CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction

Identification of putative allosteric inhibitors of BCKDK via virtual screening and biological evaluation

Abnormal accumulation of branched-chain amino acids (BCAAs) can lead to metabolic diseases and cancers. Branched-chain α-keto acid dehydrogenase kinase (BCKDK) is a key negative regulator of BCAA catabolism, and targeting BCKDK provides a promising therapeutic approach for diseases caused by BCAA accumulation. Here, we screened PPHN and POAB as novel putative allosteric inhibitors by integrating allosteric binding site prediction, large-scale ligand database virtual screening, and bioactivity evaluation assays. Both of them showed a high binding affinity to BCKDK, with Kd values of 3.9 μM and 1.86 μM, respectively. In vivo experiments, the inhibitors demonstrated superior kinase inhibitory activity and notable antiproliferative and proapoptotic effects on diverse cancer cells. Finally, bulk RNA-seq analysis revealed that PPHN and POAB suppressed cell growth through a range of signalling pathways. Taken together, our findings highlight two novel BCKDK inhibitors as potent therapeutic candidates for metabolic diseases and cancers associated with BCAA dysfunctional metabolism.

Computational exploration of compounds in Xylocarpus granatum as a potential inhibitor of Plasmodium berghei using docking, molecular dynamics, and DFT studies

Malaria remains a global health concern, with the emergence of resistance to the antimalarial drug atovaquone through cytochrome b (cyt b) being well-documented. This study was prompted by the presence of this mutation in cyt b to enable new drug candidates capable of overcoming drug resistance. Our objective was to identify potential drug candidates from compounds of Xylocarpus granatum by computationally assessing their interactions with Plasmodium berghei cyt b. Using computational methods, we modeled cyt b (GenBank: AF146076.1), identified the binding cavity, and analyzed the Ramachandran plot against cyt b. Additionally, we conducted drug-likeness and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, along with density functional theory (DFT) analysis of the compounds. Molecular docking and molecular dynamics simulation (MDS) were used to evaluate the binding energy and stability of the cyt b-ligand complex. Notably, our investigation highlighted kaempferol as a promising compound due to its high binding energy of 7.67 kcal/mol among all X. granatum compounds, coupled with favorable pharmacological properties (ADMET) and antiprotozoal properties at Pa 0.345 > Pi 0.009 (PASS value). DFT analysis showed that kaempferol has an energy gap of 4.514 eV. MDS indicated that all tested ligands caused changes in bonding and affected the structural conformation of cyt b, as observed before MDS (0 ns) and after MDS (100 ns). The most notable differences were observed in the types of hydrogen bonds between 0 and 100 ns. Nevertheles, MDS results from a 100 ns simulation revealed consistent behavior for kaempferol across various parameters including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), and hydrogen bonds. The cyt b-kaempferol complex demonstrated favorable energy stability, as supported by the internal energy distribution values observed in principal component analysis (PCA), which closely resembled those of the atovaquone control. Additionally, trajectory stability analysis indicated structural stability, with a cumulative eigenvalue of 24.7 %. Dynamic cross-correlation matrix (DCCM) analysis revealed a positive correlation among catalytic cytochrome residues within the amino acid residues range 119-268. The results of our research indicate that the structure of kaempferol holds promise as a potential candidate against Plasmodium.

GPCR-BSD: a database of binding sites of human G-protein coupled receptors under diverse states

G-protein coupled receptors (GPCRs), the largest family of membrane proteins in human body, involve a great variety of biological processes and thus have become highly valuable drug targets. By binding with ligands (e.g., drugs), GPCRs switch between active and inactive conformational states, thereby performing functions such as signal transmission. The changes in binding pockets under different states are important for a better understanding of drug-target interactions. Therefore it is critical, as well as a practical need, to obtain binding sites in human GPCR structures. We report a database (called GPCR-BSD) that collects 127,990 predicted binding sites of 803 GPCRs under active and inactive states (thus 1,606 structures in total). The binding sites were identified from the predicted GPCR structures by executing three geometric-based pocket prediction methods, fpocket, CavityPlus and GHECOM. The server provides query, visualization, and comparison of the predicted binding sites for both GPCR predicted and experimentally determined structures recorded in PDB. We evaluated the identified pockets of 132 experimentally determined human GPCR structures in terms of pocket residue coverage, pocket center distance and redocking accuracy. The evaluation showed that fpocket and CavityPlus methods performed better and successfully predicted orthosteric binding sites in over 60% of the 132 experimentally determined structures. The GPCR Binding Site database is freely accessible at https://gpcrbs.bigdata.jcmsc.cn . This study not only provides a systematic evaluation of the commonly-used fpocket and CavityPlus methods for the first time but also meets the need for binding site information in GPCR studies.

Renalase rs2296545 variant improve hypertension susceptibility by modifying binding affinity to catecholamines in obstructive sleep apnea

Obstructive sleep apnea (OSA), a condition often linked with hypertension, has an undefined relationship with renalase, a protein known for regulating blood pressure. This study aimed to investigate the relationship between serum renalase levels as well as renalase functional single nucleotide polymorphism (SNP) rs2296545 variant and hypertension in a Han Chinese OSA population. 126 subjects underwent serum renalase detection, with linear regression being performed to evaluate the relationship between serum renalase levels and OSA-related traits. Additional 4275 subjects were obtained rs2296545 genotype information by SNP microarray. And binary logistic regression was used to assess the effect of rs2296545 on hypertension risk. Molecular dynamics simulation and molecular docking were utilized to access the protein structures and the interplay between protein and catecholamines of wild-type and rs2296545 mutant renalase. The results showed that serum renalase levels were significantly higher in the severe OSA group. Further analysis showed renalase levels were positively correlated with blood pressure in the non-OSA group and negatively correlated in the severe OSA group. For rs2296545 polymorphism analysis, the hypertension risk significantly increased for the recessive model CC/GG + CG (OR = 1.211, 95% CI: 1.025-1.431) and the additive model CC/CG (OR = 1.223, 95% CI: 1.025-1.458) in the severe OSA. The rs2296545 polymorphism affected protein structure, and led to increase binding free energy, weakening interactions between renalase and catecholamines. In conclusion, serum renalase levels had independent association with blood pressure. And rs2296545 polymorphism may influence on susceptibility to hypertension by altering protein ability to bind to catecholamines, which might contribute to the intervention of hypertension in the OSA population.

Recent advances from computer-aided drug design to artificial intelligence drug design

Computer-aided drug design (CADD), a cornerstone of modern drug discovery, can predict how a molecular structure relates to its activity and interacts with its target using structure-based and ligand-based methods. Fueled by ever-increasing data availability and continuous model optimization, artificial intelligence drug design (AIDD), as an enhanced iteration of CADD, has thrived in the past decade. AIDD demonstrates unprecedented opportunities in protein folding, property prediction, and molecular generation. It can also facilitate target identification, high-throughput screening (HTS), and synthetic route prediction. With AIDD involved, the process of drug discovery is greatly accelerated. Notably, AIDD offers the potential to explore uncharted territories of chemical space beyond current knowledge. In this perspective, we began by briefly outlining the main workflows and components of CADD. Then through showcasing exemplary cases driven by AIDD in recent years, we describe the evolving role of artificial intelligence (AI) in drug discovery from three distinct stages, that is, chemical library screening, linker generation, and de novo molecular generation. In this process, we attempted to draw comparisons between the features of CADD and AIDD.

Target Fishing Reveals a Novel Mechanism of N-Acylamino Saccharin Derivatives Targeting Glyceraldehyde-3-Phosphate Dehydrogenase toward Cyanobacterial Blooms Control

Based on current challenges of poor targeting and limited choices in chemical control methods of cyanobacterial blooms (CBs), identifying new targets is an urgent and formidable task in the quest for target-based algaecides. This study discovered N-acylamino saccharin derivatives exhibiting potent algicidal activity. Thus, using N-acylamino saccharin as the probes, glyceraldehyde-3-phosphate dehydrogenase from cyanobacterial (CyGAPDH) was identified as a new target of algaecides through the activity-based protein profiling (ABPP) strategy for the first time. Building upon the structure of Probe2, a series of derivatives were designed and synthesized, with compound b6 demonstrating the most potent inhibitory activity against CyGAPDH and Synechocystis sp. PCC6803 (IC50 = 1.67 μM and EC50 = 1.15 μM). Furthermore, the potential covalent binding model of b6 to the cysteine residue C154 was explored through covalent possibility prediction, LC-MS experiments, substrate competitive inhibition experiments, and molecular docking. Especially, the results revealed C154 as a crucial covalent binding site, with residues T184 and R11 forming robust hydrophobic interactions and H181 establishing significant hydrogen-bonding interactions with b6, highlighting their potential as essential pharmacophores. In summary, this study not only identifies a novel target of algaecides for the control of CB but also lays the solid foundation for the development of targeted covalent algaecides.

