayette, IN, USA
- Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, IN, USA
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11
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Mih N, Brunk E, Bordbar A, Palsson BO. A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism. PLoS Comput Biol 2016; 12:e1005039. [PMID: 27467583 PMCID: PMC4965186 DOI: 10.1371/journal.pcbi.1005039] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 06/27/2016] [Indexed: 12/31/2022] Open
Abstract
Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.
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Affiliation(s)
- Nathan Mih
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, United States of America
| | - Elizabeth Brunk
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
- * E-mail: (EB); (BOP)
| | - Aarash Bordbar
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- * E-mail: (EB); (BOP)
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12
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Swift RV, Jusoh SA, Offutt TL, Li ES, Amaro RE. Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles. J Chem Inf Model 2016; 56:830-42. [PMID: 27097522 PMCID: PMC4881196 DOI: 10.1021/acs.jcim.5b00684] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
![]()
Ensemble docking
can be a successful virtual screening technique
that addresses the innate conformational heterogeneity of macromolecular
drug targets. Yet, lacking a method to identify a subset of conformational
states that effectively segregates active and inactive small molecules,
ensemble docking may result in the recommendation of a large number
of false positives. Here, three knowledge-based methods that construct
structural ensembles for virtual screening are presented. Each method
selects ensembles by optimizing an objective function calculated using
the receiver operating characteristic (ROC) curve: either the area
under the ROC curve (AUC) or a ROC enrichment factor (EF). As the
number of receptor conformations, N, becomes large,
the methods differ in their asymptotic scaling. Given a set of small
molecules with known activities and a collection of target conformations,
the most resource intense method is guaranteed to find the optimal
ensemble but scales as O(2N). A recursive approximation to the optimal solution scales
as O(N2), and a more
severe approximation leads to a faster method that scales linearly, O(N). The techniques are generally applicable
to any system, and we demonstrate their effectiveness on the androgen
nuclear hormone receptor (AR), cyclin-dependent kinase 2 (CDK2), and
the peroxisome proliferator-activated receptor δ (PPAR-δ)
drug targets. Conformations that consisted of a crystal structure
and molecular dynamics simulation cluster centroids were used to form
AR and CDK2 ensembles. Multiple available crystal structures were
used to form PPAR-δ ensembles. For each target, we show that
the three methods perform similarly to one another on both the training
and test sets.
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Affiliation(s)
- Robert V Swift
- Department of Chemistry and Biochemistry, University of California, San Diego , La Jolla, California 92093-0340, United States
| | - Siti A Jusoh
- Faculty of Pharmacy, Universiti Teknologi MARA , 42300 Bandar Puncak Alam, Malaysia
| | - Tavina L Offutt
- Department of Chemistry and Biochemistry, University of California, San Diego , La Jolla, California 92093-0340, United States
| | - Eric S Li
- Department of Chemistry and Biochemistry, University of California, San Diego , La Jolla, California 92093-0340, United States
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California, San Diego , La Jolla, California 92093-0340, United States
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13
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Di Martino GP, Masetti M, Ceccarini L, Cavalli A, Recanatini M. An Automated Docking Protocol for hERG Channel Blockers. J Chem Inf Model 2013; 53:159-75. [DOI: 10.1021/ci300326d] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Giovanni Paolo Di Martino
- Department of Pharmacy and Biotechnology,
Alma Mater Studiorum, Università di Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology,
Alma Mater Studiorum, Università di Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Luisa Ceccarini
- Department of Pharmacy and Biotechnology,
Alma Mater Studiorum, Università di Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Andrea Cavalli
- Department of Pharmacy and Biotechnology,
Alma Mater Studiorum, Università di Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Department of Drug Discovery
and Development, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
| | - Maurizio Recanatini
- Department of Pharmacy and Biotechnology,
Alma Mater Studiorum, Università di Bologna, Via Belmeloro 6, 40126 Bologna, Italy
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14
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Zhou W, Scocchera EW, Wright DL, Anderson AC. Antifolates as effective antimicrobial agents: new generations of trimethoprim analogs. MEDCHEMCOMM 2013. [DOI: 10.1039/c3md00104k] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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15
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Minh DDL. Implicit ligand theory: rigorous binding free energies and thermodynamic expectations from molecular docking. J Chem Phys 2012; 137:104106. [PMID: 22979849 PMCID: PMC3460968 DOI: 10.1063/1.4751284] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2012] [Accepted: 08/23/2012] [Indexed: 01/24/2023] Open
Abstract
A rigorous formalism for estimating noncovalent binding free energies and thermodynamic expectations from calculations in which receptor configurations are sampled independently from the ligand is derived. Due to this separation, receptor configurations only need to be sampled once, facilitating the use of binding free energy calculations in virtual screening. Demonstrative calculations on a host-guest system yield good agreement with previous free energy calculations and isothermal titration calorimetry measurements. Implicit ligand theory provides guidance on how to improve existing molecular docking algorithms and insight into the concepts of induced fit and conformational selection in noncovalent macromolecular recognition.
