research-article

Public Access

Geometry Analysis for Protein Secondary Structures Matching Problem

Authors:

Christopher Jones,

Ruba JebrilAuthors Info & Claims

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

Pages 716 - 721

https://doi.org/10.1145/3107411.3107505

Published: 20 August 2017 Publication History

Abstract

De novo modeling is a promising computational approach to model the structure of proteins using Cryo-Electron Microscopy data. Not all data produced is within a resolution that enables us to visualize the atomic-structure of the molecule. However, information like secondary structure locations is detectable. At an intermediate step in de novo modeling, the matching between these locations and the amino acid sequence segments that underlie these secondary structure elements needs to be addressed. Many tools have been developed to address this matching problem including DP-TOSS. In this paper, we propose a recast of DP-TOSS that incorporates a geometry-based analytical function to improve accuracy. A test of 15 proteins shows that our analytical function has a positive impact on the accuracy of DP-TOSS.

References

[1]

H. Zheng, K. B. Handing, M. D. Zimmerman, I. G. Shabalin, S. C. Almo, and W. Minor. 2015. X-ray crystallography over the past decade for novel drug discovery -- where are we heading next? Expert Opinion on Drug Discovery, 10, 9 (2015), 975--989.

[2]

A. R. Pearson and A. Mozzarelli. 2011. X-ray crystallography marries spectroscopy to unveil structure and function of biological macromolecules. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics, 1814, 6 (2011), 731--733.

[3]

A.-H. Emwas. 2015. The Strengths and Weaknesses of NMR Spectroscopy and Mass Spectrometry with Particular Focus on Metabolomics Research. Methods in Molecular Biology. Springer New York. 161--193.

[4]

K. Mitra and J. Frank. 2006. Ribosome dynamics: insights from atomic structure modeling into cryo-electron microscopy maps. Annual Review of Biophysics and Biomolecular Structure, 35 (2006), 299--317.

[5]

J. Frank. 2009. Single-particle reconstruction of biological macromolecules in electron microscopy -- 30 years. Quarterly Reviews of Biophysics, 42, 03 (2009), 139--158.

[6]

A. Fiser. 2010. Template-Based Protein Structure Modeling. Methods in Molecular Biology. Humana Press. 73--94.

[7]

M. Källberg, H. Wang, S. Wang, J. Peng, Z. Wang, H. Lu, and J. Xu. 2012. Template-based protein structure modeling using the RaptorX web server. Nat. Protocols, 7, 8 (2012), 1511--1522.

[8]

Y. J. Huang, B. Mao, J. M. Aramini, and G. T. Montelione. 2014. Assessment of template-based protein structure predictions in CASP10. Proteins: Structure, Function, and Bioinformatics, 82 (2014), 43--56.

[9]

D. E. Kim, D. Chivian, and D. Baker. 2004. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res, 32, Web Server issue (2004), W526--531.

[10]

K. T. Simons, C. Kooperberg, E. Huang, and D. Baker. 1997. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. Journal of Molecular Biology, 268, 1 (1997), 209--225.

[11]

B. Adhikari, D. Bhattacharya, R. Cao, and J. Cheng. 2015. CONFOLD: Residue-residue contact-guided ab initio protein folding. Proteins: Structure, Function, and Bioinformatics, 83, 8 (2015), 1436--1449.

[12]

M. L. Baker, S. S. Abeysinghe, S. Schuh, R. A. Coleman, A. Abrams, M. P. Marsh, C. F. Hryc, T. Ruths, W. Chiu, and T. Ju. 2011. Modeling protein structure at near atomic resolutions with Gorgon. Journal of Structural Biology, 174, 2 (2011), 360--373.

[13]

S. Lindert, N. Alexander, N. Wötzel, M. Karaka, Phoebe L. Stewart, and J. Meiler. 2012. EM-Fold: De Novo Atomic-Detail Protein Structure Determination from Medium-Resolution Density Maps. Structure, 20, 3 (2012), 464--478.

