CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations

By Stefan Seemayer, Markus Gruber, Johannes Söding. 2014

382 citations recorded by the source corpus.

Motivation: Recent breakthroughs in protein residue–residue contact prediction have made reliable de novo prediction of protein structures possible. The key was to apply statistical methods that can distinguish direct couplings between pairs of columns in a multiple sequence alignment from merely correlated pairs, i.e. to separate direct from indirect effects. Two classes of such methods exist, either relying on regularized inversion of the covariance matrix or on pseudo-likelihood maximization (PLM). Although PLM-based methods offer clearly higher precision, available tools are not sufficiently optimized and are written in interpreted languages that introduce additional overheads. This impedes the runtime and large-scale contact prediction for larger protein families, multi-domain proteins and protein–protein interactions. Results: Here we introduce CCMpred, our performance-optimized PLM implementation in C and CUDA C. Using graphics cards in the price range of current six-core processors, CCMpred can predict contacts for typical alignments 35–113 times faster and with the same precision as the most accurate published methods. For users without a CUDA-capable graphics card, CCMpred can also run in a CPU mode that is still 4–14 times faster. Thanks to our speed-ups (http://dictionary.cambridge.org/dictionary/british/speed-up) contacts for typical protein families can be predicted in 15–60 s on a consumer-grade GPU and 1–6 min on a six-core CPU. Availability and implementation: CCMpred is free and open-source software under the GNU Affero General Public License v3 (or later) available at https://bitbucket.org/soedinglab/ccmpred Contact: johannes.soeding@mpibpc.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, background): “(ii) unsupervised contacts predicted by the CCMpred implementation (Seemayer et al., 2014) of direct coupling analysis.”
MSA Transformer (2021, methodology): “…have been proposed to efﬁciently ﬁt the MRF, including mean-ﬁeld inference (Morcos et al., 2011), sparse-inverse covariance estimation (Jones et al., 2012), and the current state-of-the-art, pseudolikelihood maximization (Balakrishnan et al., 2011; Ekeberg et al., 2013; Seemayer et al., 2014).”
CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction (2020, methodology): “For example, both AlphaFold [6] and RaptorX [16] rely on the inter-residue contacts predicted by CCMpred, a DCA-based approach using the Potts model [17].”
Generating functional protein variants with variational autoencoders (2020, methodology): “Couplings were predicted using CCMPred on samples of 3000 sequences, and only couplings between residues separated by at least 4 sequence positions were shown.”
Evolutionary context-integrated deep sequence modeling for protein engineering (2020, methodology): “We refer the interested [38,58,59] for the details of the optimization.”
Evaluating Protein Transfer Learning with TAPE (2019, methodology): “The original RaptorX method has a more complex evolutionary featurization: they construct a Multiple Sequence Alignment for each protein, then pass it through CCMpred [60] - a Markov Random Field based contact prediction method.”
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.”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “Despite only submitting 54/57 targets for the joint set of FM and FM/TBM targets due to the submission script bug, DeepContact achieved an experiment best overall average F1 score, including 0 for the 3 missed targets, of 23.226 (iFold_1) with MetaPSICOV (Jones et al., 2015) the next best non-DeepCo”
Folding membrane proteins by deep transfer learning (2017, methodology): “ECA predicts contacts by identifying co-evolved residues, such as EVfold (Marks et al., 2011), PSICOV (Jones et al., 2012), CCMpred (Seemayer et al., 2014), Gremlin (Kamisetty et al., 2013) and others(Weigt et al., 2009), but it needs a large number of sequence homologs to be effective (Skwark et…”