secondary structure


Evaluate the accuracy of a profile-based predictor as a function of the max cosine similarity between training and test profiles.

Brewery: Deep Learning and deeper profiles for the prediction of 1D protein structure annotations

Motivation: Protein Structural Annotations are essential abstractions to deal with the prediction of Protein Structures. Many increasingly sophisticated Protein Structural Annotations have been devised in the last few decades. However the need for …

Deep learning methods in protein structure prediction

Protein Structure Prediction is a central topic in Structural Bioinformatics. Since the ’60s statistical methods, followed by increasingly complex Machine Learning and recently Deep Learning methods, have been employed to predict protein structural …


State-of-the-art ab initio prediction of 1D protein structure annotations

Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction

Protein Secondary Structure prediction has been a central topic of research in Bioinformatics for decades. In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction …

Protein Structure Annotations

This chapter aims to introduce to the specifics of protein structure annotations and their fundamental position in structural bioinformatics, bioinformatics in general. Proteins are profoundly characterised by their structure in every aspect of their …

Porter 5: fast, state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes

Motivation: Although secondary structure predictors have been developed for decades, current ab initio methods have still some way to go to reach their theoretical limits. Moreover, the continuous effort towards harnessing ever expanding data sets …


State-of-the-art ab initio prediction of protein secondary structure