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Features.html
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<p class="u-custom-font u-heading-font u-text u-text-default u-text-white u-text-1">Features</p>
<div class="u-expanded-width u-tab-links-align-center u-tabs u-tabs-1">
<ul class="u-spacing-4 u-tab-list u-unstyled" role="tablist"><li class="u-tab-item" role="presentation"><a class="active u-active-palette-1-base u-button-style u-grey-80 u-tab-link u-text-active-white u-text-white u-tab-link-1" id="link-tab-9d34" href="#tab-9d34" role="tab">Modes</a>
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</li><li class="u-tab-item" role="presentation"><a class="u-active-palette-1-base u-button-style u-grey-80 u-tab-link u-text-active-white u-text-white u-tab-link-3" id="link-tab-a5c6" href="#tab-a5c6" role="tab">Protein Preparation</a>
</li><li class="u-tab-item" role="presentation"><a class="u-active-palette-1-base u-button-style u-grey-80 u-tab-link u-text-active-white u-text-white u-tab-link-4" id="link-tab-14b7" href="#tab-14b7" role="tab">Pocket Finding</a>
</li><li class="u-tab-item" role="presentation"><a class="u-active-palette-1-base u-button-style u-grey-80 u-tab-link u-text-active-white u-text-white u-tab-link-5" id="link-tab-2917" href="#tab-2917" role="tab">Docking</a>
</li><li class="u-tab-item" role="presentation"><a class="u-active-palette-1-base u-button-style u-grey-80 u-tab-link u-text-active-white u-text-white u-tab-link-6" id="link-tab-38dc" href="#tab-38dc" role="tab" aria-controls="tab-38dc" aria-selected="false">Pose Selection</a>
</li><li class="u-tab-item" role="presentation"><a class="u-active-palette-1-base u-button-style u-grey-80 u-tab-link u-text-active-white u-text-white u-tab-link-7" id="link-tab-33f2" href="#tab-33f2" role="tab" aria-controls="tab-33f2" aria-selected="false">Rescoring</a>
</li><li class="u-tab-item" role="presentation"><a class="u-active-palette-1-base u-button-style u-grey-80 u-tab-link u-text-active-white u-text-white u-tab-link-8" id="link-tab-5d65" href="#tab-5d65" role="tab" aria-controls="tab-5d65" aria-selected="false">Consensus</a>
</li></ul>
<div class="u-tab-content">
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<h3 class="u-text u-text-default u-text-2">Single / Default Docking</h3>
<p class="u-text u-text-default u-text-3" spellcheck="false">Docking of a compound library into one protein structure.</p>
<h3 class="u-text u-text-default u-text-4">Ensemble Docking</h3>
<p class="u-text u-text-default u-text-5" spellcheck="false">Docking of a compound library into several protein structures. Outputs the compounds that are among the top-scorers for each protein structure.</p>
<h3 class="u-text u-text-default u-text-6">Active Learning <span style="font-size: 1.125rem;">(currently not implemented)</span>
</h3>
<p class="u-text u-text-default u-text-7" spellcheck="false">Docks a small fraction of the library (0.1-1%) then builds a machine-learning model to predict the consensus scores of the rest of the library. This is done iteratively to gradually improve the model. Greatly reduced computation time for large libraries. </p>
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<div class="u-container-style u-shape-rectangle u-tab-pane u-tab-pane-2" id="tab-0da5" role="tabpanel" aria-labelledby="link-tab-0da5">
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<h3 class="u-text u-text-default u-text-8">Standardisation</h3>
<p class="u-text u-text-default u-text-9" spellcheck="false">Standardisation of the compound library is handled by the ChemBL Structure Pipeline and includes salt removal, bond standardization and tautomer standardisation.<br>
<span style="font-size: 0.75rem;">Bento, A. P., Hersey, A., Félix, E., Landrum, G., Gaulton, A., Atkinson, F., Bellis, L. J., de Veij, M., & Leach, A. R. (2020). An open source chemical structure curation pipeline using RDKit. <i>Journal of Cheminformatics</i>, <i>12</i>(1), 1–16. <a href="https://doi.org/10.1186/s13321-020-00456-1" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-1">DOI</a>
