Thioamide substitutions in peptides can be used as fluorescence quenchers in protease sensors and as stabilizing modifications of hormone analogs. To guide these applications in the context of serine proteases, we here examine the cleavage of several model substrates, scanning a thioamide between the P3 and P3′ positions, and identify perturbing positions for thioamide substitution. While all serine proteases tested were affected by P1 thioamidation, certain proteases were also significantly affected by other thioamide positions. We demonstrate how these findings can be applied by harnessing the combined P3/P1 effect of a single thioamide on kallikrein proteolysis to protect two key positions in a neuropeptide Y-based imaging probe, increasing its serum half-life to >24 h while maintaining potency for binding to Y1 receptor expressing cells. Such stabilized peptide probes could find application in imaging cell populations in animal models or even in clinical applications such as fluorescence-guided surgery.

Thioamide substitutions of the peptide backbone have been shown to stabilize therapeutic and imaging peptides toward proteolysis. In order to rationally design thioamide modifications, we have developed a novel Rosetta custom score function to classify thioamide positional effects on proteolysis in substrates of serine and cysteine proteases. Peptides of interest were docked into proteases using the FlexPepDock application in Rosetta. Docked complexes were modified to contain thioamides parametrized through the creation of custom atom types in Rosetta based on ab intio simulations. Thioamide complexes were simulated, and the resultant structural complexes provided features for machine learning classification as the decomposed values of the Rosetta score function. An ensemble, majority voting model was developed to be a robust predictor of previously unpublished thioamide proteolysis holdout data. Theoretical control simulations with pseudo-atoms that modulate only one physical characteristic of the thioamide show differential effects on prediction accuracy by the optimized voting classification model. These pseudo-atom model simulations, as well as statistical analyses of the full thioamide simulations, implicate steric effects on peptide binding as being primarily responsible for thioamide positional effects on proteolytic resistance.

Using a combinatorial peptide library that is based on the one-bead one-peptide approach we identified 14 peptide substrates for the c-ABL protein tyrosine kinase, which define three distinct consensus sequence groups. This is distinct from many serine/threonine kinases, which often phosphorylate only one major consensus sequence. The three consensus sequences accurately predict phosphorylation sites in cellular ABL substrates proven to play a role in cell signaling. Our data suggest that protein tyrosine kinases have evolved to recognize multiple substrate motifs.

Information on the effects of sidechain and backbone modification on the activity of cathepsin (Cts) L, V, K, S, and B was used to design a thioamide peptide that is inert to all Cts and selectively inhibits Cts L.

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This paper proposed a new methodology for machine learning in 2-dimensional space (2-D ML) in inline coordinates. It is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. It allows discovering n-D patterns in 2-D space without loss of n-D information using graph representations of n-D data in 2-D. Specifically, it can be done with the inline based coordinates in different modifications, including static and dynamic ones. The classification and regression algorithms based on these inline coordinates were introduced. A successful case study based on a benchmark data demonstrated the feasibility of the approach. This approach helps to consolidate further a whole new area of full 2-D machine learning as a promising ML methodology. It has advantages of abilities to involve actively the end-users into the discovering of models and their justification. Another advantage is providing interpretable ML models.

Using a combinatorial peptide library method, we identified Y1YGSFK as an efficient and specific peptide substrate for pp60 c-src protein tyrosine kinase (PTK) [Lain et al., Int. J. Pept. Protein Res., 45 (1995) 587]. Employing YIYGSFK as a template, we synthesized and evaluated a series of pseudosubstrate-based inhibitors for pp60 c-src. We found that the efficiency of a given inhibitor was highly dependent on the specific tyrosine analog used at the phosphorylation site of the substrate. One of these pseudosubstrate inhibitors, YI(2'-Nal)GSFK, selectively inhibited the kinase activity of pp60 °sr~, with a K i of 24 BM. This peptide inhibitor exhibited selectivity for pp60 ..... as compared to other PTKs tested, such as c-Abl and Bcr-Abl. Our results suggest that selective inhibitors for a specific PTK can be developed when the structure of a specific and efficient small peptide substrate for this PTK can be used as a template for structure modification.

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