JOURNAL OF MOLECULAR RECOGNITION
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2016
·Vol. 30
·期 1
·e2566
·
IF 2.091
Hua-jin Zeng,Min Miao,Ran Yang,Ling-bo Qu
In this study, the fibrillation of hen egg-white lysozyme (HEWL) in the absence and presence of different concentrations of silybin was studied by thioflavin T spectroscopy, Congo red binding assays, 8-anilino-1-naphthalenesulfonic acid (ANS) fluorescence assay, circular dichroism, and transmission electron microscopy. The experimental results indicated that not only the fibrillation of HEWL at high temperature (65°C) and low pH (pH = 2.0) could be inhibited effectively by silybin but also the inhibition of HEWL by silybin followed a dose-dependent manner. Molecular docking studies indicated that 2 possible binding modes could be found in the interaction between silybin and HEWL via van der Waals forces and electrostatic forces as well as hydrogen bonding. One of these 2 conformations was directly entered into the cavity of HEWL (binding site I); the other was bound to the surface of HEWL (binding site II). In this way, silybin could not only increase the hydrophobicity of the cavity or the surface of HEWL but also influence the microenvironment of the binding site, which was able to stabilize the structure of HEWL and delay the process of HEWL fibrosis.
研究领域:
生物化学
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分子生物学
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蛋白质科学
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药理学
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结构生物学
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DOI
高影响因子
BIOESSAYS
·
2004
·Vol. 26
·期 12
·1351-1362
·
IF 6.491
Pierre De Meyts
I present here a personal perspective on more than three decades of research into the structural biology of the insulin–receptor interaction. The solution of the three-dimensional structure of insulin in 1969 provided a detailed understanding of the insulin surfaces involved in self-assembly. In subsequent years, hundreds of insulin analogues were prepared by insulin chemists and molecular biologists, with the goal of relating the structure to the biological function of the molecule. The design of methods for direct receptor-binding studies in the 1970s, and the cloning of the receptor in the mid 1980s, provided the required tools for detailed structure–function studies. In the absence of a full three-dimensional structure of the insulin–receptor complex, I attempt to assemble the existing pieces of the puzzle generated by our and other laboratories, in order to generate a plausible mechanistic model of the insulin–receptor interaction that explains its kinetics and negative cooperativity. BioEssays 26:1351–1362, 2004. © 2004 Wiley Periodicals, Inc.
研究领域:
结构生物学
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分子内分泌学
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生物化学
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受体信号传导
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DOI
高影响因子
JOURNAL OF MOLECULAR RECOGNITION
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2018
·Vol. 31
·期 7
·e2705
·
IF 1.868
Hua-xin Zhang,Dan Zhou,Qing-hua Xia
In this work, the interaction of an anti-HIV drug lamivudine and human serum albumin (HSA) was studied by multispectroscopic and molecular modeling methods. The fluorescence emission spectra showed that the fluorescence of HSA was quenched by lamivudine through static mechanism with HSA-lamivudine complex produced at ground state. According to the binding equilibriums observed at 4 different temperatures, the number of binding site, binding constant, enthalpy change, entropy change, and Gibbs free energy change of the interaction were calculated. The results indicated that there was only 1 main binding site under present concentration condition, and then, the location of this binding site was ascertained by molecular probe experiments using warfarin and ibuprofen as site markers. The interaction was a spontaneous and exothermic process. Hydrogen bonds and van der Waals force were the major power that fixed lamivudine on Sudlow's site I in subdomain IIA of HSA molecule. The distance between donor and acceptor was determined by Förster's nonradiative fluorescence resonance energy transfer theory. Circular dichroism spectra exhibited the alteration of HSA's secondary structures. Molecular modeling investigation revealed the structure of HSA-lamivudine complex, including the conformation of lamivudine in binding site, the amino residues close to lamivudine, and the interaction forces between receptor and ligand. The study may be beneficial to therapists in understanding the distribution of lamivudine in vivo and explaining its drug-resistant mechanism in clinical diagnosis.
研究领域:
药理学
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分子生物学
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生物物理化学
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药物代谢
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结构生物学
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DOI
高影响因子
MOLECULAR DIVERSITY
·
2023
·Vol. 28
·期 4
·2429-2447
·
IF 3.8
Li Guo,Li Jiaxuan,Tian Yujia,Zhao Yunyang,Pang Xiaoyang,Yan Aixia
In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton’s tyrosine kinase (BTK) and to provide interpretable and transparent explanations for these predictions. To achieve this, we gathered data on BTK inhibitors from the Reaxys and ChEMBL databases, removing compounds with covalent bonds and duplicates to obtain a dataset of 3895 inhibitors of non-covalent. These inhibitors were characterized using MACCS fingerprints and Morgan fingerprints, and four traditional machine learning algorithms (decision trees (DT), random forests (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost)) were used to build 16 classification models. In addition, four deep learning models were developed using deep neural networks (DNN). The best model, Model D_4, which was built using XGBoost and MACCS fingerprints, achieved an accuracy of 94.1% and a Matthews correlation coefficient (MCC) of 0.75 on the test set. To provide interpretable explanations, we employed the SHAP method to decompose the predicted values into the contributions of each feature. We also used K-means dimensionality reduction and hierarchical clustering to visualize the clustering effects of molecular structures of the inhibitors. The results of this study were validated using crystal structures, and we found that the interaction between the BTK amino acid residue and the important features of clustered scaffold was consistent with the known properties of the complex crystal structures. Overall, our models demonstrated high predictive ability and a qualitative model can be converted to a quantitative model to some extent by SHAP, making them valuable for guiding the design of new BTK inhibitors with desired activity.
研究领域:
计算化学
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药物发现
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生物信息学
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生物学中的机器学习
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结构生物学
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DOI
高影响因子
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