Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction.

The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. To date, the most common prediction strategies are based upon machine learning or other quantitative structure-activity relationships (QSAR) based approaches. In the present research, we propose and discuss a straightforward strategy not based on any learning modelling but exclusively relying upon the chemical similarity of a query compound to reference compounds with annotated activity against cell lines. We also compare the performance of the proposed method to machine learning predictions on the same problem. A curated database of compounds-cell lines associations derived from ChemBL version 22 was created for algorithm construction and cross-validation. Validation was done using 10-fold cross-validation and testing the models on new data obtained from ChemBL version 25. In terms of accuracy, both methods perform similarly with values around 0.65 across 750 cell lines in 10-fold cross-validation experiments. By combining both methods it is possible to achieve 66% of correct classification rate in more than 26000 newly reported interactions comprising 11000 new compounds. A Web Service implementing the described approaches (both similarity and machine learning based models) is freely available at: http://bioquimio.udla.edu.ec/cellfishing.

Unravelling the relationship between protein sequence and low-complexity regions entropies: Interactome implications.

Low-complexity regions are sub-sequences of biased composition in a protein sequence. The influence of these regions over protein evolution, specific functions and highly interactive capacities is well known. Although protein sequence entropy has been largely studied, its relationship with low-complexity regions and the subsequent effects on protein function remains unclear. In this work we propose a theoretical and empirical model integrating the sequence entropy with local complexity parameters. Our results indicate that the protein sequence entropy is related with the protein length, the entropies inside and outside the low-complexity regions as well as their number and average size. We found a small but significant increment in the sequence entropy of hubs proteins. In agreement with our theoretical model, this increment is highly dependent of the balance between the increment of protein length and average size of the low-complexity regions. Finally, our models and proteins analysis provide evidence supporting that modifications in the average size is more relevant in hubs proteins than changes in the number of low-complexity regions.

Application of desirability-based multi(bi)-objective optimization in the design of selective arylpiperazine derivates for the 5-HT1A serotonin receptor.

The multiobjective optimization technique based on the desirability estimation of several interrelated responses (MOOP-DESIRE) has been recently applied to quantitative structure-activity relationship (QSAR) studies. However, the advantage of applying this new methodology to the study of selectivity and affinity to competitive targets has been little explored. We used the MOOP-DESIRE methodology and a variation of this, to study the arylpiperazine derivates that could interact with 5-HT(1A) and 5-HT(2A), serotonin receptor subtypes with the objective of designing more selective molecules for the 5-HT(1A) receptor. We did show that the model results are in agreement with the available pharmacophore descriptions, guaranteeing an appropriate structural correlation and proving the methodology, as a useful tool for the important problem of selective drug design.