De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts

By T. Kościółek, David T. Jones. 2014

110 citations recorded by the source corpus.

The advent of high accuracy residue-residue intra-protein contact prediction methods enabled a significant boost in the quality of de novo structure predictions. Here, we investigate the potential benefits of combining a well-established fragment-based folding algorithm – FRAGFOLD, with PSICOV, a contact prediction method which uses sparse inverse covariance estimation to identify co-varying sites in multiple sequence alignments. Using a comprehensive set of 150 diverse globular target proteins, up to 266 amino acids in length, we are able to address the effectiveness and some limitations of such approaches to globular proteins in practice. Overall we find that using fragment assembly with both statistical potentials and predicted contacts is significantly better than either statistical potentials or contacts alone. Results show up to nearly 80% of correct predictions (TM-score ≥0.5) within analysed dataset and a mean TM-score of 0.54. Unsuccessful modelling cases emerged either from conformational sampling problems, or insufficient contact prediction accuracy. Nevertheless, a strong dependency of the quality of final models on the fraction of satisfied predicted long-range contacts was observed. This not only highlights the importance of these contacts on determining the protein fold, but also (combined with other ensemble-derived qualities) provides a powerful guide as to the choice of correct models and the global quality of the selected model. A proposed quality assessment scoring function achieves 0.93 precision and 0.77 recall for the discrimination of correct folds on our dataset of decoys. These findings suggest the approach is well-suited for blind predictions on a variety of globular proteins of unknown 3D structure, provided that enough homologous sequences are available to construct a large and accurate multiple sequence alignment for the initial contact prediction step.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins (2014, background): “Details of the benchmark have already been described (Kosciolek and Jones, 2014), where we evaluated the usefulness of PSICOV contacts in assisting de novo protein structure prediction.”
Transformer protein language models are unsupervised structure learners (2020, methodology): “Similarly, FragFold (Kosciolek & Jones, 2014) and Rosetta (Ovchinnikov et al., 2016) incorporate constraints from a Potts Model into a fragment assembly based pipeline.”
Accurate contact predictions using covariation techniques and machine learning (2015, background): “20 In this approach, predicted contacts serve as additional energy terms for FRAGFOLD,21–23 in addition to the pair-wise potentials of mean force and solvation.”
Protein contact prediction using metagenome sequence data and residual neural networks (2019, methodology): “The information included in a contact map can be used as distance restraints to guide protein structure modeling (Hopf et al., 2012; Kim et al., 2014; Kosciolek and Jones, 2014; Marks et al., 2011, 2012; Nugent and Jones, 2012; Ortiz et al., 1999; Ovchinnikov et al., 2015, 2016; Sadowski et al., 201”
Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12 (2017, background): “24 The success is however contingent upon precise contact map prediction that in turn depends on high volume of sequence homologs, particularly in co-evolution based contact prediction methods.”
ConEVA: a toolbox for comprehensive assessment of protein contacts (2016, methodology): “When accurately predicted contacts are supplied as input to structure prediction or reconstruction methods, accurate folds can be predicted consistently [1, 7 – 9].”
Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction (2015, methodology): “We also compared the accuracy of ReRASREC-PLM with two other recently published methods (PconsFold [33] and FRAGFOLD [31]) on the subset of targets where each publication reported actual numbers on.”
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints (2018, methodology): “Well established fragment-based methods such as Rosetta [4] and FRAGFOLD [5] add constraints from predicted contacts to an existing fragment assembly pipeline.”
Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction (2018, methodology): “…in statistical physics, led to a breakthrough in de-novo (template-free) protein structure prediction: The predicted contacts proved suﬃ-ciently accurate to be used as spatial restraints to reli-25 ably predict protein 3D structures purely from sequence information [23, 26–28, 32, 35, 36, 44–47].”
CONFOLD: Residue-Residue Contact-guided ab initio Protein Folding (2015, background): “39 For the 15 proteins in EVFOLD benchmark set, average precision of top 50 predicted contacts is 0.65 and within the range [0.38, 0.86] and predicted secondary structure have average accuracy (Q3 score) of 0.84 and within the range [0.56, 0.96].”
UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling (2016, methodology): “The final term Erirj accounts for energetic contribution due to residue-residue contacts adopted from previously published FRAGFOLD with RRCON methodology (Kosciolek and Jones, 2014).”
CONFOLD2: improved contact-driven ab initio protein structure modeling (2017, methodology): “5) of 100 out of 150 proteins [15], CONFOLD2 recovered the correct fold of 107 proteins.”
Recent developments in deep learning applied to protein structure prediction (2019, background): “76-79 It should therefore come as little surprise that models that take these features as input are successful at predicting contacts, as in these cases the model does not have to achieve much more than “ clean up ” the initial predictions by recognizing local contact patterns across the training…”