1 / 13

Density-Based Classification of Protein Structures Using Iterative TM-score

Explore density-based classification of protein structures using iterative TM-score algorithm. Learn about feature vectors extraction, scoring, and experimental results. Enhance protein classification accuracy and speed.

kingdavid
Download Presentation

Density-Based Classification of Protein Structures Using Iterative TM-score

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Density-Based Classification of Protein Structures Using Iterative TM-score David Hoksza, Jakub Galgonek Charles University in PragueDepartment of Software Engineering Czech Republic

  2. Presentation Outline • Biological background • Similarity search in protein structure databases • Method • feature vectors’ extraction • feature mapping • scoring • Experimental results • Conclusion CSBW 2009

  3. Biological Background • Proteins • molecule • translated from mRNA in ribosomes • DNA → RNA → protein • sequence of amino acids (20 AAs) • AAs coded by codons (triplet of nucleotides) • Function of a protein derived from its three dimensional structure • → similar proteins show similar functions • Identifying protein structure → finding similar proteins → getting clue to the function CSBW 2009

  4. Similarity Search in Protein Databases • Similarity between a pair of proteins • finding mapping (alignment) of atoms • transformation to minimize mutual distance • computing distance of the superposed structures • DALI, CE, TM-align, Vorometric, Vorolign, PPM, … • Classification • SCOP (Structural Classification of Proteins) • manually curated hierarchical classification • family → superfamily → fold → class • need for automated classification CSBW 2009

  5. Feature Extraction and Comparison • Features based on density (inter-residual distances) of atoms • distances among Cα atoms used • each AA represents one feature→ protein p consists of |p| features • various semantics used • based on clustering Cα atoms into rings • distance between pair of features • Euclidean distance • weighted Euclidean distance CSBW 2009

  6. Feature Extraction Semantics • Features • n-dimensional vectors of real numbers • AA ≈ viewpoint → VPT (viewpoint tag) • sDens • density of AAs in rings with a predefined width • sDensSSE • enhanced with SSE information • sRad • widths of rings containing predefined percentage of AAs • sRadSSE • enhanced with SSE information • sDir • number of AAs in a ring pointing from the viepoint • sDens enhanced with direction information CSBW 2009

  7. Finding an Alignment • Modifications • Using modified distances of features in the substitution matrix • μ … mean of s[i,j] • Structure-specific gap costs • OGP … open gap penalty • EGP … extend gap penalty • Smith-Waterman dynamic programming solution (local alignment) • originally for sequence alignment • finding optimal local alignment according to given distance (substitution) matrix • L0,j = 0, Li,0 = 0 • L … DP matrix • s … distance matrix • σ … gap penalty • max(Li,j) … value of optimal alignment CSBW 2009

  8. Superposition + Scoring • Scoring • Superposition achieved by iterative use of TM-score rotation algorithm • Algorithm tries to find a subset of alignment whose RMSD superposition maximizes the TM-score • Various initial subsets are evaluated • lenghts … L, L/2, max(L/25,4) • For each, a superposition is created and a new alignment consisting of spatially close residues is created • Iteration continues until stabilization or max. number of iteration is achieved • Superposition is used for reconstruction of the alignment achieved by dynamic programming (iteratively) CSBW 2009

  9. Superposition Improvements Prefiltration using non-iterative FAST* heuristics top kNN filtering range filtering Belt-based restriction of dynamic programming when reconstructing original alignment Sticking more closely to the original alignment • Using only single initial alignment of length L • For similar proteins (which are important for classification) the deviation from original algorithm is small CSBW 2009

  10. Experimental Results • ASTRAL compendium • Test set • ASTRAL 1.67 • only proteins not present in ASTRAL95 1.65 having < 30% sequence identity and <30 identical residues • 979 proteins • Database set • ASTRAL25 1.65 • 4357 proteins • Evaluation of • classification accuracy according to SCOP • classification of a query is based on the classification of the most similar protein • classification is correct when it is in agreement with SCOP classification • speed CSBW 2009

  11. Comparison of Classification Methods CSBW 2009

  12. Speed Evaluation CSBW 2009

  13. Conclusion • We have proposed • Alignment of protein structures • distance and density of Cα atoms • alignment based on feature distances • Improved version of superposing algorithm (TM) • Accuracy • Speed • Experimental results • best results among methods according to SCOP superfamily and fold accuracy • 95.8% superfamily classification accuracy • 98.1% fold classification accuracy CSBW 2009

More Related