Exploring potential therapeutic combinations for castration-sensitive prostate cancer using supercomputers: a proof of concept study.

To address the challenge of finding new combination therapies against castration-sensitive prostate cancer, we introduce Vini, a computational tool that predicts the efficacy of drug combinations at the intracellular level by integrating data from the KEGG, DrugBank, Pubchem, Protein Data Bank, Uniprot, NCI-60 and COSMIC databases. Vini is a computational tool that predicts the efficacy of drugs and their combinations at the intracellular level. It addresses the problem comprehensively by considering all known target genes, proteins and small molecules and their mutual interactions involved in the onset and development of cancer. The results obtained point to new, previously unexplored combination therapies that could theoretically be promising candidates for the treatment of castration-sensitive prostate cancer and could prevent the inevitable progression of the cancer to the incurable castration-resistant stage. Furthermore, after analyzing the obtained triple combinations of drugs and their targets, the most common targets became clear: ALK, BCL-2, mTOR, DNA and androgen axis. These results may help to define future therapies against castration-sensitive prostate cancer. The use of the Vini computer model to explore therapeutic combinations represents an innovative approach in the search for effective treatments for castration-sensitive prostate cancer, which, if clinically validated, could potentially lead to new breakthrough therapies.

Improving the selectivity of antimicrobial peptides from anuran skin.

Anuran skin is known to be a rich source of antimicrobial peptides although their therapeutic potential is often limited due to their toxicity against mammalian cells. The analysis of structure-activity relationships among anuran antimicrobial peptides provided the parameters to construct the "Mutator" tool for improving their selectivity for bacterial cells, by suggesting appropriate point substitutions. Double substitution analogues [K2, K16] of the Xenopus tropicalis peptide XT-7 and [I2, K19] of the Ascaphus truei peptide ascaphin-8 were predicted by this tool to have an increased 'therapeutic index' (TI = HC(50)/MIC for erythrocytes with respect to bacteria) > 80. The mutated peptides were synthesized and respectively found to have experimental TI values > 130 for S. aureus or E. coli, a considerable improvement with respect to TI < 37 for the parent compounds. Circular dichroism studies of the mutated peptides suggested this may in part be due to variations in the α-helical structure. For P. aeruginosa, which is more resistant to XT-7, the TI increased in the mutated peptide from 5 to >270, also due to a significant improvement in minimal inhibitory concentration. We have shown that the Mutator tool is capable of suggesting limited variations in natural anuran peptides capable of increasing peptide selectivity, by decreasing toxicity against mammalian erythrocytes, in general without compromising antibacterial activity. The tool is freely available on the Mutator Web server at http://split4.pmfst.hr/mutator/.

DADP: the database of anuran defense peptides.

SUMMARY: Anuran tissues, and especially skin, are a rich source of bioactive peptides and their precursors. We here present a manually curated database of antimicrobial and other defense peptides with a total of 2571 entries, most of them in the precursor form with demarcated signal peptide (SP), acidic proregion(s) and bioactive moiety(s) corresponding to 1923 non-identical bioactive sequences. Search functions on the corresponding web server facilitate the extraction of six distinct SP classes. The more conserved of these can be used for searching cDNA and UniProtKB databases for potential bioactive peptides, for creating PROSITE search patterns, and for phylogenetic analysis.

Knowledge-based computational methods for identifying or designing novel, non-homologous antimicrobial peptides.

We describe computational approaches for identifying promising lead candidates for the development of peptide antibiotics, in the context of quantitative structure-activity relationships (QSAR) studies for this type of molecule. A first approach deals with predicting the selectivity properties of generated antimicrobial peptide sequences in terms of measured therapeutic indices (TI) for known antimicrobial peptides (AMPs). Based on a training set of anuran AMPs, the concept of sequence moments was used to construct algorithms that could predict TIs for a second test set of natural AMPs and could also predict the effect of point mutations on TI values. This approach was then used to design peptide antibiotics (adepantins) not homologous to known natural or synthetic AMPs. In a second approach, many novel putative AMPs were identified from DNA sequences in EST databases, using the observation that, as a rule, specific subclasses of highly conserved signal peptides are associated exclusively with AMPs. Both anuran and teleost sequences were used to elucidate this observation and its implications. The predicted therapeutic indices of identified sequences could then be used to identify new types of selective putative AMPs for future experimental verification.