scholarly journals Molecular Characterization, Bioinformatic Analysis, and Expression Profile of Lin-28 Gene and Its Protein from Arabian Camel (Camelus dromedarius)

2019 ◽  
Vol 20 (9) ◽  
pp. 2291 ◽  
Author(s):  
Sultan N. Alharbi ◽  
Ibtehal S. Alduhaymi ◽  
Lama Alqahtani ◽  
Musaad A. Altammaami ◽  
Fahad M. Alhoshani ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Rna Binding ◽  
Secondary Structure Prediction ◽  
Bos Taurus ◽  
Sequence Similarity ◽  
Zinc Fingers ◽  
3D Structure ◽  
Bioinformatic Analysis ◽  
Peptide Mass ◽  
Arabian Camel

Lin-28 is an RNA-binding protein that is known for its role in promoting the pluripotency of stem cells. In the present study, Arabian camel Lin-28 (cLin-28) cDNA was identified and analyzed. Full length cLin-28 mRNA was obtained using the reverse transcription polymerase chain reaction (RT-PCR). It was shown to be 715 bp in length, and the open reading frame (ORF) encoded 205 amino acids. The molecular weight and theoretical isoelectric point (pI) of the cLin-28 protein were predicted to be 22.389 kDa and 8.50, respectively. Results from the bioinformatics analysis revealed that cLin-28 has two main domains: an N-terminal cold-shock domain (CSD) and a C-terminal pair of retroviral-type Cysteine3Histidine (CCHC) zinc fingers. Sequence similarity and phylogenetic analysis showed that the cLin-28 protein is grouped together Camelus bactrianus and Bos taurus. Quantitative real-time PCR (qPCR) analysis showed that cLin-28 mRNA is highly expressed in the lung, heart, liver, and esophageal tissues. Peptide mass fingerprint-mass spectrometry (PMF-MS) analysis of the purified cLin-28 protein confirmed the identity of this protein. Comparing the modeled 3D structure of cLin-28 protein with the available protein 3D structure of the human Lin-28 protein confirmed the presence of CSD and retroviral-type CCHC zinc fingers, and high similarities were noted between the two structures by using super secondary structure prediction.

Proteomic Analysis of Medicinal Plant Calotropis Gigantea by insilico Peptide Mass Fingerprinting

2020 ◽  
Vol 16 ◽  
Author(s):  
Saad Ur Rehman ◽  
Muhammad Rizwan ◽  
Sajid Khan ◽  
Azhar Mehmood ◽  
Anum Munir
Keyword(s):  
Medicinal Plant ◽  
Structure Prediction ◽  
3D Structure ◽  
Peptide Mass Fingerprinting ◽  
Protein Docking ◽  
Receptor Protein ◽  
Human Leukemia ◽  
Protein Database ◽  
Calotropis Gigantea ◽  
Peptide Mass

: Medicinal plants are the basic source of medicinal compounds traditionally used for the treatment of human diseases. Calotropis gigantea a medicinal plant belonging to the family of Apocynaceae in the plant kingdom and subfamily Asclepiadaceae usually bearing multiple medicinal properties to cure a variety of diseases. Background: The Peptide Mass Fingerprinting (PMF) identifies the proteins from a reference protein database by comparing the amino acid sequence that is previously stored in a database and identified. Method: The calculation of insilico peptide masses is done through the ExPASy PeptideMass and these masses are used to identify the peptides from MASCOT online server. Anticancer probability is calculated from the iACP server, docking of active peptides is done by CABS-dock the server. Objective: The purpose of the study is to identify the peptides having anti-cancerous properties by in-silico peptide mass fingerprinting. Results : The anti-cancerous peptides are identified with the MASCOT peptide mass fingerprinting server, the identified peptides are screened and only the anti-cancer are selected. De novo peptide structure prediction is used for 3D structure prediction by PEP-FOLD 3 server. The docking results confirm strong bonding with the interacting amino acids of the receptor protein of breast cancer BRCA1 which shows the best peptide binding to the Active chain, the human leukemia protein docking with peptides shows the accurate binding. Conclusion : These peptides are stable and functional and are the best way for the treatment of cancer and many other deadly diseases.

