In-Silico Nanobio-Design. A New Frontier in Computational Biology

Abstract microRNAs (miRNAs) are a recently discovered class of small non-coding RNAs that regulate gene expression. Rapidly accumulating evidence has revealed that miRNAs are associated with cancer. The human tissue kalli-krein gene family is the largest contiguous family of proteases in the human genome, containing 15 genes. Many kallikreins have been reported as potential tumor markers. In this review, recent bioinformatics and experimental evidence is presented indicating that kallikreins are potential miRNA targets. The available experimental approaches to investigate these interactions and the potential diagnostic and therapeutic applications are also discussed. miRNAs represent a possible regulatory mechanism for controlling kallikrein expression at the post-transcriptional level. Many miRNAs were predicted to target kallikreins and a single miRNA can target more than one kallikrein. Recent evidence suggests that miRNAs can also exert ‘quantitative’ control of kallikreins by utilizing multiple targeting sites in the kallikrein mRNA. More research is needed to experimentally verify the in silico predictions and to investigate the possible role in tumor initiation and/or progression.

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The CombineArchiveWeb application – A web based tool to handle files associated with modelling results

10.7287/peerj.preprints.639 ◽

2014 ◽

Cited By ~ 1

Author(s):

Martin Scharm ◽

Florian Wendland ◽

Martin Peters ◽

Markus Wolfien ◽

Tom Theile ◽

...

Keyword(s):

Computational Biology ◽

In Silico ◽

Simulation Studies ◽

Meta Data ◽

Web Based ◽

Manual Handling ◽

The Web

Sharing in silico experiments is essential for the advance of research in computational biology. Consequently, the COMBINE archive was designed as a digital container format. It eases the management of files related to a modelling result, fosters collaboration, and ultimately enables the exchange of reproducible simulation studies. However, manual handling of COMBINE archives is tedious and error prone. We therefore developed the CombineArchiveWeb application to support scientists in promoting and publishing their research by means of creating, exploring, modifying, and sharing archives. All files are equipped with meta data and can be distributed over the Web through shareable workspaces.

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Direct in silico visualization of ligands channelling through proteins: The next-generation frontier of computational biology

Physics of Life Reviews ◽

10.1016/j.plrev.2017.08.004 ◽

2017 ◽

Vol 22-23 ◽

pp. 82-84 ◽

Cited By ~ 1

Author(s):

Alessandra Magistrato

Keyword(s):

Computational Biology ◽

In Silico ◽

Next Generation

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The CombineArchive Toolkit - facilitating the transfer of research results

10.7287/peerj.preprints.514 ◽

2014 ◽

Author(s):

Martin Scharm ◽

Florian Wendland ◽

Martin Peters ◽

Markus Wolfien ◽

Tom Theile ◽

...

Keyword(s):

Computational Biology ◽

In Silico ◽

Reproducible Research ◽

Research Results ◽

Manual Handling

Sharing in silico experiments is essential for the advance of research in computational biology. Consequently, the COMBINE archive was designed as a digital container format. It eases the management of numerous files, fosters collaboration, and ultimately enables the exchange of reproducible research results. However, manual handling of COMBINE archives is tedious and error prone. We therefore developed the CombineArchive Toolkit. It supports scientists in promoting and publishing their work by means of creating, exploring, modifying, and sharing archives.

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IN SILICO MODELING OF THE MOLECULAR STRUCTURE OF microRNAs MARKERS FOR LIVER FIBROSIS IN HEPATITIS C

Revista de Patologia Tropical / Journal of Tropical Pathology ◽

10.5216/rpt.v48i3.58660 ◽

2019 ◽

Vol 48 (3) ◽

pp. 135-147

Author(s):

Gabriela Correia Matos de Oliveira ◽

Raymundo Paraná ◽

Luís Jesuino De Oliveira Andrade

Keyword(s):

Molecular Structure ◽

Hepatitis C ◽

Nucleotide Sequence ◽

Computational Biology ◽

Hepatic Fibrosis ◽

In Silico ◽

Computational Simulation ◽

Micro Rna ◽

Nucleotide Sequence Alignment ◽

In Silico Modeling

Molecular biology looks for evidence that microRNA (miRNAs) plays a relevant function both in the beginning and advanced stages of hepatic fibrosis (HF), and has been proposed as an additional biomarker for HF forecasting in carriers of hepatitis C virus (HCV) infection. The purpose of this study was to develop an in silico modeling of the two-dimensional (2D) molecular structure of miRNA markers for HF in carriers of HCV. A search was initially performed for the nucleotide sequence of 6 miRNAs defined as biomarkers for HF, performinga computational simulation of the molecular structure of the following miRNAs: miRNA-182, miRNA-183, miRNA-1260b, miRNA-122-3p, miRNA-378i, and miRNA-214-5p. The nucleotide sequences were chosen in the GenBank of the American National Institutes of Health genetic sequence database. The nucleotide sequence alignment was carried out with a text-based format (FASTA) tool. In the molecular modeling, the structures were built with the RNAstructure, a completely automated miRNAs structure modelling server, available through Web Servers for RNA Secondary Structure Prediction. This study presented the nucleotide sequence and the computational simulation of molecular structures for the following miRNA: miRNA-182, miRNA-183, miRNA-1260b, miRNA-122-3p, miRNA-378i, and miRNA-214-5p. The molecular structure of miRNAs markers for HF in HCV carriers, through computational biology, is essential for designing more efficient optional tools for accurate treatment. KEY WORDS: micro-RNA; Hepatitis C; Hepatic Fibrosis; Computational Biology.

