FreeContact: fast and free software for protein contact prediction from residue co-evolution

By László Kaján, Thomas A. Hopf, Matú Kala, Debora Marks, Burkhard Rost. 2014

161 citations recorded by the source corpus.

20 years of improved technology and growing sequences now renders residue-residue contact constraints in large protein families through correlated mutations accurate enough to drive de novo predictions of protein three-dimensional structure. The method EVfold broke new ground using mean-field Direct Coupling Analysis (EVfold-mfDCA); the method PSICOV applied a related concept by estimating a sparse inverse covariance matrix. Both methods (EVfold-mfDCA and PSICOV) are publicly available, but both require too much CPU time for interactive applications. On top, EVfold-mfDCA depends on proprietary software. Here, we present FreeContact, a fast, open source implementation of EVfold-mfDCA and PSICOV. On a test set of 140 proteins, FreeContact was almost eight times faster than PSICOV without decreasing prediction performance. The EVfold-mfDCA implementation of FreeContact was over 220 times faster than PSICOV with negligible performance decrease. EVfold-mfDCA was unavailable for testing due to its dependency on proprietary software. FreeContact is implemented as the free C++ library “libfreecontact”, complete with command line tool “freecontact”, as well as Perl and Python modules. All components are available as Debian packages. FreeContact supports the BioXSD format for interoperability. FreeContact provides the opportunity to compute reliable contact predictions in any environment (desktop or cloud).

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Jianlin Cheng
2. Robin Pearce
3. Yang Zhang
4. Badri Adhikari
5. Jie Hou
6. Debswapna Bhattacharya
7. David T. Jones
8. Chengxin Zhang

In Molecular Biology

1. Yang Zhang
2. Robin Pearce
3. Jianlin Cheng
4. Jinbo Xu
5. Badri Adhikari
6. David T. Jones
7. Chengxin Zhang
8. Sheng Wang

Citations in the corpus

• DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with Dropout (2019, methodology): “Table 2 shows that our method DEEPCON performs better than the baseline methods CCMpred and FreeContact, and better then PconsC4 and DeepCov.”
• CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations (2014, background): “Figure 1 shows that CCMpred is among the top tools.”
• Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “To generate the evolutionary coupling features we ran CCMPred and EVFold using default parameters on the previously-computed multiple sequence alignments (MSAs) (Seemayer et al., 2014; Kaján et al., 2014).”
• The PSIPRED Protein Analysis Workbench: 20 years on (2019, methodology): “This meta-predictor integrates contact predictions from 3 coevolutionary contact prediction methods: PSICOV (24), MF-DCA / FreeContact (25) and CCMPred (26).”
• Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, unknown): “In this study, we retrained NeBcon to improve its long-range contact prediction precision by using the naı¨ve Bayes classifier (NBC) theorem to integrate eight state-of-the-art contact prediction methods, including four deep-learning-based methods: DeepPLM (Zheng et al., 2019a), DeepCov (Jones and Ka”
• Deep learning methods in protein structure prediction (2020, background): “PSICOV [131], FreeContact [132] and CCMpred [133], which are notable results of this development, have allowed the exploitation of ever growing data banks and prompted a new wave of Deep Learning methods.”
• ConEVA: a toolbox for comprehensive assessment of protein contacts (2016, methodology): “The success of many protein residue contact prediction methods, in the recent years, has kindled a new hope to solve the long standing problem of ab initio protein structure prediction [1 – 6].”
• Deep-learning contact-map guided protein structure prediction in CASP13 (2019, methodology): “35 Among the component predictors, ResPRE (Figure 1C) is a newly developed deep-learning-based method to predict the contact-map (with C β -”