Assessing the accuracy of contact and distance predictions in CASP14

We present the results of the assessment of the intramolecular residue-residue contact and distance predictions from groups participating in the 14th round of the CASP experiment. The performance of contact prediction methods was evaluated with the measures used in previous CASPs, while distance predictions were assessed based on a new protocol, which considers individual distance pairs as well as the whole predicted distance matrix, using a graph-based framework. The results of the evaluation indicate that predictions by the tFold framework, TripletRes and DeepPotential were the most accurate in both categories. With regards to progress in method performance, the results of the assessment in contact prediction did not reveal any discernible difference when compared to CASP13. Arguably, this could be due to CASP14 FM targets being more challenging than ever before.We present the results of the assessment of the intramolecular residue-residue contact and distance predictions from groups participating in the 14th round of the CASP experiment. The performance of contact prediction methods was evaluated with the measures used in previous CASPs, while distance predictions were assessed based on a new protocol, which considers individual distance pairs as well as the whole predicted distance matrix, using a graph-based framework. The results of the evaluation indicate that predictions by the tFold framework, TripletRes and DeepPotential were the most accurate in both categories. With regards to progress in method performance, the results of the assessment in contact prediction did not reveal any discernible difference when compared to CASP13. Arguably, this could be due to CASP14 FM targets being more challenging than ever before

The SWISS-MODEL Repository: new features and functionalities

The SWISS-MODEL Repository is a database of annotated 3D protein structure models generated by the SWISS-MODEL homology-modelling pipeline. As of September 2005, the repository contained 675 000 models for 604 000 different protein sequences of the UniProt database. Regular updates ensure that the content of the repository reflects the current state of sequence and structure databases, integrating new or modified target sequences, and making use of new template structures. Each Repository entry consists of one or more 3D models accompanied by detailed information about the target protein and the model building process: functional annotation, a detailed template selection log, target-template alignment, summary of the model building and model quality assessment. The SWISS-MODEL Repository is freely accessible at

Critical assessment of methods of Protein Structure Prediction (CASP) – Round XIII

CASP (Critical Assessment of Structure Prediction) assesses the state of the art in modeling protein structure from amino acid sequence. The most recent experiment (CASP13 held in 2018) saw dramatic progress in structure modeling without use of structural templates (historically ‘ab initio’ modeling). Progress was driven by the successful application of deep learning techniques to predict inter-residue distances. In turn, these results drove dramatic improvements in three-dimensional structure accuracy: With the proviso that there are an adequate number of sequences known for the protein family, the new methods essentially solve the long-standing problem of predicting the fold topology of monomeric proteins. Further, the number of sequences required in the alignment has fallen substantially. There is also substantial improvement in the accuracy of template-based models. Other areas - model refinement, accuracy estimation, and the structure of protein assemblies - have again yielded interesting results. CASP13 placed increased emphasis on the use of sparse data together with modeling and chemical crosslinking, SAXS, and NMR all yielded more mature results. This paper summarizes the key outcomes of CASP13. The special issue of PROTEINS contains papers describing the CASP13 assessments in each modeling category and contributions from the participants

Assessment of the model refinement category in CASP12

We here report on the assessment of the model refinement predictions submitted to the 12th Experiment on the Critical Assessment of Protein Structure Prediction (CASP12). This is the fifth refinement experiment since CASP8 (2008) and, as with the previous experiments, the predictors were invited to refine selected server models received in the regular (nonrefinement) stage of the CASP experiment. We assessed the submitted models using a combination of standard CASP measures. The coefficients for the linear combination of Z‐scores (the CASP12 score) have been obtained by a machine learning algorithm trained on the results of visual inspection. We identified eight groups that improve both the backbone conformation and the side chain positioning for the majority of targets. Albeit the top methods adopted distinctively different approaches, their overall performance was almost indistinguishable, with each of them excelling in different scores or target subsets. What is more, there were a few novel approaches that, while doing worse than average in most cases, provided the best refinements for a few targets, showing significant latitude for further innovation in the field

Cryo‐EM targets in CASP13: overview and evaluation of results

Structures of seven CASP13 targets were determined using cryo‐electron microscopy (cryo‐EM) technique with resolution between 3.0 and 4.0 å. We provide an overview of the experimentally derived structures and describe results of the numerical evaluation of the submitted models. The evaluation is carried out by comparing coordinates of models to those of reference structures (CASP‐style evaluation), as well as checking goodness‐of‐fit of modeled structures to the cryo‐EM density maps. The performance of contributing research groups in the CASP‐style evaluation is measured in terms of backbone accuracy, all‐atom local geometry and similarity of inter‐subunit interfaces. The results on the cryo‐EM targets are compared with those on the whole set of eighty CASP13 targets. A‐posteriori refinement of the best models in their corresponding cryo‐EM density maps resulted in structures that are very close to the reference structure, including some regions with better fit to the density

A novel method to compare protein structures using local descriptors

<p>Abstract</p> <p>Background</p> <p>Protein structure comparison is one of the most widely performed tasks in bioinformatics. However, currently used methods have problems with the so-called "difficult similarities", including considerable shifts and distortions of structure, sequential swaps and circular permutations. There is a demand for efficient and automated systems capable of overcoming these difficulties, which may lead to the discovery of previously unknown structural relationships.</p> <p>Results</p> <p>We present a novel method for protein structure comparison based on the formalism of local descriptors of protein structure - DEscriptor Defined Alignment (DEDAL). Local similarities identified by pairs of similar descriptors are extended into global structural alignments. We demonstrate the method's capability by aligning structures in difficult benchmark sets: curated alignments in the SISYPHUS database, as well as SISY and RIPC sets, including non-sequential and non-rigid-body alignments. On the most difficult RIPC set of sequence alignment pairs the method achieves an accuracy of 77% (the second best method tested achieves 60% accuracy).</p> <p>Conclusions</p> <p>DEDAL is fast enough to be used in whole proteome applications, and by lowering the threshold of detectable structure similarity it may shed additional light on molecular evolution processes. It is well suited to improving automatic classification of structure domains, helping analyze protein fold space, or to improving protein classification schemes. DEDAL is available online at <url>http://bioexploratorium.pl/EP/DEDAL</url>.</p