</span>
<br>
</p>
<h3 class="u-text u-text-default u-text-10">Protonation</h3>
<p class="u-text u-text-default u-text-11" spellcheck="false">Protonation of the library can be handled in one of two ways:</p>
<ul class="u-text u-text-default u-text-12" spellcheck="false">
<li>
<span style="font-weight: 700;">GypsumDL<span style="background-color: var(--darkreader-bg--palette-2-base); font-family: Ubuntu,sans-serif; font-size: 1rem; "></span>
</span>
<span style="background-color: var(--darkreader-bg--palette-2-base); color: var(--darkreader-text--palette-2-base-text-color); font-family: Ubuntu,sans-serif; font-size: 1rem; ">(https://durrantlab.pitt.edu/gypsum-dl/) </span>
<span style="background-color: var(--darkreader-bg--palette-2-base); color: var(--darkreader-text--palette-2-base-text-color); font-family: Ubuntu,sans-serif; font-size: 0.75rem; Durrant, J. D. (2019). GypSUm-DL: An open-source program for preparing small-molecule libraries for structure-based virtual screening. <i>Journal of Cheminformatics</i>, <i>11</i>(1), 1–13. <a href=" https:="" doi.org="" 10.1186="" s13321-019-0358-3"="" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-2">DOI
</span>
</li>
<li>No protonation</li>
</ul>
<h3 class="u-text u-text-default u-text-13">Conformer Generation</h3>
<p class="u-text u-text-default u-text-14" spellcheck="false">Generation of conformers is handled by GypsumDL (https://durrantlab.pitt.edu/gypsum-dl/) <span style="font-size: 0.75rem;">Ropp, P. J., Spiegel, J. O., Walker, J. L., Green, H., Morales, G. A., Milliken, K. A., Ringe, J. J., & Durrant, J. D. (2019). GypSUm-DL: An open-source program for preparing small-molecule libraries for structure-based virtual screening. <i>Journal of Cheminformatics</i>, <i>11</i>(1), 1–13. <a href="https://doi.org/10.1186/s13321-019-0358-3" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-3">DOI</a>
</span>
</p>
</div>
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<h3 class="u-text u-text-default u-text-15">Protonation</h3>
<p class="u-text u-text-default u-text-16" spellcheck="false">Addition of hydrogens and prediction of tautomers can be done automatically via the Protoss API :</p>
<ul class="u-text u-text-default u-text-17" spellcheck="false">
<li>
<span style="font-weight: 700;">Protoss </span>: <span style="font-size: 0.75rem;">Bietz S, Urbaczek S, Schulz B, Rarey M (2014) Protoss: a holistic approach to predict tautomers and protonation states in protein-ligand complexes. <span style="font-style: italic;">J Cheminform</span> 6:12. <a href="1. Bietz S, Urbaczek S, Schulz B, Rarey M (2014) Protoss: a holistic approach to predict tautomers and protonation states in protein-ligand complexes. J Cheminform 6:12. https://doi.org/10.1186/1758-2946-6-12" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-4">DOI</a>
</span>
</li>
</ul>
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<h3 class="u-text u-text-default u-text-18">Using a reference ligand</h3>
<p class="u-text u-text-19" spellcheck="false">If a binding mode of a ligand is available, this can be used as a reference to define the pocket of the target enzyme</p>
<h3 class="u-text u-text-default u-text-20">Using a reference ligand's Radius of Gyration</h3>
<p class="u-text u-text-default u-text-21" spellcheck="false">If a binding mode of a ligand is available, this can be used as a reference to define the pocket of the target enzyme using the ligand's radius of gyration. This was shown to be an effective method to determine the optimal binding site size : <span style="font-size: 0.75rem;">Feinstein, W. P.; Brylinski, M. Calculating an Optimal Box Size for Ligand Docking and Virtual Screening against Experimental and Predicted Binding Pockets. J. Cheminform. 2015, 7 (1), 18. <a href="https://doi.org/10.1186/s13321-015-0067-5" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-5">DOI</a>.
