A new paradigm for applying deep learning to protein-ligand interaction prediction.

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function.

The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein-ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization.

Reticular Heterojunction for Organic Photoelectrochemical Transistor Detection of Neuron-Specific Enolase.

Reticular heterojunctions on the basis of metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have sparked considerable interest in recent research endeavors, which nevertheless have seldom been studied in optoelectronic biosensing. In this work, its utilization for organic photoelectrochemical transistor (OPECT) detection of the important cancer biomarker of neuron-specific enolase (NSE) is reported. A MOF@COF@CdS quantum dots (QDs) heterojunction is rationally designed to serve as the photogating module against the polymeric channel. Linking with a sandwich complexing event, target-dependent alternation of the photogate is achieved, leading to the changed photoelectric conversion efficiency as indicated by the amplified OPECT signals. The proposed assay demonstrates good analytical performance in detecting NSE, featuring a linear detection range from 0.1 pg mL-1 to 100 ng mL-1, with a detection limit of 0.033 pg mL-1.

Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term.

Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein-ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to the Vina score, the model considerably enhances the AutoDock Vina prediction abilities for docking and screening tasks based on different benchmarks (such as cross-docking dataset, CASF-2016, DUD-E and DUD-AD). Furthermore, our model could be combined with multiple docking applications to increase pose selection accuracies and screening abilities, indicating its wide usage for structure-based drug discoveries. Furthermore, in a reverse practice, the combined scoring strategy successfully identified multiple known receptors of a plant hormone. To summarize, the results show that the combination of data-driven model (OnionNet-SFCT) and empirical scoring function (Vina score) is a good scoring strategy that could be useful for structure-based drug discoveries and potentially target fishing in future.

Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling.

Accurate protein-ligand binding poses are the prerequisites of structure-based binding affinity prediction and provide the structural basis for in-depth lead optimization in small molecule drug design. However, it is challenging to provide reasonable predictions of binding poses for different molecules due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlated to higher success rates. However, molecular similarity is not consistently high because molecules often require changes to achieve specific purposes, leading to reduced alignment precision. To address this issue, we propose a new alignment method─Z-align. This method uses topological structural information as a criterion for evaluating similarity, reducing the reliance on molecular fingerprint similarity. Our method has achieved success rates significantly higher than those of other methods at moderate levels of similarity. Additionally, our approach can comprehensively and flexibly optimize bond lengths and angles of molecules, maintaining a high accuracy even when dealing with larger molecules. Consequently, our proposed solution helps in achieving more accurate binding poses in protein-ligand docking problems, facilitating the development of small molecule drugs. Z-align is freely available as a web server at https://cloud.zelixir.com/zalign/home.

zPoseScore model for accurate and robust protein-ligand docking pose scoring in CASP15.

We introduce a deep learning-based ligand pose scoring model called zPoseScore for predicting protein-ligand complexes in the 15th Critical Assessment of Protein Structure Prediction (CASP15). Our contributions are threefold: first, we generate six training and evaluation data sets by employing advanced data augmentation and sampling methods. Second, we redesign the "zFormer" module, inspired by AlphaFold2's Evoformer, to efficiently describe protein-ligand interactions. This module enables the extraction of protein-ligand paired features that lead to accurate predictions. Finally, we develop the zPoseScore framework with zFormer for scoring and ranking ligand poses, allowing for atomic-level protein-ligand feature encoding and fusion to output refined ligand poses and ligand per-atom deviations. Our results demonstrate excellent performance on various testing data sets, achieving Pearson's correlation R = 0.783 and 0.659 for ranking docking decoys generated based on experimental and predicted protein structures of CASF-2016 protein-ligand complexes. Additionally, we obtain an averaged local distance difference test (lDDT pli = 0.558) of AIchemy LIG2 in CASP15 for de novo protein-ligand complex structure predictions. Detailed analysis shows that accurate ligand binding site prediction and side-chain orientation are crucial for achieving better prediction performance. Our proposed model is one of the most accurate protein-ligand pose prediction models and could serve as a valuable tool in small molecule drug discovery.

Neutrophil-Derived IL-6 Potentially Drives Ferroptosis Resistance in B Cells in Lupus Kidney.

