Travis Hoppe

Dependence of Internal Friction on Folding Mechanism

An outstanding challenge in protein folding is understanding the origin of “internal friction” in folding dynamics, experimentally identified from the dependence of folding rates on solvent viscosity. A possible origin suggested by simulation is the crossing of local torsion barriers. However, it was unclear why internal friction varied from protein to protein or for different folding barriers of the same protein. Using all-atom simulations with variable solvent viscosity, in conjunction with transition-path sampling to obtain reaction rates and analysis via Markov state models, we are able to determine the internal friction in the folding of several peptides and miniproteins. In agreement with experiment, we find that the folding events with greatest internal friction are those that mainly involve helix formation, while hairpin formation exhibits little or no evidence of friction. Via a careful analysis of folding transition paths, we show that internal friction arises when torsion angle changes are an important part of the folding mechanism near the folding free energy barrier. These results suggest an explanation for the variation of internal friction effects from protein to protein and across the energy landscape of the same protein.

Programmable nanoscaffolds that control ligand display to a G-protein-coupled receptor in membranes to allow dissection of multivalent effects.

A programmable ligand display system can be used to dissect the multivalent effects of ligand binding to a membrane receptor. An antagonist of the A2A adenosine receptor, a G-protein-coupled receptor that is a drug target for neurodegenerative conditions, was displayed in 35 different multivalent configurations, and binding to A2A was determined. A theoretical model based on statistical mechanics was developed to interpret the binding data, suggesting the importance of receptor dimers. Using this model, extended multivalent arrangements of ligands were constructed with progressive improvements in binding to A2A. The results highlight the ability to use a highly controllable multivalent approach to determine optimal ligand valency and spacing that can be subsequently optimized for binding to a membrane receptor. Models explaining the multivalent binding data are also presented.

An Equilibrium Model for the Combined Effect of Macromolecular Crowding and Surface Adsorption on the Formation of Linear Protein Fibrils

The formation of linear protein fibrils has previously been shown to be enhanced by volume exclusion or crowding in the presence of a high concentration of chemically inert protein or polymer, and by adsorption to membrane surfaces. An equilibrium mesoscopic model for the combined effect of both crowding and adsorption upon the fibrillation of a dilute tracer protein is presented. The model exhibits behavior that differs qualitatively from that observed in the presence of crowding or adsorption alone. The model predicts that in a crowded solution, at critical values of the volume fraction of crowder or intrinsic energy of the tracer-wall interaction, the tracer protein will undergo an extremely cooperative transition-approaching a step function-from existence as a slightly self-associated species in solution to existence as a highly self-associated and completely adsorbed species. Criteria for a valid experimental test of these predictions are presented.

Singular Value Decomposition of the Radial Distribution Function for Hard Sphere and Square Well Potentials

We compute the singular value decomposition of the radial distribution function for hard sphere, and square well solutions. We find that decomposes into a small set of basis vectors allowing for an extremely accurate representation at all interpolated densities and potential strengths. In addition, we find that the coefficient vectors describing the magnitude of each basis vector are well described by a low-order polynomial. We provide a program to calculate in this compact representation for the investigated parameter range.

Additional support for RCR: A validated article-level measure of scientific influence

In their comment, Janssens et al. [1] offer a critique of the Relative Citation Ratio (RCR), objecting to the construction of both the numerator and denominator of the metric. While we strongly agree that any measure used to assess the productivity of research programs should be thoughtfully designed and carefully validated, we believe that the specific concerns outlined in their correspondence are unfounded. Our original article acknowledged that RCR or, for that matter, any bibliometric measure has limited power to quantify the influence of any very recently published paper, because citation rates are inherently noisy when the absolute number of citations is small [2]. For this reason, in our iCite tool, we have not reported RCRs for papers published in the calendar year previous to the current year [3]. However, while agreeing with our initial assertion that RCR cannot be used to conclusively evaluate recent papers, Janssens et al. also suggest that the failure to report RCRs for new publications might unfairly penalize some researchers. While it is widely understood that it takes time to accurately assess the influence that new papers have on their field, we have attempted to accommodate this concern by updating iCite so that RCRs are now reported for all papers in the database that have at least 5 citations and by adding a visual indicator to flag values for papers published in the last 18 months, which should be considered provisional [3]. This modified practice will be maintained going forward. Regarding article citation rates of older articles, we have added data on the stability of RCR values to the “Statistics” page of the iCite website [4, 5]. We believe that these new data, which demonstrate that the vast majority of influential papers retain their influence over the period of an investigator’s career, should reassure users that RCR does not unfairly disadvantage older papers. Our analysis of the year-by-year changes in RCR values of National Institutes of Health (NIH)-funded articles published in 1991 reinforces this point (Fig 1). From 1992–2014, both on the individual level and in aggregate, RCR values are remarkably stable. For cases in which RCRs change significantly, the values typically increase. That said, we strongly believe that the potential for RCR to decrease over time is necessary and important; as knowledge advances and old models are replaced, publications rooted in those outdated models naturally become less influential. The RCR denominator (the expected citation rate [ECR]) is calculated by aggregating the article citation rates of peer papers that have the same field citation rate (FCR) and are published in the same year as the article in the numerator. FCR, as noted by Janssens et al., is defined as the collective 2-year journal citation rate for all papers in the co-citation network of the article being evaluated. This calculation is conceptually distinct from that used in determining journal impact factors; rather than relying on journal of publication to define an

Integer sequence discovery from small graphs

We have exhaustively enumerated all simple, connected graphs of a finite order and have computed a selection of invariants over this set. Integer sequences were constructed from these invariants and checked against the Online Encyclopedia of Integer Sequences (OEIS). 141 new sequences were added and six sequences were extended. From the graph database, we were able to programmatically suggest relationships among the invariants. It will be shown that we can readily visualize any sequence of graphs with a given criteria. The code has been released as an open-source framework for further analysis and the database was constructed to be extensible to invariants not considered in this work.