Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins (Q28269843)

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29 February 2020

title

Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins (English)

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29 February 2020

5

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29 February 2020

Yaoqi Zhou

8

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29 February 2020

Yuedong Yang

9

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29 February 2020

author name string

Rhys Heffernan

1

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29 February 2020

Abdollah Dehzangi

2

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29 February 2020

James Lyons

3

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29 February 2020

Kuldip Paliwal

4

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29 February 2020

Jihua Wang

6

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29 February 2020

Abdul Sattar

7

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29 February 2020

language of work or name

English

0 references

publication date

14 November 2015

1 reference

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29 February 2020

number of pages

7

1 reference

based on heuristic

inferred from page(s)

2 references

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29 February 2020

volume

32

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29 February 2020

issue

6

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29 February 2020

page(s)

843-849

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Accurate prediction of solvent accessibility using neural networks-based regression.

29 February 2020

cites work

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Exploring the extremes of sequence/structure space with ensemble fold recognition in the program Phyre

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Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction

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Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure.

21 January 2018

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An amino acid has two sides: a new 2D measure provides a different view of solvent exposure

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Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.

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Evaluation of local structure alphabets based on residue burial.

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TOUCHSTONE: an ab initio protein structure prediction method that uses threading-based tertiary restraints.

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CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks.

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Reliable B cell epitope predictions: impacts of method development and improved benchmarking

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ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions

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The interpretation of protein structures: Estimation of static accessibility

21 January 2018

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Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes

21 January 2018

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Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.

21 January 2018

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DL-PRO: A Novel Deep Learning Method for Protein Model Quality Assessment.

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Prediction of coordination number and relative solvent accessibility in proteins.

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Conservation and prediction of solvent accessibility in protein families

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Deep learning in neural networks: an overview

21 January 2018

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HSEpred: predict half-sphere exposure from protein sequences

21 January 2018

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PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure

21 January 2018

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Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy.

21 January 2018

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Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

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Prediction of protein accessible surface areas by support vector regression

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Analysis and prediction of RNA-binding residues using sequence, evolutionary conservation, and predicted secondary structure and solvent accessibility.

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Accurate sequence-based prediction of catalytic residues

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Highly accurate and high-resolution function prediction of RNA binding proteins by fold recognition and binding affinity prediction

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10.1093/BIOINFORMATICS/BTV665

21 January 2018

Identifiers

DOI

2 references

Consolidated OpenCitations Corpus – April 2017

OpenCitations bibliographic resource ID

1618996

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:26568622%20AND%20SRC:MED&resulttype=core&format=json

journals/bioinformatics/HeffernanDLPSWS16

29 February 2020

DBLP publication ID

1 reference

DBLP Dataset 2021-01-02

OpenCitations bibliographic resource ID

28 January 2021

1618996

1 reference

Consolidated OpenCitations Corpus – April 2017

OpenCitations bibliographic resource ID

1618996

1 reference