Eleven popular scoring functions have been tested on 100 protein-ligand complexes to evaluate their abilities to reproduce experimentally determined structures and binding affinities. They include four scoring functions implemented in the LigFit module in Cerius2 (LigScore, PLP, PMF, and LUDI), four scoring functions implemented in the CScore module in SYBYL (F-Score, G-Score, D-Score, and ChemScore), the scoring function implemented in the AutoDock program, and two stand-alone scoring functions (DrugScore and X-Score). These scoring functions are not tested in the context of a particular docking program. Instead, conformational sampling and scoring are separated into two consecutive steps. First, an exhaustive conformational sampling is performed by using the AutoDock program to generate an ensemble of docked conformations for each ligand molecule. This conformational ensemble is required to cover the entire conformational space as much as possible rather than to focus on a few energy minima. Then, each scoring function is applied to score this conformational ensemble to see if it can identify the experimentally observed conformation from all of the other decoys. Among all of the scoring functions under test, six of them, i.e., PLP, F-Score, LigScore, DrugScore, LUDI, and X-Score, yield success rates higher than the AutoDock scoring function. The success rates of these six scoring functions range from 66% to 76% if using root-mean-square deviation < or =2.0 A as the criterion. Combining any two or three of these six scoring functions into a consensus scoring scheme further improves the success rate to nearly 80% or even higher. However, when applied to reproduce the experimentally determined binding affinities of the 100 protein-ligand complexes, only X-Score, PLP, DrugScore, and G-Score are able to give correlation coefficients over 0.50. All of the 11 scoring functions are further inspected by their abilities to construct a descriptive, funnel-shaped energy surface for protein-ligand complexation. The results indicate that X-Score and DrugScore perform better than the other ones at this aspect.

We have screened the entire Protein Data Bank (Release No. 103, January 2003) and identified 5671 protein-ligand complexes out of 19 621 experimental structures. A systematic examination of the primary references of these entries has led to a collection of binding affinity data (K(d), K(i), and IC(50)) for a total of 1359 complexes. The outcomes of this project have been organized into a Web-accessible database named the PDBbind database.

We have developed the PDBbind database to provide a comprehensive collection of binding affinities for the protein-ligand complexes in the Protein Data Bank (PDB). This paper gives a full description of the latest version, i.e., version 2003, which is an update to our recently reported work. Out of 23 790 entries in the PDB release No.107 (January 2004), 5897 entries were identified as protein-ligand complexes that meet our definition. Experimentally determined binding affinities (K(d), K(i), and IC(50)) for 1622 of these were retrieved from the references associated with these complexes. A total of 900 complexes were selected to form a "refined set", which is of particular value as a standard data set for docking and scoring studies. All of the final data, including binding affinity data, reference citations, and processed structural files, have been incorporated into the PDBbind database accessible on-line at http:// www.pdbbind.org/.

We have designed MI-219 as a potent, highly selective and orally active small-molecule inhibitor of the MDM2-p53 interaction. MI-219 binds to human MDM2 with a Ki value of 5 nM and is 10,000-fold selective for MDM2 over MDMX. It disrupts the MDM2-p53 interaction and activates the p53 pathway in cells with wild-type p53, which leads to induction of cell cycle arrest in all cells and selective apoptosis in tumor cells. MI-219 stimulates rapid but transient p53 activation in established tumor xenograft tissues, resulting in inhibition of cell proliferation, induction of apoptosis, and complete tumor growth inhibition. MI-219 activates p53 in normal tissues with minimal p53 accumulation and is not toxic to animals. MI-219 warrants clinical investigation as a new agent for cancer treatment.cancer therapy ͉ MDM2-p53 protein-protein interaction ͉ selective toxicity to tumors ͉ small-molecule inhibitor T he tumor suppressor p53 plays a central role in the regulation of cell cycle, apoptosis, DNA repair, and senescence (1-4). Because of the prominent role played by p53 in suppressing oncogenesis (5), it is not surprising that p53 function is impaired in all human cancers. Several distinct approaches have been pursued to restore p53 function as a new cancer therapeutic strategy (6-9). Three recent studies, using unique genetic mouse models, have demonstrated that the restoration of p53 leads universally to a rapid and robust regression of established sarcomas, lymphomas, and liver tumors (10)(11)(12)(13)(14). These studies provide strong evidence that established tumors remain persistently vulnerable to p53 tumorsuppressor function and that restoration of p53 function is therefore a powerful cancer therapeutic strategy (13).In Ϸ50% of human cancers, the gene encoding p53 is either deleted or mutated, rendering the p53 protein inactive (5, 15). In the remaining cancers, p53 retains its wild-type status but its function is effectively inhibited by its primary cellular inhibitor, the human MDM2 oncoprotein (mouse double minute 2, also termed HDM2 in humans) (5,16,17). One attractive pharmacological approach to p53 reactivation is to use a small molecule to block the MDM2-p53 interaction (6)(7)(8)18). The discovery of the Nutlins provided the important proof of the concept for this approach (7). Nutlins were shown to bind to MDM2, block the MDM2-p53 interaction, and activate wild-type p53 (7,(19)(20)(21). Nutlin-3a exhibits strong anti-tumor activity in multiple xenograft mouse models of human cancer (7,19). The discovery of the Nutlins has fueled enthusiasm for the development of small-molecule MDM2 inhibitors as a new class of anticancer therapy (6,8,22,23).One critical question in the development of MDM2 inhibitors for cancer treatment is their potential toxicity to normal tissues. This concern was heightened by a recent genetic study, which showed that p53 activation in the absence of the MDM2 gene causes severe toxicity to radiosensitive normal adult mouse tissues, leading to rapid animal death (24). Previous studies on ...

A successful structure-based design of a class of non-peptide small-molecule MDM2 inhibitors targeting the p53-MDM2 protein-protein interaction is reported. The most potent compound 1d binds to MDM2 protein with a Ki value of 86 nM and is 18 times more potent than a natural p53 peptide (residues 16-27). Compound 1d is potent in inhibition of cell growth in LNCaP prostate cancer cells with wild-type p53 and shows only a weak activity in PC-3 prostate cancer cells with a deleted p53. Importantly, 1d has a minimal toxicity to normal prostate epithelial cells. Our studies provide a convincing example that structure-based strategy can be employed to design highly potent, non-peptide, cell-permeable, small-molecule inhibitors to target protein-protein interaction, which remains a very challenging area in chemical biology and drug design.

Potent, specific, non-peptide small-molecule inhibitors of the MDM2-p53 interaction were successfully designed. The most potent inhibitor (MI-63) has a K(i) value of 3 nM binding to MDM2 and greater than 10,000-fold selectivity over Bcl-2/Bcl-xL proteins. MI-63 is highly effective in activation of p53 function and in inhibition of cell growth in cancer cells with wild-type p53 status. MI-63 has excellent specificity over cancer cells with deleted p53 and shows a minimal toxicity to normal cells.

Cell entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is mediated by its surface glycoprotein, Spike. The S1 subunit of Spike contains the N-terminal domain (NTD) and the receptor-binding domain (RBD), which mediates recognition of the host cell receptor angiotensinconverting enzyme 2 (ACE2). The S2 subunit drives fusion

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.