Protein Secondary Structure Prediction Using Data Mining Tool C5

1. Protein Secondary Structure Prediction Using Data Mining Tool C5 Meiliu Lu†, Du Zhang†, Hongjun Xu†, Ken Tse-yau Lau‡, and Li Lu§ † Dept. of Computer Science California State University ‡ Intel Corporation, Folsom CA § Sierra Systems Consultants Inc., Washington DC ICTAI-99, Chicago

2. Introduction • Advancement of medical sciences depends critically on understanding of structures of proteins, the fundamental molecules for all living organisms. • Proteins have different structures based upon their locations (intracellular, extracellular, membrane, cytosolic, neuclear ) and functions (structural, enzyme, or antibodies, etc.) • All protein molecules are polymers built up from 20 different amino acid residues linked end to end by peptide bonds. ICTAI-99, Chicago

3. Protein Structures • Primary structure is the linear sequences of amino acid. • Secondary structure is the spatial relationship of amino acid residues that are close to one another in the linear sequence. • Tertiary structure is the spatial relationship of residues that are far apart in the linear sequence. • Quaternary structure is the way some proteins are packed together to form polypeptide chain. ICTAI-99, Chicago

4. The Secondary Structure • The function of every protein depends on its tertiary (3D) structure. • Secondary structure plays a pivotal role between the final 3D structure and the linear amino acid sequence of a protein. • Determining a protein’s secondary structure from its primary one would greatly help us unlock its 3D structure. ICTAI-99, Chicago

5. Types of Secondary Structure • -helix: a rod-like structure. • -sheet: several regions of the polypeptide chain. • turns: part where direction of the polypeptide chain is changed. • coil: any part of the polypeptide chains not belonging to the above three. ICTAI-99, Chicago

6. Protein Structure Example 1: p21Ras ICTAI-99, Chicago

7. Protein Structure Example 2: MHC1 ICTAI-99, Chicago

8. State-of-the-Art in Protein Secondary Structure prediction • Physical methods such as x-ray crystallography, or nuclear magnetic resonance, slow and expensive. • There are 3 broad groups of secondary structure prediction methods: • empirical statistical methods, accuracy around 50% • stereochemical criteria based methods, accuracy 50% • machine learning based methods, accuracy up to 70-80% ICTAI-99, Chicago

9. The Challenge • The slow experimental determination of 3D structure vs. the fast accumulation of amino acid sequence data. • Different amino acid sequences may yield similar 3D structure. • Very difficult to predict 3D structure from its sequence of an unknown protein. ICTAI-99, Chicago

10. Our Research Experiment • To predict the secondary structure of an unknown protein, Spermidine/Spermine N1-Acetyltransferase (SSAT), a target of cancer chemotherapy. • A machine learning tool called C5 (by J. Ross Quinlan), which is based on a decision tree learning method, is used for the prediction task. ICTAI-99, Chicago

11. Comparison of ML Tools ICTAI-99, Chicago

12. Prediction Considerations • Use of functional similarity and sequence homology in selecting training proteins. • Incorporation of amino acid hydrophobicity into the process. • Choices of training set sizes and sequence attribute sizes. ICTAI-99, Chicago

13. Selections of Training Proteins • A set (FS) of 23 known proteins that are functionally similar to SSAT is selected. • A set (SH) of 32 known proteins that have sequence homology to SSAT is selected. • A third set (MX) is constructed that consists of proteins from both FS and SH. ICTAI-99, Chicago

14. Incorporation of Hydrophobicity • Hydrophobic character of each amino acid residue is incorporated into the prediction process. • The levels considered in our experiments are: none (NH), residual-level (RH) and atomic-level (AH.) • Two methods used in calculating the values. ICTAI-99, Chicago

15. Decision Tree Based Learning • Collect a large set of examples. • Divide it into two disjoint sets: training set (TR) and test set (TT). • Use the learning algorithm with TR to generate decision trees (if-then rules). • Measure the percentage of examples in TT that are correctly classified by the trees (rules). • Repeat the above steps for diff. sizes of TR and diff. randomly selected TR of each size. ICTAI-99, Chicago

16. Training Sets and Test Sets • Total number of cases for FS, SH and MX are 6288, 7165 and 13453, respectively. • Selection of training set and test set: • Category 1: equal sized training/test sets. • Category 2: 20% of total cases for test set varying sized training set (25%, 50%, 75% and 100% of the remaining cases ) ICTAI-99, Chicago

17. MX FS SH Size of the test set 2691 1258 1433 Size of training set one 2701 1258 1433 Size of training set two 5401 2515 2866 Size of training set three 8101 3773 4299 Size of training set four 10762 5030 5732 Training/Test Sets in Category 2 ICTAI-99, Chicago

18. Sequence Attribute Sizes • The size of sequence attributes indicates how many neighboring amino acid residues are included in a C5 case. • Eight different sizes are considered in our experiments: 5, 9, 13, 17, 21, 25, 29, and 33). ICTAI-99, Chicago

19. Results • Six hundred runs are performed, each producing a decision tree as a classifier. • Those runs are made with regard to the following factors: • Different data sets (FS, SH, MX). • Hydrophobicity attributes (NH, RH, AH). • Hydrophobicity value calculating methods. • Varying training set sizes and sequence attributes. ICTAI-99, Chicago

20. Results (continued) • Results obtained using training cases from SH are consistently better. • Differences among three data sets (FS, SH, MX) are significantly different. • Hydrophobicity and its calculation method choice do not show improvement in predictive accuracy. • Error rate decreases as training set size increases. • No significant difference among error rates of different sequence attribute sizes. ICTAI-99, Chicago

21. MX FS SH Category one 41.4 60.7 23.8 Category two 42.2 61.4 25.7 Average Error Percentage ICTAI-99, Chicago

22. Predicted Secondary Structure of SSAT ICTAI-99, Chicago

23. Conclusions • C5 can be used to predict protein secondary structure. • The prediction accuracy depends critically on selection of training data. • Training data selected based on sequence homology are superior to functional similarity or hydrophobicity. • The SH classifier achieves 75% accuracy. ICTAI-99, Chicago

24. Future Work • Improve predictive accuracy by setting new data selection criteria. • Develop on-line service for protein structure prediction. ICTAI-99, Chicago