Crystallization of alcohol oxidase fromPichia pastoris. Secondary structure predictions indicate a domain with the eightfold ?/?-barrel fold

1990 ◽  
Vol 9 (1) ◽  
pp. 83-86 ◽  
Author(s):  
Ewa Tykarska ◽  
Lukasz Lebioda ◽  
Elzbieta Marchut ◽  
Janusz Steczko ◽  
Boguslaw Stec
Keyword(s):  
Secondary Structure ◽  
Alcohol Oxidase ◽  
Secondary Structure Predictions

Analysis of X-Ray Scattering by Human AA Fibrils Using Secondary Structure Predictions of Human SAA1

Amyloidosis ◽  
1986 ◽  
pp. 49-55 ◽  
Author(s):  
W. G. Turnell ◽  
R. Sarra ◽  
I. D. Glover ◽  
J. O. Baum ◽  
D. Caspi ◽  
...  
Keyword(s):  
Secondary Structure ◽  
X Ray ◽  
X Ray Scattering ◽  
Secondary Structure Predictions ◽  
Ray Scattering

A sequence-based approach for identifying protein fold switchers

2018 ◽  
Author(s):  
Soumya Mishra ◽  
Loren L. Looger ◽  
Lauren L. Porter
Keyword(s):  
Secondary Structure ◽  
Drug Targets ◽  
Prediction Method ◽  
Data Bank ◽  
Amino Acid Sequences ◽  
Protein Fold ◽  
Secondary Structure Predictions ◽  
Underlying Mechanisms ◽  
Cellular Control ◽  
Potential Drug Targets

AbstractAlthough most proteins conform to the classical one-structure/one-function paradigm, an increasing number of proteins with dual structures and functions are emerging. These fold-switching proteins remodel their secondary structures in response to cellular stimuli, fostering multi-functionality and tight cellular control. Accurate predictions of fold-switching proteins could both suggest underlying mechanisms for uncharacterized biological processes and reveal potential drug targets. Previously, we developed a prediction method for fold-switching proteins based on secondary structure predictions and structure-based thermodynamic calculations. Given the large number of genomic sequences without homologous experimentally characterized structures, however, we sought to predict fold-switching proteins from their sequences alone. To do this, we leveraged state-of-the-art secondary structure predictions, which require only amino acid sequences but are not currently designed to identify structural duality in proteins. Thus, we hypothesized that incorrect and inconsistent secondary structure predictions could be good initial predictors of fold-switching proteins. We found that secondary structure predictions of fold-switching proteins with solved structures are indeed less accurate than secondary structure predictions of non-fold-switching proteins with solved structures. These inaccuracies result largely from the conformations of fold-switching proteins that are underrepresented in the Protein Data Bank (PDB), and, consequently, the training sets of secondary structure predictors. Given that secondary structure predictions are homology-based, we hypothesized that decontextualizing the inaccurately-predicted regions of fold-switching proteins could weaken the homology relationships between these regions and their overpopulated structural representatives. Thus, we reran secondary structure predictions on these regions in isolation and found that they were significantly more inconsistent than in regions of non-fold-switching proteins. Thus, inconsistent secondary structure predictions can serve as a preliminary marker of fold switching. These findings have implications for genomics and the future development of secondary structure predictors.

scholarly journals OPUS-TASS: a protein backbone torsion angles and secondary structure predictor based on ensemble neural networks

2020 ◽  
Vol 36 (20) ◽  
pp. 5021-5026 ◽  
Author(s):  
Gang Xu ◽  
Qinghua Wang ◽  
Jianpeng Ma
Keyword(s):  
Neural Networks ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Supplementary Information ◽  
Learning Approaches ◽  
Protein Backbone ◽  
Torsion Angles ◽  
Secondary Structure Predictions ◽  
The Mean

