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1.
Identification of Strategic Residues at the Interface of Antigen-Antibody Interactions by In Silico Mutagenesis.
Xin, L, Yu, H, Hong, Q, Bi, X, Zhang, X, Zhang, Z, Kong, Z, Zheng, Q, Gu, Y, Zhao, Q, et al
Interdisciplinary sciences, computational life sciences. 2018;(2):438-448
Abstract
Structural information pertaining to antigen-antibody interactions is fundamental in immunology, and benefits structure-based vaccine design. Modeling of antigen-antibody immune complexes from co-crystal structures or molecular docking simulations provides an extensive profile of the epitope at the interface; however, the key amino acids involved in the interaction must be further clarified, often through the use of experimental mutagenesis and subsequent binding assays. Here, we describe an in silico mutagenesis method to identify key sites at antigen-antibody interfaces, using significant increase in pH-dependency energy among saturated point mutations. Through a comprehensive analysis of the crystal structures of three antigen-antibody immune complexes, we show that a cutoff value of 1 kcal/mol of increased interaction energy provides good congruency with the experimental non-binding mutations conducted in vitro. This in silico mutagenesis strategy, in association with energy calculations, may provide an efficient tool for antibody-antigen interface analyses, epitope optimization, and/or conformation prediction in structure-based vaccine design.
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2.
Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs.
Hasan, MM, Zhou, Y, Lu, X, Li, J, Song, J, Zhang, Z
PloS one. 2015;(6):e0129635
Abstract
Prokaryotic proteins are regulated by pupylation, a type of post-translational modification that contributes to cellular function in bacterial organisms. In pupylation process, the prokaryotic ubiquitin-like protein (Pup) tagging is functionally analogous to ubiquitination in order to tag target proteins for proteasomal degradation. To date, several experimental methods have been developed to identify pupylated proteins and their pupylation sites, but these experimental methods are generally laborious and costly. Therefore, computational methods that can accurately predict potential pupylation sites based on protein sequence information are highly desirable. In this paper, a novel predictor termed as pbPUP has been developed for accurate prediction of pupylation sites. In particular, a sophisticated sequence encoding scheme [i.e. the profile-based composition of k-spaced amino acid pairs (pbCKSAAP)] is used to represent the sequence patterns and evolutionary information of the sequence fragments surrounding pupylation sites. Then, a Support Vector Machine (SVM) classifier is trained using the pbCKSAAP encoding scheme. The final pbPUP predictor achieves an AUC value of 0.849 in 10-fold cross-validation tests and outperforms other existing predictors on a comprehensive independent test dataset. The proposed method is anticipated to be a helpful computational resource for the prediction of pupylation sites. The web server and curated datasets in this study are freely available at http://protein.cau.edu.cn/pbPUP/.
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3.
hCKSAAP_UbSite: improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties.
Chen, Z, Zhou, Y, Song, J, Zhang, Z
Biochimica et biophysica acta. 2013;(8):1461-7
Abstract
As one of the most common post-translational modifications, ubiquitination regulates the quantity and function of a variety of proteins. Experimental and clinical investigations have also suggested the crucial roles of ubiquitination in several human diseases. The complicated sequence context of human ubiquitination sites revealed by proteomic studies highlights the need of developing effective computational strategies to predict human ubiquitination sites. Here we report the establishment of a novel human-specific ubiquitination site predictor through the integration of multiple complementary classifiers. Firstly, a Support Vector Machine (SVM) classier was constructed based on the composition of k-spaced amino acid pairs (CKSAAP) encoding, which has been utilized in our previous yeast ubiquitination site predictor. To further exploit the pattern and properties of the ubiquitination sites and their flanking residues, three additional SVM classifiers were constructed using the binary amino acid encoding, the AAindex physicochemical property encoding and the protein aggregation propensity encoding, respectively. Through an integration that relied on logistic regression, the resulting predictor termed hCKSAAP_UbSite achieved an area under ROC curve (AUC) of 0.770 in 5-fold cross-validation test on a class-balanced training dataset. When tested on a class-balanced independent testing dataset that contains 3419 ubiquitination sites, hCKSAAP_UbSite has also achieved a robust performance with an AUC of 0.757. Specifically, it has consistently performed better than the predictor using the CKSAAP encoding alone and two other publicly available predictors which are not human-specific. Given its promising performance in our large-scale datasets, hCKSAAP_UbSite has been made publicly available at our server (http://protein.cau.edu.cn/cksaap_ubsite/).
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4.
G/U and certain wobble position mismatches as possible main causes of amino acid misincorporations.
Zhang, Z, Shah, B, Bondarenko, PV
Biochemistry. 2013;(45):8165-76
Abstract
A mass spectrometry-based method was developed to measure amino acid substitutions directly in proteins down to a level of 0.001%. When applied to recombinant proteins expressed in Escherichia coli, monoclonal antibodies expressed in mammalian cells, and human serum albumin purified from three human subjects, the method revealed a large number of amino acid misincorporations at levels of 0.001-0.1%. The detected misincorporations were not random but involved a single-base difference between the codons of the corresponding amino acids. The most frequent base differences included a change from G to A, corresponding to a G(mRNA)/U(tRNA) base pair mismatch during translation. We concluded that under balanced nutrients, G(mRNA)/U(tRNA) mismatches at any of the three codon positions and certain additional wobble position mismatches (C/U and/or U/U) are the main causes of amino acid misincorporations. The hypothesis was tested experimentally by monitoring the levels of misincorporation at several amino acid sites encoded by different codons, when a protein with the same amino acid sequence was expressed in E. coli using 13 different DNA sequences. The observed levels of misincorporation were different for different codons and agreed with the predicted levels. Other less frequent misincorporations may occur due to G(DNA)/U(mRNA) mismatch during transcription, mRNA editing, U(mRNA)/G(tRNA) mismatch during translation, and tRNA mischarging.
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5.
SecretP: a new method for predicting mammalian secreted proteins.
Yu, L, Guo, Y, Zhang, Z, Li, Y, Li, M, Li, G, Xiong, W, Zeng, Y
Peptides. 2010;(4):574-8
Abstract
In contrast to a large number of classically secreted proteins (CSPs) and non-secreted proteins (NSPs), only a few proteins have been experimentally proved to enter non-classical secretory pathways. So it is difficult to identify non-classically secreted proteins (NCSPs), and no methods are available for distinguishing the three types of proteins simultaneously. In order to solve this problem, a data mining has been taken firstly, and mammalian proteins exported via ER-Golgi-independent pathways are collected through extensive literature searches. In this paper, a support vector machine (SVM)-based ternary classifier named SecretP is proposed to predict mammalian secreted proteins by using pseudo-amino acid composition (PseAA) and five additional features. When distinguishing the three types of proteins, SecretP yielded an accuracy of 88.79%. Evaluating the performance of our method by an independent test set of 92 human proteins, 76 of them are correctly predicted as NCSPs. When performed on another public independent data set, the prediction result of SecretP is comparable to those of other existing computational methods. Therefore, SecretP can be a useful supplementary tool for future secretome studies. The web server SecretP and all supplementary tables listed in this paper are freely available at http://cic.scu.edu.cn/bioinformatics/secretp/index.htm.