Wei Zhao1, Meng Xu, Zhi Liang, Bo Ding, Liwen Niu, Haiyan Liu, Maikun Teng. 1. Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China and Key Laboratory of Structural Biology, Chinese Academy of Sciences, 96 Jinzhai Road, Hefei, Anhui, China.
Abstract
MOTIVATION: Zinc-binding proteins are the most abundant metallo-proteins in Protein Data Bank (PDB). Accurate prediction of zinc-binding sites in proteins of unknown function may provide important clues for the inference of protein function. As zinc binding is often associated with characteristic 3D arrangements of zinc ligand residues, its prediction may benefit from using not only the sequence information but also the structure information of proteins. RESULTS: In this work, we present a structure-based method, TEMSP (3D TEmplate-based Metal Site Prediction), to predict zinc-binding sites. TEMSP significantly improves over previously reported best methods in predicting as many as possible true ligand residues for zinc with minimum overpredictions: if only those results in which all zinc ligand residues have been correctly predicted are defined as true positives, our method improves sensitivity from less than 30% to above 60%, and selectivity from around 25% to 80%. These results are for predictions based on apo state structures. In addition, the method can predict the zinc-bound local structures reliably, generating predictions useful for function inference. We applied TEMSP to 1888 protein structures of the 'Unknown Function' class in the PDB database. A number of zinc-binding sites have been discovered de novo, i.e. based solely on the protein structures. Using the predicted local structures of these sites, possible functional roles were analyzed. AVAILABILITY: TEMSP is freely available from http://netalign.ustc.edu.cn/temsp/.
MOTIVATION: Zinc-binding proteins are the most abundant metallo-proteins in Protein Data Bank (PDB). Accurate prediction of zinc-binding sites in proteins of unknown function may provide important clues for the inference of protein function. As zinc binding is often associated with characteristic 3D arrangements of zinc ligand residues, its prediction may benefit from using not only the sequence information but also the structure information of proteins. RESULTS: In this work, we present a structure-based method, TEMSP (3D TEmplate-based Metal Site Prediction), to predict zinc-binding sites. TEMSP significantly improves over previously reported best methods in predicting as many as possible true ligand residues for zinc with minimum overpredictions: if only those results in which all zinc ligand residues have been correctly predicted are defined as true positives, our method improves sensitivity from less than 30% to above 60%, and selectivity from around 25% to 80%. These results are for predictions based on apo state structures. In addition, the method can predict the zinc-bound local structures reliably, generating predictions useful for function inference. We applied TEMSP to 1888 protein structures of the 'Unknown Function' class in the PDB database. A number of zinc-binding sites have been discovered de novo, i.e. based solely on the protein structures. Using the predicted local structures of these sites, possible functional roles were analyzed. AVAILABILITY: TEMSP is freely available from http://netalign.ustc.edu.cn/temsp/.
Authors: Katarzyna B Handing; Ewa Niedzialkowska; Ivan G Shabalin; Misty L Kuhn; Heping Zheng; Wladek Minor Journal: Nat Protoc Date: 2018-04-19 Impact factor: 13.491
Authors: A A Aptekmann; J Buongiorno; D Giovannelli; M Glamoclija; D U Ferreiro; Y Bromberg Journal: Bioinformatics Date: 2022-05-27 Impact factor: 6.931