International Journal for Computational Biology (IJCB)
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Accurate Demarcation of Protein Domain Linkers Based on Structural Analysis of Linker Probable Region
Get PDFIn multi-domainproteins, the domainsare connected by a flexible unstructured region called as protein domain linker. The accurate demarcation of these linkers holds a key to understanding of their biochemical and evolutionary attributes. This knowledge helps in designing a suitable linker for engineering stable multi-domain chimeric proteins. Here we propose a novel method for the demarcation of the linker based on a three-dimensional protein structure and a domain definition. The proposed method is based on biological knowledge about structural flexibility of the linkers. We performed structural analysis on a linker probable region (LPR) around domain boundary points of known SCOP domains. The LPR was described using a set of overlapping peptide fragments of fixed size. Each peptide fragment was then described by geometricinvariants (GIs) and subjected to clustering process where the fragments corresponding to actual linker comeupasoutliers.We then discover the actual linkers by finding the longest continuous stretch ofoutlier fragments from LPRs. This method was evaluated on a benchmark dataset of 51 continuous multi-domain proteins, where it achieves F1 score of 0.745 (0.83precision and 0.66recall). When the method was applied on 725 continuous multi-domain proteins, it was able to identify novel linkers that were not reported previously. This method can be used in combination with supervised /sequence based linker prediction methods for accurate linker demarcation.
MatSAM: a Matlab implementation for Significance Analysis of Microarrays
Get PDFMicroarray experiments enable the simultaneous measure of expression levels of large amount of genes and have many applications. A widespread one is finding set of genes that are differentially expressed. Significance Analysis of Microarrays (SAM) helps to produce those sets using multiple testing techniques. There is unfortunately not yet a public tool enabling to do SAM using the Matlab platform. We here define MatSAM, a SAM implementation in Matlab, and show that it yields results of high confidence comparatively to those obtained by putative tools available in the R programming environment. MatSAM can be used in conjunction with Matlab Bioinformatics toolbox to perform further analysis.Availability: MatSAM is available as source code at http://www.bioinfoindia.org/MatSA
Protein Local Tertiary Structure Prediction by Super Granule Support Vector Machines with Chou-Fasman Parameter
Get PDFPrediction of a protein's tertiary structure from its sequence information alone is considered a major task in modern computational biology. In order to closer the gap between protein sequences to its tertiary structures, we discuss the correlation between protein sequence and local tertiary structure information in this paper. The strategy we used in this work is predict small portions (local) of protein tertiary structure with high confidence from conserved protein sequences, which are called “protein sequence motifs”. 799 protein sequence motifs that transcend protein family boundaries were obtained from our previous work. The prediction accuracy generated from the best group of protein sequence motifs always keep higher than 90% while more than 8% of the independent testing data segments are predicted. Since the most meaningful result published in latest publication is merely 70.02% accuracy under the coverage of 4.45%, the research results achieved in this paper are obviously outperformed. Besides, we also set up a stricter evaluation to our prediction to further understand the relation between protein sequence motifs and tertiary structure predictions. The results suggest that the hidden sequence-to-structure relationship can be uncovered using the Super Granule SVM Model with the Chou-Fasman Parameter. With the high local tertiary structure prediction accuracy provided in this article, the hidden relation between protein primary sequences and their 3D structure are uncovered considerably
Application of Support Vector Machines in Virtual Screening
Get PDFTraditionally drug discovery has been a labor intensive effort, since it is difficult to identify a possible drug candidate from an extremely large small molecule library for any given target. Most of the small molecules fail to show any activity against the target because of electrochemical, structural and other incompatibilities. Virtual screening is an in-silico approach to identify drug candidates which are unlikely to show any activity against a given target, thus reducing an enormous amount of experimentation which is most likely to end up as failures. Important approaches in virtual screening have been through docking studies and using classification techniques. Support vector machines based classifiers, based on the principles of statistical learning theory have found several applications in virtual screening. In this paper, first the theory and main principles of SVM are briefly outlined. Thereafter a few successful applications of SVM in virtual screening have been discussed. It further underlines the pitfalls of the existing approaches and highlights the area which needs further contribution to improve the state of the art for application of SVM in virtual screening
Converting Life into Numbers
Get PDFBiological data exists in several layers from genome sequence to networks and beyond. Information that comes from environment passes through layers of viscosity within organisms and is transformed into an output released back into the environment. Given enormous data generation, biology is increasingly becoming a computational problem. Here in this article, various computational needs are abstracted with a view to offer the future requirement of the community
TpPred: A Tool for Hierarchical Prediction of Transport Proteins Using Cluster of Neural Networks and Sequence Derived Features
Get PDFA top–down predictor, called TpPred, is developed which consists of 3 level of hierarchical classification using cascade of neural networks from sequence derived features. The 1st layer of the prediction engine is for identifying a query protein as transport protein or not; the 2nd layer for the main functional class; and the 3rd layer for the sub-functional class. The overall success rates for all the three layers are higher than 65% that were obtained through rigorous cross-validation tests on the very stringent benchmark datasets in which none of the proteins has 30% sequence identity with any other in the same class or subclass. TpPred achieved good prediction accuracies and could nicely complement experimental approaches for identification of transport proteins. TpPred is freely available to be use in-house as a standalone version and is accessible at http://www.juit.ac.in/attachments/tppred/Home.html
Studies on 8-tertbutyl Caffeine: An in silico approach to mechanistic studies
Get PDFAmminecobalt (III) promoted aerial oxidation of alkyl hydrazines undergoing homolytic alkylation of xanthines selectively at C-8 position. No modeling studies have been done previously on these compounds. An attempt was made to predict the mechanism involved in this spontaneous reaction using molecular modeling. The predictions revealed that homolytic aromatic substitution of alkyl radical exhibits primary isotopic effect. We try to correlate the importance of in silico approaches towards mechanistic studies in such compounds
A Mathematical Model of Central Dogma of Molecular Biology employing a Novel Irrational-Integral-Imaginary (i3) Encoding and Numerical Approximation based on Cellular Automaton
Get PDFCellular Automaton (CA) is usually used to model the spatio-temporal evolution of dynamical systems. In this work, a special class of the same known as 'Outer-totalistic' Cellular Automaton is applied to examine if there is a rationale behind the correlation between 64 possible codons and the resulting 20 amino-acids. Also, an attempt is made to mathematically model the central dogma of molecular biology in an intelligible format, including transcription and translation. Results suggest that our irrational-integral-imaginary (i3) encoding approach forms not only a satisfactory basis for a mathematical model of translation of mRNA to protein but also that of transcription from ssDNA (single stranded DNA) to mRNA (messenger RNA)
SMDB: Soybean Marker DataBase
Get PDFSoybean Marker Database (SMDB) is a repository of important genomic information for soybean. At present several genomic databases are available for plants. Some of the important oilseeds plant databases are ATPID database, Castor Bean Genome Database, CGPDB, SoyBase, Legume Information System (LIS), Brassica database, Sinbase, etc. To gain comprehensive information from varied amount of resources, we developed this database which provides general as well as specific information at universal level. Along with this it also furnishes gene level information for various functional categories such as transcription factor, disease resistant varieties, heat shock protein, genetically modified strain of soybean. The bunch of information available to researchers today increases in tremendous manner. Hence understanding the plant genome specific databases for acquiring specific information is the demand of time for crop improvement and research programmes. SMDB is designed for the purpose of exploring potential gene differences in different plant genotypes, including genetically modified and disease resistant crops beneficial to the farmer who cultivate this crop. SMDB is publicly accessible for academic and research purpose at: http://www.bioinfoindia.org/smdb/
Editorial Note by Prof. Achuth Sankar Nair
No full textI am very much pleased to release this issue of IJCB with some very good research works as discussed aforementioned. With a detailed view about the cutting edge computational techniques being applied to solve the mystery of biology, these works published in this issue, we believe, would possibly explore a wide area of applications in the area of Biotechnology, Pharmacology, Systems Biology and Biomedicine. I do hope the articles would catch interest of the readers and motivate them to contribute new original articles in this journal