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Journal articles on the topic 'Ligand to protein docking'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Wang, Kai, Nan Lyu, Hongjuan Diao, et al. "GM-DockZn: a geometry matching-based docking algorithm for zinc proteins." Bioinformatics 36, no. 13 (2020): 4004–11. http://dx.doi.org/10.1093/bioinformatics/btaa292.

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Abstract Motivation Molecular docking is a widely used technique for large-scale virtual screening of the interactions between small-molecule ligands and their target proteins. However, docking methods often perform poorly for metalloproteins due to additional complexity from the three-way interactions among amino-acid residues, metal ions and ligands. This is a significant problem because zinc proteins alone comprise about 10% of all available protein structures in the protein databank. Here, we developed GM-DockZn that is dedicated for ligand docking to zinc proteins. Unlike the existing doc

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2

Bottegoni, Giovanni. "Protein-ligand docking." Frontiers in Bioscience 16, no. 1 (2011): 2289. http://dx.doi.org/10.2741/3854.

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Velesinović, Aleksandar, and Goran Nikolić. "Protein-protein interaction networks and protein-ligand docking: Contemporary insights and future perspectives." Acta Facultatis Medicae Naissensis 38, no. 1 (2021): 5–17. http://dx.doi.org/10.5937/afmnai38-28322.

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Traditional research means, such as in vitro and in vivo models, have consistently been used by scientists to test hypotheses in biochemistry. Computational (in silico) methods have been increasingly devised and applied to testing and hypothesis development in biochemistry over the last decade. The aim of in silico methods is to analyze the quantitative aspects of scientific (big) data, whether these are stored in databases for large data or generated with the use of sophisticated modeling and simulation tools; to gain a fundamental understanding of numerous biochemical processes related, in p

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Chakraborty, Sandeep. "DOCLASP - Docking ligands to target proteins using spatial and electrostatic congruence extracted from a known holoenzyme and applying simple geometrical transformations." F1000Research 3 (June 16, 2016): 262. http://dx.doi.org/10.12688/f1000research.5145.3.

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The ability to accurately and effectively predict the interaction between proteins and small drug-like compounds has long intrigued researchers for pedagogic, humanitarian and economic reasons. Protein docking methods (AutoDock, GOLD, DOCK, FlexX and Glide to name a few) rank a large number of possible conformations of protein-ligand complexes using fast algorithms. Previously, it has been shown that structural congruence leading to the same enzymatic function necessitates the congruence of electrostatic properties (CLASP). The current work presents a methodology for docking a ligand into a ta

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5

Pérez, Carlos, and Angel R. Ortiz. "Evaluation of Docking Functions for Protein−Ligand Docking." Journal of Medicinal Chemistry 44, no. 23 (2001): 3768–85. http://dx.doi.org/10.1021/jm010141r.

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Ng, Marcus C. K., Simon Fong, and Shirley W. I. Siu. "PSOVina: The hybrid particle swarm optimization algorithm for protein–ligand docking." Journal of Bioinformatics and Computational Biology 13, no. 03 (2015): 1541007. http://dx.doi.org/10.1142/s0219720015410073.

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Protein–ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden–Fletcher–Goldfarb–Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein–ligand complexes from the PDBbind database and a ful

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Xiao, Wei, Disha Wang, Zihao Shen, Shiliang Li, and Honglin Li. "Multi-Body Interactions in Molecular Docking Program Devised with Key Water Molecules in Protein Binding Sites." Molecules 23, no. 9 (2018): 2321. http://dx.doi.org/10.3390/molecules23092321.

