abstract: Peptides offer great promise as targeted affinity ligands, but the space of possible peptide sequences is vast, making experimental identification of lead candidates expensive, difficult, and uncertain. Computational modeling can narrow the search by estimating the affinity and specificity of a given peptide in relation to a predetermined protein target. The predictive performance of computational models of interactions of intermediate-length peptides with proteins can be improved by taking into account the stochastic nature of the encounter and binding dynamics. A theoretical case is made for the hypothesis that, because of the flexibility of the peptide and the structural complexity of the target protein, interactions are best characterized by an ensemble of possible bound configurations rather than a single “lock and key” fit. A model incorporating these factors is proposed and evaluated. A comprehensive dataset of 3,924 peptide-protein interface structures was extracted from the Protein Data Bank (PDB) and descriptors were computed characterizing the geometry and energetics of each interface. The characteristics of these interfaces are shown to be generally consistent with the proposed model, and heuristics for design and selection of peptide ligands are derived. The curated and energy-minimized interface structure dataset and a relational database containing the detailed results of analysis and energy modeling are made publicly available via a web repository. A novel analytical technique based on the proposed theoretical model, Virtual Scanning Probe Mapping (VSPM), is implemented in software to analyze the interaction between a target protein of known structure and a peptide of specified sequence, producing a spatial map indicating the most likely peptide binding regions on the protein target. The resulting predictions are shown to be superior to those of two other published methods, and support the validity of the stochastic binding model. / Dissertation/Thesis / Ph.D. Bioengineering 2010

Affinity ligands are important molecules used in affinity chromatography for purification of significant substances from complex mixtures. To find affinity ligands specific to important target molecules could be a challenging process. Cytiva uses the powerful phage display technique to find new promising affinity ligands. The phage display technique is a method run in several enrichment cycles. When developing new affinity ligands, a protein scaffold library with a diversity of up to 1010-1011 different protein scaffold variants is run through the enrichment cycles.  The result from the phage display rounds is screened for target molecule binding followed by sequencing, usually with one of the conventional screening methods ELISA or Biacore followed by Sanger sequencing. However, the throughput of these analyses are unfortunately very low, often with only a few hundred screened clones. Therefore, Next Generation Sequencing or NGS, has become an increasingly popular screening method for phage display libraries which generates millions of sequences from each phage display round. This creates a need for a robust data analysis pipeline to be able to interpret the large amounts of data.  In this project, a pipeline for analysis of NGS data of phage displayed libraries has been developed at Cytiva. Cytiva uses NGS as one of their screening methods of phage displayed protein libraries because of the high throughput compared to the conventional screening methods. The purpose is to find new affinity ligands for purification of essential substances used in drugs.  The pipeline has been created using the object-oriented programming language R and consists of several analyses covering the most important steps to be able to find promising results from the NGS data. With the developed pipeline the user can analyze the data on both DNA and protein sequence level and per position residue breakdown, as well as filter the data based on specific amino acids and positions. This gives a robust and thorough analysis which can lead to promising results that can be used in the development of novel affinity ligands for future purification products.