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Specific interactions between ribosome recycling factor (RRF) and elongation factor-G (EFG) mediate disassembly of post-termination ribosomal complexes for new rounds of initiation. The interactions between RRF and EFG are also important in peptidyl-tRNA release from stalled pre-termination complexes. Unlike the post-termination complexes (harboring deacylated tRNA), the pre-termination complexes (harboring peptidyl-tRNA) are not recycled by RRF and EFG in vitro, suggesting participation of additional factor(s) in the process. Using a combination of biochemical and genetic approaches, we show that, (i) Inclusion of IF3 with RRF and EFG results in recycling of the pre-termination complexes; (ii) IF3 overexpression in Escherichia coli LJ14 rescues its temperature sensitive phenotype for RRF; (iii) Transduction of infC135 (which encodes a functionally compromised IF3) in E.coli LJ14 generates a ‘synthetic severe’ phenotype; (iv) The infC135 and frr1 (containing an insertion in the RRF gene promoter) alleles synergistically rescue a temperature sensitive mutation in peptidyl-tRNA hydrolase in E.coli; and (v) IF3 facilitates ribosome recycling by Thermus thermophilus RRF and E.coli EFG in vivo and in vitro. These lines of evidence clearly demonstrate the physiological importance of IF3 in the overall mechanism of ribosome recycling in E.coli.

Peptidyl-tRNA hydrolase catalyses the cleavage of the ester link between the peptide and the tRNA in peptidyl-tRNAs that, for various reasons, have dropped off the translating ribosomes. This enzyme from Mycobacterium tuberculosis has been crystallized in three related but distinct forms: P2 1 2 1 2 1 , unit-cell parameters a = 36.

Recently, Panama wilt disease that attacks banana leaves has caused enormous economic losses to farmers. Early detection of this disease and necessary preventive measures can avoid economic damage. This paper proposes an improved method to predict Panama wilt disease based on symptoms using an agro deep learning algorithm. The proposed deep learning model for detecting Panama wilts disease is essential because it can help accurately identify infected plants in a timely manner. It can be instrumental in large-scale agricultural operations where Panama wilts disease could spread quickly and cause significant crop loss. Additionally, deep learning models can be used to monitor the effectiveness of treatments and help farmers make informed decisions about how to manage the disease best. This method is designed to predict the severity of the disease and its consequences based on the arrangement of color and shape changes in banana leaves. The present proposed method is compared with its previous methods, and it achieved 91.56% accuracy, 91.61% precision, 88.56% recall and 81.56% F1-score.