Investigation into the role of sequence- driven-features and amino

243 Pages·2013·5.31 MB·English

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Investigation into the role of sequence-
driven-features and amino acid indices for
the prediction of structural classes of
proteins
By
Mr. Sundeep Singh Nanuwa
Submitted in partial fulfilment of the
requirements for the award of Doctor of
Philosophy of
De Montfort University
April 2013
Abstract
The work undertaken within this thesis is towards the development of a representative set of
sequence driven features for the prediction of structural classes of proteins. Proteins are
biological molecules that make living things function, to determine the function of a protein
the structure must be known because the structure dictates its physical capabilities. A protein
is generally classified into one of the four main structural classes, namely all-α, all-β, α + β or α
/ β, which are based on the arrangements and gross content of the secondary structure
elements. Current methods manually assign the structural classes to the protein by manual
inspection, which is a slow process. In order to address the problem, this thesis is concerned
with the development of automated prediction of structural classes of proteins and extraction
of a small but robust set of sequence driven features by using the amino acid indices. The first
main study undertook a comprehensive analysis of the largest collection of sequence driven
features, which includes an existing set of 1479 descriptor values grouped by ten different
feature groups. The results show that composition based feature groups are the most
representative towards the four main structural classes, achieving a predictive accuracy of
63.87%. This finding led to the second main study, development of the generalised amino acid
composition method (GAAC), where amino acid index values are used to weigh corresponding
amino acids. GAAC method results in a higher accuracy of 68.02%. The third study was to
refine the amino acid indices database, which resulted in the highest accuracy of 75.52%. The
main contributions from this thesis are the development of four computationally extracted
sequence driven feature-sets based on the underused amino acid indices. Two of these
methods, GAAC and the hybrid method have shown improvement over the usage of traditional
sequence driven features in the context of smaller and refined feature sizes and classification
accuracy. The development of six non-redundant novel sets of the amino acid indices dataset,
of which each are more representative than the original database. Finally, the construction of
two large 25% and 40% homology datasets consisting over 5000 and 7000 protein samples,
respectively. A public webserver has been developed located at http://www.generalised-
protein-sequence-features.com, which allows biologists and bioinformaticians to extract GAAC
sequence driven features from any inputted protein sequence.
Keywords: protein structural classes, sequence driven features, amino acid indices, test
procedures, largest protein structural class datasets, generalised amino acid composition
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Publications
Nanuwa, S. S. and H. Seker (2008). Investigation into the role of sequence-driven-features for
prediction of protein structural classes. 8th IEEE International Conference on Bioinformatics
and BioEngineering, BIBE 2008, Athens Greece. Volume 1. Pages 583-586
Nanuwa, S. S., A. Dziurla and H. Seker. (2009). Weighted amino acid composition based on
amino acid indices for prediction of protein structural classes. Final Program and Abstract
Book - 9th International Conference on Information Technology and Applications in
Biomedicine, ITAB 2009, Larnaca Cyprus. Volume 1. Pages 327-332
Nanuwa, S. S. and H. Seker (2011). Prediction of a protein’s structural class using amino acid
indices. The International Congress on Bioinformatics and Biomics, 2009 Izmir Turkey

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This thesis is dedicated to my wife Sharan, dad Amarjit, mum Harjinder, Brother Jamie & the
Nanuwa family.

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Acknowledgements
I would like to offer my sincerest gratitude to my supervisor, Dr Huseyin Seker, who has
supported me throughout my Masters and PhD. The work undertaken is through his
encouragement, which gave me the determination to complete the thesis. One simply could
not wish for a better supervisor. I would like to also offer my sincerest gratitude to my current
place of work The Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and
Inflammation Laboratory (JDRF/WT DIL), centred at the Cambridge Institute for Medical
Research at University of Cambridge, allowing me to have the time needed to complete the
thesis.

