PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures

Proteome-Wide Identification and Comparison of Drug Pockets for Discovering New Drug Indications and Side Effects

Drug development faces significant financial and time challenges, highlighting the need for more efficient strategies. This study evaluated the druggability of the entire human proteome using Fpocket. We identified 15,043 druggable pockets in 20,255 predicted protein structures, significantly expanding the estimated druggable proteome from 3000 to over 11,000 proteins. Notably, many druggable pockets were found in less studied proteins, suggesting untapped therapeutic opportunities. The results of a pairwise pocket similarity analysis identified 220,312 similar pocket pairs, with 3241 pairs across different protein families, indicating shared drug-binding potential. In addition, 62,077 significant matches were found between druggable pockets and 1872 known drug pockets, highlighting candidates for drug repositioning. We repositioned progesterone to ADGRD1 for pemphigus and breast cancer, as well as estradiol to ANO2 for shingles and medulloblastoma, which were validated via molecular docking. Off-target effects were analyzed to assess the safety of drugs such as axitinib, linking newly identified targets with known side effects. For axitinib, 127 new targets were identified, and 46 out of 48 documented side effects were linked to these targets. These findings demonstrate the utility of pocket similarity in drug repositioning, target expansion, and improved drug safety evaluation, offering new avenues for the discovery of new indications and side effects of existing drugs.

Cyclocurcumin potently inhibits human aromatase as a potential therapeutic agent

Curcuminoids, including curcumin and its derivatives, show potent inhibition of aromatase (CYP19A1), crucial for estradiol synthesis and breast cancer metastasis. Our study evaluated the efficacy and mechanism of 10 curcuminoids and their metabolites against human and rat CYP19A1 using placental microsomes, revealing species-specific IC50 values. Cyclocurcumin (IC50, 4.43 μM) and curcumin (IC50, 3.49 μM) were the most effective inhibitors for human and rat CYP19A1, respectively. These compounds acted as mixed or competitive inhibitors, reducing estradiol production in human BeWo cells. Docking analysis showed that curcuminoids interact with CYP19A1 active site, forming a hydrogen bond with Met374. 3D-QSAR analysis highlighted the importance of hydrogen bonding in inhibition. A negative correlation was observed between the pKa values and IC50 values for human CYP19A1. A positive correlation was observed between the lowest binding energy and IC50 values for human CYP19A1. These findings underscore the potential of curcuminoids as therapeutic agents against breast cancer.

Advances in structure-based allosteric drug design

The identification of allosteric binding sites forms a critical connection between structural and computational biology, substantially advancing the discovery of allosteric drugs. However, the prevailing strategies for allosteric drug development predominantly rely on high-throughput screening, which suffers from high failure rates due to a limited understanding of allosteric mechanisms. This review collects insights from case studies on allosteric mechanisms, protein structure databases and computation algorithm developments, aiming to enhance our comprehension of allostery and guide more effective allosteric drug development. A crucial element in this area is the integration of structural biology with computational biology, which is vital for translating three-dimensional structural datasets into available drug discovery knowledge. These datasets and AI algorithms underpin the establishment of the allosteric binding site identification leading to structure-activity relationships (SARs) and are fueling the development of computational algorithms tailored for allosteric proteins, thereby driving forward the field of allosteric drug discovery.

Essential Oil From Tetrapanax papyrifer (Hook.) K. Koch: Chemical Composition, Antioxidant Activity, α-Glucosidase Inhibitory Effect Integrating Molecular Docking Analysis

This study investigated the chemical composition of essential oil from Tetrapanax papyrifer and evaluated its antioxidant and anti-α-glucosidase activity. A total of 61 essential oil compounds from T. papyrifer were identified by gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID). The essential oil is rich in sesquiterpenoids (96.05%), with major components identified as β-cubebene (13.96%), caryophyllene (13.43%), α-cubebene (12.12%), copaene (7.89%), δ-cadinene (7.29%), eremophilene (6.15%), humulene (5.26%). The essential oil showed dose-dependent antioxidant activity with IC50 values of 723.15 ± 24.10 and 200.74 ± 9.90 µg/mL for 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assays, respectively. Ferric reducing antioxidant power (FRAP) antioxidant capacity was 222.73 ± 25.49 µmol/g. The essential oil exhibited a 17.86% ± 0.56% inhibitory effect against α-glucosidase at 500 µg/mL. Moreover, in silico molecular docking analyses were utilized to validate the in vitro anti-α-glucosidase results further.

