Trends in Bioinformatics最新文献_第5页

Signal Peptide Sequence Analysis of Selected Protein Sequences from Cryptosporidium parvum 小隐孢子虫选定蛋白序列的信号肽序列分析

Trends in Bioinformatics Pub Date : 1900-01-01 DOI: 10.3923/TB.2018.33.43

A. M. Yusof, Mohd Aiman Baru, M. Lokman

引用次数: 2

Molecular Docking Analysis of Phyto-Ligands with Multi Drug Resistant ß-lactamases of Staphylococcus aureus 金黄色葡萄球菌多重耐药ß-内酰胺酶与植物配体的分子对接分析

Trends in Bioinformatics Pub Date : 1900-01-01 DOI: 10.3923/TB.2011.23.34

P. Lakshmi, S. Radhika, A. Annamalai

引用次数: 7

SVM Model to Predict Human Death Domain Protein-Protein Interactions Based on Amino Acid Composition 基于氨基酸组成预测人类死亡结构域蛋白-蛋白相互作用的SVM模型

Trends in Bioinformatics Pub Date : 1900-01-01 DOI: 10.3923/TB.2015.14.25

Prakash A. Nemade, K. Pardasani

{"title":"SVM Model to Predict Human Death Domain Protein-Protein Interactions Based on Amino Acid Composition","authors":"Prakash A. Nemade, K. Pardasani","doi":"10.3923/TB.2015.14.25","DOIUrl":"https://doi.org/10.3923/TB.2015.14.25","url":null,"abstract":"Protein-Protein Interactions (PPIs) play crucial role in regulation of virtually all biological processes in any living system such as DNA transcription, replication, metabolic cycles and signaling cascades. The PPIs also play an important role in the complex process of cell death which occurs via apoptosis and necrosis in eukaryotic cells. The PPIs detection via high throughput experimental methods are time consuming, expensive and are generating huge amount of PPIs data. Therefore, there is need to develop computational methods to efficiently and accurately predict PPIs. This study attempts to develop computational model for predicting human death domain PPIs. First, the protein primary sequences are encoded into descriptors based on amino acid composition of proteins which are monomers of protein. Then, the support vector machine and sequential minimal optimization of WEKA tool is employed to classify interacting and non interacting protein pairs. The various kernel functions were evaluated to build the model and it is observed that libSVM with linear kernel is found to be the best on the basis of performance measures. The validation has been performed by 10 fold cross validation technique. The optimum model gives us the accuracy of 76.47% in predicting human death domain protein-protein interactions. Such models can be useful in providing PPI information of death domain proteins which can be useful in understanding the molecular mechanisms involved in death of cells taking place due to ageing, programmed cell death and various diseases.","PeriodicalId":164864,"journal":{"name":"Trends in Bioinformatics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116661714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mutational Analysis on Human Granulocyte Macrophage-Colony Stimulating Factor Stability Using Computational Approaches 基于计算方法的人粒细胞巨噬细胞集落刺激因子稳定性突变分析

Trends in Bioinformatics Pub Date : 1900-01-01 DOI: 10.3923/TB.2015.1.13

S. Vignesh, S. Narayanan, M. Sivanandha

{"title":"Mutational Analysis on Human Granulocyte Macrophage-Colony Stimulating Factor Stability Using Computational Approaches","authors":"S. Vignesh, S. Narayanan, M. Sivanandha","doi":"10.3923/TB.2015.1.13","DOIUrl":"https://doi.org/10.3923/TB.2015.1.13","url":null,"abstract":"Granulocyte Macrophage Colony Stimulating Factor (GM-CSF) is a 16.29 kDa cytokine that regulates the leukocyte production, migration and functions. The GM-CSF receptor ligand interaction stability plays vital role for prolonged differentiation of haematopoietic stem cell into granulocytes and monocytes. In the present investigation attempts were made to increase the number of stabilization centres in GM-CSF ligand using molecular simulation. This improves half-life stability of GM-CSF receptor ligand interaction complex. The numbers of stabilization centres were increased by amino-acid substitution which led to change in contact energy, hydrophobicity index and unfolding Gibbs free energy without altering receptor ligand interaction. Multiple sequence alignment of GM-CSF sequence using ClustalW with Ovies aries, Homo sapiens, Mus musculus and Gallus gallus species revealed the conserved domain regions and aminoacid dissimilarities in conserved and other regions. Based on the above, 21N, 25L, 42V, 55L, 56Q, 93E and 102T were mutated with its aminoacid substitution property. Different combinations of mutation were incorporated in the amino acid sequence and mutant proteins were modelled using structure of GM-CSF ligand (PDB ID: 1CSG) as a template by MODELLER. After mutation, the GLU21, LEU25, LEU55 and THR102 positions were identified as stability centre using SCide. Mutations at residues LEU55 and THR102 had 16.71% lesser energy value than the wild type GMCSF energy value which is 6831.73. The result suggested that, the stability of human GM-CSF has been increased (as the energy decreases) due to mutagenesis by computational tools.","PeriodicalId":164864,"journal":{"name":"Trends in Bioinformatics","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122809891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Study on the Conserved and the Polymorphic Sites of MTHFR Using Bioinformatics Approaches 利用生物信息学方法研究MTHFR的保守位点和多态性位点

Trends in Bioinformatics Pub Date : 1900-01-01 DOI: 10.3923/TB.2008.7.17

Muhummadh Khan, K. Jamil

引用次数: 13

Generation of 2D-QSAR Model for Angiogenin Inhibitors: A Ligand-Based Approach for Cancer Drug Design 血管生成素抑制剂2D-QSAR模型的生成:基于配体的抗癌药物设计方法

Trends in Bioinformatics Pub Date : 1900-01-01 DOI: 10.3923/TB.2016.1.13

J. C. Rosy, S. Balamurali, J. Mary, Rajaiah Shenbagara, K. Sundar

引用次数: 1

ArchaeVir: A Comprehensive Genometrics Database of Archaeal Viruses ArchaeVir:一个全面的古细菌病毒基因组数据库

Trends in Bioinformatics Pub Date : 1900-01-01 DOI: 10.3923/tb.2018.1.6

K. Ojha, Swapnil Mishra, Lalit Kumar Pand