B-cell lymphoma-2 family proteins in the crosshairs: Small molecule inhibitors and activators for cancer therapy.

The B-cell lymphoma-2 (BCL-2) family of proteins plays a crucial role in the regulation of apoptosis, offering a dual mechanism for its control. Numerous studies have established a strong association between gene disorders of these proteins and the proliferation of diverse cancer cell types. Consequently, the identification and development of drugs targeting BCL-2 family proteins have emerged as a prominent area in antitumor therapy. Over the last two decades, several small-molecules have been designed to modulate the protein-protein interactions between anti- and proapoptotic BCL-2 proteins, effectively suppressing tumor growth and metastasis in vivo. The primary focus of research has been on developing BCL-2 homology 3 (BH3) mimetics to target antiapoptotic BCL-2 proteins, thereby competitively releasing proapoptotic BCL-2 proteins and restoring the blocked intrinsic apoptotic program. Additionally, for proapoptotic BCL-2 proteins, exogenous small molecules have been explored to activate cell apoptosis by directly interacting with executioner proteins such as BCL-2-associated X protein (BAX) or BCL-2 homologous antagonist/killer protein (BAK). In this comprehensive review, we summarize the inhibitors and activators (sensitizers) of BCL-2 family proteins developed over the past decades, highlighting their discovery, optimization, preclinical and clinical status, and providing an overall landscape of drug development targeting these proteins for therapeutic purposes.

PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction.

Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.

Mechanistic basis for receptor-mediated pathological α-synuclein fibril cell-to-cell transmission in Parkinson's disease.

The spread of pathological α-synuclein (α-syn) is a crucial event in the progression of Parkinson's disease (PD). Cell surface receptors such as lymphocyte activation gene 3 (LAG3) and amyloid precursor-like protein 1 (APLP1) can preferentially bind α-syn in the amyloid over monomeric state to initiate cell-to-cell transmission. However, the molecular mechanism underlying this selective binding is unknown. Here, we perform an array of biophysical experiments and reveal that LAG3 D1 and APLP1 E1 domains commonly use an alkaline surface to bind the acidic C terminus, especially residues 118 to 140, of α-syn. The formation of amyloid fibrils not only can disrupt the intramolecular interactions between the C terminus and the amyloid-forming core of α-syn but can also condense the C terminus on fibril surface, which remarkably increase the binding affinity of α-syn to the receptors. Based on this mechanism, we find that phosphorylation at serine 129 (pS129), a hallmark modification of pathological α-syn, can further enhance the interaction between α-syn fibrils and the receptors. This finding is further confirmed by the higher efficiency of pS129 fibrils in cellular internalization, seeding, and inducing PD-like α-syn pathology in transgenic mice. Our work illuminates the mechanistic understanding on the spread of pathological α-syn and provides structural information for therapeutic targeting on the interaction of α-syn fibrils and receptors as a potential treatment for PD.

HydraMap v.2: Prediction of Hydration Sites and Desolvation Energy with Refined Statistical Potentials.

The complex network of water molecules within the binding pocket of a target protein undergoes alterations upon ligand binding, presenting a significant challenge for conventional molecular modeling methods to accurately characterize and compute the associated energy changes. We have previously developed an empirical method, HydraMap (J. Chem. Inf. Model. 2020, 60, 4359-4375), which employs statistical potentials to predict hydration sites and compute desolvation energy, achieving a reasonable balance between accuracy and speed. In this work, we present its improved version, namely, HydraMap v.2. We updated the statistical potentials for protein-water interactions through an analysis of 17 042 crystal protein structures. We also introduced a new feature to evaluate ligand-water interactions by incorporating statistical potentials derived from the solvated structures of 9878 small organic molecules produced by molecular dynamics simulations. By combining these potentials, HydraMap v.2 can predict and compare the hydration sites in a binding pocket before and after ligand binding, identifying key water molecules involved in the binding process, such as those forming bridging hydrogen bonds and unstable ones that can be replaced. We demonstrated the application of HydraMap v.2 in explaining the structure-activity relationship of a panel of MCL-1 inhibitors. The desolvation energies calculated by summing the energy change of each hydration site before and after ligand binding showed good correlation with known ligand binding affinities on six target proteins. In conclusion, HydraMap v.2 offers a cost-effective solution for estimating the desolvation energy during protein-ligand binding and also is practical in guiding lead optimization in structure-based drug discovery.