Pharmacodynamics of the orexin type 1 (OX1) receptor in colon cancer cell models: A two-sided nature of antagonistic ligands resulting from partial dissociation of Gq

BACKGROUND AND PURPOSE

Orexins have important biological effects on the central and peripheral nervous systems. Their primary ability is to regulate the sleep-wake cycle. Orexins and their antagonists, via OX1 receptor have been shown to have proapoptotic and antitumor effects on various digestive cancers cell models. We investigated, (1) the ability of orexin-A and its antagonists to regulate OX1 receptor expression at the cell surface and (2), how OX1 antagonists induced proapoptotic effect in cancer cells models.

EXPERIMENTAL APPROACH

The OX1 receptor internalisation is determined by imaging flow cytometry in colon cancer cell models and the OX1 receptor coupling to G proteins via bioluminescence resonance energy transfer and molecular dynamic simulation.

KEY RESULTS

Orexin-A induced rapid receptor internalisation within 15 min via β-arrestin 2 recruitment, whereas antagonists had no effect. Furthermore, Gq is critical for receptor internalisation and signalling pathways, and no other G proteins appear to be recruited. Surprisingly, antagonists induced recruitment and conformational changes in Gq protein. Simulated molecular dynamics of agonists/orexin receptor/Gq complexes show that antagonists exhibits a similar binding mode, stable at the binding site and show conformational changes of ECL2, similar to that of the agonists.

CONCLUSION AND IMPLICATIONS

OX1 receptor activation induced orexin/β-arrestin-dependent internalisation, which was independent of the apoptotic pathway induced by orexins and antagonists. In addition, antagonists activate the Gq protein, suggesting its putative partial dissociation. These results suggest that the development of OX1 receptor targeting molecules, including orexin antagonists with antitumor properties, may pave the way for innovative cancer therapies.

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Integrating Network Pharmacology and In Silico Analysis to Explore the Bioactive Compounds Against Gastric Cancer Treatment

Gastric cancer (GC) has become a major challenge in oncology research, primarily due to its detection at advanced stages. In this study, we identified and validated the pharmacological mechanisms involved in treating gastric cancer using an integrated approach combining network pharmacology, molecular docking, and a dynamic approach. Gastric cancer-related genes were obtained from DisGeNET, Genecard, and Malacard databases, while potential targets of bioactive compounds were predicted using SwissTargetPrediction. Network pharmacology and gene ontology (GO) enrichment analyses were employed to understand the molecular mechanisms of action. This should further be investigated to isolate bioactive compounds that can be used to treat different ailments. Albumin (ALB), B-cell lymphoma 2 (BCL-2), nuclear factor kappa B subunit 1 (NFKB1), hypoxia-inducible factor 1 alpha (HIF1A), and interleukin 6 (IL-6) had a higher expression in gastric cancer than in normal conditions. Top genes were validated by using the GEPIA (Gene Expression Profiling Interactive Analysis) database. Furthermore, the lead compounds dehydroxy-isocalamendiol and spathulenol exhibited the highest binding affinity with NFKB1 and HIF1A (-6.3 and -6 kJ/mol) in the molecular docking study. Enrichment analysis indicated enrichment of these hub targets in the programmed cell death-ligand 1 (PD-L1) checkpoint, phosphatidylinositol 3-kinases/protein kinase B (PI3K-Akt), Ras, and hypoxia-inducible factor-1 (HIF-1) signalling pathways with significant cut-offs of FDR < 0.01 and p < 0.05. Therefore, network pharmacology and molecular docking analyses revealed that dehydroxy-isocalamendiol and spathulenol exert therapeutic efficacy on gastric cancer by multiple targets, NFKB1 and HIF1A, and pathways (MAPK, PD-L1 checkpoint, PI3K-Akt, Ras, and HIF-1 pathways).

Asoprisnil as a Novel Ligand Interacting with Stress-Associated Glucocorticoid Receptor

Background/objective: The glucocorticoid receptor (GR) is critical in regulating cortisol production during stress. This makes it a key target for treating conditions associated with hypothalamic-pituitary-adrenal (HPA) axis dysregulation, such as mental disorders. This study explores novel ligands beyond mifepristone for their potential to modulate GR with improved efficacy and safety. By investigating these interactions, we seek to identify new pharmacotherapeutic options for stress-related mental illness. Methods: The ligands asoprisnil, campestanol, and stellasterol were selected based on structural similarities to mifepristone (reference ligand) and evaluated for pharmacological and ADME (absorption, distribution, metabolism, and excretion) properties using the SwissADME database. Molecular docking with AutoDock 4.2.6 and molecular dynamics simulations were performed to investigate ligand-protein interactions with the human glucocorticoid receptor, and binding free energies were calculated using MMPBSA. Results: Pharmacokinetic analysis revealed that asoprisnil exhibited high gastrointestinal absorption and obeyed Lipinski's rule, while mifepristone crossed the blood-brain barrier. Toxicological predictions showed that mifepristone was active for neurotoxicity and immunotoxicity, while asoprisnil, campestanol, and stellasterol displayed lower toxicity profiles. Asoprisnil demonstrated the highest stability in molecular dynamics simulations, with the highest negative binding energy of -62.35 kcal/mol, when compared to mifepristone, campestanol, and stellasterol, with binding energies of -57.08 kcal/mol, -49.99 kcal/mol, and -46.69 kcal/mol, respectively. Conclusion: This makes asoprisnil a potentially favourable therapeutic candidate compared to mifepristone. However, further validation of asoprisnil's interaction, efficacy, and safety in stress-related mental disorders through experimental studies and clinical trials is needed.

