mGPfusion is a Gaussian process based method for predicting stability changes upon single and multiple mutations of proteins that complements the available experimental data with large amounts of simulated data. Our Bayesian data fusion model re-calibrates the experimental and in silico data sources and then learns a predictive model from this combined data. Our model is protein-specific and requires experimental data only regarding the protein of interest. mGPfusion has been developed at Aalto University.
- For a comprehensive description of mGPfusion see [1]
- For an example usage of mGPfusion, see example.m
Our example data contains information for 15 proteins: pdb-structures from The Protein data bank, experimentally measured ddG-values from Protherm [2], and ddG-values simulated with Rosetta [3].
The first column in the cell arrays (second column in csv-files) contains the mutations defined using
consecutive numbering. The third column in csv-files uses Rosetta's numbering. The signs of the ddG-values
simulated with Rosetta have been changed to match the notation used for the muations from Protherm.
The scaling suggested by [4], 0.57 * y_S, can be applied if the values are used as they are.
ddg_protherm contains experimentally measured ddG-values gathered from Protherm, ddg_rosetta_single contains
ddG-values for all single mutations simulated with Rosetta, and ddg_rosetta_multi contains ddG-values for all
multiple mutations simulated with Rosetta.
The experimentally measured ddG-values have been obtained from Protherm. If multiple measurements for the ddg-change upon a mutation were provided, an average was taken.
We use consecutive numbering for mutations, which means that the first residue in the amino acid sequence has number 1, second 2, and so on, regardless if the residues are present in the pdb structure. For our 15 example proteins, this corresponds to the numbering used in the pdb structures, but this may not be the case for all proteins.
The numbering that is used by Rosetta gives numbers only to residues that are present in the pdb-structure. This numbering often differes from our consecutive numbering and that used with pdb-structures.
At the time of adding this file, Protherm database does not seem to be accessible. This file was earlier downloaded from Protherm and it should contain all data, that was available at that time. Unfortunately, in my knowledge, further descriptions of this file were only available at the site, wich is not accessible at the moment.
This data is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License
For the minFunc software, see the copyright.txt file in repository code/minConf
Copyright 2017-2018 Emmi Jokinen
Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
[1] Jokinen, E., Heinonen, M., and Lähdesmäki, H. mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion. (accepted)
[2] Kumar MD, Bava KA, Gromiha MM, Parabakaran P, Kitajima K, Uedaira H, Sarai A. (2006). ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions. Nuleic Acids Res., 34:D204-6, Database issue
[3] Leaver-Fay, A., Tyka, M., Lewis, S. M., Lange, O. F., Thompson, J., Jacak, R., Kaufman, K., Renfrew, P. D., Smith, C. A., Sheffler, W., et al. (2011). ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods in enzymology, 487, 545
[4] Kellogg, E. H., Leaver-Fay, A., and Baker, D. (2011). Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins: Structure, Function, and Bioinformatics, 79(3), 830-838.