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      Ligand-based virtual screening interface between PyMOL and LiSiCA

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          Abstract

          Ligand-based virtual screening of large small-molecule databases is an important step in the early stages of drug development. It is based on the similarity principle and is used to reduce the chemical space of large databases to a manageable size where chosen ligands can be experimentally tested. Ligand-based virtual screening can also be used to identify bioactive molecules with different basic scaffolds compared to already known bioactive molecules, thus having the potential to increase the structural variability of compounds. Here, we present an interface between the popular molecular graphics system PyMOL and the ligand-based virtual screening software LiSiCA available at http://insilab.org/lisica-plugin and demonstrate how this interface can be used in the early stages of drug discovery process.

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          Ligand-based virtual screening interface between PyMOL and LiSiCA.

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          Most cited references 15

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          Ligand docking and binding site analysis with PyMOL and Autodock/Vina

          Docking of small molecule compounds into the binding site of a receptor and estimating the binding affinity of the complex is an important part of the structure-based drug design process. For a thorough understanding of the structural principles that determine the strength of a protein/ligand complex both, an accurate and fast docking protocol and the ability to visualize binding geometries and interactions are mandatory. Here we present an interface between the popular molecular graphics system PyMOL and the molecular docking suites Autodock and Vina and demonstrate how the combination of docking and visualization can aid structure-based drug design efforts.
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            Recognizing pitfalls in virtual screening: a critical review.

            The aim of virtual screening (VS) is to identify bioactive compounds through computational means, by employing knowledge about the protein target (structure-based VS) or known bioactive ligands (ligand-based VS). In VS, a large number of molecules are ranked according to their likelihood to be bioactive compounds, with the aim to enrich the top fraction of the resulting list (which can be tested in bioassays afterward). At its core, VS attempts to improve the odds of identifying bioactive molecules by maximizing the true positive rate, that is, by ranking the truly active molecules as high as possible (and, correspondingly, the truly inactive ones as low as possible). In choosing the right approach, the researcher is faced with many questions: where does the optimal balance between efficiency and accuracy lie when evaluating a particular algorithm; do some methods perform better than others and in what particular situations; and what do retrospective results tell us about the prospective utility of a particular method? Given the multitude of settings, parameters, and data sets the practitioner can choose from, there are many pitfalls that lurk along the way which might render VS less efficient or downright useless. This review attempts to catalogue published and unpublished problems, shortcomings, failures, and technical traps of VS methods with the aim to avoid pitfalls by making the user aware of them in the first place.
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              CAVER: a new tool to explore routes from protein clefts, pockets and cavities

              Background The main aim of this study was to develop and implement an algorithm for the rapid, accurate and automated identification of paths leading from buried protein clefts, pockets and cavities in dynamic and static protein structures to the outside solvent. Results The algorithm to perform a skeleton search was based on a reciprocal distance function grid that was developed and implemented for the CAVER program. The program identifies and visualizes routes from the interior of the protein to the bulk solvent. CAVER was primarily developed for proteins, but the algorithm is sufficiently robust to allow the analysis of any molecular system, including nucleic acids or inorganic material. Calculations can be performed using discrete structures from crystallographic analysis and NMR experiments as well as with trajectories from molecular dynamics simulations. The fully functional program is available as a stand-alone version and as plug-in for the molecular modeling program PyMol. Additionally, selected functions are accessible in an online version. Conclusion The algorithm developed automatically finds the path from a starting point located within the interior of a protein. The algorithm is sufficiently rapid and robust to enable routine analysis of molecular dynamics trajectories containing thousands of snapshots. The algorithm is based on reciprocal metrics and provides an easy method to find a centerline, i.e. the spine, of complicated objects such as a protein tunnel. It can also be applied to many other molecules. CAVER is freely available from the web site .
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                Author and article information

                Contributors
                athiradilip@gmail.com
                samo.lesnik@gmail.com
                tanja.stular@gmail.com
                dusanka.janezic@upr.si
                +386-1-4760-273 , konc@cmm.ki.si
                Journal
                J Cheminform
                J Cheminform
                Journal of Cheminformatics
                Springer International Publishing (Cham )
                1758-2946
                7 September 2016
                7 September 2016
                2016
                : 8
                : 1
                Affiliations
                [1 ]National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
                [2 ]Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, 6000 Koper, Slovenia
                [3 ]Laboratory for Physical Chemistry and Thermodynamics, Faculty of Chemistry and Chemical Technology, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia
                Article
                157
                10.1186/s13321-016-0157-z
                5013575
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004329, Javna Agencija za Raziskovalno Dejavnost RS;
                Award ID: J1-6743
                Award ID: P1-0002
                Award Recipient :
                Categories
                Software
                Custom metadata
                © The Author(s) 2016

                Chemoinformatics

                virtual screening, similarity search, lisica, pymol, molecular graphics

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