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Affiliation(s)
- David D L Minh
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA.
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16
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Impact of X-Ray Structure on Predictivity of Scoring Functions: PPARγ Case Study. Mol Inform 2012; 31:631-3. [DOI: 10.1002/minf.201200040] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2012] [Accepted: 07/23/2012] [Indexed: 11/07/2022]
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17
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Oberlin M, Kroemer R, Mikol V, Minoux H, Tastan E, Baurin N. Engineering protein therapeutics: predictive performances of a structure-based virtual affinity maturation protocol. J Chem Inf Model 2012; 52:2204-14. [PMID: 22788756 DOI: 10.1021/ci3001474] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The implementation of a structure based virtual affinity maturation protocol and evaluation of its predictivity are presented. The in silico protocol is based on conformational sampling of the interface residues (using the Dead End Elimination/A* algorithm), followed by the estimation of the change of free energy of binding due to a point mutation, applying MM/PBSA calculations. Several implementations of the protocol have been evaluated for 173 mutations in 7 different protein complexes for which experimental data were available: the use of the Boltzamnn averaged predictor based on the free energy of binding (ΔΔG(*)) combined with the one based on its polar component only (ΔΔE(pol*)) led to the proposal of a subset of mutations out of which 45% would have successfully enhanced the binding. When focusing on those mutations that are less likely to be introduced by natural in vivo maturation methods (99 mutations with at least two base changes in the codon), the success rate is increased to 63%. In another evaluation, focusing on 56 alanine scanning mutations, the in silico protocol was able to detect 89% of the hot-spots.
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Affiliation(s)
- Michael Oberlin
- SANOFI R&D, Centre de Recherche de Vitry/Alfortville, LGCR/SDI, 13 quai Jules Guesde-BP 14-94403 Vitry-sur-Seine Cedex, France
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18
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Korb O, Olsson TSG, Bowden SJ, Hall RJ, Verdonk ML, Liebeschuetz JW, Cole JC. Potential and limitations of ensemble docking. J Chem Inf Model 2012; 52:1262-74. [PMID: 22482774 DOI: 10.1021/ci2005934] [Citation(s) in RCA: 120] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
A major problem in structure-based virtual screening applications is the appropriate selection of a single or even multiple protein structures to be used in the virtual screening process. A priori it is unknown which protein structure(s) will perform best in a virtual screening experiment. We investigated the performance of ensemble docking, as a function of ensemble size, for eight targets of pharmaceutical interest. Starting from single protein structure docking results, for each ensemble size up to 500,000 combinations of protein structures were generated, and, for each ensemble, pose prediction and virtual screening results were derived. Comparison of single to multiple protein structure results suggests improvements when looking at the performance of the worst and the average over all single protein structures to the performance of the worst and average over all protein ensembles of size two or greater, respectively. We identified several key factors affecting ensemble docking performance, including the sampling accuracy of the docking algorithm, the choice of the scoring function, and the similarity of database ligands to the cocrystallized ligands of ligand-bound protein structures in an ensemble. Due to these factors, the prospective selection of optimum ensembles is a challenging task, shown by a reassessment of published ensemble selection protocols.
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Affiliation(s)
- Oliver Korb
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK.
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19
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Asses Y, Venkatraman V, Leroux V, Ritchie DW, Maigret B. Exploring c-Met kinase flexibility by sampling and clustering its conformational space. Proteins 2012; 80:1227-38. [PMID: 22275094 DOI: 10.1002/prot.24021] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2011] [Revised: 11/14/2011] [Accepted: 12/13/2011] [Indexed: 11/09/2022]
Abstract
It is now widely recognized that the flexibility of both partners has to be considered in molecular docking studies. However, the question how to handle the best the huge computational complexity of exploring the protein binding site landscape is still a matter of debate. Here we investigate the flexibility of c-Met kinase as a test case for comparing several simulation methods. The c-Met kinase catalytic site is an interesting target for anticancer drug design. In particular, it harbors an unusual plasticity compared with other kinases ATP binding sites. Exploiting this feature may eventually lead to the discovery of new anticancer agents with exquisite specificity. We present in this article an extensive investigation of c-Met kinase conformational space using large-scale computational simulations in order to extend the knowledge already gathered from available X-ray structures. In the process, we compare the relevance of different strategies for modeling and injecting receptor flexibility information into early stage in silico structure-based drug discovery pipeline. The results presented here are currently being exploited in on-going virtual screening investigations on c-Met.