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Water molecules play an important role in modeling protein-ligand interactions. However, traditional molecular docking methods often ignore the impact of the water molecules by removing them without any analysis or keeping them as a static part of the proteins or the ligands. Hence, the accuracy of the docking simulations will inevitably be damaged. Here, we introduce a multi-body docking program which incorporates the fixed or the variable number of the key water molecules in protein-ligand docking simulations. The program employed NSGA II, a multi-objective optimization algorithm, to identif

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Aziz, Fitri Kusvila, Cantika Nukitasari, Fauziyah Ardli Oktavianingrum, Lita Windy Aryati, and Broto Santoso. "Hasil In Silico Senyawa Z12501572, Z00321025, SCB5631028 dan SCB13970547 dibandingkan Turunan Zerumbon terhadap Human Liver Glycogen Phosphorylase (1l5Q) sebagai Antidiabetes." Jurnal Kimia VALENSI 2, no. 2 (2016): 120–24. http://dx.doi.org/10.15408/jkv.v2i2.4170.

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Abstrak Human Liver Glycogen Phosphorylase (HLGP), suatu katalis glikogen yang mengontrol pelepasan glukosa-1-fosfat glikogen dari hati. Enzim ini mempunyai peran sentral dalam luaran glukosa hati sehingga menjadi target obat antidiabetik. Kajian docking dilakukan pada komputer dengan prosesor Intel Pentium, RAM 1 GB dan Windows 7. Ligan yang digunakan adalah senyawa obat (Z12501572, Z00321025, SCB5631028 dan SCB13970547), dataset pembanding aktif glycogen phosphorylase outer dimer site (PYGL-out) dan decoysdari www.dekois.com dan turunan zerumbon. Protein dipisahkan dari ligan nativ dan semua

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9

Ulzurrun, Eugenia, Yorley Duarte, Esteban Perez-Wohlfeil, Fernando Gonzalez-Nilo, and Oswaldo Trelles. "PLIDflow: an open-source workflow for the online analysis of protein–ligand docking using galaxy." Bioinformatics 36, no. 14 (2020): 4203–5. http://dx.doi.org/10.1093/bioinformatics/btaa481.

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Abstract Motivation Molecular docking is aimed at predicting the conformation of small-molecule (ligands) within an identified binding site (BS) in a target protein (receptor). Protein–ligand docking plays an important role in modern drug discovery and biochemistry for protein engineering. However, efficient docking analysis of proteins requires prior knowledge of the BS, which is not always known. The process which covers BS identification and protein–ligand docking usually requires the combination of different programs, which require several input parameters. This is furtherly aggravated whe

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Sulimov, Vladimir B., Danil C. Kutov, and Alexey V. Sulimov. "Advances in Docking." Current Medicinal Chemistry 26, no. 42 (2020): 7555–80. http://dx.doi.org/10.2174/0929867325666180904115000.

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Background: Design of small molecules which are able to bind to the protein responsible for a disease is the key step of the entire process of the new medicine discovery. Atomistic computer modeling can significantly improve effectiveness of such design. The accurate calculation of the free energy of binding a small molecule (a ligand) to the target protein is the most important problem of such modeling. Docking is one of the most popular molecular modeling methods for finding ligand binding poses in the target protein and calculating the protein-ligand binding energy. This energy is used for

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11

Fu, Yi, Ji Zhao, and Zhiguo Chen. "Insights into the Molecular Mechanisms of Protein-Ligand Interactions by Molecular Docking and Molecular Dynamics Simulation: A Case of Oligopeptide Binding Protein." Computational and Mathematical Methods in Medicine 2018 (December 4, 2018): 1–12. http://dx.doi.org/10.1155/2018/3502514.

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Protein-ligand interactions are a necessary prerequisite for signal transduction, immunoreaction, and gene regulation. Protein-ligand interaction studies are important for understanding the mechanisms of biological regulation, and they provide a theoretical basis for the design and discovery of new drug targets. In this study, we analyzed the molecular interactions of protein-ligand which was docked by AutoDock 4.2 software. In AutoDock 4.2 software, we used a new search algorithm, hybrid algorithm of random drift particle swarm optimization and local search (LRDPSO), and the classical Lamarck

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Mehta, Simpi, and Seema R. Pathak. "INSILICO DRUG DESIGN AND MOLECULAR DOCKING STUDIES OF NOVEL COUMARIN DERIVATIVES AS ANTI-CANCER AGENTS." Asian Journal of Pharmaceutical and Clinical Research 10, no. 4 (2017): 335. http://dx.doi.org/10.22159/ajpcr.2017.v10i4.16826.