scholarly journals Prediction of 8-state protein secondary structures by 1D-Inception and BD-LSTM

2019 ◽  
Author(s):  
Aminur Rab Ratul ◽  
Marcel Turcotte ◽  
M. Hamed Mozaffari ◽  
WonSook Lee
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
3D Structure ◽  
Protein Secondary Structure ◽  
Research Area ◽  
Protein Secondary Structures ◽  
Precise Prediction

AbstractProtein secondary structure is crucial to create an information bridge between the primary structure and the tertiary (3D) structure. Precise prediction of 8-state protein secondary structure (PSS) significantly utilized in the structural and functional analysis of proteins in bioinformatics. In this recent period, deep learning techniques have been applied in this research area and raise the Q8 accuracy remarkably. Nevertheless, from a theoretical standpoint, there still lots of room for improvement, specifically in 8-state (Q8) protein secondary structure prediction. In this paper, we presented two deep learning architecture, namely 1D-Inception and BD-LSTM, to improve the performance of 8-classes PSS prediction. The input of these two architectures is a carefully constructed feature matrix from the sequence features and profile features of the proteins. Firstly, 1D-Inception is a Deep convolutional neural network-based approach that was inspired by the InceptionV3 model and containing three inception modules. Secondly, BD-LSTM is a recurrent neural network model which including bidirectional LSTM layers. Our proposed 1D-Inception method achieved 76.65%, 71.18%, 76.86%, and 74.07% Q8 accuracy respectively on benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets. Moreover, BD-LSTM acquired 74.71%, 69.49%, 74.07%, and 72.37% state-8 accuracy after evaluated on CullPdb6133, CB513, CASP10, and CASP11 datasets, respectively. Both these architectures enable the efficient processing of local and global interdependencies between amino acids to make an accurate prediction of each class is very beneficial in the deep neural network. To the best of our knowledge, experiment results of the 1D-Inception model demonstrate that it outperformed all the state-of-art methods on the benchmark CullPdb6133, CB513, and CASP10 datasets.

Protein Structure Prediction using Homology Modeling

2016 ◽  
pp. 339-359 ◽  
Author(s):  
Akanksha Gupta ◽  
Pallavi Mohanty ◽  
Sonika Bhatnagar
Keyword(s):  
Homology Modeling ◽  
Structure Prediction ◽  
High Throughput Sequencing ◽  
Tertiary Structure ◽  
Structural Information ◽  
Sequence Similarity ◽  
3D Structure ◽  
Comparative Modeling ◽  
Spatial Arrangement ◽  
Good Sequence

Sequence-structure deficit marks one of the critical problems in today's scenario where high-throughput sequencing has resulted in large datasets of protein sequences but their corresponding 3D structures still needs to be determined. Homology modeling, also termed as Comparative modeling refers to modeling of 3D structure of a protein by exploiting structural information from other known protein structures with good sequence similarity. Homology models contain sufficient information about the spatial arrangement of important residues in the protein and are often used in drug design for screening of large libraries by molecular docking techniques. This chapter provides a brief description about protein tertiary structure prediction and Homology modeling. The authors provide a description of the steps involved in homology modeling protocols and provide information on the various resources available for the same.

scholarly journals New functionality of RNAComposer: application to shape the axis of miR160 precursor structure

2017 ◽  
Vol 63 (4) ◽  
Author(s):  
Maciej Antczak ◽  
Mariusz Popenda ◽  
Tomasz Zok ◽  
Joanna Sarzynska ◽  
Tomasz Ratajczak ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Web Application ◽  
3D Model ◽  
Secondary Structure Prediction ◽  
3D Structure ◽  
Structure Topology ◽  
Precursor Structure ◽  
Secondary Structure Topology ◽  
Main Components

