Protein Secondary Structure Prediction
13 papers with code • 8 benchmarks • 1 datasets
Protein secondary structure prediction is a vital task in bioinformatics, aiming to determine the arrangement of amino acids in proteins, including α-helices, β-sheets, and coils. By analyzing amino acid sequences, computational algorithms and machine learning techniques predict these structural elements. This knowledge is crucial for understanding protein function and interactions. While progress has been made, challenges remain, especially with non-local interactions and low sequence homology. Advancements in machine learning hold promise for improving prediction accuracy, furthering our understanding of protein biology.
Benchmarks
These leaderboards are used to track progress in Protein Secondary Structure Prediction
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
PS4
Most implemented papers
High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures
idrori/cu-ssp
•
•
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.
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.
ProteinBERT: a universal deep-learning model of protein sequence and function
nadavbra/protein_bert
•
•
Bioinformatics, Volume 38, Issue 8 2022
We introduce ProteinBERT, a deep language model specifically designed for proteins.
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction
LucaAngioloni/ProteinSecondaryStructure-CNN
•
•
Here we present a new supervised generative stochastic network (GSN) based method to predict local secondary structure with deep hierarchical representations.
Protein secondary structure prediction using deep convolutional neural fields
LucaAngioloni/ProteinSecondaryStructure-CNN
•
•
Protein secondary structure (SS) prediction is important for studying protein structure and function.
Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks
Inspired by the recent successes of deep neural networks, in this paper, we propose an end-to-end deep network that predicts protein secondary structures from integrated local and global contextual features.
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.
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.
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.
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.