The Amaryllidaceae alkaloid, montanine, is a potential inhibitor of the Trypanosoma cruzi trans-sialidase enzyme

Trypanosoma cruzi is the parasite that causes the chronic malady known as Chagas disease (CD). Only nifurtimox and benznidazole are currently approved to treat CD in acute and chronic phases. To minimize the danger of disease transmission and as a therapy, new compounds that are safer and more effective are required. It has been demonstrated that Amaryllidaceae plants suppress the growth of T. cruzi - the causative agent of CD. However, little research has been done on their potential protein targets in the parasite. In this study, an in-silico approach was used to investigate the interactions of the Amaryllidaceae alkaloids with trans-sialidase, a confirmed protein target of T. cruzi. The nature and efficiency of the main binding modes of the alkaloids were investigated by molecular docking. Trans-sialidase active site residues were bound by the alkaloids with binding energies varying from -8.9 to -6.9 kcal/mol. From the molecular docking investigation, all the alkaloids had strong interactions with the crucial amino acid residues (Glu230, Tyr342, and Asp59) required for trans-sialidase catalysis. Montanine was the most stable compound throughout the molecular dynamics simulation and had a favorable docking binding energy (-8.9 kcal/mol). The binding free energy (MM-GBSA) of the montanine complex was -14.6 kcal/mol. The pharmacokinetic properties investigated demonstrated that all the evaluated compounds exhibit suitable oral administration requirements. Overall, this in silico study suggests that the Amaryllidaceae alkaloids could potentially act as inhibitors of trans-sialidase.Communicated by Ramaswamy H. Sarma.

Molecular hybridization assisted multi-technique approach for designing USP21 inhibitors to halt catalytic triad-mediated nucleophilic attack and suppress pancreatic ductal adenocarcinoma progression: A molecular dynamics study

AIMS

Pancreatic cancer, the 12th-most common cancer, globally, is highly challenging to treat due to its complex epigenetic, metabolic, and genomic characteristics. In pancreatic ductal adenocarcinoma, USP21 acts as an oncogene by stabilizing the long isoform of Transcription Factor 7, thereby activating the Wnt signaling pathway. This study aims to inhibit activation of this pathway through computer-aided drug discovery. Accordingly, four libraries of compounds were designed to target the USP21's catalytic domain (Cys221, His518, Asp534), responsible for its deubiquitinating activity.

MAIN METHODS

Utilizing an array of computer-aided drug design methodologies, such as molecular docking, virtual screening, principal component analysis, molecular dynamics simulation, and dynamic cross-correlation matrix, the structural and functional characteristics of the USP21-inhibitor complex were examined. Following the evaluation of the binding affinities, 20 potential ligands were selected, and the best ligand was subjected to additional molecular dynamics simulation study.

KEY FINDINGS

The results indicated that the ligand-bound USP21 exhibited reduced structural fluctuations compared to the unbound form, as evident from RMSD, RMSF, Rg, and SASA graphs. ADMET analysis of the top ligand showed promising pharmacokinetic and pharmacodynamic profiles, good bioavailability, and low toxicity. The stable conformations of the proposed drug when bound to their target cavities indicate a robust binding affinity of -9.3 kcal/mol. The drug exhibits an elevated pKi value of 6.82, a noteworthy pIC50 value of 5.972, and a pKd value of 6.023 proving its high affinity and inhibitory potential towards the target.

SIGNIFICANCE

In-vitro testing of the top compound (MOLHYB-0436) could lead to its use as a potential treatment for pancreatic cancer.

GraphNABP: Identifying nucleic acid-binding proteins with protein graphs and protein language models

The computational identification of nucleic acid-binding proteins (NABP) is of great significance for understanding the mechanisms of these biological activities and drug discovery. Although a bunch of sequence-based methods have been proposed to predict NABP and achieved promising performance, the structure information is often overlooked. On the other hand, the power of popular protein language models (pLM) has seldom been harnessed for predicting NABPs. In this study, we propose a novel framework called GraphNABP, to predict NABP by integrating sequence and predicted 3D structure information. Specifically, sequence embeddings and protein molecular graphs were first obtained from ProtT5 protein language model and predicted 3D structures, respectively. Then, graph attention (GAT) and bidirectional long short-term memory (BiLSTM) neural networks were used to enhance feature representations. Finally, a fully connected layer is used to predict NABPs. To the best of our knowledge, this is the first time to integrate AlphaFold and protein language models for the prediction of NABPs. The performances on multiple independent test sets indicate that GraphNABP outperforms other state-of-the-art methods. Our results demonstrate the effectiveness of pLM embeddings and structural information for NABP prediction. The codes and data used in this study are available at https://github.com/lixiangli01/GraphNABP.

Target-Specific De Novo Peptide Binder Design with DiffPepBuilder

Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic data set, referred to as PepPC-F, from the abundant protein-protein interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures. We tested DiffPepBuilder on 30 experimentally verified strong peptide binders with available protein-peptide complex structures. DiffPepBuilder was able to effectively recall the native structures and sequences of the peptide ligands and to generate novel peptide binders with improved binding free energy. We subsequently conducted de novo generation case studies on three targets. In both the regeneration test and case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with ProteinMPNN, in terms of sequence and structure recall, interface quality, and structural diversity. Molecular dynamics simulations confirmed that the introduction of disulfide bonds enhanced the structural rigidity and binding performance of the generated peptides. As a general peptide binder de novo design tool, DiffPepBuilder can be used to design peptide binders for given protein targets with three-dimensional and binding site information.

Allosteric Activation of Protein Phosphatase 5 with Small Molecules

The activation of PP5 is essential for a variety of cellular processes, as it participates in a variety of biological pathways by dephosphorylating substrates. However, activation of PP5 by small molecules has been a challenge due to its native "self-inhibition" mechanism, which is controlled by the N-terminal TPR domain and the C-terminal αJ helix. Here, we reported the discovery of DDO-3733, a well-identified TPR-independent PP5 allosteric activator, which facilitates the dephosphorylation process of downstream substrates. Considering the negative regulatory effect of PP5 on heat shock transcription factor HSF1, pharmacologic activation of PP5 by DDO-3733 was found to reduce the HSP90 inhibitor-induced heat shock response. These results provide a chemical tool to advance the exploration of PP5 as a potential therapeutic target and highlight the value of pharmacological activation of PP5 to reduce heat shock toxicity of HSP90 inhibitors.

TIMM9 as a prognostic biomarker in multiple cancers and its associated biological processes

TIMM9 has been identified as a mediator of essential functions in mitochondria, but its association with pan-cancer is poorly understood. We herein employed bioinformatics, computational chemistry techniques and experiments to investigate the role of TIMM9 in pan-cancer. Our analysis revealed that overexpression of TIMM9 was significantly associated with tumorigenesis, pathological stage progression, and metastasis. Missense mutations (particularly the S49L variant), copy number variations (CNV) and methylation alterations in TIMM9 were found to be associated with poor cancer prognosis. Moreover, TIMM9 was positively related with cell cycle progression, mitochondrial and ribosomal function, oxidative phosphorylation, TCA cycle activity, innate and adaptive immunity. Additionally, we discovered that TIMM9 could be regulated by cancer-associated signaling pathways, such as the mTOR pathway. Using molecular simulations, we identified ITFG1 as the protein that has the strongest physical association with TIMM9, which show a promising structural complement.