Analysis of the Binding Sites on BAX and the Mechanism of BAX Activators through Extensive Molecular Dynamics Simulations.

The BAX protein is a pro-apoptotic member of the Bcl-2 family, which triggers apoptosis by causing permeabilization of the mitochondrial outer membrane. However, the activation mechanism of BAX is far from being understood. Although a few small-molecule BAX activators have been reported in the literature, their crystal structures in complex with BAX have not been resolved. So far, their binding modes were modeled at most by simple molecular docking efforts. Lack of an in-depth understanding of the activation mechanism of BAX hinders the development of more effective BAX activators. In this work, we employed cosolvent molecular dynamics simulation to detect the potential binding sites on the surface of BAX and performed a long-time molecular dynamics simulation (50 µs in total) to derive the possible binding modes of three BAX activators (i.e., BAM7, BTC-8, and BTSA1) reported in the literature. Our results indicate that the trigger, S184, and vMIA sites are the three major binding sites on the full-length BAX structure. Moreover, the canonical hydrophobic groove is clearly detected on the α9-truncated BAX structure, which is consistent with the outcomes of relevant experimental studies. Interestingly, it is observed that solvent probes bind to the trigger bottom pocket more stably than the PPI trigger site. Each activator was subjected to unbiased molecular dynamics simulations started at the three major binding sites in five parallel jobs. Our MD results indicate that all three activators tend to stay at the trigger site with favorable MM-GB/SA binding energies. BAM7 and BTSA1 can enter the trigger bottom pocket and thereby enhance the movement of the α1-α2 loop, which may be a key factor at the early stage of BAX activation. Our molecular modeling results may provide useful guidance for designing smart biological experiments to further explore BAX activation and directing structure-based efforts toward discovering more effective BAX activators.

Analysis of the Clinical Efficacy of Conservative Treatment for an Unruptured Cornual Pregnancy.

Context: Early diagnosis and early treatment of cornual pregnancy are very important. Conservative treatment before rupture can greatly reduce the patient's trauma. It's very important to choose a treatment method for cornual pregnancy with a high level of effectiveness, few adverse reactions, and no effects on fertility. Objective: The study intended to compare the clinical efficacy of different treatments for unruptured cornual pregnancy to find a safe, effective, minimally invasive treatment for unruptured cornual pregnancy that has few side effects and doesn't affect fertility. Design: The research team retrospectively collected the clinical data of patients to analyze the benefits of treatments for cornual pregnancy. Setting: The study took place in the Department of Obstetrics and Gynecology at the Wuhan Third Hospital in Wuhan, Hubei Province, China. Participants: Participants were 61 patients with an unruptured cornual pregnancy who had been admitted to the hospital between September 2002 and May 2012. Intervention: Participants were divided into four groups according to the treatment they received: (1) 20 patients who had been orally administered mifepristone combined with misoprostol and received uterine curettage were included in the drug abortion + curettage group (D group); (2) 16 patients who had received ultrasound-guided uterine aspiration were included in the uterine aspiration group (U group); (3) 15 patients who had received methotrexate (MTX) chemotherapy were included in the chemotherapy group (C group); and (4) 10 patients who had received ultrasound-guided hysteroscope operation were included in the hysteroscope operation group (H group). Outcome Measures: Adverse reactions and the decrease in participants' blood ß-HCG were recorded in detail. The participants were followed up for two months. Results: Of the 61 participants, 12 underwent surgery after failed conservative treatment, one in the D group, four in the U group, three in the C group, and four in the H group. No significant difference existed in the baseline data among the four groups. The decline rates of ß-HCG at seven days after treatment and the treatment success rates of participants in the D group were significantly higher than those in the U group, the C group, and the H group (all P < .05). The time at which the ß-HCG turned negative and the average hospital stays weren't significantly different among the four groups. Conclusions: The current study found that oral administration of mifepristone, combined with misoprostol, plus uterine curettage was superior to the other three methods in treatment of unruptured cornual pregnancy. The drug abortion + curettage treatment was found to be a safe, effective, minimally invasive treatment for unruptured cornual pregnancy, which has few side effects and doesn't affect fertility.