Abstract Motivation Predictions of protein backbone torsion angles (ϕ and ψ) and secondary structure from sequence are crucial subproblems in protein structure prediction. With the development of deep learning approaches, their accuracies have been significantly improved. To capture the long-range interactions, most studies integrate bidirectional recurrent neural networks into their models. In this study, we introduce and modify a recently proposed architecture named Transformer to capture the interactions between the two residues theoretically with arbitrary distance. Moreover, we take advantage of multitask learning to improve the generalization of neural network by introducing related tasks into the training process. Similar to many previous studies, OPUS-TASS uses an ensemble of models and achieves better results. Results OPUS-TASS uses the same training and validation sets as SPOT-1D. We compare the performance of OPUS-TASS and SPOT-1D on TEST2016 (1213 proteins) and TEST2018 (250 proteins) proposed in the SPOT-1D paper, CASP12 (55 proteins), CASP13 (32 proteins) and CASP-FM (56 proteins) proposed in the SAINT paper, and a recently released PDB structure collection from CAMEO (93 proteins) named as CAMEO93. On these six test sets, OPUS-TASS achieves consistent improvements in both backbone torsion angles prediction and secondary structure prediction. On CAMEO93, SPOT-1D achieves the mean absolute errors of 16.89 and 23.02 for ϕ and ψ predictions, respectively, and the accuracies for 3- and 8-state secondary structure predictions are 87.72 and 77.15%, respectively. In comparison, OPUS-TASS achieves 16.56 and 22.56 for ϕ and ψ predictions, and 89.06 and 78.87% for 3- and 8-state secondary structure predictions, respectively. In particular, after using our torsion angles refinement method OPUS-Refine as the post-processing procedure for OPUS-TASS, the mean absolute errors for final ϕ and ψ predictions are further decreased to 16.28 and 21.98, respectively. Availability and implementation The training and the inference codes of OPUS-TASS and its data are available at https://github.com/thuxugang/opus_tass. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Inaccurate secondary structure predictions often indicate protein fold switching

2019 ◽  
Vol 28 (8) ◽  
pp. 1487-1493 ◽  
Author(s):  
Soumya Mishra ◽  
Loren L. Looger ◽  
Lauren L. Porter
Keyword(s):  
Secondary Structure ◽  
Protein Fold ◽  
Secondary Structure Predictions

Getting into mitochondria

2016 ◽  
Vol 473 (21) ◽  
pp. 3755-3758 ◽  
Author(s):  
Ján A. Miernyk
Keyword(s):  
Secondary Structure ◽  
Glutamate Dehydrogenase ◽  
Protein Import ◽  
Terminal Region ◽  
Nuclear Genes ◽  
Cleavage Sites ◽  
Mitochondrial Targeting ◽  
Limited Ability ◽  
Secondary Structure Predictions ◽  
High Matrix

The human mitochondrial glutamate dehydrogenase isoenzymes (hGDH1 and hGDH2) are abundant matrix-localized proteins encoded by nuclear genes. The proteins are synthesized in the cytoplasm, with an atypically long N-terminal mitochondrial targeting sequence (MTS). The results of secondary structure predictions suggest the presence of two α-helices within the N-terminal region of the MTS. Results from deletion analyses indicate that individual helices have limited ability to direct protein import and matrix localization, but that there is a synergistic interaction when both helices are present [Biochem. J. (2016) 473, 2813–2829]. Mutagenesis of the MTS cleavage sites blocked post-import removal of the presequences, but did not impede import. The authors propose that the high matrix levels of hGDH can be attributed to the unusual length and secondary structure of the MTS.

scholarly journals Novel Polybasic Cleavage Site in SARS-CoV-2 Genome Is Likely to Induce a Major Change in the RNA Secondary Structure

2020 ◽  
Author(s):  
Amirhossein Manzourolajdad ◽  
Zhenming Xu ◽  
Diako Ebrahimi
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Major Change ◽  
Receptor Protein ◽  
Base Sequence ◽  
The Novel ◽  
Viral Receptor ◽  
Secondary Structure Predictions ◽  
Potential Impact ◽  
Host Cell Membranes

Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) has claimed nearly 180,000 lives and continues to spread. There are currently no approved medications or vaccines for this new coronavirus. Studies have shown that the positive RNA genome of SARS-CoV-2 contains unique features, including a 12-base sequence inserted between the two subunits of viral receptor protein Spike. This inserted sequence facilitates the cleavage of Spike by the cellular proteases Furin and TMPRSS2, leading to the fusion of virus and host cell membranes. Current studies are mostly focused on the SARS-CoV-2 Spike protein and its interacting cellular proteins ACE2, Furin, and TMPRSS2. RNA structural studies are limited and little is known about the potential impact of the 12-base sequence insert on the secondary structure of SARS-CoV-2 genomic RNA and/or its transcripts. Here, by using local and global RNA secondary structure predictions, we show that the novel 12-base insert of SARS-CoV-2 genome likely induces a major RNA secondary structure change.

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