Jianfeng Pei

Biosynthesis, Purification, and Substrate Specificity of Severe Acute Respiratory Syndrome Coronavirus 3C-like Proteinase

The 3C-like proteinase of severe acute respiratory syndrome (SARS) coronavirus has been proposed to be a key target for structural-based drug design against SARS. In order to understand the active form and the substrate specificity of the enzyme, we have cloned, expressed, and purified SARS 3C-like proteinase. Analytic gel filtration shows a mixture of monomer and dimer at a protein concentration of 4 mg/ml and mostly monomer at 0.2 mg/ml, which correspond to the concentration used in the enzyme assays. The linear decrease of the enzymatic-specific activity with the decrease of enzyme concentration revealed that only the dimeric form is active and the dimeric interface could be targeted for structural-based drug design against SARS 3C-like proteinase. By using a high pressure liquid chromatography assay, SARS 3C-like proteinase was shown to cut the 11 peptides covering all of the 11 cleavage sites on the viral polyprotein with different efficiency. The two peptides corresponding to the two self-cleavage sites are the two with highest cleavage efficiency, whereas peptides with non-canonical residues at P2 or P1′ positions react slower. The P2 position of the substrates seems to favor large hydrophobic residues. Secondary structure studies for the peptide substrates revealed that substrates with more β-sheetlike structure tend to react fast. This study provides a basic understanding of the enzyme catalysis and a full substrate specificity spectrum for SARS 3C-like proteinase, which are helpful for structural-based inhibitor design against SARS and other coronavirus.

Discovery of multitarget inhibitors by combining molecular docking with common pharmacophore matching

Multitarget drugs have been to be found effective in controlling complex diseases. However, how to design multitarget drugs presents a great challenge. We have developed a computer-assisted strategy to screen for multitarget inhibitors using a combination of molecular docking and common pharmacophore matching. This strategy was successfully applied to screen for dual-target inhibitors against both the human leukotriene A(4) hydrolase (LTA4H-h) and the human nonpancreatic secretory phospholipase A2 (hnps-PLA2). Three compounds screened from the chemical database MDL Available Chemical Directory were found to inhibit these two enzymes at the 10 microM level. Moreover, one synthetic compound matching the common pharmacophores inhibits LTA4H-h and hnps-PLA2 with IC(50) values of 35 nM and 7.3 microM, respectively. The common pharmacophore model can also be used to search small molecule databases or collections of existing inhibitors, as well as to guide combinatorial library design to search for ideal multitarget inhibitors.

LigBuilder 2: A Practical de Novo Drug Design Approach

We have developed a new version (2.0) of the de novo drug design program LigBuilder. With LigBuilder 2.0, the synthesis accessibility of designed compounds can be analyzed, and a cavity detection procedure is implemented to detect the positions and shapes of the binding sites on the surface of a given protein structure and to quantitatively estimate drugability. Ligands are designed to best fit the detected cavities using a set of rules for evaluation. Drug-like and privileged fragments are used to construct the ligands with the aid of internal and external absorption, distribution, metabolism, excretion, and toxicity (ADME/T) and drug-like filters.

Discovering potent small molecule inhibitors of cyclophilin A using de novo drug design approach.

This work describes an integrated approach of de novo drug design, chemical synthesis, and bioassay for quick identification of a series of novel small molecule cyclophilin A (CypA) inhibitors (1-3). The activities of the two most potent CypA inhibitors (3h and 3i) are 2.59 and 1.52 nM, respectively, which are about 16 and 27 times more potent than that of cyclosporin A. This study clearly demonstrates the power of our de novo drug design strategy and the related program LigBuilder 2.0 in drug discovery.

PSI-DOCK: towards highly efficient and accurate flexible ligand docking.

We have developed a new docking method, Pose‐Sensitive Inclined (PSI)‐DOCK, for flexible ligand docking. An improved SCORE function has been developed and used in PSI‐DOCK for binding free energy evaluation. The improved SCORE function was able to reproduce the absolute binding free energies of a training set of 200 protein–ligand complexes with a correlation coefficient of 0.788 and a standard error of 8.13 kJ/mol. For ligand binding pose exploration, a unique searching strategy was designed in PSI‐DOCK. In the first step, a tabu‐enhanced genetic algorithm with a rapid shape‐complementary scoring function is used to roughly explore and store potential binding poses of the ligand. Then, these predicted binding poses are optimized and compete against each other by using a genetic algorithm with the accurate SCORE function to determine the binding pose with the lowest docking energy. The PSI‐DOCK 1.0 program is highly efficient in identifying the experimental binding pose. For a test dataset of 194 complexes, PSI‐DOCK 1.0 achieved a 67% success rate (RMSD <2.0 Å) for only one run and a 74% success rate for 10 runs. PSI‐DOCK can also predict the docking binding free energy with high accuracy. For a test set of 64 complexes, the correlation between the experimentally observed binding free energies and the docking binding free energies for 64 complexes is r = 0.777 with a standard deviation of 7.96 kJ/mol. Moreover, compared with other docking methods, PSI‐DOCK 1.0 is extremely easy to use and requires minimum docking preparations. There is no requirement for the users to add hydrogen atoms to proteins because all protein hydrogen atoms and the flexibility of the terminal protein atoms are intrinsically taken into account in PSI‐DOCK. There is also no requirement for the users to calculate partial atomic charges because PSI‐DOCK does not calculate an electrostatic energy term. These features are not only convenient for the users but also help to avoid the influence of different preparation methods. Proteins 2006.