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The CombineArchive Toolkit - facilitating the transfer of research results

10.7287/peerj.preprints.514v1 ◽

2014 ◽

Cited By ~ 1

Author(s):

Martin Scharm ◽

Florian Wendland ◽

Martin Peters ◽

Markus Wolfien ◽

Tom Theile ◽

...

Keyword(s):

Computational Biology ◽

In Silico ◽

Reproducible Research ◽

Research Results ◽

Manual Handling

Sharing in silico experiments is essential for the advance of research in computational biology. Consequently, the COMBINE archive was designed as a digital container format. It eases the management of numerous files, fosters collaboration, and ultimately enables the exchange of reproducible research results. However, manual handling of COMBINE archives is tedious and error prone. We therefore developed the CombineArchive Toolkit. It supports scientists in promoting and publishing their work by means of creating, exploring, modifying, and sharing archives.

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Target2drug : A Novel Programmatic Workflow to Automate in Silico Drug Discovery

10.26434/chemrxiv.13262603 ◽

2020 ◽

Author(s):

Ben Geoffrey A S ◽

Rafal Madaj ◽

Akhil Sanker ◽

Pavan Preetham Valluri ◽

Judith Gracia ◽

...

Keyword(s):

Drug Discovery ◽

Computational Biology ◽

In Silico ◽

Drug Target ◽

Drug Targets ◽

Qsar Model ◽

Learning Tools ◽

Active Compounds ◽

Qsar Modelling ◽

Drug Leads

As the Big Data and Artificial Intelligence (AI) revolution continues to affect every area of our lives, it’s influence is also exerted in the areas of bioinformatics, computational biology and drug discovery. Machine/Deep Learning tools have been developed to predict compounds-drug target interactions and the vice-versa process of predicting target interactions for an compound. In our presented work, we report a programmatic tool, which incorporates many features of the bioinformatics, computational biology and AI-driven drug discovery revolutions into a single workflow assembly. When a user is required to identify drugs against a new drug target, the user provides target signatures in the form of amino acid sequence of the target or it’s corresponding nucleotide sequence as input to the tool and the tool carries out a BLAST protocol to identify known protein drug targets that are similar to the new target submitted by the user and collects data linked to the target involving, active compounds against the target, the activity value and molecular descriptors of active compounds to perform QSAR modelling and to generate drug leads with predictions from the validated QSAR model. The tool performs an In-Silico modelling to generate In-Silico interaction profiles of compounds generated as drug leads and the target and stores the results in the working folder of the user. To demonstrate the use of the tool, we have carried out a demonstration with the target signatures of the current pandemic causing virus, SARS-CoV 2. However the tool can be used against any target and is expected to help in growing our knowledge graph of targets and interacting compounds. <br>

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Not just BLAST nt: WGS database joins the party

10.1101/653592 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jose Manuel Martí ◽

Carlos P. Garay

Keyword(s):

Molecular Biology ◽

Computational Biology ◽

In Silico ◽

Confidence Score ◽

Taxonomic Classification ◽

Local Alignment ◽

Whole Genome ◽

Culture Independent ◽

Search Tool ◽

Ncbi Blast

AbstractSince its introduction in 1990 and with over 50k citations, the NCBI BLAST family has been an essential tool of in silico molecular biology. The BLAST nt database, based on the traditional divisions of GenBank, has been the default and most comprehensive database for nucleotide BLAST searches and for taxonomic classification software in metagenomics. Here we argue that this is no longer the case. Currently, the NCBI WGS database contains one billion reads (almost five times more than GenBank), and with 4.4 trillion nucleotides, WGS has about 14 times more nucleotides than GenBank. This ratio is growing with time. We advocate a change in the database paradigm in taxonomic classification by systematically combining the nt and WGS databases in order to boost taxonomic classifiers sensitivity. We present here a case in which, by adding WGS data, we obtained over five times more classified reads and with a higher confidence score. To facilitate the adoption of this approach, we provide the draftGenomes script.Author summaryCulture-independent methods are revolutionizing biology. The NIH/NCBI Basic Local Alignment Search Tool (BLAST) is one of the most widely used methods in computational biology. The BLAST nt database has become a de facto standard for taxonomic classifiers in metagenomics. We believe that it is time for a change in the database paradigm for such a classification. We advocate the systematic combination of the BLAST nt database with genomes of the massive NCBI Whole-Genome Shotgun (WGS) database. We make draftGenomes available, a script that eases the adoption of this approach. Current developments and technologies make it feasible now. Our recent results in several metagenomic projects indicate that this strategy boosts the sensitivity in taxonomic classifications.

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Prospective Application of Parallelization to Accelerate Biological Model Simulations

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7405.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 5195-5201

Keyword(s):

Computational Biology ◽

In Silico ◽

Biological Systems ◽

State Variables ◽

Biological Modeling ◽

Model Simulations ◽

Long Time ◽

Prospective Application ◽

Mathematical Techniques ◽

Gpu Architecture

Biological systems can be modeled using mathematical techniques to carry out in silico experiments and research. These models tend to have a lot of state variables and hence take a long time to simulate the model. Modern GPU architecture provides a framework for parallelizing computation-heavy processes. With the advent of GPU technology, it is increasingly used in the field of computational biology, aiming to reduce simulation times and increase the size of inputs. This paper surveys the use of GPU architecture in the field of biological modeling..

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