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Table of Contents
Abstract ....................................................................................................................................... 2
Publications ................................................................................................................................ 3
Acknowledgements .................................................................................................................. 5
Acronyms .................................................................................................................................. 19
Mathematical Symbols ........................................................................................................... 20
1 Chapter 1 - Introduction ..................................................................................................... 21
1.1 Background ................................................................................................................. 21
1.2 Prediction of structural classes of proteins ................................................................ 23
1.3 Sequence-driven-features & Amino acid indices ........................................................ 24
1.4 Organisation of PhD thesis .......................................................................................... 25
2 Chapter 2 - Literature Review ............................................................................................. 27
2.1 Introduction ................................................................................................................ 27
2.2 Cells, DNA, Proteins .................................................................................................... 27
2.2.1 Primary Structure ................................................................................................ 38
2.2.2 Secondary structure ............................................................................................ 39
2.2.3 Tertiary structure ................................................................................................ 42
2.2.4 Current experimental procedures to determine protein structures .................. 43
2.2.5 Transition from secondary structures to structural classes ................................ 44
2.2.6 Structural classes ................................................................................................ 44
2.3 Bioinformatics for prediction of structural classes of proteins .................................. 50
2.3.1 Real world bioinformatics applications ............................................................... 51
2.3.2 Bioinformatics and Proteomics ........................................................................... 51
2.3.3 Bioinformatics for the prediction for structural classes of proteins by using
sequence information ......................................................................................................... 51
2.3.4 Data resources .................................................................................................... 52
2.3.5 Datasets constructed using PDB and SCOP ......................................................... 52
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2.3.6 Sequence driven features for protein representation ........................................ 54
2.3.7 Amino acid indices .............................................................................................. 55
2.3.8 Predictive models ................................................................................................ 55
2.3.9 Assessment of the predictive models (test procedures) .................................... 57
2.3.10 Sequence homology ............................................................................................ 58
2.3.11 Feature selection................................................................................................. 59
2.3.12 Current prediction accuracies ............................................................................. 62
2.4 Conclusions ................................................................................................................. 67
3 Chapter 3 - Materials and Methods .................................................................................... 68
3.1 Introduction ................................................................................................................ 68
3.2 Datasets ...................................................................................................................... 68
3.3 Dataset filtering .......................................................................................................... 71
3.4 Classification algorithms ............................................................................................. 72
3.4.1 K-nearest neighbour classifier ............................................................................ 73
3.4.2 Support Vector Machine ..................................................................................... 77
3.4.3 Differences between KNN and SVM ................................................................... 80
3.4.4 Classification performance ................................................................................. 80
3.4.5 Test procedures .................................................................................................. 81
3.5 Hierarchical clustering ................................................................................................ 84
3.5.1 Bioinformatics application of hierarchical clustering ......................................... 86
3.6 Principal Component Analysis..................................................................................... 86
3.6.1 Bioinformatics application of PCA ....................................................................... 88
3.7 Conclusions ................................................................................................................. 88
4 Chapter 4 - Analysis of existing sequence driven features ................................................. 90
4.1 Introduction ................................................................................................................ 90
4.2 Sequence representation: Sequence-driven features ................................................ 90
4.3 Sequence driven features technical details ................................................................ 91
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4.3.1 Amino Acid Composition ..................................................................................... 92
4.3.2 Dipeptide Composition ....................................................................................... 93
4.3.3 Autocorrelation feature groups .......................................................................... 94
4.3.4 Composition, Transition and Distribution ........................................................... 97
4.4 Sequence Order ........................................................................................................ 103
4.5 Pseudo amino acid composition ............................................................................... 106
4.6 Results and discussion .............................................................................................. 109
4.6.1 Results for amino acid composition feature group .......................................... 112
4.6.2 Results for dipeptide composition feature group ............................................. 112
4.6.3 Results for autocorrelation feature groups ...................................................... 112