</span>
</p>
<h3 class="u-text u-text-default u-text-22">DogSiteScorer</h3>
<p class="u-text u-text-23" spellcheck="false">If a binding mode is not known or only the apo form of the target is available, DogSiteScorer (<a href="https://proteins.plus/pages/about" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-6">more info</a>) can be used to the search for suitable pockets using different metrics to rank the pockets (volume, druggability score etc.). <br>
<span style="font-size: 0.75rem;">Volkamer, A., Kuhn, D., Rippmann, F., & Rarey, M. (2012). DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. <i>Bioinformatics</i>, <i>28</i>(15), 2074–2075. <a href="https://doi.org/10.1093/bioinformatics/bts310" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-7">DOI</a>
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<h3 class="u-text u-text-default u-text-24">GNINA</h3>
<p class="u-text u-text-default u-text-25" spellcheck="false"> Gnina is a molecular docking program with integrated support for scoring and optimizing ligands using convolutional neural networks. It is a fork of <a href="http://smina.sf.net/" rel="nofollow" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-8">smina</a>, which is a fork of <a href="http://vina.scripps.edu/" rel="nofollow" class="u-active-none u-border-none u-btn u-button-style u-hover-none u-none u-text-palette-1-base u-btn-9">AutoDock Vina</a>.<br>
<span style="font-size: 0.75rem;">McNutt, A. T., Francoeur, P., Aggarwal, R., Masuda, T., Meli, R., Ragoza, M., Sunseri, J., & Koes, D. R. (2021). GNINA 1.0: molecular docking with deep learning. <i>Journal of Cheminformatics</i>, <i>13</i>(1), 1–20. <a href="https://doi.org/10.1186/s13321-021-00522-2" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-10">DOI</a>
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<h3 class="u-text u-text-default u-text-26">SMINA</h3>
<p class="u-text u-text-default u-text-27" spellcheck="false"> A fork of AutoDock Vina that is customized to better support scoring function development and high-performance energy minimization.<br>
<span style="font-size: 0.75rem;">Koes, D. R., Baumgartner, M. P., & Camacho, C. J. (2013). Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. <i>Journal of Chemical Information and Modeling</i>, <i>53</i>(8), 1893–1904. <a href="https://doi.org/10.1021/ci300604z" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-11">DOI</a>
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<h3 class="u-text u-text-default u-text-28">PLANTS</h3>
<p class="u-text u-text-default u-text-29" spellcheck="false"> The docking algorithm PLANTS is based on a class of stochastic optimization algorithms called ant colony optimization (ACO).<br>
<span style="font-size: 0.75rem;">Korb, O., Stützle, T., & Exner, T. E. (2009). Empirical scoring functions for advanced Protein-Ligand docking with PLANTS. <i>Journal of Chemical Information and Modeling</i>, <i>49</i>(1), 84–96. <a href="https://doi.org/10.1021/ci800298z" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-12">DOI</a>
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<h3 class="u-text u-text-default u-text-30">QVINA-W</h3>
<p class="u-text u-text-default u-text-31" spellcheck="false">A version of Autodock Vina using Average Sum of Proximity relative Frequencies (ASoF).<br>
<span style="font-size: 0.75rem;"></span>
<span style="font-style: italic; font-size: 0.75rem;">Hassan, N. M.; Alhossary, A. A.; Mu, Y.; Kwoh, C.-K. Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration. Sci. Rep. 2017, 7 (1), 15451. <a href="https://doi.org/10.1038/s41598-017-15571-7." class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-13">DOI</a>
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<h3 class="u-text u-text-default u-text-32">QVINA-2</h3>
<p class="u-text u-text-default u-text-33" spellcheck="false">A parallelised version of Autodock Vina.<br>
<span style="font-size: 0.75rem;"></span>
<span style="font-size: 0.75rem;">Alhossary, A.; Handoko, S. D.; Mu, Y.; Kwoh, C.-K. Fast, Accurate, and Reliable Molecular Docking with QuickVina 2. <span style="font-style: italic;">Bioinformatics </span>2015, 31 (13), 2214–2216. <a href="https://doi.org/10.1093/bioinformatics/btv082." class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-14">DOI</a>
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<h3 class="u-text u-text-default u-text-34">Best Pose</h3>
<p class="u-text u-text-default u-text-35" spellcheck="false">Classically, out of the number of poses generated by a docking algorithm, the best-scoring docking pose is selected for further analysis. This can also be done in DockM8 by selecting either the best pose for all docking algorithms or by considering the best pose from only one of the docking algorithms.</p>
<h3 class="u-text u-text-default u-text-36">Descriptor-based clustering</h3>
