Diversity and motif conservation in protein 3D structural landscape: exploration by a new multivariate simulation method.

In this paper, diversity and conservation in the 'landscape' of random variation of protein tertiary structures are explored for quantitative feature-vector models of major types of functionally important 3D structural motifs. For this, I have deployed a recently developed nonparametric regression (NPR)-based multidimensional copula method of simulation. Apart from improved accuracy of multidimensional random sample generation, the simulation provides additional insight into diversity in the protein structural landscape in terms of random variation in the feature-vector.

Quantitative characterization of protein tertiary motifs.

A quantitative feature-vector representation/model of tertiary structural motifs of proteins is presented. Multiclass logistic regression and a probabilistic neural network were employed to apply this representation to large data sets in order to classify them into major families of distinct motif types (including those of functional importance) with high statistical confidence. Scatter plots of random samples of these motifs were obtained through two-dimensional transformation of the feature vector by metric MDS (multidimensional scaling).

Dimensionality reduction in computational demarcation of protein tertiary structures.

Predictive classification of major structural families and fold types of proteins is investigated deploying logistic regression. Only five to seven dimensional quantitative feature vector representations of tertiary structures are found adequate. Results for benchmark sample of non-homologous proteins from SCOP database are presented.

Characteristic peptides of protein secondary structural motifs.

Characteristic peptides of the protein segments having common secondary folds are obtained for the I-sites library using maximal position specific probability scores. The secondary structures of these peptides are predicted deploying two best-known computational methods. These are validated with significant accuracy against the corresponding motifs.

Gibbs sampling shows possibilities of B-cell epitope signatures.

We have attempted finding common sequential patterns among protein antigens. For this, we have used Gibbs multiple motif sampler on the set of all non-redundant antigenic sequences available in curated databanks. Several sequential motifs are obtained on these sequences when the amino acids are represented according to their similarity clusters.

Peptide vaccine models using statistical data mining.

Design and synthesis of peptide vaccines is of significant pharmaceutical importance. A knowledge based statistical model is fitted here for prediction of binding of an antigenic site of a protein or a B-cell epitope on a CDR (complementarity determining region) of an immunoglobulin. Linear analogues of the 3D structure of the epitopes are computed using this model.

Statistics of catalytic domain sequences in protein kinases vs non-kinases.

The catalytic regions of Protein Kinases are known to have similarity in primary chains. However, it is not known whether there is a signature profile specific to a particular catalytic region? Whether the signature profile, if any, is unique to a protein kinases family in a particular species or in a group of species? We have attempted analyzing some of these aspects by statistical data mining using an authentic and exhaustive database of Protein Kinases. The results reveal interesting features and provide some new directions to look at their applications.

Bayesian data mining of protein domains gives an efficient predictive algorithm and new insight.

Identification of structural domains in uncharacterized protein sequences is important in the prediction of protein tertiary folds and functional sites, and hence in designing biologically active molecules. We present a new predictive computational method of classifying a protein into single, two continuous or two discontinuous domains using Bayesian Data Mining. The algorithm requires only the primary sequence and computer-predicted secondary structure.

Data mining of VDJ genes reveals interesting clues.

Hypervariability of the complementary determining regions in characteristic structure of Immunoglobulins and the distinct, cell-specific expressions of the genes coding for this important class of proteins pose intriguing problems in experimental and computational/informatics research requiring a special approach different from those for the other proteins. We present here an Average Linkage Hierarchical Clustering of the Homosapien VDJ genes and the Immunoglobulin polypeptides generated by them using special kind of data structures and correlation matrices in place of the microarray data. The results reveal interesting clues on the heterogeneity of exon - intron locations in these gene-families and its possible role in hypervariability of the Immunoglobulins.

Fast prediction of protein domain boundaries using conserved local patterns.

We have found certain conserved motifs and secondary structural patterns present in the vicinity of interior domain boundary points (dbps) by a data-driven approach without any a priori constraint on the type and number of such features, and without any requirement of sequence homology. We have used these motifs and patterns to rerank the solutions obtained by the well-known domain guess by size (DGS) algorithm. We predict, overall, five solutions.

Structure prediction of a multi-domain EF-hand Ca2+ binding protein by PROPAINOR.

PROPAINOR is a new algorithm developed for ab initio prediction of the 3D structures of proteins using knowledge-based nonparametric multivariate statistical methods. This algorithm is found to be most efficient in terms of computational simplicity and prediction accuracy for single-domain proteins as compared to other ab initio methods. In this paper, we have used the algorithm for the atomic structure prediction of a multi-domain (two-domain) calcium-binding protein, whose solution structure has been deposited in the PDB recently (PDB ID: 1JFK).

A biostatistical approach to ayurveda: quantifying the tridosha.

Objective: To compute quantitative estimates of the tridosha--the qualitative characterization that constitutes the core of diagnosis and treatment in Ayurveda--to provide a basis for biostatistical analysis of this ancient Indian science, which is a promising field of alternative medicine.

Subjects: The data sources were 280 persons from among the residents and visitors/training students at the Brahmvarchas Research Centre and Shantikuj, Hardwar, India.

Design/methodology: A quantitative measure of the tridosha level (for vata, pitta, and kapha) is obtained by applying an algorithmic heuristic approach to the exhaustive list of qualitative features/factors that are commonly used by Ayurvedic doctors.

Ab-initio prediction and reliability of protein structural genomics by PROPAINOR algorithm.

We have formulated the ab-initio prediction of the 3D-structure of proteins as a probabilistic programming problem where the inter-residue 3D-distances are treated as random variables. Lower and upper bounds for these random variables and the corresponding probabilities are estimated by nonparametric statistical methods and knowledge-based heuristics. In this paper we focus on the probabilistic computation of the 3D-structure using these distance interval estimates.

Ab initio structure of human seminal plasma prostatic inhibin gives significant insight into its biological functions.

Human seminal plasma prostatic inhibin (HSPI) is a protein isolated from the human prostate gland. Despite its profound biomedical and biotechnological importance, the 3D structure of this protein of 94 amino acids remains undeciphered. The difficulties in extracting it in pure form and crystallizing it have restrained the determination of its structure experimentally.