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A great challenge in bioinformatics is to accurately predict protein structure and function from its amino acid sequence, including annotation of protein domains, identification of protein disordered regions and detecting protein stability changes resulting from amino acid mutations. The combination of bioinformatics, genomics and proteomics becomes essential for the investigation of biological, cellular and molecular aspects of disease, and therefore can greatly contribute to the understanding of protein structures and facilitating drug discovery.

In this thesis, a PREDICTOR, which consists of three machine learning methods applied to three different but related structure bioinformatics tasks, is presented: using profile Hidden Markov Models (HMMs) to identify remote sequence homologues, on the basis of protein domains; predicting order and disorder in proteins using Conditional Random Fields (CRFs); applying Support Vector Machines (SVMs) to detect protein stability changes due to single mutation.

To facilitate structural instability and disease studies, these methods are implemented in three web servers: FISH, OnD-CRF and ProSMS, respectively.

For FISH, most of the work presented in the thesis focuses on the design and construction of the web-server. The server is based on a collection of structure-anchored hidden Markov models (saHMM), which are used to identify structural similarity on the protein domain level.

For the order and disorder prediction server, OnD-CRF, I implemented two schemes to alleviate the imbalance problem between ordered and disordered amino acids in the training dataset. One uses pruning of the protein sequence in order to obtain a balanced training dataset. The other tries to find the optimal p-value cut-off for discriminating between ordered and disordered amino acids.  Both these schemes enhance the sensitivity of detecting disordered amino acids in proteins. In addition, the output from the OnD-CRF web server can also be used to identify flexible regions, as well as predicting the effect of mutations on protein stability.

For ProSMS, we propose, after careful evaluation with different methods, a clustered by homology and a non-clustered model for a three-state classification of protein stability changes due to single amino acid mutations. Results for the non-clustered model reveal that the sequence-only based prediction accuracy is comparable to the accuracy based on protein 3D structure information. In the case of the clustered model, however, the prediction accuracy is significantly improved when protein tertiary structure information, in form of local environmental conditions, is included. Comparing the prediction accuracies for the two models indicates that the prediction of mutation stability of proteins that are not homologous is still a challenging task.

Benchmarking results show that, as stand-alone programs, these predictors can be comparable or superior to previously established predictors. Combined into a program package, these mutually complementary predictors will facilitate the understanding of structural instability and disease from protein sequence.