Pattern recognition methods for the prediction of chemical structures of fungal secondary metabolites

Non-Ribosomal Peptide Synthetases (NRPS) are mega synthetases that are predominantly found in bacteria and fungi. They produce small peptides that serve numerous biological functions and crucial ecological roles. Adenylation (A) domains of NRPSs catalyze ATP dependent activation of substrates harboring carboxy terminus. A-domain substrates include not only natural amino acids (D and L forms) but also non-proteinogenic amino acids. As the substrate repertoire is large and specificity rules for fungi are not established well, there is a difficulty in predicting substrates for fungal A-domains. In bacteria, ten amino acid residues were established as NRPS code, which determine specificity of A-domains. To study relationships between fungal A-domains and their specificity, the cluster analysis of NRPS code residues was done. NRPS code residues were encoded by physicochemical properties essential for binding small molecules and these residues were clustered. Cluster analysis showed similar NRPS codes for α-amino adipic acid, and tryptophan, etc. between bacteria and fungi. Fungal NRPS codes for substrates such as tyrosine, and proline, did not cluster together with bacteria, which indicates an independent evolution of substrate specificity in fungi. This emphasizes the need for the development of a fungus-specific prediction tool. Currently available A-domain substrate specificity prediction tools accurately identify substrates for bacteria but fail to provide correct predictions for fungi. A novel approach for fungal A-domain substrate specificity prediction is presented here. Neural Network based A-domain substrate specificity classifier (NNassc) was developed using Keras with TensorFlow backend. NNassc was trained solely using fungal NRPS codes and combines physicochemical and structural features for specificity predictions. Internal and external validation datasets of experimentally verified NRPS codes were used to assess the performance of NNassc

Discovery of Molecules that Modulate Protein-Protein Interactions in the Context of Human Proliferating Cell Nuclear Antigen-Associated Processes of DNA Replication and Damage Repair

Integral to cell viability is the homotrimeric protein complex Proliferating Cell Nuclear Antigen (PCNA) that encircles chromatin-bound DNA and functionally acts as a DNA clamp that provides topological sites for recruitment of proteins necessary for DNA replication and damage repair. PCNA has critical roles in the survival and proliferation of cells, as disease-associated dysregulation of associated functions can have dire effects on genome stability, leading to the formation of various malignancies ranging from non-Hodgkin’s lymphoma to skin, laryngeal, ocular, prostate and breast cancers. Here, a strategy was explored with PCNA as a drug target that may have wider implications for targeting protein-protein interactions (PPIs) as well as for fragment-based drug design. A design platform using peptidomimetic small molecules was developed that maps ideal surface binding interaction sites at a PPI interface before considering detailed conformations of an optimal ligand. A novel in silico multi-fragment, combinatorial screening approach was used to guide the selection and subsequent synthesis of tripeptoid ligands, which were evaluated in a PCNA-based competitive displacement assay. From the results, some of the peptoid-based compounds that were synthesized displayed the ability to disrupt the interaction between PCNA and a PIP box-containing peptide. The IC50 values of these compounds had similar or improved affinity to that of T2AA, an established inhibitor of PCNA-PIP box interactions. The information gained here could be useful for subsequent drug lead candidate identification