AAnchor: CNN guided detection of anchor amino acids in high resolution cryo-EM density maps

Mark Rozanov, Haim J. Wolfson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The recent cryo-EM resolution revolution enables the development of algorithms for direct de-novo modeling of protein structures into cryo-EM density maps. Here we present a machine learning based method for the detection of high confidence anchor amino acid residues in such a map. Such anchor residues can be exploited in several local de-novo modeling tasks, such as the reliable positioning of secondary structures, loop modeling and general fragment based modeling. In the experimental results we show the ability of the proposed procedure to locate and classify a significant number of amino acids in density maps of 3. 1 A° (or better) resolution. Our performance analysis indicates that the main factor affecting the detection accuracy is the lack of sufficient experimental data for the training stage of the algorithm. Thus, our method is expected to improve significantly in the near future, due to the rapid increase in the release of novel high resolution cryo-EM maps.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages88-91
Number of pages4
ISBN (Electronic)9781538654880
DOIs
StatePublished - 21 Jan 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/TerritorySpain
CityMadrid
Period3/12/186/12/18

Keywords

  • CNN
  • cryo-EM maps
  • machine learning
  • molecular modeling
  • proteins

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