[14]

S. Lindert, R. Staritzbichler, N. Wötzel, M. Karakas, P. L. Stewart, and J. Meiler. 2009. EM-fold: De novo folding of alpha-helical proteins guided by intermediate-resolution electron microscopy density maps. Structure, 17, 7 (2009), 990--1003.

[15]

J. He, Y. Lu, and E. Pontelli. 2004. A Parallel Algorithm for Helix Mapping between 3-D and 1-D Protein Structure using the Length Constraints. Lecture Notes in Computer Science, 3358 (2004), 746--756.

Digital Library

[16]

A. Dal Palu, E. Pontelli, J. He, and Y. Lu. 2006. A constraint logic programming approach to 3D structure determination of large protein complexes. In Proceedings of the 2006 ACM Symposium on Applied Computing. ACM, New York, NY, 131--136.

Digital Library

[17]

Y. Wu, M. Chen, M. Lu, Q. Wang, and J. Ma. 2005. Determining protein topology from skeletons of secondary structures. Journal of Molecular Biology, 350, 3 (2005), 571--586.

[18]

S. S. Abeysinghe and T. Ju. 2009. Interactive skeletonization of intensity volumes. Vis. Comput., 25, 5--7 (2009), 627--635.

Digital Library

[19]

K. Al Nasr, C. Liu, M. Rwebangira, L. Burge, and J. He. 2013. Intensity-Based Skeletonization of CryoEM Gray-Scale Images Using a True Segmentation-Free Algorithm. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 10, 5 (2013), 1289--1298.

Digital Library

[20]

K. Al Nasr, D. Ranjan, M. Zubair, L. Chen, and J. He. 2014. Solving the Secondary Structure Matching Problem in Cryo-EM De Novo Modeling Using a Constrained K-Shortest Path Graph Algorithm. Computational Biology and Bioinformatics, IEEE/ACM Transactions on, 11, 2 (2014), 419--430.

Digital Library

[21]

F. DiMaio, M. D. Tyka, M. L. Baker, W. Chiu, and D. Baker. 2009. Refinement of protein structures into low-resolution density maps using rosetta. Journal of Molecular Biology, 392, 1 (2009), 181--190.

[22]

D. Si, S. Ji, K. Al Nasr, and J. He. 2012. A machine learning approach for the identification of protein secondary structure elements from cryoEM density maps. Biopolymers, 97 (2012), 698--708.

[23]

K. Lasker, O. Dror, M. Shatsky, R. Nussinov, and H. J. Wolfson. 2007. EMatch: discovery of high resolution structural homologues of protein domains in intermediate resolution cryo-EM maps. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4, 1 (2007), 28--39.

Digital Library

[24]

M. L. Baker, T. Ju, and W. Chiu. 2007. Identification of secondary structure elements in intermediate-resolution density maps. Structure, 15, 1 (2007), 7--19.

[25]

A. Del Palu, J. He, E. Pontelli, and Y. Lu. 2006. Identification of Alpha-Helices from Low Resolution Protein Density Maps. Proceeding of Computational Systems Bioinformatics Conference(CSB) (2006), 89--98.

[26]

D. Si and J. He. 2014. Tracing Beta Strands Using StrandTwister from Cryo-EM Density Maps at Medium Resolutions. Structure, 22, 11 (2014), 1665--1676.

[27]

G. Pollastri and A. McLysaght. 2005. Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics, 21, 8 (2005), 1719--1720.

Digital Library

[28]

D. T. Jones. 1999. Protein secondary structure prediction based on position-specific scoring matrices. Journal of Molecular Biology, 292, 2 (1999), 195--202.

[29]

K. Al Nasr, D. Ranjan, M. Zubair, and J. He. 2011. Ranking Valid Topologies of the Secondary Structure elements Using a constraint Graph. Journal of Bioinformatics and Computational Biology, 9, 3 (2011), 415--430.

[30]

K. Al Nasr, L. Chen, D. Si, D. Ranjan, M. Zubair, and J. He. 2012. Building the initial chain of the proteins through de novo modeling of the cryo-electron microscopy volume data at the medium resolutions. In Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM, New York, NY, 490--497.

