TY - JOUR
T1 - Assessing the accuracy of contact predictions in CASP13
AU - Shrestha, Rojan
AU - Fajardo, Eduardo
AU - Gil, Nelson
AU - Fidelis, Krzysztof
AU - Kryshtafovych, Andriy
AU - Monastyrskyy, Bohdan
AU - Fiser, Andras
N1 - Funding Information:
This work was supported by the following grants and agencies: National Institutes of Health (NIH) grant GM118709, GM100482, and AI141816.
Publisher Copyright:
© 2019 Wiley Periodicals, Inc.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
KW - CASP13
KW - contact prediction
KW - protein structure modeling
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U2 - 10.1002/prot.25819
DO - 10.1002/prot.25819
M3 - Article
C2 - 31587357
AN - SCOPUS:85074557304
SN - 0887-3585
VL - 87
SP - 1058
EP - 1068
JO - Proteins: Structure, Function and Bioinformatics
JF - Proteins: Structure, Function and Bioinformatics
IS - 12
ER -