New Descriptors of Amino Acids and Its Applications to Peptide Quantitative Structure-activity Relationship

A new set of descriptors, HSEHPCSV （component score vector of hydrophobic, steric, and electronic properties together with hydrogen bonding contributions）, were derived from principal component analyses of 95 physicochemical variables of 20 natural amino acids separately according to different kinds of properties described, namely, hydrophobic, steric, and electronic properties as well as hydrogen bonding contributions. HSEHPCSV scales were then employed to express structures of angiotensin-converting enzyme inhibitors, bitter tasting thresholds and bactericidal 18 peptide, and to construct QSAR models based on partial least square （PLS）. The results obtained are as follows： the multiple correlation coefficient （R2cum） of 0.846, 0.917 and 0.993, leave-one-out cross validated Q2cm of 0.835, 0.865 and 0.899, and root-mean-square error for estimated error （RMSEE） of 0.396, 0.187and 0.22, respectively. Satisfactory results showed that, as new amino acid scales, data of HSEHPCSV may be a useful structural expression methodology＇for the studies on peptide QSAR （quantitative structure-activity relationship） due to many advantages such as plentiful structural information, definite physical and chemical meaning and easy interpretation.

Molecular characteristics and structure–activity relationships of food-derived bioactive peptides

Peptides are functional active fragments of proteins which can provide nutrients needed for human growth and development,and they also have unique physiological activity characteristics relative to proteins.Bioactive peptides contain a great deal of development potential.More specifically,food-derived bioactive peptides have the advantages of a wide variety of sources,unique structures,high efficiency and safety,so they have broad development prospects.This review provides an overview of the current advances regarding the preparation,functional characteristics,and structure–activity relationships of food-derived bioactive peptides.Moreover,the prospects for the future development and application of food-derived bioactive peptides are discussed.This review may provide a better understanding of foodderived bioactive peptides,and some constructive inspirations for further research and applications in the food industry.

Quantitative structure-activity relationship study on the biodegradation of acid dyestuffs

Quantitative structure-biodegradability relationships （QSBRs） were established to develop predictive models and mechanistic explanations for acid dyestuffs as well as biological activities. With a total of four descriptors, molecular weight （MW）, energies of the highest occupied molecular orbital （EHOMO）, the lowest unoccupied molecular orbital （ELUMO）, and the excited state （EES）, calculated using quantum chemical semi-empirical methodology, a series of models were analyzed between the dye biodegradability and each descriptor. Results showed that EHOMO and Mw were the dominant parameters controlling the biodegradability of acid dyes. A statistically robust QSBR model was developed for all studied dyes, with the combined application of EHOMO and Mw. The calculated biodegradations fitted well with the experimental data monitored in a facultative-aerobic process, indicative of the reliable prediction and mechanistic character of the developed model.

Quantitative Structure-activity Relationship Study on the Antioxidant Activity of Carotenoids

Carotenoids are a family of effective active oxygen scavengers, which can reduce the danger of occurrence of chronic diseases such as cardiovascular disease, cataract, cancer, and so on. The quantitative structure-activity relationship （QSAR） equation between carotenoids and antioxidant activity was established by quantum chemistry AM1, molecular mechanism （MM＋） and stepwise regression analysis methods, and the model was evaluated by leave-one-out approach. The results showed that the significant molecular descriptors related to the antioxidant activity of carotenoids were the energy difference （E_HL） between the lowest unoccupied molecular orbital （LUMO） and the highest occupied molecular orbital （HOMO） and ionization energy （Eiso）. The model showed a good predictive ability （Q^2 〉 0.5）.

Genotoxicity of substituted nitrobenzenes and the quantitative structure－activity relationship

The genotoxicity of 22 substituted nitrobenzenes were evaluated by the chromosome aberrations test in in vitro human peripheral lymphocytes.18 of 22 compounds exhibit genotoxic activities.Quantitative structure-activity relationship model was established to correlate the genotoxicity of substituted nitrobenzenes with the characteristics of the substituents on benzene ring.

