S2: Disease Understanding and Drug Discovery
Crowdsourcing towards Disease Understanding and Drug Discovery
CSIR-Institute of Microbial Technology
The first problem addressed by crowdsourcing was probably the 1714 Longitude Prize—for a marine clock that would enable accurate calculation of longitude at sea. Since then efforts such as Wikipedia in 2001 and Open Source Drug Discovery in 2007 have proven the power of this approach. The talk will discuss examples of crowdsourcing as a scientific tool for genome annotation as well as for prediction of potential inhibitors. The first example will explain extensive genome re-annotation for constructing a systems level protein interaction map of Mycobacterium tuberculosis (Mtb) with an objective of finding novel drug target candidates.
In addition, a novel method to comprehend the metabolic map of Mtb is also developed. The second part of the talk will discuss the development of dPABBs, a web server that facilitates the prediction and design of anti-biofilm peptides. Biofilms are being recognised for their causative role in persistent infections (like cystic fibrosis, otitis media, diabetic foot ulcers) and nosocomial diseases (biofilm-infected vascular catheters, implants and prosthetics). Given the clinical relevance of biofilms and their recalcitrance to conventional antibiotics, it is imperative that alternative therapeutics are proactively sought. dPABBs facilitates the prediction and design of anti-biofilm peptides.
The six SVM and Weka models implemented on dPABBs were observed to identify anti-biofilm peptides on the basis of their whole amino acid composition, selected residue features and the positional preference of the residues (maximum accuracy, sensitivity, specificity and MCC of 95.24%, 92.50%, 97.73% and 0.91, respectively, on the training datasets). Positive predictions were also obtained for 29 FDA-approved peptide drugs and ten antimicrobial peptides in clinical development, indicating at their possible repurposing for anti-biofilm therapy. The talk will conclude with recommendations of crowdsourcing models towards systems toxicology.