Targeting the potent Beclin 1-UVRAG coiled-coil interaction with designed peptides enhances autophagy and endolysosomal trafficking.

The Beclin 1-Vps34 complex, known as "mammalian class III PI3K," plays essential roles in membrane-mediated transport processes including autophagy and endosomal trafficking. Beclin 1 acts as a scaffolding molecule for the complex and readily transits from its metastable homodimeric state to interact with key modulators such as Atg14L or UVRAG and form functionally distinct Atg14L/UVRAG-containing Beclin 1-Vps34 subcomplexes. The Beclin 1-Atg14L/UVRAG interaction relies critically on their coiled-coil domains, but the molecular mechanism remains poorly understood. We determined the crystal structure of Beclin 1-UVRAG coiled-coil complex and identified a strengthened interface with both hydrophobic pairings and electrostatically complementary interactions. This structure explains why the Beclin 1-UVRAG interaction is more potent than the metastable Beclin 1 homodimer. Potent Beclin 1-UVRAG interaction is functionally significant because it renders UVRAG more competitive than Atg14L in Beclin 1 binding and is critical for promoting endolysosomal trafficking. UVRAG coiled-coil mutants with weakened Beclin 1 binding do not outcompete Atg14L and fail to promote endolysosomal degradation of the EGF receptor (EGFR). We designed all-hydrocarbon stapled peptides that specifically targeted the C-terminal part of the Beclin 1 coiled-coil domain to interfere with its homodimerization. One such peptide reduced Beclin 1 self-association, promoted Beclin 1-Atg14L/UVRAG interaction, increased autophagic flux, and enhanced EGFR degradation. Our results demonstrate that the targeting Beclin 1 coiled-coil domain with designed peptides to induce the redistribution of Beclin 1 among its self-associated form or Atg14L/UVRAG-containing complexes enhances both autophagy and endolysosomal trafficking.

Synthesis of (1,3,4-thiadiazol-2-yl)-acrylamide derivatives as potential antitumor agents against acute leukemia cells.

A lead compound with the (1,3,4-thiadiazol-2-yl)-acrylamide scaffold was discovered to have significant cytotoxicity on several tumor cell lines in an in-house cell-based screening. A total of 60 derivative compounds were then synthesized and tested in a CCK-8 cell viability assay. Some of them exhibited improved cytotoxic activities. The most potent compounds had IC50 values of 1-5 µM on two acute leukemia tumor cell lines, i.e. RS4;11 and HL-60. Flow cytometry analysis of several active compounds and detection of caspase activation indicated that they induced caspase-dependent apoptosis. It was also encouraging to observe that these compounds did not have obvious cytotoxicity on normal cells, i.e. IC50 > 50 µM on HEK-293T cells. Although the molecular targets of this class of compound are yet to be revealed, our current results suggest that this class of compound represents a new possibility for developing drug candidates against acute leukemia.

Prediction of the Favorable Hydration Sites in a Protein Binding Pocket and Its Application to Scoring Function Formulation.

The important role of water molecules in protein-ligand binding energetics has attracted wide attention in recent years. A range of computational methods has been developed to predict the favorable locations of water molecules in a protein binding pocket. Most of the current methods are based on extensive molecular dynamics or Monte Carlo simulations. They are time-consuming and thus cannot be applied to high-throughput tasks. To overcome this difficulty, we have developed an empirical method, called HydraMap, to predict the favorable hydration sites in the binding pocket of a protein molecule. This method uses statistical potentials to quantify the interactions between protein atoms and water molecules. Such statistical potentials were derived from 10,987 crystal structures selected from the Protein Data Bank. The probability of placing a water probe at each spot in the binding pocket was evaluated to derive a density map. The density map was then deduced into explicit hydration sites through a clustering process. HydraMap was validated on two external test sets, where it produced comparable results as 3D-RISM and WATsite but was 30-1000 times faster. In addition, we have attempted to estimate the desolvation energy associated with water molecule replacement upon ligand binding based on the outcomes of HydraMap. This desolvation term, called DEWED, was incorporated into the framework of four scoring functions, i.e., ASP, ChemPLP, GoldScore, and X-Score. The derivative scoring functions were tested in terms of scoring power, docking power, and screening power on a range of data sets. It was observed that X-Score exhibited the most obvious improvement in accuracy after adding the DEWED terms. Moreover, all scoring functions augmented with the DEWED terms exhibited improved or comparable performance on most data sets as the corresponding ones augmented with the GB/SA terms. Our study has demonstrated the potential application of HydraMap and DEWED to the formulation of new scoring functions. A beta-version of the HydraMap software is freely available from our Web site (http://www.sioc-ccbg.ac.cn/software/hydramap/) for testing.

Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?

In recent years, protein-ligand interaction scoring functions derived through machine-learning are repeatedly reported to outperform conventional scoring functions. However, several published studies have questioned that the superior performance of machine-learning scoring functions is dependent on the overlap between the training set and the test set. In order to examine the true power of machine-learning algorithms in scoring function formulation, we have conducted a systematic study of six off-the-shelf machine-learning algorithms, including Bayesian Ridge Regression (BRR), Decision Tree (DT), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Linear Support Vector Regression (L-SVR), and Random Forest (RF). Model scoring functions were derived with these machine-learning algorithms on various training sets selected from over 3700 protein-ligand complexes in the PDBbind refined set (version 2016). All resulting scoring functions were then applied to the CASF-2016 test set to validate their scoring power. In our first series of trial, the size of the training set was fixed; while the overall similarity between the training set and the test set was varied systematically. In our second series of trial, the overall similarity between the training set and the test set was fixed, while the size of the training set was varied. Our results indicate that the performance of those machine-learning models are more or less dependent on the contents or the size of the training set, where the RF model demonstrates the best learning capability. In contrast, the performance of three conventional scoring functions (i.e., ChemScore, ASP, and X-Score) is basically insensitive to the use of different training sets. Therefore, one has to consider not only "hard overlap" but also "soft overlap" between the training set and the test set in order to evaluate machine-learning scoring functions. In this spirit, we have complied data sets based on the PDBbind refined set by removing redundant samples under several similarity thresholds. Scoring functions developers are encouraged to employ them as standard training sets if they want to evaluate their new models on the CASF-2016 benchmark.

Fragment-Based Computational Method for Designing GPCR Ligands.

G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors, which is arguably the most important family of drug target. With the technology breakthroughs in X-ray crystallography and cryo-electron microscopy, more than 300 GPCR-ligand complex structures have been publicly reported since 2007, covering about 60 unique GPCRs. Such abundant structural information certainly will facilitate the structure-based drug design by targeting GPCRs. In this study, we have developed a fragment-based computational method for designing novel GPCR ligands. We first extracted the characteristic interaction patterns (CIPs) on the binding interfaces between GPCRs and their ligands. The CIPs were used as queries to search the chemical fragments derived from GPCR ligands, which were required to form similar interaction patterns with GPCR. Then, the selected chemical fragments were assembled into complete molecules by using the AutoT&T2 software. In this work, we chose ß-adrenergic receptor (ß-AR) and muscarinic acetylcholine receptor (mAChR) as the targets to validate this method. Based on the designs suggested by our method, samples of 63 compounds were purchased and tested in a cell-based functional assay. A total of 15 and 22 compounds were identified as active antagonists for ß2-AR and mAChR M1, respectively. Molecular dynamics simulations and binding free energy analysis were performed to explore the key interactions (e.g., hydrogen bonds and π-π interactions) between those active compounds and their target GPCRs. In summary, our work presents a useful approach to the de novo design of GPCR ligands based on the relevant 3D structural information.

AlloFinder: a strategy for allosteric modulator discovery and allosterome analyses.