A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction

Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein-ligand binding sites remains a challenging task. To address this, we propose PGpocket, a geometric deep learning-based framework to improve protein-ligand binding site prediction. Initially, the protein surface is converted into a point cloud, and then the geometric and chemical properties of each point are calculated. Subsequently, the point cloud graph is constructed based on the inter-point distances, and the point cloud graph neural network (GNN) is applied to extract and analyze the protein surface information to predict potential binding sites. PGpocket is trained on the scPDB dataset, and its performance is verified on two independent test sets, Coach420 and HOLO4K. The results show that PGpocket achieves a 58% success rate on the Coach420 dataset and a 56% success rate on the HOLO4K dataset. These results surpass competing algorithms, demonstrating PGpocket's advancement and practicality for protein-ligand binding site prediction.

Small molecular exogenous modulators of active forms of MMPs

Matrix metalloproteinases (MMPs) are endopeptidases, and their activity depends on calcium and zinc metal ions. These enzymes are expressed originally in zymogenic form, where the active site of proteins is closed by a prodomain which is removed during activation. A homeostatic balance of their activity is primarily regulated by a 'cysteine switch' located on a consensus sequence of the prodomain and natural endogenous inhibitors, called tissue inhibitors of metalloproteinases (TIMPs). Breakage of this homeostasis may lead to various pathological conditions, which may require further activation and/or inhibition of these enzymes to regenerate that balance. Here, we report four modulators, more specifically, three inhibitors (I1, I2 and I3), and one exogenous activator (L) of the active form of human collagenase MMP-1 (without prodomain). The results were confirmed by binding studies using fluorescence-based enzyme assays.

An in-silico receptor-pharmacophore based multistep molecular docking and simulation study to evaluate the inhibitory potentials against NS1 of DENV-2

DENV-2 strain is the most fatal and infectious of the five dengue virus serotypes. The non-structural protein NS1 encoded by its genome is the most significant protein required for viral pathogenesis and replication inside the host body. Thus, targeting the NS1 protein and designing an inhibitor to limit its stability and secretion is a propitious attempt in our fight against dengue. Four novel inhibitors are designed to target the conserved cysteine residues (C55, C313, C316, and C329) and glycosylation sites (N130 and N207) of the NS1 protein in an attempt to halt the spread of the dengue infection in the host body altogether. Numerous computer-aided drug designing techniques including molecular docking, molecular dynamics simulation, virtual screening, principal component analysis, and dynamic cross-correlation matrix were employed to determine the structural and functional activity of the NS1-inhibitor complexes. From our analysis, it was evident that the extent of structural and atomic level fluctuations of the ligand-bound protein exhibited a declining trend in contrast to unbound protein which was prominently noticeable through the RMSD, RMSF, Rg, and SASA graphs. The ADMET analysis of the four ligands revealed a promising pharmacokinetics and pharmacodynamic profile, along with good bioavailability and toxicity properties. The proposed drugs when bound to the targeted cavities resulted in stable conformations in comparison to their unbound state, implying they have good affinity promising effective drug action. Thus, they can be tested in vitro and used as potential anti-dengue drugs.Communicated by Ramaswamy H. Sarma.

A first-in-class dimethyl 2-acetamido terephthalate inhibitor targeting Conyza canadensis SHMT1 with a novel herbicidal mode-of-action

INTRODUCTION

Herbicide application is a highly efficiency method of weed control that boots agricultural output and assures food security. The development of novel herbicides focuses on improved bioactivity and new modes of action. The amino acid biosynthesis was validated as a promising novel mode of action for herbicidal compounds. However, the amino acid biosynthesis enzyme remains largely unexplored for herbicidal targets.

OBJECTIVES

Serine hydroxymethyl transferase (SHMT) is an essentialenzyme in the photorespiratory cycle. The study aims to explore Conyza canadensis SHMT1 (CcSHMT1) as a promising target for herbicide discovery.

METHODS

Structure determination of CcSHMT1 was resolved by X-ray crystallography. Virtual screening docking experiments were performed with Glide version 5.5. Novel derivatives of dimethyl 2-acetamido terephthalate were further designed, synthesized, and bioassay. The druggability of the inhibitor was evidenced by ultrastructural changes in mitochondria, in vivo and vitro enzyme activity assays, and genetics analysis.

RESULTS

CcSHMT1 has a typical PLP-dependent enzyme 3D structure. The dimethyl 2-acetamido terephthalate-containing compounds had herbicidal activity. Dimethyl 2-(2-(4-(2-(4-bromo-2-chlorophenoxy) acetyl)piperazin-1-yl)acetamido) terephthalate (Compound 9ay, EC50 = 193.8 g a.i./ ha) exhibited the highest herbicidal activity on tested weed among the synthesized compounds. Compound 9ay had no obvious adverse effect on the growth of maize and honeybees. Compound 9ay was verified to target CcSHMT1 as an herbicide candidate.

CONCLUSION

A first-in-class CcSHMT1 inhibitor that could be developed as a potent herbicide with a new mode of action and provide an avenue for discovering novel inhibitors of pyridoxal-5-phosphate-dependent enzymes.

3D Porous Polymer Scaffold-Conjugated KGF-Mimetic Peptide Promotes Functional Skin Regeneration in Chronic Diabetic Wounds

The re-epithelialization process gets severely dysregulated in chronic nonhealing diabetic foot ulcers/wounds. Keratinocyte growth factor (KGF or FGF-7) is the major modulator of the re-epithelialization process, which regulates the physiological phenotypes of cutaneous keratinocytes. The existing therapeutic strategies of growth factor administration have several limitations. To overcome these, we have designed a KGF-mimetic peptide (KGFp, 13mer) based on the receptor interaction sites in murine KGF. KGFp enhanced migration and transdifferentiation of mouse bone marrow-derived MSCs toward keratinocyte-like cells (KLCs). A significant increase in the expression of skin-specific markers Bnc1 (28.5-fold), Ck5 (14.6-fold), Ck14 (26.1-fold), Ck10 (187.7-fold), and epithelial markers EpCam (23.3-fold) and Cdh1 (64.2-fold) was associated with the activation of ERK1/2 and STAT3 molecular signaling in the KLCs. Further, to enhance the stability of KGFp in the wound microenvironment, it was conjugated to biocompatible 3D porous polymer scaffolds without compromising its active binding sites followed by chemical characterization using Fourier transform infrared spectroscopy, field-emission scanning electron microscopy, dynamic mechanical analysis, and thermogravimetry. In vitro evaluation of the KGFp-conjugated 3D polymer scaffolds revealed its potential for transdifferentiation of MSCs into KLCs. Transplantation of allogeneic MSCGFP using KGFp-conjugated 3D polymer scaffolds in chronic nonhealing type 2 diabetic wounds (db/db transgenic, 50-52 weeks old male mice) significantly enhanced re-epithelialization-mediated wound closure rate (79.3%) as compared to the control groups (Untransplanted -22.4%, MSCGFP-3D polymer scaffold -38.5%). Thus, KGFp-conjugated 3D porous polymer scaffolds drive the fate of the MSCs toward keratinocytes that may serve as potential stem cell delivery platform technology for tissue engineering and transplantation.

Exploring transmembrane allostery in the MexB: DB08385 variant as a promising inhibitor-like candidate against Pseudomonas aeruginosa antibiotic resistance: a computational study

Pseudomonas aeruginosa, a formidable pathogen renowned for its antimicrobial resistance, poses a significant threat to immunocompromised individuals. In this regard, the MexAB-OprM efflux pump acts as a pivotal line of defense by extruding antimicrobials from bacterial cells. The inner membrane homotrimeric protein MexB captures antibiotics and translocates them into the outer membrane OprM channel protein connected through the MexA adaptor protein. Despite extensive efforts, competitive inhibitors targeting the tight (T) protomer of the MexB protein have not received FDA approval for medical use. Over the past few years, allosteric inhibitors have become popular as alternatives to the classical competitive inhibitor-based approach because of their higher specificity, lower dosage, and reduced toxicological effects. Hence, in this study, we unveiled the existence of a transmembrane allosteric binding pocket of MexB inspired by the recent discovery of an important allosteric inhibitor, BDM88855, for the homolog AcrB protein. While repurposing BDM88855 proved ineffective in controlling the MexB loose (L) protomer, our investigation identified a promising alternative: a chlorine-containing variant of DB08385 (2-Cl DB08385 or Variant 1). Molecular dynamics simulations, including binding free energy estimation coupled with heterogeneous dielectric implicit membrane model (implicit-membrane MM/PBSA), interaction entropy (IE) analysis and potential of mean force (PMF) calculation, demonstrated Variant 1's superior binding affinity to the transmembrane pocket, displaying the highest energy barrier in the ligand unbinding process. To elucidate the allosteric crosstalk between the transmembrane and porter domain of MexB, we employed the 'eigenvector centrality' measure in the linear mutual information obtained from the protein correlation network. Notably, this study confirmed the presence of an allosteric transmembrane site in the MexB L protomer. In addition to this, Variant 1 emerged as a potent regulator of allosteric crosstalk, inducing an 'O-L intermediate state' in the MexB L protomer. This induced state might hold the potential to diminish substrate intake into the access pocket, leading to the ineffective efflux of antibiotics.

