Analyzing the Topology of Active Sites: On the Prediction of Pockets and Subpockets

Subpocket-Based Analysis Approach for the Protein Pocket Dynamics

Structural and dynamic characteristics of protein pockets remarkably influence their biological functions and are also important for enzyme engineering and new drug research and development. To date, several softwares have been developed to analyze the dynamic properties of protein pockets. However, due to the complexity and diversity of the pocket information during the kinetic relaxation, further improvement and capacity expansion of current tools are required. Here, we developed a platform software AlphaTraj in which a computational strategy that divides the whole protein pocket into subpockets and examines various properties of the subpockets such as survival time, stability, and correlation was proposed and implemented. We also proposed a scoring function for the subpockets as well as the whole pocket to visualize the quality of the pocket. Furthermore, we implemented automated conformational search functions for ligand docking and ligand optimization. These functions may help us to gain a deep understanding of the dynamic properties of protein pockets and accelerate the protein engineering and the design of inhibitors and small-molecule drugs. The software is freely available at https://github.com/dooo12332/AlphaTraj.git under the GNU GPL license.

Network toxicology and molecular docking for the toxicity analysis of food contaminants: A case of Aflatoxin B1

The present study aims to promote network toxicology and molecular docking strategies for the efficient evaluation of the toxicity of food contaminants. With the example of liver injury induced by the food contaminant Aflatoxin B1(AFB1), this study effectively investigated the putative toxicity of food contaminants and the potentially molecular mechanisms. The study found that AFB1 regulates multiple signalling pathways by modulating core targets such as AKT1, BCL2, TNF, CASP3, SRC and EGFR. These pathways encompass Pathways in cancer, PI3K-Akt signalling pathway, Endocrine resistance, Lipid and atherosclerosis, Apoptosis and other pathways, subsequently impacting immunotoxicity, inflammatory responses, apoptosis, cytogenetic mutations, and ultimately leading to liver injury. We provide a theoretical basis for understanding the molecular mechanisms of AFB1 hepatotoxicity and for the prevention and treatment of cancers caused by the food contaminant AFB1. Furthermore, our network toxicology and molecular docking methods also provide an effective method for the rapid evaluation of the toxicity of food contaminants, which effectively solves the cost and ethical problems associated with the use of experimental animals.

SiteMine: Large-scale binding site similarity searching in protein structure databases

Drug discovery and design challenges, such as drug repurposing, analyzing protein-ligand and protein-protein complexes, ligand promiscuity studies, or function prediction, can be addressed by protein binding site similarity analysis. Although numerous tools exist, they all have individual strengths and drawbacks with regard to run time, provision of structure superpositions, and applicability to diverse application domains. Here, we introduce SiteMine, an all-in-one database-driven, alignment-providing binding site similarity search tool to tackle the most pressing challenges of binding site comparison. The performance of SiteMine is evaluated on the ProSPECCTs benchmark, showing a promising performance on most of the data sets. The method performs convincingly regarding all quality criteria for reliable binding site comparison, offering a novel state-of-the-art approach for structure-based molecular design based on binding site comparisons. In a SiteMine showcase, we discuss the high structural similarity between cathepsin L and calpain 1 binding sites and give an outlook on the impact of this finding on structure-based drug design. SiteMine is available at https://uhh.de/naomi.

A comprehensive assessment of the cholinergic-supporting and cognitive-enhancing effects of Rosa damascena Mill. (Damask rose) essential oil on scopolamine-induced amnestic rats

INTRODUCTION

Alzheimer's disease (AD) is a neurodegenerative condition characterized by gradual loss of cognitive abilities (dementia) and is a major public health problem. Here, we aimed at investigating the effects of Rosa damascena essential oil (RDEO) on learning and memory functions in a rat model of amnesia induced by scopolamine, as well as on changes in acetylcholinesterase (AChE) activity, M1 muscarinic acetylcholine receptor (mAChR) expression, and brain-derived neurotrophic factor (BDNF) levels in the extracted brain tissues.

METHODS

The control, amnesia (scopolamine, 1 mg/kg/i.p.) and treatment (RDEO, 100 μL/kg/p.o. or galantamine, 1.5 mg/kg/i.p.) groups were subjected to Morris water maze and new object recognition tests. AChE activity was assayed by ELISA, and M1 mAChR and BDNF concentration changes were determined by western blotting. Also, using computational tools, human M1 mAChR was modeled in an active conformation, and the major components of RDEO were docked onto this receptor.

RESULTS

According to our behavioral tests, RDEO was able to mitigate the learning and memory impairments caused by scopolamine in vivo. Our in vitro assays showed that the observed positive effects correlated well with a decrease in AChE activity and an increase in M1 mAChR and BDNF levels in amnestic rat brains. We also demonstrated in an in silico setting that the major components of RDEO, specifically -citronellol, geraniol, and nerol, could be accommodated favorably within the allosteric binding pocket of active-state human M1 mAChR and anchored here chiefly by hydrogen-bonding and alkyl-π interactions.

CONCLUSION

Our findings offer a solid experimental foundation for future RDEO-based medicinal product development for patients suffering from AD.

Structural Characterization and Rat Aortic Vascular Reactivity of Bradykinin-Potentiating Peptides (BPPs) from the Snake Venom of Bothrops moojeni from Delta do Parnaíba Region, Brazil

Snake venoms contain various bradykinin-potentiating peptides (BPPs). First studied for their vasorelaxant properties due to angiotensin converting enzyme (ACE) inhibition, these molecules present a range of binding partners, among them the argininosuccinate synthase (AsS) enzyme. This has renewed interest in their characterization from biological sources and the evaluation of their pharmacological activities. In the present work, the low molecular weight fraction of Bothrops moojeni venom was obtained and BPPs were characterized by mass spectrometry. Eleven BPPs or related peptides were sequenced, and one of them, BPP-Bm01, was new. Interestingly, some oxidized BPPs were detected. The three most abundant peptides were BPP-Bm01, BPP-Bax12, and BPP-13a, and their putative interactions with the AsS enzyme were investigated in silico. A binding cavity for these molecules was predicted, and docking studies allowed their ranking. Three peptides were synthesized and submitted to vasorelaxation assays using rat aortic rings. While all BPPs were active, BPP-Bm01 showed the highest potency in this assay. This work adds further diversity to BPPs from snake venoms and suggests, for the first time, a putative binding pocket for these molecules in the AsS enzyme. This can guide the design of new and more potent AsS activators.

Explanatory review on pyrimidine/fused pyrimidine derivatives as anticancer agents targeting Src kinase

The pyrimidine and fused pyrimidine ring systems play vital roles to inhibit the c-Src kinase. The Src kinase is made of different domains but the kinase domain is responsible for inhibition of Src kinase. In which the kinase domain is the main domain that is made of several amino acids. The Src kinase is inhibited by its inhibitors when it is activated by phosphorylation. Although dysregulation of Src kinase caused cancer in the late nineteenth century, medicinal chemists have not explored it extensively; therefore it is still regarded as a cult pathway. There are numerous FDA-approved drugs on the market, yet novel anticancer drugs are still in demand. Existing medications have adverse effects and drug resistance owing to rapid protein mutation. In this review, we discussed the activation process of Src kinase, chemistry of pyrimidine ring and its different synthetic routes, as well as the recent development in c-Src kinase inhibitors containing pyrimidine and their biological activity, SAR, and selectivity. The c-Src binding pocket has been predicted in detail to discover the vital amino acids which will interact with inhibitors. The potent derivatives were docked to discover the binding pattern. The derivative 2 established three hydrogen bonds with the amino acid residues Thr341 and Gln278 and had the greatest binding energy of -13.0 kcal/mol. The top docked molecules were further studied for ADMET studies. The derivative 1, 2, and 43 did not show any violation of Lipinski's rule. All derivatives used for the prediction of toxicity showed toxicity.

Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly improved with the advent and development of systematic and targeted therapies such as sorafenib and lenvatinib; however, the rise of drug resistance and the high mortality rate necessitate the continuous discovery of effective targeting agents. To discover novel anti-HCC compounds, we first constructed a deep learning-based chemical representation model to screen more than 6 million compounds in the ZINC15 drug-like library. We successfully identified LGOd1 as a novel anticancer agent with a characteristic levoglucosenone (LGO) scaffold. The mechanistic studies revealed that LGOd1 treatment leads to HCC cell death by interfering with cellular copper homeostasis, which is similar to a recently reported copper-dependent cell death named cuproptosis. While the prototypical cuproptosis is brought on by copper ionophore-induced copper overload, mechanistic studies indicated that LGOd1 does not act as a copper ionophore, but most likely by interacting with the copper chaperone protein CCS, thus LGOd1 represents a potentially new class of compounds with unique cuproptosis-inducing property. In summary, our findings highlight the critical role of bioavailable copper in the regulation of cell death and represent a novel route of cuproptosis induction.

CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm

Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces "flow transfer algorithm" for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT , complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/ .Scientific contribution: Flow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.

Bioinspired Pyrano[2,3-f]chromen-8-ones: Ring C-Opened Analogues of Calanolide A: Synthesis and Anti-HIV-1 Evaluation

We have designed and synthesized a series of bioinspired pyrano[2,3-f]coumarin-based Calanolide A analogs with anti-HIV activity. The design of these new calanolide analogs involved incorporating nitrogen heterocycles or aromatic groups in lieu of ring C, effectively mimicking and preserving their bioactive properties. Three directions for the synthesis were explored: reaction of 5-hydroxy-2,2-dimethyl-10-propyl-2H,8H-pyrano[2,3-f]chromen-8-one with (i) 1,2,4-triazines, (ii) sulfonylation followed by Suzuki cross-coupling with (het)aryl boronic acids, and (iii) aminomethylation by Mannich reaction. Antiviral assay of the synthesized compounds showed that compound 4 has moderate activity against HIV-1 on enzymes and poor activity on the cell model. A molecular docking study demonstrates a good correlation between in silico and in vitro HIV-1 reverse transcriptase (RT) activity of the compounds when docked to the nonnucleoside RT inhibitor binding site, and alternative binding modes of the considered analogs of Calanolide A were established.

Redocking the PDB

Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

Ligand-binding properties of XaffOBP9, a Minus-C odorant-binding protein from Xyleborus affinis (Coleoptera: Curculionidae: Scolytinae)

Xyleborus affinis, one of the most important pests of rubber trees, has caused severe damage to the natural rubber industry in Hainan province. The ability to detect host plants through a sensitive and specific olfactory system is crucial for Xyleborus affinis. Odorant binding proteins (OBPs) are believed to bind and carry hydrophobic active compounds from the environment to the surface of olfactory receptor neurons. To investigate the potential functional role of the highly expressed XaffOBP9 in binding with semiochemicals, we cloned and analyzed the cDNA sequence of XaffOBP9. The results showed that XaffOBP9 contains a 411bp open reading frame that encodes 136 amino acids. Then XaffOBP9 was expressed in Escherichia coli. The binding affinity of the recombinant OBP to 15 different ligands (14 host plant volatiles and 1 aggregation pheromone) was then examined using a fluorescence competitive binding approach. The results demonstrated that XaffOBP9 exhibited broad binding capabilities and strong affinities for 14 ligands. The structure of XaffOBP9 and its interactions with fourteen ligands were further analyzed by modeling and molecular docking, respectively. Based on the docking result, we found hydrophobic interactions are important between XaffOBP9 to these ligands and three amino acid residues (L71, Y106, and L114) were highly overlapped and contributed to the interaction with ligands. Mutation functional assays confirmed that the mutant L114A showed significantly reduced binding capacity to these ligands. This study suggested that XaffOBP9 may be involved in the chemoreception of semiochemicals and that it is helpful for the integrated management of X. affinis.

Ivermectin affects Arabidopsis thaliana microtubules through predicted binding site of β-tubulin

The ivermectin is a potent nematocide and insecticide, which has low toxicity for humans and domestic animals, but due to low biotransformation, it can be dangerous for non-target organisms. The recent determination of ivermectin absorption and accumulation in tissues of higher plants and multiple shreds of evidence of its negative impact on plant physiology provide a basis for the search for ivermectin's molecular targets and mechanisms of action in plant cells. In this research, for the first time, the ivermectin effect on microtubules of Arabidopsis thaliana cells was studied. It was revealed that ivermectin (250 μg mL-1) disrupts the microtubule network, induces the loss of microtubule orientation, leads to microtubule curvature and shrinkage, and their longitudinal and cross-linked bundling in various cells of A. thaliana primary roots. Further, the previously proposed binding of ivermectin to the β1-tubulin taxane site was developed and confirmed using molecular dynamics simulations of ivermectin complexes with Haemonchus contortus and A. thaliana β1-tubulins. It was predicted that similar to other microtubule stabilizing agents ivermectin binding causes M-loop stabilization in both H. contortus and A. thaliana β-tubulin, which leads to the enhancement of lateral contacts between subunits of adjacent protofilaments preventing microtubule depolymerization.

In Vivo and In Silico Studies of the Hepatoprotective Activity of Tert-Butylhydroquinone

Tert-butylhydroquinone (TBHQ) is a synthetic food antioxidant with biological activities, but little is known about its pharmacological benefits in liver disease. Therefore, this work aimed to evaluate TBHQ during acute liver damage induced by CCl4 (24 h) or BDL (48 h) in Wistar rats. It was found that pretreatment with TBHQ prevents 50% of mortality induced by a lethal dose of CCl4 (4 g/kg, i.p.), and 80% of BDL+TBHQ rats survived, while only 50% of the BDL group survived. Serum markers of liver damage and macroscopic and microscopic (H&E staining) observations suggest that TBHQ protects from both hepatocellular necrosis caused by the sublethal dose of CCl4 (1.6 g/kg, i.p.), as well as necrosis/ductal proliferation caused by BDL. Additionally, online databases identified 49 potential protein targets for TBHQ. Finally, a biological target candidate (Keap1) was evaluated in a proof-of-concept in silico molecular docking assay, resulting in an interaction energy of -5.5491 kcal/mol, which was higher than RA839 and lower than monoethyl fumarate (compounds known to bind to Keap1). These findings suggest that TBHQ increases the survival of animals subjected to CCl4 intoxication or BDL, presumably by reducing hepatocellular damage, probably due to the interaction of TBHQ with Keap1.

SNAP25 is a potential target for early stage Alzheimer's disease and Parkinson's disease

BACKGROUND

Alzheimer's disease (AD) and Parkinson's disease (PD), two common irreversible neurodegenerative diseases, share similar early stage syndromes, such as olfaction dysfunction. Yet, the potential comorbidity mechanism of AD and PD was not fully elucidated.

METHODS

The gene expression profiles of GSE5281 and GSE8397 were downloaded from the Gene Expression Omnibus (GEO) database. We utilized a series of bioinformatics analyses to screen the overlapped differentially expressed genes (DEGs). The hub genes were further identified by the plugin CytoHubba of Cytoscape and validated in the hippocampus (HIP) samples of APP/PS-1 transgenic mice and the substantial nigra (SN) samples of A53T transgenic mice by real-time quantitative polymerase chain reaction (RT-qPCR). Meanwhile, the expression of the target genes in the olfactory epithelium/bulb was detected by RT-qPCR. Finally, molecular docking was used to screen potential compounds for the target gene.

