Characterization of the tracrARN-DRARN genetic complex associated with the CRISPR-Cas9 system of the phytosymbiont Acholeplasma palmae: biotechnological interest

The CRISPR-Cas9 technology used in plant biotechnology is based on the use of Cas9 endonucleases to generate precise cuts in the genome, and a duplex consisting of a trans-activating CRISPR RNA (tracrRNA) and a CRISPR RNA (DRRNA) which are precursors of guide RNA (sgRNA) commercially redesigned (sgRNA-Cas9) to guide gene cleavage. Most of these tools come from clinical bacteria. However, there are several CRISPR-Cas9 systems in environmental microorganisms such as phytoendosymbionts of plants of the genus Acholeplasma. But the exploitation of these systems more compatible with plants requires using bioinformatics tools for prediction and study. We identified and characterized the elements associated with the duplex in the genome of A. palmae. For this, the protein information was obtained from the Protein Data Bank and the genomics from GenBank/NCBI. The CRISPR system was studied with the CRISPRfinder software. Alignment algorithms and NUPACK software were used to identify the tracrRNA and DRRNA modules, together with various computational software for genetic, structural and biophysical characterization. A CRISPR-Cas system was found in A. palmae with type II-C characteristics, as well as a thermodynamically very stable duplex, with flexible regions, exhibiting a docking power with Cas9 thermodynamically favored. These results are desirable in programmable gene editing systems and show the possibility of exploring native molecular tools in environmental microorganisms applicable to the genetic manipulation of plants, as more research is carried out. This study represents the first report on the thermodynamic stability and molecular docking of elements associated with the tracrRNA-DRRNA duplex in the phytosymbiont A. palmae.

An evaluation of combined strategies for improving the performance of molecular docking

Molecular docking is a fast and efficient computational method for the prediction of the binding mode and binding affinity between a ligand and a target protein at the atomic level. However, the performance of current docking programs is less than satisfactory. Herein, with a focus on free programs and scoring functions, the performances of LeDock and three standalone scoring functions were tested by 195 high-quality protein–ligand complexes. Results showed that the success rate for the best pose of the free available docking program LeDock achieved 89.20%, indicative of a strong sampling power. Based on the poses generated by LeDock, a comparative evaluation on other three non-commercial scoring functions, including DSX (DrugScore X), PoseScore and X-score was performed. Among all the evaluated scoring functions, DSX and X-score exhibited the best scoring power and ranking power, respectively. The performances of LeDock, DSX and X-score were similar in docking power test, which was much better than the PoseScore. Accordingly, it was suggested that the combination of pose sampling by LeDock with rescoring by DSX or X-score could improve the prediction accuracy of molecular docking and applied in the lead discovery.

MCSS-based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

Computational fragment-based approaches are widely used in drug design and drug discovery. One of the limitations of their application is the lack of performance of docking methods, mainly the scoring functions. With the emergence of new fragment-based approaches for single-stranded RNA ligands, we propose an analysis of an MCSS-based approach evaluated for its docking power on nucleotide-binding sites. Hybrid solvent models based on some partial explicit representation are shown to improve docking and screening powers. Clustering of the n best-ranked poses can also contribute to a lesser extent to better performance. The results suggest that we can apply the approach to the fragment-based design of sequence-selective oligonucleotides.