Domain Architecture Based Methods for Comparative Functional Genomics Toward Therapeutic Drug Target Discovery.

Co-occurrence networks Domain architecture and evolution Domains’ Versatility index Regulatory network Therapeutic targets

Journal

Journal of molecular evolution
ISSN: 1432-1432
Titre abrégé: J Mol Evol
Pays: Germany
ID NLM: 0360051

Informations de publication

Date de publication:
Oct 2023
Historique:
received: 13 08 2022
accepted: 06 08 2023
pubmed: 26 8 2023
medline: 26 8 2023
entrez: 25 8 2023
Statut: ppublish

Résumé

Genes duplicate, mutate, recombine, fuse or fission to produce new genes, or when genes are formed from de novo, novel functions arise during evolution. Researchers have tried to quantify the causes of these molecular diversification processes to know how these genes increase molecular complexity over a period of time, for instance protein domain organization. In contrast to global sequence similarity, protein domain architectures can capture key structural and functional characteristics, making them better proxies for describing functional equivalence. In Prokaryotes and eukaryotes it has proven that, domain designs are retained over significant evolutionary distances. Protein domain architectures are now being utilized to categorize and distinguish evolutionarily related proteins and find homologs among species that are evolutionarily distant from one another. Additionally, structural information stored in domain structures has accelerated homology identification and sequence search methods. Tools for functional protein annotation have been developed to discover, protein domain content, domain order, domain recurrence, and domain position as all these contribute to the prediction of protein functional accuracy. In this review, an attempt is made to summarise facts and speculations regarding the use of protein domain architecture and modularity to identify possible therapeutic targets among cellular activities based on the understanding their linked biological processes.

Identifiants

pubmed: 37626222
doi: 10.1007/s00239-023-10129-w
pii: 10.1007/s00239-023-10129-w
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

598-615

Informations de copyright

© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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Auteurs

Pavan Gollapalli (P)

Center for Bioinformatics and Biostatistics, Nitte (Deemed to be University), Mangalore, Karnataka, 575018, India.

Sushmitha Rudrappa (S)

Department of Biotechnology and Bioinformatics, Jnana Sahyadri Campus, Kuvempu University, Shankaraghatta, Shivamogga, Karnataka, 577451, India.

Vadlapudi Kumar (V)

Department of Biochemistry, Davangere University, Shivagangothri, Davangere, Karnataka, 577007, India.

Hulikal Shivashankara Santosh Kumar (HS)

Department of Biotechnology and Bioinformatics, Jnana Sahyadri Campus, Kuvempu University, Shankaraghatta, Shivamogga, Karnataka, 577451, India. sk.genesan@gmail.com.

Classifications MeSH