Quantitative Structure-activity Relationship(QSAR) Study of Toxicity of Substituted Aromatic Compounds to Photobacterium Phosphoreum

With the artificial neural network（ANN） method combined with the multiple linear regression（MLR）,based on a series of quantum chemical descriptors and molecular connectivity indexes,quantitative structure-activity relationship（QSAR） models to predict the acute toxicity（-lgEC50） of substituted aromatic compounds to Photobacterium phosphoreum were established.Four molecular descriptors that appear in the MLR model,namely,the second order valence molecular connectivity index（2XV）,the energy of the highest occupied molecular orbital（EHOMO）,the logarithm of n-octyl alcohol/water partition coefficient（logKow） and the Connolly molecular area（MA）,were inputs of the ANN model.The root-mean-square error（RMSE） of the training and validation sets of the ANN model are 0.1359 and 0.2523,and the correlation coefficient（R） is 0.9810 and 0.8681,respectively.The leave-one-out（LOO） cross validated correlation coefficient（Q L2OO） of the MLR and ANN models is 0.6954 and 0.6708,respectively.The result showed that the two methods are complementary in the calculations.The regression method gave support to the neural network with physical explanation,and the neural network method gave a more accurate model for QSAR.In addition,some insights into the structural factors affecting the acute toxicity and toxicity mechanism of substituted aromatic compounds were discussed.

Quantitative Structure-activity Relationship Studies on the Antioxidant Activity and Gap Junctional Communication of Carotenoids

The antioxidant and gap junctional communication（GJC） activities of carotenoids are known to be the two main anticancer mechanisms.Quantitative structure-activity relationship（QSAR） models of the two activities were developed using stepwise regression and multilayer perceptron neural network based on the calculated descriptors of quantum chemistry.The results showed that the significant molecular descriptor related to the antioxidant activity of carotenoids was the HOMO-LUMO energy gap（EHL） and the molecular descriptor related to the GJC was the lowest unoccupied molecular orbital energy（ELUMO）.The two models of antioxidant activity both showed good predictive power,but the predictive power of the neural network QSAR model of antioxidant activity was better.In addition,the two GJC models have similar,moderate predictive power.The possible mechanisms of antioxidant activity and GJC of carotenoids were discussed.

On quantitative structure-activity relationships between hydrazine derivatives and β irradiation

In this study, solutions of hydrazine and its derivatives were irradiated using a pulsed electron beam to determine the half-reaction time of radiolysis. 3 D structures of the hydrazine derivatives were optimized, and their energies were calculated using density functional theory with the B3 LYP method and 6-311 +(3 d, 3 p) basis set.For the first time, the 3 D quantitative structure-activity relationship(QSAR) equation describing the relationship between the hydrazine derivative structures and rate of radiolysis has been established using SPSS software.Pearson correlation analysis revealed a close correlation between the total energies of the molecules and half-reaction times. In the QSAR equation, Y =-7583.464 +54.687 X_1+94333.586 X_2,Y,X_1,and X_2 are the half-reaction time, total energy of the molecule, and orbital transition energy, respectively. The significance levels of the regression coefficients were 0.006 and 0.031, i.e., both less than 0.05. Thus, this model fully explains the relationship between hydrazine derivatives and β radiolysis stability.The results show that the total energy of the molecule and orbital transition energy are the main factors that influence the β radiolysis stability of these hydrazine derivatives.

Predicting quantitative structure-activity relationship of substituted 17α-acetoxyprogesterones by molecular hybridization electronegativity-distance vector

A set of novel structural descriptors (molecular hybridization electronegativity-distance vector, VMEDh) was put forward, and the quantitative structure–activity relationship (QSAR) of a series of 17α-Acetoxyprogesterones (APs) was investigated. Taking into account the effect of various hybridized orbits on atomic electronegativities, we developed the structure descriptors with amended electronegativities to build a QSAR model. The 10-parameter model based on VMEDh yields a correlation coefficient R=0.972 and standard deviation SD=0.262, which are more desirable than those of the previous molecular electonegativity-distance vector (MEDV-4) (R=0.969, SD=0.275). By stepwise multiple linear regression, several parameters are selected to construct optimal models. The 7-parameter model based on VMEDh has R=0.960 and SD=0.276; its correlation coefficient (RCV) and standard deviation (SDCV) for leave-one-out procedure crossvalidation are respectively RCV=0.890 and SDCV=0.445. The 6-parameter MEDV-4 model has R=0.946, SD=0.304, RCV=0.903 and SDCV=0.406. It is demonstrated that VMEDh has desirable estimation performance and good predictive capability for this series of chemical compounds.

Quantitative Structure-activity Relationship Models of Monomer Reactivity

The reactivity parameters,Q and e,in the Q-e scheme reflect the reactivities of a monomer(or a radical)in free-radical copolymerizations.By applying multiple linear regression(MLR)analysis,the optimal quantitative structure-activity relationship(QSAR)model for the reactivity parameter lnQ was developed based on five descriptors(NAF,NOF,EαLUMO,EβHOMO,and EβLUMO)and 69 monomers with the root mean square(rms)error of 0.61.The optimal MLR model of the parameter e obtained from five descriptors(TOcl,NpN,NSO,EαHOMO and DH)and 68 monomers produced rms error of 0.42.Compared with previous models,the two optimal MLR models in this paper show satisfactory statistical characteristics.The feasibility of combining 2D descriptors obtained from the monomers and 3D descriptors calculated from the radical structures(formed from monomers+H )to predict parameters Q and e has been demonstrated.

