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Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition.
Wang, C, Wang, W, Lu, K, Zhang, J, Chen, P, Wang, B
International journal of molecular sciences. 2020;(16)
Abstract
The task of drug-target interaction (DTI) prediction plays important roles in drug development. The experimental methods in DTIs are time-consuming, expensive and challenging. To solve these problems, machine learning-based methods are introduced, which are restricted by effective feature extraction and negative sampling. In this work, features with electrotopological state (E-state) fingerprints for drugs and amphiphilic pseudo amino acid composition (APAAC) for target proteins are tested. E-state fingerprints are extracted based on both molecular electronic and topological features with the same metric. APAAC is an extension of amino acid composition (AAC), which is calculated based on hydrophilic and hydrophobic characters to construct sequence order information. Using the combination of these feature pairs, the prediction model is established by support vector machines. In order to enhance the effectiveness of features, a distance-based negative sampling is proposed to obtain reliable negative samples. It is shown that the prediction results of area under curve for Receiver Operating Characteristic (AUC) are above 98.5% for all the three datasets in this work. The comparison of state-of-the-art methods demonstrates the effectiveness and efficiency of proposed method, which will be helpful for further drug development.
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2.
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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3.
Design, synthesis and biological evaluation of novel tripeptidyl epoxyketone derivatives constructed from β-amino acid as proteasome inhibitors.
Zhang, J, Cao, J, Xu, L, Zhou, Y, Liu, T, Li, J, Hu, Y
Bioorganic & medicinal chemistry. 2014;(11):2955-65
Abstract
A series of novel tripeptidyl epoxyketone derivatives constructed from β-amino acid were designed, synthesized and evaluated as proteasome inhibitors. All target compounds were tested for their proteasome inhibitory activities and selected compounds were tested for their anti-proliferation activities against two multiple myeloma (MM) cell lines RPMI 8226 and NCI-H929. Among them, eleven compounds exhibited proteasome inhibitory rates of more than 50% at the concentration of 1 μg/mL and nine compounds showed anti-proliferation activities with IC50 values at low micromolar level. Compound 20h displayed the most potent proteasome inhibitory activities (IC50: 0.11 ± 0.01 μM) and anti-proliferation activities with IC50 values at 0.23 ± 0.01 and 0.17 ± 0.02 μM against two tested cell lines. Additionally, the poly-ubiquitin accumulation in the western blot analysis supported that proteasome inhibition in a cellular system was induced by compound 20h. All these experimental results confirmed that β-amino acid can be introduced as a building block for the development of proteasome inhibitors.
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4.
Design, synthesis and biological evaluation of peptidyl epoxyketone proteasome inhibitors composed of β-amino acids.
Zhang, J, Han, M, Ma, X, Xu, L, Cao, J, Zhou, Y, Li, J, Liu, T, Hu, Y
Chemical biology & drug design. 2014;(5):497-504
Abstract
A series of novel di- and tripeptidyl epoxyketone derivatives composed of β-amino acids were designed, synthesized and evaluated for their proteasome inhibitory activities and anti-proliferation activities against two multiple myeloma cell lines RPMI 8226 and NCI-H929 and normal cells (peripheral blood mononucleated cells). Among these tested compounds, tripeptidyl analogues showed much more potent activities than dipeptides, and four tripeptidyl compounds exhibited proteasome inhibitory activities with IC50 values ranging from 0.97 ± 0.05 to 1.85 ± 0.11 μM. In addition, all the four compounds showed anti-proliferation activities with IC50 values at low micromolar levels against two multiple myeloma cell lines and weak activities against normal cells. Furthermore, Western blot analysis was performed to verify the proteasome inhibition induced by compounds 21d and 21e. All the experimental results validated that the β-amino acid building block has the potential for the development of proteasome inhibitors.
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5.
Prediction of methylation sites using the composition of K-spaced amino acid pairs.
Zhang, W, Xu, X, Yin, M, Luo, N, Zhang, J, Wang, J
Protein and peptide letters. 2013;(8):911-7
Abstract
Protein methylation is one of the most important post-translational modifications. Typically methylation occurs on arginine or lysine residues in the protein sequence. In the biological system, methylation is catalyzed by enzymes, and should be involved in modification of heavy metals, regulation of gene expression, regulation of protein function, and RNA metabolism. Thus the prediction of methylation sites plays a crucial role. As we know, traditional experiment approaches to predict the sites are accurate, but that are always labor-intensive and time-consuming. Thus, it is common to see that computational methods receive increasingly attentions due to their convenience and fast speed in recent years. In this study, we develop a computational approach to predict the performance of methylarginine and methyllysine sites. First, a new encoding schema as called the CKASSP is used in our method. Then, the support vector machine (SVM) algorithm is used as a predictor. Experimental results show that our method can obtain average prediction accuracy of 87.46%, sensitivity of 99.09%, specificity of 86.89% for arginine methylation sites, and average prediction accuracy of 88.78%, sensitivity of 93.75%, specificity of 81.79% for lysine methylation sites as well, which is better than those of other state-of-art predictors. The online service is implemented by java 1.4.2 and is freely available at http://202.198.129.219:8080/cksaap_methsite.