Label-free cell phenotypic profiling of histamine H4R receptor and discovery of non-competitive H4R antagonist from natural products

Histamine 4 receptor (H4R), the most recently identified subtype of histamine receptor, primarily induces inflammatory reactions upon activation. Several H4R antagonists have been developed for the treatment of inflammatory bowel disease (IBD) and atopic dermatitis (AD), but their use has been limited by adverse side effects, such as a short half-life and toxicity. Natural products, as an important source of anti-inflammatory agents, offer minimal side effects and reduced toxicity. This work aimed to identify novel H4R antagonists from natural products. An H4R target-pathway model deconvoluted downstream Gi and MAPK signaling pathways was established utilizing cellular label-free integrative pharmacology (CLIP), on which 148 natural products were screened. Cryptotanshinone was identified as selective H4R antagonist, with an IC50 value of 11.68 ± 1.30 μM, which was verified with Fluorescence Imaging Plate Reader (FLIPR) and Cellular Thermal Shift (CTS) assays. The kinetic binding profile revealed the noncompetitive antagonistic property of cryptotanshinone. Two allosteric binding sites of H4R were predicted using SiteMap, Fpocket and CavityPlus. Subsequent molecular docking and dynamics simulation indicated that cryptotanshinone interacts with H4R at a pocket formed by the outward interfaces between TM3/4/5, potentially representing a new allosteric binding site for H4R. Overall, this study introduced cryptotanshinone as a novel H4R antagonist, offering promise as a new hit for drug design of H4R antagonist. Additionally, this study provided a novel screening model for the discovery of H4R antagonists.

Repurposing FDA approved drugs against monkeypox virus DNA dependent RNA polymerase: virtual screening, normal mode analysis and molecular dynamics simulation studies

Zoonotic monkeypox disease, caused by the double-stranded DNA monkeypox virus, has become a global concern. Due to the absence of a specific small molecule drug for the disease, this report aims to identify potential inhibitor drugs for monkeypox. This study explores a drug repurposing strategy using virtual screening to evaluate 1615 FDA approved drugs against the monkeypox virus DNA dependent RNA polymerase subunit A6R. Normal mode analysis and molecular dynamics simulation assessed the flexibility and stability of the target protein in complex with the top screened drugs. The analysis identified Nilotinib (ZINC000006716957), Conivaptan (ZINC000012503187), and Ponatinib (ZINC000036701290) as the most potential RNA polymerase inhibitors with binding energies of - 7.5 kcal/mol. These drugs mainly established hydrogen bonds and hydrophobic interactions with the protein active sites, including LEU95, LEU90, PRO96, MET110, and VAL113, and residues nearby. Normal mode analysis and molecular dynamics simulation confirmed the stability of interactions between the top drugs and the protein. In conclusion, we have discovered promising drugs that can potentially control the monkeypox virus and should be further explored through experimental assays and clinical trials to assess their actual activity against the disease. The findings of this study could lay the foundation for screening repurposed compounds as possible antiviral treatments against various highly pathogenic viruses.

Enzymatic Metabolic Switches of Astrocyte Response to Lipotoxicity as Potential Therapeutic Targets for Nervous System Diseases

Astrocytes play a pivotal role in maintaining brain homeostasis. Recent research has highlighted the significance of palmitic acid (PA) in triggering pro-inflammatory pathways contributing to neurotoxicity. Furthermore, Genomic-scale metabolic models and control theory have revealed that metabolic switches (MSs) are metabolic pathway regulators by potentially exacerbating neurotoxicity, thereby offering promising therapeutic targets. Herein, we characterized these enzymatic MSs in silico as potential therapeutic targets, employing protein-protein and drug-protein interaction networks alongside structural characterization techniques. Our findings indicate that five MSs (P00558, P04406, Q08426, P09110, and O76062) were functionally linked to nervous system drug targets and may be indirectly regulated by specific neurological drugs, some of which exhibit polypharmacological potential (e.g., Trifluperidol, Trifluoperazine, Disulfiram, and Haloperidol). Furthermore, four MSs (P00558, P04406, Q08426, and P09110) feature ligand-binding or allosteric cavities with druggable potential. Our results advocate for a focused exploration of P00558 (phosphoglycerate kinase 1), P04406 (glyceraldehyde-3-phosphate dehydrogenase), Q08426 (peroxisomal bifunctional enzyme, enoyl-CoA hydratase, and 3-hydroxyacyl CoA dehydrogenase), P09110 (peroxisomal 3-ketoacyl-CoA thiolase), and O76062 (Delta(14)-sterol reductase) as promising targets for the development or repurposing of pharmacological compounds, which could have the potential to modulate lipotoxic-altered metabolic pathways, offering new avenues for the treatment of related human diseases such as neurological diseases.

Diverse models of cavity engineering in enzyme modification: Creation, filling, and reshaping

Most enzyme modification strategies focus on designing the active sites or their surrounding structures. Interestingly, a large portion of the enzymes (60%) feature active sites located within spacious cavities. Despite recent discoveries, cavity-mediated enzyme engineering remains crucial for enhancing enzyme properties and unraveling folding-unfolding mechanisms. Cavity engineering influences enzyme stability, catalytic activity, specificity, substrate recognition, and docking. This article provides a comprehensive review of various cavity engineering models for enzyme modification, including cavity creation, filling, and reshaping. Additionally, it also discusses feasible tools for geometric analysis, functional assessment, and modification of cavities, and explores potential future research directions in this field. Furthermore, a promising universal modification strategy for cavity engineering that leverages state-of-the-art technologies and methodologies to tailor cavities according to the specific requirements of industrial production conditions is proposed.

Unraveling the Ligand-Binding Sites of CYP3A4 by Molecular Dynamics Simulations with Solvent Probes

Cytochrome P450 3A4 (CYP3A4) is one of the most important drug-metabolizing enzymes in the human body and is well known for its complicated, atypical kinetic characteristics. The existence of multiple ligand-binding sites in CYP3A4 has been widely recognized as being capable of interfering with the active pocket through allosteric effects. The identification of ligand-binding sites other than the canonical active site above the heme is especially important for understanding the atypical kinetic characteristics of CYP3A4 and the intriguing association between the ligand and the receptor. In this study, we first employed mixed-solvent molecular dynamics (MixMD) simulations coupled with the online computational predictive tools to explore potential ligand-binding sites in CYP3A4. The MixMD approach demonstrates better performance in dealing with the receptor flexibility compared with other computational tools. From the sites identified by MixMD, we then picked out multiple sites for further exploration using ensemble docking and conventional molecular dynamics (cMD) simulations. Our results indicate that three extra sites are suitable for ligand binding in CYP3A4, including one experimentally confirmed site and two novel sites.

Discovery of novel covalent inhibitors of DJ-1 through hybrid virtual screening

Background: DJ-1 is a ubiquitously expressed protein with multiple functions. Its overexpression has been associated with the occurrence of several cancers, positioning DJ-1 as a promising therapeutic target for cancer treatment. Methods: To find novel inhibitors of DJ-1, we employed a hybrid virtual screening strategy that combines structure-based and ligand-based virtual screening on a comprehensive compound library. Results: In silico study identified six hit compounds as potential DJ-1 inhibitors that were assessed in vitro at the cellular level. Compound 797780-71-3 exhibited antiproliferation activity in ACHN cells with an IC50 value of 12.18 μM and was able to inhibit the Wnt signaling pathway. This study discovers a novel covalent inhibitor for DJ-1 and paves the way for further optimization.

Study on the saltiness-enhancing mechanism of chicken-derived umami peptides by sensory evaluation and molecular docking to transmembrane channel-like protein 4 (TMC4)

The previously obtained chicken-derived umami peptides in the laboratory were evaluated for their saltiness-enhancing effect by sensory evaluation and S-curve, and the results revealed that peptides TPPKID, PKESEKPN, TEDWGR, LPLQDAH, NEFGYSNR, and LPLQD had significant saltiness-enhancing effects. In the binary solution system with salt, the ratio of the experimental detection threshold (129.17 mg/L) to the theoretical detection threshold (274.43 mg/L) of NEFGYSNR was 0.47, which had a synergistic saltiness-enhancing effect with salt. The model of transmembrane channel-like protein 4 (TMC4) channel protein was constructed by homology modeling, which had a 10-fold transmembrane structure and was well evaluated. Molecular docking and frontier molecular orbitals showed that the main active sites of TMC4 were Lys 471, Met 379, Cys 475, Gln 377, and Pro 380, and the main active sites of NEFGYSNR were Tyr, Ser and Asn. This study may provide a theoretical reference for low-sodium diets.

The interaction of InvF-RNAP is mediated by the chaperone SicA in Salmonella sp: an in silico prediction

In this work we carried out an in silico analysis to understand the interaction between InvF-SicA and RNAP in the bacterium Salmonella Typhimurium strain LT2. Structural analysis of InvF allowed the identification of three possible potential cavities for interaction with SicA. This interaction could occur with the structural motif known as tetratricopeptide repeat (TPR) 1 and 2 in the two cavities located in the interface of the InvF and α-CTD of RNAP. Indeed, molecular dynamics simulations showed that SicA stabilizes the Helix-turn-Helix DNA-binding motifs, i.e., maintaining their proper conformation, mainly in the DNA Binding Domain (DBD). Finally, to evaluate the role of amino acids that contribute to protein-protein affinity, an alanine scanning mutagenesis approach, indicated that R177 and R181, located in the DBD motif, caused the greatest changes in binding affinity with α-CTD, suggesting a central role in the stabilization of the complex. However, it seems that the N-terminal region also plays a key role in the protein-protein interaction, especially the amino acid R40, since we observed conformational flexibility in this region allowing it to interact with interface residues. We consider that this analysis opens the possibility to validate experimentally the amino acids involved in protein-protein interactions and explore other regulatory complexes where chaperones are involved.

Multiple integrated computational approach to analyse wound healing potential of Symplocos racemosa bark as Matrix metalloproteinase inhibitors

The healing of wounds is the flagging concern in chronic wound cases especially when accompanied by pathogenic, diabetic comorbidities. Matrix metalloproteinases are associated with widespread pathological ailments, and the selective inhibitors for metalloproteinases can be of great interest in wound healing strategies. In the present research study, six constituents of Symplocos racemosa Roxb were evaluated for the docking aptitudes on human matrix metalloproteinase MMP 2 (PDB ID: 1QIB) and MMP 9 (PDB ID: 4H1Q) utilising Autodock Vina followed by the visualisation using Discovery studio (DS). The Pymol was used to generate the poses and the best binding pose was chosen for the docking aptitudes. 2D interactions and the 3D poses of the docked complex were accomplished using DS and LigPlot + software respectively. Working on SWISS ADME and OSIRIS software accomplished the physicochemical characteristics, absorption, distribution, metabolism, excretion, molecular properties, bioactivity score, and toxicity predictions. The molecule's physiochemical investigations discovered that all of the ligands comply with Lipinski's rule of five except compound 6, which deviated with two violations. Docking studies against 4H1Q revealed that compounds 1, 3, 5 and 6 exhibited maximum interactions with the target protein, with the free binding energies of -8.3 kJ Mol-1, -9.3 kJ Mol-1, -7.2 kJ Mol-1 and -11.0 kJ Mol-1 respectively. In case of the 1QIB target, compounds 1, 3 and 6 displayed remarkable binding energies of -8.7 kJ mol-1, -9.0 kJ mol-1 and -8.8 kJ mol-1. Bioactivity prediction study revealed that all of the selected Phytoconstituents displayed incredible Bioactivity scores. None of the selected chemical compounds was found to be irritant to the skin as discovered by toxicity studies. The contacts of the ligand-protein complex during the simulation studies revealed that the H-bond interactions of the ligands with LEU188, ALA189, GLN402, ARG420, MET422, PRO421, and ARG424 of 4H1Q were stable for more than 30% of the simulation time. It was thus concluded that the tested compounds predominantly compounds 1, 5 and 6 might rank among the vital supplementary lead drugs in chronic wounds and healing complexities. It is also worth noting the potential aptitude of the compound 3, however, its toxicity concern must be considered.

Functional analysis of transmembrane terpene cyclases involved in fungal meroterpenoid biosynthesis

Pyr4-family terpene cyclases are noncanonical transmembrane class II terpene cyclases that catalyze a variety of cyclization reactions in the biosynthesis of microbial terpenoids, such as meroterpenoids. However, although these cyclases are widely distributed in microorganisms, their three-dimensional structures have not been determined, possibly due to the transmembrane locations of these enzymes. In this chapter, we describe procedures for the functional analysis of transmembrane terpene cyclases based on their model structures generated using AlphaFold2. We used AdrI, the Pyr4-family terpene cyclase required for the biosynthesis of andrastin A and its homologs, as an example.

Computational investigation of impact of Pb(II) and Ni(II) ions on hUNG enzyme: insights from molecular dynamics simulations

Human uracil DNA glycosylase (hUNG), a crucial player in the initiation of the base excision repair pathway, is susceptible to alterations in function and conformation induced by the accumulation of toxic metals. Despite the recognized impact of toxic metals on DNA repair enzymes, there exists a notable deficiency in theoretical investigations addressing this phenomenon. This study investigates the impact of toxic heavy metal ions, Pb(II) and Ni(II), on the stability of hUNG through molecular dynamics (MD) simulations. The initial analysis involved the identification of key cavities in the hUNG enzyme. Notably, the active site cavity emerged as a promising site for ligand binding. Subsequently, AutoDockTools software was employed to dock Pb(II) and Ni(II) onto the identified cavities, followed by extensive MD simulations. The MD analysis, encompassing parameters such as root mean square deviation, radius of gyration, solvent accessible surface area, hydrogen bond variations, Ramachandran plot, principal component analysis, and root mean square fluctuations, collectively revealed distinct alterations in the behavior of the enzyme upon complexation with Pb(II) and Ni(II). Interestingly, the enzyme exhibited enhanced structural stability, reduced flexibility, and modified hydrogen bonding patterns in the presence of these toxic metal ions. The observed limitation in structural flexibility implies a more rigid and stable conformation when the enzyme complex with Pb(II) and Ni(II) compared to its free form. This structural alteration may lead to a potential reduction in enzymatic activity, suggesting that toxic metal ions influence the functional dynamics of hUNG. These computational findings offer valuable insights into the molecular interactions between metal ions and enzymes.Communicated by Ramaswamy H. Sarma.

ASD2023: towards the integrating landscapes of allosteric knowledgebase

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.

Necrotic activity of ExhC from Mammaliicoccus sciuri is mediated by specific amino acid residues

Mammaliicoccus sciuri, a commensal and pathogenic bacterium of significant clinical and veterinary relevance, expresses exfoliative toxin C (ExhC), a specific glutamyl endopeptidase belonging to the chymotrypsin family as the principal virulence factor. However, unlike most members of this family, ETs are inactive against a wide range of substrates and possess exquisite specificity for desmoglein-1 (Dsg1), a cadherin-like adhesion molecule that is crucial to maintain tissue integrity, thereby preventing the separation of skin cells and the entry of pathogens. ExhC is of clinical importance since in addition to causing exfoliation in pigs and mice, it induces necrosis in multiple mammalian cell lines, a property not observed for other ETs. Previous experiments have implicated the ExhC79-128 fragment in causing necrosis. Site-directed mutagenesis of specific residues within this fragment were studied and led to the design of an ExhC variant containing four-point mutations (ExhCmut4) lacking necrotic potential but retaining nearly wild-type (wt) levels of enzymatic activity. Moreover, the determination of the ExhCwt and ExhCmut4 crystal structures identified the conformation in the necrosis-linked region. These results constitute an important step toward the understanding of the mechanisms underlying the necrotic and epidermolytic activity of ExhC.

