Biology subjects

Oas, T. G.

Publications and source records attributed to Oas, T. G..

4 recordsLinked to original sources

bioRxiv2026

The protein binding domains of staphylococcal protein A fold independently and form an N- to C-terminal gradient of increasing stability.

Surface factors that contribute to the virulence of Staphylococcus aureus have become therapeutic targets in the treatment of illness associated with this bacterium. Staphylococcal protein A (SpA) is a well-known contributor to S. aureus toxicity and virulence, although relatively little is known about protein A and how its biological function has evolved. SpA is displayed on the surface of the bacterium and contains 5 nearly identical helical ({approx} 60 aa) domains that bind antibodies with high affinity (Kd {approx} 10 nM). The folding free energies of only domains E and B have been determined. In this study we used intrinsic fluorescence detected denaturation to measure the folding thermodynamics of each domain in isolation and in the native multidomain context using a construct that includes the N-terminal half of the mature protein (SpA-N). We also constructed a series of proteins with 1 to 5 repeats of B domain, linked exactly as the five domains of WT SpA are linked. We used nearest neighbor thermodynamic models to explicitly demonstrate that the domains in B domain repeat proteins fold independently. We also showed that the domains in SpA-N fold independently by comparing the folding free energies of domains in isolation and in their multidomain context. Previous dynamic NMR experiments detected highly flexible linkers between domains in 5B, suggesting that the domains of SpA are structurally independent, which is likely responsible for the lack of thermodynamic coupling. Our results also showed a steep increase in domain stability from the N-to C-terminus in SpA-N, from 0.97 {+/-} 0.05 to 5.57 {+/-} 0.28 kcal/mol. We hypothesize that this stability gradient is related to efficient secretion of protein A.

biophysics↗

bioRxiv2025

Predicting pose distribution of protein domains connected by flexible linkers is an unsolved problem

In CASP16, we assessed the ability of computational methods to predict the distribution of relative orientations of two domains tethered by a flexible linker. The range of interdomain distances and orientations (poses) of such domain-linker-domain (D-L-D) proteins can play an important role in protein function, allostery, aggregation, and the thermodynamics of binding. The CASP16 Conformational Ensembles Experiment included two challenges to predict the interdomain pose distribution of a Staphylococcal protein A (SpA) D-L-D construct, called ZLBT-C, in which two of SpAs five nearly identical domains are connected by either (1) a six-residue wild-type (WT) linker (kadnkf), or (2) an all-glycine (Gly6) linker. The wild-type linker has a highly conserved sequence and is thought to contribute to the energetic barrier for binding with host antibodies. Ground truth was provided by nuclear magnetic resonance (NMR) residual dipolar coupling (RDC) data on WT protein and small angle X-ray scattering (SAXS) data on both proteins in solution. Twenty-five predictor groups submitted 35 sets of predicted conformational distributions, in the form of population-weighted finite ensembles of discrete structures. Unlike traditional CASP assessments that compare predicted atomic models to experimental atomic models, the accuracy of these predictions was assessed by back-calculating NMR RDCs and SAXS curves from each ensemble of atomic models and comparing these results to respective experimental data. Accuracy was also assessed by using kernelization to compare ensembles to the continuous orientational distributions optimally fit to experimental data. In our assessment, predictions spanned a wide range of accuracy, but none were close fits to the combined NMR and SAXS data. In addition, none were able to recapitulate the observed difference between WT and Gly6 proteins, as observed in the SAXS data. These results, and our analysis, highlighted strengths and weaknesses, plus complementarity of NMR RDC and SAXS analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/650885v2_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@148f520org.highwire.dtl.DTLVardef@d5450eorg.highwire.dtl.DTLVardef@1a8c6aborg.highwire.dtl.DTLVardef@18b2b5b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2023

Efficient Enumeration and Visualization of Helix-coil Ensembles

Helix-coil models are routinely used to interpret CD data of helical peptides or predict the helicity of naturally-occurring and designed polypeptides. However, a helix-coil model contains significantly more information than mean helicity alone, as it defines the entire ensemble - the equilibrium population of every possible helix-coil configuration - for a given sequence. Many desirable quantities of this ensemble are either not obtained as ensemble averages, or are not available using standard helicity-averaging calculations. Enumeration of the entire ensemble can allow calculation of a wider set of ensemble properties, but the exponential size of the configuration space typically renders this intractable. We present an algorithm that efficiently approximates the helix-coil ensemble to arbitrary accuracy, by sequentially generating a list of the M highest populated configurations in descending order of population. Truncating this list of (configuration, population) pairs at a desired accuracy provides an approximating sub-ensemble. We demonstrate several uses of this approach for providing insight into helix-coil ensembles and folding mechanisms, including landscape visualization. O_TEXTBOXSIGNIFICANCE Helix-coil models define the probability distribution of helix-coil configurations for a polypeptide (a helix-coil ensemble). Each configuration specifies which residues are -helical and which are not. We used an accurate helix-coil model, paired with concepts from the field of probabilistic graphical modeling, to devise an algorithm capable of enumerating helix-coil configurations in order of decreasing probability. By enumerating ensembles for a representative set of peptides we find that helix-coil ensembles tend to be highly concentrated, with the vast majority of probability mass assigned to a relatively small set of configurations from a configuration space that is often astronomical in size. This result facilitates the development of new and accurate methods for analyzing and predicting the behavior of helical polypeptides. C_TEXTBOX

biophysics↗

bioRxiv2017

Continuous interdomain orientation distributions reveal components of binding thermodynamics

1The flexibility of biological macromolecules is an important structural determinant of function. Unfortunately, the correlations between different motional modes are poorly captured by discrete ensemble representations. Here, we present new ways to both represent and visualize correlated interdomain motions. Interdomain motions are determined directly from residual dipolar couplings (RDCs), represented as a continuous conformational distribution, and visualized using the disk-on-sphere (DoS) representation. Using the DoS representation, features of interdomain motions, including correlations, are intuitively visualized. The representation works especially well for multidomain systems with broad conformational distributions. This analysis also can be extended to multiple probability density modes, using a Bingham mixture model. We use this new paradigm to study the interdomain motions of staphylococcal protein A, which is a key virulence factor contributing to the pathogenicity of S. aureus. We capture the smooth transitions between important states and demonstrate the utility of continuous distribution functions for computing components of binding thermodynamics. Such insights allow the dissection the dynamic structural components of functionally important intermolecular interactions.

biophysics↗