It is found that divergent sequences, in the identity range of 25–55% provide the largest accuracy gain and that above 65% identity there is almost no advantage ...
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The effect of training a neural network secondary structure prediction algorithm with different types of multiple sequence alignment profiles derived from the ...
It is found that divergent sequences, in the identity range of 25–55% provide the largest accuracy gain and that above 65% identity there is almost no advantage ...
Abstract. Secondary structure prediction methods are widely used bioinformatics algorithms providing initial insights about protein struc-.
Analysis of the Effects of Multiple Sequence Alignments in Protein Secondary Structure Prediction. https://doi.org/10.1007/11532323_14 · Full text.
Oct 9, 2023 · Mainstream protein structure prediction pipelines rely heavily on co-evolution information extracted from multiple sequence alignments (MSAs).
In this paper, a multiple alignment algorithm is presented, explicitly designed for exploiting any available secondary-structure information. A layered ...
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This method uses co-evolution of alignment columns to predict the contact map of real protein sequences, for which accurate 3D structural information is known.
Using multiple sequence alignments as input the method has a prediction accuracy of 73.5%. Prediction of secondary structure by the SSPAL method is available ...
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Apr 7, 2021 · We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features.