In silico prediction of type I PKS gene modules in nine lichenized fungi


TÜRKTAŞ ERKEN M., CANSARAN DUMAN D., Tanman U.

BIOTECHNOLOGY & BIOTECHNOLOGICAL EQUIPMENT, cilt.35, sa.1, ss.376-383, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35 Sayı: 1
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/13102818.2021.1879679
  • Dergi Adı: BIOTECHNOLOGY & BIOTECHNOLOGICAL EQUIPMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, CAB Abstracts, Compendex, Food Science & Technology Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Sayfa Sayıları: ss.376-383
  • Gazi Üniversitesi Adresli: Evet

Özet

The novel biologically active molecules could play a significant role in the treatment of human diseases. Natural products have been and continue to be a major source of pharmaceuticals, and lichen secondary metabolites emerge as never-ending potential for bioactive molecules with a variety of pharmacological activities. Polyketides, which are synthesized by enzymes encoded by PKS genes, constitute the major group of these secondary metabolites. To date, there is a lack of information about identification of PKS gene modules. Functional validation studies in lichens are difficult because of the slow growth rates of lichens, the symbiotic partners of lichens cannot be cultured in the laboratory or the fact that most of them cannot be grown in culture. Consequently, the importance of genomic mining approach is increasing as a unique tool for natural product discovery studies. Here, we bioinformatically investigated the type I PKS module candidates in nine publicly available lichen-forming fungi genomes through the use of the in silico screening tools. We also predicted putative secondary metabolites produced in these lichens which indicated the pharmaceutical potential of these nine lichen-forming fungi by bioinformatics tools.