Ferroptosis resistance is vital for B cell development, especially in inflammatory diseases, yet the underlying mechanism is still unclear. In this study, based on the scRNA-seq technique and flow cytometry, we discovered a proportion of neutrophils exhibited upregulated expression of the IL-6 and correlated with the expression of IL-6 receptor and SLC7A11 from B cells in lupus kidney. Moreover, we identified that in lupus kidney, neutrophils could provide IL-6 to facilitate ferroptosis resistance in B cells via SLC7A11, and inhibition of SLC7A11 could significantly enhance ferroptosis in B cells and could decrease B cell proliferation. This study helps understand the crosstalk between neutrophils and B cells in the kidney in the development of lupus.

Strategy to Lengthen the On-Time of Photochromic Rhodamine Spirolactam for Super-resolution Photoactivated Localization Microscopy.

Rhodamine derivatives and analogues have been widely used for different super-resolution imaging techniques, including photoactivated localization microscopy (PALM). Among them, rhodamine spirolactams exhibit great superiority for PALM imaging due to a desirable bright-dark contrast during the photochromic switching process. Although considerable attention has been paid to the chemical modifications on rhodamine spirolactams, the on-time of photochromic switching, one of the key characteristics for PALM imaging, has never been optimized in previous developments. In this study, we proposed that simply installing a carboxyl group close to the lactam site could impose an intramolecular acidic environment, stabilize the photoactivated zwitterionic structure, and thus effectively increase the on-time. On the basis of this idea, we have synthesized a new rhodamine spirolactam, Rh-Gly, that demonstrated considerably longer on-time than the other tested analogues, as well as an enhancement of single-molecule brightness, an improvement on signal-to-noise ratio and an enlargement of total collected photons of a single molecule before photobleaching. Finally, super-resolution images of live cell mitochondria stained with Rh-Gly have been obtained with a good temporal resolution of 10 s, as well as a satisfactory localization precision of ∼25 nm. Through self-labeling protein tags, Rh-Gly modified with a HaloTag ligand enabled super-resolution imaging of histone H2B proteins in live HeLa cells; through immunostaining antibodies labeled with an isothiocyanate-substituted Rh-Gly, super-resolution imaging of microtubules was achieved in fixed cells. Therefore, our simple and effective strategy provides novel insight for developing further enhanced rhodamine spirolactams recommendable for PALM imaging.

Detection of respiration changes inside biofilms with microelectrodes during exposure to antibiotics.

In order to evaluate the influence of antibiotics on the respiration of subpopulations in biofilms, dissolved oxygen microelectrodes were used to determine the oxygen concentration inside E. Coli biofilms. Based on the results, the oxygen consumption rate varying with depth in the biofilms was calculated before and after the treatment with antibiotics. For both ceftriaxone sodium and chloramphenicol, significant attenuation of oxygen consumption was identified when the system reached steady state. However, by monitoring the oxygen concentration change induced by ceftriaxone sodium, an increased oxygen consumption rate and a shortened oxygen penetration depth were identified in the first few hours, which indicated the accelerated respiration of the subpopulation. Followed by cellular death, the induced respiration acceleration seems to be part of the action mechanism of ceftriaxone sodium. Compared to the planktonic cells, the biofilms showed much higher tolerance to ceftriaxone sodium, which may be attributed to the extended hypoxic zone induced by the accelerated respiration.

Dual functional cholinesterase and MAO inhibitors for the treatment of Alzheimer's disease: synthesis, pharmacological analysis and molecular modeling of homoisoflavonoid derivatives.

Because of the complexity of Alzheimer's disease (AD), the multi-target-directed ligand (MTDL) strategy is expected to provide superior effects for the treatment of AD, instead of the classic one-drug-one-target strategy. In this context, we focused on the design, synthesis and evaluation of homoisoflavonoid derivatives as dual acetyl cholinesterase (AChE) and monoamine oxidase (MAO-B) inhibitors. Among all the synthesized compounds, compound 10 provided a desired balance of AChE and hMAO-B inhibition activities, with IC50 value of 3.94 and 3.44 µM, respectively. Further studies revealed that compound 10 was a mixed-type inhibitor of AChE and an irreversible inhibitor of hMAO-B, which was also confirmed by molecular modeling studies. Taken together, the data indicated that 10 was a promising dual functional agent for the treatment of AD.

