Deciphering the Selectivity of CBL-B Inhibitors Using All-Atom Molecular Dynamics and Machine Learning.

We employ a combination of accelerated molecular dynamics and machine learning to unravel how the dynamic characteristics of CBL-B and C-CBL confer their binding affinity and selectivity for ligands from subtle structural disparities within their binding pockets and dissociation pathways. Our predictive model of dissociation rate constants (k off) demonstrates a moderate correlation between predicted k off and experimental IC50 values, which is consistent with experimental k off and τ-random accelerated molecular dynamics (τRAMD) results. By employing a linear regression of dissociation trajectories, we identified key amino acids in binding pockets and along the dissociation paths responsible for activity and selectivity. These amino acids are statistically significant in achieving activity and selectivity and contribute to the primary structural discrepancies between CBL-B and C-CBL. Moreover, the binding free energies calculated from molecular mechanics with generalized Born and surface area solvation (MM/GBSA) highlight the ΔG difference between CBL-B and C-CBL. The k off prediction, together with the key amino acids, provides important guides for designing drugs with high selectivity.

Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential.

Exclusively prioritizing the precision of energy prediction frequently proves inadequate in satisfying multifaceted requirements. A heightened focus is warranted on assessing the rationality of potential energy curves predicted by machine learning-based force fields (MLFFs), alongside evaluating the pragmatic utility of these MLFFs. This study introduces SWANI, an optimized neural network potential stemming from the ANI framework. Through the incorporation of supplementary physical constraints, SWANI aligns more cohesively with chemical expectations, yielding rational potential energy profiles. It also exhibits superior predictive precision compared with that of the ANI model. Additionally, a comprehensive comparison is conducted between SWANI and a prominent graph neural network-based model. The findings indicate that SWANI outperforms the latter, particularly for molecules exceeding the dimensions of the training set. This outcome underscores SWANI's exceptional capacity for generalization and its proficiency in handling larger molecular systems.

An efficient method by combining different basis sets and SAPT levels.

In symmetry-adapted perturbation theory (SAPT), accurate calculations on non-covalent interaction (NCI) for large complexes with more than 50 atoms are time-consuming using large basis sets. More efficient ones with smaller basis sets usually result in poor prediction in terms of dispersion and overall energies. In this study, we propose two composite methods with baseline calculated at SAPT2/aug-cc-pVDZ and SAPT2/aug-cc-pVTZ with dispersion term corrected at SAPT2+ level using bond functions and smaller basis set with δ MP2 corrections respectively. Benchmark results on representative NCI data sets, such as S22, S66, and so forth, show significant improvements on the accuracy compared to the original SAPT Silver standard and comparable to SAPT Gold standard in some cases with much less computational cost.

Metabolic switch regulates lineage plasticity and induces synthetic lethality in triple-negative breast cancer.

Metabolic reprogramming is key for cancer development, yet the mechanism that sustains triple-negative breast cancer (TNBC) cell growth despite deficient pyruvate kinase M2 (PKM2) and tumor glycolysis remains to be determined. Here, we find that deficiency in tumor glycolysis activates a metabolic switch from glycolysis to fatty acid ß-oxidation (FAO) to fuel TNBC growth. We show that, in TNBC cells, PKM2 directly interacts with histone methyltransferase EZH2 to coordinately mediate epigenetic silencing of a carnitine transporter, SLC16A9. Inhibition of PKM2 leads to impaired EZH2 recruitment to SLC16A9, and in turn de-represses SLC16A9 expression to increase intracellular carnitine influx, programming TNBC cells to an FAO-dependent and luminal-like cell state. Together, these findings reveal a new metabolic switch that drives TNBC from a metabolically heterogeneous-lineage plastic cell state to an FAO-dependent-lineage committed cell state, where dual targeting of EZH2 and FAO induces potent synthetic lethality in TNBC.

Patient-specific analysis of co-expression to measure biological network rewiring in individuals.

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.

Using graph-based model to identify cell specific synthetic lethal effects.

Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cell's survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, current computational approaches provide only limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. As a result, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we applied cell-line specific multi-omics data to a specially designed deep learning model to predict cell-line specific SL pairs. Through incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach achieves the prediction of SL pairs in a cell-specific manner and demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. The code and data of our approach can be found at https://github.com/promethiume/SLwise.

Paralog-based synthetic lethality: rationales and applications.

