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We developed a new approach for detecting sHSPs and delineating ACDs based on an iterative Hidden Markov Model algorithm using a multiple alignment profile generated from structural data on ACD.
Multiple alignment profile of CpARF proteins obtained with ClustalW program.
Since multiple alignment profile scores provide a major contribution to the prediction, it remains reasonably reliable even without the missing 3-D information of LPH.
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Thus, it still remains difficult to build a relevant multiple alignment or profile to identify conserved residues or motifs for this region.
The sensitivity of these methods derives from comparing a multiple sequence alignment profile built from query homologs to similarly derived profiles of existing structures.
To further understand the sequence and structure affinities of toxin domains, we also used their multiple alignments in profile-profile comparisons with the HHpred program to recover distant homologs and determine their protein fold.
The functionality of a putative polypeptide is predicted by matching against nonredundant databases of protein sequences, motifs, and family; this is because proteins act as better templates for functional annotation implementing multiple-sequence alignment, profile, HMM generation, phylogenetic analysis, domains, and motif analysis.
Multiple sequence alignment profiles were created using the Muscle sequence comparison tool from Embl-EBI (Edgar, 2004a, 2004b).
Following the above description of the algorithm, we provide a summary of all parameters that can influence the PHOG-BLAST score between any two multiple alignments: (i) Profile clusters.
Because the profile for H2 had the most significant alignments to the other profiles, as measured by COMPASS E-values, it was used to create a seeded multiple alignment for the profiles similar to a PSI-BLAST [13] alignment.
Picasso [ 10] uses multiple alignments as profiles which are then merged hierarchically.
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