christophfeinauer / GaussDCA.jl

Multivariate Gaussian Direct Coupling Analysis for residue contact prediction in protein families - Julia module

Gaussian Direct Coupling Analysis for protein contacts predicion

Overview

This is the code which accompanies the paper "Fast and accurate multivariate Gaussian modeling of protein families: Predicting residue contacts and protein-interaction partners" by Carlo Baldassi, Marco Zamparo, Christoph Feinauer, Andrea Procaccini, Riccardo Zecchina, Martin Weigt and Andrea Pagnani, (2014) PLoS ONE 9(3): e92721. doi:10.1371/journal.pone.0092721

See also this Wikipedia article for a general overview of the Direct Coupling Analysis technique.

This code is released under the GPL version 3 (or later) license; see the LICENSE.md file for details.

The code is written in Julia and requires julia version 1.5 or later; it provides a function which reads a multiple sequence alignment (in FASTA format) and returns a ranking of all pairs of residue positions in the aligned amino-acid sequences.

Since version 2, most of the internal functions used to parse and manipulate the data have been factored out into the package DCAUtils.jl. The code in this module is essentially a wrapper around those utilities.

Installation

To install the package, enter in Pkg mode by pressing the ] key, then in the pkg prompt enter

(@v1.5) pkg> add https://github.com/carlobaldassi/GaussDCA.jl"

Usage

To load the code, just type using GaussDCA.

This software provides one main function, gDCA(filname::String, ...). This function takes the name of a (possibly gzipped) FASTA file, and returns a predicted contact ranking, in the form of a Vector of triples, each triple containing two indices i and j (with i < j) and a score. The indices start counting from 1, and denote pair of residue positions in the given alignment; pairs which are separated by less than a given number of residues (by default 5) are filtered out. The triples are sorted by score in descending order, such that predicted contacts should come up on top.

For convenience, a utility function is also provided, printrank(output, R), which prints the result of gDCA either in a file or to a stream, given as first argument. If the first argument output is omitted, the standard terminal output will be used.

The gDCA function takes some additional, optional keyword arguments:

• pseudocount: the value of the pseudo-count parameter, between 0 and 1. the default is 0.8, which gives good results when the Frobenius norm score is used (see below); a good value for the Direct Information score is 0.2.
• θ: the value of the similarity threshold. By default it is :auto, which means it will be automatically computed (this takes additional time); otherwise, a real value between 0 and 1 can be given.
• max_gap_fraction: maximum fraction of gap symbols in a sequence; sequences that exceed this threshold are discarded. The default value is 0.9.
• score: the scoring function to use. There are two possibilities, :DI for the Direct Information, and :frob for the Frobenius norm. The default is :frob. (Note the leading colon: this argument is passed as a symbol).
• min_separation: the minimum separation between residues in the output ranking. Must be ≥ 1. The default is 5.

The code is multi-threaded: if you start julia with the -t option, for example as julia -t 8, the computations will run in parallel on the given number of threads.

Examples

Here is a basic usage example, assuming an alignment in FASTA format is found in the file "alignment.fasta.gz":

julia> using GaussDCA

julia> FNR = gDCA("alignment.fasta.gz");

julia> printrank("results_FN.txt", FNR)

The above uses the Frobenius norm ranking with default parameters. This is how to get the Direct Information ranking instead:

julia> DIR = gDCA("alignment.fasta.gz", pseudocount = 0.2, score = :DI);

julia> printrank("results_DI.txt", DIR)