Xun Li

Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes

Many molecular docking programs are available nowadays, and thus it is of great practical value to evaluate and compare their performance. We have conducted an extensive evaluation of four popular commercial molecular docking programs, including Glide, GOLD, LigandFit, and Surflex. Our test set consists of 195 protein‐ligand complexes with high‐resolution crystal structures (resolution ≤2.5 Å) and reliable binding data [dissociation constant (Kd) or inhibition constant (Ki)], which are selected from the PDBbind database with an emphasis on diversity. The top‐ranked solutions produced by these programs are compared to the native ligand binding poses observed in crystal structures. Glide and GOLD demonstrate better accuracy than the other two on the entire test set. Their results are also less sensitive to the starting structures for docking. Comparison of the results produced by these programs at three different computation levels reveal that their accuracy are not always proportional to CPU cost as one may expect. The binding scores of the top‐ranked solutions produced by these programs are in low to moderate correlations with experimentally measured binding data. Further analyses on the outcomes of these programs on three suites of subsets of protein‐ligand complexes indicate that these programs are less capable to handle really flexible ligands and relatively flat binding sites, and they have different preferences to hydrophilic/hydrophobic binding sites. Our evaluation can help other researchers to make reasonable choices among available molecular docking programs. It is also valuable for program developers to improve their methods further.

Discovery and Development of Thiazolo[3,2-a]pyrimidinone Derivatives as General Inhibitors of Bcl-2 Family Proteins

A class of compounds with a common thiazolo[3,2‐a]pyrimidinone motif has been developed as general inhibitors of Bcl‐2 family proteins. The lead compound was originally identified in a random screening of a small compound library using a fluorescence polarization‐based competitive binding assay. Its binding to the Bcl‐xL protein was further confirmed by 15N‐HSQC NMR experiments. Structural modifications on the lead compound were guided by the outcomes of molecular modeling studies. Among the 42 compounds obtained, a number of them exhibited much improved binding affinities to Bcl‐2 family proteins as compared to the lead compound. The most potent compound, BCL‐LZH‐40, inhibited the binding of BH3 peptides to Bcl‐xL, Bcl‐2, and Mcl‐1 with inhibition constants (Ki) of 17, 534, and 200 nM, respectively.

Interpretation of the binding affinities of PTP1B inhibitors with the MM-GB/SA method and the X-score scoring function.

We have studied the binding affinities of a set of 45 small-molecule inhibitors to protein tyrosine phosphatase 1B (PTP1B) through computational approaches. All of these compounds share a common oxalylamino benzoic acid (OBA) moiety. The complex structure of each compound was modeled by using the GOLD program plus the ASP scoring function. Each complex structure was then subjected to a molecular dynamics (MD) simulation of 2 ns long by using the AMBER program. Based on the configurational ensembles retrieved from MD trajectories, both MM-GB/SA and MM-PB/SA were employed to compute the binding free energies of all 45 PTP1B inhibitors. The correlation coefficient between the MM-GB/SA results and experimental binding data was 0.87 and the standard deviation was 0.60 kcal/mol. The performance of MM-PB/SA was slightly inferior to that of MM-GB/SA. Several aspects of the MM-GB(PB)/SA method were explored in our study to obtain optimized results. The X-Score scoring function was found to produce equally good results as MM-GB/SA on both the complex structures prepared by molecular docking and the configurational ensembles obtained through lengthy MD simulations. The structure-activity relationship of this set of compounds is also discussed based on the computed results. The computational approaches validated in our study are hopefully applicable to the study of other classes of PTP1B inhibitors.

Probing the Key Interactions between Human Atg5 and Atg16 Proteins: A Prospective Application of Molecular Modeling

Autophagy, a conserved catabolic process in eukaryotes, mediates the nutrient and energy homeostasis via the degradation of intracellular cytoplasmic materials and organelles by lysosomal machinery. It serves as a recycling mechanism for the rejuvenation of cell contents, which is known to be critical in various biological events, such as starvation response, neurodegeneration, immunity, inflammation, cancer and so on. Due to its complex nature, autophagy actually plays a dual role in relevant diseases. For example, as most anticancer drugs cause cellular stress, autophagy is often activated in cancer cells after drug treatment to promote cell survival under stress. Thus, both inducers and inhibitors of autophagy have been investigated for their potential therapeutic applications. Studies on the molecular mechanism of autophagy began with the identification of the autophagy-related (ATG) genes in yeast in the 1990s. So far, many protein factors have been found to participate in the formation of autophagosome. In particular, formation of the preautophagosomal structure (PAS) is essential for autophagy execution in yeast as well as mammals (Figure 1). Two ubiquitin-like protein conjugates are known to be involved in this process: Atg8–phosphatidylethanolamine (PE) and the Atg12–Atg5–Atg16 complex. Atg8–PE is equivalent to the LC3–phosphatidylethanolamine conjugate (LC3-II) in mammals. Atg8 was found to act as an important component of the scaffold during membrane expansion of PAS, and its amount in cell quantitatively relates to the PAS vesicle size. The Atg12–Atg5–Atg16 complex, which facilitates the lipidation of Atg8, is also believed to help direct Atg8–PE towards PAS. Mutations in the Atg12– Atg5–Atg16 complex lead to a decrease in Atg8–PE/LC3-II levels as well as a defective autophagosome. The studies mentioned above illustrate the importance of the Atg12–Atg5–Atg16 complex in autophagy. It is possible to develop effective autophagy inhibitors by targeting the protein–protein interactions (PPI) between Atg5–Atg16 and Atg5– Atg12. Atg12 is known to be covalently linked with Atg5 via a glycine residue at its C terminus. Mediated by the oligomerization of Atg16, the Atg12–Atg5 complex interacts noncova-

A Statistical Survey on the Binding Constants of Covalently Bound Protein-Ligand Complexes.

We have conducted a statistical survey to compare the binding constants of covalently and noncovalently bound protein–ligand complexes as two groups. In our study, a total of 1602 complexes formed between various types of proteins and small‐molecule ligands were selected from the PDBbind database (version 2008), all of which had high‐resolution three‐dimensional structures and reliable experimentally measured binding constants. These complexes were further classified as 79 covalent complexes, 131 Zn‐containing complexes, and 1392 noncovalent complexes. Covalent complexes formed through reversible mechanisms are found to be associated with higher binding constants than noncovalent complexes. Two‐sample T‐test indicates that the difference is statistically significant. The advantage, however, is only modest (<20 folds). The same trend is also observed on a set of covalent and noncovalent complexes formed by thrombin. Our results indicate that reversible covalent bonding formed between protein and ligand will not automatically lead to a much tighter binding in general. Thus, our survey does not provide any supporting evidence for Houk’s hypothesis which states that covalent bonding formed between enzyme and transition state accounts for the extraordinary proficiency of enzymes.