An In Silico Investigation of the Pathogenic G151R G Protein-Gated Inwardly Rectifying K+ Channel 4 Variant to Identify Small Molecule Modulators

Primary aldosteronism is characterised by the excessive production of aldosterone, which is a key regulator of salt metabolism, and is the most common cause of secondary hypertension. Studies have investigated the association between primary aldosteronism and genetic alterations, with pathogenic mutations being identified. This includes a glycine-to-arginine substitution at position 151 (G151R) of the G protein-activated inward rectifier potassium (K+) channel 4 (GIRK4), which is encoded by the KCNJ5 gene. Mutations in GIRK4 have been found to reduce the selectivity for K+ ions, resulting in membrane depolarisation, the activation of voltage-gated Ca2+ channels, and an increase in aldosterone secretion. As a result, there is an interest in identifying and exploring the mechanisms of action of small molecule modulators of wildtype (WT) and mutant channels. In order to investigate the potential modulation of homotetrameric GIRK4WT and GIRK4G151R channels, homology models were generated. Molecular dynamics (MD) simulations were performed, followed by a cluster analysis to extract starting structures for molecular docking. The central cavity has been previously identified as a binding site for small molecules, including natural compounds. The OliveNetTM database, which consists of over 600 compounds from Olea europaea, was subsequently screened against the central cavity. The binding affinities and interactions of the docked ligands against the GIRK4WT and GIRK4G151R channels were then examined. Based on the results, luteolin-7-O-rutinoside, pheophorbide a, and corosolic acid were identified as potential lead compounds. The modulatory activity of olive-derived compounds against the WT and mutated forms of the GIRK4 channel can be evaluated further in vitro.

Binding of MAP3773c Protein of Mycobacterium avium subsp. paratuberculosis in the Mouse Ferroportin1 Coding Region

Mycobacterium avium subsp. paratuberculosis (MAP) is known to cause paratuberculosis. One notable protein, MAP3773c, plays a critical role in iron metabolism as a transcription factor. This study aims to investigate the binding affinity of MAP3773c to the chromatin of the Ferroportin1 (FPN1) gene in murine macrophage J774 A.1. We conducted a sequence alignment to identify potential interaction sites for MAP3773c. Following this, we used in silico analysis to predict binding interactions, complemented by electrophoretic mobility shift assay (EMSA) to confirm in vitro binding of MAP3773c. The map3773c gene was cloned into the pcDNA3.1 vector, with subsequent expression analysis carried out via Western blotting and real-time PCR. Chromatin immunoprecipitation (CHiP) assays were performed on transfected macrophages to confirm binding in the native chromatin context. Our in silico and in vitro analysis indicated that MAP3773c interacts with two binding motifs within the FPN1 coding region. The ChiP results provided additional validation, demonstrating the binding of MAP3773c to the FPN1 chromatin through successful amplification of the associated chromatin fragment via PCR. Our study demonstrated that MAP3773c binds to FPN1 and provides insight into the role of MAP3773c and its effect on host iron transport.

Screening and Characterization of Sialic Acid-Binding Variable Lymphocyte Receptors from Hagfish

Sialic acid is a diverse group of monosaccharides often found on the termini of N- and O-linked glycans as well as being components of glycoconjugates. Hypersialylation has been associated with the progression of chronic inflammation-mediated diseases such as cardiovascular disease and cancer. Given its role in infection and disease-related processes, sialic acid is a promising target for therapeutic approaches that utilize carbohydrate-binding molecules. In this study, we screened for sialic acid-recognizing variable lymphocyte receptors (VLRBs) or ccombodies from inshore hagfish (Eptatretus burgeri) using a synthetic Neu5Ac-glycoconjugate as an antigen in immunoassay. Resulting ccombodies, 2D8, 5G11, 4A1, and 5F8 were further characterized in terms of their binding activity and specificity. A competitive ELISA using free haptens showed strong inhibition using either N-acetylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc). The half-maximal inhibitory concentrations (IC50) for Neu5Ac ranged from 7.02 to 17.06 mM, with candidates 4A1 and 5G11 requiring the least and highest amounts, respectively. IC50 values for Neu5Gc ranged from 8.12 to 13.91 mM, for 4A1 and 5G11, respectively. Candidate ccombodies also detected naturally occurring sialic acid from known sialoglycoproteins using a dot blot assay. Neu5Gc-5G11 and Neu5Ac-2D8 yielded the strongest and weakest docking interactions with affinity values of -5.9 kcal/mol and -4.9 kcal/mol, respectively. Hydrogen bonding and hydrophobic interactions were predicted to be the predominant noncovalent forces observed between the ccombodies and sialic acid. This study demonstrates that glycan-binding VLRBs from hagfish hold promise in augmenting the glycobiologists' toolkit in investigating the roles of glycans in human and animal health and disease.