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Objective: Cancer is the major worldwide problem. It arises due to uncontrolled growth of cells. In the present study a series of novel coumarin derivatives were designed and computationallyoptimized to investigate the interaction between designed ligands and 10 pdb files of five selected proteins. The objective here was to analyse in silico anticancerous activity of designed ligands to reduce cost and time for getting novel anticancerous drug with minimum side effects.Methods: Docking studies were performed to find outmaximum interaction between designed ligands and selected five proteins usi

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Sulimov, A. V., D. C. Kutov, E. V. Katkova, and V. B. Sulimov. "Combined Docking with Classical Force Field and Quantum Chemical Semiempirical Method PM7." Advances in Bioinformatics 2017 (January 16, 2017): 1–6. http://dx.doi.org/10.1155/2017/7167691.

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Results of the combined use of the classical force field and the recent quantum chemical PM7 method for docking are presented. Initially the gridless docking of a flexible low molecular weight ligand into the rigid target protein is performed with the energy function calculated in the MMFF94 force field with implicit water solvent in the PCM model. Among several hundred thousand local minima, which are found in the docking procedure, about eight thousand lowest energy minima are chosen and then energies of these minima are recalculated with the recent quantum chemical semiempirical PM7 method.

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14

Bentham Science Publisher, Bentham Science Publisher. "Scoring Functions for Protein-Ligand Docking." Current Protein & Peptide Science 7, no. 5 (2006): 407–20. http://dx.doi.org/10.2174/138920306778559395.

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15

Roberts, Benjamin C., and Ricardo L. Mancera. "Ligand−Protein Docking with Water Molecules." Journal of Chemical Information and Modeling 48, no. 2 (2008): 397–408. http://dx.doi.org/10.1021/ci700285e.

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16

Goto, Junichi, Ryoichi Kataoka, and Noriaki Hirayama. "Ph4Dock: Pharmacophore-Based Protein−Ligand Docking." Journal of Medicinal Chemistry 47, no. 27 (2004): 6804–11. http://dx.doi.org/10.1021/jm0493818.

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17

Verdonk, Marcel L., Jason C. Cole, Michael J. Hartshorn, Christopher W. Murray, and Richard D. Taylor. "Improved protein-ligand docking using GOLD." Proteins: Structure, Function, and Bioinformatics 52, no. 4 (2003): 609–23. http://dx.doi.org/10.1002/prot.10465.

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18

Pippel, Martin, Michael Scharfe, René Meier, and Wolfgang Sippl. "Einfach und frei: Protein-Ligand-Docking." Nachrichten aus der Chemie 60, no. 6 (2012): 656–57. http://dx.doi.org/10.1002/nadc.201290238.

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19

Morris, Connor J., and Dennis Della Corte. "Using molecular docking and molecular dynamics to investigate protein-ligand interactions." Modern Physics Letters B 35, no. 08 (2021): 2130002. http://dx.doi.org/10.1142/s0217984921300027.

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Molecular docking and molecular dynamics (MD) are powerful tools used to investigate protein-ligand interactions. Molecular docking programs predict the binding pose and affinity of a protein-ligand complex, while MD can be used to incorporate flexibility into docking calculations and gain further information on the kinetics and stability of the protein-ligand bond. This review covers state-of-the-art methods of using molecular docking and MD to explore protein-ligand interactions, with emphasis on application to drug discovery. We also call for further research on combining common molecular d

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20

Verdonk, Marcel L., Paul N. Mortenson, Richard J. Hall, Michael J. Hartshorn, and Christopher W. Murray. "Protein−Ligand Docking against Non-Native Protein Conformers." Journal of Chemical Information and Modeling 48, no. 11 (2008): 2214–25. http://dx.doi.org/10.1021/ci8002254.

