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Assessment of chemical‐crosslink‐assisted protein structure modeling in CASP13
Author(s) -
Fajardo J. Eduardo,
Shrestha Rojan,
Gil Nelson,
Belsom Adam,
Crivelli Silvia N.,
Czaplewski Cezary,
Fidelis Krzysztof,
Grudinin Sergei,
Karasikov Mikhail,
Karczyńska Agnieszka S.,
Kryshtafovych Andriy,
Leitner Alexander,
Liwo Adam,
Lubecka Emilia A.,
Monastyrskyy Bohdan,
Pagès Guillaume,
Rappsilber Juri,
Sieradzan Adam K.,
Sikorska Celina,
Trabjerg Esben,
Fiser Andras
Publication year - 2019
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25816
Subject(s) - context (archaeology) , computer science , workflow , protein structure prediction , protein structure , data science , machine learning , artificial intelligence , chemistry , database , biology , paleontology , biochemistry
With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches. This largest‐to‐date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases. However, in a broader context, it also suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows.