Nicholas C. Fitzkee

The Gaussian-distributed random coil has been the dominant model for denatured proteins since the 1950s, and it has long been interpreted to mean that proteins are featureless, statistical coils in 6 M guanidinium chloride. Here, we demonstrate that random-coil statistics are not a unique signature of featureless polymers. The random-coil model does predict the experimentally determined coil dimensions of denatured proteins successfully. Yet, other equally convincing experiments have shown that denatured proteins are biased toward specific conformations, in apparent conflict with the random-coil model. We seek to resolve this paradox by introducing a contrived counterexample in which largely native protein ensembles nevertheless exhibit random-coil characteristics. Specifically, proteins of known structure were used to generate disordered conformers by varying backbone torsion angles at random for approximately 8% of the residues; the remaining approximately 92% of the residues remained fixed in their native conformation. Ensembles of these disordered structures were generated for 33 proteins by using a torsion-angle Monte Carlo algorithm with hard-sphere sterics; bulk statistics were then calculated for each ensemble. Despite this extreme degree of imposed internal structure, these ensembles have end-to-end distances and mean radii of gyration that agree well with random-coil expectations in all but two cases.

Gold nanoparticles (AuNPs) are an attractive delivery vector in biomedicine because of their low toxicity and unique electronic and chemical properties. AuNP bioconjugates can be used in many applications, including nanomaterials, biosensing, and drug delivery. While the phenomenon of spontaneous protein–AuNP adsorption is well-known, the structural and mechanistic details of this interaction remain poorly understood. As a result, predicting the orientation and structure of proteins on the nanoparticle surface remains a challenge. New techniques are therefore needed to characterize the structural properties of proteins as they bind to AuNPs. We have developed a straightforward and rapid NMR-based approach to quantitatively characterize the protein–AuNP interaction. This approach is immune to the inner filter effect, which complicates fluorescence measurements, and it can be performed without prior centrifugation of samples. Using a data set of six proteins, ranging in size from 3 to 583 residues, we measured the stoichiometry of binding to AuNPs with a diameter of 15 nm. The stoichiometry of binding can be predicted based on simple geometric considerations assuming that proteins remain globular on the AuNP surface. Using our approach, we find that a protein lacking cysteine residues can be displaced from AuNPs using a small organothiol compound, but proteins with surface cysteines are resistant to displacement. From this data we develop a model for adsorption consisting of three steps: an initial reversible association step, a rearrangement/reorientation step on the AuNP surface, and a final cysteine-dependent “hardening” step, after which binding becomes irreversible.

Biochar has been proposed as a soil amendment in agricultural applications due to its advantageous adsorptive properties, high porosity, and low cost. These properties allow biochar to retain soil nutrients, yet the effects of biochar on bacterial growth remain poorly understood. To examine how biochar influences microbial metabolism, Escherichia coli was grown in a complex, well-defined media and treated with either biochar or activated carbon. The concentration of metabolites in the media were then quantified at several time points using NMR spectroscopy. Several metabolites were immediately adsorbed by the char, including l-asparagine, l-glutamine, and l-arginine. However, we find that biochar quantitatively adsorbs less of these metabolic precursors when compared to activated carbon. Electron microscopy reveals differences in surface morphology after cell culture, suggesting that Escherichia coli can form biofilms on the surfaces of the biochar. An examination of significant compounds in the tricarboxylic acid cycle and glycolysis reveals that treatment with biochar is less disruptive than activated carbon throughout metabolism. While both biochar and activated carbon slowed growth compared to untreated media, Escherichia coli in biochar-treated media grew more efficiently, as indicated by a longer logarithmic growth phase and a higher final cell density. This work suggests that biochar can serve as a beneficial soil amendment while minimizing the impact on bacterial viability. In addition, the experiments identify a mechanism for biochar's effectiveness in soil conditioning and reveal how biochar can alter specific bacterial metabolic pathways.

Protein phase separation is thought to be a primary driving force for the formation of membrane-less organelles, which control a wide range of biological functions from stress response to ribosome biogenesis. Among phase-separating (PS) proteins, many have intrinsically disordered regions (IDRs) that are needed for phase separation to occur. Accurate identification of IDRs that drive phase separation is important for testing the underlying mechanisms of phase separation, identifying biological processes that rely on phase separation, and designing sequences that modulate phase separation. To identify IDRs that drive phase separation, we first curated datasets of folded, ID, and PS ID sequences. We then used these sequence sets to examine how broadly existing amino acid property scales can be used to distinguish between the three classes of protein regions. We found that there are robust property differences between the classes and, consequently, that numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. This result indicates that multiple, redundant mechanisms contribute to the formation of phase-separated droplets from IDRs. The top-performing scales were used to further optimize our previously developed predictor of PS IDRs, ParSe. We then modified ParSe to account for interactions between amino acids and obtained reasonable predictive power for mutations that have been designed to test the role of amino acid interactions in driving protein phase separation. Collectively, our findings provide further insight into the classification of IDRs and the elements involved in protein phase separation.