Predicting Continuous Local Structure and the Effect of Its Substitution for Secondary Structure in Fragment-Free Protein Structure Prediction

Faraggi, Eshel; Yang, Yuedong; Zhang, Shesheng; Zhou, Yaoqi

doi:10.1016/j.str.2009.09.006

Cited by 110 publications

(126 citation statements)

References 82 publications

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“…Smoothed scores of residues were standardized for each of the 18 chlamydial proteins and discrimination of epitope residues from remaining total residues was tested for each of the 18 proteins individually. b Coils (Spine-X) indicate the coils predicted in secondary structure (36). the IncA protein are plotted against IncA residue number.…”

Section: Tablementioning

confidence: 99%

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction

Rahman

Chowdhury

Sachse

et al. 2016

Journal of Biological Chemistry

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X-ray crystallography has shown that an antibody paratope typically binds 15-22 amino acids (aa) of an epitope, of which 2-5 randomly distributed amino acids contribute most of the binding energy. In contrast, researchers typically choose for B-cell epitope mapping short peptide antigens in antibody binding assays. Furthermore, short 6 -11-aa epitopes, and in particular non-epitopes, are over-represented in published B-cell epitope datasets that are commonly used for development of B-cell epitope prediction approaches from protein antigen sequences. We hypothesized that such suboptimal length peptides result in weak antibody binding and cause false-negative results. We tested the influence of peptide antigen length on antibody binding by analyzing data on more than 900 peptides used for B-cell epitope mapping of immunodominant proteins of Chlamydia spp. We demonstrate that short 7-12-aa peptides of B-cell epitopes bind antibodies poorly; thus, epitope mapping with short peptide antigens falsely classifies many B-cell epitopes as non-epitopes. We also show in published datasets of confirmed epitopes and non-epitopes a direct correlation between length of peptide antigens and antibody binding. Elimination of short, <11-aa epitope/non-epitope sequences improved datasets for evaluation of in silico B-cell epitope prediction. Achieving up to 86% accuracy, protein disorder tendency is the best indicator of B-cell epitope regions for chlamydial and published datasets. For B-cell epitope prediction, the most effective approach is plotting disorder of protein sequences with the IUPred-L scale, followed by antibody reactivity testing of 16 -30-aa peptides from peak regions. This strategy overcomes the well known inaccuracy of in silico B-cell epitope prediction from primary protein sequences.Knowledge of B-cell epitopes of proteins is essential in many fields of applied biomedical research, such as antibody diagnostics and therapeutics, vaccines, as well basic research. Laboratory methods for identification of such epitopes are time-consuming and labor-intensive. Hence, any reduction in the need for discovery and confirmatory wet-lab research by epitope prediction algorithms is highly desirable. Among in silico predictive methods from primary sequence information, epitope prediction algorithms are distinguished for their lack of reliability (1). This underperformance prompted us to examine current approaches to B-cell epitope prediction by use of extensive data on epitopes and confirmed non-epitope regions of the Chlamydia spp. proteome, accumulated in research on chlamydial molecular serology (2).Recent three-dimensional antibody-antigen complex studies (3-7) show that about 15-22-aa 2 antigen peptide residues are structurally involved in binding of epitopes to ϳ17-aa residues in antibody complementarity-determining regions (CDRs; paratopes). Among these 15-22 structural epitope residues, about 2-5 aa, termed functional residues, contribute most of the total binding energy to antibodies (6). These functional residues lie...

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Section: Tablementioning

confidence: 99%

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction

Rahman

Chowdhury

Sachse

et al. 2016

Journal of Biological Chemistry

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“…This is a simplification of the real situation where the supposed constant bond lengths and angles depend on the environment of atoms. Examples of recent proposals that predict protein torsion angles are Faraggi et al [17] and Furuta et al [21].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary decision rules for predicting protein contact maps

Chamorro

Asencio-Cortés

Divina

et al. 2012

Pattern Anal Applic

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Protein structure prediction is currently one of the main open challenges in Bioinformatics. The protein contact map is an useful, and commonly used, representation for protein 3D structure and represents binary proximities (contact or non-contact) between each pair of amino acids of a protein. In this work, we propose a multiobjective evolutionary approach for contact map prediction based on physico-chemical properties of amino acids. The evolutionary algorithm produces a set of decision rules that identifies contacts between amino acids. The rules obtained by the algorithm impose a set of conditions based on amino acid properties to predict contacts. We present results obtained by our approach on four different protein data sets. A statistical study was also performed to extract valid conclusions from the set of prediction rules generated by our algorithm. Results obtained confirm the validity of our proposal.

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“…More signifi cant improvement is expected in the near future. More accurately predicted torsion angles will likely replace the dominant role played by predicted secondary structure in protein three -dimensional structure prediction as found in the development and application of SPINE XI [222] .…”

Section: Discussionmentioning

confidence: 99%

Prediction of One‐Dimensional Structural Properties Of Proteins by Integrated Neural Networks

Zhou

Faraggi

2010

Introduction to Protein Structure Prediction

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Predicting Continuous Local Structure and the Effect of Its Substitution for Secondary Structure in Fragment-Free Protein Structure Prediction

Cited by 110 publications

References 82 publications

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction

Evolutionary decision rules for predicting protein contact maps

Prediction of One‐Dimensional Structural Properties Of Proteins by Integrated Neural Networks

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