Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library

Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact––increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.

Synergistic sequence contributions bias glycation outcomes

The methylglyoxal-derived hydroimidazolone isomer, MGH-1, is an abundant advanced glycation end-product (AGE) associated with disease and age-related disorders. As AGE formation occurs spontaneously and without an enzyme, it remains unknown why certain sites on distinct proteins become modified with specific AGEs. Here, we use a combinatorial peptide library to determine the chemical features that favor MGH-1. When properly positioned, tyrosine is found to play an active mechanistic role that facilitates MGH-1 formation. This work offers mechanistic insight connecting multiple AGEs, including MGH-1 and carboxyethylarginine (CEA), and reconciles the role of negative charge in influencing glycation outcomes. Further, this study provides clear evidence that glycation outcomes can be influenced through long- or medium-range cooperative interactions. This work demonstrates that these chemical features also predictably template selective glycation on full-length protein targets expressed in mammalian cells. This information is vital for developing methods that control glycation in living cells and will enable the study of glycation as a functional post-translational modification.

Efficient Screening of Combinatorial Peptide Libraries by Spatially Ordered Beads Immobilized on Conventional Glass Slides

Screening of one-bead-one-compound (OBOC) libraries is a proven procedure for the identification of protein-binding ligands. The demand for binders with high affinity and specificity towards various targets has surged in the biomedical and pharmaceutical field in recent years. The traditional peptide screening involves tedious steps such as affinity selection, bead picking, sequencing, and characterization. Herein, we present a high-throughput “all-on-one chip” system to avoid slow and technically complex bead picking steps. On a traditional glass slide provided with an electrically conductive tape, beads of a combinatorial peptide library are aligned and immobilized by application of a precision sieve. Subsequently, the chip is incubated with a fluorophore-labeled target protein. In a fluorescence scan followed by matrix-assisted laser desorption/ionization (MALDI)-time of flight (TOF) mass spectrometry, high-affinity binders are directly and unambiguously sequenced with high accuracy without picking of the positive beads. The use of an optimized ladder sequencing approach improved the accuracy of the de-novo sequencing step to nearly 100%. The new technique was validated by employing a FLAG-based model system, identifying new peptide binders for the monoclonal M2 anti-FLAG antibody, and was finally utilized to search for IgG-binding peptides. In the present format, more than 30,000 beads can be screened on one slide.