Allostery tweaks innumerable biological processes and plays a fundamental role in human disease and drug discovery. Exploration of allostery has thus been regarded as a crucial requirement for research on biological mechanisms and the development of novel therapeutics. Here, based on our previously developed allosteric data and methods, we present an interactive platform called AlloFinder that identifies potential endogenous or exogenous allosteric modulators and their involvement in human allosterome. AlloFinder automatically amalgamates allosteric site identification, allosteric screening and allosteric scoring evaluation of modulator-protein complexes to identify allosteric modulators, followed by allosterome mapping analyses of predicted allosteric sites and modulators in human proteome. This web server exhibits prominent performance in the reemergence of allosteric metabolites and exogenous allosteric modulators in known allosteric proteins. Specifically, AlloFinder enables identification of allosteric metabolites for metabolic enzymes and screening of potential allosteric compounds for disease-related targets. Significantly, the feasibility of AlloFinder to discover allosteric modulators was tested in a real case of signal transduction and activation of transcription 3 (STAT3) and validated by mutagenesis and functional experiments. Collectively, AlloFinder is expected to contribute to exploration of the mechanisms of allosteric regulation between metabolites and metabolic enzymes, and to accelerate allosteric drug discovery. The AlloFinder web server is freely available to all users at http://mdl.shsmu.edu.cn/ALF/.

Revisiting the Relationship Between Correlation Coefficient, Confidence Level, and Sample Size.

In the field of computational chemistry, it is a very common task to compare the predictive power of theoretical models with Pearson correlation coefficients. A general understanding is that larger sample sizes lead to increased precision. However, what is the minimum sample size required for comparing two models? This issue has not been well addressed in this field. To the best of our knowledge, the only serious study of this kind was published by Carlson in 2013 [ J. Chem. Inf. Model. 2013 , 53 1837 - 1841 ], where they proposed a method for estimating the minimum sample size required by this task. Considering how a benchmark comparison is conducted in reality, we want to point out that (i) the possible intercorrelation between two models should not be neglected and (ii) the one-sided test is more reasonable because comparison direction is known a priori. Carlson's method has significantly overestimated the required minimum sample size due to these two issues. Here, we will describe a more appropriate method based on Dunn and Clark's test statistic, and we have designed an extensive numerical test to validate our method. The minimum sample sizes required by comparing two models under various conditions are computed with our method. Our study has shown that the required minimum sample size is determined by several factors, including confidence, power, correlation coefficients as well as the intercorrelation between two models. As a rule of thumb, a couple of hundred samples are sufficient at 90% confidence or above for comparing two models producing meaningful R values.

Comparative Assessment of Scoring Functions: The CASF-2016 Update.

In structure-based drug design, scoring functions are often employed to evaluate protein-ligand interactions. A variety of scoring functions have been developed so far, and thus, some objective benchmarks are desired for assessing their strength and weakness. The comparative assessment of scoring functions (CASF) benchmark developed by us provides an answer to this demand. CASF is designed as a "scoring benchmark", where the scoring process is decoupled from the docking process to depict the performance of scoring function more precisely. Here, we describe the latest update of this benchmark, i.e., CASF-2016. Each scoring function is still evaluated by four metrics, including "scoring power", "ranking power", "docking power", and "screening power". Nevertheless, the evaluation methods have been improved considerably in several aspects. A new test set is compiled, which consists of 285 protein-ligand complexes with high-quality crystal structures and reliable binding constants. A panel of 25 scoring functions are tested on CASF-2016 as a demonstration. Our results reveal that the performance of current scoring functions is more promising in terms of docking power than scoring, ranking, and screening power. Scoring power is somewhat correlated with ranking power, so are docking power and screening power. The results obtained on CASF-2016 may provide valuable guidance for the end users to make smart choices among available scoring functions. Moreover, CASF is created as an open-access benchmark so that other researchers can utilize it to test a wider range of scoring functions. The complete CASF-2016 benchmark will be released on the PDBbind-CN web server ( http://www.pdbbind-cn.org/casf.asp/ ) once this article is published.

TopP-S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility.

Aqueous solubility and partition coefficient are important physical properties of small molecules. Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery. The prediction accuracy depends crucially on molecular descriptors which are typically derived from a theoretical understanding of the chemistry and physics of small molecules. This work introduces an algebraic topology-based method, called element-specific persistent homology (ESPH), as a new representation of small molecules that is entirely different from conventional chemical and/or physical representations. ESPH describes molecular properties in terms of multiscale and multicomponent topological invariants. Such topological representation is systematical, comprehensive, and scalable with respect to molecular size and composition variations. However, it cannot be literally translated into a physical interpretation. Fortunately, it is readily suitable for machine learning methods, rendering topological learning algorithms. Due to the inherent correlation between solubility and partition coefficient, a uniform ESPH representation is developed for both properties, which facilitates multi-task deep neural networks for their simultaneous predictions. This strategy leads to a more accurate prediction of relatively small datasets. A total of six datasets is considered in this work to validate the proposed topological and multitask deep learning approaches. It is demonstrated that the proposed approaches achieve some of the most accurate predictions of aqueous solubility and partition coefficient. Our software is available online at http://weilab.math.msu.edu/TopP-S/. © 2018 Wiley Periodicals, Inc.

Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions.

In structure-based drug design, scoring functions are widely used for fast evaluation of protein-ligand interactions. They are often applied in combination with molecular docking and de novo design methods. Since the early 1990s, a whole spectrum of protein-ligand interaction scoring functions have been developed. Regardless of their technical difference, scoring functions all need data sets combining protein-ligand complex structures and binding affinity data for parametrization and validation. However, data sets of this kind used to be rather limited in terms of size and quality. On the other hand, standard metrics for evaluating scoring function used to be ambiguous. Scoring functions are often tested in molecular docking or even virtual screening trials, which do not directly reflect the genuine quality of scoring functions. Collectively, these underlying obstacles have impeded the invention of more advanced scoring functions. In this Account, we describe our long-lasting efforts to overcome these obstacles, which involve two related projects. On the first project, we have created the PDBbind database. It is the first database that systematically annotates the protein-ligand complexes in the Protein Data Bank (PDB) with experimental binding data. This database has been updated annually since its first public release in 2004. The latest release (version 2016) provides binding data for 16 179 biomolecular complexes in PDB. Data sets provided by PDBbind have been applied to many computational and statistical studies on protein-ligand interaction and various subjects. In particular, it has become a major data resource for scoring function development. On the second project, we have established the Comparative Assessment of Scoring Functions (CASF) benchmark for scoring function evaluation. Our key idea is to decouple the "scoring" process from the "sampling" process, so scoring functions can be tested in a relatively pure context to reflect their quality. In our latest work on this track, i.e. CASF-2013, the performance of a scoring function was quantified in four aspects, including "scoring power", "ranking power", "docking power", and "screening power". All four performance tests were conducted on a test set containing 195 high-quality protein-ligand complexes selected from PDBbind. A panel of 20 standard scoring functions were tested as demonstration. Importantly, CASF is designed to be an open-access benchmark, with which scoring functions developed by different researchers can be compared on the same grounds. Indeed, it has become a popular choice for scoring function validation in recent years. Despite the considerable progress that has been made so far, the performance of today's scoring functions still does not meet people's expectations in many aspects. There is a constant demand for more advanced scoring functions. Our efforts have helped to overcome some obstacles underlying scoring function development so that the researchers in this field can move forward faster. We will continue to improve the PDBbind database and the CASF benchmark in the future to keep them as useful community resources.

Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints.

BACKGROUND: In structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach (BMC Bioinformatics, 2010, 11, 193-208). The key idea is to compute the binding affinity of a given protein-ligand complex based on the known binding data of an appropriate reference complex, so the error in binding affinity prediction can be reduced effectively. RESULTS: In this study, we have developed an upgraded version, i.e. KGS2, by employing 3D protein-ligand interaction fingerprints in reference selection. KGS2 was evaluated in combination with four scoring functions (X-Score, ChemPLP, ASP, and GoldScore) on five drug targets (HIV-1 protease, carbonic anhydrase 2, beta-secretase 1, beta-trypsin, and checkpoint kinase 1). In the in situ scoring test, considerable improvements were observed in most cases after application of KGS2. Besides, the performance of KGS2 was always better than KGS in all cases. In the more challenging molecular docking test, application of KGS2 also led to improved structure-activity relationship in some cases. CONCLUSIONS: KGS2 can be applied as a convenient "add-on" to current scoring functions without the need to re-engineer them, and its application is not limited to certain target proteins as customized scoring functions. As an interpolation method, its accuracy in principle can be improved further with the increasing knowledge of protein-ligand complex structures and binding affinity data. We expect that KGS2 will become a practical tool for enhancing the performance of current scoring functions in binding affinity prediction. The KGS2 software is available upon contacting the authors.

Alloscore: a method for predicting allosteric ligand-protein interactions.