RNAComposer is a fully automated, web-interfaced system for RNA 3D structure prediction, freely available at http://rnacomposer.cs.put.poznan.pl/ and http://rnacomposer.ibch.poznan.pl/. Its main components are: manually curated database of RNA 3D structure elements, highly efficient computational engine and user-friendly web application. In this paper, we demonstrate how the latest additions to the system allow the user to significantly affect the process of 3D model composition on several computational levels. Although in general our method is based on the knowledge of secondary structure topology, currently RNAComposer offers a choice of six incorporated programs for secondary structure prediction. It allows also to apply conditional search in the database of 3D structure elements and introduce user-provided elements into the final 3D model. This new functionality contributes to a significant improvement of the predicted 3D model reliability and it facilitates better model adjustment to the experimental data. This is exemplified based on RNAComposer application for modelling of the 3D structures of precursors of miR160 family members.

scholarly journals Contrastive learning on protein embeddings enlightens midnight zone at lightning speed

2021 ◽  
Author(s):  
Michael Heinzinger ◽  
Maria Littmann ◽  
Ian Sillitoe ◽  
Nicola Bordin ◽  
Christine Orengo ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Sequence Similarity ◽  
3D Structure ◽  
Three Dimensional ◽  
Hierarchical Classification ◽  
Language Models ◽  
Sequence Alignments ◽  
Sequence Comparisons ◽  
Multiple Sequence ◽  
3D Structures

Thanks to the recent advances in protein three-dimensional (3D) structure prediction, in particular through AlphaFold 2 and RoseTTAFold, the abundance of protein 3D information will explode over the next year(s). Expert resources based on 3D structures such as SCOP and CATH have been organizing the complex sequence-structure-function relations into a hierarchical classification schema. Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI) transferring annotations from a protein with experimentally known annotation to a query without annotation. Here, we presented a novel approach that expands the concept of HBI from a low-dimensional sequence-distance lookup to the level of a high-dimensional embedding-based annotation transfer (EAT). Secondly, we introduced a novel solution using single protein sequence representations from protein Language Models (pLMs), so called embeddings (Prose, ESM-1b, ProtBERT, and ProtT5), as input to contrastive learning, by which a new set of embeddings was created that optimized constraints captured by hierarchical classifications of protein 3D structures. These new embeddings (dubbed ProtTucker) clearly improved what was historically referred to as threading or fold recognition. Thereby, the new embeddings enabled the intrusion into the midnight zone of protein comparisons, i.e., the region in which the level of pairwise sequence similarity is akin of random relations and therefore is hard to navigate by HBI methods. Cautious benchmarking showed that ProtTucker reached much further than advanced sequence comparisons without the need to compute alignments allowing it to be orders of magnitude faster. Code is available at https://github.com/Rostlab/EAT .

scholarly journals 2dSS: a web server for protein secondary structure visualization

2019 ◽  
Author(s):  
Diksha Priya Lotun ◽  
Charlotte Cochard ◽  
Fabio R.J Vieira ◽  
Juliana Silva Bernardes
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
3D Structure ◽  
Protein Secondary Structure ◽  
Web Server ◽  
Homologous Proteins ◽  
Multiple Sequence ◽  
Prediction Tools ◽  
Secondary Structure Predictions

2dSS is a web-server for visualising and comparing secondary structure predictions. It provides two main functionalities: 2D-alignment and compare predictions. The “2D-alignment” has been designed to visualise conserved secondary structure elements in a multiple sequence alignment (MSA). From this we can study the secondary structure content of homologous proteins (a protein family) and highlight its structural patterns. The “compare predictions” has been designed to compare the output of several secondary structure prediction tools, and check their accuracy when compared with real secondary structure elements extracted from 3D-structure. 2dSS provides a comprehensive representation of protein secondary structure elements, and it can be used to visualise and compare secondary structures of any prediction tool.Availabilityhttp://genome.lcqb.upmc.fr/2dss/

scholarly journals Zebra2: advanced and easy-to-use web-server for bioinformatic analysis of subfamily-specific and conserved positions in diverse protein superfamilies

