Critical assessment of methods of protein structure prediction (CASP)—Round XIII

Abstract

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.

Small angle X‐ray scattering‐assisted protein structure prediction in CASP13 and emergence of solution structure differences

“…As assessed in Critical Assessment of protein Structure Prediction (CASP12) and now in CASP13, 1 protein structure prediction algorithms have made major leaps toward improving prediction accuracy.…”

The data universe of structural biology

“…Modelers make particularly heavy use of the PDB. For example, the CASP project uses PDB data to develop methods for structure prediction (Kryshtafovych et al, 2019). Biochemists and biophysicists use structures to help explain their findings and structures in the PDB have facilitated the discovery of several new drugs .…”

Machine Learning Approaches for Quality Assessment of Protein Structures

“…It is worth mentioning that the use of DL techniques has greatly improved the performances of the participating structure prediction methods. The overall accuracy of predicted models has improved dramatically in CASP13, especially for the more difficult targets that lack templates [13]. AlphaFold, the top-performing free modeling (FM) method in CASP13, includes one generative neural network for fragment generation and two deep residual convolutional neural networks for scoring, which together calculate inter-residue distances and evaluate structure geometry [50,51].…”

“…To overcome these problems, researchers have developed computational methods for protein-structure prediction. Popular methods include Modeller [3], SWISS-MODEL [4], Rosetta [5,6], I-TASSER [7], FALCON [8], Raptor/RaptorX [9,10], and IntFOLD [11] (see [12,13] for recent comprehensive reviews of the prediction theory and methods). Prediction functions are also available in some commercial software packages such as Internal Coordinate Mechanics, Molecular Operating Environment, and Schrödinger.…”