Protein Secondary Structure Prediction
13 papers with code • 8 benchmarks • 1 datasets
Latest papers
PS4: a Next-Generation Dataset for Protein Single Sequence Secondary Structure Prediction
Protein secondary structure prediction is a subproblem of protein folding.
Convolutional ProteinUnetLM competitive with long short-term memory-based protein secondary structure predictors
In recent years, a new generation of algorithms for SS prediction based on embeddings from protein language models (pLMs) is emerging.
DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts
Here, we adapted this concept to the problem of protein sequence analysis, by developing DistilProtBert, a distilled version of the successful ProtBert model.
ProteinBERT: a universal deep-learning model of protein sequence and function
We introduce ProteinBERT, a deep language model specifically designed for proteins.
DLPAlign: A Deep Learning based Progressive Alignment Method for Multiple Protein Sequences
This paper proposed a novel and straightforward approach to improve the accuracy of progressive multiple protein sequence alignment method.
ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids.
Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction
In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction accuracy (88–90%), while only a few predict more than the 3 traditional Helix, Strand and Coil classes.
ProteinNet: a standardized data set for machine learning of protein structure
We have created the ProteinNet series of data sets to provide a standardized mechanism for training and assessing data-driven models of protein sequence-structure relationships.
High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures
In the spirit of reproducible research we make our data, models and code available, aiming to set a gold standard for purity of training and testing sets.
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.