Protein-sol is a simple and free, web based suite of theoretical calculations and predictive algorithms for understanding protein solubility and stability. The software currently has 4 tools

1. Sequence solubility: Predicting protein solubility from sequence
2. Patches: Calculating surface charge and hydrophobicity from structure
3. Heatmaps: Predicting the fold state stability of a protein from structure
4. Abpred: Predicting the biophysical properties of potential therapeutic antibody

Protein-sol was built by the Warwicker group at the University of Manchester, with major contributions from Max Hebditch, Sonia Nicolaou, Alejandro Carballo and Spyros Charonis, and hosted on a virtual machine provided by the Computational Shared Facility at the University of Manchester.

Please contact us at protein-sol@manchester.ac.uk if you have any questions, comments or further interest in using the tool.

Sequence

Hebditch M, Carballo-Amador M A, Charonis S, Curtis R, Warwicker J.
Protein-Sol: A web tool for predicting protein solubility from sequence.
Bioinformatics (2017)

Patches and Heatmap

Hebditch and Warwicker.
Web-based display of protein surface and pH-dependent properties for assessing the developability of biotherapeutics.
Scientific Reports (2019)

Abpred

Hebditch and Warwicker.
Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies
PeerJ (2019)

pka

Hebditch M and Warwicker J.
protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses
Bioinformatics (2020)

To run multiple sequences, the predictive algorithm is available for download. Methods for the protein-sol sequence software are described in the paper.

Building on work published in

Chan P, Curtis R, Warwicker J.
Soluble expression of proteins correlates with a lack of positively-charged surface.
Scientific Reports (2013)

Warwicker J, Charonis S, Curtis R.

The patches and heatmap software calculates and visualises the distribution of charge, and hydrophobicity, across the protein surface. See here for an example of patches, and here for an example of heatmaps.

Given a protein PDB structure our in-house electrostatics software, using finite-difference Poisson–Boltzmann (FDPB) methods, calculates the distribution of potential across a surface, and can calculate the degree of surface patch hydrophobicity by comparing the ratio of non-polar to polar (NPP ratio) solvent accessible surface area (SASA) for all non-hydrogen atoms. The resulting surface properties are added to the PDB file in the B factor column, which can be viewed and manipulated on the Protein-sol website directly using the embedded NGL viewer, or locally in the user's preferred visualisation software.

For the potential display, the corresponding PDB file can be downloaded and viewed with approximately the same color scheme in PyMOL (The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC.) using the command.

For the non-polar / polar surface display, the corresponding PDB file can be downloaded and viewed with a similar color scheme in PyMOL (The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC.) using the command.

Methods

The methods are described in our paper, building on work published in

Moutevelis E & Warwicker J.
Prediction of pKa and redox properties in the thioredoxin superfamily.
Protein science (2004)

Warwicker J.
Improved pKa calculations through flexibility based sampling of a water-dominated interaction scheme.
Protein Science (2004)

Bate P & Warwicker J.
Enzyme/non-enzyme discrimination and prediction of enzyme active site location using charge-based methods.
Journal of molecular biology, (2004)

Cole C & Warwicker J.
Side-chain conformational entropy at protein-protein interfaces.
Protein Science (2002)

Warwicker J.
Simplified methods for pKa and acid pH-dependent stability estimation in proteins: removing dielectric and counterion boundaries.
Protein Science (1999)

The NGL software is used under the MIT license, with fullscreen code adapted from here

AS Rose, AR Bradley, Y Valasatava, JM Duarte, A Prlić and PW Rose.
Web-based molecular graphics for large complexes.
2016