English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Algorithms that detect covariance between pairs of columns in multiplesequence alignments are commonly employed to predict functionally importantresidues and structural contacts. However, the assumption that co-variance onlyoccurs between individual residues in the protein is more driven bycomputational convenience rather than fundamental protein architecture. Here wedevelop a novel algorithm that defines a covariance score across two groups ofcolumns where each group represents a stretch of contiguous columns in thealignment. We define a test set that consists of secondary structure elements({\alpha}-helixes and {\beta}-strands) across more than 1,100 PFAM families.Using these alignments to predict segments that are physically close instructure, we show that our method substantially out-performs approaches thataggregate the results of algorithms that operate on individual column pairs.Our approach demonstrates that considering units of proteins beyond pairs ofcolumns can improve the power and utility of covariance algorithms.

Covariance in protein multiple sequence alignments using groups of columns