Pan-cancer functional analysis of somatic mutations in G protein-coupled receptors.

G Protein-coupled receptors (GPCRs) are the most frequently exploited drug target family, moreover they are often found mutated in cancer. Here we used a dataset of mutations found in patient samples derived from the Genomic Data Commons and compared it to the natural human variance as exemplified by data from the 1000 genomes project. We explored cancer-related mutation patterns in all GPCR classes combined and individually. While the location of the mutations across the protein domains did not differ significantly in the two datasets, a mutation enrichment in cancer patients was observed among class-specific conserved motifs in GPCRs such as the Class A "DRY" motif. A Two-Entropy Analysis confirmed the correlation between residue conservation and cancer-related mutation frequency. We subsequently created a ranking of high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). Our approach was confirmed by re-discovery of established cancer targets such as the LPA and mGlu receptor families, but also discovered novel GPCRs which had not been linked to cancer before such as the P2Y Receptor 10 (P2RY10). Overall, this study presents a list of GPCRs that are amenable to experimental follow up to elucidate their role in cancer.

Computational inhibitor design against malaria plasmepsins.

Plasmepsins are aspartic proteases involved in the degradation of the host cell hemoglobin that is used as a food source by the malaria parasite. Plasmepsins are highly promising as drug targets, especially when combined with the inhibition of falcipains that are also involved in hemoglobin catabolism. In this review, we discuss the mechanism of plasmepsins I-IV in view of the interest in transition state mimetics as potential compounds for lead development. Inhibitor development against plasmepsin II as well as relevant crystal structures are summarized in order to give an overview of the field. Application of computational techniques, especially binding affinity prediction by the linear interaction energy method, in the development of malarial plasmepsin inhibitors has been highly successful and is discussed in detail. Homology modeling and molecular docking have been useful in the current inhibitor design project, and the combination of such methods with binding free energy calculations is analyzed.

Molecular diversity sample generation on the basis of quantum-mechanical computations and principal component analysis.

The present study introduces a new strategy of selection of a maximum diversity sample of n compounds from N available in a molecular database. This strategy can be useful in pharmacological screening, combinatorial chemistry or parallel synthesis planning. It consists of first describing the compounds by means of parameters derived from quantum mechanical computations (water solvation deltaG, benzene solvation deltaG, octanol solvation deltaG, dipolar moment), as well as standard molecular parameters such as solvent-accessible surface area and molecular weight. Solvation parameters are used because of the importance of this phenomenon in the pharmacological behaviour. Redundant information in the description of the compounds is eliminated by using principal components (PC) instead of the original descriptors. Based on the similarity between the N compounds in the PC space, they are classified into n groups by k-means cluster analysis. The compounds that are nearest to the centroid of each cluster constituted the maximum diversity sample. When practical difficulties exist for the use of one of the proposed compounds, another also close to the cluster centroid can substitute for it. This strategy has been tested in the selection of a sample of 50 amines from the 923 available in the Aldrich catalogue. The results have been contrasted with those obtained from an optimal, distance-based experimental design, resulting in an 86% of agreement between both approaches. An R(2)-like diversity coefficient has been used to assess the quality of the proposed solutions.