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21

Jakhar, Ritu, Mehak Dangi, Alka Khichi, and Anil Kumar Chhillar. "Relevance of Molecular Docking Studies in Drug Designing." Current Bioinformatics 15, no. 4 (2020): 270–78. http://dx.doi.org/10.2174/1574893615666191219094216.

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Molecular Docking is used to positioning the computer-generated 3D structure of small ligands into a receptor structure in a variety of orientations, conformations and positions. This method is useful in drug discovery and medicinal chemistry providing insights into molecular recognition. Docking has become an integral part of Computer-Aided Drug Design and Discovery (CADDD). Traditional docking methods suffer from limitations of semi-flexible or static treatment of targets and ligand. Over the last decade, advances in the field of computational, proteomics and genomics have also led to the de

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22

Lu, Qiangna, Lian-Wen Qi, and Jinfeng Liu. "Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock." Journal of Theoretical and Computational Chemistry 18, no. 05 (2019): 1950027. http://dx.doi.org/10.1142/s0219633619500275.

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Water plays a significant role in determining the protein–ligand binding modes, especially when water molecules are involved in mediating protein–ligand interactions, and these important water molecules are receiving more and more attention in recent years. Considering the effects of water molecules has gradually become a routine process for accurate description of the protein–ligand interactions. As a free docking program, Autodock has been most widely used in predicting the protein–ligand binding modes. However, whether the inclusion of water molecules in Autodock would improve its docking p

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23

Oferkin, I. V., A. V. Sulimov, E. V. Katkova, et al. "Supercomputer investigation of the protein-ligand system low-energy minima." Biomeditsinskaya Khimiya 61, no. 6 (2015): 712–16. http://dx.doi.org/10.18097/pbmc20156106712.

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The accuracy ofthe protein-ligand binding energy calculations andligand positioning isstrongly influenced by the choice of the docking target function. This work demonstrates the evaluation of the five different target functions used in docking: functions based on MMFF94 force field and functions based on PM7 quantum-chemical method accounting orwithout accounting the implicit solvent model (PCM, COSMO or SGB). For these purposes the ligand positions corresponding to the minima of the target function and the experimentally known ligand positions in the protein active site (crystal ligand posit

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24

Kuldeep Patel, Richa Dubey, Shaifali Soni, Jagdish Chandra Rathi, and Neerupma Dhiman. "Molecular Docking and ADME Study of Quinoline and Chalcone based Derivatives for Anti-Cancer Activity." International Journal of Research in Pharmaceutical Sciences 12, no. 3 (2021): 2252–64. http://dx.doi.org/10.26452/ijrps.v12i3.4849.

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Cancer is a big issue that affects people all over the world. It develops as a result of uncontrolled cell growth. The interaction between developed ligands and thymine phosphorylation was investigated in this study, which was computationally optimized. The aim of this study was to examine the anticancerous activity of designed ligands in thymine phosphorylation (PDB ID: 1UOU) in order to minimize the cost and time required to develop a novel anticancer drug with minimal side effects. All the designed ligands showed mild to excellent binding with proteins. Most of the ligands exhibited better

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Luise, Chiara, Dina Robaa, and Wolfgang Sippl. "Exploring aromatic cage flexibility of the histone methyllysine reader protein Spindlin1 and its impact on binding mode prediction: an in silico study." Journal of Computer-Aided Molecular Design 35, no. 6 (2021): 695–706. http://dx.doi.org/10.1007/s10822-021-00391-9.

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AbstractSome of the main challenges faced in drug discovery are pocket flexibility and binding mode prediction. In this work, we explored the aromatic cage flexibility of the histone methyllysine reader protein Spindlin1 and its impact on binding mode prediction by means of in silico approaches. We first investigated the Spindlin1 aromatic cage plasticity by analyzing the available crystal structures and through molecular dynamic simulations. Then we assessed the ability of rigid docking and flexible docking to rightly reproduce the binding mode of a known ligand into Spindlin1, as an example

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Jacquemard, Célien, Viet-Khoa Tran-Nguyen, Malgorzata N. Drwal, Didier Rognan, and Esther Kellenberger. "Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses." Molecules 24, no. 14 (2019): 2610. http://dx.doi.org/10.3390/molecules24142610.

