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.

Drug discovery by targeting the protein-protein interactions involved in autophagy.

Autophagy is a cellular process in which proteins and organelles are engulfed in autophagosomal vesicles and transported to the lysosome/vacuole for degradation. Protein-protein interactions (PPIs) play a crucial role at many stages of autophagy, which present formidable but attainable targets for autophagy regulation. Moreover, selective regulation of PPIs tends to have a lower risk in causing undesired off-target effects in the context of a complicated biological network. Thus, small-molecule regulators, including peptides and peptidomimetics, targeting the critical PPIs involved in autophagy provide a new opportunity for innovative drug discovery. This article provides general background knowledge of the critical PPIs involved in autophagy and reviews a range of successful attempts on discovering regulators targeting those PPIs. Successful strategies and existing limitations in this field are also discussed.

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.

The potent BECN2-ATG14 coiled-coil interaction is selectively critical for endolysosomal degradation of GPRASP1/GASP1-associated GPCRs.

ABBREVIATIONS: AMBRA1 autophagy and beclin 1 regulator 1; ATG14 autophagy related 14; ATG5 autophagy related 5; ATG7 autophagy related 7; BECN1 beclin 1; BECN2 beclin 2; CC coiled-coil; CQ chloroquine CNR1/CB1R cannabinoid receptor 1 DAPI 4',6-diamidino-2-phenylindole; dCCD delete CCD; DRD2/D2R dopamine receptor D2 GPRASP1/GASP1 G protein-coupled receptor associated sorting protein 1 GPCR G-protein coupled receptor; ITC isothermal titration calorimetry; IP immunoprecipitation; KD knockdown; KO knockout; MAP1LC3/LC3 microtubule associated protein 1 light chain 3; NRBF2 nuclear receptor binding factor 2; OPRD1/DOR opioid receptor delta 1 PIK3C3/VPS34 phosphatidylinositol 3-kinase catalytic subunit type 3; PIK3R4/VPS15 phosphoinositide-3-kinase regulatory subunit 4; PtdIns3K class III phosphatidylinositol 3-kinase; PtdIns3P phosphatidylinositol-3-phosphate; RUBCN rubicon autophagy regulator; SQSTM1/p62 sequestosome 1; UVRAG UV radiation resistance associated; VPS vacuolar protein sorting; WT wild type.

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.

Discovery of Small-Molecule Autophagy Inhibitors by Disrupting the Protein-Protein Interactions Involving Autophagy-Related 5.

One possible strategy for modulating autophagy is to disrupt the critical protein-protein interactions (PPIs) formed during this process. Our attention is on the autophagy-related 12 (ATG12)-autophagy-related 5 (ATG5)-autophagy-related 16-like 1 (ATG16L1) heterotrimer complex, which is responsible for ATG8 translocation from ATG3 to phosphatidylethanolamine. In this work, we discovered a compound with an (E)-3-(2-furanylmethylene)-2-pyrrolidinone core moiety (T1742) that blocked the ATG5-ATG16L1 and ATG5-TECAIR interactions in the in vitro binding assay (IC50 = 1-2 µM) and also exhibited autophagy inhibition in cellular assays. The possible binding mode of T1742 to ATG5 was predicted through molecular modeling, and a batch of derivatives sharing essentially the same core moiety were synthesized and tested. The outcomes of the in vitro binding assay and the flow cytometry assay of those newly synthesized compounds were generally consistent. This work has validated our central hypothesis that small-molecule inhibitors of the PPIs involving ATG5 can tune down autophagy effectively, and their pharmaceutical potential may be further explored.

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.

Public Data Set of Protein-Ligand Dissociation Kinetic Constants for Quantitative Structure-Kinetics Relationship Studies.

