The ‘30K’ superfamily of viral movement proteins

Microbiology ◽  
2000 ◽  
Vol 81 (1) ◽  
pp. 257-266 ◽  
Author(s):  
Ulrich Melcher
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Consensus Sequence ◽  
Amino Acid Sequences ◽  
Movement Proteins ◽  
Consensus Sequences ◽  
Viral Movement ◽  
Secondary Structure Predictions ◽  
Α Helix

Relationships among the amino acid sequences of viral movement proteins related to the 30 kDa (‘30K’) movement protein of tobacco mosaic virus – the 30K superfamily – were explored. Sequences were grouped into 18 families. A comparison of secondary structure predictions for each family revealed a common predicted core structure flanked by variable N- and C-terminal domains. The core consisted of a series of β-elements flanked by an α-helix on each end. Consensus sequences for each of the families were generated and aligned with one another. From this alignment an overall secondary structure prediction was generated and a consensus sequence that can recognize each family in database searches was obtained. The analysis led to criteria that were used to evaluate other virus-encoded proteins for possible membership of the 30K superfamily. A rhabdoviral and a tenuiviral protein were identified as 30K superfamily members, as were plant-encoded phloem proteins. Parsimony analysis grouped tubule-forming movement proteins separate from others. Establishment of the alignment of residues of diverse families facilitates comparison of mutagenesis experiments done on different movement proteins and should serve as a guide for further such experiments.

scholarly journals Use of molecular mechanics for secondary structure prediction. Is it possible to reveal α-helix?

FEBS Letters ◽  
2001 ◽  
Vol 510 (1-2) ◽  
pp. 13-16 ◽  
Author(s):  
Gelena T Kilosanidze ◽  
Alexey S Kutsenko ◽  
Natalia G Esipova ◽  
Vladimir G Tumanyan
Keyword(s):  
Secondary Structure ◽  
Molecular Mechanics ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Α Helix

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 A method for predicting evolved fold switchers exclusively from their sequences

2020 ◽  
Author(s):  
Allen K. Kim ◽  
Loren L. Looger ◽  
Lauren L. Porter
Keyword(s):  
Secondary Structure ◽  
Structural Biology ◽  
Matthews Correlation Coefficient ◽  
Statistical Significance ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Biological Functions ◽  
Predictive Methods ◽  
Secondary Structure Predictions ◽  
Α Helix

AbstractAlthough most proteins with known structures conform to the longstanding rule-of-thumb that high levels of aligned sequence identity tend to indicate similar folds and functions, an increasing number of exceptions is emerging. In spite of having highly similar sequences, these “evolved fold switchers” (1) can adopt radically different folds with disparate biological functions. Predictive methods for identifying evolved fold switchers are desirable because some of them are associated with disease and/or can perform different functions in cells. Previously, we showed that inconsistencies between predicted and experimentally determined secondary structures can be used to predict fold switching proteins (2). The usefulness of this approach is limited, however, because it requires experimentally determined protein structures, whose magnitude is dwarfed by the number of genomic proteins. Here, we use secondary structure predictions to identify evolved fold switchers from their amino acid sequences alone. To do this, we looked for inconsistencies between the secondary structure predictions of the alternative conformations of evolved fold switchers. We used three different predictors in this study: JPred4, PSIPRED, and SPIDER3. We find that overall inconsistencies are not a significant predictor of evolved fold switchers for any of the three predictors. Inconsistencies between α-helix and β-strand predictions made by JPred4, however, can discriminate between the different conformations of evolved fold switchers with statistical significance (p < 1.7*10−13). In light of this observation, we used these inconsistencies as a classifier and found that it could robustly discriminate between evolved fold switchers and evolved non-fold-switchers, as evidenced by a Matthews correlation coefficient of 0.90. These results indicate that inconsistencies between secondary structure predictions can indeed be used to identify evolved fold switchers from their genomic sequences alone. Our findings have implications for genomics, structural biology, and human health.

scholarly journals 2dSS: a web server for protein secondary structure visualization

2019 ◽  
Author(s):  
Diksha Priya Lotun ◽  
Charlotte Cochard ◽  
Fabio R.J Vieira ◽  
Juliana Silva Bernardes
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
3D Structure ◽  
Protein Secondary Structure ◽  
Web Server ◽  
Homologous Proteins ◽  
Multiple Sequence ◽  
Prediction Tools ◽  
Secondary Structure Predictions

