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Abstract Peptides are important biomolecules, and their interactions with proteins make them useful in sensing and therapeutic applications. Computational peptide design methods can benefit from high-quality peptide-protein structures paired with thermodynamic data. The Predicted and Experimental Peptide Binding Information (PEPBI) database provides 329 predicted peptide-protein complexes, each based on an experimentally determined structure, with corresponding experimental measurements of changes in Gibbs free energy, enthalpy, and entropy. For each complex, 40 properties calculated using Rosetta’s Interface Analyzer are included. Complexes were selected for inclusion in PEPBI using eight stringent structural criteria, including peptide length (5–20 residues), structure resolution (≤2.0 Å), less than 30% sequence identity between complexes, and having a corresponding unbound protein structure in the Protein Data Bank with at least 90% sequence identity to the bound form with minimal changes in the binding pocket. PEPBI is expected to be of use for the development of computational methods for peptide design with desired binding properties to protein targets. 

ABSTRACT The last few years have seen the rapid proliferation of machine learning methods to design binding proteins. Although these methods have shown large increases in experimental success rates compared to prior approaches, the majority of their predictions fail when they are experimentally tested. It is evident that computational methods still struggle to distinguish the features of real protein binding interfaces from false predictions. Short molecular dynamics simulations of 20 antibody–protein complexes were conducted to identify features of interactions that should occur in binding interfaces. Intermolecular salt bridges, hydrogen bonds, and hydrophobic interactions were evaluated for their persistences, energies, and stabilities during the simulations. It was found that only the hydrogen bonds where both residues are stabilized in the bound complex are expected to persist and meaningfully contribute to binding between the proteins. In contrast, stabilization was not a requirement for salt bridges and hydrophobic interactions to persist. Still, interactions where both residues are stabilized in the bound complex persist significantly longer and have significantly stronger energies than other interactions. Two hundred and twenty real antibody–protein complexes and 8194 decoy complexes were used to train and test a random forest classifier using the features of expected persistent interactions identified in this study and the macromolecular features of interaction energy (IE), buried surface area (BSA), IE/BSA, and shape complementarity. It was compared to a classifier trained only on the expected persistent interaction features and another trained only on the macromolecular features. Inclusion of the expected persistent interaction features reduced the false positive rate of the classifier by two‐ to five‐fold across a range of true positive classification rates. 

We present scanning electrochemical cell microscopy (SECCM) studies of Ag nucleation and growth on carbon and indium tin oxide (ITO) electrodes. 

Elastin-like polymers are a class of stimuliresponsive protein polymers that hold immense promise in applications such as drug delivery, hydrogels, and biosensors. Yet, understanding the intricate interplay of factors influencing their stimuli-responsive behavior remains a challenging frontier. Using temperature-controlled dynamic light scattering and zeta potential measurements, we investigate the interactions between buffer, pH, salt, water, and protein using an elastin-like polymer containing ionizable lysine residues. We observed the elevation of transition temperature in the presence of the common buffering agent HEPES at low concentrations, suggesting a “salting-in” effect of HEPES as a cosolute through weak association with the protein. Our findings motivate a more comprehensive investigation of the influence of buffer and other cosolute molecules on elastin-like polymer behavior. 

Two-dimensional semiconductors (2DSCs) are attractive materials for a variety of applications in electronics, photovoltaics, and catalysis. Despite their promise, it is often unclear how the performance of 2DSCs is influenced by structural defects present in these materials such as atomic vacancies or step-edges. A better fundamental understanding of how such structural features influence the generation and transport of charge carriers in 2DSCs will be critical in the pursuit of improved practical devices moving forward. In this Opinion, we highlight how electrochemistry can be leveraged to reveal fascinating insights into the behavior of 2DSCs. Recent advancements in techniques for mapping the rate of photoelectrochemical processes at 2DSCs are outlined and salient experiments employing these techniques are discussed. We conclude with sharing our perspective on opportunities within this field moving forward. 

Well-ordered nanoparticle arrays are attractive platforms for a variety of analytical applications, but the fabrication of such arrays is generally challenging. Here, it is demonstrated that scanning electrochemical cell microscopy (SECCM) can be used as a powerful, instantly reconfigurable tool for the fabrication of ordered nanoparticle arrays. Using SECCM, Ag nanoparticle arrays were straightforwardly fabricated via electrodeposition at the interface between a substrate electrode and an electrolyte-filled pipet. By dynamically monitoring the currents flowing in an SECCM cell, individual nucleation and growth events could be detected and controlled to yield individual nanoparticles of controlled size. Characterization of the resulting arrays demonstrate that this SECCM-based approach enables spatial control of nanoparticle location comparable with the terminal diameter of the pipet employed and straightforward control over the volume of material deposited at each site within an array. These results provide further evidence for the utility of probe-based electrochemical techniques such as SECCM as tools for surface modification in addition to analysis. 

Abstract The use of engineered cells, tissues, and organs has the opportunity to change the way injuries and diseases are treated. Commercialization of these groundbreaking technologies has been limited in part by the complex and costly nature of their manufacture. Process-related variability and even small changes in the manufacturing process of a living product will impact its quality. Without real-time integrated detection, the magnitude and mechanism of that impact are largely unknown. Real-time and non-destructive sensor technologies are key for in-process insight and ensuring a consistent product throughout commercial scale-up and/or scale-out. The application of a measurement technology into a manufacturing process requires cell and tissue developers to understand the best way to apply a sensor to their process, and for sensor manufacturers to understand the design requirements and end-user needs. Furthermore, sensors to monitor component cells’ health and phenotype need to be compatible with novel integrated and automated manufacturing equipment. This review summarizes commercially relevant sensor technologies that can detect meaningful quality attributes during the manufacturing of regenerative medicine products, the gaps within each technology, and sensor considerations for manufacturing.