Towards multi-target glioblastoma therapy: Structural, distribution, and functional insights into protein target candidates

Glioblastoma is the most commonly occurring and most lethal primary brain tumor. Treatment options are limited in number and therapeutic development remains a major challenge. However, substantial progress has been made in better understanding the underlying biology of the disease. A recent proteomic meta-analysis revealed that 270 proteins were commonly dysregulated in glioblastoma, highlighting the complexity of the disease. This motivated us to explore potential protein targets which may be collectively inhibited, based on common upregulation, as part of a multi-target therapeutic strategy. Herein, we identify and characterize structural attributes relevant to the druggability of six protein target candidates. Computational analysis of crystal structures revealed druggable cavities in each of these proteins, and various parameters of these cavities were determined. For proteins with inhibitor-bound structures available, inhibitor compounds were found to overlap with the computationally determined cavities upon structural alignment. We also performed bioinformatic analysis for normal transcriptional expression distribution of these proteins across various brain regions and various tissues, as well as gene ontology curation to gain functional insights, as this information is useful for understanding the potential for off-target adverse effects. Our findings represent initial steps towards the development of multi-target glioblastoma therapy and may aid future work exploring similar therapeutic strategies.

In Silico Screening of Multi-Domain Targeted Inhibitors for PTK6: A Strategy Integrating Drug Repurposing and Consensus Docking

Protein tyrosine kinase 6 (PTK6), also known as breast tumor kinase (BRK), serves as a non-receptor intracellular tyrosine kinase within the Src kinases family. Structurally resembling other Src kinases, PTK6 possesses an Src homology 3 (SH3) domain, an Src homology 2 (SH2) domain, and a tyrosine kinase domain (SH1). While considerable efforts have been dedicated to designing PTK6 inhibitors targeting the SH1 domain, which is responsible for kinase activity in various pathways, it has been observed that solely inhibiting the SH1 domain does not effectively suppress PTK6 activity. Subsequent investigations have revealed the involvement of SH2 and SH3 domains in intramolecular and substrate binding interactions, which are crucial for PTK6 function. Consequently, the identification of PTK6 inhibitors targeting not only the SH1 domain but also the SH2 and SH3 domains becomes imperative. Through an in silico structural-based virtual screening approach, incorporating drug repurposing and a consensus docking approach, we have successfully identified four potential ligands capable of concurrently inhibiting the tyrosine kinase domain and SH2/SH3 domains of PT6K simultaneously. This finding suggests potential pathways for therapeutic interventions in PTK6 inhibition.

De Novo Potent Peptide Nucleic Acid Antisense Oligomer Inhibitors Targeting SARS-CoV-2 RNA-Dependent RNA Polymerase via Structure-Guided Drug Design

Global reports of novel SARS-CoV-2 variants and recurrence cases continue despite substantial vaccination campaigns, raising severe concerns about COVID-19. While repurposed drugs offer some treatment options for COVID-19, notably, nucleoside inhibitors like Remdesivir stand out as curative therapies for COVID-19 that are approved by the US Food and Drug Administration (FDA). The emergence of highly contagious SARS-CoV-2 variants underscores the imperative for antiviral drugs adaptable to evolving viral mutations. RNA-dependent RNA polymerase (RdRp) plays a key role in viral genome replication. Currently, inhibiting viral RdRp function remains a pivotal strategy to tackle the notorious virus. Peptide nucleic acid (PNA) therapy shows promise by effectively targeting specific genome regions, reducing viral replication, and inhibiting infection. In our study, we designed PNA antisense oligomers conjugated with cell-penetrating peptides (CPP) aiming to evaluate their antiviral effects against RdRp target using structure-guided drug design, which involves molecular docking simulations, drug likeliness and pharmacokinetic evaluations, molecular dynamics simulations, and computing binding free energy. The in silico analysis predicts that chemically modified PNAs might act as antisense molecules in order to disrupt ribosome assembly at RdRp's translation start site, and their chemically stable and neutral backbone might enhance sequence-specific RNA binding interaction. Notably, our findings demonstrate that PNA-peptide conjugates might be the most promising inhibitors of SARS-CoV-2 RdRp, with superior binding free energy compared to Remdesivir in the current COVID-19 medication. Specifically, PNA-CPP-1 could bind simultaneously to the active site residues of RdRp protein and sequence-specific RdRp-RNA target in order to control viral replication.

Molecular Design of Novel Inhibitor by Targeting IL-6Rα using Combined Pharmacophore and Experimentally Verified Plant Products with Scaffold-Hopping Techniques: A Dual Therapeutic Strategy for COVID-19 and Cancer

The IL-6/IL-6R/gp130 complex serves as a significant indicator of cytokine release syndrome in COVID-19 and chronic inflammation, increasing the risk of cancer. Therefore, we identified IL-6Rα as a potential target to block gp130 interaction. Notably, there has been no reception of approval for an orally available drug to serve this purpose, to date. In this study, we targeted IL-6Rα to inhibit IL-6Rα/gp130 interaction. The selection of the lead candidate L821 involved the amalgamation of three drug discovery approaches. This library was screened employing tertiary structure-based pharmacophore models followed by molecular docking models, scaffold-hopping, MM/PBSA as well as MM/GBSA analysis, and assessments of pKi and ADMET properties. After evaluating the binding interactions with key amino acids, 15 potential ligands were chosen, with the top ligand undergoing further investigation by means of molecular dynamics simulations. Considering the stability of the complexes, the strong interactions observed between ligand and residues of IL-6Rα/gp130, and the favorable binding free energy calculations, L821 emerged as the prime candidate for inhibiting IL-6Rα. Notably, L821 exhibited a docking-based binding affinity of -9.5 kcal/mol. Our study presents L821 as a promising inhibitor for future in vitro analysis, potentially combatting SARS-CoV-2-related cytokine storms and serving as an oncogenic drug therapy.

CSC01 shows promise as a potential inhibitor of the oncogenic G13D mutant of KRAS: an in silico approach

About 30% of malignant tumors include KRAS mutations, which are frequently required for the development and maintenance of malignancies. KRAS is now a top-priority cancer target as a result. After years of research, it is now understood that the oncogenic KRAS-G12C can be targeted. However, many other forms, such as the G13D mutant, are yet to be addressed. Here, we used a receptor-based pharmacophore modeling technique to generate potential inhibitors of the KRAS-G13D oncogenic mutant. Using a comprehensive virtual screening workflow model, top hits were selected, out of which CSC01 was identified as a promising inhibitor of the oncogenic KRAS mutant (G13D). The stability of CSC01 upon binding the switch II pocket was evaluated through an exhaustive molecular dynamics simulation study. The several post-simulation analyses conducted suggest that CSC01 formed a stable complex with KRAS-G13D. CSC01, through a dynamic protein-ligand interaction profiling analysis, was also shown to maintain strong interactions with the mutated aspartic acid residue throughout the simulation. Although binding free energy analysis through the umbrella sampling approach suggested that the affinity of CSC01 with the switch II pocket of KRAS-G13D is moderate, our DFT analysis showed that the stable interaction of the compound might be facilitated by the existence of favorable molecular electrostatic potentials. Furthermore, based on ADMET predictions, CSC01 demonstrated a satisfactory drug likeness and toxicity profile, making it an exemplary candidate for consideration as a potential KRAS-G13D inhibitor.

