Renxiao Wang

Further development and validation of empirical scoring functions for structure-based binding affinity prediction

New empirical scoring functions have been developed to estimate the binding affinity of a given protein-ligand complex with known three-dimensional structure. These scoring functions include terms accounting for van der Waals interaction, hydrogen bonding, deformation penalty, and hydrophobic effect. A special feature is that three different algorithms have been implemented to calculate the hydrophobic effect term, which results in three parallel scoring functions. All three scoring functions are calibrated through multivariate regression analysis of a set of 200 protein-ligand complexes and they reproduce the binding free energies of the entire training set with standard deviations of 2.2 kcal/mol, 2.1 kcal/mol, and 2.0 kcal/mol, respectively. These three scoring functions are further combined into a consensus scoring function, X-CSCORE. When tested on an independent set of 30 protein-ligand complexes, X-CSCORE is able to predict their binding free energies with a standard deviation of 2.2 kcal/mol. The potential application of X-CSCORE to molecular docking is also investigated. Our results show that this consensus scoring function improves the docking accuracy considerably when compared to the conventional force field computation used for molecular docking.

A New Atom-Additive Method for Calculating Partition Coefficients

A new method is presented for the calculation of partition coefficients of solutes in octanol/water. Our algorithm, XLOGP, is based on the summation of atomic contributions and includes correction factors for some intramolecular interactions. Using this method, we calculate the log P of 1831 organic compounds and analyze the derived parameters by multivariate regression to generate the final model. The correlation coefficient for fitting this training database is 0.968, and the standard deviation is 0.37. The result shows that our method for log P estimation is applicable to quantitative structure−activity relationship studies and gives better results than other more complicated atom-additive methods.

Discovery of embelin as a cell-permeable, small-molecular weight inhibitor of XIAP through structure-based computational screening of a traditional herbal medicine three-dimensional structure database.

The X-linked inhibitor of apoptosis (XIAP) is a promising new molecular target for the design of novel anticancer drugs aiming at overcoming apoptosis-resistance of cancer cells to chemotherapeutic agents and radiation therapy. Recent studies demonstrated that the BIR3 domain of XIAP where caspase-9 and Smac proteins bind is an attractive site for designing small-molecule inhibitors of XIAP. Through computational structure-based screening of an in-house traditional herbal medicine three-dimensional structure database of 8221 individual natural products, followed by biochemical testing of selected candidate compounds, we discovered embelin from the Japanese Ardisia herb as a small-molecular weight inhibitor that binds to the XIAP BIR3 domain. We showed that embelin binds to the XIAP BIR3 protein with an affinity similar to that of the natural Smac peptide using a fluorescence polarization-based binding assay. Our NMR analysis further conclusively confirmed that embelin interacts with several crucial residues in the XIAP BIR3 domain with which Smac and caspsase-9 bind. Embelin inhibits cell growth, induces apoptosis, and activates caspase-9 in prostate cancer cells with high levels of XIAP, but has a minimal effect on normal prostate epithelial and fibroblast cells with low levels of XIAP. In stably XIAP-transfected Jurkat cells, embelin effectively overcomes the protective effect of XIAP to apoptosis and enhances the etoposide-induced apoptosis and has a minimal effect in Jurkat cells transfected with vector control. Taken together, our results showed that embelin is a fairly potent, nonpeptidic, cell-permeable, small-molecule inhibitor of XIAP and represents a promising lead compound for designing an entirely new class of anticancer agents that target the BIR3 domain of XIAP.

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.

