Biology subjects

Bale, A.

Publications and source records attributed to Bale, A..

3 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↗

bioRxiv2024

Glioblastoma Drives Protease-Independent Extracellular Matrix Invasion of Microglia

Glioblastoma (GBM) is the most common and lethal form of primary brain cancer. Microglia infiltration into the tumor microenvironment is associated with immunosuppression and poor prognosis. Improved physicochemical understanding of microglia activation and invasion may provide novel GBM therapeutic strategies essential for improving long-term treatment efficacy. Here, we combine microfluidic systems with 3-D collagen hydrogels to systematically investigate microglia activation, invasion, contractility and cytokine secretion in response of GBM-microglia crosstalk. GBM inflammatory biomolecules significantly promote activation and 3D invasion of microglia. Interestingly, microglia invasion is not significantly affected by inhibitors of MMP activity or cellular glycolysis. In contrast, ROCK-pathway inhibition significantly impedes microglia invasion. Infrared microscopy analyses show that GBM co-culture does not significantly alter microglia lipid content. Further, GBM conditioned media resulted in significantly increased collagen hydrogel contraction, suggesting the importance of microglia contractility to physically remodel the local extracellular matrix (ECM). We also identify a panel of soluble proteins that may contribute to microglia chemotaxis, such as TIMP-1 and CXCL12. Taken together, this study suggests that the presence of GBM cells can enhance microglia invasion via increased cellular contractility, independent of MMP activity and cellular glycolysis.

bioengineering↗

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↗