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Prediction of protein structure [PDF]

open access: yesCurrent Biology, 2000
Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.Nucleic Acids Res 1997, 25:3389-3402Bowie JU, Eisenberg D: Inverted protein structure prediction.
openaire   +2 more sources

Improved protein structure prediction using predicted interresidue orientations

open access: yesProceedings of the National Academy of Sciences of the United States of America, 2019
Significance Protein structure prediction is a longstanding challenge in computational biology. Through extension of deep learning-based prediction to interresidue orientations in addition to distances, and the development of a constrained optimization ...
Jianyi Yang   +5 more
semanticscholar   +1 more source

High‐accuracy protein structure prediction in CASP14

open access: yesProteins: Structure, Function, and Bioinformatics, 2021
The application of state‐of‐the‐art deep‐learning approaches to the protein modeling problem has expanded the “high‐accuracy” category in CASP14 to encompass all targets.
J. Pereira   +5 more
semanticscholar   +1 more source

Enhancing fragment-based protein structure prediction by customising fragment cardinality according to local secondary structure

open access: yesBMC Bioinformatics, 2020
Background Whenever suitable template structures are not available, usage of fragment-based protein structure prediction becomes the only practical alternative as pure ab initio techniques require massive computational resources even for very small ...
Jad Abbass, Jean-Christophe Nebel
doaj   +1 more source

Single-sequence protein structure prediction using supervised transformer protein language models

open access: yesbioRxiv, 2022
It remains challenging for single-sequence protein structure prediction with AlphaFold2 and other deep learning methods. In this work, we introduce trRosettaX-Single, a novel algorithm for singlesequence protein structure prediction.
Wenkai Wang, Zhenling Peng, Jianyi Yang
semanticscholar   +1 more source

PS4: a next-generation dataset for protein single-sequence secondary structure prediction

open access: yesBioTechniques, 2023
Protein secondary structure prediction is a subproblem of protein folding. A light-weight algorithm capable of accurately predicting secondary structure from only the protein residue sequence could provide useful input for tertiary structure prediction ...
Omar Peracha
doaj   +1 more source

Peptide-binding specificity prediction using fine-tuned protein structure prediction networks

open access: yesbioRxiv, 2022
Peptide binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone,
Amir Motmaen   +5 more
semanticscholar   +1 more source

Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning

open access: yesMolecules, 2023
Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network ...
Matic Broz, Marko Jukič, Urban Bren
doaj   +1 more source

Recent Progress of Protein Tertiary Structure Prediction

open access: yesMolecules
The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades.
Qiqige Wuyun   +7 more
doaj   +1 more source

The MULTICOM toolbox for protein structure prediction

open access: yesBMC Bioinformatics, 2012
Background As genome sequencing is becoming routine in biomedical research, the total number of protein sequences is increasing exponentially, recently reaching over 108 million. However, only a tiny portion of these proteins (i.e.
Cheng Jianlin   +4 more
doaj   +1 more source

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