Discovering Protein Functional Regions and Protein-Protein Interaction using Co-occurring Aligned Pattern Clusters

Bioinformatics is a rapidly expanding field of research due to multiple recent advancements: 1) the advent of machine intelligence, 2) the increase of computing power, 3) our better understanding of the underlying biomolecular mechanisms, and 4) the drastic reduction of biosequencing cost and time. Since wet laboratory approaches to analysing the protein sequencing is still labour intensive and time consuming, more cost-effective computational approaches for analyzing protein sequences and their biochemical interactions are crucial. This is especially true when we encounter a large collection of protein sequences. Aligned Pattern CLustering (APCL), an algorithm which combines machine intelligence methodologies such as pattern recognition, pattern discovery, pattern clustering and alignment, formulated by my research group and myself, is one such technique. APCL discovers, prunes, and clusters aligned statistically significant patterns to assemble a related, or specifically, a homologous group of patterns in the form of an Aligned Pattern Cluster (APC). The APC obtained is found to correspond to statistically and functionally significant association patterns, which corresponds as conserved regions, such as binding segments within and between protein sequences as well as between Protein Transcription Factor (TF) and DNA Transcription Factor Binding Sites (TFBS) in many of our empirical experiments. While several known algorithms also exist to find functionally conserved segments in biosequences, they are less flexible and require more parameters than what APCL requires. Hence, APCL is a powerful tool to analyze biosequences. Because of its effectiveness, the usefulness of APCL is further expanded from the assist of discovering and analyzing functional regions of protein sequences to the exploration of co-occurrence of patterns on the same sequences or on interacting patterns between sequences from the discovered APCs. Two new algorithms are introduced and reported in this thesis in the exploration of 1) APCs containing patterns residing within the same biosequences and 2) APCs containing patterns residing between interacting biosequences. The first algorithm attempts to cluster APCs from APCs that share patterns on the same biosequences. It uses a co-occurrence score between APCs in a co-occurrence APC pair (two APCs containing co-occurrence patterns) to account for the proportion of biosequences of co-occurrence patterns they share against the total number of sequences containing them. Using this score as a similarity measure (or more precisely, as a co-occurring measure), we devise a Co-occurrence APC Clustering Algorithm to cluster APCs obtained from a collection of related biosequences into a Co-Occurrence Cluster of APCs abbreviated by cAPC. It is then analyzed and verified to see whether or not there are essential biological functions associating with the APCs within that cluster. Cytochrome c and ubiquitin families were analyzed in depth, and it was validated that members in the same cAPC do cover the functional regions that have essential cooperative biological functions. The second algorithm takes advantage of the effectiveness of APCL to create a protein-protein interaction (PPI) identification and prediction algorithm. PPI prediction is a hot research problem in bioinformatics and proteomic. A good number of algorithms exist. The state of the art algorithm is one which could achieve high success rate in prediction performance, but provides results that are difficult to interpret. The research in this thesis tries to overcome this hurdle. This second algorithm uses an APC-PPI score between two APCs to account for the proportion of patterns residing on two different protein sequences. This score measures how often patterns in both APCs co-occur in the sequence data of two known interacting proteins. The scores are then used to construct feature vectors to first train a learning model from the known PPI data and later used to predict the possible PPI between a protein pair. The algorithm performance was comparable to the state of the art algorithms, but provided results that are interpretable. The results from both algorithms built upon the extension of APCL in finding co-occurring patterns via co-occurrence of APCs are proved to be effective and useful since its performance in finding APCs is fast and effective. The first algorithm discovered biological insights, supported by biological literature, which are typically unable to be discovered solely through the analysis of biosequences. The second algorithm succeeded in providing accurate and descriptive PPI predictions. Hence, these two algorithms are useful in the analysis and prediction of proteins. In addition, through continued research and development to the second algorithm, it will be a powerful tool for the drug industry, as it can help find new PPI, an important step in developing new drugs for different drug targets

Discovering Patterns from Sequences with Applications to Protein-Protein and Protein-DNA Interaction