In-silico assessment of novel peptidomimetics inhibitor targeting STAT3 and STAT4 N-terminal domain dimerization: A comprehensive study using molecular docking, molecular dynamics simulation, and binding free energy analysis

Dysregulation in Janus kinase-Signal Transducer and Activation of Transcription (JAK-STAT) pathway is closely linked to various cancer types. The N-terminal domain (NTD) of STAT proteins, upon dimerization, assumes a multifaceted role with remarkable adaptability in mediating interactions between proteins. Consequently, the strategic targeting of the N-terminal domain of STATs has emerged as a promising tactic for disrupting dimerization and impeding the translocation of STAT proteins. In this study, we have deployed an integrated in-silico methodology to rationally design Peptidomimetic foldamers as inhibitors of the N-terminal domains of STAT3 and STAT4, with the objective of disrupting protein dimerization. Consequently, we have judiciously designed a series of peptidomimetics that encompass β3-amino acids, bearing side chains that mimic the residues within interface II of the dimeric structures of the NTDs. Employing molecular docking techniques; we have assessed the binding affinity of these designed peptidomimetics toward both the NTDs. Furthermore, we have conducted an evaluation of the stability and conformational alterations within the docked complexes over an extensive Molecular Dynamics, subsequently computing the binding free energy utilizing MM/PBSA calculations. Our findings unequivocally demonstrate that the peptidomimetic foldamers we have devised (Peptide-A, Peptide-B, and Peptide-C) exhibit a propensity to bind to and impede the dimerization process of the NTDs of both STAT3 and STAT4. These outcomes serve to underscore the potential of these meticulously designed peptidomimetics as potential candidates meriting further exploration in the realm of cancer prevention and management.

Comparative evaluation of methods for the prediction of protein-ligand binding sites

The accurate identification of protein-ligand binding sites is of critical importance in understanding and modulating protein function. Accordingly, ligand binding site prediction has remained a research focus for over three decades with over 50 methods developed and a change of paradigm from geometry-based to machine learning. In this work, we collate 13 ligand binding site predictors, spanning 30 years, focusing on the latest machine learning-based methods such as VN-EGNN, IF-SitePred, GrASP, PUResNet, and DeepPocket and compare them to the established P2Rank, PRANK and fpocket and earlier methods like PocketFinder, Ligsite and Surfnet. We benchmark the methods against the human subset of our new curated reference dataset, LIGYSIS. LIGYSIS is a comprehensive protein-ligand complex dataset comprising 30,000 proteins with bound ligands which aggregates biologically relevant unique protein-ligand interfaces across biological units of multiple structures from the same protein. LIGYSIS is an improvement for testing methods over earlier datasets like sc-PDB, PDBbind, binding MOAD, COACH420 and HOLO4K which either include 1:1 protein-ligand complexes or consider asymmetric units. Re-scoring of fpocket predictions by PRANK and DeepPocket display the highest recall (60%) whilst IF-SitePred presents the lowest recall (39%). We demonstrate the detrimental effect that redundant prediction of binding sites has on performance as well as the beneficial impact of stronger pocket scoring schemes, with improvements up to 14% in recall (IF-SitePred) and 30% in precision (Surfnet). Finally, we propose top-N+2 recall as the universal benchmark metric for ligand binding site prediction and urge authors to share not only the source code of their methods, but also of their benchmark.Scientific contributionsThis study conducts the largest benchmark of ligand binding site prediction methods to date, comparing 13 original methods and 15 variants using 10 informative metrics. The LIGYSIS dataset is introduced, which aggregates biologically relevant protein-ligand interfaces across multiple structures of the same protein. The study highlights the detrimental effect of redundant binding site prediction and demonstrates significant improvement in recall and precision through stronger scoring schemes. Finally, top-N+2 recall is proposed as a universal benchmark metric for ligand binding site prediction, with a recommendation for open-source sharing of both methods and benchmarks.