RESULTS

One hundred seventy-four overlapped DEGs were identified in AD and PD. Five of the top ten enrichment pathways mainly focused on the synapse. Five hub genes were identified and further validated. As a common factor in AD and PD, the changes of synaptosomal-associated protein 25 (SNAP25) mRNA in olfactory epithelium/bulb were significantly decreased and had a strong association with those in the HIP and SN samples. Pazopanib was the optimal compound targeting SNAP25, with a binding energy of - 9.2 kcal/mol.

CONCLUSIONS

Our results provided a theoretical basis for understanding the comorbidity mechanism of AD and PD and highlighted that SNAP25 in the olfactory epithelium may serve as a potential target for early detection and intervention in both AD and PD.

In silico drug repurposing carvedilol and its metabolites against SARS-CoV-2 infection using molecular docking and molecular dynamic simulation approaches

The pandemic of coronavirus disease 2019 (COVID-19) caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a significant impact on the economy and public health worldwide. Therapeutic options such as drugs and vaccines for this newly emerged disease are eagerly desired due to the high mortality. Using the U.S. Food and Drug Administration (FDA) approved drugs to treat a new disease or entirely different diseases, in terms of drug repurposing, minimizes the time and cost of drug development compared to the de novo design of a new drug. Drug repurposing also has some other advantages such as reducing safety evaluation to accelerate drug application on time. Carvedilol, a non-selective beta-adrenergic blocker originally designed to treat high blood pressure and manage heart disease, has been shown to impact SARS-CoV-2 infection in clinical observation and basic studies. Here, we applied computer-aided approaches to investigate the possibility of repurposing carvedilol to combat SARS-CoV-2 infection. The molecular mechanisms and potential molecular targets of carvedilol were identified by evaluating the interactions of carvedilol with viral proteins. Additionally, the binding affinities of in vivo metabolites of carvedilol with selected targets were evaluated. The docking scores for carvedilol and its metabolites with RdRp were - 10.0 kcal/mol, - 9.8 kcal/mol (1-hydroxyl carvedilol), - 9.7 kcal/mol (3-hydroxyl carvedilol), - 9.8 kcal/mol (4-hydroxyl carvedilol), - 9.7 kcal/mol (5-hydroxyl carvedilol), - 10.0 kcal/mol (8-hydroxyl carvedilol), and - 10.1 kcal/mol (O-desmethyl carvedilol), respectively. Using the molecular dynamics simulation (100 ns) method, we further confirmed the stability of formed complexes of RNA-dependent RNA polymerase (RdRp) and carvedilol or its metabolites. Finally, the drug-target interaction mechanisms that contribute to the complex were investigated. Overall, this study provides the molecular targets and mechanisms of carvedilol and its metabolites as repurposed drugs to fight against SARS-CoV-2 infection.

A Computational Method for the Binding Mode Prediction of COX-1 and COX-2 Inhibitors: Analyzing the Union of Coxibs, Oxicams, Propionic and Acetic Acids

Among the biological targets extensively investigated to improve inflammation and chronic inflammatory conditions, cyclooxygenase enzymes (COXs) occupy a prominent position. The inhibition of these enzymes, essential for mitigating inflammatory processes, is chiefly achieved through Non-Steroidal Anti-Inflammatory Drugs (NSAIDs). In this work, we introduce a novel method-based on computational molecular docking-that could aid in the structure-based design of new compounds or the description of the anti-inflammatory activity of already-tested compounds. For this, we used eight crystal complexes (four COX-1 and COX-2 each), and each pair had a specific NSAID: Celecoxib, Meloxicam, Ibuprofen, and Indomethacin. This selection was based on the ligand selectivity towards COX-1 or COX-2 and their binding mode. An interaction profile of each NSAID was compiled to detect the residues that are key for their binding mode, highlighting the interaction made by the Me group. Furthermore, we rigorously validated our models based on structural accuracy (RMSD < 1) and (R2 > 70) using eight NSAIDs and thirteen compounds with IC50 values for each enzyme. Therefore, this model can be used for the binding mode prediction of small and structurally rigid compounds that work as COX inhibitors or the prediction of new compounds that are designed by means of a structure-based approach.

Crystal Structure of Fungal Nonspecific Phospholipase C Unveils a Distinct Catalytic Mechanism

Nonspecific phospholipase C (NPC) plays a pivotal role in hydrolyzing phospholipids, releasing diacylglycerol─an essential second messenger. Extensive research has elucidated the structure and function of bacterial and plant NPCs, but our understanding of their fungal counterparts remains limited. Here, we present the first crystal structure of a fungal NPC derived from Rasamsonia emersonii (RePLC), unraveling its distinguishable features divergent from other known phospholipase C. Remarkably, the structure of RePLC contains solely the phosphoesterase domain without the crucial C-terminal domain (CTD) found in plant NPCs, although CTD is important for their activity. Through a comparative analysis of structural features among NPCs from diverse species combined with structure-based mutation analyses and bioinformatics methods, we propose a potential molecular mechanism that may universally underlie the catalysis of phospholipid hydrolysis in fungal NPCs. Furthermore, our study sheds light on the captivating evolutionary trajectory of enzymes across diverse species.

Exogenous H2S initiating Nrf2/GPx4/GSH pathway through promoting Syvn1-Keap1 interaction in diabetic hearts

Excessive ROS accumulation contributes to cardiac injury in type 2 diabetes mellitus. Hydrogen sulfide (H2S) is a vital endogenous gasotransmitter to alleviate cardiac damage in diabetic cardiomyopathy (DCM). However, the underlying mechanisms remain unclear. In this study, we investigated the effects of NaHS administration in db/db mice via intraperitoneal injection for 20 weeks and the treatment of high glucose (HG), palmitate (PA) and NaHS in HL-1 cardiomyocytes for 48 h, respectively. H2S levels were decreased in hearts of db/db mice and HL-1 cardiomyocytes exposed to HG and PA, which were restored by NaHS. Exogenous H2S activated the nuclear factor erythroid 2-related factor 2 (Nrf2)/glutathione peroxidase 4 (GPx4)/glutathione (GSH) pathway, suppressed ferroptosis and mitigated mitochondrial apoptosis in db/db mice. However, these effects were abrogated after Nrf2 knockdown. NaHS treatment elevated the ubiquitination level of Kelch-like ECH-associated protein (Keap1) by preserving its E3 ligase synoviolin (Syvn1), resulting in Nrf2 nuclear translocation. H2S facilitated the sulfhydration of Syvn1-cys115 site, a post-translational modification. Transfecting Syvn1 C115A in cardiomyocytes exposed to HG and PA partially attenuated the effects of NaHS on Nrf2 and cell death. Our findings suggest that exogenous H2S regulates Nrf2/GPx4/GSH pathway by promoting the Syvn1-Keap1 interaction to reduce ferroptosis and mitochondrial apoptosis in DCM.