Understanding Protein-Protein and Protein-DNA interaction is of fundamental importance in deciphering gene regulation and other biological processes in living cells. Traditionally, new interaction knowledge is discovered through biochemical experiments that are often labor intensive, expensive and time-consuming. Thus, computational approaches are preferred. Due to the abundance of sequence data available today, sequence-based interaction analysis becomes one of the most readily applicable and cost-effective methods. One important problem in sequence-based analysis is to identify the functional regions from a set of sequences within the same family or demonstrating similar biological functions in experiments. The rationale is that throughout evolution the functional regions normally remain conserved (intact), allowing them to be identified as patterns from a set of sequences. However, there are also mutations such as substitution, insertion, deletion in these functional regions. Existing methods, such as those based on position weight matrices, assume that the functional regions have a fixed width and thus cannot not identify functional regions with mutations, particularly those with insertion or deletion mutations. Recently, Aligned Pattern Clustering (APCn) was introduced to identify functional regions as Aligned Pattern Clusters (APCs) by grouping and aligning patterns with variable width. Nevertheless, APCn cannot discover functional regions with substitution, insertion and/or deletion mutations, since their frequencies of occurrences are too low to be considered as patterns. To overcome such an impasse, this thesis proposes a new APC discovery algorithm known as Pattern-Directed Aligned Pattern Clustering (PD-APCn). By first discovering seed patterns from the input sequence data, with their sequence positions located and recorded on an address table, PD-APCn can use the seed patterns to direct the incremental extension of functional regions with minor mutations. By grouping the aligned extended patterns, PD-APCn can recruit patterns adaptively and efficiently with variable width without relying on exhaustive optimal search. Experiments on synthetic datasets with different sizes and noise levels showed that PD-APCn can identify the implanted pattern with mutations, outperforming the popular existing motif-finding software MEME with much higher recall and Fmeasure over a computational speed-up of up to 665 times. When applying PD-APCn on datasets from Cytochrome C and Ubiquitin protein families, all key binding sites conserved in the families were captured in the APC outputs. In sequence-based interaction analysis, there is also a lack of a model for co-occurring functional regions with mutations, where co-occurring functional regions between interaction sequences are indicative of binding sites. This thesis proposes a new representation model Co-Occurrence APCs to capture co-occurring functional regions with mutations from interaction sequences in database transaction format. Applications on Protein-DNA and Protein-Protein interaction validated the capability of Co-Occurrence APCs. In Protein-DNA interaction, a new representation model, Protein-DNA Co-Occurrence APC, was developed for modeling Protein-DNA binding cores. The new model is more compact than the traditional one-to-one pattern associations, as it packs many-to-many associations in one model, yet it is detailed enough to allow site-specific variants. An algorithm, based on Co-Support Score, was also developed to discover Protein-DNA Co-Occurrence APCs from Protein-DNA interaction sequences. This algorithm is 1600x faster in run-time than its contemporaries. New Protein-DNA binding cores indicated by Protein-DNA Co-Occurrence APCs were also discovered via homology modeling as a proof-of-concept. In Protein-Protein interaction, a new representation model, Protein-Protein Co-Occurrence APC, was developed for modeling the co-occurring sequence patterns in Protein-Protein Interaction between two protein sequences. A new algorithm, WeMine-P2P, was developed for sequence-based Protein-Protein Interaction machine learning prediction by constructing feature vectors leveraging Protein-Protein Co-Occurrence APCs, based on novel scores such as Match Score, MaxMatch Score and APC-PPI score. Through 40 independent experiments, it outperformed the well-known algorithm, PIPE2, which also uses co-occurring functional regions while not allowing variable widths and mutations. Both applications on Protein-Protein and Protein-DNA interaction have indicated the potential use of Co-Occurrence APC for exploring other types of biosequence interaction in the future

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Quantitative methods for reconstructing protein-protein interaction histories

