Earticle

초록

영어

Remote protein homology detection is a problem of detecting evolutionary relationship between proteins at low sequence similarity level. Among several problems in remote protein homology detection include the questions of determining which combination of multiple alignment and classification techniques is the best as well as the misalignment of protein sequences during the alignment process. Therefore, this paper deals with remote protein homology detection via assessing the impact of using structural information on protein multiple alignments over sequence information. This paper further presents the best combinations of multiple alignment and classification programs to be chosen. This paper also improves the quality of the multiple alignments via integration of a refinement algorithm. The framework of this paper began with datasets preparation on datasets from SCOP version 1.73, followed by multiple alignments of the protein sequences using CLUSTALW, MAFFT, ProbCons and T-Coffee for sequence-based multiple alignments and 3DCoffee, MAMMOTH-mult, MUSTANG and PROMALS3D for structural-based multiple alignments. Next, a refinement algorithm was applied on the protein sequences to reduce misalignments. Lastly, the aligned protein sequences were classified using the pHMMs generative classifier such as HMMER and SAM and also SVMs discriminative classifier such as SVM-Fold and SVM-Struct. The performances of assessed programs were evaluated using ROC, Precision and Recall tests. The result from this paper shows that the combination of refined SVM-Struct and PROMALS3D performs the best against other programs, which suggests that this combination is the best for RPHD. This paper also shows that the use of the refinement algorithm increases the performance of the multiple alignments programs by at least 4%.

목차

Abstract
 1. Introduction
 2. Framework for Finding the Optimal Mesh Algorithm
  2.1. Dataset Generation
  2.2. Multiple Alignments
  2.3. Refinement Algorithm
  2.4. Classification Algorithm
  2.5. Performance Evaluation
 3. Results
  3.1. HMMER Performance
  3.2. SAM Performance
  3.3. HMMER and SAM Performance
  3.4. SVM-Fold Performance
  3.5. SVM-Struct Performance
  3.6. SVM-Fold and SVM-Struct Performance
  3.7. PHMMs and SVMs Performance
 4. Discussion
  4.1. PROMALS3D: The Best Multiple Alignments
  4.2. SVM-Struct: The Best Classification Algorithm
  4.3. REFINER: The Impact or Refinement Algorithm
  4.4. PRS: The Optimal Mesh Algorithm
 5. Conclusions
 References

키워드

저자

• Firdaus M Abdullah [ Laboratory of Computational Intelligence and Biotechnology, Universiti Teknologi Malaysia ]
• Razib M. Othman [ Laboratory of Computational Intelligence and Biotechnology, Universiti Teknologi Malaysia ] Corresponding author
• Shahreen Kasim [ Department of Web Technology, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia ]
• Rathiah Hashim [ Department of Web Technology, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia ]
• Rohayanti Hassan [ Laboratory of Computational Intelligence and Biotechnology, Universiti Teknologi Malaysia ]
• Hishammuddin Asmuni [ Laboratory of Computational Intelligence and Biotechnology, Universiti Teknologi Malaysia ]
• Jumail Taliba [ Laboratory of Computational Intelligence and Biotechnology, Universiti Teknologi Malaysia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605