<p class="u-text u-text-37" spellcheck="false">Clustering of a set of docking poses can also be done by generating a similarity matrix between all of the poses from a single compound. Metrics such as <span style="font-weight: 700;">RMSD</span>, symmetry-corrected RMSD (<span style="font-weight: 700;">spyRMSD</span>), electrostatic shape similarity (<span style="font-weight: 700;">espsim</span>), protein-ligand interaction fingerprint similarity (<span style="font-weight: 700;">S</span>
<span style="font-weight: 700;">PLIF</span>), shape similarity (<span style="font-weight: 700;">USRCAT</span>), and <span style="font-weight: 700;">3DScore </span>can then be calculated for each pair of compounds. A clustering algorithm is then use to output representative poses according to each metric. Clustering can be done either via the <span style="font-weight: 700;">K-Medoids</span> or <span style="font-weight: 700;">Affinity Propagation</span> algorithms.<br>
<span style="font-weight: 700;">spyRMSD </span>: <span style="font-size: 0.75rem;">Meli, R., & Biggin, P. C. (2020). Spyrmsd: Symmetry-corrected RMSD calculations in Python. Journal of Cheminformatics, 12(1), 1–7. <a href="https://doi.org/10.1186/S13321-020-00455-2/FIGURES/7" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-15">DOI</a>
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<span style="font-weight: 700;">espsim </span>: <span style="font-size: 0.75rem;">Bolcato, G., Heid, E., & Boström, J. (2022). On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods. Journal of Chemical Information and Modeling, 62(6), 1388–1398.<a href=" https://doi.org/10.1021/acs.jcim.1c01535" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-16">DOI</a>
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<span style="font-weight: 700;">3DScore </span>: <span style="font-size: 0.75rem;">Plewczynski, D., Łaźniewski, M., Grotthuss, M. von, Rychlewski, L., & Ginalski, K. (2011). VoteDock: Consensus docking method for prediction of protein–ligand interactions. <i>Journal of Computational Chemistry</i>, <i>32</i>(4), 568–581. <a href="https://doi.org/10.1002/JCC.21642" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-17">DOI</a>
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<span style="font-weight: 700;">USRCAT </span>: <span style="font-size: 0.75rem;">Schreyer, A. M., & Blundell, T. (2012). USRCAT: real-time ultrafast shape recognition with pharmacophoric constraints. Journal of Cheminformatics, 4(1), 27. <a href="https://doi.org/10.1186/1758-2946-4-27" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-18">DOI</a>
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<span style="font-weight: 700;">SPLIF </span>: <span style="font-size: 0.75rem;">Da, C., & Kireev, D. (2014). Structural Protein–Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study. Journal of Chemical Information and Modeling, 54(9), 2555–2561. <a href="https://doi.org/10.1021/ci500319f" class="u-active-none u-border-none u-btn u-button-link u-button-style u-hover-none u-none u-text-palette-1-base u-btn-19">DOI</a>
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<h3 class="u-text u-text-default u-text-38">Rescoring</h3>
<p class="u-text u-text-39" spellcheck="false">Any of the scoring functions available in DockM8 can be used to rescored the docked poses and output the best scoring pose. Please see the Rescoring tab for more details about which scoring functions are available.</p>
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<h3 class="u-align-left u-text u-text-40">GNINA Scoring functions</h3>
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<li>GNINA-Affinity</li>
<li>CNN-Score</li>
<li>CNN-Affinity</li>
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<h3 class="u-align-left u-text u-text-42">Empirical Scoring functions</h3>
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<li>AAScore</li>
<li>LinF9</li>
<li>CHEMPLP</li>
<li>PLP<br>
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<li>Vinardo</li>
<li>AutoDock 4<br>
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<li>KORP-PL</li>
<li>Convex-PLR</li>
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<h3 class="u-align-left u-text u-text-44">ML Scoring functions</h3>
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<li>RF-Score-VS</li>
<li>SCORCH</li>
<li>RTMScore</li>
<li>AAScore</li>
<li>PLECScore (oddt)</li>
<li>NNScore (oddt)</li>
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<h3 class="u-text u-text-default u-text-46">Consensus methods</h3>
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<li>Exponential Consensus Ranking (ECR) : 3 methods</li>
<li>Rank by Rank (RbR)</li>
<li>Rank by Vote (RbV)</li>
<li>Z-score : 2 methods</li>
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