Protein-protein interactions (PPIs) are vital for the function of a cell and the evolution of these interactions produce much of the evolution of phenotype of an organism. However, as the evolutionary process cannot be observed, methods are required to infer evolution from existing data. An understanding of the resulting evolutionary relationships between species can then provide information for PPI prediction and function assignment. This thesis further develops and applies the interaction tree method for modelling PPI evolution within and between protein families. In this approach, a phylogeny of the protein family/ies of interest is used to explicitly construct a history of duplication and specification events. Given a model relating sequence change in this phylogeny to the probability of a rewiring event occurring, this method can then infer probabilities of interaction between the ancestral proteins described in the phylogeny. It is shown that the method can be adapted to infer the evolution of PPIs within obligate protein complexes, using a large set of such complexes to validate this application. This approach is then applied to reconstruct the history of the proteasome complex, using x-ray crystallography structures of the complex as input, with validation to show its utility in predicting present day complexes for which we have no structural data. The methodology is then adapted for application to transient PPIs. It is shown that the approach used in the previous chapter is inadequate here and a new scoring system is described based on a likelihood score of interaction. The predictive ability of this score is shown in predicting known two component systems in bacteria and its use in an interaction tree setting is demonstrated through inference of the interaction history between the histidine kinase and response regulator proteins responsible for sporulation onset in a set of bacteria. This thesis demonstrates that with suitable modifications the interaction tree approach is widely applicable to modelling PPI evolution and also, importantly, predicting existing PPIs. This demonstrates the need to incorporate phylogenetic data in to methods of predicting PPIs and gives some measure of the benefit in doing so

Revealing mammalian evolutionary relationships by comparative analysis of gene clusters

Many software tools for comparative analysis of genomic sequence data have been released in recent decades. Despite this, it remains challenging to determine evolutionary relationships in gene clusters due to their complex histories involving duplications, deletions, inversions, and conversions. One concept describing these relationships is orthology. Orthologs derive from a common ancestor by speciation, in contrast to paralogs, which derive from duplication. Discriminating orthologs from paralogs is a necessary step in most multispecies sequence analyses, but doing so accurately is impeded by the occurrence of gene conversion events. We propose a refined method of orthology assignment based on two paradigms for interpreting its definition: by genomic context or by sequence content. X-orthology (based on context) traces orthology resulting from speciation and duplication only, while N-orthology (based on content) includes the influence of conversion events

Using evolutionary covariance to infer protein sequence-structure relationships

During the last half century, a deep knowledge of the actions of proteins has emerged from a broad range of experimental and computational methods. This means that there are now many opportunities for understanding how the varieties of proteins affect larger scale behaviors of organisms, in terms of phenotypes and diseases. It is broadly acknowledged that sequence, structure and dynamics are the three essential components for understanding proteins. Learning about the relationships among protein sequence, structure and dynamics becomes one of the most important steps for understanding the mechanisms of proteins. Together with the rapid growth in the efficiency of computers, there has been a commensurate growth in the sizes of the public databases for proteins. The field of computational biology has undergone a paradigm shift from investigating single proteins to looking collectively at sets of related proteins and broadly across all proteins. we develop a novel approach that combines the structure knowledge from the PDB, the CATH database with sequence information from the Pfam database by using co-evolution in sequences to achieve the following goals: (a) Collection of co-evolution information on the large scale by using protein domain family data; (b) Development of novel amino acid substitution matrices based on the structural information incorporated; (c) Higher order co-evolution correlation detection. The results presented here show that important gains can come from improvements to the sequence matching. What has been done here is simple and the pair correlations in sequence have been decomposed into singlet terms, which amounts to discarding much of the correlation information itself. The gains shown here are encouraging, and we would like to develop a sequence matching method that retains the pair (or higher order) correlation information, and even higher order correlations directly, and this should be possible by developing the sequence matching separately for different domain structures. The many body correlations in particular have the potential to transform the common perceptions in biology from pairs that are not actually so very informative to higher-order interactions. Fully understanding cellular processes will require a large body of higher-order correlation information such as has been initiated here for single proteins

Graph Theory and Networks in Biology

In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss recent work on identifying and modelling the structure of bio-molecular networks, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape