Jiazhong Li

Molecular modeling study of checkpoint kinase 1 inhibitors by multiple docking strategies and prime/MM–GBSA calculation

Developing chemicals that inhibit checkpoint kinase 1 (Chk1) is a promising adjuvant therapeutic to improve the efficacy and selectivity of DNA‐targeting agents. Reliable prediction of binding‐free energy and binding affinity of Chk1 inhibitors can provide a guide for rational drug design. In this study, multiple docking strategies and Prime/Molecular Mechanics Generalized Born Surface Area (Prime/MM‐GBSA) calculation were applied to predict the binding mode and free energy for a series of benzoisoquinolinones as Chk1 inhibitors. Reliable docking results were obtained using induced‐fit docking and quantum mechanics/molecular mechanics (QM/MM) docking, which showed superior performance on both ligand binding pose and docking score accuracy to the rigid‐receptor docking. Then, the Prime/MM–GBSA method based on the docking complex was used to predict the binding‐free energy. The combined use of QM/MM docking and Prime/MM–GBSA method could give a high correlation between the predicted binding‐free energy and experimentally determined pIC50. The molecular docking combined with Prime/MM–GBSA simulation can not only be used to rapidly and accurately predict the binding‐free energy of novel Chk1 inhibitors but also provide a novel strategy for lead discovery and optimization targeting Chk1.

QSAR study of malonyl‐CoA decarboxylase inhibitors using GA‐MLR and a new strategy of consensus modeling

Quantitative structure‐activity relationship (QSAR) of a series of structural diverse malonyl‐CoA decarboxylase (MCD) inhibitors have been investigated by using the predictive single model as well as the consensus analysis based on a new strategy proposed by us. Self‐organizing map (SOM) neural network was employed to divide the whole data set into representative training set and test set. Then a multiple linear regressions (MLR) model population was built based on the theoretical molecular descriptors selected by Genetic Algorithm using the training set. In order to analyze the diversity of these models, multidimensional scaling (MDS) was employed to explore the model space based on the Hamming distance matrix calculated from each two models. In this space, Q2 (cross‐validated R2) guided model selection (QGMS) strategy was performed to select submodels. Then consensus modeling was built by two strategies, average consensus model (ACM) and weighted consensus model (WCM), where each submodel had a different weight according to the contribution of model expressed by MLR regression coefficients. The obtained results prove that QGMS is a reliable and practical method to guide the submodel selection in consensus modeling building and our weighted consensus model (WCM) strategy is superior to the simple ACM.

Exploring the Influence of Carbon Nanoparticles on the Formation of β-Sheet-Rich Oligomers of IAPP22–28 Peptide by Molecular Dynamics Simulation

Recent advances in nanotechnologies have led to wide use of nanomaterials in biomedical field. However, nanoparticles are found to interfere with protein misfolding and aggregation associated with many human diseases. It is still a controversial issue whether nanoparticles inhibit or promote protein aggregation. In this study, we used molecular dynamics simulations to explore the effects of three kinds of carbon nanomaterials including graphene, carbon nanotube and C60 on the aggregation behavior of islet amyloid polypeptide fragment 22–28 (IAPP22–28). The diverse behaviors of IAPP22–28 peptides on the surfaces of carbon nanomaterials were studied. The results suggest these nanomaterials can prevent β-sheet formation in differing degrees and further affect the aggregation of IAPP22–28. The π–π stacking and hydrophobic interactions are different in the interactions between peptides and different nanoparticles. The subtle differences in the interaction are due to the difference in surface curvature and area. The results demonstrate the adsorption interaction has competitive advantages over the interactions between peptides. Therefore, the fibrillation of IAPP22–28 may be inhibited at its early stage by graphene or SWCNT. Our study can not only enhance the understanding about potential effects of nanomaterials to amyloid formation, but also provide valuable information to develop potential β-sheet formation inhibitors against type II diabetes.

Global, local and novel consensus quantitative structure-activity relationship studies of 4-(Phenylaminomethylene) isoquinoline-1, 3 (2H, 4H)-diones as potent inhibitors of the cyclin-dependent kinase 4

Quantitative structure-activity relationship (QSAR) studies on a series of selective inhibitors of the cyclin-dependent kinase 4 (CDK4) were performed by using two conventional global modeling methods (multiple linear regression (MLR) and support vector machine (SVM)), local lazy regression (LLR) as well as three consensus models. It is remarkable that the LLR model could improve the performance of the QSAR model significantly. In addition, due to the fact that each model can predict certain compounds more accurately than other models, the above three derived models were all used as submodels to build consensus models using three different strategies: average consensus model (ACM), simple weighted consensus model (SWCM) and hat weighted consensus model (HWCM). Through the analysis of the results, the HWCM consensus strategy, firstly proposed in this work, proved to be more reliable and robust than the best single LLR model, ACM and SWCM models.

QSAR classification of estrogen receptor binders and pre-screening of potential pleiotropic EDCs

Endocrine disrupting chemicals (EDCs) are suspected of posing serious threats to human and wildlife health through a variety of mechanisms, these being mainly receptor-mediated modes of action. It is reported that some EDCs exhibit dual activities as estrogen receptor (ER) and androgen receptor (AR) binders. Indeed, such compounds can affect the normal endocrine system through a dual complex mechanism, so steps should be taken not only to identify them a priori from their chemical structure, but also to prioritize them for experimental tests in order to reduce and even forbid their usage. To date, very few EDCs with dual activities have been identified. The present research uses QSARs, to investigate what, so far, is the largest and most heterogeneous ER binder data set (combined METI and EDKB databases). New predictive classification models were derived using different modelling methods and a consensus approach, and these were used to virtually screen a large AR binder data set after strict validation. As a result, 46 AR antagonists were predicted from their chemical structure to also have potential ER binding activities, i.e. pleiotropic EDCs. In addition, 48 not yet recognized ER binders were in silico identified, which increases the number of potential EDCs that are substances of very high concern (SVHC) in REACH. Thus, the proposed screening models, based only on structure information, have the main aim to prioritize experimental tests for the highlighted compounds with potential estrogenic activities and also to design safer alternatives.

A novel method for protein‐ligand binding affinity prediction and the related descriptors exploration

In this study, a novel method was developed to predict the binding affinity of protein‐ligand based on a comprehensive set of structurally diverse protein‐ligand complexes (PLCs). The 1300 PLCs with binding affinity (493 complexes with Kd and 807 complexes with Ki) from the refined dataset of PDBbind Database (release 2007) were studied in the predictive model development. In this method, each complex was described using calculated descriptors from three blocks: protein sequence, ligand structure, and binding pocket. Thereafter, the PLCs data were rationally split into representative training and test sets by full consideration of the validation of the models. The molecular descriptors relevant to the binding affinity were selected using the ReliefF method combined with least squares support vector machines (LS‐SVMs) modeling method based on the training data set. Two final optimized LS‐SVMs models were developed using the selected descriptors to predict the binding affinities of Kd and Ki. The correlation coefficients (R) of training set and test set for Kd model were 0.890 and 0.833. The corresponding correlation coefficients for the Ki model were 0.922 and 0.742, respectively. The prediction method proposed in this work can give better generalization ability than other recently published methods and can be used as an alternative fast filter in the virtual screening of large chemical database.

QSAR classification models for the screening of the endocrine-disrupting activity of perfluorinated compounds

Perfluorinated compounds (PFCs) are a class of emerging pollutants still widely used in different materials as non-adhesives, waterproof fabrics, fire-fighting foams, etc. Their toxic effects include potential for endocrine-disrupting activity, but the amount of experimental data available for these pollutants is limited. The use of predictive strategies such as quantitative structure–activity relationships (QSARs) is recommended under the REACH regulation, to fill data gaps and to screen and prioritize chemicals for further experimentation, with a consequent reduction of costs and number of tested animals. In this study, local classification models for PFCs were developed to predict their T4-TTR (thyroxin-transthyretin) competing potency. The best models were selected by maximizing the sensitivity and external predictive ability. These models, characterized by robustness, good predictive power and a defined applicability domain, were applied to predict the activity of 33 other PFCs of environmental concern. Finally, classification models recently published by our research group for T4-TTR binding of brominated flame retardants and for estrogenic and anti-androgenic activity were applied to the studied perfluorinated chemicals to compare results and to further evaluate the potential for these PFCs to cause endocrine disruption.

Novel approaches for retention time prediction of oligonucleotides in ion-pair reversed-phase high-performance liquid chromatography

The base sequence autocorrelation (BSA) descriptors were used to describe structures of oligonucleotides and to develop accurate quantitative structure-retention relationship (QSRR) models of oligonucleotides in ion-pair reversed-phase high-performance liquid chromatography. Through the combination use of multiple linear regression (MLR) and genetic algorithm (GA), QSRR models were developed at temperatures of 30 degrees C, 40 degrees C, 50 degrees C, 60 degrees C and 80 degrees C, respectively. Satisfactory results were obtained for the single-temperature models (STM). Multi-temperature model (MTM) was also developed that can be used for predicting the retention time at any temperature. The correlation coefficients of retention time prediction for the test set based on the MTM model at 30 degrees C, 40 degrees C, 50 degrees C, 60 degrees C and 80 degrees C were 0.978, 0.982, 0.989, 0.988 and 0.996, respectively. The corresponding absolute average relative deviations (AARD) for the test set at each temperature were all less than 1%. The new strategy of feature representation and multi-temperatures modeling is a very promising tool for QSRR modeling with good predictive ability for predicting retention time of oligonucleotides at multiple temperatures under the studied condition.

In Silico Identification of Protein S-Palmitoylation Sites and Their Involvement in Human Inherited Disease.

S-Palmitoylation is a key regulatory mechanism controlling protein targeting, localization, stability, and activity. Since increasing evidence shows that its disruption is implicated in many human diseases, the identification of palmitoylation sites is attracting more attention. However, the computational methods that are published so far for this purpose have suffered from a poor balance of sensitivity and specificity; hence, it is difficult to get a good generalized prediction ability on an external validation set, which holds back the further analysis of associations between disruption of palmitoylation and human inherited diseases. In this work, we present a reliable identification method for protein S-palmitoylation sites, called SeqPalm, based on a series of newly composed features from protein sequences and the synthetic minority oversampling technique. With only 16 extracted key features, this approach achieves the most favorable prediction performance up to now with sensitivity, specificity, and Matthews correlation coefficient values of 95.4%, 96.3%, and 0.917, respectively. Then, all known disease-associated variations are studied by SeqPalm. It is found that 243 potential loss or gain of palmitoylation sites are highly associated with human inherited disease. The analysis presents several potential therapeutic targets for inherited diseases associated with loss or gain of palmitoylation function. There are even biological evidence that are coordinate with our prediction results. Therefore, this work presents a novel approach to discover the molecular basis of pathogenesis associated with abnormal palmitoylation. SeqPalm is now available online at http://lishuyan.lzu.edu.cn/seqpalm , which can not only annotate the palmitoylation sites of proteins but also distinguish loss or gain of palmitoylation sites by protein variations.

Rational prediction of the herbicidal activities of novel protoporphyrinogen oxidase inhibitors by quantitative structure-activity relationship model based on docking-guided active conformation.

Molecular docking-guided active conformation selection was used in a quantitative structure-activity relationship (QSAR) study of a series of novel protoporphyrinogen oxidase (PPO) inhibitors with herbicidal activities. The developed model can be used for the rational and accurate prediction of herbicidal activities of these inhibitors from their molecular structures. Molecular docking study was carried out to dock the inhibitors into the PPO active site and to obtain the rational active conformations. Based on the conformations generated from molecular docking, satisfactory predictive results were obtained by a genetic algorithm-multiple linear regression (GA-MLR) model according to the internal and external validations. The model gave a correlation coefficient R(2) of 0.972 and 0.953 and an absolute average relative deviation AARD of 2.24% and 2.75% for the training set and test set, respectively. The results from this work demonstrate that the molecular docking-guided active conformation selection strategy is rational and useful in the QSAR study of these PPO inhibitors and for the quantitative prediction of their herbicidal activities. The results obtained could be helpful to the design of new derivatives with potential herbicidal activities.