Phang C. Tai

Improved K-means clustering algorithm for exploring local protein sequence motifs representing common structural property

Information about local protein sequence motifs is very important to the analysis of biologically significant conserved regions of protein sequences. These conserved regions can potentially determine the diverse conformation and activities of proteins. In this work, recurring sequence motifs of proteins are explored with an improved K-means clustering algorithm on a new dataset. The structural similarity of these recurring sequence clusters to produce sequence motifs is studied in order to evaluate the relationship between sequence motifs and their structures. To the best of our knowledge, the dataset used by our research is the most updated dataset among similar studies for sequence motifs. A new greedy initialization method for the K-means algorithm is proposed to improve traditional K-means clustering techniques. The new initialization method tries to choose suitable initial points, which are well separated and have the potential to form high-quality clusters. Our experiments indicate that the improved K-means algorithm satisfactorily increases the percentage of sequence segments belonging to clusters with high structural similarity. Careful comparison of sequence motifs obtained by the improved and traditional algorithms also suggests that the improved K-means clustering algorithm may discover some relatively weak and subtle sequence motifs, which are undetectable by the traditional K-means algorithms. Many biochemical tests reported in the literature show that these sequence motifs are biologically meaningful. Experimental results also indicate that the improved K-means algorithm generates more detailed sequence motifs representing common structures than previous research. Furthermore, these motifs are universally conserved sequence patterns across protein families, overcoming some weak points of other popular sequence motifs. The satisfactory result of the experiment suggests that this new K-means algorithm may be applied to other areas of bioinformatics research in order to explore the underlying relationships between data samples more effectively.

Improved protein secondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier

Prediction of protein secondary structures is an important problem in bioinformatics and has many applications. The recent trend of secondary structure prediction studies is mostly based on the neural network or the support vector machine (SVM). The SVM method is a comparatively new learning system which has mostly been used in pattern recognition problems. In this study, SVM is used as a machine learning tool for the prediction of secondary structure and several encoding schemes, including orthogonal matrix, hydrophobicity matrix, BLOSUM62 substitution matrix, and combined matrix of these, are applied and optimized to improve the prediction accuracy. Also, the optimal window length for six SVM binary classifiers is established by testing different window sizes and our new encoding scheme is tested based on this optimal window size via sevenfold cross validation tests. The results show 2% increase in the accuracy of the binary classifiers when compared with the instances in which the classical orthogonal matrix is used. Finally, to combine the results of the six SVM binary classifiers, a new tertiary classifier which combines the results of one-versus-one binary classifiers is introduced and the performance is compared with those of existing tertiary classifiers. According to the results, the Q/sub 3/ prediction accuracy of new tertiary classifier reaches 78.8% and this is better than the best result reported in the literature.

A Significant Fraction of Functional SecA Is Permanently Embedded in the Membrane SecA CYCLING ON AND OFF THE MEMBRANE IS NOT ESSENTIAL DURING PROTEIN TRANSLOCATION

SecA has been suggested to cycle on and off the cytoplasmic membrane of Escherichia coli during protein translocation. We have reconstituted 35S-SecA onto SecA-depleted membrane vesicles and followed the fate of the membrane-associated 35S-SecA during protein translocation. Some 35S-SecA was released from the membranes in a translocation-independent manner. However, a significant fraction of 35S-SecA remained on the membranes even after incubation with excess SecA. This fraction of 35S-SecA was shown to be integrated into the membrane and was active in protein translocation, indicating that SecA cycling on and off membrane is not required for protein translocation. Proteolysis experiments did not support the model of SecA insertion and deinsertion during protein translocation; instead, a major 48-kDa domain was found persistently embedded in the membrane regardless of translocation status. Thus, in addition to catalyzing ATP hydrolysis, certain domains of SecA probably play an important structural role in the translocation machinery, perhaps forming part of the protein-conducting channels.

Nonclassical Protein Secretion by Bacillus subtilis in the Stationary Phase Is Not Due to Cell Lysis

The carboxylesterase Est55 has been cloned and expressed in Bacillus subtilis strains. Est55, which lacks a classical, cleavable N-terminal signal sequence, was found to be secreted during the stationary phase of growth such that there is more Est55 in the medium than inside the cells. Several cytoplasmic proteins were also secreted in large amounts during late stationary phase, indicating that secretion in B. subtilis is not unique to Est55. These proteins, which all have defined cytoplasmic functions, include GroEL, DnaK, enolase, pyruvate dehydrogenase subunits PdhB and PdhD, and SodA. The release of Est55 and those proteins into the growth medium is not due to gross cell lysis, a conclusion that is supported by several lines of evidence: constant cell density and secretion in the presence of chloramphenicol, constant viability count, the absence of EF-Tu and SecA in the culture medium, and the lack of effect of autolysin-deficient mutants. The shedding of these proteins by membrane vesicles into the medium is minimal. More importantly, we have identified a hydrophobic α-helical domain within enolase that contributes to its secretion. Thus, upon the genetic deletion or replacement of a potential membrane-embedding domain, the secretion of plasmid gene-encoded mutant enolase is totally blocked, while the wild-type chromosomal enolase is secreted normally in the same cultures during the stationary phase, indicating differential specificity. We conclude that the secretion of Est55 and several cytoplasmic proteins without signal peptides in B. subtilis is a general phenomenon and is not a consequence of cell lysis or membrane shedding; instead, their secretion is through a process(es) in which protein domain structure plays a contributing factor.