Digital Library

[31]

K. Al Nasr, W. Sun, and J. He. 2010. Structure prediction for the helical skeletons detected from the low resolution protein density map. BMC Bioinformatics, 11, Suppl 1 (2010), S44.

[32]

S. Abeysinghe, T. Ju, M. L. Baker, and W. Chiu. 2008. Shape modeling and matching in identifying 3D protein structures. Computer-Aided Design, 40, 6 (2008), 708--720.

Digital Library

[33]

G. Wang and R. L. Dunbrack Jr. 2003. PISCES: a protein sequence culling server. Bioinformatics, 19, 12 (2003), 1589--1591.

[34]

D. P. Doane and L. E. Seward. 2011. Measuring Skewness: A Forgotten Statistic? Journal of Statistics Education, 19, 2 (2011).

[35]

E. F. Pettersen, T. D. Goddard, C. C. Huang, G. S. Couch, D. M. Greenblatt, E. C. Meng, and T. E. Ferrin. 2004. UCSF Chimera--A visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25, 13 (2004), 1605--1612.

[36]

K. Al Nasr, C. Liu, M. Rwebangira, and L. I. Burge. 2013. A Graph Approach to Bridge the Gaps in Volumetric Electron Cryo-microscopy Skeletons. Lecture Notes in Computer Science. Springer Berlin Heidelberg. 211--223.

Cited By

Alshammari MHe J(2021)Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure TopologiesMolecules10.3390/molecules2622704926:22(7049)Online publication date: 22-Nov-2021
https://doi.org/10.3390/molecules26227049
Al Nasr KYousef FJebril RJones C(2018)Analytical Approaches to Improve Accuracy in Solving the Protein Topology ProblemMolecules10.3390/molecules2302002823:2(28)Online publication date: 23-Jan-2018
https://doi.org/10.3390/molecules23020028

Index Terms

Geometry Analysis for Protein Secondary Structures Matching Problem
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
    2. Computational biology
      1. Molecular structural biology

Recommendations

Revealing protein structures: a new method for mapping antibody epitopes
RECOMB '02: Proceedings of the sixth annual international conference on Computational biology

A recent idea for determining the three-dimensional structure of a protein uses antibody recognition of surface structure and random peptide libraries to map antibody epitope combining sites. Antibodies that bind to the surface of the protein of ...
Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structures and Support Vector Machines
BIBM '09: Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine

Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish ...
A reference dataset for the analyses of membrane protein secondary structures and transmembrane residues using circular dichroism spectroscopy

Motivation: Empirical analyses of protein secondary structures based on circular dichroism (CD) and synchrotron radiation circular dichroism (SRCD) spectroscopic data rely on the availability of reference datasets comprised of spectra of relevant ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

August 2017

800 pages

ISBN:9781450347228

DOI:10.1145/3107411

General Chairs:
Nurit Haspel
University of Massachusetts Boston, USA
,
Lenore J. Cowen
Tufts University, USA
,
Program Chairs:
Amarda Shehu
George Mason University, USA
,
Tamer Kahveci
University of Florida, USA
,
Giuseppe Pozzi
Politecnico di Milano, Italy

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGBio: ACM Special Interest Group on Bioinformatics

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

BCB '17

Sponsor:

SIGBio

BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 20 - 23, 2017

Massachusetts, Boston, USA

Acceptance Rates

ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;

Overall Acceptance Rate 254 of 885 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
113
Total Downloads

Downloads (Last 12 months)25
Downloads (Last 6 weeks)5

Reflects downloads up to 19 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Alshammari MHe J(2021)Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure TopologiesMolecules10.3390/molecules2622704926:22(7049)Online publication date: 22-Nov-2021
https://doi.org/10.3390/molecules26227049
Al Nasr KYousef FJebril RJones C(2018)Analytical Approaches to Improve Accuracy in Solving the Protein Topology ProblemMolecules10.3390/molecules2302002823:2(28)Online publication date: 23-Jan-2018
https://doi.org/10.3390/molecules23020028

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents