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WebFEATURE : Tool to identify and visualize Functional Sites in Macromolecules

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The identification and assignment of functions of unknown macro molecules has been observed to be faster and reliable than the sequence-based methods. This may be due to the structure-based methods which can identify molecules beyond their residues with the help of 3D space.

WebFEATURE is a web-based analysis tool for the identification of macro molecules. The users can easily identify the functional sites in the query structures. It scans the query structures beyond the residue identity and also consider the biophysical and biochemical properties of the functional sites in 3D space.

Fig.1

Fig.1 Web interface of WebFEATURE.

The FEATURE system uses a supervised learning algorithm to find the conserved properties from the similar structures and then builds statistical models. These models represents the statistical distribution of physico-chemical properties of the functional sites at distances from the site of interest. It better explains the chemical patterns behind the residues. The supervised learning algorithm automatically discover the physico-chemical properties of the macro molecules.

Fig.2

Figure 2. The output of a WebFEATURE scan for an ATP binding site in Casein Kinase-1 (PDB ID: 1csn) shows the hits, above cutoff, superimposed on the structure and crystallographically bound ATP. Hit score statistics are plotted in a histogram to the right of the Chime viewer. By entering a new cutoff in the Cutoff text field, or by clicking on the histogram, the user can change the displayed hits by score. Buttons are provided to change the representation of the molecule and hits. Details on the statistical model are also provided.

The user can also specify the sites and the non-sites in the query structure. Sites are the locations for functional or structural roles and non-sites are those where that function does not occur. The training algorithm generates an output model which differentiates the sites from the non-sites. This generated model is then used as a part of input to the scanning algorithm of FEATURE. The scanning algorithm then analyze the grid points over a query structure for similar sites with in a significant cut-off. The log-odd scoring function of the physico-chemical properties around each grid point is calculated. This score provides a probability (likelihood) that a grid point is a site of interest. The higher the score, more likely the point is of interest.

WebFEATURE is a user-friendly web based tools which also provides offline analysis of the outputs. The result can be downloaded and can be visualized in Chimera, PyMol, etc. The WebFEATURE provides the results in real-time manner.

For further details click here

Note:

An exhaustive list of references for this article is available with the author and is available on personal request, for more details write to [email protected]

Dr. Muniba is a Bioinformatician based in New Delhi, India. She has completed her PhD in Bioinformatics from South China University of Technology, Guangzhou, China. She has cutting edge knowledge of bioinformatics tools, algorithms, and drug designing. When she is not reading she is found enjoying with the family. Know more about Muniba

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Algorithms

MOCCA- A New Suite to Model cis- regulatory Elements for Motif Occurrence Combinatorics

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MOCCA- A New Suite to Model cis- regulatory Elements for Motif Occurrence Combinatorics

cis-regulatory elements are DNA sequence segments that regulate gene expression. cis-regulatory elements consist of some regions such as promoters, enhancers, and so on. These regions consist of specific sequence motifs. (more…)

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vs_Analysis.py: A Python Script to Analyze Virtual Screening Results of Autodock Vina

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VS-Analysis: A Python Script to Analyze Virtual Screening Results of Autodock Vina

The output files obtained as a result of virtual screening (VS) using Autodock Vina may be large in number. It is difficult or quite impossible to analyze them manually. Therefore, we are providing a Python script to fetch top results (i.e., compounds showing low binding affinities). (more…)

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How to search motif pattern in FASTA sequences using Perl hash?

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Here is a simple Perl script to search for motif patterns in a large FASTA file with multiple sequences.

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How to read fasta sequences from a file using PHP?

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Here is a simple function in PHP to read fasta sequences from a file. (more…)

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How to read fasta sequences as hash using perl?

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This is a simple Perl script to read a multifasta file as a hash. (more…)

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BETSY: A new backward-chaining expert system for automated development of pipelines in Bioinformatics

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Bioinformatics analyses have become long and difficult as it involves a large number of steps implemented for data processing. Bioinformatics pipelines are developed to make this process easier, which on one hand automate a specific analysis, while on the other hand, are still limited for investigative analyses requiring changes to the parameters used in the process. (more…)

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Algorithm and workflow of miRDB

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As mentioned in the previous article, Micro RNAs (miRNAs) are the short endogenous RNAs (~22 nucleotides) and originate from the non-coding RNAs [1], produced in single-celled eukaryotes, viruses, plants, and animals [2]. They play significant roles in various biological processes such as degradation of mRNA [3]. Several databases exist storing a large amount of information about miRNAs, one of such databases miRBase [4] was explained in the previous article, today we will explain the algorithm of miRDB [5,6], another database for miRNA target prediction. (more…)

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miRBase: Explained

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Micro RNAs (miRNAs) are the short endogenous RNAs (~22 nucleotides) and originate from the non-coding RNAs [1], produced in single-celled eukaryotes, viruses, plants, and animals [2]. miRNAs are capable of controlling homeostasis [2] and play significant roles in various biological processes such as degradation of mRNA and post-translational inhibition through complementary base pairing [3].  (more…)

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Prediction of biochemical reactions catalyzed by enzymes in humans

