Abstract
Phage Display is a powerful method for the identification of peptide binding to targets of variable complexities and tissues, from unique molecules to the internal surfaces of vessels of living organisms. Particularly for in vivo screenings, the resulting repertoires can be very complex and difficult to study with traditional approaches. Next Generation Sequencing (NGS) opened the possibility to acquire high resolution overviews of such repertoires and thus facilitates the identification of binders of interest. Additionally, the ever-increasing amount of available genome/proteome information became satisfactory regarding the identification of putative mimicked proteins, due to the large scale on which partial sequence homology is assessed. However, the subsequent production of massive data stresses the need for high-performance computational approaches in order to perform standardized and insightful molecular network analysis. Systems-level analysis is essential for efficient resolution of the underlying molecular complexity and the extraction of actionable interpretation, in terms of systemic biological processes and pathways that are systematically perturbed. In this work we introduce PepSimili, an integrated workflow tool, which performs mapping of massive peptide repertoires on whole proteomes and delivers a streamlined, systems-level biological interpretation. The tool employs modules for modeling and filtering of background noise due to random mappings and amplifies the biologically meaningful signal through coupling with BioInfoMiner, a systems interpretation tool that employs graph-theoretic methods for prioritization of systemic processes and corresponding driver genes. The current implementation exploits the Galaxy environment and is available online. A case study using public data is presented, with and without a control selection.
Highlights
Phage Display has been widely used to select peptides binding to a variety of targets, in vitro or in vivo, with complexities ranging from a single macromolecule (Rodi and Makowski, 1999; Bábícková et al, 2013) to diffuse pathological lesions (Pasqualini and Ruoslahti, 1996)
Complex selections were poorly analyzed before the introduction of Generation Sequencing (NGS), which offered a detailed view of the peptide sequences (Dias-Neto et al, 2009)
A tool more adapted to analysis of phage display data, named PepTeam was developed by Hume et al (2013), based on an algorithm producing all the ungapped matches of the peptides of a repertoire, compared against a set of proteins
Summary
Phage Display has been widely used to select peptides binding to a variety of targets, in vitro or in vivo, with complexities ranging from a single macromolecule (Rodi and Makowski, 1999; Bábícková et al, 2013) to diffuse pathological lesions (Pasqualini and Ruoslahti, 1996). The hypothesis of mimicked proteins was advanced, based on the assumption that some peptides share sequence similarity with protein domains, and mimic the physiological interaction of the protein domain with its targets In this scope, sequence comparison was usually performed using available tools, performing probabilistic (BLAST) (Altschul et al, 1990) or best-match (Needleman– Wunsch) (Smith and Waterman, 1981) mappings. PepSimili integrates the mapping function of PepTeam and extends the analysis with (a) an evaluation and subtraction of the local noise due to random mappings, (b) the subtraction of the signals produced by a control repertoire, and (c) filtering of derived proteins using a mapping score
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
