Computational Framework for Generating Synthetic Signal Peptides

被引：2

作者：

Johnsten, Tom ^{[1
]}

Prakash, Aishwarya ^{[2
,3
]}

Daly, Grant T. ^{[2
]}

Benton, Ryan G. ^{[1
]}

Clark, Tristan ^{[1
]}

机构：

[1] Univ S Alabama, Comp Sci, Mobile, AL 36688 USA

[2] Univ S Alabama, Dept Pharmacol, Mobile, AL USA

[3] Univ S Alabama, Mitchell Canc Inst, Mobile, AL USA

来源：

13TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND HEALTH INFORMATICS, BCB 2022 | 2022年

关键词：

Signal peptides; Targeting Sequences; Eukaryotic; Mitochondria; Multi-Layer Vector Space Model; Amino Acids; Building Blocks;

D O I：

10.1145/3535508.3545530

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We have developed a computational framework for constructing synthetic signal peptides from a base set of protein sequences. A large number of structured "building blocks", represented as m-step ordered pairs of amino acids, are extracted from the base sequences. Using a straightforward procedure, the building blocks enable the construction of a diverse set of synthetic signal peptides and targeting sequences that have the potential for industrial and therapeutic purposes. We have validated the proposed framework using several state-of-the-art sequence prediction platforms such as Signal-BLAST, SignalP-5.0, MULocDeep, and DeepMito. Experimental results show the computational framework can successfully generate synthetic signal peptides and targeting sequences and transform non-signaling sequences into synthetic signal peptides.

引用

页数：7

共 15 条

[1]

Akkoc Can, 2011, Proceedings of the ISCA 3rd International Conference on Bioinformatics and Computational Biology, P160

[2]

[Anonymous], SignalP-5.0 Signal peptide and cleavage sites in gram+, gramand eukaryotic amino acid sequences

[3] SignalP 5.0 improves signal peptide predictions using deep neural networks [J].