[Show abstract][Hide abstract] ABSTRACT: Cyclin dependent kinases (CDK) associate with cyclins to regulate cell cycle progression and gene transcription by phosphorylating key proteins. The different cyclin-CDK complexes display differences in substrate specificities with substrates binding across a shallow, hydrophobic, substrate-binding pocket known as the cyclin groove. However the mechanism underlying this differential substrate recognition remains largely unknown and cannot be explained merely on the basis of sequence variability. A subset of cyclins, cyclins A2, E1 and B1 despite being structurally and functionally similar, show marked differences in their interactions with recruitment peptides derived from their substrate or inhibitor proteins p27, p21, p57, E2F1, p53, pRb and p107. While these peptides (characterized by a cyclin binding motif of four residues ZRXL where Z and X are cationic residues) inhibit the activity of cyclins A2 and E1, no such inhibition is observed for cyclin B1. Electrostatic potentials of cyclins A2, E1 and B1 show that anionic regions of cyclins A2 and E1 enable them to bind peptides while cationic regions at homologous locations in cyclin B1 abrogate binding. These arise from charged residues that are conserved. Mutations that switch these characters are suggested. Computed energetics of binding confirms this. Deregulation of the enzymatic activity of this class of enzymes is a ubiquitous feature of human neoplasia, but attempts to exploit this therapeutically have been confounded by a lack of understanding of the precise specificity of the different cyclin complexes. Here we begin to clarify this issue by explaining the mechanism by which cyclin B1 escapes regulation by the p21 family of CDKIs.
[Show abstract][Hide abstract] ABSTRACT: A variety of protein physicochemical as well as topological properties, demonstrate a scaling behavior relative to chain length. Many of the scalings can be modeled as a power law which is qualitatively similar across the examples. In this article, we suggest a rational explanation to these observations on the basis of both protein connectivity and hydrophobic constraints of residues compactness relative to surface volume. Unexpectedly, in an examination of these relationships, a singularity was shown to exist near 255-270 residues length, and may be associated with an upper limit for domain size. Evaluation of related G-factor data points to a wide range of conformational plasticity near this point. In addition to its theoretical importance, we show by an application of CASP experimental and predicted structures, that the scaling is a practical filter for protein structure prediction.
Proteins Structure Function and Bioinformatics 03/2007; 66(3):621-9. · 3.34 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: P53 is probably the most important tumor suppressor known. Over the years, information about this gene has increased dramatically. We have built a comprehensive knowledgebase of p53, which aims to facilitate wet-lab biologists to formulate their experiments and new-comers to learn whatever they need about the gene and bioinformaticians to make new discoveries through data analysis. Using the information curated, including mutation information, transcription factors, transcriptional targets, and single nucleotide polymorphisms, we have performed extensive bioinformatics analysis, and made several new discoveries about p53. We have identified point missense mutations that are over-represented in cancers, but lack of functional studies. By assessing the capability of six p53 transcriptional targets' tag SNPs selected from HapMap to capture SNPs obtained from National Institute of Environmental Health Sciences (NIEHS) Environmental Genome project and vice versa, we conclude that NIEHS data is a better source for tagSNP selections of these genes in future association studies. Analysis of microRNA regulation in the transcriptional network of the p53 gene reveals potentially important regulatory relationships between oncogenic microRNAs and transcription factors of p53. By mapping transcription factors of p53 to pathways involved in cell cycle and apoptosis, we have identified distinctive transcriptional controls of p53 in these two physiological states.
[Show abstract][Hide abstract] ABSTRACT: Some research has suggested that patches of six constitute an important amino acid window length in proteins for conveying information. We present database evidence that supports this conjecture, as well as additional recurrence-based data that characterization and quantification of these words affect the folding/aggregation features of proteins. Other indirect evidence is presented and discussed.
[Show abstract][Hide abstract] ABSTRACT: Protein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of prediction algorithms have been developed based on amino acid compositions or on the N-terminal characteristics (signal peptides) of proteins. However, such approaches lead to a loss of contextual information. Moreover, where information about the physicochemical properties of amino acids has been used, the methods employed to exploit that information are less than optimal and could use the information more effectively.
In this paper, we propose a new algorithm called pSLIP which uses Support Vector Machines (SVMs) in conjunction with multiple physicochemical properties of amino acids to predict protein subcellular localization in eukaryotes across six different locations, namely, chloroplast, cytoplasmic, extracellular, mitochondrial, nuclear and plasma membrane. The algorithm was applied to the dataset provided by Park and Kanehisa and we obtained prediction accuracies for the different classes ranging from 87.7%-97.0% with an overall accuracy of 93.1%.
This study presents a physicochemical property based protein localization prediction algorithm. Unlike other algorithms, contextual information is preserved by dividing the protein sequences into clusters. The prediction accuracy shows an improvement over other algorithms based on various types of amino acid composition (single, pair and gapped pair). We have also implemented a web server to predict protein localization across the six classes (available at http://pslip.bii.a-star.edu.sg/).