4.6.4 Results for composition feature group ............................................................. 115
4.6.5 Results for transition and distribution feature group ....................................... 116
4.6.6 Results for pseudo amino acid composition ..................................................... 116
4.6.7 Results of test procedures performance .......................................................... 120
4.6.8 Individual class performance ............................................................................ 128
4.7 Conclusions ............................................................................................................... 130
5 Chapter 5 - Amino acid indices based sequence driven features ..................................... 133
5.1 Introduction .............................................................................................................. 133
5.2 Amino acid indices .................................................................................................... 133
5.3 Amino acid indices database..................................................................................... 134
5.3.1 Normalisation of amino acid indices ................................................................. 135
5.4 Novel feature extraction methods based on amino acid indices ............................. 136
5.4.1 Hybrid computational method for the analysis of amino acid indices – method 1
136
5.4.2 Generalised amino acid composition – method 2 ............................................ 139
5.4.3 Novel feature extraction methods over sequence representation matrix based
on amino acid indices........................................................................................................ 140
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5.4.4 Sequence representation matrix ...................................................................... 140
5.4.5 Feature extraction using the mean of sequence representation matrix - method
3 143
5.4.6 Feature extraction using principal component analysis over sequence
representation matrix - method 4 .................................................................................... 143
5.5 Results ....................................................................................................................... 146
5.5.1 Hybrid computational method for the analysis of amino acid indices reveals
novel indices – Method 1 .................................................................................................. 146
5.5.2 Assessment of amino acid indices for GAAC - method 2 .................................. 156
5.5.3 Comparison with the published and benched mark study results ................... 156
5.5.4 Individual class performance ............................................................................ 165
5.5.5 Assessment of performance based on test procedures ................................... 166
5.5.6 Results obtained using the novel feature extraction methods based on amino
acid indices – methods 3 and 4 ......................................................................................... 170
5.6 Generalised Amino Acid Composition webserver .................................................... 173
5.7 Conclusions ............................................................................................................... 176
6 Chapter 6 - Feature selection ............................................................................................ 178
6.1 Introduction .............................................................................................................. 178
6.2 Feature selection categories ..................................................................................... 178
6.3 F-select ...................................................................................................................... 180
6.4 Minimum redundancy maximum relevance feature selection................................. 181
6.5 Results from feature selection methods ................................................................... 183
6.5.1 Feature selection results over the traditional sequence driven features
presented in c hapter 4 ..................................................................................................... 183
6.5.2 Feature selection results based on the sequence driven features presented in
chapter 5 – method 3 ........................................................................................................ 187
6.5.3 Feature selection results based on the sequence driven features presented in
chapter 5– method 4 ........................................................................................................ 187
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6.6 Conclusions ............................................................................................................... 188
7 Chapter 7 – Discussion, conclusion and future work ........................................................ 190
7.1 Introduction .............................................................................................................. 190
7.2 Critical evaluation of traditional sequence-driven features ..................................... 190
7.2.1 Composition based sequence-driven-feature groups ...................................... 190
7.2.2 Autocorrelation feature groups ........................................................................ 191
7.2.3 Composition, transition and distribution feature groups ................................. 192
7.2.4 Pseudo amino acid composition ....................................................................... 192
7.3 Critical evaluation of amino acid indices based sequence-driven-features ............. 193
7.3.1 Updated amino acid indices dataset ................................................................. 193
7.3.2 Generalised amino acid composition ................................................................ 194
7.3.3 Identification of a candidate set of amino acid indices .................................... 194
7.3.4 Generalised amino acid composition webserver .............................................. 196
7.3.5 Amino Acid Indices based sequence-driven-feature extraction methods ........ 196
7.3.6 Hybrid sequence driven feature extraction ...................................................... 197
7.4 Feature selection ...................................................................................................... 198
7.5 Test procedures ........................................................................................................ 199
7.6 Assessment of multiple k-nearest neighbour ........................................................... 199
7.7 Conclusions ............................................................................................................... 201
7.8 Future work ............................................................................................................... 204
References ................................................................................................................................ 205
Appendix I – Sequence Driven Features ................................................................................... 216
Appendix II Full list of amino acid indices from the AAindex database .................................... 218
Appendix III Full list of amino acid indices found through literature searches ........................ 224
Appendix IV Generated amino acid indices using SINGLE Linkage and Minimum Cluster
Distance = 1 ............................................................................................................................... 226
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