2020 ◽  
Vol 48 (W1) ◽  
pp. W65-W71 ◽  
Author(s):  
Dmitry Suplatov ◽  
Yana Sharapova ◽  
Elizaveta Geraseva ◽  
Vytas Švedas
Keyword(s):  
Rational Design ◽  
Sequence Similarity ◽  
3D Structure ◽  
Web Server ◽  
Bioinformatic Analysis ◽  
Query Protein ◽  
Systematic Analysis ◽  
External Resources ◽  
Protein Superfamilies ◽  
Network Interfaces

Abstract Zebra2 is a highly automated web-tool to search for subfamily-specific and conserved positions (i.e. the determinants of functional diversity as well as the key catalytic and structural residues) in protein superfamilies. The bioinformatic analysis is facilitated by Mustguseal—a companion web-server to automatically collect and superimpose a large representative set of functionally diverse homologs with high structure similarity but low sequence identity to the selected query protein. The results are automatically prioritized and provided at four information levels to facilitate the knowledge-driven expert selection of the most promising positions on-line: as a sequence similarity network; interfaces to sequence-based and 3D-structure-based analysis of conservation and variability; and accompanied by the detailed annotation of proteins accumulated from the integrated databases with links to the external resources. The integration of Zebra2 and Mustguseal web-tools provides the first of its kind out-of-the-box open-access solution to conduct a systematic analysis of evolutionarily related proteins implementing different functions within a shared 3D-structure of the superfamily, determine common and specific patterns of function-associated local structural elements, assist to select hot-spots for rational design and to prepare focused libraries for directed evolution. The web-servers are free and open to all users at https://biokinet.belozersky.msu.ru/zebra2, no login required.

scholarly journals Molecular cloning and characterization of Antheraea mylitta cytoplasmic polyhedrosis virus genome segment 9

2002 ◽  
Vol 83 (6) ◽  
pp. 1483-1491 ◽  
Author(s):  
Kaustubha R. Qanungo ◽  
Subhas C. Kundu ◽  
James I. Mullins ◽  
Ananta K. Ghosh
Keyword(s):  
Structure Prediction ◽  
Rna Binding ◽  
Secondary Structure Prediction ◽  
Virus Genome ◽  
Genome Segment ◽  
Viral Rna ◽  
Structural Protein ◽  
Antheraea Mylitta ◽  
Cytoplasmic Polyhedrosis Virus ◽  
Polyhedrosis Virus

Genome segment 9 of the 11-segment RNA genomes of three cytoplasmic polyhedrosis virus (CPV) isolates from Antheraea mylitta (AmCPV), Antheraea assamensis (AaCPV) and Antheraea proylei (ApCPV) were converted to cDNA, cloned and sequenced. In each case, this genome segment consists of 1473 nucleotides with one long ORF of 1035 bp and encodes a protein of 345 amino acids, termed NSP38, with a molecular mass of 38 kDa. Secondary structure prediction showed the presence of nine α-helices in the central and terminal domains with localized similarity to RNA-binding motifs of bluetongue virus and infectious bursal disease virus RNA polymerases. Nucleotide sequences were 99·6% identical between these three strains of CPVs, but no similarity was found to any other nucleotide or protein sequence in public databases. The ORF from AmCPV cDNA was expressed as a His-tagged fusion protein in E. coli and polyclonal antibody was raised against the purified protein. Immunoblot as well as immunofluorescence analysis with anti-NSP38 antibody showed that the protein was not present in polyhedra or uninfected cells but was present in AmCPV-infected host midgut cells. NSP38 was expressed in insect cells as soluble protein via a baculovirus expression vector and shown to possess the ability to bind poly(rI)–(rC) agarose, which was competitively removed by AmCPV viral RNA. These results indicate that NSP38 is expressed in virus-infected cells as a non-structural protein. By binding to viral RNA, it may play a role in the regulation of genomic RNA function and packaging.

