Lili Xi

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.

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.

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.

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.

A combined molecular modeling study on gelatinases and their potent inhibitors

Zinc‐dependent matrix metalloproteinase (MMP) family is considered to be an attractive target because of its important role in many physiological and pathological processes. In the present work, a molecular modeling study combining protein‐, ligand‐ and complex‐based computational methods was performed to analyze a new series of β‐N‐biaryl ether sulfonamide hydroxamates as potent inhibitors of gelatinase A (MMP‐2) and gelatinase B (MMP‐9). Firstly, the similarities and differences between the binding sites of MMP‐2 and MMP‐9 were analyzed through sequence alignment and structural superimposition. Secondly, in order to extract structural features influencing the activities of these inhibitors, quantitative structure‐activity relationship (QSAR) models using genetic algorithm‐multiple linear regression (GA‐MLR), comparative molecular field (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed. The proposed QSAR models could give good predictive ability for the studied inhibitors. Thirdly, docking study was employed to further explore the binding mode between the ligand and protein. The results from all the above analyses could provide the information about the similarities and differences of the binding mode between the MMP‐2, MMP‐9 and their potent inhibitors. The obtained results can provide very useful information for the design of new potential inhibitors.

Molecular modeling studies of Rho kinase inhibitors using molecular docking and 3D-QSAR analysis

Rho kinase (ROCK) has become an attractive target for the treatment of many diseases such as hypertension, stroke and cancer. In this work, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed on a series of ROCK inhibitors. Molecular docking was used to explore the binding mode between the ligands and the receptor. Based on the docked conformations, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed to gain insights into the key structural factors affecting the activity. The results of the molecular modeling studies suggested that further modification of these inhibitors with bulky and hydrophobic groups that accommodated in the distal region of the ROCK binding pocket would improve the activity.

A sequence-based computational model for the prediction of the solvent accessible surface area for α-helix and β-barrel transmembrane residues.

Predicting the solvent accessible surface area (ASA) of transmembrane (TM) residues is of great importance for experimental researchers to elucidate diverse physiological processes. TM residues fall into two major structural classes (α‐helix membrane protein and β‐barrel membrane protein). The reported solvent ASA prediction models were developed for these two types of TM residues respectively. However, this prevents the general use of these methods because one cannot determine which model is suitable for a given TM residue without information of its type. To conquer this limitation, we developed a new computational model that can be used for predicting the ASA of both TM α‐helix and β‐barrel residues. The model was developed from 78 α‐helix membrane protein chains and 24 β‐barrel membrane protein. Its prediction ability was evaluated by cross validation method and its prediction result on an independent test set of 20 membrane protein chains. The results show that our model performs well for both types of TM residues and outperforms other prediction model which was developed for the specific type of TM residues. The prediction results also proved that the random forest model incorporating conservation score is an effective sequence‐based computational approach for predicting the solvent ASA of TM residues.

Global and local prediction of protein folding rates based on sequence autocorrelation information.

To understand the folding behavior of proteins is an important and challenging problem in modern molecular biology. In the present investigation, a large number of features representing protein sequences were developed based on sequence autocorrelation weighted by properties of amino acid residues. Genetic algorithm (GA) combined with multiple linear regression (MLR) was employed to select significant features related to protein folding rates, and to build global predictive model. Moreover, local lazy regression (LLR) method was also used to predict the protein folding rates. The obtained results indicated that LLR performed much better than the global MLR model. The important properties of amino acid residues affecting protein folding rates were also analyzed. The results of this study will be helpful to understand the mechanism of protein folding. Our results also demonstrate that the features of amino acid sequence autocorrelation is effective in representing the relationship between protein sequence and folding rates, and the local method is a powerful tool to predict the protein folding rates.

Structure‐based quantitative structure‐activity relationship studies of checkpoint kinase 1 inhibitors

Structure‐based quantitative structure‐activity relationship (QSAR) studies on a series of checkpoint kinase 1 (Chk1) inhibitors were performed to find the key structural features responsible for their inhibitory activity. Molecular docking was employed to explore the binding mode of all inhibitors at the active site of Chk1 and determine the active conformation for the QSAR studies. Ligand and structure‐based descriptors incorporating the ligand‐receptor interaction were generated based on the docked complex. Genetic Algorithm‐Multiple Linear Regression (GA‐MLR) method was used to build 2D QSAR model. The 2D QSAR model gave a squared correlation coefficient R2 of 0.887, cross‐validated Q2 of 0.837 and the prediction squared correlation coefficient R  2pred of 0.849, respectively. Furthermore, three‐dimensional quantitative structure‐activity relationship (3D QSAR) model using comparative molecular field analysis (CoMFA) with R2 of 0.983, Q2 of 0.550 and R  2pred of 0.720 was also developed. The obtained results are helpful for the design of novel Chk1 inhibitors with improved activities.