Algorithms for Computational Biology: Second International by Adrian-Horia Dediu, Francisco Hernández-Quiroz, Carlos

By Adrian-Horia Dediu, Francisco Hernández-Quiroz, Carlos Martín-Vide, David A. Rosenblueth

This booklet constitutes the court cases of the second one overseas convention on Algorithms for Computational Biology, AICoB 2015, held in Mexico urban, Mexico, in August 2015.

The eleven papers awarded during this quantity have been rigorously reviewed and chosen from 23 submissions. They have been prepared in topical sections named: genetic processing; molecular recognition/prediction; and phylogenetics.

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Extra resources for Algorithms for Computational Biology: Second International Conference, AlCoB 2015, Mexico City, Mexico, August 4-5, 2015, Proceedings

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In most types of SBVS, docking algorithms are used to predict possible ligand-binding interactions. It is recommended to focus docking to a protein cavity of interest to limit the search space of possible conformations. -H. Dediu et al. ): AlCoB 2015, LNBI 9199, pp. 41–52, 2015. 1007/978-3-319-21233-3 4 42 R. Kriv´ ak and D. g. confirmed active sties), it may be necessary to perform blind docking which scans the whole protein surface. Compared to local docking it is generally less accurate and significantly more time consuming, which limits the size of compound libraries that is possible to screen [34].

Details of the datasets are presented in Table 1. 1 Experimental Evaluation Evaluation Measures To evaluate predictive performance of our method and compare it with Fpocket we have used methodology based on ligand-centric counting and DCA (distance between the center of the pocket and any ligand atom) pocket identification criterion with 4 ˚ A threshold. Ligand-centric counting means, that for every relevant ligand in the dataset, its binding site must be correctly predicted for a method to achieve 100 % identification success rate.

Parameters were optimized with respect to the success rate achieved on the JOINED dataset. This was done to avoid bias towards CHEN11 dataset so the Table 2. Results: the numbers represent identification success rate [%] measured by A threshold considering only pockets ranked at the top of the list DCA criterion with 4 ˚ (n is the number of ligands in considered structure). *average results of 10 independent 5-fold cross-validation runs. 8 P2RANK: Knowledge-Based Ligand Binding Site Prediction 49 Fig.

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