Tumor cells can result from gene mutations and over-expression. Synthetic lethality (SL) offers a desirable setting where cancer cells bearing one mutated gene of an SL gene pair can be specifically targeted by disrupting the function of the other genes, while leaving wide-type normal cells unharmed. Paralogs, a set of homologous genes that have diverged from each other as a consequence of gene duplication, make the concept of SL feasible as the loss of one gene does not affect the cell's survival. Furthermore, homozygous loss of paralogs in tumor cells is more frequent than singletons, making them ideal SL targets. Although high-throughput CRISPR-Cas9 screenings have uncovered numerous paralog-based SL pairs, the unclear mechanisms of targeting these gene pairs and the difficulty in finding specific inhibitors that exclusively target a single but not both paralogs hinder further clinical development. Here, we review the potential mechanisms of paralog-based SL given their function and genetic combination, and discuss the challenge and application prospects of paralog-based SL in cancer therapeutic discovery.

Targeting the PRC2-dependent epigenetic program alleviates urinary tract infections.

Urinary tract infection (UTI) is a pervasive health problem worldwide. Patients with a history of UTIs suffer increased risk of recurrent infections, a major risk of antibiotic resistance. Here, we show that bladder infections induce expression of Ezh2 in bladder urothelial cells. Ezh2 is the methyltransferase of polycomb repressor complex 2 (PRC2)-a potent epigenetic regulator. Urothelium-specific inactivation of PRC2 results in reduced urine bacterial burden, muted inflammatory response, and decreased activity of the NF-κB signaling pathway. PRC2 inactivation also facilitates proper regeneration after urothelial damage from UTIs, by attenuating basal cell hyperplasia and increasing urothelial differentiation. In addition, treatment with Ezh2-specific small-molecule inhibitors improves outcomes of the chronic and severe bladder infections in mice. These findings collectively suggest that the PRC2-dependent epigenetic reprograming controls the amplitude of inflammation and severity of UTIs and that Ezh2 inhibitors may be a viable non-antibiotic strategy to manage chronic and severe UTIs.

Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation.

Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (koff) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 and obtained the protein-ligand interaction fingerprints (IFPs) on their dissociation pathways and their influencing weights on the koff value. We observed a high correlation among the simulated, predicted, and experimental -log(koff) values. Combining ML, molecular dynamics (MD) simulation, and IFPs derived from accelerated MD helps design a drug for specific kinetic properties and selectivity profiles to the target of interest. To further validate our koff predictive ML model, we tested our model on two new N-HSP90 inhibitors, which have experimental koff values and are not in our ML training dataset. The predicted koff values are consistent with experimental data, and the mechanism of their kinetic properties can be explained by IFPs, which shed light on the nature of their selectivity against N-HSP90 protein. We believe that the ML model described here is transferable to predict koff of other proteins and will enhance the kinetics-based drug design endeavor.

Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures.

Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the treatment of cancer. Even though somatic mutations have been linked to tumorigenesis and metastasis, it is less explored whether metastatic events can be identified through genomic mutational signatures, which are concise descriptions of the mutational processes. Here, we developed MetaWise, a Deep Neural Network (DNN) model, by applying mutational signatures as input features calculated from Whole-Exome Sequencing (WES) data of TCGA and other metastatic cohorts. This model can accurately classify metastatic tumors from primary tumors and outperform traditional machine learning (ML) models and a deep learning (DL) model, DiaDeL. Signatures of non-coding mutations also have a major impact on the model's performance. SHapley Additive exPlanations (SHAP) and Local Surrogate (LIME) analyses identify several mutational signatures which are directly correlated to metastatic spread in cancers, including APOBEC-mutagenesis, UV-induced signatures, and DNA damage response deficiency signatures.

G2GT: Retrosynthesis Prediction with Graph-to-Graph Attention Neural Network and Self-Training.

Retrosynthesis prediction, the task of identifying reactant molecules that can be used to synthesize product molecules, is a fundamental challenge in organic chemistry and related fields. To address this challenge, we propose a novel graph-to-graph transformation model, G2GT. The model is built on the standard transformer structure and utilizes graph encoders and decoders. Additionally, we demonstrate the effectiveness of self-training, a data augmentation technique that utilizes unlabeled molecular data, in improving the performance of the model. To further enhance diversity, we propose a weak ensemble method, inspired by reaction-type labels and ensemble learning. This method incorporates beam search, nucleus sampling, and top-k sampling to improve inference diversity. A simple ranking algorithm is employed to retrieve the final top-10 results. We achieved new state-of-the-art results on both the USPTO-50K data set, with a top-1 accuracy of 54%, and the larger more challenging USPTO-Full data set, with a top-1 accuracy of 49.3% and competitive top-10 results. Our model can also be generalized to all other graph-to-graph transformation tasks. Data and code are available at https://github.com/Anonnoname/G2GT_2.

A pocket-based 3D molecule generative model fueled by experimental electron density.

