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This contradiction happens when domains are of a different predictability, e.g. one has a close template, but the other one does not.
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When close templates are identified, high-resolution models could be built by the template-based methods.
Some targets are easy to predict, having very close templates among known structures, while other targets are quite challenging.
Conversely, a protein without close templates in PDB, which may require contact prediction, usually has few sequence homologs even if millions of protein sequences are now available.
The homology-based methods can have a high accuracy of predictions when close templates are identified, but the accuracy decreases sharply when the sequence identity of target and template is low (e.g. <30%).
A two-level strategy could be designed: in each region, a dictionary of templates of incompatible folding patterns would be collected and matched in a way or another using rare architectural information, while individual subjects would be aligned using diffeomorphisms to the closest template.
The presented results show that predicted structures can be successfully used for virtual ligand screening, and by exploiting the ideas of LHM, diverse novel small molecule binders can be identified even when the closest template is distantly related to the protein target of interest.
The original implementation used kmer searching with 7-mers to identify the closest template sequence in the reference database.
To understand the structural and molecular conformational differences between the members of the rice ALDH protein superfamily as well as their protein-protein interaction characteristics and ligand-protein interaction properties, the 21 deduced ALDH protein sequences were modeled using the top ten PDB closed template structures by I-Tasser [62].
Sequences that had >50% identity to the sequence of the closest template were selected.
The D2 domain of ClpA (PDB entry 1R6B; Xia et al., 2004) was picked as the closest template structure.
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