Abstract:
AIM AND OBJECTIVE:The rapid increase in the amount of protein sequence data available leads to an urgent need for novel computational algorithms to analyze and compare these sequences. This study is undertaken to develop an efficient computational approach for timely encoding protein sequences and extracting the hidden information. METHODS:Based on two physicochemical properties of amino acids, a protein primary sequence was converted into a three-letter sequence, and then a graph without loops and multiple edges and its geometric line adjacency matrix were obtained. A generalized PseAAC (pseudo amino acid composition) model was thus constructed to characterize a protein sequence numerically. RESULTS:By using the proposed mathematical descriptor of a protein sequence, similarity comparisons among β-globin proteins of 17 species and 72 spike proteins of coronaviruses were made, respectively. The resulting clusters agreed well with the established taxonomic groups. In addition, a generalized PseAAC based SVM (support vector machine) model was developed to identify DNA-binding proteins. Experiment results showed that our method performed better than DNAbinder, DNA-Prot, iDNA-Prot and enDNA-Prot by 3.29-10.44% in terms of ACC, 0.056-0.206 in terms of MCC, and 1.45-15.76% in terms of F1M. When the benchmark dataset was expanded with negative samples, the presented approach outperformed the four previous methods with improvement in the range of 2.49-19.12% in terms of ACC, 0.05-0.32 in terms of MCC, and 3.82- 33.85% in terms of F1M. CONCLUSION:These results suggested that the generalized PseAAC model was very efficient for comparison and analysis of protein sequences, and very competitive in identifying DNA-binding proteins.
journal_name
Comb Chem High Throughput Screenjournal_title
Combinatorial chemistry & high throughput screeningauthors
Li C,Zhao J,Wang C,Yao Ydoi
10.2174/1386207321666180130100838subject
Has Abstractpub_date
2018-01-01 00:00:00pages
100-110issue
2eissn
1386-2073issn
1875-5402pii
CCHTS-EPUB-88267journal_volume
21pub_type
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journal_title:Combinatorial chemistry & high throughput screening
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journal_title:Combinatorial chemistry & high throughput screening
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journal_title:Combinatorial chemistry & high throughput screening
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journal_title:Combinatorial chemistry & high throughput screening
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abstract::The National Institute of Mental Health (NIMH) Psychoactive Drug Screening Program (PDSP) is a resource that provides free screening of novel compounds to academic investigators. This program differs from other public-sector screening programs in that compounds are screened against a large panel of transmembrane recep...
journal_title:Combinatorial chemistry & high throughput screening
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journal_title:Combinatorial chemistry & high throughput screening
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更新日期:2013-06-28 00:00:00
abstract:Aims & Objective:Corona Virus Disease 2019 (COVID-19) caused by the human coronavirus 2019 (HCoV-19, also known as SARS-CoV-2) infection is currently in a global outbreak. COVID-19 has posed a huge threat to public health and economic stability worldwide. CR3022, a human monoclonal neutralizing antibody isolated from a...
journal_title:Combinatorial chemistry & high throughput screening
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更新日期:2020-10-26 00:00:00
abstract:AIM AND OBJECTIVE:To determine the mechanisms present in the etiopathogenesis of nasal polyposis. It is not clear whether amino acids contribute in a causal way to the development of the disease. Therefore, the aim of this study was to determine the plasma-free amino acid profile in patients with nasal polyposis and to...
journal_title:Combinatorial chemistry & high throughput screening
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更新日期:2019-01-01 00:00:00