Quantitative Structure Activity Relationship Studies of Benzoxazinone Derivative Antithrombotic Drug Using New Three-dimensional Structure Descriptors

A novel three-dimensional holographic vector of atomic interaction field（3D-HoVAIF） was used to describe the chemical structures of 23 benzoxazinone derivatives as antithrombotic drugs.Here a quantitative structure activity relationship（QSAR） model was built by partial least-squares（PLS） regression.The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations.The correlation coefficients of established PLS model,leave-one-out（LOO） cross-validation,and predicted values versus experimental ones of external samples were R2=0.899,RCV2=0.854 and Qext2=0.868,respectively.These values indicated that the built PLS model had both favorable estimation stability and good prediction capabilities.Furthermore,the satisfactory results showed that 3D-HoVAIF could preferably express the information related to the biological activity of benzoxazinone derivatives.

Some substrates of P-glycoprotein targeting <i>β</i>-amyloid clearance by quantitative structure-activity relationship (QSAR)/membrane-interaction (MI)-QSAR analysis

The pathogenesis of Alzheimer’s disease (AD) putatively involves a compromised blood-brain barrier (BBB). In particular, the importance of brain-to-blood transport of brain-derived metabolites across the BBB has gained increasing attention as a potential mechanism in the pathogenesis of neurodegenerative disorders such as AD, which is characterized by the aberrant polymerization and accumulation of specific misfolded proteins, particularly β-amyloid (Aβ), a neuropathological hallmark of AD. P-glycoprotein (P-gp), a major component of the BBB, plays a role in the etiology of AD through Aβ clearance from the brain. Our QSAR models on a series of purine-type and propafenone-type substrates of P-gp showed that the interaction between P-gp and its modulators depended on Molar Refractivity, LogP, and Shape Attribute of drugs it transports. Meanwhile, another model on BBB partitioning of some compounds revealed that BBB partitioning relied upon the polar surface area, LogP, Balaban Index, the strength of a molecule combined with the membrane-water complex, and the changeability of the structure of a solute-membrane-water complex. The predictive model on BBB partitioning contributes to the discovery of some molecules through BBB as potential AD therapeutic drugs. Moreover, the interaction model of P-gp and modulators for treatment of multidrug resistance (MDR) indicates the discovery of some molecules to increase Aβ clearance from the brain and reduce Aβ brain accumulation by regulating BBB P-gp in the early stages of AD. The mechanism provides a new insight into the therapeutic strategy for AD.

Quantitative structure－activity study on the reductive dehalogenation potency of the halogenated aromatics

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Quantum Chemical Study on Structure-activity Relationship of Several Kinds of Drugs

The structure-activity relationship of several drugs with similar structure has been investigated by using ab initio method. The relation between the dipole moments and biological activities of these drugs was judged after comparing their geometric structures, dipole moments and inhibitory concentrations. In principle, new drug molecule could be reasonably designed by altering the place of groups and ultimately, the potential drug could be screened by comparing the dipole moments of obtained molecules.

Effects of C-terminal amidation and heptapeptide ring on the biological activities and advanced structure of amurin-9KY, a novel antimicrobial peptide identified from the brown frog, Rana kunyuensis

Rana kunyuensis is a species of brown frog that lives exclusively on Kunyu Mountain,Yantai,China.In the current study,a 279-bp cDNA sequence encoding a novel antimicrobial peptide (AMP),designated as amurin-9KY,was cloned from synthesized double-strand skin cDNA of R.kunyuensis.The amurin-9KY precursor was composed of 62 amino acid (aa) residues,whereas the mature peptide was composed of 14 aa and contained two cysteines forming a C-terminal heptapeptide ring (Rana box domain) and an amidated C-terminus.These structural characters represent a novel amphibian AMP family.Although amurin-9KY exhibited high similarity to the already identified amurin-9AM from R.amurensis,little is known about the structures and activities of amurin-9 family AMPs so far.Therefore,amurin-9KY and its three derivatives (amurin-9KY1-3) were designed and synthesized.The structures and activities were examined to evaluate the influence of C-terminal amidation and the heptapeptide ring on the activities and structure of amurin-9KY..Results indicated that C-terminal amidation was essential for antimicrobial activity,whereas both C-terminal amidation and the heptapeptide ring played roles in the low hemolytic activity.Circular dichroism (CD) spectra showed that the four peptides adopted an α-helical conformation in THF/H2O (v/v 1∶1) solution,but a random coil in aqueous solution.Elimination of the C-terminal heptapeptide ring generated two free cysteine residues with unpaired thiol groups,which greatly increased the concentration-dependent anti-oxidant activity.Scanning electron microscopy (SEM) was also performed to determine the possible bactericidal mechanisms.

