Assessing the accuracy of contact predictions in CASP13

By Rojan Shrestha, Eduardo Fajardo, Nelson Gil, Krzysztof Fidelis, Andriy Kryshtafovych, Bohdan Monastyrskyy, András Fiser. 2019

63 citations recorded by the source corpus.

The accuracy of sequence‐based tertiary contact predictions was assessed in a blind prediction experiment at the CASP13 meeting. After 4 years of significant improvements in prediction accuracy, another dramatic advance has taken place since CASP12 was held 2 years ago. The precision of predicting the top L/5 contacts in the free modeling category, where L is the corresponding length of the protein in residues, has exceeded 70%. As a comparison, the best‐performing group at CASP12 with a 47% precision would have finished below the top 1/3 of the CASP13 groups. Extensively trained deep neural network approaches dominate the top performing algorithms, which appear to efficiently integrate information on coevolving residues and interacting fragments or possibly utilize memories of sequence similarities and sometimes can deliver accurate results even in the absence of virtually any target specific evolutionary information. If the current performance is evaluated by F‐score on L contacts, it stands around 24% right now, which, despite the tremendous impact and advance in improving its utility for structure modeling, also suggests that there is much room left for further improvement.

Subfields: Materials Chemistry, Molecular Biology

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In Materials Chemistry

1. Robin Pearce
2. Yang Zhang
3. Chengxin Zhang
4. Yang Li
5. Wei Zheng
6. Eric W. Bell
7. Quan Le
8. Mirko Torrisi

In Molecular Biology

1. Robin Pearce
2. Yang Zhang
3. Chengxin Zhang
4. Eric W. Bell
5. Yang Li
6. Wei Zheng
7. Quan Le
8. Mirko Torrisi

Citations in the corpus

• Deep learning methods in protein structure prediction (2020, background): “are largely to be attributed to improved map predictions [127,136].”
• Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, methodology): “Nevertheless, given the dominantly important role of contact predictions (Shrestha et al., 2019) and the fact that the most”