Targeting cyclin-dependent kinase 2 CDK2: Insights from molecular docking and dynamics simulation - A systematic computational approach to discover novel cancer therapeutics

Global public health is confronted with significant challenges due to the prevalence of cancer and the emergence of treatment resistance. This work focuses on the identification of cyclin-dependent kinase 2 (CDK2) through a systematic computational approach to discover novel cancer therapeutics. A ligand-based pharmacophore model was initially developed using a training set of seven potent CDK2 inhibitors. The obtained most robust model was characterized by three features: one donor (|Don|) and two acceptors (|Acc|). Screening this model against the ZINC database resulted in identifying 108 hits, which underwent further molecular docking studies. The docking results indicated binding affinity, with energy values ranging from -6.59 kcal mol⁻¹ to -7.40 kcal mol⁻¹ compared to the standard Roscovitine. The top 10 compounds (Z1-Z10) selected from the docking data were further screened for ADMET profiling, ensuring their compliance with pharmacokinetic and toxicological criteria. The top 3 compounds (Z1-Z3) chosen from the docking were subjected to Density Functional Theory (DFT) studies. They revealed significant variations in electronic properties, providing insights into the reactivity, stability, and polarity of these compounds. Molecular dynamics simulations confirmed the stability of the ligand-protein complexes, with acceptable RMSD and RMSF values. Specifically, compound Z1 demonstrated stability, around 2.4 Å, and maintained throughout the 100 ns simulation period with minimal conformational changes, stable RMSD, and consistent protein-ligand interactions.

PMSFF: Improved Protein Binding Residues Prediction through Multi-Scale Sequence-Based Feature Fusion Strategy

Proteins perform different biological functions through binding with various molecules which are mediated by a few key residues and accurate prediction of such protein binding residues (PBRs) is crucial for understanding cellular processes and for designing new drugs. Many computational prediction approaches have been proposed to identify PBRs with sequence-based features. However, these approaches face two main challenges: (1) these methods only concatenate residue feature vectors with a simple sliding window strategy, and (2) it is challenging to find a uniform sliding window size suitable for learning embeddings across different types of PBRs. In this study, we propose one novel framework that could apply multiple types of PBRs Prediciton task through Multi-scale Sequence-based Feature Fusion (PMSFF) strategy. Firstly, PMSFF employs a pre-trained language model named ProtT5, to encode amino acid residues in protein sequences. Then, it generates multi-scale residue embeddings by applying multi-size windows to capture effective neighboring residues and multi-size kernels to learn information across different scales. Additionally, the proposed model treats protein sequences as sentences, employing a bidirectional GRU to learn global context. We also collect benchmark datasets encompassing various PBRs types and evaluate our PMSFF approach to these datasets. Compared with state-of-the-art methods, PMSFF demonstrates superior performance on most PBRs prediction tasks.

An outlook on structural biology after AlphaFold: tools, limits and perspectives

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

Dual inhibitory potential of ganoderic acid A on GLUT1/3: computational and in vitro insights into targeting glucose metabolism in human lung cancer

Human glucose transporters (GLUTs) facilitate the uptake of hexoses into cells. In cancer, the increased proliferation necessitates higher expression of GLUTs, with particular emphasis on GLUT1 and GLUT3. Thus, inhibiting GLUTs holds promise as an anticancer therapy by starving these cells of fuel. Ganoderic acid A (GAA), a triterpene found in Ganoderma lucidum, has anticancer and antidiabetic properties. Recent studies show that GAA reduces glucose uptake in cancer cells, which indicates that GAA may affect GLUT1/GLUT3 by inhibiting glucose uptake. Therefore, this study aimed to inspect whether GAA could target GLUT1/GLUT3 and play an inhibitory role in changing their endofacial and exofacial conformations. To this end, AlphaFold2 was employed to model the endofacial and exofacial conformations of GLUT3 and GLUT1, respectively. Molecular docking, molecular dynamics simulation, cell viability, cellular thermal shift assays (CETSA), glucose uptake, qPCR, and western blotting were harnessed. In comparison to the endofacial (cytochalasin B) and exofacial (phloretin) GLUT1/3 inhibitors, the computational findings unveiled GAA's capacity to bind and stabilize GLUT1/3 in their two conformational states, with a preference for binding the endofacial conformation. A low, non-cytotoxic dose of GAA thermally stabilized both transporters and inhibited glucose uptake in human lung cancer cells, similar to cytochalasin B and phloretin. In conclusion, this study has unearthed novel functionalities of GAA, suggesting its potential utility in cancer therapy by targeting glucose metabolism.

