scholarly journals In Silico Structure and Sequence Analysis of Bacterial Porins and Specific Diffusion Channels for Hydrophilic Molecules: Conservation, Multimericity and Multifunctionality

2016 ◽  
Vol 17 (4) ◽  
pp. 599 ◽  
Author(s):  
Hilde Vollan ◽  
Tone Tannæs ◽  
Gert Vriend ◽  
Geir Bukholm
Keyword(s):  
Sequence Analysis ◽  
In Silico ◽  
Bacterial Porins

In silico prediction of influenza vaccine effectiveness by sequence analysis

Vaccine ◽  
2021 ◽  
Vol 39 (7) ◽  
pp. 1030-1034
Author(s):  
Lirong Cao ◽  
Jingzhi Lou ◽  
Shi Zhao ◽  
Renee W.Y. Chan ◽  
Martin Chan ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Influenza Vaccine ◽  
In Silico ◽  
Vaccine Effectiveness ◽  
In Silico Prediction

scholarly journals In Silico Sequence Analysis Reveals New Characteristics of Fungal NADPH Oxidase Genes

Mycobiology ◽  
2014 ◽  
Vol 42 (3) ◽  
pp. 241-248 ◽  
Author(s):  
Nicolas Détry ◽  
Jaeyoung Choi ◽  
Hsiao-Che Kuo ◽  
Fred O. Asiegbu ◽  
Yong-Hwan Lee
Keyword(s):  
Sequence Analysis ◽  
Nadph Oxidase ◽  
In Silico

scholarly journals A Novel Approach to Single Cell RNA-Sequence Analysis Facilitates In Silico Gene Reporting of Human Pluripotent Stem Cell-Derived Retinal Cell Types

Stem Cells ◽  
2017 ◽  
Vol 36 (3) ◽  
pp. 313-324 ◽  
Author(s):  
M. Joseph Phillips ◽  
Peng Jiang ◽  
Sara Howden ◽  
Patrick Barney ◽  
Jee Min ◽  
...  
Keyword(s):  
Stem Cell ◽  
Sequence Analysis ◽  
Single Cell ◽  
In Silico ◽  
Pluripotent Stem Cell ◽  
Cell Types ◽  
Retinal Cell ◽  
Human Pluripotent Stem Cell ◽  
Rna Sequence ◽  
Novel Approach

scholarly journals Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes

2017 ◽  
Vol 8 (2) ◽  
pp. 87-95 ◽  
Author(s):  
Iain D. Kerr ◽  
Hannah C. Cox ◽  
Kelsey Moyes ◽  
Brent Evans ◽  
Brianna C. Burdett ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Cancer Risk ◽  
Protein Sequence ◽  
In Silico ◽  
Protein Sequence Analysis ◽  
Clinical Classification ◽  
Risk Genes

In silico Prediction and Designing of Potential siRNAs to be Used as Antivirals Against SARS-CoV-2

2021 ◽  
Vol 27 ◽  
Author(s):  
Sayed Sartaj Sohrab ◽  
Sherif Aly El-Kafrawy ◽  
Aymn T. Abbas ◽  
Leena H. Bajrai ◽  
Esam Ibraheem Azhar
Keyword(s):  
Genetic Diversity ◽  
Sequence Analysis ◽  
Phylogenetic Relationships ◽  
In Silico ◽  
Full Genome ◽  
In Silico Prediction ◽  
High Sequence Identity ◽  
Minor Parent ◽  
Sequence Identity ◽  
The City

