helixCAM: A platform for programmable cellular assembly in bacteria and human cells.

Interactions between cells are indispensable for signaling and creating structure. The ability to direct precise cell-cell interactions would be powerful for engineering tissues, understanding signaling pathways, and directing immune cell targeting. In humans, intercellular interactions are mediated by cell adhesion molecules (CAMs). However, endogenous CAMs are natively expressed by many cells and tend to have cross-reactivity, making them unsuitable for programming specific interactions. Here, we showcase "helixCAM," a platform for engineering synthetic CAMs by presenting coiled-coil peptides on the cell surface. helixCAMs were able to create specific cell-cell interactions and direct patterned aggregate formation in bacteria and human cells. Based on coiled-coil interaction principles, we built a set of rationally designed helixCAM libraries, which led to the discovery of additional high-performance helixCAM pairs. We applied this helixCAM toolkit for various multicellular engineering applications, such as spherical layering, adherent cell targeting, and surface patterning.

Sitetack: A deep learning model that improves PTM prediction by using known PTMs.

MOTIVATION: Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their analyses compromise success. RESULTS: We evaluated the use of known PTM sites in prediction via sequence-based deep learning algorithms. For each PTM, known locations of that PTM were encoded as a separate amino acid before sequences were encoded via word embedding and passed into a convolutional neural network that predicts the probability of that PTM at a given site. Without labeling known PTMs, our models are on par with others. With labeling, however, we improved significantly upon extant models. Moreover, knowing PTM locations can increase the predictability of a different PTM. Our findings highlight the importance of PTMs for the installation of additional PTMs. We anticipate that including known PTM locations will enhance the performance of other proteomic machine learning algorithms. AVAILABILITY AND IMPLEMENTATION: Sitetack is available as a web tool at https://sitetack.net; the source code, representative datasets, instructions for local use, and select models are available at https://github.com/clair-gutierrez/sitetack. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mammalian Esterase Activity: Implications for Peptide Prodrugs.

As a traceless, bioreversible modification, the esterification of carboxyl groups in peptides and proteins has the potential to increase their clinical utility. An impediment is the lack of strategies to quantify esterase-catalyzed hydrolysis rates for esters in esterified biologics. We have developed a continuous Förster resonance energy transfer (FRET) assay for esterase activity based on a peptidic substrate and a protease, Glu-C, that cleaves a glutamyl peptide bond only if the glutamyl side chain is a free acid. Using pig liver esterase (PLE) and human carboxylesterases, we validated the assay with substrates containing simple esters (e.g., ethyl) and esters designed to be released by self-immolation upon quinone methide elimination. We found that simple esters were not cleaved by esterases, likely for steric reasons. To account for the relatively low rate of quinone methide elimination, we extended the mathematics of the traditional Michaelis-Menten model to conclude with a first-order intermediate decay step. By exploring two regimes of our substrate → intermediate → product (SIP) model, we evaluated the rate constants for the PLE-catalyzed cleavage of an ester on a glutamyl side chain (kcat/KM = 1.63 × 103 M-1 s-1) and subsequent spontaneous quinone methide elimination to regenerate the unmodified peptide (kI = 0.00325 s-1; t1/2 = 3.55 min). The detection of esterase activity was also feasible in the human intestinal S9 fraction. Our assay and SIP model increase the understanding of the release kinetics of esterified biologics and facilitate the rational design of efficacious peptide prodrugs.

Chemoselective Caging of Carboxyl Groups for On-Demand Protein Activation with Small Molecules.

Tools for on-demand protein activation enable impactful gain-of-function studies in biological settings. Thus far, however, proteins have been chemically caged at primarily Lys, Tyr, and Sec, typically through the genetic encoding of unnatural amino acids. Herein, we report that the preferential reactivity of diazo compounds with protonated acids can be used to expand this toolbox to solvent-accessible carboxyl groups with an elevated pKa value. As a model protein, we employed lysozyme (Lyz), which has an active-site Glu35 residue with a pKa value of 6.2. A diazo compound with a bioorthogonal self-immolative handle esterified Glu35 selectively, inactivating Lyz. The hydrolytic activity of the caged Lyz on bacterial cell walls was restored with two small-molecule triggers. The decaging was more efficient by small molecules than by esterases. This simple chemical strategy was also applied to a hemeprotein and an aspartyl protease, setting the stage for broad applicability.

Sitetack: A Deep Learning Model that Improves PTM Prediction by Using Known PTMs.

Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their analyses compromise success. Here we evaluate the use of known PTM sites in prediction via sequence-based deep learning algorithms. Specifically, PTM locations were encoded as a separate amino acid before sequences were encoded via word embedding and passed into a convolutional neural network that predicts the probability of a modification at a given site. Without labeling known PTMs, our model is on par with others. With labeling, however, we improved significantly upon extant models. Moreover, knowing PTM locations can increase the predictability of a different PTM. Our findings highlight the importance of PTMs for the installation of additional PTMs. We anticipate that including known PTM locations will enhance the performance of other proteomic machine learning algorithms.