The Identification of a Strain for the Biological Purification of Soy Molasses to Produce Functional Soy Oligosaccharides and Optimize Purification Conditions.

Soy molasses is rich in oligosaccharides like sucrose, stachyose, and raffinose, with stachyose and raffinose being functional oligosaccharides. Harnessing soy molasses for the production of functional soy oligosaccharides (FSO) can significantly elevate its value. Biological purification, a method leveraging the selective utilization of different carbon sources by microorganisms, allows for the specific removal of sucrose from soy molasses while preserving stachyose and raffinose, thereby increasing the FSO content. This research identified a yeast named YT312 with strong purification capabilities for soy molasses and optimized the purification conditions. The study revealed that yeast YT312 was Wickerhamomyces anomalus, exhibiting a broad range of growth temperatures and pH levels alongside a high tolerance to glucose, sucrose, and NaCl. Through single-factor and orthogonal experiments, it was established that under specific conditions-0.375% inoculum size, 30 °C fermentation temperature, 150 rpm shaking speed, 10-fold dilution ratio, pH of 7, and 12 h of fermentation-sucrose was completely removed from soy molasses, while functional raffinose and stachyose were retained at rates of 96.1% and 90.2%, respectively. Consequently, W. anomalus YT312 displayed exceptional characteristics for the biological purification of soy molasses and the production of FSO.

ThermoLink: Bridging disulfide bonds and enzyme thermostability through database construction and machine learning prediction.

Disulfide bonds, covalently formed by sulfur atoms in cysteine residues, play a crucial role in protein folding and structure stability. Considering their significance, artificial disulfide bonds are often introduced to enhance protein thermostability. Although an increasing number of tools can assist with this task, significant amounts of time and resources are often wasted owing to inadequate consideration. To enhance the accuracy and efficiency of designing disulfide bonds for protein thermostability improvement, we initially collected disulfide bond and protein thermostability data from extensive literature sources. Thereafter, we extracted various sequence- and structure-based features and constructed machine-learning models to predict whether disulfide bonds can improve protein thermostability. Among all models, the neighborhood context model based on the Adaboost-DT algorithm performed the best, yielding "area under the receiver operating characteristic curve" and accuracy scores of 0.773 and 0.714, respectively. Furthermore, we also found AlphaFold2 to exhibit high superiority in predicting disulfide bonds, and to some extent, the coevolutionary relationship between residue pairs potentially guided artificial disulfide bond design. Moreover, several mutants of imine reductase 89 (IR89) with artificially designed thermostable disulfide bonds were experimentally proven to be considerably efficient for substrate catalysis. The SS-bond data have been integrated into an online server, namely, ThermoLink, available at guolab.mpu.edu.mo/thermoLink.

The investigation of oncolytic viruses in the field of cancer therapy.

Oncolytic viruses (OVs) have emerged as a potential strategy for tumor treatment due to their ability to selectively replicate in tumor cells, induce apoptosis, and stimulate immune responses. However, the therapeutic efficacy of single OVs is limited by the complexity and immunosuppressive nature of the tumor microenvironment (TME). To overcome these challenges, engineering OVs has become an important research direction. This review focuses on engineering methods and multi-modal combination therapies for OVs aimed at addressing delivery barriers, viral phagocytosis, and antiviral immunity in tumor therapy. The engineering approaches discussed include enhancing in vivo immune response, improving replication efficiency within the tumor cells, enhancing safety profiles, and improving targeting capabilities. In addition, this review describes the potential mechanisms of OVs combined with radiotherapy, chemotherapy, cell therapy and immune checkpoint inhibitors (ICIs), and summarizes the data of ongoing clinical trials. By continuously optimizing engineering strategies and combination therapy programs, we can achieve improved treatment outcomes and quality of life for cancer patients.

Novel biaryloxazolidinone derivatives with broad-spectrum antibacterial activity, favorable drug-like profiles and in vivo efficacy against linezolid-resistant Staphylococcusaureus.

