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The profile-based fold recognition approach was first described by Bowie, Lüthy and David Eisenberg in 1991. [1] The term threading was first coined by David Jones, William R. Taylor and Janet Thornton in 1992, [2] and originally referred specifically to the use of a full 3-D structure atomic representation of the protein template in fold ...
The method of homology modeling is based on the observation that protein tertiary structure is better conserved than amino acid sequence. [3] Thus, even proteins that have diverged appreciably in sequence but still share detectable similarity will also share common structural properties, particularly the overall fold.
Generally scores below 0.20 corresponds to randomly chosen unrelated proteins whereas structures with a score higher than 0.5 assume roughly the same fold. [2] A quantitative study [3] shows that proteins of TM-score = 0.5 have a posterior probability of 37% in the same CATH topology family and of 13% in the same SCOP fold family. The ...
This type of method is also known as 3D-1D fold recognition due to its compatibility analysis between three-dimensional structures and linear protein sequences. This method has also given rise to methods performing an inverse folding search by evaluating the compatibility of a given structure with a large database of sequences, thus predicting ...
The aim of the PRISMA statement is to help authors improve the reporting of systematic reviews and meta-analyses. [3] PRISMA has mainly focused on systematic reviews and meta-analysis of randomized trials, but it can also be used as a basis for reporting reviews of other types of research (e.g., diagnostic studies, observational studies).
The term "ultra-deep" can sometimes also refer to higher coverage (>100-fold), which allows for detection of sequence variants in mixed populations. [ 5 ] [ 6 ] [ 7 ] In the extreme, error-corrected sequencing approaches such as Maximum-Depth Sequencing can make it so that coverage of a given region approaches the throughput of a sequencing ...
This is the aim of multiple factor analysis which balances the different issues (i.e. the different groups of variables) within a global analysis and provides, beyond the classical results of factorial analysis (mainly graphics of individuals and of categories), several results (indicators and graphics) specific of the group structure.
The chart portion of the forest plot will be on the right hand side and will indicate the mean difference in effect between the test and control groups in the studies. A more precise rendering of the data shows up in number form in the text of each line, while a somewhat less precise graphic representation shows up in chart form on the right.