Uncovering the anti-obesity constituents in Ginkgo biloba extract and deciphering their synergistic effects

Obesity has been recognized as a key risk factor for multiple metabolic disorders, including diabetes, cardiovascular diseases and many types of cancer. Herbal medicines have been frequently used for preventing and treating obesity in many countries, but in most cases, the key anti-obesity constituents in herbs and their anti-obesity mechanisms are poorly understood. This study demonstrated a case study for uncovering the anti-obesity constituents in an anti-obesity herbal medicine (Ginkgo biloba extract) and deciphering their synergistic effects via targeting human pancreatic lipase (hPL). Following screening the anti-hPL effects of eighty herbal medicines, Ginkgo biloba extract (GBE50) was found with the most potent anti-hPL activity. Global chemical profiling of herbal constituents coupling with hPL inhibition assay revealed that the bioflavonoids and several flavonoids in GBE50 were key anti-hPL constituents. Among all tested thirty-eight constituents, sciadopitysin, bilobetin, quercetin, isoginkgetin, and ginkgetin showed potent anti-hPL effects (IC50 values <2.5 μM). Inhibition kinetic analyses suggested that sciadopitysin, bilobetin, quercetin, isoginkgetin, and ginkgetin acted as non-competitive inhibitors of hPL, with the Ki values were <2 μM. Docking simulations revealed that four bioflavonoids (sciadopitysin, bilobetin, isoginkgetin, and ginkgetin) could tightly bind on hPL at cavity 2, which it is different from the binding cavity of quercetin on hPL. Further investigations demonstrated that the combinations of quercetin and one bioflavonoid-type hPL inhibitor (sciadopitysin or bilobetin) showed synergistic anti-hPL effects, suggesting that the multi-components in GBE50 may generate more potent anti-hPL effect. Collectively, our findings uncovered the anti-obesity constituents in GBE50, and explored their anti-hPL mechanisms as well as synergistic effects at molecular levels, which will be very helpful for further understanding the anti-obesity mechanisms of Ginkgo biloba.

Naringenin improves muscle endurance via activation of the Sp1-ERRγ transcriptional axis

Skeletal muscle function declines in the aging process or disease; however, until now, skeletal muscle has remained one of the organs most undertreated with medication. In this study, naringenin (NAR) was found to build muscle endurance in wild-type mice of different ages by increasing oxidative myofiber numbers and aerobic metabolism, and it ameliorates muscle dysfunction in mdx mice. The transcription factor Sp1 was identified as a direct target of NAR and was shown to mediate the function of NAR on muscle. Moreover, the binding site of NAR on Sp1 was further validated as GLN-110. NAR enhances the binding of Sp1 to the CCCTGCCCTC sequence of the Esrrg promoter by promoting Sp1 phosphorylation, thus upregulating Esrrg expression. The identification of the Sp1-ERRγ transcriptional axis is of great significance in basic muscle research, and this function of NAR has potential implications for the improvement of muscle function and the prevention of muscle atrophy.

Crystal structure of the GDSL family esterase EstL5 in complex with PMSF reveals a branch channel of the active site pocket

Esterases/lipases from the GDSL family have potential applications in the hydrolysis and synthesis of important esters of pharmaceutical, food, and biotechnical interests. However, the structural and functional understanding of GDSL enzymes is still limited. Here, we report the crystal structure of the GDSL family esterase EstL5 complexed with PMSF at 2.34 Å resolution. Intriguingly, the PMSF binding site is not located at the active site pocket but is situated in a surface cavity. At the active site, we note that there is a trapped crystallization solvent 1,6-hexanediol, which mimics the bound ester chain, allowing for further definition of the active site pocket of EstL5. The most striking structural feature of EstL5 is the presence of a unique channel, which extends approximately 18.9 Å, with a bottleneck radius of 6.8 Å, connecting the active-site pocket and the surface cavity. Replacement of Ser205 with the bulk aromatic residue Trp or Phe could partially block the channel at one end and perturb its access. Reduced enzymatic activity is found in the EstL5 S205W and EstL5 S205F mutants, suggesting the functional relevance of the channel to enzyme catalysis. Our study provides valuable information regarding the properties of the GDSL-family enzymes for designing more efficient and robust biocatalysts.

GAABind: a geometry-aware attention-based network for accurate protein-ligand binding pose and binding affinity prediction

Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket-ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset; the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the severe acute respiratory syndrome coronavirus 2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.

Exploring the potential of biologically active phenolic acids from marine natural products as anticancer agents targeting the epidermal growth factor receptor

The epidermal growth factor receptor (EGFR) dimerizes upon ligand bindings to the extracellular domain that initiates the downstream signaling cascades and activates intracellular kinase domain. Thus, activation of autophosphorylation through kinase domain results in metastasis, cell proliferation, and angiogenesis. The main objective of this research is to discover more promising anti-cancer lead compound against EGRF from the phenolic acids of marine natural products using in-silico approaches. Phenolic compounds reported from marine sources are reviewed from previous literatures. Furthermore, molecular docking was carried out using the online tool CB-Dock. The molecules with good docking and binding energies scores were subjected to ADME, toxicity and drug-likeness analysis. Subsequently, molecules from the docking experiments were also evaluated using the acute toxicity and MD simulation studies. Fourteen phenolic compounds from the reported literatures were reviewed based on the findings, isolation, characterized and applications. Molecular docking studies proved that the phenolic acids have good binding fitting by forming hydrogen bonds with amino acid residues at the binding site of EGFR. Chlorogenic acid, Chicoric acid and Rosmarinic acid showed the best binding energies score and forming hydrogen bonds with amino acid residues compare to the reference drug Erlotinib. Among these compounds, Rosmarinic acid showed the good pharmacokinetics profiles as well as acute toxicity profile. The MD simulation study further revealed that the lead complex is stable and could be future drug to treat the cancer disease. Furthermore, in a wet lab environment, both in-vitro and in-vivo testing will be employed to validate the existing computational results.Communicated by Ramaswamy H. Sarma.

Allosteric modulation of SHP2: Quest from known to unknown

Src homology-2 domain-containing protein tyrosine phosphatase-2 (SHP2) is a key regulatory factor in the cell cycle and its activating mutations play an important role in the development of various cancers, making it an important target for antitumor drugs. Due to the highly conserved amino acid sequence and positively charged nature of the active site of SHP2, it is difficult to discover inhibitors with high affinity for the catalytic site of SHP2 and sufficient cell permeability, making it considered an "undruggable" target. However, the discovery of allosteric regulation mechanisms provides new opportunities for transforming undruggable targets into druggable ones. Given the limitations of orthosteric inhibitors, SHP2 allosteric inhibitors have become a more selective and safer research direction. In this review, we elucidate the oncogenic mechanism of SHP2 and summarize the discovery methods of SHP2 allosteric inhibitors, providing new strategies for the design and improvement of SHP2 allosteric inhibitors.

Structural Insights into the Binding and Degradation Mechanisms of Protoporphyrin IX by the Translocator Protein TSPO

The 18 kDa translocator protein (TSPO) has gained considerable attention as a clinical biomarker for neuroinflammation and a potential therapeutic target. However, the mechanisms by which TSPO associates with ligands, particularly the endogenous porphyrin ligand protoporphyrin IX (PpIX), remain poorly understood. In this study, we employed mutagenesis- and spectroscopy-based functional assays to investigate TSPO-mediated photo-oxidative degradation of PpIX and identify key residues involved in the reaction. We provide structural evidence using electron spin resonance, which sheds light on the highly conserved intracellular loop (LP1) connecting transmembrane 1 (TM1) and TM2. Our findings show that LP1 does not act as a lid to regulate ligand binding; instead, it interacts strongly with the TM3-TM4 linker (LP3) to stabilize the local structure of LP3. This LP1-LP3 interaction is crucial for maintaining the binding pocket structure, which is essential for proper ligand binding. Our results also demonstrate that PpIX accesses the pocket through the lipid bilayer without requiring conformational changes in TSPO. This study provides an improved understanding of TSPO-mediated PpIX degradation, highlighting potential therapeutic strategies to regulate the reaction.