In silico evidence of bitopertin's broad interactions within the SLC6 transporter family

The Glycine Transporter Type 1 (GlyT1) significantly impacts central nervous system functions, influencing glycinergic and glutamatergic neurotransmission. Bitopertin, the first GlyT1 inhibitor in clinical trials, was developed for schizophrenia treatment but showed limited efficacy. Despite this, bitopertin's repositioning could advance treating various pathologies. This study aims to understand bitopertin's mechanism of action using computational methods, exploring off-target effects, and providing a comprehensive pharmacological profile. Similarity Ensemble Approach (SEA) and SwissTargetPrediction initially predicted targets, followed by molecular modeling on SWISS-MODEL and GalaxyWeb servers. Binding sites were identified using PrankWeb, and molecular docking was performed with DockThor and GOLD software. Molecular dynamics analyses were conducted on the Visual Dynamics platform. Reverse screening on SEA and SwissTargetPrediction identified GlyT1 (SLC6A9), GlyT2 (SLC6A5), PROT (SLC6A7), and DAT (SLC6A3) as potential bitopertin targets. Homology modeling on SwissModel generated high-resolution models, optimized further on GalaxyWeb. PrankWeb identified similar binding sites in GlyT1, GlyT2, PROT, and DAT, indicating potential interaction. Docking studies suggested bitopertin's interaction with GlyT1 and proximity to GlyT2 and PROT. Molecular dynamics confirmed docking results, highlighting bitopertin's target stability beyond GlyT1. The study concludes that bitopertin potentially interacts with multiple SLC6 family targets, indicating a broader pharmacological property.

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.

Reassessment of the genetic basis of natural rifampin resistance in the genus Rickettsia

Rickettsia, a genus of obligate intracellular bacteria, includes species that cause significant human diseases. This study challenges previous claims that the Leucine-973 residue in the RNA polymerase beta subunit is the primary determinant of rifampin resistance in Rickettsia. We investigated a previously untested Rickettsia species, R. lusitaniae, from the Transitional group and found it susceptible to rifampin, despite possessing the Leu-973 residue. Interestingly, we observed the conservation of this residue in several rifampin-susceptible species across most Rickettsia phylogenetic groups. Comparative genomics revealed potential alternative resistance mechanisms, including additional amino acid variants that could hinder rifampin binding and genes that could facilitate rifampin detoxification through efflux pumps. Importantly, the evolutionary history of Rickettsia genomes indicates that the emergence of natural rifampin resistance is phylogenetically constrained within the genus, originating from ancient genetic features shared among a unique set of closely related Rickettsia species. Phylogenetic patterns appear to be the most reliable predictors of natural rifampin resistance, which is confined to a distinct monophyletic subclade known as Massiliae. The distinctive features of the RNA polymerase beta subunit in certain untested Rickettsia species suggest that R. raoultii, R. amblyommatis, R. gravesii, and R. kotlanii may also be naturally rifampin-resistant species.

Genomic insights into redox-driven microbial processes for carbon decomposition in thawing Arctic soils and permafrost

Climate change is rapidly transforming Arctic landscapes where increasing soil temperatures speed up permafrost thaw. This exposes large carbon stocks to microbial decomposition, possibly worsening climate change by releasing more greenhouse gases. Understanding how microbes break down soil carbon, especially under the anaerobic conditions of thawing permafrost, is important to determine future changes. Here, we studied the microbial community dynamics and soil carbon decomposition potential in permafrost and active layer soils under anaerobic laboratory conditions that simulated an Arctic summer thaw. The microbial and viral compositions in the samples were analyzed based on metagenomes, metagenome-assembled genomes, and metagenomic viral contigs (mVCs). Following the thawing of permafrost, there was a notable shift in microbial community structure, with fermentative Firmicutes and Bacteroidota taking over from Actinobacteria and Proteobacteria over the 60-day incubation period. The increase in iron and sulfate-reducing microbes had a significant role in limiting methane production from thawed permafrost, underscoring the competition within microbial communities. We explored the growth strategies of microbial communities and found that slow growth was the major strategy in both the active layer and permafrost. Our findings challenge the assumption that fast-growing microbes mainly respond to environmental changes like permafrost thaw. Instead, they indicate a common strategy of slow growth among microbial communities, likely due to the thermodynamic constraints of soil substrates and electron acceptors, and the need for microbes to adjust to post-thaw conditions. The mVCs harbored a wide range of auxiliary metabolic genes that may support cell protection from ice formation in virus-infected cells.