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Affiliation(s)
- Yasmine Asses
- Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, Vandœuvre-lès-Nancy Cedex, France
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20
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Abstract
Computational simulation of pandemic diseases provides important insight into many disease features that may benefit public health. This is especially true for the influenza virus, a continuing global pandemic threat. Molecular or atomic-level investigation of influenza has predominantly focused on the two major virus glycoproteins, neuraminidase (NA) and hemagglutinin (HA). In this chapter, we walk the readers through major considerations for studying pandemic influenza glycoproteins, from choosing the most useful choice of system(s) to avoiding common pitfalls in experimental design and execution. While a brief discussion of several potential simulation and docking techniques is presented, we emphasize molecular dynamics (MD) and Brownian dynamics (BD) simulation techniques and molecular docking, within the context of biologically outstanding questions in influenza research.
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Affiliation(s)
- Rommie E Amaro
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, USA.
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21
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Abstract
Proteomic and genomic discoveries have identified vast numbers of new drug targets for investigation. In the quest to discover drugs that modulate the function of these targets, identification of small-molecule drug leads is one of the earliest steps. Structure-based drug design has emerged as a valuable, inexpensive, and rapid computational resource that identifies lead compounds that are complementary to the structure of the target. Leads identified through this process are biologically evaluated and "hit compounds" with affinity and activity are further optimized. This chapter introduces the process of structure-based drug design, including preparation of the ligand database, preparation of the target structure, docking and scoring, and evaluation.
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22
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Nichols SE, Swift RV, Amaro RE. Rational prediction with molecular dynamics for hit identification. Curr Top Med Chem 2012; 12:2002-12. [PMID: 23110535 PMCID: PMC3636520 DOI: 10.2174/156802612804910313] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2012] [Revised: 08/27/2012] [Accepted: 08/27/2012] [Indexed: 12/05/2022]
Abstract
Although the motions of proteins are fundamental for their function, for pragmatic reasons, the consideration of protein elasticity has traditionally been neglected in drug discovery and design. This review details protein motion, its relevance to biomolecular interactions and how it can be sampled using molecular dynamics simulations. Within this context, two major areas of research in structure-based prediction that can benefit from considering protein flexibility, binding site detection and molecular docking, are discussed. Basic classification metrics and statistical analysis techniques, which can facilitate performance analysis, are also reviewed. With hardware and software advances, molecular dynamics in combination with traditional structure-based prediction methods can potentially reduce the time and costs involved in the hit identification pipeline.
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Affiliation(s)
- Sara E Nichols
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA.
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23
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Yu L, Xu L, Xu M, Wan B, Yu L, Huang Q. Role of Mg2+ions in protein kinase phosphorylation: insights from molecular dynamics simulations of ATP-kinase complexes. MOLECULAR SIMULATION 2011. [DOI: 10.1080/08927022.2011.561430] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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24
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Korb O, McCabe P, Cole J. The Ensemble Performance Index: An Improved Measure for Assessing Ensemble Pose Prediction Performance. J Chem Inf Model 2011; 51:2915-9. [DOI: 10.1021/ci2002796] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Oliver Korb
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, United Kingdom
| | - Patrick McCabe
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, United Kingdom
| | - Jason Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, United Kingdom
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25
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Yuan P, Liang K, Ma B, Zheng N, Nussinov R, Huang J. Multiple-targeting and conformational selection in the estrogen receptor: computation and experiment. Chem Biol Drug Des 2011; 78:137-49. [PMID: 21443691 PMCID: PMC3115459 DOI: 10.1111/j.1747-0285.2011.01119.x] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Conformational selection is a primary mechanism in biomolecular recognition. The conformational ensemble may determine the ability of a drug to compete with a native ligand for a receptor target. Traditional docking procedures which use one or few protein structures are limited and may not be able to represent a complex competition among closely related protein receptors in agonist and antagonist ensembles. Here, we test a protocol aimed at selecting a drug candidate based on its ability to synergistically bind to distinct conformational states. We demonstrate, for the case of estrogen receptor α (ERα) and estrogen receptor β (ERβ), that the functional outcome of ligand binding can be inferred from its ability to simultaneously bind both ERα and ERβ in agonist and antagonist conformations as calculated docking scores. Combining a conformational selection method with an experimental reporter gene system in yeast, we propose that several phytoestrogens can be novel estrogen receptor β selective agonists. Our work proposes a computational protocol to select estrogen receptor subtype selective agonists. Compared with other models, present method gives the best prediction in ligands' function.