Identification of potential phytochemical inhibitors for the proven six chronic obstructive pulmonary disease biomarkers - A molecular dynamics study

COPD is a multifactorial lung disease causing breathing difficulties in individuals and is becoming a major health concern worldwide. The unclear pathogenic mechanism and high mortality rate urge the development of drugs against this disease. In this study, around six COPD biomarkers identified from the preceding research through integrated gene expression analysis were taken as COPD target proteins. A total of 3307 phytocompounds were included in the COPD phytocompound library from 59 therapeutic plant sources. Furthermore, a crucial three-step virtual screening process (Absorption, Distribution, Metabolism, and Excretion, respiratory nontoxicity, less harmful and nontoxic compound category) was implemented to filter the potential drug-like phytocompounds. As a result, 160 phytocompounds were filtered with desired Absorption, Distribution, Metabolism, Excretion and Toxicity properties. The filtered 160 phytocompounds were docked against six COPD target proteins and the best-docked complexes were identified through visual inspection based on six unique parameters in SeeSAR. Furthermore, the best docked complexes were subjected to molecular dynamics simulation studies to assess the stability and conformational changes of the complexes. The presence of few amino acid residues within the crucial active, allosteric and catalytic sites of the six target proteins were revealed from the binding interaction types and major residual fluctuations from molecular docking and dynamic simulation studies respectively. This is indicative of the potential inhibitory activity of phytocompounds against the COPD biomarkers. Here we report the identified phytocompounds as a promising lead molecule for the six COPD biomarkers through in silico analysis opening new avenues in COPD pathogenesis and treatments.Communicated by Ramaswamy H. Sarma.

DataDTA: a multi-feature and dual-interaction aggregation framework for drug-target binding affinity prediction

MOTIVATION

Accurate prediction of drug-target binding affinity (DTA) is crucial for drug discovery. The increase in the publication of large-scale DTA datasets enables the development of various computational methods for DTA prediction. Numerous deep learning-based methods have been proposed to predict affinities, some of which only utilize original sequence information or complex structures, but the effective combination of various information and protein-binding pockets have not been fully mined. Therefore, a new method that integrates available key information is urgently needed to predict DTA and accelerate the drug discovery process.

RESULTS

In this study, we propose a novel deep learning-based predictor termed DataDTA to estimate the affinities of drug-target pairs. DataDTA utilizes descriptors of predicted pockets and sequences of proteins, as well as low-dimensional molecular features and SMILES strings of compounds as inputs. Specifically, the pockets were predicted from the three-dimensional structure of proteins and their descriptors were extracted as the partial input features for DTA prediction. The molecular representation of compounds based on algebraic graph features was collected to supplement the input information of targets. Furthermore, to ensure effective learning of multiscale interaction features, a dual-interaction aggregation neural network strategy was developed. DataDTA was compared with state-of-the-art methods on different datasets, and the results showed that DataDTA is a reliable prediction tool for affinities estimation. Specifically, the concordance index (CI) of DataDTA is 0.806 and the Pearson correlation coefficient (R) value is 0.814 on the test dataset, which is higher than other methods.

AVAILABILITY AND IMPLEMENTATION

The codes and datasets of DataDTA are available at https://github.com/YanZhu06/DataDTA.

Medicinal Chemistry Perspectives on Recent Advances in Src Kinase Inhibitors as a Potential Target for the Development of Anticancer Agents: Biological Profile, Selectivity, Structure-Activity Relationship

The physiological Src proto-oncogene is a protein tyrosine kinase receptor that served as the essential signaling pathway in different types of cancer. Src kinase receptor is divided into different domains: a unique domain, an SH3 domain, an SH2 domain, a protein tyrosine kinase domain, and a regulatory tail, which runs from the N-terminus to the C-terminus. Src kinase inhibitors bind in the kinase domain and are activated by phosphorylation. The etiology of cancer involved various signaling pathways and Src signaling pathways are also involved in those clusters. Although the dysregulation of Src kinase resulted in cancer being discovered in the late 19th century it is still considered a cult pathway because it is not much explored by different medicinal chemists and oncologists. The Src kinase regulated through different kinase pathways (MAPK, PI3K/Akt/mTOR, JAK/STAT3, Hippo kinase, PEAK1, and Rho/ROCK pathways) and proceeded downstream signaling to conduct cell proliferation, angiogenesis, migration, invasion, and metastasis of cancer cells. There are numerous FDA-approved drugs flooded the market but still, there is a huge demand for the creation of novel anticancer drugs. As the existing drugs are accompanied by several adverse effects and drug resistance due to rapid mutation in proteins. In this review, we have elaborated about the structure and activation of Src kinase, as well as the development of Src kinase inhibitors. Our group also provided a comprehensive overview of Src inhibitors throughout the last two decades, including their biological activity, structure-activity relationship, and Src kinase selectivity. The Src binding pocket has been investigated in detail to better comprehend the interaction of Src inhibitors with amino acid residues. We have strengthened the literature with our contribution in terms of molecular docking and ADMET studies of top compounds. We hope that the current analysis will be a useful resource for researchers and provide glimpse of direction toward the design and development of more specific, selective, and potent Src kinase inhibitors.

Unraveling the Mechanism of Action of Myricetin in the Inhibition of hUba1∼Ubiquitin Thioester Bond Formation via In Silico Molecular Modeling Techniques

Ubiquitination is a crucial type of protein modification which helps to control substrate degradation and maintain cell homeostasis. Recent studies suggest that ubiquitination and deubiquitination are involved in regulating metabolic reprogramming in cancer cells and maintaining cancer stem cells. Uba1, a crucial protein in the ubiquitination cascade, can be targeted to develop effective inhibitors for cancer treatment. In previous work, we showed that myricetin (Myr) acts as a potential human Uba1 (hUba1) inhibitor. In this study, we have utilized computational modeling techniques to attempt to illustrate the mechanism of action of Myr. Through extra-precision docking, we confirmed that Myr binds to the adenosine triphosphate (ATP)-binding site of hUba1 (referred to as hotspot 1) with the highest binding affinity. The dynamics of this interaction revealed that hUba1 undergoes a conformational shift from open to closed upon binding of Myr. Myr also migrates outward to interact with the crossover loop simultaneously as the rotational shift of the ubiquitin fold domain (UFD) takes place, thereby blocking access to the ubiquitin binding interface of hUba1 and the crossover loop. The outward migration also explains the reversible nature of Myr binding to hUba1 in previous experiments. We hypothesize that Myr acts as an inhibitor of Uba1∼Ub thioester bond formation by causing a large domain shift toward a closed conformation. Few other analogues of Myr containing the same flavone skeleton showed promising docking scores against hUba1 and could be considered for further validation. We propose that Myr and some of its analogues reported in this study may be promising candidates for developing effective Uba1 inhibitors for cancer treatment.

SiteFerret: Beyond Simple Pocket Identification in Proteins

We present a novel method for the automatic detection of pockets on protein molecular surfaces. The algorithm is based on an ad hoc hierarchical clustering of virtual probe spheres obtained from the geometrical primitives used by the NanoShaper software to build the solvent-excluded molecular surface. The final ranking of putative pockets is based on the Isolation Forest method, an unsupervised learning approach originally developed for anomaly detection. A detailed importance analysis of pocket features provides insight into which geometrical (clustering) and chemical (amino acidic composition) properties characterize a good binding site. The method also provides a segmentation of pockets into smaller subpockets. We prove that subpockets are a convenient representation to pinpoint the binding site with great precision. SiteFerret is outstanding in its versatility, accurately predicting a wide range of binding sites, from those binding small molecules to those binding peptides, including difficult shallow sites.

