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Enhancement for MAINMAST, De Novo Main-Chain Tracing Method: Symmetric Multi-Chain Modeling, Local Refinement, and Graphical User Interface

Published online by Cambridge University Press:  05 August 2019

Genki Terashi ,
Yuhong Zha  and
Daisuke Kihara
Genki Terashi
Affiliation:
Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
Yuhong Zha
Affiliation:
School of Computer Science, Carnegie Mellon University, Pittsburg, PA, USA.
Daisuke Kihara*
Affiliation:
Department of Biological Sciences, Purdue University, West Lafayette, IN, USA. Department of Computer Science, Purdue University, West Lafayette, IN, USA.
*
*Corresponding author: dkihara@purdue.edu

Abstract

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Type
Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy
Information
Microscopy and Microanalysis , Volume 25 , Supplement S2 , August 2019 , pp. 146 - 147
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Terashi, G and Kihara, D, Nature Communications [Online] 9.1 1618 (2018), https://www.nature.com/articles/s41467-018-04053-7 (accessed Feb 20th, 2019).Google Scholar
[2]Terashi, G and Kihara, D, Journal of Structural Biology 204.2 (2018) p.351-359.Google Scholar
[3]MAINMAST website, http://kiharalab.org/mainmast/index.html (accessed Feb 20th, 2019).Google Scholar
[4]Pintilie, GD, et al. , Journal of Structural Biology. 170.3 (2010): 427-438.Google Scholar
[5]Pettersen, EF, et al. , Journal of Computational Chemistry. 25 (2004) p.1605–1612.Google Scholar
[6]This work was supported by the Purdue Institute of Drug Discovery. Supports from the National Institutes of Health (R01GM123055), the National Science Foundation (DMS1614777, CMMI1825941), are also acknowledged.Google Scholar