UNLABELLED: Allosteric ligands have increasingly gained attention as potential therapeutic agents due to their higher target selectivity and lower toxicity compared with classic orthosteric ligands. Despite the great interest in the development of allosteric drugs as a new tactic in drug discovery, the understanding of the ligand-protein interactions underlying allosteric binding represents a key challenge. Herein, we introduce Alloscore, a web server that predicts the binding affinities of allosteric ligand-protein interactions. This method exhibits prominent performance in describing allosteric binding and could be useful in allosteric virtual screening and the structural optimization of allosteric agonists/antagonists. AVAILABILITY AND IMPLEMENTATION: The Alloscore server and tutorials are freely available at http://mdl.shsmu.edu.cn/alloscore CONTACT: jian.zhang@sjtu.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Overexpression of microRNA-194 suppresses the epithelial-mesenchymal transition in targeting stem cell transcription factor Sox3 in endometrial carcinoma stem cells.

The epithelial-mesenchymal transition is the key process driving cancer metastasis. MicroRNA-194 inhibits epithelial-mesenchymal transition in several cancers and its downregulation indicates a poor prognosis in human endometrial carcinoma. Self-renewal factor Sox3 induces epithelial-mesenchymal transition at gastrulation and is also involved epithelial-mesenchymal transition in several cancers. We intended to determine the roles of Sox3 in inducing epithelial-mesenchymal transition in endometrial cancer stem cells and the possible role of microRNA-194 in controlling Sox3 expression. Firstly, we found that Sox3 and microRNA-194 expressions were associated with the status of endometrial cancer stem cells in a panel of endometrial carcinoma tissue, the CD133+ cell was higher in tumorsphere than in differentiated cells, and overexpression of microRNA-194 would decrease CD133+ cell expression. Silencing of Sox3 in endometrial cancer stem cell upregulated the epithelial marker E-cadherin, downregulated the mesenchymal marker vimentin, and significantly reduced cell invasion in vitro; overexpression of Sox3 reversed these phenotypes. Furthermore, we discovered that the expression of Sox3 was suppressed by microRNA-194 through direct binding to the Sox3 3'-untranslated region. Ectopic expression of microRNA-194 in endometrial cancer stem cells induced a mesenchymal-epithelial transition by restoring E-cadherin expression, decreasing vimentin expression, and inhibiting cell invasion in vitro. Moreover, overexpression of microRNA-194 inhibited endometrial cancer stem cell invasion or metastasis in vivo by injection of adenovirus microRNA-194. These findings demonstrate the novel mechanism by which Sox3 contributes to endometrial cancer stem cell invasion and suggest that repression of Sox3 by microRNA-194 may have therapeutic potential to suppress endometrial carcinoma metastasis. The cancer stem cell marker, CD133, might be the surface marker of endometrial cancer stem cell.

Proposed Hydrogen-Bonding Index of Donor or Acceptor Reflecting Its Intrinsic Contribution to Hydrogen-Bonding Strength.

In this work, we tentatively propose that the hydrogen-bonding strength EHB (referring to the minimal hydrogen-bonding energy) and its corresponding hydrogen-bond (HB) distance (referring to the optimal HB distance dHB) for simple mono-HB systems have an exponential relationship on the basis of MP2 and DFT computational results. We take a step further and propose that the hydrogen-bonding indices of the donor (Idonor) and acceptor (Iacceptor), reflecting their intrinsic contributions to hydrogen-bonding strength, also have an exponential relation with the hypothetical effective hydrogen-bond radii of the donor (rdonor) and acceptor (racceptor), respectively. On the basis of extensive quantum-mechanical calculations, relevant assumptions about the hydrogen-bonding index are rationalized. Moreover, the hydrogen-bonding index is also suggested as an additional prefiltering criterion for virtual screening besides the widely accepted Lipinski's rule of five. Finally, a "Hydrogen-Bond Index Estimator (HBIE)" module has been implemented in our Visual Force Field Derivation Toolkit (VFFDT) program to approximately and rapidly estimate the hydrogen-bonding indices of any small molecules in batch and screen possible stronger donors or acceptors from the small-molecule database. To the best of our knowledge, the concept of the hydrogen-bonding index and its potential application are proposed here for the first time.