Markov State Models Reconcile Conformational Plasticity of GTPase with Its Substrate Binding Event

Ras GTPase is an enzyme that catalyzes the hydrolysis of guanosine triphosphate (GTP) and plays an important role in controlling crucial cellular signaling pathways. However, this enzyme has always been believed to be undruggable due to its strong binding affinity with its native substrate GTP. To understand the potential origin of high GTPase/GTP recognition, here we reconstruct the complete process of GTP binding to Ras GTPase via building Markov state models (MSMs) using a 0.1 ms long all-atom molecular dynamics (MD) simulation. The kinetic network model, derived from the MSM, identifies multiple pathways of GTP en route to its binding pocket. While the substrate stalls onto a set of non-native metastable GTPase/GTP encounter complexes, the MSM accurately discovers the native pose of GTP at its designated catalytic site in crystallographic precision. However, the series of events exhibit signatures of conformational plasticity in which the protein remains trapped in multiple non-native conformations even when GTP has already located itself in its native binding site. The investigation demonstrates mechanistic relays pertaining to simultaneous fluctuations of switch 1 and switch 2 residues which remain most instrumental in maneuvering the GTP-binding process. Scanning of the crystallographic database reveals close resemblance between observed non-native GTP binding poses and precedent crystal structures of substrate-bound GTPase, suggesting potential roles of these binding-competent intermediates in allosteric regulation of the recognition process.

Binding Mechanism of CD47 with SIRPα Variants and Its Antibody: Elucidated by Molecular Dynamics Simulations

The intricate complex system of the differentiation 47 (CD47) and the signal-regulatory protein alpha (SIRPα) cluster is a crucial target for cancer immunotherapy. Although the conformational state of the CD47-SIRPα complex has been revealed through crystallographic studies, further characterization is needed to fully understand the binding mechanism and to identify the hot spot residues involved. In this study, molecular dynamics (MD) simulations were carried out for the complexes of CD47 with two SIRPα variants (SIRPαv1, SIRPαv2) and the commercially available anti-CD47 monoclonal antibody (B6H12.2). The calculated binding free energy of CD47-B6H12.2 is lower than that of CD47-SIRPαv1 and CD47-SIRPαv2 in all the three simulations, indicating that CD47-B6H12.2 has a higher binding affinity than the other two complexes. Moreover, the dynamical cross-correlation matrix reveals that the CD47 protein shows more correlated motions when it binds to B6H12.2. Significant effects were observed in the energy and structural analyses of the residues (Glu35, Tyr37, Leu101, Thr102, Arg103) in the C strand and FG region of CD47 when it binds to the SIRPα variants. The critical residues (Leu30, Val33, Gln52, Lys53, Thr67, Arg69, Arg95, and Lys96) were identified in SIRPαv1 and SIRPαv2, which surround the distinctive groove regions formed by the B2C, C'D, DE, and FG loops. Moreover, the crucial groove structures of the SIRPα variants shape into obvious druggable sites. The C'D loops on the binding interfaces undergo notable dynamical changes throughout the simulation. For B6H12.2, the residues Tyr32_LC, His92_LC, Arg96_LC, Tyr32_HC, Thr52_HC, Ser53_HC, Ala101_HC, and Gly102_HC in its initial half of the light and heavy chains exhibit obvious energetic and structural impacts upon binding with CD47. The elucidation of the binding mechanism of SIRPαv1, SIRPαv2, and B6H12.2 with CD47 could provide novel perspectives for the development of inhibitors targeting CD47-SIRPα.

Binding Site Detection Remastered: Enabling Fast, Robust, and Reliable Binding Site Detection and Descriptor Calculation with DoGSite3

Binding site prediction on protein structures is a crucial step in early phase drug discovery whenever experimental or predicted structure models are involved. DoGSite belongs to the widely used tools for this task. It is a grid-based method that uses a Difference-of-Gaussian filter to detect cavities on the protein surface. We recently reimplemented the first version of this method, released in 2010, focusing on improved binding site detection in the presence of ligands and optimized parameters for more robust, reliable, and fast predictions and binding site descriptor calculations. Here, we introduce the new version, DoGSite3, compare it to its predecessor, and re-evaluate DoGSite on published data sets for a large-scale comparative performance evaluation.

Characteristics and Application of a Novel Cold-Adapted and Salt-Tolerant Protease EK4-1 Produced by an Arctic Bacterium Mesonia algae K4-1

Mesonia algae K4-1 from the Arctic secretes a novel cold-adapted and salt-tolerant protease EK4-1. It has the highest sequence similarity with Stearolysin, an M4 family protease from Geobacillus stearothermophilus, with only 45% sequence identity, and is a novel M4 family protease. Ek4-1 has a low optimal catalytic temperature (40 °C) and is stable at low temperatures. Moreover, EK4-1 is still active in 4 mol/L NaCl solution and is tolerant to surfactants, oxidizing agents and organic solvents; furthermore, it prefers the hydrolysis of peptide bonds at the P1' position as the hydrophobic residues, such as Leu, Phe and Val, and amino acids with a long side chain, such as Phe and Tyr. Mn²⁺and Mg²⁺ significantly promoted enzyme activity, while Fe³⁺, Co⁺, Zn²⁺ and Cu²⁺ significantly inhibited enzyme activity. Amino acid composition analysis showed that EK4-1 had more small-side-chain amino acids and fewer large-side-chain amino acids. Compared with a thermophilic protease Stearolysin, the cold-adapted protease EK4-1 contains more random coils (48.07%) and a larger active pocket (727.42 ų). In addition, the acidic amino acid content of protease EK4-1 was higher than that of the basic amino acid, which might be related to the salt tolerance of protease. Compared with the homologous proteases EB62 and E423, the cold-adapted protease EK4-1 was more efficient in the proteolysis of grass carp skin, salmon skin and casein at a low temperature, and produced a large number of antioxidant peptides, with DPPH, ·OH and ROO· scavenging activities. Therefore, cold-adapted and salt-tolerant protease EK4-1 offers wide application prospects in the cosmetic and detergent industries.

Chemical Space Virtual Screening against Hard-to-Drug RNA Methyltransferases DNMT2 and NSUN6

Targeting RNA methyltransferases with small molecules as inhibitors or tool compounds is an emerging field of interest in epitranscriptomics and medicinal chemistry. For two challenging RNA methyltransferases that introduce the 5-methylcytosine (m⁵C) modification in different tRNAs, namely DNMT2 and NSUN6, an ultra-large commercially available chemical space was virtually screened by physicochemical property filtering, molecular docking, and clustering to identify new ligands for those enzymes. Novel chemotypes binding to DNMT2 and NSUN6 with affinities down to K_D,app = 37 µM and K_D,app = 12 µM, respectively, were identified using a microscale thermophoresis (MST) binding assay. These compounds represent the first molecules with a distinct structure from the cofactor SAM and have the potential to be developed into activity-based probes for these enzymes. Additionally, the challenges and strategies of chemical space docking screens with special emphasis on library focusing and diversification are discussed.

Engineered aldoxime dehydratase to enable the chemoenzymatic conversion of benzyl amines to aromatic nitriles

A chemoenzymatic strategy has been implemented to synthesize nitriles from benzyl amines under mild conditions. Aldoxime dehydratase (Oxd) plays a decisive role to convert aldoximes into corresponding nitriles. However, natural Oxds commonly exhibit extremely low catalytic capacity toward benzaldehyde oximes. Here, we engineered the OxdF1 from Pseudomonas putida F1 to enhance its catalytic efficiency toward benzaldehyde oximes by a semi-rational design strategy. The protein structure-based CAVER analysis indicates that M29, A147, F306, and L318 are located adjacent to the substrate tunnel entrance of OxdF1, which were responsible for the transportation of substrate into the active site. After two rounds of mutagenesis, the maximum activities of the mutants L318F and L318F/F306Y were 2.6 and 2.8 U/mg respectively, which were significantly higher than the wild OxdF1 of 0.7 U/mg. Meanwhile, the lipase type B from Candida antarctica was functionally expressed in Escherichia coli cells to selectively oxidize benzyl amines to aldoximes using urea-hydrogen peroxide adduct (UHP) as an oxidant in ethyl acetate. To merge the oxidation and dehydration reactions, a reductive extraction solution was added to remove the residue UHP, which is critical to eliminate its inhibition on the Oxd activity. Consequently, nine benzyl amines were efficiently converted into corresponding nitriles by the chemoenzymatic sequence.

