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Abstract We have developed an algorithm, ParSe, which accurately identifies from the primary sequence those protein regions likely to exhibit physiological phase separation behavior. Originally, ParSe was designed to test the hypothesis that, for flexible proteins, phase separation potential is correlated to hydrodynamic size. While our results were consistent with that idea, we also found that many different descriptors could successfully differentiate between three classes of protein regions: folded, intrinsically disordered, and phase‐separating intrinsically disordered. Consequently, numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. Built from that finding, ParSe 2.0 uses an optimal set of property scales to predict domain‐level organization and compute a sequence‐based prediction of phase separation potential. The algorithm is fast enough to scan the whole of the human proteome in minutes on a single computer and is equally or more accurate than other published predictors in identifying proteins and regions within proteins that drive phase separation. Here, we describe a web application for ParSe 2.0 that may be accessed through a browser by visitinghttps://stevewhitten.github.io/Parse_v2_FASTAto quickly identify phase‐separating proteins within large sequence sets, or by visitinghttps://stevewhitten.github.io/Parse_v2_webto evaluate individual protein sequences. 

Abstract The orientation adopted by proteins on nanoparticle surfaces determines the nanoparticle’s bioactivity and its interactions with living systems. Here, we present a residue-based affinity scale for predicting protein orientation on citrate-gold nanoparticles (AuNPs). Competitive binding between protein variants accounts for thermodynamic and kinetic aspects of adsorption in this scale. For hydrophobic residues, the steric considerations dominate, whereas electrostatic interactions are critical for hydrophilic residues. The scale rationalizes the well-defined binding orientation of the small GB3 protein, and it subsequently predicts the orientation and active site accessibility of two enzymes on AuNPs. Additionally, our approach accounts for the AuNP-bound activity of five out of six additional enzymes from the literature. The model developed here enables high-throughput predictions of protein behavior on nanoparticles, and it enhances our understanding of protein orientation in the biomolecular corona, which should greatly enhance the performance and safety of nanomedicines used in vivo. 

Abstract In the last decade, nanoparticles (NPs) have become a key tool in medicine and biotechnology as drug delivery systems, biosensors and diagnostic devices. The composition and surface chemistry of NPs vary based on the materials used: typically organic polymers, inorganic materials, or lipids. Nanoparticle classes can be further divided into sub‐categories depending on the surface modification and functionalization. These surface properties matter when NPs are introduced into a physiological environment, as they will influence how nucleic acids, lipids, and proteins will interact with the NP surface. While small‐molecule interactions are easily probed using NMR spectroscopy, studying protein‐NP interactions using NMR introduces several challenges. For example, globular proteins may have a perturbed conformation when attached to a foreign surface, and the size of NP‐protein conjugates can lead to excessive line broadening. Many of these challenges have been addressed, and NMR spectroscopy is becoming a mature technique forin situanalysis of NP binding behavior. It is therefore not surprising that NMR has been applied to NP systems and has been used to study biomolecules on NP surfaces. Important considerations include corona composition, protein behavior, and ligand architecture. These features are difficult to resolve using classical surface and material characterization strategies, and NMR provides a complementary avenue of characterization. In this review, we examine how solution NMR can be combined with other analytical techniques to investigate protein behavior on NP surfaces. 

Abstract Each year, bovine respiratory disease (BRD) results in significant economic loss in the cattle sector, and novel metabolic profiling for early diagnosis represents a promising tool for developing effective measures for disease management. Here, 1 H-nuclear magnetic resonance ( 1 H-NMR) spectra were used to characterize metabolites from blood plasma collected from male dairy calves (n = 10) intentionally infected with two of the main BRD causal agents, bovine respiratory syncytial virus (BRSV) and Mannheimia haemolytica (MH), to generate a well-defined metabolomic profile under controlled conditions. In response to infection, 46 metabolites (BRSV = 32, MH = 33) changed in concentration compared to the uninfected state. Fuel substrates and products exhibited a particularly strong effect, reflecting imbalances that occur during the immune response. Furthermore, 1 H-NMR spectra from samples from the uninfected and infected stages were discriminated with an accuracy, sensitivity, and specificity ≥ 95% using chemometrics to model the changes associated with disease, suggesting that metabolic profiles can be used for further development, understanding, and validation of novel diagnostic tools.