The emergence of multidrug-resistant bacteria along with a declining pipeline of clinically useful antibiotics has led to the urgent need for the development of more effective antibacterial agents to treat drug-resistant bacteria. We previously discovered compound OB-158 with potent antibacterial activity but exhibited poor oral bioavailability. Herein, a systematic structural optimization of OB-158 to improve pharmacokinetic profiles yielded 26 novel biaryloxazolidinone analogues, and their activities against Gram-positive S. aureus, multidrug resistant S. aureus and Enterococcus faecalis were evaluated. Remarkably, compound 8b was identified with potent antibacterial activity against S. aureus (MIC = 0.06 µg/mL), MSSA (MIC = 0.125 µg/mL), MRSA (MIC = 0.06 µg/mL), LRSA (MIC = 0.125 µg/mL) and LREFa (MIC = 0.5 µg/mL). Compound 8b was demonstrated as a promising candidate through druglikeness evaluation including metabolism in microsomes and plasma, Caco-2 cell permeability, plasma protein binding, cytotoxicity, and inhibition of CYP450 and human monoamine oxidase. Notably, compound 8b displayed excellent PK profile with appropriate T1/2 of 1.49 h, high peak plasma concentration (Cmax = 2320 ng/mL), high plasma exposure (AUC0-t = 8310 h ng/mL), and superior oral bioavailability (F = 68.1 %) in Sprague-Dawley rats. Ultimately, in vivo efficacy of compound 8b in a mouse model of LRSA systemic infection was also demonstrated. Taken together, compound 8b represents a promising drug candidate for the treatment of linezolid-resistant Gram-positive bacterial strains infection.

Tackling FGFR3-driven bladder cancer with a promising synergistic FGFR/HDAC targeted therapy.

Bladder cancer (BC) is one of the most prevalent malignancies worldwide and FGFR3 alterations are particularly common in BC. Despite approval of erdafitinib, durable responses for FGFR inhibitors are still uncommon and most patients relapse to metastatic disease. Given the necessity to discover more efficient therapies for BC, herein, we sought to explore promising synergistic combinations for BC with FGFR3 fusions. Our studies confirmed the synergy between FGFR and HDAC inhibitors in vitro and demonstrated its benefits in vivo. Mechanistic studies revealed that quisinostat can downregulate FGFR3 expression by suppressing FGFR3 translation. Additionally, quisinostat can also sensitize BC cells to erdafitinib by downregulating HDGF. Furthermore, the synergy was also confirmed in BC cells with FGFR3 S249C. This study discovers a new avenue for treatment of FGFR3-driven BC and uncovers new mechanistic insights. These preclinical studies pave the way for a direct translation of this combination to early phase clinical trials.

Medicinal chemistry strategies targeting NLRP3 inflammasome pathway: A recent update from 2019 to mid-2023.

Nod-like receptor protein 3 (NLRP3), a therapeutic target that has a close relationship with inflammatory diseases, has drawn significant attention from researchers in the field. An increasing number of NLRP3 inhibitors have been reported since NLRP3 was identified as a biomarker and inflammatory therapeutic target. Inhibiting NLRP3 has been widely studied as therapeutics for the treatment of cryopyrin associated periodic syndrome (CAPS), inflammatory bowel disease (IBD), nonalcoholic steatohepatitis (NASH), arthrolithiasis, Alzheimer's disease (AD) and Parkinson's disease (PD). This review updates the recently reported (2019 to mid-2023) molecule inhibitors targeting the NLRP3 inflammasome pathway, summarizes their structure-activity relationships (SARs), and discusses the therapeutic effects on inflammatory diseases. I hope this review will contribute to the development of novel inhibitors targeting NLRP3 inflammasome pathway as potential drugs.

The Expression of ARMCX1 in Gastric Cancer Contributes to Prognosis and Influences Chemotherapy.

