ArticleBMC bioinformatics2014
FreeContact: fast and free software for protein contact prediction from residue co-evolution.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
82 citing papers in PubMed.
- MAKA-Map: Real-Valued Distance Prediction for Protein Folding Mechanisms via a Hybrid Neural Framework Integrating the Mamba and Kolmogorov-Arnold Networks.Biomolecules · 2026Article
- Promiscuity Guided Evolution of Decarboxylative Aldolases for Synthesis of Tertiary γ-Hydroxy Amino Acids.Angewandte Chemie (International ed. in English) · 2025Article
- Friends and relatives: insight into conformational regulation from orthologues and evolutionary lineages using KIF and KIN.Faraday discussions · 2024Article
- TMKit: a Python interface for computational analysis of transmembrane proteins.Briefings in bioinformatics · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Article
- Deep learning methods for 3D structural proteome and interactome modeling.Current opinion in structural biology · 2022Review
- SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model.Bioinformatics (Oxford, England) · 2022Article
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.Scientific reports · 2022Article
- Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function.Computational and structural biotechnology journal · 2022Article
- Inter-Residue Distance Prediction From Duet Deep Learning Models.Frontiers in genetics · 2022Article
- SARS-CoV-2 structural coverage map reveals viral protein assembly, mimicry, and hijacking mechanisms.Molecular systems biology · 2021Article
- Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions.Nature communications · 2021Article
- Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.Cell reports methods · 2021Article
- COMTOP: Protein Residue-Residue Contact Prediction through Mixed Integer Linear Optimization.Membranes · 2021Article
- DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning.Scientific reports · 2021Article
- Alignments of biomolecular contact maps.Interface focus · 2021Article
- Evaluation of residue-residue contact prediction methods: From retrospective to prospective.PLoS computational biology · 2021Article
- Episodic evolution of coadapted sets of amino acid sites in mitochondrial proteins.PLoS genetics · 2021Article
- Hybrid methods for combined experimental and computational determination of protein structure.The Journal of chemical physics · 2020Review
22 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
background20 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.
resultsHere, 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.
conclusionsFreeContact provides the opportunity to compute reliable contact predictions in any environment (desktop or cloud).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.