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Max Hebditch

dblp:209/7834 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2020
0000-0002-5424-193XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › molecular property prediction
pka prediction
0.412020
Protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses · Bioinform. 2020
Bioinformatics and computational biology › protein structure analysis
protein electrostatics
0.412020
Protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses · Bioinform. 2020
Bioinformatics and computational biology › molecular property prediction
protein property prediction
0.312017
Protein-Sol: a web tool for predicting protein solubility from sequence · Bioinform. 2017
Bioinformatics and computational biology › molecular property prediction › protein property prediction
protein solubility prediction
0.312017
Protein-Sol: a web tool for predicting protein solubility from sequence · Bioinform. 2017
Bioinformatics and computational biology
protein structure analysis
0.112020
Protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses · Bioinform. 2020
Bioinformatics and computational biology › protein structure analysis › protein surface analysis
solvent accessible surface
0.112020
Protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses · Bioinform. 2020

Methods — techniques the papers use, named apart from their topics

structure-based pka calculation · 0.4solvent accessibility computation · 0.4sequence-based feature calculation · 0.3feature weighting · 0.3
YearPublicationVenuePosition
2020 Protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses
abstract
MOTIVATION: 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. RESULTS: Since prediction of buried group pKas is computationally intensive, solvent accessibility of ionizable 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 benchmarking against known pH sensing sites in influenza virus hemagglutinin and in variants of murine hepatitis virus, a coronavirus. A pair of histidine residues, which are conserved in coronavirus spike proteins, are predicted to be electrostatically frustrated at acidic pH in both pre- and post-fusion conformations. We suggest that an intermediate expanded conformation at endosomal pH could relax the frustration, allowing histidine protonation and facilitating conformational conversion of coronavirus spike protein. AVAILABILITY AND IMPLEMENTATION: This tool is available at http://www.protein-sol.manchester.ac.uk/pka/.
Max Hebditch, Jim Warwicker
Bioinform.1
2017 Protein-Sol: a web tool for predicting protein solubility from sequence
abstract
MOTIVATION: Protein solubility is an important property in industrial and therapeutic applications. Prediction is a challenge, despite a growing understanding of the relevant physicochemical properties. RESULTS: Protein-Sol is a web server for predicting protein solubility. Using available data for Escherichia coli protein solubility in a cell-free expression system, 35 sequence-based properties are calculated. Feature weights are determined from separation of low and high solubility subsets. The model returns a predicted solubility and an indication of the features which deviate most from average values. Two other properties are profiled in windowed calculation along the sequence: fold propensity, and net segment charge. The utility of these additional features is demonstrated with the example of thioredoxin. AVAILABILITY AND IMPLEMENTATION: The Protein-Sol webserver is available at http://protein-sol.manchester.ac.uk. CONTACT: [email protected].
Max Hebditch, M. Alejandro Carballo-Amador, Spyros Charonis, Robin Curtis, Jim Warwicker
Bioinform.1