The altered expression of ARMCX1 in patients with gastric cancer has been reported frequently, yet its correlation to prognosis and chemotherapy needs to be unveiled. In combination of the gene expression data retrieved from TCGA database and bioinformatic analysis, this study discovered 590 differentially expressed genes in the cancerous biopsies isolated from gastric patients, compared with controls. Among which, ARMCX1 exhibited great potential to serve as a prognostic biomarker for gastric patients; furthermore, patients with low expression of ARMCX1 could be more sensitive to these 9 chemotherapeutic agents: A-770041, AMG-706, ATRA, BEZ235, bortezomib, CGP60474, dasatinib, HG-64-1, and pazopanib, rather than the other chemotherapeutic agents. This study helps the improvement of evaluating the prognosis of gastric cancer patients, and would help optimize chemotherapeutic strategies in consideration of the expression of ARMCX1.

Therapeutic Targeting of FGFR Signaling in Head and Neck Cancer.

ABSTRACT: Squamous cell carcinoma of the head and neck (HNSCC) is the sixth most prevalent cancer worldwide, with an annual incidence of 600,000 new cases. Despite advances in surgery, chemotherapy, and radiotherapy, the overall survival for HNSCC patients has not been significantly improved over the past several decades. Fibroblast growth factor (FGF)/fibroblast growth factor receptor (FGFR) genomic alterations are frequently detected in HNSCC, including amplification, activating mutation, and chromosomal rearrangement. Among them, FGFR1 amplification, FGF amplifications, and FGFR3 mutations are the most prevalent. In addition, FGF/FGFR expression has also been observed in most HNSCCs. However, the prognostic value of FGF/FGFR aberrations remains unclear, especially for gene amplification and overexpression. Nonetheless, FGF/FGFR has been a promising target for HNSCC treatment, and recent preclinical studies demonstrate the potential of the combination treatment regimens involving FGFR inhibitors on HNSCC. Therefore, there are a number of FGFR inhibitors currently in clinical trials for the treatment of head and neck cancers.

Update to the Treatment of Parkinson's Disease Based on the Gut-Brain Axis Mechanism.

Gastrointestinal (GI) symptoms represented by constipation were significant non-motor symptoms of Parkinson's disease (PD) and were considered early manifestations and aggravating factors of the disease. This paper reviewed the research progress of the mechanism of the gut-brain axis (GBA) in PD and discussed the roles of α-synuclein, gut microbiota, immune inflammation, neuroendocrine, mitochondrial autophagy, and environmental toxins in the mechanism of the GBA in PD. Treatment of PD based on the GBA theory has also been discussed, including (1) dietary therapy, such as probiotics, vitamin therapy, Mediterranean diet, and low-calorie diet, (2) exercise therapy, (3) drug therapy, including antibiotics; GI peptides; GI motility agents, and (4) fecal flora transplantation can improve the flora. (5) Vagotomy and appendectomy were associated but not recommended.

Single-thermal synthesis of bimetallic Co/Zn@NC under solvent-free conditions as an efficient dual-functional oxygen electrocatalyst in Zn-air batteries.

A straightforward in situ thermal (IST) method is developed to synthesize bimetallic Co/Zn embedded in nitrogen-doped three-dimensional carbon (CoZn@NC_IST). The facile IST process is a single-step thermal treatment of a mixture of metals (Co/Zn) and 2-methylimidazole precursors under solvent-free conditions. This straightforward method is advantageous over the traditional synthesis derived from CoZn-ZIF (CoZn@NC_Solv). During the IST method, the bimetallic Co/Zn bridged with 2-methylimidazole forming zeolitic-imidazole frameworks (ZIFs) under low-temperature (<200 °C) conditions before being de-coordinated and sacrificed their structure into a carbon material at high temperature (>500 °C). Loading zinc into the mixture of precursors contributed to the metal distribution and increased the surface area compared with the sample without zinc (Co@NC_IST). CoZn@NC_IST exhibits a bifunctional electrocatalytic ability for the ORR (0.855 V@E1/2) and OER (overpotential of 325 mV@10 mA cm-2). Applying CoZn@NC_IST in a zinc-air battery confirmed its excellent and effective dual-function electrocatalytic performance. Herein, using the advanced single-step method of IST in the absence of any solvent, we provide a powerful and green synthesis of an electrocatalyst that is a potential candidate for industrial applications.