2dSS is a web-server for visualising and comparing secondary structure predictions. It provides two main functionalities: 2D-alignment and compare predictions. The “2D-alignment” has been designed to visualise conserved secondary structure elements in a multiple sequence alignment (MSA). From this we can study the secondary structure content of homologous proteins (a protein family) and highlight its structural patterns. The “compare predictions” has been designed to compare the output of several secondary structure prediction tools, and check their accuracy when compared with real secondary structure elements extracted from 3D-structure. 2dSS provides a comprehensive representation of protein secondary structure elements, and it can be used to visualise and compare secondary structures of any prediction tool.Availabilityhttp://genome.lcqb.upmc.fr/2dss/

scholarly journals Secondary Structure Prediction for RNA Sequences Including N6-methyladenosine

2021 ◽  
Author(s):  
Elzbieta Kierzek ◽  
Xiaoju Zhang ◽  
Richard M. Watson ◽  
Ryszard Kierzek ◽  
David H. Mathews
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
High Throughput Sequencing ◽  
Nearest Neighbor ◽  
Secondary Structure Prediction ◽  
Consensus Sequence ◽  
Structure Mapping ◽  
Sequencing Technologies ◽  
Stability Of Structures ◽  
Melting Experiments

AbstractThere is increasing interest in the roles played by covalently modified nucleotides in mRNAs and non-coding RNAs. New high-throughput sequencing technologies localize these modifications to exact nucleotide positions. There has been, however, and inability to account for these modifications in secondary structure prediction because of a lack of software tools for handling modifications and a lack of thermodynamic parameters for modifications. Here, we report that we solved these issues for N6-methyladenosine (m6A), for the first time allowing secondary structure prediction for a nucleotide alphabet of A, C, G, U, and m6A. We revised the RNAstructure software package to work with any user-defined alphabet of nucleotides. We also developed a set of nearest neighbor parameters for helices and loops containing m6A, using a set of 45 optical melting experiments. Interestingly, N6-methylation decreases the folding stability of structures with adenosines in the middle of a helix, has little effect on the folding stability of adenosines at the ends of helices, and stabilizes the folding stability for structures with unpaired adenosines stacked on the end of a helix. The parameters were tested against an additional two melting experiments, including a consensus sequence for methylation and an m6A dangling end. The utility of the new software was tested using predictions of the structure of a molecular switch in the MALAT1 lncRNA, for which a conformation change is triggered by methylation. Additionally, human transcriptome-wide calculations for the effect of N6-methylation on the probability of an adenosine being buried in a helix compare favorably with PARS structure mapping data. Now users of RNAstructure are able to develop hypothesis for structure-function relationships for RNAs with m6A, including conformational switching triggered by methylation.

Predicting Protein Secondary Structure Using Artificial Neural Networks and Information Theory

2011 ◽  
pp. 335-360
Author(s):  
Saad O.A. Subair ◽  
Safaai Deris
Keyword(s):  
Neural Networks ◽  
Information Theory ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
3D Structure ◽  
Protein Secondary Structure ◽  
Amino Acid Sequences ◽  
Prediction Methods ◽  
Hardware Platforms

Protein secondary-structure prediction is a fundamental step in determining the 3D structure of a protein. In this chapter, a new method for predicting protein secondary structure from amino-acid sequences has been proposed and implemented. Cuff and Barton 513 protein data set is used in training and testing the prediction methods under the same hardware, platforms, and environments. The newly developed method utilizes the knowledge of the GOR-V information theory and the power of the neural networks to classify a novel protein sequence in one of its three secondary-structures classes (i.e., helices, strands, and coils). The newly developed method (NN-GORV-I) is further improved by applying a filtering mechanism to the searched database and hence named NN-GORV-II. The developed prediction methods are rigorously analyzed and tested together with the other five well-known prediction methods in this domain to allow easy comparison and clear conclusions.

General Framework for Two-stage Approaches to Protein Secondary Structure Prediction

2003 ◽  
Vol 07 (03) ◽  
pp. 122-128
Author(s):  
Jagath C. Rajapakse ◽  
Minh N. Aguyen
Keyword(s):  
Neural Network ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Amino Acid Sequences ◽  
Single Stage ◽  
Support Vector ◽  
Protein Secondary Structure Prediction ◽  
Two Stage

Bioinformatics techniques to protein secondary structure prediction, such as Support Vector Machine (SVM) and GOR approaches, are mostly single-stage approaches; they predict secondary structures of the protein by taking into account only the information available in amino acid sequences. On the other hand, PHD (Profile network from HeiDelberg) method is a two-stage technique where two Multi-Layer Perceptrons (MLPs) are cascaded; the second neural network receives the output of the first neural network captures any contextual relationships among the secondary structure elements predicted by the first neural network. In this paper, we argue that it is feasible to extend the current single-stage approaches by adding a second-stage prediction scheme to capture the contextual information among secondary structural elements and thereby improving their accuracies. We demonstrate that two-stage SVMs perform better than present techniques for protein secondary structure prediction.

Sign in / Sign up

Export Citation Format

Share Document