An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes

Fourteen popular scoring functions, i.e., X-Score, DrugScore, five scoring functions in the Sybyl software (D-Score, PMF-Score, G-Score, ChemScore, and F-Score), four scoring functions in the Cerius2 software (LigScore, PLP, PMF, and LUDI), two scoring functions in the GOLD program (GoldScore and ChemScore), and HINT, were tested on the refined set of the PDBbind database, a set of 800 diverse protein-ligand complexes with high-resolution crystal structures and experimentally determined Ki or Kd values. The focus of our study was to assess the ability of these scoring functions to predict binding affinities based on the experimentally determined high-resolution crystal structures of proteins in complex with their ligands. The quantitative correlation between the binding scores produced by each scoring function and the known binding constants of the 800 complexes was computed. X-Score, DrugScore, Sybyl::ChemScore, and Cerius2::PLP provided better correlations than the other scoring functions with standard deviations of 1.8-2.0 log units. These four scoring functions were also found to be robust enough to carry out computation directly on unaltered crystal structures. To examine how well scoring functions predict the binding affinities for ligands bound to the same target protein, the performance of these 14 scoring functions were evaluated on three subsets of protein-ligand complexes from the test set: HIV-1 protease complexes (82 entries), trypsin complexes (45 entries), and carbonic anhydrase II complexes (40 entries). Although the results for the HIV-1 protease subset are less than desirable, several scoring functions are able to satisfactorily predict the binding affinities for the trypsin and the carbonic anhydrase II subsets with standard deviation as low as 1.0 log unit (corresponding to 1.3-1.4 kcal/mol at room temperature). Our results demonstrate the strengths as well as the weaknesses of current scoring functions for binding affinity prediction.

Analysis of ligand-bound water molecules in high-resolution crystal structures of protein-ligand complexes.

We have performed a comprehensive analysis of water molecules at the protein-ligand interfaces observed in 392 high-resolution crystal structures. There are a total of 1829 ligand-bound water molecules in these 392 complexes; 18% are surface water molecules, and 72% are interfacial water molecules. The number of ligand-bound water molecules in each complex structure ranges from 0 to 21 and has an average of 4.6. Of these interfacial water molecules, 76% are considered to be bridging water molecules, characterized by having polar interactions with both ligand and protein atoms. Among a number of factors that may influence the number of ligand-bound water molecules, the polar van der Waals (vdw) surface area of ligands has the highest Pearson linear correlation coefficient of 0.63. Our regression analysis predicted that one more ligand-bound water molecule is expected for every additional 24 A2 in the polar vdw surface area of the ligand. In contrast to the observation that the resolution is the primary factor influencing the number of water molecules in crystallographic models of proteins, we found that there is only a weak relationship between the number of ligand-bound water molecules and the resolution of the crystal structures. An analysis of the isotropic B factors of buried ligand-bound water molecules suggested that, when water molecules have fewer than two polar interactions with the protein-ligand complex, they are more mobile than protein atoms in the crystal structures; when they have more than three polar interactions, they are significantly less mobile than protein atoms.

PDB-wide collection of binding data: current status of the PDBbind database

MOTIVATION Molecular recognition between biological macromolecules and organic small molecules plays an important role in various life processes. Both structural information and binding data of biomolecular complexes are indispensable for depicting the underlying mechanism in such an event. The PDBbind database was created to collect experimentally measured binding data for the biomolecular complexes throughout the Protein Data Bank (PDB). It thus provides the linkage between structural information and energetic properties of biomolecular complexes, which is especially desirable for computational studies or statistical analyses. RESULTS Since its first public release in 2004, the PDBbind database has been updated on an annual basis. The latest release (version 2013) provides experimental binding affinity data for 10,776 biomolecular complexes in PDB, including 8302 protein-ligand complexes and 2474 other types of complexes. In this article, we will describe the current methods used for compiling PDBbind and the updated status of this database. We will also review some typical applications of PDBbind published in the scientific literature. AVAILABILITY AND IMPLEMENTATION All contents of this database are freely accessible at the PDBbind-CN Web server at http://www.pdbbind-cn.org/. CONTACT wangrx@mail.sioc.ac.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Breast Cancer Cells Can Evade Apoptosis-Mediated Selective Killing by a Novel Small Molecule Inhibitor of Bcl-2