We report for the first time the use of experimental electron density (ED) as training data for the generation of drug-like three-dimensional molecules based on the structure of a target protein pocket. Similar to a structural biologist building molecules based on their ED, our model functions with two main components: a generative adversarial network (GAN) to generate the ligand ED in the input pocket and an ED interpretation module for molecule generation. The model was tested on three targets: a kinase (hematopoietic progenitor kinase 1), protease (SARS-CoV-2 main protease), and nuclear receptor (vitamin D receptor), and evaluated with a reference dataset composed of over 8000 compounds that have their activities reported in the literature. The evaluation considered the chemical validity, chemical space distribution-based diversity, and similarity with reference active compounds concerning the molecular structure and pocket-binding mode. Our model can generate molecules with similar structures to classical active compounds and novel compounds sharing similar binding modes with active compounds, making it a promising tool for library generation supporting high-throughput virtual screening. The ligand ED generated can also be used to support fragment-based drug design. Our model is available as an online service to academic users via https://edmg.stonewise.cn/#/create .

Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design.

In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high-level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Different ESP prediction ML models were proposed to generate surface molecular charge distribution. Electrostatic complementarity (EC) can apply ESP data to quantify the complementarity between a ligand and its binding pocket, leading to the potential to increase the efficiency of drug design. However, there is not much research discussing EC score functions and their applicability domain. We propose a new EC score function modified from the one originally developed by Bauer and Mackey, and confirm its effectiveness against the available Pearson's R correlation coefficient. Additionally, the applicability domain of the EC score and two indices used to define the EC score application scope will be discussed.

Arid1a regulates bladder urothelium formation and maintenance.

Epigenetic regulation of gene expression plays a central role in bladder urothelium development and maintenance. ATPase-dependent chromatin remodeling is a major epigenetic regulatory mechanism, but its role in the bladder has not been explored. Here, we show the functions of Arid1a, the largest subunit of the SWI/SNF or BAF chromatin remodeling ATPase complex, in embryonic and adult bladder urothelium. Knockout of Arid1a in urothelial progenitor cells significantly increases cell proliferation during bladder development. Deletion of Arid1a causes ectopic cell proliferation in the terminally differentiated superficial cells in adult mice. Consistently, gene-set enrichment analysis of differentially expressed genes demonstrates that the cell cycle-related pathways are significantly enriched in Arid1a knockouts. Gene-set of the polycomb repression complex 2 (PRC2) pathway is also enriched, suggesting that Arid1a antagonizes the PRC2-dependent epigenetic gene silencing program in the bladder. During acute cyclophosphamide-induced bladder injury, Arid1a knockouts develop hyperproliferative and hyperinflammatory phenotypes and exhibit a severe loss of urothelial cells. A Hallmark gene-set of the oxidative phosphorylation pathway is significantly reduced in Aria1a mutants before injury and is unexpectedly enriched during injury response. Together, this study uncovers functions of Arid1a in both bladder progenitor cells and the mature urothelium, suggesting its critical roles in urothelial development and regeneration.

Observing Noncovalent Interactions in Experimental Electron Density for Macromolecular Systems: A Novel Perspective for Protein-Ligand Interaction Research.

We report for the first time the use of experimental electron density (ED) in the Protein Data Bank for modeling of noncovalent interactions (NCIs) for protein-ligand complexes. Our methodology is based on reduced electron density gradient (RDG) theory describing intermolecular NCIs by ED and its first derivative. We established a database named Experimental NCI Database (ExptNCI; http://ncidatabase.stonewise.cn/#/nci) containing ED saddle points, indicating ∼200,000 NCIs from over 12,000 protein-ligand complexes. We also demonstrated the usage of the database in the case of depicting amide-π interactions in protein-ligand binding systems. In summary, the database provides details on experimentally observed NCIs for protein-ligand complexes and can support future studies including studies on rarely documented NCIs and the development of artificial intelligence models for protein-ligand binding prediction.

TET2 directs mammary luminal cell differentiation and endocrine response.

Epigenetic regulation plays an important role in governing stem cell fate and tumorigenesis. Lost expression of a key DNA demethylation enzyme TET2 is associated with human cancers and has been linked to stem cell traits in vitro; however, whether and how TET2 regulates mammary stem cell fate and mammary tumorigenesis in vivo remains to be determined. Here, using our recently established mammary specific Tet2 deletion mouse model, the data reveals that TET2 plays a pivotal role in mammary gland development and luminal lineage commitment. We show that TET2 and FOXP1 form a chromatin complex that mediates demethylation of ESR1, GATA3, and FOXA1, three key genes that are known to coordinately orchestrate mammary luminal lineage specification and endocrine response, and also are often silenced by DNA methylation in aggressive breast cancers. Furthermore, Tet2 deletion-PyMT breast cancer mouse model exhibits enhanced mammary tumor development with deficient ERα expression that confers tamoxifen resistance in vivo. As a result, this study elucidates a role for TET2 in governing luminal cell differentiation and endocrine response that underlies breast cancer resistance to anti-estrogen treatments.

