Being able to predict the sequence-structure relationship in proteins will extend the scope of many bioinformatics tools relying on structure information. Here we use Hidden Markov models (HMM) to recognize and pinpoint the location in target sequences of local structural motifs (local descriptors of protein structure, LDPS) These substructures are composed of three or more segments of amino acid backbone structures that are in proximity with each other in space but not necessarily along the amino acid sequence. We were able to align descriptors to their proper locations in 41.1% of the cases when using models solely built from amino acid information. Using models that also incorporated secondary structure information, we were able to assign 57.8% of the local descriptors to their proper location. Further enhancements in performance was yielded when threading a profile through the Hidden Markov models together with the secondary structure, with this material we were able assign 58,5% of the descriptors to their proper locations. Hidden Markov models…
Contents
1 Introduction
1.1 Background
1.1.1 Protein structure prediction
1.1.1.1 Homology modeling
1.1.1.2 Protein Threading
1.1.1.3 Ab initio protein modeling
1.1.2 Local Descriptors of protein structures
1.1.3 Amino acid sequence analysis
1.1.3.1 Patterns
1.1.3.2 Profiles
1.1.4 Hidden Markov Models
1.1.4.1 Definition for Markov Models
1.1.4.2 Definition for Hidden Markov Models
1.2 Aims of the Master’s thesis
2 Results
2.1 General information
2.2 Different Models
2.2.1 Simple Amino Acid Model
2.2.2 Extended Amino Acid Model
2.2.3 Combined Dual Hidden Markov Model
2.2.4 Dual Emitting Hidden Markov Model
2.2.5 Hybrid Hidden Markov Inspired Model
2.3 Different Target Data
2.3.1 Ungapped Blast Alignment
2.3.2 Gapped Blast Alignment
2.3.2 Profile PSSM
3 Discussion
3.1 Discussion – Amino Acid models
3.2 Discussion – Dual Data Set Models
3.3 Discussion – Different Target Data
4 Conclusion and Outlook
4.1 Related Research
4.1.1 Hidden Markov Models used for structure predicting
4.1.2 Hidden Markov Model concept improvements
4.2 Conclusion
4.3 Outlook
5 Acknowledgements
References
Appendix A
Author: Bjorkholm, Patrik
Source: Linkoping University
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