Hic Sunt Dracones: Molecular Docking in Uncharted Territories with Structures from AlphaFold2 and RoseTTAfold

AlphaFold2 and RoseTTAfold impress with their high accuracy in protein structure prediction. However, for structure-based virtual screenings, not only the overall structure but especially the binding sites need to be accurately predicted. In this work, the docking performance for 66 targets with known ligands but without experimental structures available in the protein data bank was elucidated. The results suggest that using an experimental surrogate-ligand complex is often superior over homology models, and only at low sequence identity to the closest homologue AlphaFold2 structures show an equal performance. The generally high fluctuation of receiver operating characteristic area under the curve values obtained for different homology models suggests that multiple combinations of docking programs and homology models should be tested prior to prospective virtual screenings, and in some cases post-processing of crude models might be necessary.

Cytoplasmic isoleucyl tRNA synthetase as an attractive multistage antimalarial drug target

Development of antimalarial compounds into clinical candidates remains costly and arduous without detailed knowledge of the target. As resistance increases and treatment options at various stages of disease are limited, it is critical to identify multistage drug targets that are readily interrogated in biochemical assays. Whole-genome sequencing of 18 parasite clones evolved using thienopyrimidine compounds with submicromolar, rapid-killing, pan-life cycle antiparasitic activity showed that all had acquired mutations in the P. falciparum cytoplasmic isoleucyl tRNA synthetase (cIRS). Engineering two of the mutations into drug-naïve parasites recapitulated the resistance phenotype, and parasites with conditional knockdowns of cIRS became hypersensitive to two thienopyrimidines. Purified recombinant P. vivax cIRS inhibition, cross-resistance, and biochemical assays indicated a noncompetitive, allosteric binding site that is distinct from that of known cIRS inhibitors mupirocin and reveromycin A. Our data show that Plasmodium cIRS is an important chemically and genetically validated target for next-generation medicines for malaria.

Resveratrol, a New Allosteric Effector of Hemoglobin, Enhances Oxygen Supply Efficiency and Improves Adaption to Acute Severe Hypoxia

Acute altitude hypoxia represents the cause of multiple adverse consequences. Current treatments are limited by side effects. Recent studies have shown the protective effects of resveratrol (RSV), but the mechanism remains unknown. To address this, the effects of RSV on the structure and function of hemoglobin of adult (HbA) were preliminarily analyzed using surface plasmon resonance (SPR) and oxygen dissociation assays (ODA). Molecular docking was conducted to specifically analyze the binding regions between RSV and HbA. The thermal stability was characterized to further validate the authenticity and effect of binding. Changes in the oxygen supply efficiency of HbA and rat RBCs incubated with RSV were detected ex vivo. The effect of RSV on the anti-hypoxic capacity under acute hypoxic conditions in vivo was evaluated. We found that RSV binds to the heme region of HbA following a concentration gradient and affects the structural stability and rate of oxygen release of HbA. RSV enhances the oxygen supply efficiency of HbA and rat RBCs ex vivo. RSV prolongs the tolerance times of mice suffering from acute asphyxia. By enhancing the oxygen supply efficiency, it alleviates the detrimental effects of acute severe hypoxia. In conclusion, RSV binds to HbA and regulates its conformation, which enhances oxygen supply efficiency and improves adaption to acute severe hypoxia.

In-silico drug design for the novel Karachi-NF001 strain of brain-eating amoeba: Naegleria fowleri

Naegleria fowleri (N. fowleri) is a free-living thermophilic amoeba of fresh water and soil. The amoeba primarily feeds on bacteria but can be transmitted to humans upon contact with freshwater sources. Furthermore, this brain-eating amoeba enters the human body through the nose and travels to the brain to cause primary amebic meningoencephalitis (PAM). N. fowleri has been reported globally since its discovery in 1961. Recently a new strain of N. fowleri named Karachi-NF001 was found in a patient who had traveled from Riyadh, Saudi Arabia to Karachi in 2019. There were 15 unique genes identified in the genome of the Karachi-NF001 strain compared to all the previously reported strains of N. fowleri worldwide. Six of these genes encode well-known proteins. In this study, we performed in-silico analysis on 5 of these 6 proteins, namely, Rab family small GTPase, NADH dehydrogenase subunit 11, two Glutamine-rich protein 2 proteins (locus tags: 12086 and 12110), and Tigger transposable element-derived protein 1. We conducted homology modeling of these 5 proteins followed by their active site identification. These proteins were subjected to molecular docking against 105 anti-bacterial ligand compounds as potential drugs. Subsequently, the 10 best-docked compounds were determined for each protein and ranked according to the number of interactions and their binding energies. The highest binding energy was recorded for the two Glutamine-rich protein 2 proteins with different locus tags, and results have shown that the protein-inhibitor complex was stable throughout the simulation run. Moreover, future in-vitro studies could validate the findings of our in-silico analysis and identify potential therapeutic drugs against N. fowleri infections.

SARS-CoV-2 proteases Mpro and PLpro: Design of inhibitors with predicted high potency and low mammalian toxicity using artificial neural networks, ligand-protein docking, molecular dynamics simulations, and ADMET calculations

The main (Mpro) and papain-like (PLpro) proteases are highly conserved viral proteins essential for replication of the COVID-19 virus, SARS-COV-2. Therefore, a logical plan for producing new drugs against this pathogen is to discover inhibitors of these enzymes. Accordingly, the goal of the present work was to devise a computational approach to design, characterize, and select compounds predicted to be potent dual inhibitors - effective against both Mpro and PLpro. The first step employed LigDream, an artificial neural network, to create a virtual ligand library. Ligands with computed ADMET profiles indicating drug-like properties and low mammalian toxicity were selected for further study. Initial docking of these ligands into the active sites of Mpro and PLpro was done with GOLD, and the highest-scoring ligands were redocked with AutoDock Vina to determine binding free energies (ΔG). Compounds 89-00, 89-07, 89-32, and 89-38 exhibited favorable ΔG values for Mpro (-7.6 to -8.7 kcal/mol) and PLpro (-9.1 to -9.7 kcal/mol). Global docking of selected compounds with the Mpro dimer identified prospective allosteric inhibitors 89-00, 89-27, and 89-40 (ΔG -8.2 to -8.9 kcal/mol). Molecular dynamics simulations performed on Mpro and PLpro active site complexes with the four top-scoring ligands from Vina demonstrated that the most stable complexes were formed with compounds 89-32 and 89-38. Overall, the present computational strategy generated new compounds with predicted drug-like characteristics, low mammalian toxicity, and high inhibitory potencies against both target proteases to form stable complexes. Further preclinical studies will be required to validate the in silico findings before the lead compounds could be considered for clinical trials.

QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets

The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target protein-identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence-based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy. Here, we combined the sequence-based self-supervised pretraining protein language model ESM1b with a graph convolutional neural network classifier to develop an improved, sequence-based drug target protein identification method. This complete model, named QuoteTarget, efficiently encodes proteins based on sequence information alone and achieves an accuracy of 95% with the nonredundant drug target and nondrug target datasets constructed for this study. When applied to all proteins from Homo sapiens, QuoteTarget identified 1213 potential undeveloped drug target proteins. We further inferred residue-binding weights from the well-trained network using the gradient-weighted class activation mapping (Grad-Cam) algorithm. Notably, we found that without any binding site information input, significant residues inferred by the model closely match the experimentally confirmed drug molecule-binding sites. Thus, our work provides a highly effective sequence-based identifier for drug target proteins, as well to yield new insights into recognizing drug molecule-binding sites. The entire model is available at https://github.com/Chenjxjx/drug-target-prediction.

