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The ligands of the top ranked templates are used as template ligands for searching against compound library.
Similar as in FINDSITEfilt, the ligands of the top ranked templates are used as reference small molecules for ligand based virtual screening, where the appropriate template ligands are obtained from the DrugBank [48] and ChEMBL [49] databases.
It ranks template structures and selects the most likely candidates and estimates the reliability of the obtained lowest energy model.
To identify and rank templates, we used profile profile alignments, profile sequence alignments and a machine learning approach (Cheng and Baldi, 2006).
This is to say that for easy targets, MULTICOM-CMFR (or MULTICOM-RANK) used only PSI-BLAST (or PSI-BLAST and then hhsearch) to identify and rank templates according to E-value as in MULTICOM-CLUSTER.
To speed-up, our method first aligns a target to all the templates using only the local alignment potential and then ranks all the templates using both the local and global alignment potentials described in Section 2. After ranking, only the first-ranked templates are used to build a 3D model by MODELLER for each target.
CNFpred first generates target template alignments and then ranks the templates using a neural network, which predicts the quality (TM-score) of an alignment.
Experimental results on the 1000 × 6000 threading set and the CASP10 set indicate that the pairwise potential indeed improves template selection, as shown in Figures 6 and 7. On the 1000 × 6000 set, the average TM-score increases from 0.547 to 0.566 when the pairwise potential is used to rank the templates.
Continuous segments are then excised from the top-ranked template structures following the query-template alignments, and these are reassembled into full-length atomic models by Monte Carlo simulations.
After entering the loop sequence and selecting the type of secondary structures that flank the loop and the classification on which to perform the search, the user receives a list of potential templates ranked by sequence score.
Templates are ranked by their SP-scores and the ligands corresponding to the top 100 templates are selected as template ligands for ligand virtual screening.
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