Protein-ligand binding affinity reflects the equilibrium thermodynamics of the protein-ligand binding process. Binding/unbinding kinetics is the other side of the coin. Computational models for interpreting the quantitative structure-kinetics relationship (QSKR) aim at predicting protein-ligand binding/unbinding kinetics based on protein structure, ligand structure, or their complex structure, which in principle can provide a more rational basis for structure-based drug design. Thus far, most of the public data sets used for deriving such QSKR models are rather limited in sample size and structural diversity. To tackle this problem, we have compiled a set of 680 protein-ligand complexes with experimental dissociation rate constants (k off), which were mainly curated from the references accumulated for updating our PDBbind database. Three-dimensional structure of each protein-ligand complex in this data set was either retrieved from the Protein Data Bank or carefully modeled based on a proper template. The entire data set covers 155 types of protein, with their dissociation kinetic constants (k off) spanning nearly 10 orders of magnitude. To the best of our knowledge, this data set is the largest of its kind reported publicly. Utilizing this data set, we derived a random forest (RF) model based on protein-ligand atom pair descriptors for predicting k off values. We also demonstrated that utilizing modeled structures as additional training samples will benefit the model performance. The RF model with mixed structures can serve as a baseline for testifying other more sophisticated QSKR models. The whole data set, namely, PDBbind-koff-2020, is available for free download at our PDBbind-CN web site (http://www.pdbbind.org.cn/download.php).

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.

Optimization of Beclin 1-Targeting Stapled Peptides by Staple Scanning Leads to Enhanced Antiproliferative Potency in Cancer Cells.

Beclin 1 is an essential autophagy gene and a haploinsufficient tumor suppressor. Beclin 1 is the scaffolding member of the Class III phosphatidylinositol-3-kinase complex (PI3KC3) and recruits two positive regulators Atg14L and UVRAG through its coiled-coil domain to upregulate PI3KC3 activity. Our previous work has shown that hydrocarbon-stapled peptides targeted to the Beclin 1 coiled-coil domain reduced Beclin 1 homodimerization and promoted the Beclin 1-Atg14L/UVRAG interaction. These peptides also induced autophagy and enhanced the endolysosomal degradation of cell surface receptors like EGFR. Here, we present the optimization of these Beclin 1-targeting peptides by staple scanning and sequence permutation. Placing the hydrocarbon staple closer to the Beclin 1-peptide interface enhanced their binding affinity by ∼10- to 30-fold. Optimized peptides showed potent antiproliferative efficacy in cancer cells that overexpressed EGFR and HER2 by inducing necrotic cell death but not apoptosis. Our Beclin 1-targeting stapled peptides may serve as effective therapeutic candidates for EGFR- or HER2-driven cancer.

Structure-Based Optimization of 3-Phenyl-N-(2-(3-phenylureido)ethyl)thiophene-2-sulfonamide Derivatives as Selective Mcl-1 Inhibitors.

Selective Mcl-1 inhibitors may overcome the drug resistance caused by current anti-apoptotic Bcl-2 protein inhibitors in tumors with Mcl-1 overexpression. Based on previously discovered compounds with a 3-phenylthiophene-2-sulfonamide core moiety, in this work, we have obtained new compounds with improved binding affinity and/or selectivity under the guidance of structure-based design. The most potent compounds achieved sub-micromolar binding affinities to Mcl-1 (Ki ∼ 0.4 µM) and good cytotoxicity (IC50 < 10 µM) on several tumor cells. 15N-heteronuclear single-quantum coherence NMR spectra suggested that these compounds bound to the BH3-binding groove on Mcl-1. Several cellular assays revealed that FWJ-D4 as well as its precursor FWJ-D5 effectively induced caspase-dependent apoptosis, and their target engagement at Mcl-1 was confirmed by co-immunoprecipitation experiments. Treatment with FWJ-D5 at 50 mg/kg every 2 days on an RS4;11 xenograft mouse model for 22 days led to 75% reduction in tumor volume without body weight loss.

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.

The phytochemical hyperforin triggers thermogenesis in adipose tissue via a Dlat-AMPK signaling axis to curb obesity.