Anaerobic hydrolysis of recalcitrant tetramethylammonium from semiconductor wastewater: Performance and mechanisms

The treatment of tetramethylammonium hydroxide (TMAH)-bearing wastewater, generated in the electronic and semiconductor industries, raises significant concerns due to the neurotoxic, recalcitrant, and bio-inhibiting effects of TMAH. In this study, we proposed the use of an anaerobic hydrolysis bioreactor (AHBR) for TMAH removal, achieving a high removal efficiency of approximately 85%, which greatly surpassed the performance of widely-used advanced oxidation processes (AOPs). Density functional theory calculations indicated that the unexpectedly poor efficiency (5.8-8.0%) of selected AOPs can be attributed to the electrostatic repulsion between oxidants and the tightly bound electrons of TMAH. Metagenomic analyses of the AHBR revealed that Proteobacteria and Euryarchaeota played a dominant role in the transformation of TMAH through processes such as methyl transfer, methanogenesis, and acetyl-coenzyme A synthesis, utilizing methyl-tetrahydromethanopterin as a substrate. Moreover, several potential functional genes (e.g., mprF, basS, bcrB, sugE) related to TMAH resistance have been identified. Molecular docking studies between five selected proteins and tetramethylammonium further provided evidence supporting the roles of these potential functional genes. This study demonstrates the superiority of AHBR as a pretreatment technology compared to several widely-researched AOPs, paving the way for the proper design of treatment processes to abate TMAH in semiconductor wastewater.

Inhibition of the predicted allosteric site of the SARS-CoV-2 main protease through flavonoids

Since its emergence in 2019, coronavirus infection (COVID-19) has become a global pandemic and killed several million people worldwide. Even though several types of vaccines are available against the COVID-19 virus, SARS-CoV-2, new strains are emerging that pose a constant danger to vaccine effectiveness. In this computational study, we identified and predicted potent allosteric inhibitors of the SARS-CoV-2 main protease (Mpro). Via molecular docking and simulations, more than 100 distinct flavonoids were docked with the allosteric site of Mpro. Docking experiments revealed four top hit compounds (Hesperidin, Schaftoside, Brickellin, and Marein) that bound strongly to the Mpro predicted allosteric site. Simulation analyses further revealed that these continually interacted with the enzyme's allosteric region throughout the simulation time. ADMET and Lipinski drug likenesses were calculated to indicate the therapeutic value of the top four hits: They were non-toxic and exhibited high human intestinal absorption concentrations. These novel allosteric site inhibitors provide a higher chance of drugging SARS-CoV2 Mpro due to the rapid mutation rate of the viral enzyme's active sites. Our findings provide a new avenue for developing novel allosteric inhibitors of SARS-CoV-2 Mpro.Communicated by Ramaswamy H. Sarma.

Identified Isosteric Replacements of Ligands' Glycosyl Domain by Data Mining

Biologically equivalent replacements of key moieties in molecules rationalize scaffold hopping, patent busting, or R-group enumeration. Yet, this information may depend upon the expert-defined space, and might be subjective and biased toward the chemistries they get used to. Most importantly, these practices are often informatively incomplete since they are often compromised by a try-and-error cycle, and although they depict what kind of substructures are suitable for the replacement occurrence, they fail to explain the driving forces to support such interchanges. The protein data bank (PDB) encodes a receptor-ligand interaction pattern and could be an optional source to mine structural surrogates. However, manual decoding of PDB has become almost impossible and redundant to excavate the bioisosteric know-how. Therefore, a text parsing workflow has been developed to automatically extract the local structural replacement of a specific structure from PDB by finding spatial and steric interaction overlaps between the fragments in endogenous ligands and particular ligand fragments. Taking the glycosyl domain for instance, a total of 49 520 replacements that overlap on nucleotide ribose were identified and categorized based on their SMILE codes. A predominately ring system, such as aliphatic and aromatic rings, was observed; yet, amide and sulfonamide replacements also occur. We believe these findings may enlighten medicinal chemists on the structure design and optimization of ligands using the bioisosteric replacement strategy.

CavityPlus 2022 Update: An Integrated Platform for Comprehensive Protein Cavity Detection and Property Analyses with User-friendly Tools and Cavity Databases

Ligand binding sites provide essential information for uncovering protein functions and structure-based drug discovery. To facilitate cavity detection and property analysis process, we developed a comprehensive web server, CavityPlus in 2018. CavityPlus applies the CAVITY program to detect potential binding sites in a given protein structure. The CavPharmer, CorrSite, and CovCys tools can then be applied to generate receptor-based pharmacophore models, identify potential allosteric sites, or detect druggable cysteine residues for covalent drug design. While CavityPlus has been widely used, the constantly evolving knowledge and methods make it necessary to improve and extend its functions. This study presents a new version of CavityPlus, CavityPlus 2022 through a series of upgrades. We upgraded the CAVITY tool to greatly speed up cavity detection calculation. We optimized the CavPharmer tool for fast speed and more accurate results. We integrated the newly developed CorrSite2.0 into the CavityPlus 2022 web server for its improved performance of allosteric site prediction. We also added a new CavityMatch module for drug repurposing and protein function studies by searching similar cavities to a given cavity from pre-constructed cavity databases. The new version of CavityPlus is freely available at http://pkumdl.cn:8000/cavityplus/.

Different Structures-Similar Effect: Do Substituted 5-(4-Methoxyphenyl)-1H-indoles and 5-(4-Methoxyphenyl)-1H-imidazoles Represent a Common Pharmacophore for Substrate Selective Inhibition of Linoleate Oxygenase Activity of ALOX15?

Mammalian 15-lipoxygenases (ALOX15) are lipid peroxidizing enzymes that exhibit variable functionality in different cancer and inflammation models. The pathophysiological role of linoleic acid- and arachidonic acid-derived ALOX15 metabolites rendered this enzyme a target for pharmacological research. Several indole and imidazole derivatives inhibit the catalytic activity of rabbit ALOX15 in a substrate-specific manner, but the molecular basis for this allosteric inhibition remains unclear. Here, we attempt to define a common pharmacophore, which is critical for this allosteric inhibition. We found that substituted imidazoles induce weaker inhibitory effects when compared with the indole derivatives. In silico docking studies and molecular dynamics simulations using a dimeric allosteric enzyme model, in which the inhibitor occupies the substrate-binding pocket of one monomer, whereas the substrate fatty acid is bound at the catalytic center of another monomer within the ALOX15 dimer, indicated that chemical modification of the core pharmacophore alters the enzyme-inhibitor interactions, inducing a reduced inhibitory potency. In our dimeric ALOX15 model, the structural differences induced by inhibitor binding are translated to the hydrophobic dimerization cluster and affect the structures of enzyme-substrate complexes. These data are of particular importance since substrate-specific inhibition may contribute to elucidation of the putative roles of ALOX15 metabolites derived from different polyunsaturated fatty acids in mammalian pathophysiology.

A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing

Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.

Variability in phenylalanine side chain conformations facilitates broad substrate tolerance of fatty acid binding in cockroach milk proteins

Diploptera punctata, also known as the Pacific beetle cockroach, is a viviparous cockroach that gives birth to live offspring and secretes a highly concentrated mixture of glycosylated proteins as a source of nourishment for developing embryos. These proteins are lipocalins that bind to lipids and crystallize in the gut of the embryo. A structure of milk crystals harvested from the embryos showed that the milk-derived crystals were heterogeneous and made of three proteins (called Lili-Mips). We hypothesized that the isoforms of Lili-Mip would display different affinities for fatty acids due to the ability of the pocket to bind multiple acyl chain lengths. We previously reported the structures of Lili-Mip from crystals grown in vivo and recombinantly expressed Lili-Mip2. These structures are similar, and both bind to several fatty acids. This study explores the specificity and affinity of fatty acid binding to recombinantly expressed Lili-Mip 1, 2 & 3. We show that all isoforms can bind to different fatty acids with similar affinities. We also report the thermostability of Lili-Mip is pH dependent, where stability is highest at acidic pH and declines as the pH increases to physiological levels near 7.0. We show that thermostability is an inherent property of the protein, and glycosylation and ligand binding do not change it significantly. Measuring the pH in the embryo's gut lumen and gut cells suggests that the pH in the gut is acidic and the pH inside the gut cells is closer to neutral pH. In various crystal structures (reported here and previously by us), Phe-98 and Phe-100 occupy multiple conformations in the binding pocket. In our earlier work, we had shown that the loops at the entrance could adapt various conformations to change the size of the binding pocket. Here we show Phe-98 and Phe-100 can reorient to stabilize interactions at the bottom of the cavity-and change the volume of the cavity from 510 Å3 to 337 Å3. Together they facilitate the binding of fatty acids of different acyl chain lengths.