Inhibition of Neutral Sphingomyelinase 2 by Novel Small Molecule Inhibitors Results in Decreased Release of Extracellular Vesicles by Vascular Smooth Muscle Cells and Attenuated Calcification

Vascular calcification (VC) is an important contributor and prognostic factor in the pathogenesis of cardiovascular diseases. VC is an active process mediated by the release of extracellular vesicles by vascular smooth muscle cells (VSMCs), and the enzyme neutral sphingomyelinase 2 (nSMase2 or SMPD3) plays a key role. Upon activation, the enzyme catalyzes the hydrolysis of sphingomyelin, thereby generating ceramide and phosphocholine. This conversion mediates the release of exosomes, a type of extracellular vesicles (EVs), which ultimately forms the nidus for VC. nSMase2 therefore represents a drug target, the inhibition of which is thought to prevent or halt VC progression. In search of novel druglike small molecule inhibitors of nSMase2, we have used virtual ligand screening to identify potential ligands. From an in-silico collection of 48,6844 small druglike molecules, we selected 996 compounds after application of an in-house multi-step procedure combining different filtering and docking procedures. Selected compounds were functionally tested in vitro; from this, we identified 52 individual hit molecules that inhibited nSMase2 activity by more than 20% at a concentration of 150 µM. Further analysis showed that five compounds presented with IC₅₀s lower than 2 µM. Of these, compounds ID 5728450 and ID 4011505 decreased human primary VSMC EV release and calcification in vitro. The hit molecules identified here represent new classes of nSMase2 inhibitors that may be developed into lead molecules for the therapeutic or prophylactic treatment of VC.

Structural Modelling of Platelet Activating Factor Acetyl Hydrolase in Leishmania donovani, Trypanosoma cruzi, and Trypanosoma brucei: Implications on Therapeutics for Leishmaniasis, Chagas Disease, and Sleeping Sickness

Purpose

Leishmaniasis, Chagas disease, and sleeping sickness are caused by protozoa Leishmania donovani, Trypanosoma cruzi, and Trypanosoma brucei, respectively. Platelet activating factor acetyl hydrolase (PAF-AH) is an inflammatory protein implicated in pathogenesis of these three infections, thereby making them attractive drug targets.

Methods

PAF-AH sequences were retrieved from UniProt and aligned using Clustal Omega. Homologous models of parasitic proteins were built based on crystal structure of human PAF-AH and validated using PROCHECK server. Volumes of substrate-binding channel were calculated using the ProteinsPlus program. High throughput virtual screening using Glide program in Schrodinger was done with ZINC drug library against parasitic PAF-AH enzymes. Complexes with best hits were energy-minimized and subjected to 100 ns molecular dynamic simulation and analyzed.

Results

PAF-AH enzyme sequences from protozoa Leishmania donovani, Trypanosoma cruzi, Trypanosoma brucei, and human have a minimum of 34% sequence similarity with each other. Corresponding structures show a globular conformation consisting of twisted β-pleated sheets, flanked by α-helices on either side. Catalytic triad of serine-histidine-aspartate is conserved. Substrate-binding channel residues are conserved to an extent, with a lower channel volume in human as compared to target enzymes. Drug screening resulted in identification of three molecules that had better affinities than the substrate to the target enzymes. These molecules fulfill Lipinski's rules for drug likeness and also bind with less affinity to the human counterpart, thereby establishing a high selective index.

Conclusion

Structures of PAF-AH from protozoan parasites and humans belong to the same family of enzymes and have a similar three-dimensional fold. However, they show subtle variations in residue composition, secondary structure composition, substrate-binding channel volume, and conformational stability. These differences result in certain specific molecules being potent inhibitors of the target enzymes while simultaneously having weaker binding to human homologue.

Small Molecular Antimicrobial Ligands of YspD are Potential Therapeutic Agents Against Yersinia enterocolitica Infection

YspD is a hydrophilic translocator forming the platform for assemblage of functional translocon. Exposure to the extra-cellular milieu makes YspD a potential therapeutic target. DoGSiteScorer predicted best druggable pocket (P0) within YspD, encompassing predominantly the C-terminal helical bundles and the long helices-9 & 5. COACH metaserver also identified ligand binding residues within the aforementioned druggable pocket mapping to helix-9. Amino acids of helix-9 are involved in oligomerization of YspD. Interaction of helix-9 and parts of C-terminal of YspD with hydrophobic translocator protein (YspB), is essential for translocation of bacterial effectors to initiate an infection. Helices-9 & 5 form an intramolecular coiled-coil structure, required for protein-protein interaction. Targeting intramolecular coiled-coil and parts of C-terminal would be important for functional inactivation of YspD. Solvent exposed surface in YspD, particularly in P0, enhances its accessibility to ligands. Nine small molecular inhibitors of TIIISS were identified and retrieved from ZINC15 database (drug-library) as putative drug candidates. Molecular docking of potential ligands with P0 was done using SwissDock server and Achilles Blind Docking server. Considering the "Significance" threshold of binding score and region of interaction, Salicylidene Acyl Hydrazide derivatives (INP0400) and Phenoxyacetamide derivative (MBX1641) were found to bind effectively with YspD. These potential ligands interact with functional domains of YspD including parts of C-terminal and the intramolecular coiled-coil, which may affect the oligomerization of YspD and disrupt the interaction of YspD with YspB, inhibiting formation of functional translocon. The identified small molecular antimicrobial ligands of YspD could be tested in vivo to attenuate Y. enterocolitica infection by deregulation of Ysa-Ysp TIIISS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40011-022-01443-2.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Magnet for the Needle in Haystack: "Crystal Structure First" Fragment Hits Unlock Active Chemical Matter Using Targeted Exploration of Vast Chemical Spaces

Fragment-based drug discovery (FBDD) has successfully led to approved therapeutics for challenging and "undruggable" targets. In the context of FBDD, we introduce a novel, multidisciplinary method to identify active molecules from purchasable chemical space. Starting from four small-molecule fragment complexes of protein kinase A (PKA), a template-based docking screen using Enamine's multibillion REAL Space was performed. A total of 93 molecules out of 106 selected compounds were successfully synthesized. Forty compounds were active in at least one validation assay with the most active follow-up having a 13,500-fold gain in affinity. Crystal structures for six of the most promising binders were rapidly obtained, verifying the binding mode. The overall success rate for this novel fragment-to-hit approach was 40%, accomplished in only 9 weeks. The results challenge the established fragment prescreening paradigm since the standard industrial filters for fragment hit identification in a thermal shift assay would have missed the initial fragments.

Substrate Specificity of GSDA Revealed by Cocrystal Structures and Binding Studies

In plants, guanosine deaminase (GSDA) catalyzes the deamination of guanosine for nitrogen recycling and re-utilization. We previously solved crystal structures of GSDA from Arabidopsis thaliana (AtGSDA) and identified several novel substrates for this enzyme, but the structural basis of the enzyme activation/inhibition is poorly understood. Here, we continued to solve 8 medium-to-high resolution (1.85-2.60 Å) cocrystal structures, which involved AtGSDA and its variants bound by a few ligands, and investigated their binding modes through structural studies and thermal shift analysis. Besides the lack of a 2-amino group of these guanosine derivatives, we discovered that AtGSDA's inactivity was due to the its inability to seclude its active site. Furthermore, the C-termini of the enzyme displayed conformational diversities under certain circumstances. The lack of functional amino groups or poor interactions/geometries of the ligands at the active sites to meet the precise binding and activation requirements for deamination both contributed to AtGSDA's inactivity toward the ligands. Altogether, our combined structural and biochemical studies provide insight into GSDA.