Management of Combined Atlas Fracture with Type II Odontoid Fracture: A Review of 21 Cases.

PURPOSE: To evaluate the therapeutic effects of combined atlas fracture with type II (C1-type II) odontoid fractures and to outline a management strategy for it. PATIENTS AND METHODS: Twenty three patients with C1-type II odontoid fractures were treated according to our management strategy. Nonoperative external immobilization in the form of cervical collar and halo vest was used in 13 patients with stable atlantoaxial joint. Surgical treatment was early performed in 10 patients whose fractures with traumatic transverse atlantal ligament disruption or atlantoaxial instability. The visual analog scale (VAS), neck disability index (NDI) scale, and American Spinal Injury Association (ASIA) scale at each stage of followup were then collected and compared. RESULTS: Compared to pretreatment, the VAS score, NDI score, and ASIA scale were improved among both groups at followup evaluation after treatment. However, in the nonsurgical group, one patient (1/11) developed nonunion which required surgical treatment in later stage and one patient (1/13) with halo vest immobilization had happened pin site infection. Two patients of the surgical group (2/11) had appeared minor complications: occipital cervical pain in one case and cerebrospinal fluid leakage in one case. Two patients (2/23) were excluded from nonsurgical treatment group because their followup period was less than 12 months. Twenty one patients were followed up regularly with an average of 23.9 months (range 15-45 months). CONCLUSIONS: We outlined our concluding management principle for the treatment of C1-type II odontoid fractures based on the nature of C1 fracture and atlantoaxial stability. The treatment principle can obtain satisfactory results for the management of C1-type II odontoid fractures.

Preparation of Iron-Loaded Granular Activated Carbon Catalyst and Its Application in Tetracycline Antibiotic Removal from Aqueous Solution.

The removal of tetracycline antibiotics from water is currently an important environmental issue. Here we prepared an iron-loaded granular activated carbon catalyst (GAC-Fe) through a one-step calcination method to remove tetracycline antibiotics from aqueous solution. The GAC-Fe was characterized by Fourier transform infrared absorption spectroscopy, X-ray photoelectron spectroscopy, and X-ray diffraction analysis. The effect of different influencing factors on the removal behavior of tetracycline antibiotics was studied, such as the solid-to-liquid ratio, H2O2 dosage, environmental temperature, initial pH, and contact time. The removal mechanism was explored through Fe ion dissolution and a free radical quenching experiment. The results show that the optimum solid-to-liquid ratio was 3.0 g∙L-1 and the suitable H2O2 dosage was 1.0 mL (3%). The applicable environmental temperature was 25 °C and the appropriate pH value was 2.0. The removal rate of tetracycline antibiotics tended to be stable in a contact time of 600 min. The main mechanism of tetracycline antibiotic removal by GAC-Fe was heterogeneous catalytic reaction through iron ion leaching and free radical inhibition experiment. The hydroxyl radical played a major role during the removal process. The partially dissolved iron ions initiated a homogeneous catalytic reaction. However, heterogeneous catalytic degradation was the main reaction. The GAC-Fe could still remove tetracycline antibiotics after five cycles, especially for methacycline and minocycline. Our work suggests that the GAC-Fe catalyst has potential as a remediation agent for tetracycline antibiotics in aqueous solution.

Epithelial-Mesenchymal Transition Directs Stem Cell Polarity via Regulation of Mitofusin.

Mitochondria are dynamic organelles that have been linked to stem cell homeostasis. However, the mechanisms involved in mitochondrial regulation of stem cell fate determination remain elusive. Here we discover that epithelial-mesenchymal transition (EMT), a key process in cancer progression, induces mitochondrial fusion through regulation of the miR200c-PGC1α-MFN1 pathway. EMT-activated MFN1 forms a complex with PKCζ and is required for PKCζ-mediated NUMB phosphorylation and dissociation from the cortical membrane to direct asymmetric division of mammary stem cells, where fused mitochondria are tethered by MFN1-PKCζ to the cortical membrane and asymmetrically segregated to the stem cell-like progeny with enhanced glutathione synthesis and reactive oxygen species scavenging capacities, allowing sustaining of a self-renewing stem cell pool. Suppression of MFN1 expression leads to equal distribution of the fragmented mitochondria in both progenies that undergo symmetric luminal cell differentiation. Together, this study elucidates an essential role of mitofusin in stem cell fate determination to mediate EMT-associated stemness.