IMPORTANCE

As the Arctic warms, thawing permafrost unlocks carbon, potentially accelerating climate change by releasing greenhouse gases. Our research delves into the underlying biogeochemical processes likely mediated by the soil microbial community in response to the wet and anaerobic conditions, akin to an Arctic summer thaw. We observed a significant shift in the microbial community post-thaw, with fermentative bacteria like Firmicutes and Bacteroidota taking over and switching to different fermentation pathways. The dominance of iron and sulfate-reducing bacteria likely constrained methane production in the thawing permafrost. Slow-growing microbes outweighed fast-growing ones, even after thaw, upending the expectation that rapid microbial responses to dominate after permafrost thaws. This research highlights the nuanced and complex interactions within Arctic soil microbial communities and underscores the challenges in predicting microbial response to environmental change.

Molecular docking of daunorubicin and etoposide drugs against Leishmania donovani : A theoretical study

BACKGROUND OBJECTIVES

The human blood parasite Leishmania donovani causes visceral leishmaniasis or grayish discoloration of the skin (black fever/kala-azar). Antitumor drugs such as daunorubicin and etoposide can help to treat such diseases. The computational approach is used to find a better interaction of drugs with the active site of the protein and help to design new drugs.

METHODS

In this study, we have optimized two antitumor drugs, daunorubicin and etoposide. We studied frontier molecular orbitals, electrostatic potential (MEP) maps, and the natural bond order analysis of these anticancer drugs, followed by molecular docking with Leishmania donovani protein.

RESULTS

The three-dimensional structure of MapK from Leishmania donovani is LDBPK-331470. Our computational calculations reveal that daunorubicin and etoposide drugs can have an affinity with MapK from Leishmania donovani .

INTERPRETATION CONCLUSION

Our study predicted that both daunorubicin and etoposide could have a similar affinity with the protein (UvrD) Leishmania donovani .

The apo-acyl coenzyme A binding protein of Leishmania major forms a unique 'AXXA' motif mediated dimer

Acyl-Coenzyme A binding domain containing proteins (ACBDs) are ubiquitous in nearly all eukaryotes. They can exist as a free protein, or a domain of a large, multidomain, multifunctional protein. Besides modularity, ACBDs also display multiplicity. The same organism may have multiple ACBDs, differing in sequence and organization. By virtue of this diversity, ACBDs perform functions ranging from transport, synthesis, trafficking, signal transduction, transcription, and gene regulation. In plants and some microorganisms, these ACBDs are designated ACBPs (acyl-CoA binding proteins). The simplest ACBD/ACBP is a small, ∼10 kDa, soluble protein, comprising the acyl-CoA binding (ACB) domain. Most of these small ACBDs exist as monomers, while a few show a tendency to oligomerize. In sync with those studies, we report the crystal structure of two ACBDs from Leishmania major, named ACBP103, and ACBP96 based on the number of residues present. Interestingly, ACBP103 crystallized as a monomer and a dimer under different crystallization conditions. Careful examination of the dimer disclosed an exposed 'AXXA' motif in the helix I of the two ACBP103 monomers, aligned in a head-to-tail arrangement in the dimer. Glutaraldehyde cross-linking studies confirm that apo-ACBP103 can self-associate in solution. Isothermal titration calorimetry studies further show that ACBP103 can bind ligands ranging from C8 - to C20-CoA, and the data could be best fit to a 'two sets of sites'/sequential binding site model. Taken together, our studies show that Leishmania major ACBP103 can self-associate in the apo-form through a unique dimerization motif, an interaction that may play an important role in its function.

Unique Binding Sites of Uricosuric Agent Dotinurad for Selective Inhibition of Renal Uric Acid Reabsorptive Transporter URAT1

Dotinurad was developed as a uricosuric agent, inhibiting urate (UA) reabsorption through the UA transporter URAT1 in the kidneys. Due to its high selectivity for URAT1 among renal UA transporters, we investigated the mechanism underlying this selectivity by identifying dotinurad binding sites specific to URAT1. Dotinurad was docked to URAT1 using AutoDock4, utilizing the AlphaFold2-predicted structure. The inhibitory effects of dotinurad on wild-type and mutated URAT1 at the predicted binding sites were assessed through URAT1-mediated [14C]UA uptake in Xenopus oocytes. Nine amino acid residues in URAT1 were identified as dotinurad-binding sites. Sequence alignment with UA-transporting organic anion transporters (OATs) revealed that H142 and R487 were unique to URAT1 among renal UA-transporting OATs. For H142, IC50 values of dotinurad increased to 62, 55, and 76 nM for mutated URAT1 (H142A, H142E, and H142R, respectively) compared with 19 nM for the wild type, indicating that H142 contributes to URAT1-selective interaction with dotinurad. H142 was predicted to interact with the phenyl-hydroxyl group of dotinurad. The IC50 of the hydroxyl group methylated dotinurad (F13141) was 165 μM, 8420-fold higher than dotinurad, suggesting the interaction of H142 and the phenyl-hydroxyl group by forming a hydrogen bond. Regarding R487, URAT1-R487A exhibited a loss of activity. Interestingly, the URAT1-H142A/R487A double mutant restored UA transport activity, with the IC50 value of dotinurad for the mutant (388 nM) significantly higher than that for H142A (73.5 nM). These results demonstrate that H142 and R487 of URAT1 determine its selectivity for dotinurad, a uniqueness observed only in URAT1 among UA-transporting OATs. SIGNIFICANCE STATEMENT: Dotinurad selectively inhibits the urate reabsorption transporter URAT1 in renal urate-transporting organic ion transporters (OATs). This study demonstrates that dotinurad interacts with H142 and R487 of URAT1, located in the extracellular domain and unique among OATs when aligning amino acid sequences. Mutations in these residues reduce affinity of dotinurad for URAT1, confirming their role in conferring selective inhibition. Additionally, the interaction between dotinurad and URAT1 involving H142 is found to mediate hydrogen bonding.