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Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software

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Huang, Sheng-You, and Xiaoqin Zou. "Advances and Challenges in Protein-Ligand Docking." International Journal of Molecular Sciences 11, no. 8 (2010): 3016–34. http://dx.doi.org/10.3390/ijms11083016.

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Thilagavathi, Ramasamy, and Ricardo L. Mancera. "Ligand−Protein Cross-Docking with Water Molecules." Journal of Chemical Information and Modeling 50, no. 3 (2010): 415–21. http://dx.doi.org/10.1021/ci900345h.

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29

Taufer, M., R. Armen, Jianhan Chen, P. Teller, and C. Brooks. "Computational multiscale modeling in protein--ligand docking." IEEE Engineering in Medicine and Biology Magazine 28, no. 2 (2009): 58–69. http://dx.doi.org/10.1109/memb.2009.931789.

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Cole, Jason C., Christopher W. Murray, J. Willem M. Nissink, Richard D. Taylor, and Robin Taylor. "Comparing protein-ligand docking programs is difficult." Proteins: Structure, Function, and Bioinformatics 60, no. 3 (2005): 325–32. http://dx.doi.org/10.1002/prot.20497.

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31

Hoffmann, Daniel, Bernd Kramer, Takumi Washio, Torsten Steinmetzer, Matthias Rarey, and Thomas Lengauer. "Two-Stage Method for Protein−Ligand Docking." Journal of Medicinal Chemistry 42, no. 21 (1999): 4422–33. http://dx.doi.org/10.1021/jm991090p.

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32

Lybrand, Terry P. "Ligand—protein docking and rational drug design." Current Opinion in Structural Biology 5, no. 2 (1995): 224–28. http://dx.doi.org/10.1016/0959-440x(95)80080-8.

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33

Wong, Chung F. "Flexible ligand–flexible protein docking in protein kinase systems." Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1784, no. 1 (2008): 244–51. http://dx.doi.org/10.1016/j.bbapap.2007.10.005.

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Shin, Woong-Hee, and Chaok Seok. "GalaxyDock: Protein–Ligand Docking with Flexible Protein Side-chains." Journal of Chemical Information and Modeling 52, no. 12 (2012): 3225–32. http://dx.doi.org/10.1021/ci300342z.

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35

Oferkin, Igor V., Ekaterina V. Katkova, Alexey V. Sulimov, et al. "Evaluation of Docking Target Functions by the Comprehensive Investigation of Protein-Ligand Energy Minima." Advances in Bioinformatics 2015 (November 26, 2015): 1–12. http://dx.doi.org/10.1155/2015/126858.

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The adequate choice of the docking target function impacts the accuracy of the ligand positioning as well as the accuracy of the protein-ligand binding energy calculation. To evaluate a docking target function we compared positions of its minima with the experimentally known pose of the ligand in the protein active site. We evaluated five docking target functions based on either the MMFF94 force field or the PM7 quantum-chemical method with or without implicit solvent models: PCM, COSMO, and SGB. Each function was tested on the same set of 16 protein-ligand complexes. For exhaustive low-energy

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Budin, N., N. Majeux, and A. Caflisch. "Fragment-Based Flexible Ligand Docking by Evolutionary Optimization." Biological Chemistry 382, no. 9 (2001): 1365–72. http://dx.doi.org/10.1515/bc.2001.168.

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Abstract A new computational approach for the efficient docking of flexible ligands in a rigid protein is presented. It exploits the binding modes of functional groups determined by an exhaustive search with solvation. The search in ligand conformational space is performed by a genetic algorithm whose scoring function approximates steric effects and intermolecular hydrogen bonds. Ligand conformations generated by the genetic algorithm are docked in the protein binding site by optimizing the fit of their fragments to optimal positions of chemically related functional groups. We show that the us

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Huang, Sheng-You, Min Li, Jianxin Wang, and Yi Pan. "HybridDock: A Hybrid Protein–Ligand Docking Protocol Integrating Protein- and Ligand-Based Approaches." Journal of Chemical Information and Modeling 56, no. 6 (2015): 1078–87. http://dx.doi.org/10.1021/acs.jcim.5b00275.

