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Method for computing stability changes in proteins upon mutations, published in

Authors: Fan Zheng, Gevorg Grigoryan

This package contains the source code for a method of predicting the effect of amino-acid point mutations on protein thermostability, reported in our paper:

Sequence statistics of tertiary structural motifs reflect protein stability, F. Zheng, G. Grigoryan, PLoS ONE, 12(5): e0178272, 2017


This package is written in Python and MATLAB and works under a UNIX/Linux-like environment (should be possible to make it work in MacOS, but we have not tried). We tested our code with Python 2.7 and MATLAB 2015b. It requires the following to work:

python libraries:

third-party programs:

  • USEARCH8.0 -- a program for rapidly finding similarities within sets of sequences (
  • Matlab 2015b or higher (Octave should also work, but we have not tested this extensively)

other tools from our lab:

For our paper, we created a database for MASTER to search for tertiary motif sequence preferences by considering a relatively non-redundant subset of the Protein Data Bank (PDB). The specific database used in th epaper can be downloaded from the Grigoryan lab website via rsync -varz /local/path. One can also follow our protocol at to customize the database, or simply update the database with the latest PDB data.

Whichever database you end up using, it will be important to exclude from consideration proteins closely related to the protein you are trying to run predictions for (otherwise, we think, the results will be biased). In the running instructoins, you will see how to specify database entries to be excluded. But to make the determination of what entries to exclude, it may be useful to store within a single FASTA file the sequences of all the protein chains in the MASTER database. For this, we provide the script, which writes a FASTA file from a MASTER database. A FASTA file corresponding to the specific database we used in our paper is already included in the above download (in file list.fasta).

confind comes with an appropriately pre-formatted datafile, derived from the Dubrack 2010 banckbone-dependent rotamer library, which can be found under rotlibs in your downloaded confind folder

Once the required programs and data sources are installed, their paths should be updated in the "Interface" section of In particular, set the following variables:

  • PATH_confind -- path to confind binary
  • PATH_rotLib -- path to directory with rotamer rotamer info that comes with ConFind
  • PATH_master -- path to directory with MASTER binaries (master and createPDS)
  • PATH_usearch -- path to USEARCH binary
  • PATH_seqdb -- path to the sequence database that corresponds to the structural database used for prediction

Because the calculation involves a large number of MASTER searches and other procedures, the code is set up to submit "jobs." By default, submitting a job just means running it locally and waiting until it finishes (i.e., running in serial), but the code also provisions the use of a Sun Grid Engine (SGE) computing cluster (i.e., where jobs are submitted to an SGE cluster and the code waits for them to finish). It is easy to generalize this to an arbitrary cluster by changing Specifically, only functions submit and checkJobRun need to change. We recommend that you designate a new value for the member variable type of class jobOnCluster that corresponds to your specific cluster, and define a corresponding additional case in functions submit and checkJobRun to deal with that cluster.

Getting started

An example of program output can be found in data_demo. The following instructions will show how these results can be generated from scratch.

  1. Prepare a PDB file for the protein to be predicted. For example, 1EY0.pdb. NOTE: the PDB file name cannot contain the underscore character.

  2. Prepare a list of point mutations. For an example, see The should be a tab-delimited file, with the following entries on each line:

    • the base name of the PDB file (i.e., for 1EY0.pdb this would be 1EY0)
    • chain ID of the chain where the desired mutation locates
    • wild-type amino-acid name (single-letter code). This need not necessarily match the residue name at the mutated positoin in the PDB file, and is used in defining what the mutation is (i.e., the final ddG will correspond to this being the wild type amino acid)
    • position within the above chain that is being mutated
    • mutated amino-acid name (single-letter code)
    • experimental ddG -- this is used only for the purposes of comparing measured and computed values in the final output, so you can specify any number here and it will not affect the resulting computed ddG values.
  3. Run the following command:
    python --l --db PATH_TO_MASTER_DB --homof 1EY0.homo

Here PATH_TO_MASTER_DB is the path to a directory with the MASTER database that you would like to use for the calculation (either the one we used in our paper, or a custom one; see above for instructions). 1EY0.homo is a file that contains information on homology within the database that is relevant for the current predictoin. Each line lists a set of chains in the database that are all homologous to the chain lister first (and thus should be ignored when predicting mutations in the first listed chain). In this case, the file contains only a single line that starts with the chain 1EY0_A. And because our predictions here are for positions from this chain, all chains listed on the first line will be ignored.

In general, it us up to the user to decide which homology to ignore. We have used the NCBI blastpgb program, with an E-value cutoff of 1.0, to generate a list of chains from our database that are too similar to the proteins we analyze. A FASTA file with all chains in the MASTER database (created via, see above) is very helpful for this.

  1. The above command will perform all of the necessary database mining to enable the prediction of the desired set of ddG's. To actually compute predicted values, you will need to perform parameter optimization in Matlab via: python --l --o 1ey0.dat  where 1ey0.dat is the file with final results. Each line of the output file will first list the same fields as the input file (here, which identify the mutation in questoin, and then will provide two components of the resulting ddG prediction: the self and pair contributions, respectively (see our paper). The final ddG prediction is the sum of the two.

  2. We also provide the option to score all amino acid at each position. For that, simply add the option --scan: python --l --o 1ey0.dat.scan --scan
    As in the output file above, lines in 1ey0.dat.scan will first repeat the input-file entries, with the following 20 values representing the total predicted ddG (NOTE: no splitting into pair and self in this case) for mutating the specified wild-type amino acid to each of the 20 natural amino acids, in the order: A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y.

Step 5 is independent of step 4. However, if step 4 has been carried out, adding a flag --oo to the step 5 command will ultilize the existing results and give output much faster.


If you use this tool in your research, please cite the following publications:

  • Zheng, Fan and Gevorg Grigoryan. "Sequence statistics of tertiary structural motifs reflect protein stability", F. Zheng, G. Grigoryan, PLoS ONE, 12.5 (2017): e0178272.

  • Zheng, Fan, Jian Zhang, and Gevorg Grigoryan. "Tertiary structural propensities reveal fundamental sequence/structure relationships." Structure 23.5 (2015): 961-971.

We also appreciate the authors of the following works that are utilized in this tool:

  • Zhou, Jianfu, and Gevorg Grigoryan. "Rapid search for tertiary fragments reveals protein sequence–structure relationships." Protein Science 24.4 (2015): 508-524.

  • Bakan, Ahmet, et al. "Evol and ProDy for bridging protein sequence evolution and structural dynamics." Bioinformatics 30.18 (2014): 2681-2683.

  • Edgar, Robert C. "Search and clustering orders of magnitude faster than BLAST." Bioinformatics 26.19 (2010): 2460-2461.


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