Correlation of Quantitative Structure and Inhibition Phytotoxicity for Aromatic Compounds Using Ab Initio Method

29 aromatic compounds were computed at the HF/6-31G^＊ level. Based on linear solvation energy theory firstly, the parameters of molecular structure were taken as theoretical descriptors, and the corresponding linear solvation energy relationship （LSER） （r^2= 0.8993, q^2=0.8559） between the structural parameters and inhibition phytotoxicity to the seed germination rate of cucumis （-lgGC50） was thus obtained. Then the parameters of molecular structure and thermodynamics were taken as theoretical descriptors, and as a result the other corresponding correlation equation （r^2=0.9268, q^2=0.8960） relating to -lgGC50 was achieved. The two equations obtained in this work by HF/6-31G^＊ are both more advantageous than that from AM 1.

Computation of Structure Activity and Design of Chalcone Derivatives

Based on the antitumor activity of chalcone derivatives and their structure, the structure-activity relationship of chalcone derivatives was analyzed by CoMFA (comparative molecular field analysis) method and then the 3D-QSAR (the three dimensional quantitative structures activity relationship) model was established. The analytical results showed that the model had good stability and prediction ability with cross-validated Q2 and non-cross-validated R2 values of 0.527 and 0.995, respectively. The contour map of the model explained the relationship between the structure of chalcone derivatives and antitumor activity, and could be analyzed to design antitumor chalcone derivatives. We designed some structure of chalcone derivatives and calculated their antitumor activity. In this paper, 24 chalcone derivatives were studied by CoMFA, 3D-QSAR, molecular design, which provided theoretical for designing good activities chalcone derivatives.

Synthesis and Structure-Antitumor Activity of 4,6-Diamino-1,2-Dihydro-2 ,2-Dimethyl-1-(Substituted-Naphthyl- 2)-1,3,5-Triazines

Introduction 4,6-Diamino-1,2-dihydro-2,2-dimethyl-1-substituted-1,3,5-triazines (Ⅰ) are dihydrofolate reductase(DHFR) inhibitors, hence they possess the inhibition to the growth of bacteria and cancer cells. Baker’s antifolate (Ⅱ) has shown promise as an antitumor agertt in clinical trials. The study of the structure-activity relationships of I shows that the inhibition to vertebrate DHFR is significantly influenced by the hydrophobicity of 1-substituent, i.e., the stronger the hydrophobicity of the 1-substituent, the more potent the inhibition of the compound to vertebrate DHFR.

新型查尔酮衍生物抗乳腺癌活性的3D-QSAR模型构建及分子设计

为了获得较高抗乳腺癌活性的新型化合物,对18个新型查尔酮衍生物抗乳腺癌活性(pIC_(50))进行了三维定量构效关系(3D-QSAR)研究。其中14个化合物作为训练集用于构建3D-QSAR模型,其余化合物(含模板分子)作为测试集对所建模型进行验证。所建3D-QSAR模型的交叉验证系数R^(2)_(CV)为0.569,非交叉验证系数R^(2)为0.974,说明所建模型具有良好的稳定性和预测能力。该模型中立体场、静电场对pIC_(50)的贡献分别为58.8%和41.2%,表明影响该类化合物抗肿瘤活性的主要因素是取代基的疏水性、空间位阻和电荷分布。通过对模型的分析,设计了5个具有较高pIC_(50)的新化合物,有待通过后续医学实验加以验证。

机器学习预测有机水污染物光催化降解速率

为了预测有机污染物的光催化降解速率,探究污染物分子结构与其降解速率之间的构效关系,设计了一种基于分子指纹的机器学习模型。该模型使用81种有机污染物的523条记录作为模型数据,将污染物MACCS分子指纹与5种实验条件(辐照度、温度、催化剂用量、污染物初始浓度和pH值)作为输入特征,采用10种机器学习算法进行建模。结果显示LightGBM算法性能最佳(R~2=0.909 4)。利用沙普利加法解释(Shapley additive explanations, SHAP)框架评估了各输入特征对光催化降解速率的贡献程度,探讨了各输入特征影响光催化降解速率的具体原因。分析表明,在光催化降解中污染物本身结构特征是影响光催化降解速率的主要原因。而且结构中含有卤素原子、N原子和不饱和碳的污染物分子降解速率最快,而结构中含有醚键或羰基的污染物分子降解速率最慢。