Biology subjects

Prior, C.

Publications and source records attributed to Prior, C..

4 recordsLinked to original sources

bioRxiv2026

Carbonara: a SAXS-guided seeding framework for exploring protein solution-state dynamics

Proteins in solution often populate conformational ensembles that differ from the static states captured by crystallography or AI-based structure prediction. Conventional molecular dynamics (MD) simulations often fail to cross the energy barriers separating these states on accessible timescales, and statistical reweighting cannot recover conformations never sampled. Here we present Carbonara, a framework that uses experimental small-angle X-ray scattering (SAXS) data to predict alternative physically plausible protein conformations. Carbonara builds on Wiggle, a standalone C-based SAXS forward model validated against explicit-solvent calculations and experimental benchmarks. Using two case studies, an AI-predicted multi-domain helicase (SMAR-CAL1) and a crystallographic antibody fragment (ChiLob7/4 IgG2), we show how seeding MD simulations from Carbonara conformations enables efficient exploration of solution-state conformational landscapes. In both cases, MD ensembles initiated from available models either fail to match the SAXS data or do so only after discarding nearly all sampled conformations, whereas Carbonara-seeded ensembles reach agreement while retaining the majority of conformations. Our modelling framework provides a route from static structural models of flexible multi-domain proteins and multimeric assemblies to solution-state ensembles.

biophysics↗

bioRxiv2023

Novel topological methods for identifying surprising protein tertiary structure relationships.

We present fast and simple-to-implement measures of the entanglement of protein tertiary structures which are appropriate for highly flexible structure comparison. These quantities are based on the writhing and crossing numbers heavily utilised in DNA topology studies which and which have shown some promising results when applied to proteins recently. Here we show how they can be applied in a novel manner across various scales of the proteins backbone to identify similar topologies which can be missed by more common RMSD, secondary structure or primary sequence based comparison methods. We derive empirical bounds on the entanglement implied by these measures and show how they can be used to constrain the search space of a protein for solution scattering, a method highly suited to determining the likely structure of proteins in solution where crystal structure or machine learning based predictions often fail to match experimental data. In addition we identify large scale helical geometries present in a large array of proteins, which are consistent across a number of different protein structure types and sequences. This is used in one specific case to demonstrate significant structural similarity between Rossmann fold and TIM Barrel proteins, a link which is potentially significant as attempts to engineer the latter have in the past produced the former. Finally we provide the SWRITHE python notebook to calculate these metrics. Author summaryThere is much interest in developing quantitative methods to compare different protein structures or identify common sub-structures across protein families. We present novel methods for studying and comparing protein structures based on the entanglement of their amino-acid backbone and demonstrate a number of their critical properties. First, they are shown to be especially useful in identifying similar protein entanglement for structures which may be seen as distinct via more established methods. Second, by studying the distribution of entanglement across a wide sample of proteins, we show that there exists a minimum expected amount (a lower bound) of entanglement given the proteins length. This bound is shown to be useful in ensuring realistic predictions from experimental structural determination methods. Third, using fundamental properties of this entanglement measure, we identify two common classes of protein sub-structure. The first are large scale helices, which provide stability to the structure. These helical structures indicate strong structural similarity of two protein families usually regarded as differing significantly. The second class of substructure is one which, though complex, has a small net entanglement. This configuration is physically useful in other disciplines, but its function in proteins is not yet clear. Finally, we provide an interactive python notebook to compute these measures for a given protein.

bioinformatics↗

bioRxiv2020

WASP: A software package for correctly characterizing the topological development of ribbon structures

We introduce the Writhe Application Software Package (WASP) which can be used to characterise the topology of ribbon structures, the underlying mathematical model of DNA, Biopolymers, superfluid vorticies, elastic ropes and magnetic flux ropes. This characterisation is achieved by the general twist-writhe decomposition of both open and closed ribbons, in particular through a quantity termed the polar writhe. We demonstrate how this decomposition is far more natural and straightforward than artificial closure methods commonly utilized in DNA modelling. In particular, we demonstrate how the decomposition of the polar writhe in local and non-local components distinctly characterizes local helical structure and knotting/linking of the ribbon. This decomposition provides additional information not given by alternative approaches. As an example application, the WASP routines are used to characterise the evolving topology (writhe) of DNA minicircle and open ended plectoneme formation magnetic/optical tweezer simulations. Finally it is demonstrated that a number of well known alternative writhe expressions are actually simplifications of the polar writhe measure.

biophysics↗

bioRxiv2019

A tertiary structure protein model for the ab-initio interpretation of small angle X-ray scattering data

Small angle X-ray scattering (SAXS) has become an important tool to investigate the structure of proteins in solution. In this paper we present a novel ab-initio method to represent polypeptide chains as discrete curves that can be used to derive a meaningful three-dimensional model from only the primary sequence and experimental SAXS data. High resolution crystal structures were used to generate probability density functions for each of the common secondary structural elements found in proteins. These are used to place realistic restraints on the model curves geometry. To evaluate the quality of potential models and demonstrate the efficacy of this novel technique we developed a new statistic to compare the entangled geometry of two open curves, based on mathematical techniques from knot theory. The chain model is coupled with a novel explicit hydration shell model in order derive physically meaningful 3D models by optimizing configurations against experimental SAXS data using a monte-caro based algorithm. We show that the combination of our ab-initio method with spatial restraints based on contact predictions successfully derives a biologically plausible model of the coiled-coil component of the human synaptonemal complex central element protein.\n\nSIGNIFICANCESmall-angle X-ray scattering allows for structure determination of biological macromolecules and their complexes in aqueous solution. Using a discrete curve representation of the polypeptide chain and combining it with empirically determined constraints and a realistic solvent model we are now able to derive realistic ab-initio 3-dimensional models from BioSAXS data. The method only require a primary sequence and the scattering data form the user.

molecular biology↗