scholarly journals Predicting the effects of the single nucleotide polymorphism A122V on CXC chemokine receptor type 1 of Bos taurus (Artiodactyla: Bovidae) cattle by in silico analyses

Biotemas ◽  
2017 ◽  
Vol 30 (4) ◽  
pp. 1-6 ◽  
Author(s):  
Anete Ferraz Guzzi ◽  
Felipe Santos de Luna Oliveira ◽  
Márcia Maria de Souza Amaro ◽  
Paulo Fernando Tavares Filho ◽  
Jane Eyre Gabriel
Keyword(s):  
In Silico ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Bos Taurus ◽  
Protein Secondary Structure ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Cxc Chemokine ◽  
In Silico Analyses

Este estudo objetivou predizer bioquimicamente as estruturas primárias e secundárias da proteína receptora de quimiocina CXCR1 bovina não polimórfica e polimórfica carreando o polimorfismo A122V por análises in silico. Duas sequências da proteína CXCR1 de bovino Bos taurus foram selecionadas a partir da base de dados de sequências de proteínas UniProtKB/Swiss-Prot: a) uma sequência não polimórfica (A7KWG0), contendo o aminoácido alanina (A) na posição 122, e b) uma sequência polimórfica, apresentando o aminoácido valina (V) na mesma posição. As estruturas primárias e secundárias da proteína foram preditas empregando os programas ProtParam e Chou & Fasman Protein Secondary Structure Prediction CFSSP. Diferenças nos parâmetros físicos e químicos não foram previstas a partir da estrutura primária de ambas as proteínas analisadas. A presença de um domínio helicoidal, situado nas posições 100 e 150, foi exclusivamente encontrada na proteína CXCR1 não polimórfica. Resíduos de aminoácidos de propriedades bioquímicas variáveis foram detectados na posição 122 na proteína CXCR1 dos ruminantes e humana, sugerindo que esse peptídeo é altamente polimórfico em vertebrados. Os resultados aqui descritos predizem diferenças no padrão da estrutura secundária da proteína CXCR1 bovina não polimórfica e polimórfica A122V empregando ferramentas de Bioinformática. 

scholarly journals Prediction and analysis of functional RNA structures within the integrative genomics viewer

2022 ◽  
Vol 4 (1) ◽  
Author(s):  
Warren B Rouse ◽  
Ryan J Andrews ◽  
Nicholas J Booher ◽  
Jibo Wang ◽  
Michael E Woodman ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Drug Targets ◽  
Rna Binding ◽  
Secondary Structure Prediction ◽  
Rna Binding Proteins ◽  
Published Data ◽  
Nucleotide Polymorphisms ◽  
Integrative Genomics

ABSTRACT In recent years, interest in RNA secondary structure has exploded due to its implications in almost all biological functions and its newly appreciated capacity as a therapeutic agent/target. This surge of interest has driven the development and adaptation of many computational and biochemical methods to discover novel, functional structures across the genome/transcriptome. To further enhance efforts to study RNA secondary structure, we have integrated the functional secondary structure prediction tool ScanFold, into IGV. This allows users to directly perform structure predictions and visualize results—in conjunction with probing data and other annotations—in one program. We illustrate the utility of this new tool by mapping the secondary structural landscape of the human MYC precursor mRNA. We leverage the power of vast ‘omics’ resources by comparing individually predicted structures with published data including: biochemical structure probing, RNA binding proteins, microRNA binding sites, RNA modifications, single nucleotide polymorphisms, and others that allow functional inferences to be made and aid in the discovery of potential drug targets. This new tool offers the RNA community an easy to use tool to find, analyze, and characterize RNA secondary structures in the context of all available data, in order to find those worthy of further analyses.

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