By Adrian-Horia Dediu, Francisco Hernández-Quiroz, Carlos Martín-Vide, David A. Rosenblueth
This publication constitutes the court cases of the second one foreign convention on Algorithms for Computational Biology, AICoB 2015, held in Mexico urban, Mexico, in August 2015.
The eleven papers provided during this quantity have been rigorously reviewed and chosen from 23 submissions. They have been geared up in topical sections named: genetic processing; molecular recognition/prediction; and phylogenetics.
Read Online or Download Algorithms for Computational Biology: Second International Conference, AlCoB 2015, Mexico City, Mexico, August 4-5, 2015, Proceedings (Lecture Notes in Computer Science) PDF
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Extra info for Algorithms for Computational Biology: Second International Conference, AlCoB 2015, Mexico City, Mexico, August 4-5, 2015, Proceedings (Lecture Notes in Computer Science)
This work-ﬂow is described in Fig. 3. e. with cloning sequences that do not appear in the 32 C. Ladroue and S. Kalvala whole sequence. In a ﬁrst pass, in case of conﬂict with calculated RBSs, the RBS are re-calculated to achieve the previously set translation rates but with diﬀerent sequences. This is done at most twice (to avoid inﬁnite loops). In a second pass, if there is a conﬂict with some coding sequences, the biocompiler proceeds to ﬁnd the optimal codon change that will both: (1) free up previously conﬂicting restriction enzymes, and (2) minimize the disruption to the CDS (by applying a cost dependent on the codon usage).
Several categories of methods (or rather distinctive approaches) have been recognized. We present them together with their representative examples, although in reality the actual methods may use a combination of those approaches: – Geometrical Methods. Methods focused mainly on the algorithmic side of the problem of ﬁnding concave pockets and clefts on the surface of a 3D structure [12,15,41], some of them incorporating additional physico-chemical information like polarity or charge [21,24]. – Energetic Methods.
Ultimately we have decided to use 100 trees (using 10× more trees leads only to marginal improvements) and tessellation level of 2 (using higher levels leads to some improvements but also unproportionally longer running times). 4 Conclusion In the present paper we have proposed P2RANK, a novel method for ligandbinding site prediction based on classiﬁcation of points lying on a protein’s Connolly surface. Each point represents potential location of a binding ligand atom and is described by a feature vector generated from its spatial neighborhood.