Proteins of the Bcl-2 family are key regulators of caspase activation and apoptosis. Some members of this family, notably Bcl-2 and Bcl-xL, are overexpressed in cancer cells, which have been associated with chemoresistance. We have designed and synthesized a small molecule inhibitor of Bcl-2, named YC137, and studied its role in cancer cells. In vitro studies showed that YC137 inhibits the binding of the Bid BH3 peptide to Bcl-2, thus disrupting an interaction essential for the antiapoptotic activity of Bcl-2. This inhibitor induces apoptosis of hematopoietic progenitors overexpressing Bcl-2 but not Bcl-xL and breast cancer cells that express high levels of Bcl-2. On the contrary, a variety of normal primary cells, including CD34+ progenitors, myoblasts, and peripheral blood mononuclear cells, do not respond to the inhibitor. A breast cancer cell line resistant to YC137 was generated. Analysis of resistant cells revealed a reduced expression of Bcl-2, which correlated with low activation of signal transducer and activator of transcription-3 (Stat3) and reduced expression of the human epidermal growth factor receptor-2 (HER2). Of note, YC137-resistant cells were more sensitive to apoptosis induced by chemotherapy. Because HER2 has not been linked previously to the Stat3-Bcl-2 transcriptional pathway, we additionally confirmed that specific blockade of HER2 in breast cancer cells resulted in down-regulation of Stat3 activity and reduced levels of Bcl-2. Consistently, HER2 blockade led to YC137 resistance. These data provide evidence for the selective killing of tumor cells by YC137 and represent the first example of in vitro selection of cancer cells refractory to a Bcl-2 inhibitor.

A computational analysis of the binding affinities of FKBP12 inhibitors using the MM‐PB/SA method

The FK506‐binding proteins have been targets of pharmaceutical interests over years. We have studied the binding of a set of 12 nonimmunosuppressive small‐molecule inhibitors to FKBP12 through molecular dynamics simulations. Each complex was subjected to 1‐ns MD simulation conducted in an explicit solvent environment under constant temperature and pressure. The binding free energy of each complex was then computed by the MM‐PB/SA method in the AMBER program. Our MM‐PB/SA computation produced a good correlation between the experimentally determined and the computed binding free energies with a correlation coefficient (R2) of 0.93 and a standard deviation as low as 0.30 kcal/mol. The vibrational entropy term given by the normal mode analysis was found to be helpful for achieving this correlation. Moreover, an adjustment to one weight factor in the PB/SA model was essential to correct the absolute values of the final binding free energies to a reasonable range. A head‐to‐head comparison of our MM‐PB/SA model with a previously reported Linear Response Approximation (LRA) model suggested that the MM‐PB/SA method is more robust in binding affinity prediction for this class of compounds. Proteins 2006.

CRISPR-Cpf1 assisted genome editing of Corynebacterium glutamicum

Corynebacterium glutamicum is an important industrial metabolite producer that is difficult to genetically engineer. Although the Streptococcus pyogenes (Sp) CRISPR-Cas9 system has been adapted for genome editing of multiple bacteria, it cannot be introduced into C. glutamicum. Here we report a Francisella novicida (Fn) CRISPR-Cpf1-based genome-editing method for C. glutamicum. CRISPR-Cpf1, combined with single-stranded DNA (ssDNA) recombineering, precisely introduces small changes into the bacterial genome at efficiencies of 86–100%. Large gene deletions and insertions are also obtained using an all-in-one plasmid consisting of FnCpf1, CRISPR RNA, and homologous arms. The two CRISPR-Cpf1-assisted systems enable N iterative rounds of genome editing in 3N+4 or 3N+2 days. A proof-of-concept, codon saturation mutagenesis at G149 of γ-glutamyl kinase relieves L-proline inhibition using Cpf1-assisted ssDNA recombineering. Thus, CRISPR-Cpf1-based genome editing provides a highly efficient tool for genetic engineering of Corynebacterium and other bacteria that cannot utilize the Sp CRISPR-Cas9 system.