Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII

By Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, John Moult. 2019

438 citations recorded by the source corpus.

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.

Subfields: Materials Chemistry, Molecular Biology, Evaluation & Benchmarks, 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, methodology): “We evaluate models on the CB513 test set (Cuff & Barton, 1999) and the CASP13 domains (Kryshtafovych et al., 2019).”
AlphaFold2 and its applications in the fields of biology and medicine (2023, methodology): “Recently Chowdhury et al. 51 developed an end-to-end deep neural network model, namely differentiable recurrent geometric network (RGN) model, in which a protein language model (AminoBERT) based on the Bidirectional Encoder Representations from Transformers (BERT) 221 was used to learn latent…”
High‐accuracy protein structure prediction in CASP14 (2021, background): “1,11 Now, in CASP14 we saw a further leap in model accuracy, 36 with the best model for targets at any difficulty level reaching a GDT_TS above 90%, a range at the level of experimental accuracy.”
Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes (2022, background): “Over the past decades, the protein structure prediction ﬁ eld has moved towards achieving this goal, at least for small proteins or protein domains 13 .”
Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, background): “, without knowledge of the experimental structures (Kryshtafovych et al., 2019).”
Deep learning methods in protein structure prediction (2020, methodology): “AlphaFold [123] is a Protein Structure Prediction method that achieved the best performance in the Ab initio category of CASP13 [135].”
FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds (2020, neutral): “Methods that make more active use of residue contact inference from sequence covariation (Weinreb et al., 2016) or arti-ﬁcial neural networks (Kryshtafovych et al., 2019; Li et al., 2019; Wang et al., 2019) show promise for such improved scoring functions.”
Deep learning techniques have significantly impacted protein structure prediction and protein design. (2021, methodology): “…TBM method is probably I-TASSER [8], which is an extension of TASSER and has been consistently ranked as the top automated method in the community-wide Critical Assessment of Structure Prediction (CASP) experiment, whose goal is to benchmark the state of the art in protein structure prediction [9].”
The language of proteins: NLP, machine learning & protein sequences (2021, background): “Competitions such as CAFA (GO annotations and protein function prediction) [31,41], CASP (3D structure prediction) [47] and CAPRI (protein–protein docking prediction) [39] provide the protein research community with rigorous tests for evaluating and comparing prediction algorithms, with new, unseen…”