A large descriptor set and a probabilistic kernel-based classifier significantly improve druglikeness classification.

Probabilistic support vector machine (SVM) in combination with ECFP_4 (Extended Connectivity Fingerprints) were applied to establish a druglikeness filter for molecules. Here, the World Drug Index (WDI) and the Available Chemical Directory (ACD) were used as surrogates for druglike and nondruglike molecules, respectively. Compared with published methods using the same data sets, the classifier significantly improved the prediction accuracy, especially when using a larger data set of 341 601 compounds, which further pushed the correct classification rates up to 92.73%. On the other hand, most characteristic features for drugs and nondrugs found by the current method were visualized, which might be useful as guiding fragments for de novo drug design and fragment based drug design.

De Novo Design of Multitarget Ligands with an Iterative Fragment-Growing Strategy

The discovery of multitarget drugs has recently attracted much attention. Most of the reported multitarget ligands have been serendipitous discoveries. Although a few methods have been developed for rational multitarget drug discovery, there is a lack of elegant methods for de novo multitarget drug design and optimization, especially for multiple targets with large differences in their binding sites. In this paper, we report the first de novo multitarget ligand design method, with an iterative fragment-growing strategy. Using this method, dual-target inhibitors for COX-2 and LTA₄H were designed, with the most potent one inhibiting PGE₂ and LTB₄ production in the human whole blood assay with IC₅₀ values of 7.0 and 7.1 μM, respectively. Our strategy is generally applicable in rational and efficient multitarget drug design, especially for the design of highly integrated inhibitors for proteins with dissimilar binding pockets.

Computational Multitarget Drug Design

Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.

Quaternary Structure, Substrate Selectivity and Inhibitor Design for SARS 3C-Like Proteinase

The SARS coronavirus 3C-like proteinase is recognized as a potential drug design target for the treatment of severe acute respiratory syndrome. In the past few years, much work has been done to understand the catalytic mechanism of this target protein and to design its selective inhibitors. The protein exists as a dimer/monomer mixture in solution and the dimer was confirmed to be the active species for the enzyme reaction. Quantitative dissociation constants have been reported for the dimer by using analytic ultracentrifuge, gel filtration and enzyme assays. Though the enzyme is a cysteine protease with a chymotrypsin fold, SARS 3C-like proteinase follows the general base catalytic mechanism similar to chymotrypsin. As the enzyme can cut eleven different sites on the viral polyprotein, the substrate specificity has been studied by synthesized peptides corresponding or similar to the cleavage sites on the polyprotein. Predictive model was built for substrate structure and activity relationships and can be applied in inhibitor design. Due to the lack of potential drugs for the treatment of SARS, the discovery of inhibitors against SARS 3C-like proteinase, which can potentially be optimized as drugs appears to be highly desirable. Various groups have been working on inhibitor discovery by virtual screening, compound library screening, modification of existing compounds or natural products. High-throughput in vitro assays, auto-cleavage assays and viral replication assays have been developed for inhibition activity tests. Inhibitors with IC50 values as low as 60 nM have been reported.

Protein-Protein Interface Analysis and Hot Spots Identification for Chemical Ligand Design

Rational design for chemical compounds targeting protein-protein interactions has grown from a dream to reality after a decade of efforts. There are an increasing number of successful examples, though major challenges remain in the field. In this paper, we will first give a brief review of the available methods that can be used to analyze protein-protein interface and predict hot spots for chemical ligand design. New developments of binding sites detection, ligandability and hot spots prediction from the authors group will also be described. Pocket V.3 is an improved program for identifying hot spots in protein-protein interface using only an apo protein structure. It has been developed based on Pocket V.2 that can derive receptor-based pharmacophore model for ligand binding cavity. Given similarities and differences between the essence of pharmacophore and hot spots for guiding design of chemical compounds, not only energetic but also spatial properties of protein-protein interface are used in Pocket V.3 for dealing with protein-protein interface. In order to illustrate the capability of Pocket V.3, two datasets have been used. One is taken from ASEdb and BID having experimental alanine scanning results for testing hot spots prediction. The other is taken from the 2P2I database containing complex structures of protein-ligand binding at the original protein-protein interface for testing hot spots application in ligand design.