The Resource A deep learning method for protein model quality assessment, by Son Phong Nguyen

A deep learning method for protein model quality assessment, by Son Phong Nguyen

Label
A deep learning method for protein model quality assessment
Title
A deep learning method for protein model quality assessment
Statement of responsibility
by Son Phong Nguyen
Creator
Contributor
Author
Thesis advisor
Subject
Genre
Language
eng
Summary
Computational protein structure prediction is very important for many applications in bioinformatics. Many prediction methods have been developed, including Modeller, HHpred, I-TASSER, Robetta, and MUFOLD. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Consensus quality assessment (QA) methods, such as MUFOLD-WQA and United3D, which are based on structure similarity, performed well on QA tasks. The drawback of consensus QA methods is that they require a pool of diverse models to work well, which is not always available. More importantly, they cannot evaluate the quality of a single protein model, which is a very common task in protein predictions and other applications. Although many single-model quality assessment methods, such as OPUS-CA, DOPE, DFIRE, and RW, etc. have been developed to address that problem, their accuracy is not good enough for most real applications. In this thesis, a new approach based on C-[alpha] atoms distance matrix and machine learning methods is proposed for single-model QA and the identification of native-like models. Different from existing energy/scoring functions and consensus approaches, this new approach is purely geometry based. Furthermore, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. For a protein model, DL-Pro uses its distance matrix that contains pairwise distances between two residues' C-[alpha] atoms in the model, which sometimes is also called contact map, as an orientation-independent representation. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW
Cataloging source
MUU
http://library.link/vocab/creatorName
Nguyen, Son Phong
Degree
M.S.
Dissertation note
Thesis
Dissertation year
2014.
Government publication
government publication of a state province territory dependency etc
Granting institution
University of Missouri--Columbia
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorDate
1967-
http://library.link/vocab/relatedWorkOrContributorName
Shang, Yi
http://library.link/vocab/subjectName
  • Proteins
  • Machine learning
  • Bioinformatics
Label
A deep learning method for protein model quality assessment, by Son Phong Nguyen
Instantiates
Publication
Note
  • "JULY 2014."
  • "A Thesis Presented to The Faculty of the Graduate School At the University of Missouri In Partial Fulfillment Of the Requirements for the Degree Master of Science."
  • Advisor: Dr. Yi Shang
Accompanying material
2 supplementary abstracts
Bibliography note
Includes bibliographical references (pages 32-34)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
954589459
Extent
1 online resource (viii, 34 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (chiefly color) +
Specific material designation
remote
System control number
(OCoLC)954589459
Label
A deep learning method for protein model quality assessment, by Son Phong Nguyen
Publication
Note
  • "JULY 2014."
  • "A Thesis Presented to The Faculty of the Graduate School At the University of Missouri In Partial Fulfillment Of the Requirements for the Degree Master of Science."
  • Advisor: Dr. Yi Shang
Accompanying material
2 supplementary abstracts
Bibliography note
Includes bibliographical references (pages 32-34)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
954589459
Extent
1 online resource (viii, 34 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations (chiefly color) +
Specific material designation
remote
System control number
(OCoLC)954589459

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