Comprehensive Analysis of Potential Prognostic Values of ANGPTLs in Colorectal Cancer

Colorectal cancer (CRC) is one of the most common malignant tumors in the world. CRC recurrence and metastasis cause poor prognosis. ANGPTLs (angiopoietin-like proteins) are a family of proteins that are widely involved in metabolic disease and tumorigenesis. The roles of ANGPTLs in CRC are still controversial and deserve further research. In this study, several databases were employed to explore the expression profiles, prognostic values, genetic alterations, potential biological function, and immune infiltration correlation of ANGPTLs in CRC. The expression of ANGPTL4 was significantly positively correlated with the stage of CRC. Therefore, cell and molecular experiments were further performed to explore the roles of ANGPTL4. Our results showed that the transcriptions of ANGPTLs in colon cancer and rectal cancer tissues were lower than those in normal tissues, but the protein expression varied among different ANGPTLs. In addition, the high expression of ANGPTLs led to a relatively poor oncological outcome. Specifically, the expression of ANGPTL4 is significantly positively correlated with the stage of CRC. Further investigation revealed that ANGPTLs are mainly involved in signal transduction and the regulation of transcription, while KEGG pathway analyses demonstrated pathways in cancer. Additionally, we also observed that ANGPTL4 could promote the proliferation and migration of CRC cells, and four specific small molecule compounds had potential ANGPTL4-binding capabilities, suggesting the clinical application of these small molecule compounds on CRC treatment. Our findings imply the prognostic values and potential therapeutic targets of ANGPTLs in CRC.

Identification of novel F508del-CFTR traffic correctors among triazole derivatives

The most prevalent cystic fibrosis (CF)-causing mutation - F508del - impairs the folding of CFTR protein, resulting in its defective trafficking and premature degradation. Small molecules termed correctors may rescue F508del-CFTR and therefore constitute promising pharmacotherapies acting on the fundamental cause of the disease. Here, we screened a collection of triazole compounds to identify novel F508del-CFTR correctors. The functional primary screen identified four hit compounds (LSO-18, LSO-24, LSO-28, and LSO-39), which were further validated and demonstrated to rescue F508del-CFTR processing, plasma membrane trafficking, and function. To interrogate their mechanism of action (MoA), we examined their additivity to the clinically approved drugs VX-661 and VX-445, low temperature, and genetic revertants of F508del-CFTR. Rescue of F508del-CFTR processing and function by LSO-18, LSO-24, and LSO-28, but not by LSO-39, was additive to VX-661, whereas LSO-28 and LSO-39, but not LSO-18 nor LSO-24, were additive to VX-445. All compounds under investigation demonstrated additive rescue of F508del-CFTR processing and function to low temperature as well as to rescue by genetic revertants G550E and 4RK. Nevertheless, none of these compounds was able to rescue processing nor function of DD/AA-CFTR, and LSO-39 (similarly to VX-661) exhibited no additivity to genetic revertant R1070W. From these findings, we suggest that LSO-39 (like VX-661) has a putative binding site at the NBD1:ICL4 interface, LSO-18 and LSO-24 seem to share the MoA with VX-445, and LSO-28 appears to act by a different MoA. Altogether, these findings represent an encouraging starting point to further exploit this chemical series for the development of novel CFTR correctors.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Estimating the Similarity between Protein Pockets

With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.

The druggable genome: Twenty years later

The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target’s druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future.

Identification of a broad lipid repertoire associated to the endothelial cell protein C receptor (EPCR)

Evidence is mounting that the nature of the lipid bound to the endothelial cell protein C receptor (EPCR) has an impact on its biological roles, as observed in anticoagulation and more recently, in autoimmune disease. Phosphatidylethanolamine and phosphatidylcholine species dominate the EPCR lipid cargo, yet, the extent of diversity in the EPCR-associated lipid repertoire is still unknown and remains to be uncovered. We undertook mass spectrometry analyses to decipher the EPCR lipidome, and identified species not yet described as EPCR ligands, such as phosphatidylinositols and phosphatidylserines. Remarkably, we found further, more structurally divergent lipids classes, represented by ceramides and sphingomyelins, both in less abundant quantities. In support of our mass spectrometry results and previous studies, high-resolution crystal structures of EPCR in three different space groups point to a prevalent diacyl phospholipid moiety in EPCR’s pocket but a mobile and ambiguous lipid polar head group. In sum, these studies indicate that EPCR can associate with varied lipid classes, which might impact its properties in anticoagulation and the onset of autoimmune disease.

Diselenide-derivative of 3-pyridinol targets redox enzymes leading to cell cycle deregulation and apoptosis in A549 cells

The aim of present study was to understand the mechanism of action of 2,2'-diselenobis(3-pyridinol) or DISPOL in human lung cancer (A549) cells. A549 cells were treated with 10 µM (∼IC₅₀) of DISPOL for varying time points to corelate the intracellular redox changes with its cytotoxic effect. The results indicated that DISPOL treatment led to a time dependant decrease in the basal level of reactive oxygen species (ROS). Additionally, DISPOL treatment elevated the ratio of reduced (GSH) and oxidised (GSSG) glutathione by upregulating gamma-glutamylcysteine ligase (γ-GCL) involved in GSH biosynthesis and inhibiting the activities of redox enzymes responsible for GSH utilization and recycling, such as glutathione-S-transferase (GST) and glutathione reductase (GR). Molecular docking analysis suggests putative interactions of DISPOL with GST and GR which could account for its inhibitory effect on these enzymes. Further, DISPOL induced reductive environment preceded G1 arrest and apoptosis as evidenced by decreased expression of cell cycle genes (Cyclin D1 and Cyclin E1) and elevation of p21 and apoptotic markers (cleaved caspase 3 and cleaved PARP). The combinatorial experiments involving DISPOL and redox modulatory agents such as N-acetylcysteine (NAC) and buthionine sulfoximine (BSO) indeed confirmed the role of reductive stress in DISPOL-induced cell death. Finally, Lipinski's rule suggests attributes of drug likeness in DISPOL. Taken together, DISPOL exhibits a novel mechanism of reductive stress-mediated cell death in A549 cells that warrants future exploration as anticancer agent.

Violacein switches off low molecular weight tyrosine phosphatase and rewires mitochondria in colorectal cancer cells

In the last decade, emerging evidence has shown that low molecular weight protein tyrosine phosphatase (LMWPTP) not only contributes to the progression of cancer but is associated with prostate low survival rate and colorectal cancer metastasis. We report that LMWPTP favors the glycolytic profile in some tumors. Therefore, the focus of the present study was to identify metabolic enzymes that correlate with LMWPTP expression in patient samples. Exploratory data analysis from RNA-seq, proteomics, and histology staining, confirmed the higher expression of LMWPTP in CRC. Our descriptive statistical analyses indicate a positive expression correlation between LMWPTP and energy metabolism enzymes such as acetyl-CoA carboxylase (ACC) and fatty acid synthase (FASN). In addition, we examine the potential of violacein to reprogram energetic metabolism and LMWPTP activity. Violacein treatment induced a shift of glycolytic to oxidative metabolism associated with alteration in mitochondrial efficiency, as indicated by higher oxygen consumption rate. Particularly, violacein treated cells displayed higher proton leak and ATP-linked oxygen consumption rate (OCR) as an indicator of the OXPHOS preference. Notably, violacein is able to bind and inhibit LMWPTP. Since the LMWPTP acts as a hub of signaling pathways that offer tumor cells invasive advantages, such as survival and the ability to migrate, our findings highlight an unexplored potential of violacein in circumventing the metabolic plasticity of tumor cells.