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Cited by 3Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

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CRFalign: A Sequence-Structure Alignment of Proteins Based on a Combination of HMM-HMM Comparison and Conditional Random Fields

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Lee, Sung Jong, Joo, Keehyoung, Sim, Sangjin, Lee, Juyong, Lee, In-Ho, Lee, Jooyoung·2022·Molecules
DOI 10.3390/molecules27123711PMC PMC9231382

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Abstract

Sequence–structure alignment for protein sequences is an important task for the template-based modeling of 3D structures of proteins. Building a reliable sequence–structure alignment is a challenging problem, especially for remote homologue target proteins. We built a method of sequence–structure alignment called CRFalign, which improves upon a base alignment model based on HMM-HMM comparison by employing pairwise conditional random fields in combination with nonlinear scoring functions of structural and sequence features. Nonlinear scoring part is implemented by a set of gradient boosted regression trees. In addition to sequence profile features, various position-dependent structural features are employed including secondary structures and solvent accessibilities. Training is performed on reference alignments at superfamily levels or twilight zone chosen from the SABmark benchmark set. We found that CRFalign method produces relative improvement in terms of average alignment accuracies for validation sets of SABmark benchmark. We also tested CRFalign on 51 sequence–structure pairs involving 15 FM target domains of CASP14, where we could see that CRFalign leads to an improvement in average modeling accuracies in these hard targets (TM-CRFalign ≃42.94%) compared with that of HHalign (TM-HHalign ≃39.05%) and also that of MRFalign (TM-MRFalign ≃36.93%). CRFalign was incorporated to our template search framework called CRFpred and was tested for a random target set of 300 target proteins consisting of Easy, Medium and Hard sets which showed a reasonable template search performance.

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Research works printed but not linked: 38