Stimulation of adipose tissue thermogenesis is regarded as a promising avenue in the treatment of obesity. However, pharmacologic engagement of this process has proven difficult. Using the Connectivity Map (CMap) approach, we identified the phytochemical hyperforin (HPF) as an anti-obesity agent. We found that HPF efficiently promoted thermogenesis by stimulating AMPK and PGC-1α via a Ucp1-dependent pathway. Using LiP-SMap (limited proteolysis-mass spectrometry) combined with a microscale thermophoresis assay and molecular docking analysis, we confirmed dihydrolipoamide S-acetyltransferase (Dlat) as a direct molecular target of HPF. Ablation of Dlat significantly attenuated HPF-mediated adipose tissue browning both in vitro and in vivo. Furthermore, genome-wide association study analysis indicated that a variation in DLAT is significantly associated with obesity in humans. These findings suggest that HPF is a promising lead compound in the pursuit of a pharmacological approach to promote energy expenditure in the treatment of obesity.

The Role of Chronic Inflammation in Various Diseases and Anti-inflammatory Therapies Containing Natural Products.

Chronic inflammation represents a long-term reaction of the body's immune system to noxious stimuli. Such a sustained inflammatory response sometimes results in lasting damage to healthy tissues and organs. In fact, chronic inflammation is implicated in the development and progression of various diseases, including cardiovascular diseases, respiratory diseases, metabolic diseases, neurodegenerative diseases, and even cancers. Targeting nonresolving inflammation thus provides new opportunities for treating relevant diseases. In this review, we will go over several chronic inflammation-associated diseases first with emphasis on the role of inflammation in their pathogenesis. Then, we will summarize a number of natural products that exhibit therapeutic effects against those diseases by acting on different markers in the inflammatory response. We envision that natural products will remain a rich resource for the discovery of new drugs treating diseases associated with chronic inflammation.

The nuclear localization sequence mediates hnRNPA1 amyloid fibril formation revealed by cryoEM structure.

Human heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) serves as a key regulating protein in RNA metabolism. Malfunction of hnRNPA1 in nucleo-cytoplasmic transport or dynamic phase separation leads to abnormal amyloid aggregation and neurodegeneration. The low complexity (LC) domain of hnRNPA1 drives both dynamic phase separation and amyloid aggregation. Here, we use cryo-electron microscopy to determine the amyloid fibril structure formed by hnRNPA1 LC domain. Remarkably, the structure reveals that the nuclear localization sequence of hnRNPA1 (termed PY-NLS), which is initially known to mediate the nucleo-cytoplamic transport of hnRNPA1 through binding with karyopherin-ß2 (Kapß2), represents the major component of the fibril core. The residues that contribute to the binding of PY-NLS with Kapß2 also exert key molecular interactions to stabilize the fibril structure. Notably, hnRNPA1 mutations found in familial amyotrophic lateral sclerosis (ALS) and multisystem proteinopathoy (MSP) are all involved in the fibril core and contribute to fibril stability. Our work illuminates structural understandings of the pathological amyloid aggregation of hnRNPA1 and the amyloid disaggregase activity of Kapß2, and highlights the multiple roles of PY-NLS in hnRNPA1 homeostasis.

Revealing the Unbinding Kinetics and Mechanism of Type I and Type II Protein Kinase Inhibitors by Local-Scaled Molecular Dynamics Simulations.

Protein kinase inhibitors disrupt phosphorylation of the target kinases, which are an important class of drug for treating cancer and other diseases. Conventional structure-based design methods (such as molecular docking) focus on the static binding mode of the kinase inhibitor with its target. However, dissociation kinetic properties of a drug molecule are found to correlate with its residence time in vivo and thus have drawn the attention of drug designers in recent years. In this study, we have applied the local-scaled molecular dynamics (MD) simulation enabled in GROMACS software to explore the unbinding mechanism of a total of 41 type I and type II kinase inhibitors. Our simulation considered multiple starting configurations as well as possible protonation states of kinase inhibitors. Based on our local-scaled MD results, we discovered that the integrals of the favorable binding energy during dissociation correlated well (R2 = 0.64) with the experimental dissociation rate constants of those kinase inhibitors on the entire data set. Given its accuracy and technical advantage, this method may serve as a practical option for estimating this important property in reality. Our simulation also provided a reasonable explanation of the dynamic properties of kinase and its inhibitor as well as the role of relevant water molecules in dissociation.

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.