Background:: The unusual pneumonia outbreak that originated in the city of Wuhan, China in December 2019 was found to be caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19. Methods:: In this work, we have performed an in silico design and prediction of potential siRNAs based on genetic diversity and recombination patterns, targeting various genes of SARS-CoV-2 for antiviral therapeutics. We performed extensive sequence analysis to analyze the genetic diversity and phylogenetic relationships, and to identify the possible source of virus reservoirs and recombination patterns, and the evolution of the virus as well as we designed the siRNAs which can be used as antivirals against SARS-CoV-2. Results:: The sequence analysis and phylogenetic relationships indicated high sequence identity and closed clusters with many types of coronavirus. In our analysis, the full-genome of SARS-CoV-2 showed the highest sequence (nucleotide) identity with SARS-bat-ZC45 (87.7%). The overall sequence identity ranged from 74.3% to 87.7% with selected SARS viruses. The recombination analysis indicated the bat SARS virus is a potential recombinant and serves as a major and minor parent. We have predicted 442 siRNAs and finally selected only 19 functional, and potential siRNAs. Conclusions:: The siRNAs were predicted and selected based on their greater potency and specificity. The predicted siRNAs need to be validated experimentally for their effective binding and antiviral activity.

scholarly journals In-silico investigation of umami peptides with receptor T1R1/T1R3 for the discovering potential targets: A combined modeling approach

2021 ◽  
Author(s):  
Wenli Wang ◽  
Zhiyong Cui ◽  
Menghua Ning ◽  
Tianxing Zhou ◽  
Yuan Liu
Keyword(s):  
Amino Acids ◽  
Molecular Docking ◽  
Sequence Analysis ◽  
In Silico ◽  
Quantum Chemical ◽  
Active Sites ◽  
Food Industry ◽  
Time Saving ◽  
Animal Growth ◽  
Key Residues

Umami, providing amino acids/peptides for animal growth, represents one of the major attractive taste modalities. The biochemical and umami properties of peptide are both important for scientific research and food industry. In this study, we did the sequence analysis of 205 umami peptides with 2-18 amino acids, sought the active sites of umami peptides by quantum chemical simulations and investigated their recognition residues with receptor T1R1/T1R3 by molecular docking. The results showed the peptides with 2-3 amino acids accounting for 44% of the total umami peptides. Residues D and E are the key active sites no matter where they in peptides (N-terminal, C-terminal or middle), when umami peptides contain D/E residues. N69, D147, R151, A170, S172, S276 and R277 residues in T1R1 receptor were deem to the key residues binding umami peptides. Finally, a powerful decision rule for umami peptides was proposed to predict potential umami peptides, which was convenient, time saving and efficiently.

scholarly journals Sibe: a computation tool to apply protein sequence statistics to folding and design

2018 ◽  
Author(s):  
Ngaam J. Cheung ◽  
Wookyung Yu
Keyword(s):  
Protein Folding ◽  
Sequence Analysis ◽  
Protein Design ◽  
Protein Sequence ◽  
In Silico ◽  
Rational Design ◽  
Sequence Data ◽  
Protein Sequence Analysis ◽  
Rational Protein Design ◽  
Interface Modules

ABSTRACTStatistical analysis plays a significant role in both protein sequences and structures, expanding in recent years from the studies of co-evolution guided single-site mutations to protein folding in silico. Here we describe a computational tool, termed Sibe, with a particular focus on protein sequence analysis, folding and design. Since Sibe has various easy-interface modules, expressive architecture and extensible codes, it is powerful in statistically analyzing sequence data and building energetic potentials in boosting both protein folding and design. In this study, Sibe is used to capture positionally conserved couplings between pairwise amino acids and help rational protein design, in which the pairwise couplings are filtered according to the relative entropy computed from the positional conservations and grouped into several ‘blocks’. A human β2-adrenergic receptor (β2AR) was used to demonstrated that those ‘blocks’ could contribute rational design at functional residues. In addition, Sibe provides protein folding modules based on both the positionally conserved couplings and well-established statistical potentials. Sibe provides various easy to use command-line interfaces in C++ and/or Python. Sibe was developed for compatibility with the ‘big data’ era, and it primarily focuses on protein sequence analysis, in silico folding and design, but it is also applicable to extend for other modeling and predictions of experimental measurements.

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