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There are many biological important enzymes which exist in the human body, one of them is Cytochrome P450 (CyP450) enzymes which are mostly considered in drug discovery due to their involvement in the majority (75%) of drug metabolism [1]. Therefore, various in-silico methods have been applied to predict the possible substrates of CyP 450 enzymes [2-4]. Recently, an in-silico model has been developed to predict the potential chemical reactions mediated by the enzymes present in humans including CyP450 enzymes [5]. (more…)

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A new high-level Python interface for MD simulation using GROMACS

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The roots of the molecular simulation application can be traced back to physics where it was applied to simplified hard-sphere systems [1]. This field of molecular simulation study has gained a lot of interest since then and applied to perform simulations to fold small protein at multi-microsecond scale [2-4], predict functional properties of receptors and to capture the intermediate transitions of the complex [5], and to study the movement and behavior of ligand in a binding pocket and also to predict interactions between receptors and ligands [6,7]. (more…)

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Machine learning in prediction of ageing-related genes/proteins

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Ageing has a great impact on human health, when people’s age advance towards 80 years, approximately half of the proteins in the body get damaged through oxidation. The chemical degradations occurring in our body produce energy by the consumed food via oxidation in the presence of oxygen. (more…)

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Simulated sequence alignment software: An alternative to MSA benchmarks

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In our previous article, we discussed different multiple sequence alignment (MSA) benchmarks to compare and assess the available MSA programs. However, since the last decade, several sequence simulation software have been introduced and are gaining more interest. In this article, we will be discussing various sequence simulating software being used as alternatives to MSA benchmarks. (more…)

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Benchmark databases for multiple sequence alignment: An overview

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Multiple sequence alignment (MSA) is a very crucial step in most of the molecular analyses and evolutionary studies. Many MSA programs have been developed so far based on different approaches which attempt to provide optimal alignment with high accuracy. Basic algorithms employed to develop MSA programs include progressive algorithm [1], iterative-based [2], and consistency-based algorithm [3]. Some of the programs incorporate several other methods into the process of creating an optimal alignment such as M-COFFEE [4] and PCMA [5]. (more…)

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ab-initio prediction of protein structure: An introduction

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We have heard a lot about the ab-initio term in Bioinformatics, which could be difficult to understand for newbies in the field of bioinformatics. Today, we will discuss in detail what ab-initio is and what are the applicable methods for it. (more…)

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Intrinsically disordered proteins’ predictors and databases: An overview

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Intrinsically unstructured proteins (IUPs) are the natively unfolded proteins which must be unfolded or disordered in order to perform their functions.  They are commonly referred to as intrinsically disordered proteins (IDPs) and play significant roles in regulating and signaling biological networks [1]. IDPs are also involved in the assembly of signaling complexes and in the dynamic self-assembly of membrane-less nuclear and cytoplasmic organelles [1]. The disordered regions in a protein can be highly conserved among the species in respect of both the composition and the sequence [2]. (more…)

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An introduction to the predictors of pathogenic point mutations

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Single nucleotide variation is a change in a single nucleotide in a sequence irrespective of the frequency of the variation. Single nucleotide variants (SNVs) play a very important role in causing several diseases such as the tumor, cancer, etc. Many efforts have been made to identify the SNVs which were initially based on identifying non-synonymous mutations in coding regions of the genomes. (more…)

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SparkBLAST: Introduction

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The basic local alignment search tool (BLAST) [1,2] is known for its speed and results, which is also a primary step in sequence analysis. The ever-increasing demand for processing huge amount of genomic data has led to the development of new scalable and highly efficient computational tools/algorithms. For example, MapReduce is the most widely accepted framework which supports design patterns representing general reusable solutions to some problems including biological assembly [3] and is highly efficient to handle large datasets running over hundreds to thousands of processing nodes [4]. But the implementation frameworks of MapReduce (such as Hadoop) limits its capability to process smaller data. (more…)

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Role of Information Theory, Chaos Theory, and Linear Algebra and Statistics in the development of alignment-free sequence analysis

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Sequence alignment is customary to not only find similar regions among a pair of sequences but also to study the structural, functional and evolutionary relationship between organisms. Many tools have been discovered to achieve the goal of alignment of a pair of sequences, separately for nucleotide sequence and amino acid sequence, BLOSSUM & PAM [1] are a few to name. (more…)

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Bioinformatics Challenges and Advances in RNA interference

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RNA interference is a post-transcriptional gene regulatory mechanism to down-regulate the gene expression either by mRNA degradation or by mRNA translation inhibition. The mechanism involves a small partially complementary RNA against the target gene. To perform the action, it also requires a class of dedicated proteins to process these primary RNAs into mature microRNAs. The guide sequence determines the specificity of the miRNA. Therefore, the knowledge of the guide sequence is crucial for predicting its targets and also exploiting the sequence to create a new regulatory circuit. In this short review, we will briefly discuss the role and challenges in miRNA research for unveiling the target prediction by bioinformatics and to foster our understanding and applications of RNA interference. (more…)

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Systems pharmacology and drug development

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Systems pharmacology is an emerging area in the field of medicinal chemistry and pharmacology which utilizes systems network to understand drug action at the organ and organism level. It applies the computational and experimental systems biology approaches to pharmacology, which includes network analyzes at multiple biological organization levels facilitating the understanding of both therapeutic and adverse effects of the drugs. Nearly a decade ago, the term systems pharmacology was used to define the drug action in a specific organ system such as reproductive pharmacology [1], but to date, it has been expanded to different organ and organism levels [2]. (more…)

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