Ring-like pore structures of SecA: Implication for bacterial protein-conducting channels

SecA, an essential component of the general protein secretion pathway of bacteria, is present in Escherichia coli as soluble and membrane-integral forms. Here we show by electron microscopy that SecA assumes two characteristic forms in the presence of phospholipid monolayers: dumbbell-shaped elongated structures and ring-like pore structures. The ring-like pore structures with diameters of 8 nm and holes of 2 nm are found only in the presence of anionic phospholipids. These ring-like pore structures with larger 3- to 6-nm holes (without staining) were also observed by atomic force microscopic examination. They do not form in solution or in the presence of uncharged phosphatidylcholine. These ring-like phospholipid-induced pore-structures may form the core of bacterial protein-conducting channels through bacterial membranes.

Structure-based discovery and experimental verification of novel AI-2 quorum sensing inhibitors against Vibrio harveyi.

Quorum sensing has been implicated in the control of pathologically relevant bacterial behavior such as secretion of virulence factors, biofilm formation, sporulation, and swarming motility. The AI‐2 quorum sensing pathway is found in both Gram‐positive and Gram‐negative bacteria. Therefore, antagonizing AI‐2 quorum sensing is a possible approach to modifying bacterial behaviour. However, efforts in developing inhibitors of AI‐2‐mediated quorum sensing are especially lacking. High‐throughput virtual screening using the V. harveyi LuxP crystal structure identified two compounds that were found to antagonize AI‐2‐mediated quorum sensing in V. harveyi without cytotoxicity. The sulfone functionality of these inhibitors was identified as critical to their ability to mimic the natural ligand in their interactions with Arg 215 and Arg 310 of the active site.

Novel hybrid hierarchical-K-means clustering method (H-K-means) for microarray analysis

Hierarchical and k-means clustering are two major analytical tools for unsupervised microarray datasets. However, both have their innate disadvantages. Hierarchical clustering cannot represent distinct clusters with similar expression patterns. Also, as clusters grow in size, the actual expression patterns become less relevant. K-means clustering requires a specified number of clusters in advance and chooses initial centroids randomly: in addition, it is sensitive to outliers. We present a novel hybrid approach to combined merits of the two and discard disadvantages we mentioned above. It is different from existed method: carry out hierarchical clustering first to decide location and number of clusters in the first round and run the K-means clustering in another round. The brief idea is we cluster around half data through hierarchical clustering and succeed by K-means for the rest half in one single round. Also, our approach provides a mechanism to handle outliers. Comparing with existed hybrid clustering approach and K-means clustering in 2 different distance measure on Eisens yeast microarray data, our method always generate much higher quality clusters.

The first low μM SecA inhibitors

SecA ATPase is a critical member of the Sec family, which is important in the translocation of membrane and secreted polypeptides/proteins in bacteria. Small molecule inhibitors can be very useful research tools as well as leads for future antimicrobial agent development. Based on previous virtual screening work, we optimized the structures of two hit compounds and obtained SecA ATPase inhibitors with IC(50) in the single digit micromolar range. These represent the first low micromolar synthetic inhibitors of bacterial SecA and will be very useful for mechanistic studies.

Clustering support vector machines for protein local structure prediction

Abstract Understanding the sequence-to-structure relationship is a central task in bioinformatics research. Adequate knowledge about this relationship can potentially improve accuracy for local protein structure prediction. One of approaches for protein local structure prediction uses the conventional clustering algorithms to capture the sequence-to-structure relationship. The cluster membership function defined by conventional clustering algorithms may not reveal the complex nonlinear relationship adequately. Compared with the conventional clustering algorithms, Support Vector Machine (SVM) can capture the nonlinear sequence-to-structure relationship by mapping the input space into another higher dimensional feature space. However, SVM is not favorable for huge datasets including millions of samples. Therefore, we propose a novel computational model called Clustering Support Vector Machines (CSVMs). Taking advantage of both theory of granular computing and advanced statistical learning methodology, CSVMs are built specifically for each information granule partitioned intelligently by the clustering algorithm. This feature makes learning tasks for each CSVM more specific and simpler. CSVMs modeled for each granule can be easily parallelized so that CSVMs can be used to handle complex classification problems for huge datasets. Average accuracy for CSVMs is over 80%, which indicates that the generalization power for CSVMs is strong enough to recognize the complicated pattern of sequence-to-structure relationships. Compared with the conventional clustering algorithm, our experimental results show that accuracy for local structure prediction has been improved noticeably when CSVMs are applied.

Discovery of the first SecA inhibitors using structure-based virtual screening

Bacterial protein secretion is a critical and complex process. The Sec machinery provides a major pathway for protein translocation across and integration into the cellular membrane in bacteria. Small molecule probes that perturb the functions of individual member proteins within the Sec machinery will be very important research tools as well as leads for future antimicrobial agent development. Herein we describe the discovery of inhibitors, through virtual screening, that specifically act on SecA ATPase, which is a critical member of the Sec system. These are the very first inhibitors reported for intrinsic SecA ATPase.