Selective C-Terminal Conjugation of Protease-Derived Native Peptides for Proteomic Measurements.

Bottom-up proteomic experiments often require selective conjugation or labeling of the N- and/or C-termini of peptides resulting from proteolytic digestion. For example, techniques based on surface fluorescence imaging are emerging as a promising route to high-throughput protein sequencing but require the generation of peptide surface arrays immobilized through single C-terminal point attachment while leaving the N-terminus free. While several robust approaches are available for selective N-terminal conjugation, it has proven to be much more challenging to implement methods for selective labeling or conjugation of the C-termini that can discriminate between the C-terminal carboxyl group and other carboxyl groups on aspartate and glutamate residues. Further, many approaches based on conjugation through amide bond formation require protection of the N-terminus to avoid unwanted cross-linking reactions. To overcome these challenges, herein, we describe a new strategy for single-point selective immobilization of peptides generated by protease digestion via the C-terminus. The method involves immobilization of peptides via lysine amino acids which are found naturally at the C-terminal end of cleaved peptides from digestions of certain serine endoproteinases, like LysC. This lysine and the N-terminus, the sole two primary amines in the peptide fragments, are chemically reacted with a custom phenyl isothiocyanate (EPITC) that contains an alkyne handle. Subsequent exposure of the double-modified peptides to acid selectively cleaves the N-terminal amino acid, while the modified C-terminus lysine remains unchanged. The alkyne-modified peptides with free N-termini can then be immobilized on an azide surface through standard click chemistry. Using this general approach, surface functionalization is demonstrated using a combination of X-ray photoelectron spectroscopy (XPS), ellipsometry, and atomic force microscopy (AFM).

Addressing the unmet need for visualizing conditional random fields in biological data.

BACKGROUND: The biological world is replete with phenomena that appear to be ideally modeled and analyzed by one archetypal statistical framework - the Graphical Probabilistic Model (GPM). The structure of GPMs is a uniquely good match for biological problems that range from aligning sequences to modeling the genome-to-phenome relationship. The fundamental questions that GPMs address involve making decisions based on a complex web of interacting factors. Unfortunately, while GPMs ideally fit many questions in biology, they are not an easy solution to apply. Building a GPM is not a simple task for an end user. Moreover, applying GPMs is also impeded by the insidious fact that the "complex web of interacting factors" inherent to a problem might be easy to define and also intractable to compute upon. DISCUSSION: We propose that the visualization sciences can contribute to many domains of the bio-sciences, by developing tools to address archetypal representation and user interaction issues in GPMs, and in particular a variety of GPM called a Conditional Random Field(CRF). CRFs bring additional power, and additional complexity, because the CRF dependency network can be conditioned on the query data. CONCLUSIONS: In this manuscript we examine the shared features of several biological problems that are amenable to modeling with CRFs, highlight the challenges that existing visualization and visual analytics paradigms induce for these data, and document an experimental solution called StickWRLD which, while leaving room for improvement, has been successfully applied in several biological research projects. Software and tutorials are available at http://www.stickwrld.org/.