  1. Senior, 2020, Improved protein structure prediction using potentials from deep learning, Nature, 577, 706, 10.1038/s41586-019-1923-7 · DOI · Paper page
  2. Cheng, 2006, A Machine Learning Information Retrieval Approach to Protein Fold Recognition, Bioinformatics, 22, 1456, 10.1093/bioinformatics/btl102 · DOI
  3. Cheng, J. (2008). A multi-template combination algorithm for protein comparative modeling. BMC Struct. Biol., 8. · DOI
  4. Zhang, 2008, Progress and challenges in protein structure prediction, Curr. Opin. Struct. Biol., 18, 342, 10.1016/j.sbi.2008.02.004 · DOI
  5. Joo, 2007, High accuracy template based modeling by global optimization, Proteins, 69, 83, 10.1002/prot.21628 · DOI
  6. Rychlewski, 2000, Comparison of sequence profiles. Strategies for structural predictions using sequence information, Protein Sci., 9, 232, 10.1110/ps.9.2.232 · DOI
  7. Yona, 2002, Within the twilight zone: A sensitive profile-profile comparison tool based on information theory, J. Mol. Biol., 315, 1257, 10.1006/jmbi.2001.5293 · DOI
  8. Zimmer, 2001, Improving profile-profile alignment via log average scoring, Lecture Notes in Computer Science, Volume 2149, 11, 10.1007/3-540-44696-6_2 · DOI
  9. Tomii, 2004, FORTE: A profile-profile comparison tool for protein fold recognition, Bioinformatics, 20, 594, 10.1093/bioinformatics/btg474 · DOI
  10. Yang, 2011, Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates, Bioinformatics, 27, 2076, 10.1093/bioinformatics/btr350 · DOI
  11. Zhou, 2004, Single-Body Residue-Level Knowledge-Based Energy Score Combined With Sequence-Profile and Secondary Structure Information for Fold Recognition, Proteins, 55, 1005, 10.1002/prot.20007 · DOI
  12. Zhou, 2005, Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments, Proteins, 58, 321, 10.1002/prot.20308 · DOI
  13. Liu, 2007, Fold Recognition by Concurrent Use of Solvent Accessibility and Residue Depth, Proteins, 68, 636, 10.1002/prot.21459 · DOI
  14. Zhang, W., Liu, S., and Zhou, Y. (2008). SP5: Improving protein fold recognition by using predicted torsion angles and profile-based gap penalty. PLoS ONE, 3. · DOI
  15. 2005, Protein homology detection by HMM-HMM comparison, Bioinformatics, 21, 951, 10.1093/bioinformatics/bti125 · DOI
  16. Deng, X., and Cheng, J. (2014). Enhancing HMM-based protein profile-profile alignment with structural features and evolutionary coupling information. BMC Bioinform., 15. · DOI
  17. Do, 2006, CONTRAlign: Discriminative Training for Protein Sequence Alignment, Res. Comput. Mol. Biol., 3909, 160, 10.1007/11732990_15 · DOI
  18. Peng, 2009, Boosting Protein Threading Accuracy, Res. Comput. Mol. Biol., 5541, 31, 10.1007/978-3-642-02008-7_3 · DOI
  19. Ma, 2012, A conditional neural fields model for protein threading, Bioinformatics, 28, i59, 10.1093/bioinformatics/bts213 · DOI
  20. Ma, J., Wang, S., Wang, Z., and Xu, J. (2014). MRFalign: Protein Homology Detection through Alignment of Markov Random Fields. PLoS Comput. Biol., 10. · DOI
  21. Makigaki, 2020, Sequence alignment using machine learning for accurate template-based protein structure prediction, Bioinformatics, 36, 104, 10.1093/bioinformatics/btz483 · DOI
  22. Remmert, 2011, HHblits: Lightning-fast iterative protein sequence searching by HMM-HMM alignment, Nat. Methods, 9, 173, 10.1038/nmeth.1818 · DOI · Paper page
  23. Tyree, S., Weinberger, K.Q., Agrawal, K., and Paykin, J. (April, January 28). Parallel boosted regression trees for web search ranking. Proceedings of the 20th International Conference on World Wide Web, Hyderabad, India.
  24. Walle, 2005, SABmark-a benchmark for sequence alignment that covers the entire known fold space, Bioinformatics, 21, 1267, 10.1093/bioinformatics/bth493 · DOI
  25. Yan, 2013, A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction, Sci. Rep., 3, 2619, 10.1038/srep02619 · DOI
  26. Joo, 2016, Template based protein structure modeling by global optimization in CASP11, Proteins, 84, 221, 10.1002/prot.24917 · DOI
  27. Joo, 2014, Protein structure modeling for CASP10 by multiple layers of global optimization, Proteins, 82, 188, 10.1002/prot.24397 · DOI
  28. Moult, 2021, Special Issue:CASP14: Critical Assessment of methods of protein Structure Prediction, 14th round, Proteins, 89, 1603
  29. Durbin, R., Eddy, S.R., Krogh, A., and Mitchison, G. (1998). Biological Sequence Analysis, Cambridge University Press. [1st ed.]. · DOI
  30. Kabsch, 1983, Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features, Biopolymers, 22, 2577, 10.1002/bip.360221211 · DOI · Paper page
  31. Jones, 1999, Protein secondary structure prediction based on position-specific scoring matrices, J. Mol. Biol., 292, 195, 10.1006/jmbi.1999.3091 · DOI
  32. Hong, 2018, Protein structure modeling and refinement by global optimization in CASP12, Proteins, 86, 122, 10.1002/prot.25426 · DOI
  33. Zemla, 2003, LGA: A method for finding 3D similarities in protein structures, Nucleic Acids Res., 31, 3370, 10.1093/nar/gkg571 · DOI · Paper page
  34. Henikoff, 1992, Amino Acid Substitution Matrices from Protein Blocks, Proc. Natl. Acad. Sci. USA, 89, 10915, 10.1073/pnas.89.22.10915 · DOI · Paper page
  35. Joo, 2012, SANN: Solvent accessibility prediction of proteins by nearest neighbor method, Proteins, 80, 1791, 10.1002/prot.24074 · DOI
  36. Gonnet, 1992, Exhaustive matching of the entire protein sequence database, Science, 256, 1443, 10.1126/science.1604319 · DOI
  37. Tan, 2006, Statistical Potential-Based Amino Acid Similarity Matrices for Aligning Distantly Related Protein Sequences, Proteins, 64, 587, 10.1002/prot.21020 · DOI
  38. Kim, 2003, PROSPECT II: Protein structure prediction program for genome-scale applications, Protein Eng., 16, 641, 10.1093/protein/gzg081 · DOI

Metadata

Authors
Lee, S. J., Joo, K., Sim, S., Lee, J., Lee, I.-H., & Lee, J.
Affiliations
  • Changwon National University (KR)
  • Korea Institute for Advanced Study (KR)
  • NAVER Cloud (South Korea) (KR)
  • Korea Research Institute of Standards and Science (KRISS), Daejeon 34113, Korea
  • School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, Korea
ORCID
Venue
Molecules
Publisher
MDPI AG
Published
2022-06-09
Volume
27
Issue
12
Pages
3711
ISSN
1420-3049 (ISSN-L)
DOI
10.3390/molecules27123711
PMC
PMC9231382
OpenAlex
W4282840045
Cited by
3
References
1
Citations by year
2025: 1 · 2023: 1 · 2022: 1
FWCI
0.24
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecules (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Ministry of Science and ICT (NRF-2017R1E1A1A01077717, NRF-2018R1D1A1B07049312)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
sequence-structure alignment, protein structure prediction, template-based modeling, conditional random fields, gradient boosting regression tree, solvent accessibility, secondary structure, CASP14
MeSH terms
Algorithms, Amino Acid Sequence, Proteins, Solvents, Sequence Alignment, Protein Structure, Secondary

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