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Affiliation(s)
- Peng Yuan
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, Hubei, China
| | - Kaiwei Liang
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, Hubei, China
| | - Buyong Ma
- Center for Cancer Research Nanobiology Program, SAIC-Frederick, National Cancer Institute, Frederick, MD 21702,USA
| | - Nan Zheng
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, Hubei, China
| | - Ruth Nussinov
- Center for Cancer Research Nanobiology Program, SAIC-Frederick, National Cancer Institute, Frederick, MD 21702,USA
- Department of Human Genetics and Molecular Medicine, Sackler Institute of Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Jian Huang
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, Hubei, China
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26
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Kokh DB, Wade RC, Wenzel W. Receptor flexibility in small‐molecule docking calculations. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2011. [DOI: 10.1002/wcms.29] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Daria B. Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | - Rebecca C. Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | - Wolfgang Wenzel
- Karlsruhe Institute of Technology, Institute of Nanotechnology, Karlsruhe, Germany
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27
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Plewczynski D, Łaźniewski M, von Grotthuss M, Rychlewski L, Ginalski K. VoteDock: consensus docking method for prediction of protein-ligand interactions. J Comput Chem 2011; 32:568-81. [PMID: 20812324 PMCID: PMC4510457 DOI: 10.1002/jcc.21642] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2010] [Revised: 06/30/2010] [Accepted: 06/30/2010] [Indexed: 11/06/2022]
Abstract
Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein-ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time-consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein-ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein-ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein-ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5.
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Affiliation(s)
- Dariusz Plewczynski
- Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Pawinskiego 5a Street, 02-106 Warsaw, Poland.
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28
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Lill MA, Danielson ML. Computer-aided drug design platform using PyMOL. J Comput Aided Mol Des 2010; 25:13-9. [DOI: 10.1007/s10822-010-9395-8] [Citation(s) in RCA: 254] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2010] [Accepted: 10/20/2010] [Indexed: 12/29/2022]
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29
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Yuriev E, Agostino M, Ramsland PA. Challenges and advances in computational docking: 2009 in review. J Mol Recognit 2010; 24:149-64. [DOI: 10.1002/jmr.1077] [Citation(s) in RCA: 223] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2010] [Revised: 07/20/2010] [Accepted: 07/21/2010] [Indexed: 12/12/2022]
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30
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Novoa EM, Pouplana LRD, Barril X, Orozco M. Ensemble Docking from Homology Models. J Chem Theory Comput 2010; 6:2547-57. [DOI: 10.1021/ct100246y] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Eva Maria Novoa
- Joint IRB-BSC Research Program in Computational Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Spain, Cell and Developmental Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Institució Catalana per la Recerca i Estudis Avançats, Passeig Lluis Companys 23, Barcelona 08010, Spain, Departament de Fisicoquímica, Facultat de Farmàcia, Avgda Diagonal sn, Barcelona 08028, Spain, and Structural Bioinformatics Node Instituto Nacional de
| | - Lluis Ribas de Pouplana
- Joint IRB-BSC Research Program in Computational Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Spain, Cell and Developmental Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Institució Catalana per la Recerca i Estudis Avançats, Passeig Lluis Companys 23, Barcelona 08010, Spain, Departament de Fisicoquímica, Facultat de Farmàcia, Avgda Diagonal sn, Barcelona 08028, Spain, and Structural Bioinformatics Node Instituto Nacional de
| | - Xavier Barril
- Joint IRB-BSC Research Program in Computational Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Spain, Cell and Developmental Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Institució Catalana per la Recerca i Estudis Avançats, Passeig Lluis Companys 23, Barcelona 08010, Spain, Departament de Fisicoquímica, Facultat de Farmàcia, Avgda Diagonal sn, Barcelona 08028, Spain, and Structural Bioinformatics Node Instituto Nacional de
| | - Modesto Orozco
- Joint IRB-BSC Research Program in Computational Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Spain, Cell and Developmental Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Institució Catalana per la Recerca i Estudis Avançats, Passeig Lluis Companys 23, Barcelona 08010, Spain, Departament de Fisicoquímica, Facultat de Farmàcia, Avgda Diagonal sn, Barcelona 08028, Spain, and Structural Bioinformatics Node Instituto Nacional de
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