Biology subjects

Hebditch, M.

Publications and source records attributed to Hebditch, M..

5 recordsLinked to original sources

bioRxiv2021

PhosIDP: a web tool to visualize the location of phosphorylation sites in disordered regions

Charge is a key determinant of intrinsically disordered protein (IDP) and intrinsically disordered region (IDR) properties. IDPs and IDRs are enriched in sites of phosphorylation, which alters charge. Visualizing the degree to which phosphorylation modulates the charge profile of a sequence would assist in the functional interpretation of IDPs and IDRs. PhosIDP is a web tool that shows variation of charge and fold propensity upon phosphorylation. In combination with the displayed location of protein domains, the information provided by the web tool can lead to functional inferences for the consequences of phosphorylation. IDRs are components of many proteins that form biological condensates. It is shown that IDR charge, and its modulation by phosphorylation, is more tightly controlled for proteins that are essential for condensate formation than for those present in condensates but inessential.

bioinformatics↗

bioRxiv2020

protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses

Evolution couples differences in ambient pH to biological function through protonatable groups, in particular those that switch from buried to exposed and alter protonation state in doing so. We present a tool focusing on structure-based discovery and display of these groups. Since prediction of buried group pKas is computationally intensive, solvent accessibility of ionisable groups is displayed, from which the user can iteratively select pKa calculation centers. Results are color-coded, with emphasis on buried groups. Utility is demonstrated with coronaviruses, which exhibit variable dependence on the acidic pH of the endocytotic pathway. After benchmarking with variants of murine hepatitis virus, a pair of conserved histidine residues are identified that are predicted to be electrostatically frustrated at acidic pH in a common structural core of pre- and post-fusion coronavirus spike proteins. We suggest that an intermediate expanded conformation at endosomal pH could relax the frustration, allowing histidine protonation, and facilitating conformational conversion. This tool is available at http://www.protein-sol.manchester.ac.uk/pka/.

bioinformatics↗

bioRxiv2019

Modelling of pH-dependence to develop a strategy for stabilising mAbs at acidic steps in production

AO_SCPLOWBSTRACTC_SCPLOWEngineered proteins are increasingly being required to function or pass through environmental stresses for which the underlying protein has not evolved. A major example in health are antibody therapeutics, where a low pH step is used for purification and viral clearance. In order to develop a computational model for analysis of pH-stability, predictions are compared with experimental data for the relative pH-sensitivities of antibody domains. The model is then applied to proteases that have evolved to be functional in an acid environment, showing a clear signature for low pH-dependence of stability in the neutral to acidic pH region, largely through reduction of saltbridges. Interestingly, an extensively acidic protein surface can maintain contribution to structural stabilisation at acidic pH through replacement of basic sidechains with polar, hydrogen-bonding groups. These observations form a design principle for engineering acid-stable proteins.

bioinformatics↗

bioRxiv2019

Charge and hydrophobicity are key features in sequence trained machine learning models for predicting the biophysical properties of clinical-stage antibodies.

AO_SCPLOWBSTRACTC_SCPLOWImproved understanding of properties that mediate protein solubility and resistance to aggregation are important for developing biopharmaceuticals, and more generally in biotechnology and synthetic biology. Recent acquisition of large datasets for antibody biophysical properties enables the search for predictive models. In this report, machine learning methods are used to derive models for 12 biophysical properties. A physicochemical perspective is maintained in analysing the models, leading to the observation that models cluster largely according to charge (cross-interaction measurements) and hydrophobicity (self-interaction methods). These two properties also overlap in some cases, for example in a new interpretation of variation in hydrophobic interaction chromatography. Since the models are developed from differences of antibody variable loops, the next stage is to extend models to more diverse protein sets.\n\nAvailabilityThe web application for the sequence based algorithms are available on the protein-sol webserver, at https://protein-sol.manchester.ac.uk/abpred, with models and virtualisation software available at https://protein-sol.manchester.ac.uk/software.

bioinformatics↗

bioRxiv2018

Investigation of protein surface polarity and pH-dependent properties using a new web tool: application to antibodies shows that antibody-antigen interfaces tend to be relatively polar.

Protein instability leads to reversible self-association and irreversible aggregation which is a major concern for developing new biopharmaceutical leads. Protein solution behaviour is dictated by the physicochemical properties of the protein and the solution. Optimising protein solutions through experimental screens and targeted protein engineering can be a difficult and time consuming process. Here, we describe development of the protein-sol web server, which was previously restricted to protein solubility prediction from amino acid sequence. Tools are presented for calculating and mapping patches of hydrophobicity and charge on the protein surface. In addition, predictions of folded state stability and net charge are displayed as a heatmap for a range of pH and ionic strength conditions. Tools are evaluated in the context of antibodies, their fragments and interactions. Surprisingly, antibody-antigen interfaces are, on average, at least as polar as Fab surfaces. This benchmarking process provides the user with thresholds with which to assess non-polar surface patches, and possible solubility implications, in proteins of interest. Stability heatmaps compare favourably with experimental data for CH2 and CH3 domains. Display and quantification of surface polarity and pH / ionic strength dependence will be useful generally for investigation of protein biophysics.

bioinformatics↗