JPred4: a protein secondary structure prediction server. [PDF]
Nucleic Acids Res, 2015 JPred
Drozdetskiy A +3 more
europepmc +13 more sources
Deep learning for protein secondary structure prediction: Pre and post-AlphaFold. [PDF]
Comput Struct Biotechnol J, 2022 This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure
Ismi DP, Pulungan R, Afiahayati.
europepmc +2 more sources
Discovering the Ultimate Limits of Protein Secondary Structure Prediction. [PDF]
Biomolecules, 2021 Secondary structure prediction (SSP) of proteins is an important structural biology technique with many applications. There have been ~300 algorithms published in the past seven decades with fierce competition in accuracy.
Ho CT, Huang YW, Chen TR, Lo CH, Lo WC.
europepmc +2 more sources
Protein secondary structure: Entropy, correlations and prediction [PDF]
Bioinformatics, 2003 Is protein secondary structure primarily determined by local interactions between residues closely spaced along the amino acid backbone, or by non-local tertiary interactions?
Brenner, Steven E., Crooks, Gavin E.
core +9 more sources
Multistage Combination Classifier Augmented Model for Protein Secondary Structure Prediction. [PDF]
Front Genet, 2022 In the field of bioinformatics, understanding protein secondary structure is very important for exploring diseases and finding new treatments. Considering that the physical experiment-based protein secondary structure prediction methods are time ...
Zhang X +6 more
europepmc +2 more sources
Ensemble deep learning models for protein secondary structure prediction using bidirectional temporal convolution and bidirectional long short-term memory. [PDF]
Front Bioeng Biotechnol, 2023 Protein secondary structure prediction (PSSP) is a challenging task in computational biology. However, existing models with deep architectures are not sufficient and comprehensive for deep long-range feature extraction of long sequences.
Yuan L, Ma Y, Liu Y.
europepmc +2 more sources
Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction. [PDF]
Biomolecules, 2022 The secondary structure of proteins is significant for studying the three-dimensional structure and functions of proteins. Several models from image understanding and natural language modeling have been successfully adapted in the protein sequence study ...
Guo Y, Wu J, Ma H, Wang S, Huang J.
europepmc +2 more sources
Protein Secondary Structure Prediction With a Reductive Deep Learning Method. [PDF]
Front Bioeng Biotechnol, 2021 Protein secondary structures have been identified as the links in the physical processes of primary sequences, typically random coils, folding into functional tertiary structures that enable proteins to involve a variety of biological events in life ...
Lyu Z, Wang Z, Luo F, Shuai J, Huang Y.
europepmc +2 more sources
Impact of Multi-Factor Features on Protein Secondary Structure Prediction. [PDF]
BiomoleculesProtein secondary structure prediction (PSSP) plays a crucial role in resolving protein functions and properties. Significant progress has been made in this field in recent years, and the use of a variety of protein-related features, including amino acid
Dong B +5 more
europepmc +2 more sources
A dynamic Bayesian network approach to protein secondary structure prediction [PDF]
BMC Bioinformatics, 2008 Background Protein secondary structure prediction method based on probabilistic models such as hidden Markov model (HMM) appeals to many because it provides meaningful information relevant to sequence-structure relationship.
Zhu Huaiqiu, Yao Xin-Qiu, She Zhen-Su
doaj +5 more sources