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Literature DB >> 8481825

Classification of protein sequences by their dipeptide composition.

Abstract

A simple approach to scan quickly a large protein sequence database for homology is described. The approach used is strictly dependent on the database organization. A database has been compiled in which protein sequences are grouped into families of closely related proteins, each family being characterized by its average dipeptide composition. A new entry in the database can be allocated in a family by comparing its dipeptide composition with the average dipeptide composition of the families.

Entities: Chemical

Mesh：

Substances：
Dipeptides

Year: 1993 PMID： 8481825 DOI： 10.1093/bioinformatics/9.2.205

Source DB: PubMed Journal: Comput Appl Biosci ISSN： 0266-7061

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Cited

13 in total

1. Sequence similarities of protein kinase peptide substrates and inhibitors: comparison of their primary structures with immunoglobulin repeats.

Authors: J Kubrycht; J Borecký; K Sigler
Journal: Folia Microbiol (Praha) Date: 2002 Impact factor: 2.099

Classification of protein sequences by their dipeptide composition.

1. Sequence similarities of protein kinase peptide substrates and inhibitors: comparison of their primary structures with immunoglobulin repeats.

2. Selfish operons: horizontal transfer may drive the evolution of gene clusters.

3. Fold homology detection using sequence fragment composition profiles of proteins.

4. Gm-PLoc: A Subcellular Localization Model of Multi-Label Protein Based on GAN and DeepFM.

5. dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides.

6. In Silico Approach for Prediction of Antifungal Peptides.

7. A novel alignment-free method for comparing transcription factor binding site motifs.

8. Interpreting genomic data via entropic dissection.

9. In silico approaches for designing highly effective cell penetrating peptides.

10. Computational approach for designing tumor homing peptides.