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Sunitha Sukumaran, Meghna M, Sneha S, Arjun B, Sathianarayanan S та Saranya T S. "Insilico analysis of acridone against TNF-α and PDE4 targets for the treatment of psoriasis". International Journal of Research in Pharmaceutical Sciences 11, SPL4 (2020): 1251–59. http://dx.doi.org/10.26452/ijrps.v11ispl4.4286.

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Psoriasis is an autoimmune disorder. Phosphodiesterase is a family of 1-11 among which PDE4 is most predominant enzymes present in inflammatory cells. Commercially available drugs are synthetic, and these may cause various side effects and are expensive. Dimethyl fumarate is the most frequently used systematic treatment for psoriasis with significant side effects such as myelosuppression, hepatic fibrosis and pulmonary fibrosis. Immune compromise drugs having various side effects, so this project is aimed to propose a novel drug that has more potency, efficiency and least side effects. The doc

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Lexa, Katrina W., and Heather A. Carlson. "Protein flexibility in docking and surface mapping." Quarterly Reviews of Biophysics 45, no. 3 (2012): 301–43. http://dx.doi.org/10.1017/s0033583512000066.

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AbstractStructure-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

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Vavra, Ondrej, Jiri Filipovic, Jan Plhak, et al. "CaverDock: a molecular docking-based tool to analyse ligand transport through protein tunnels and channels." Bioinformatics 35, no. 23 (2019): 4986–93. http://dx.doi.org/10.1093/bioinformatics/btz386.

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Abstract Motivation Protein tunnels and channels are key transport pathways that allow ligands to pass between proteins’ external and internal environments. These functionally important structural features warrant detailed attention. It is difficult to study the ligand binding and unbinding processes experimentally, while molecular dynamics simulations can be time-consuming and computationally demanding. Results CaverDock is a new software tool for analysing the ligand passage through the biomolecules. The method uses the optimized docking algorithm of AutoDock Vina for ligand placement dockin

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Kroemer, R. T. "Molecular modelling probes: docking and scoring." Biochemical Society Transactions 31, no. 5 (2003): 980–84. http://dx.doi.org/10.1042/bst0310980.

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A general introduction to molecular modelling techniques in the area of protein–ligand interactions is given. Methods covered range from binding-site analysis to statistical treatment of sets of ligands. The main focus of this paper is on docking and scoring. After an outline of the main concepts, two specific application examples are given.

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Kannadasan, R., M. S. Saleembasha, and I. Arnold Emerson. "A Frame Work for Learning Drug Designing through Molecular Modelling Software Techniques and Biological Databases for Protein-Ligand Interactions." International Journal of Engineering Research in Africa 27 (December 2016): 111–18. http://dx.doi.org/10.4028/www.scientific.net/jera.27.111.

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Applications of computer and information technology are indispensable in various fields especially in the field of biology. The use of computer aided tools plays a key role in solving biological problems. The spontaneous process of molecular docking is important for finding potentially strong candidate of drug for various viruses. The binding of protein receptors with ligand molecules is essential in drug discovery process. The aim of molecular docking tools is to predict the interaction between protein and ligand. This review outlines the major tools for protein - ligand docking which in turn

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Bolcato, Giovanni, Alberto Cuzzolin, Maicol Bissaro, Stefano Moro, and Mattia Sturlese. "Can We Still Trust Docking Results? An Extension of the Applicability of DockBench on PDBbind Database." International Journal of Molecular Sciences 20, no. 14 (2019): 3558. http://dx.doi.org/10.3390/ijms20143558.

