QSAR method for prediction of protein-peptide binding affinity: Application to MHC class I molecule HLA-A*0201

Zhao, Chunyan; Zhang, Haixia; Luan, Feng; Zhang, Ruisheng; Liu, Mancang; Hu, Zhide; Fan, Botao

doi:10.1016/j.jmgm.2006.12.002

Cited by 16 publications

(7 citation statements)

References 41 publications

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“…Quantitative structure activity relationship (QSAR) approaches that utilize the physicochemical properties of interacting species as a foundation are a more appropriate method. These have been applied by one group but in the context of peptides rather than proteins or proteomes [32-36]. …”

Section: Introductionmentioning

confidence: 99%

An integrated approach to epitope analysis II: A system for proteomic-scale prediction of immunological characteristics

Bremel¹,

Homan²

2010

Immunome Res

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BackgroundImproving our understanding of the immune response is fundamental to developing strategies to combat a wide range of diseases. We describe an integrated epitope analysis system which is based on principal component analysis of sequences of amino acids, using a multilayer perceptron neural net to conduct QSAR regression predictions for peptide binding affinities to 35 MHC-I and 14 MHC-II alleles.ResultsThe approach described allows rapid processing of single proteins, entire proteomes or subsets thereof, as well as multiple strains of the same organism. It enables consideration of the interface of diversity of both microorganisms and of host immunogenetics. Patterns of binding affinity are linked to topological features, such as extracellular or intramembrane location, and integrated into a graphical display which facilitates conceptual understanding of the interplay of B-cell and T-cell mediated immunity.Patterns which emerge from application of this approach include the correlations between peptides showing high affinity binding to MHC-I and to MHC-II, and also with predicted B-cell epitopes. These are characterized as coincident epitope groups (CEGs). Also evident are long range patterns across proteins which identify regions of high affinity binding for a permuted population of diverse and heterozygous HLA alleles, as well as subtle differences in reactions with MHCs of individual HLA alleles, which may be important in disease susceptibility, and in vaccine and clinical trial design. Comparisons are shown of predicted epitope mapping derived from application of the QSAR approach with experimentally derived epitope maps from a diverse multi-species dataset, from Staphylococcus aureus, and from vaccinia virus.ConclusionsA desktop application with interactive graphic capability is shown to be a useful platform for development of prediction and visualization tools for epitope mapping at scales ranging from individual proteins to proteomes from multiple strains of an organism. The possible functional implications of the patterns of peptide epitopes observed are discussed, including their implications for B-cell and T-cell cooperation and cross presentation.

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Section: Introductionmentioning

confidence: 99%

An integrated approach to epitope analysis II: A system for proteomic-scale prediction of immunological characteristics

Bremel¹,

Homan²

2010

Immunome Res

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“…The theory of SVM has been extensively described in References (Cheng et al, 2010;Zhao et al, 2007). In this article, we introduce the basic idea simply in the following.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Simple and accurate approaches to predict the activity of benzothiadiazine derivatives as HCV inhibitors

et al. 2011

Med Chem Res

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Quantitative structure-activity relationship (QSAR) models of Hepatitis C virus (HCV) inhibitors were developed for 118 benzothiadiazine derivatives based on linear and nonlinear approaches. The best multi-linear regression (BMLR) was utilized to select appropriate molecular descriptors and develop linear model. Using the same descriptors, radial basis function neural networks (RBFNN) and support vector machine (SVM) were employed to generate the non-linear models. When comparing with the simpler BMLR model, RBFNN and SVM models had more accurate results with the square of correlation coefficients (R 2 ) of 0.850 and 0.875 for the training set, and R 2 of 0.893 and 0.854 for the test set. It proved that RBFNN and SVM were useful tools to predict the biological activity of benzothiadiazine derivatives as HCV inhibitors. Meanwhile, those factors governing the biological activity of benzothiadiazine derivatives were discussed. The proposed methods will be of significance in the HCV inhibitors research, and can be expected to apply to other similar research fields.

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“…Quantitative structure–activity relationship/quantitative sequence property relationship (QSAR/QSPR) models are powerful in silico tools that allow for the prediction of mAb behavior where the descriptors (based on structure, sequence, and/or interaction) are modeled against a biological activity (response) using a multivariate data analysis approach for approved mAbs. This is an extension of QSAR/QSPR models, which are routinely used for the development of predictive models in biopharmaceutical development and toxicological applications . QSAR models have been developed for a number of chromatographic applications for a myriad of proteins at different developmental stages .…”

Section: Introductionmentioning

confidence: 99%

Cross‐Interaction Chromatography‐Based QSAR Model for Early‐Stage Screening to Facilitate Enhanced Developability of Monoclonal Antibody Therapeutics

Kizhedath

Karlberg

2019

Biotechnology Journal

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Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by developability issues such as polyspecificity and lack of solubility, which leads to attrition as well as manufacturing failures. In this study a multitool hybrid quantitative structure–activity relationship (QSAR) model development framework is described. This framework uses four novel datasets derived from the primary sequences of IgG1‐κ‐humanized mAbs with varying degrees of resolutions. Unsupervised pattern recognition is first performed on the descriptor sets to visualize any intrinsic property‐based clustering, followed by regression of descriptors against cross‐interaction chromatography (CIC) retention times. Model optimization is performed via unsupervised variable reduction followed by supervised variable selection. Finally, the models and datasets are benchmarked based on the regression model performance metrics such as R2, Q2, and RMSE. The results show that datasets containing localized descriptors rather than averaged value over the entire protein have better predictive performance of CIC retention behavior with R2 > 0.8 and RMSE < 0.3. Furthermore, the results indicate the physicochemical, electronic, and topological properties of hypervariable regions of antibodies that contribute most to the CIC retention times. The results of these studies could contribute to early‐stage screening and better design of mAbs.

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QSAR method for prediction of protein-peptide binding affinity: Application to MHC class I molecule HLA-A*0201

Cited by 16 publications

References 41 publications

An integrated approach to epitope analysis II: A system for proteomic-scale prediction of immunological characteristics

An integrated approach to epitope analysis II: A system for proteomic-scale prediction of immunological characteristics

Simple and accurate approaches to predict the activity of benzothiadiazine derivatives as HCV inhibitors

Cross‐Interaction Chromatography‐Based QSAR Model for Early‐Stage Screening to Facilitate Enhanced Developability of Monoclonal Antibody Therapeutics

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