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The number of entries in the Protein Data Bank (PDB) has doubled in the last decade, and it has increased tenfold in the last twenty years. The availability of an ever-growing number of structures is having a huge impact on the Structure-Based Drug Discovery (SBDD), allowing investigation of new targets and giving the possibility to have multiple structures of the same macromolecule in a complex with different ligands. Such a large resource often implies the choice of the most suitable complex for molecular docking calculation, and this task is complicated by the plethora of possible posing an

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Macari, Gabriele, Daniele Toti, Carlo Del Moro, and Fabio Polticelli. "Fragment-Based Ligand-Protein Contact Statistics: Application to Docking Simulations." International Journal of Molecular Sciences 20, no. 10 (2019): 2499. http://dx.doi.org/10.3390/ijms20102499.

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In this work, the information contained in the contacts between fragments of small-molecule ligands and protein residues has been collected and its exploitability has been verified by using the scoring of docking simulations as a test case for bringing about a proof of concept. Contact statistics between small-molecule fragments and binding site residues were collected and analyzed using a dataset composed of 200,000+ binding sites and associated ligands, derived from the database of the LIBRA ligand binding site recognition software, as a starting point. The fragments were generated by applyi

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May, Andreas, and Martin Zacharias. "Accounting for global protein deformability during protein–protein and protein–ligand docking." Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1754, no. 1-2 (2005): 225–31. http://dx.doi.org/10.1016/j.bbapap.2005.07.045.

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Chen, Jui-Le, Chun-Wei Tsai, Ming-Chao Chiang, and Chu-Sing Yang. "A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm." BioMed Research International 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/909717.

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The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this paper, a novel docking prediction algorithm, named fast cloud-based protein-ligand docking prediction algorithm (FCPLDPA), is presented to accelerate the docking prediction algorithm. The proposed algori

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Butt, Sania Safdar, Yasmin Badshah, Maria Shabbir, and Mehak Rafiq. "Molecular Docking Using Chimera and Autodock Vina Software for Nonbioinformaticians." JMIR Bioinformatics and Biotechnology 1, no. 1 (2020): e14232. http://dx.doi.org/10.2196/14232.

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In the field of drug discovery, many methods of molecular modeling have been employed to study complex biological and chemical systems. Experimental strategies are integrated with computational approaches for the identification, characterization, and development of novel drugs and compounds. In modern drug designing, molecular docking is an approach that explores the confirmation of a ligand within the binding site of a macromolecule. To date, many software and tools for docking have been employed. AutoDock Vina (in UCSF [University of California, San Francisco] Chimera) is one of the computat

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Seifert, Markus H. J. "ProPose: Steered Virtual Screening by Simultaneous Protein−Ligand Docking and Ligand−Ligand Alignment." Journal of Chemical Information and Modeling 45, no. 2 (2005): 449–60. http://dx.doi.org/10.1021/ci0496393.

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Dawood, A. A., M. A. A. Altobje, and Z. T. Al-Rrassam. "Molecular Docking of SARS-CoV-2 Nucleocapsid Protein with Angiotensin-Converting Enzyme II." Mikrobiolohichnyi Zhurnal 83, no. 2 (2021): 82–92. http://dx.doi.org/10.15407/microbiolj83.02.082.

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SARS-CoV-2 remains life-threatening human pathogen witnessed in the present world. Purpose. The key objective of this research was to incorporate a bioinformatics technique to forecast the molecular docking of the ACE2-associated SARS-CoVs nucleocapsid protein. Methods. Different bioinformatics tools were used in this study in order to compare the chemical structures with their biological behaviour at the levels of atoms and the ligand-binding affinity. This research sought to investigate new data analysis. Results. It was computed the basic 2D structure that occurs in all models, requiring io

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Yang, Jinsol, Minkyung Baek, and Chaok Seok. "GalaxyDock3: Protein–ligand docking that considers the full ligand conformational flexibility." Journal of Computational Chemistry 40, no. 31 (2019): 2739–48. http://dx.doi.org/10.1002/jcc.26050.

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