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.

Expansion of pneumococcal serotype 23F and 14 lineages with genotypic changes in capsule polysaccharide locus and virulence gene profiles post introduction of pneumococcal conjugate vaccine in Blantyre, Malawi

Since the introduction of the 13-valent pneumococcal conjugate vaccine (PCV13) in Malawi in 2011, there has been persistent carriage of vaccine serotype (VT) Streptococcus pneumoniae, despite high vaccine coverage. To determine if there has been a genetic change within the VT capsule polysaccharide (cps) loci since the vaccine's introduction, we compared 1022 whole-genome-sequenced VT isolates from 1998 to 2019. We identified the clonal expansion of a multidrug-resistant, penicillin non-susceptible serotype 23F GPSC14-ST2059 lineage, a serotype 14 GPSC9-ST782 lineage and a novel serotype 14 sequence type GPSC9-ST18728 lineage. Serotype 23F GPSC14-ST2059 had an I253T mutation within the capsule oligosaccharide repeat unit polymerase Wzy protein, which is predicted in silico to alter the protein pocket cavity. Moreover, serotype 23F GPSC14-ST2059 had SNPs in the DNA binding sites for the cps transcriptional repressors CspR and SpxR. Serotype 14 GPSC9-ST782 harbours a non-truncated version of the large repetitive protein (Lrp), containing a Cna protein B-type domain which is also present in proteins associated with infection and colonisation. These emergent lineages also harboured genes associated with antibiotic resistance, and the promotion of colonisation and infection which were absent in other lineages of the same serotype. Together these data suggest that in addition to serotype replacement, modifications of the capsule locus associated with changes in virulence factor expression and antibiotic resistance may promote vaccine escape. In summary, the study highlights that the persistence of vaccine serotype carriage despite high vaccine coverage in Malawi may be partly caused by expansion of VT lineages post-PCV13 rollout.

Identification of novel single nucleotide variants in the drug resistance mechanism of Mycobacterium tuberculosis isolates by whole-genome analysis

BACKGROUND

Tuberculosis (TB) represents a major global health challenge. Drug resistance in Mycobacterium tuberculosis (MTB) poses a substantial obstacle to effective TB treatment. Identifying genomic mutations in MTB isolates holds promise for unraveling the underlying mechanisms of drug resistance in this bacterium.

METHODS

In this study, we investigated the roles of single nucleotide variants (SNVs) in MTB isolates resistant to four antibiotics (moxifloxacin, ofloxacin, amikacin, and capreomycin) through whole-genome analysis. We identified the drug-resistance-associated SNVs by comparing the genomes of MTB isolates with reference genomes using the MuMmer4 tool.

RESULTS

We observed a strikingly high proportion (94.2%) of MTB isolates resistant to ofloxacin, underscoring the current prevalence of drug resistance in MTB. An average of 3529 SNVs were detected in a single ofloxacin-resistant isolate, indicating a mutation rate of approximately 0.08% under the selective pressure of ofloxacin exposure. We identified a set of 60 SNVs associated with extensively drug-resistant tuberculosis (XDR-TB), among which 42 SNVs were non-synonymous mutations located in the coding regions of nine key genes (ctpI, desA3, mce1R, moeB1, ndhA, PE_PGRS4, PPE18, rpsA, secF). Protein structure modeling revealed that SNVs of three genes (PE_PGRS4, desA3, secF) are close to the critical catalytic active sites in the three-dimensional structure of the coding proteins.

CONCLUSION

This comprehensive study elucidates novel resistance mechanisms in MTB against antibiotics, paving the way for future design and development of anti-tuberculosis drugs.

Exploring Binding Pockets in the Conformational States of the SARS-CoV-2 Spike Trimers for the Screening of Allosteric Inhibitors Using Molecular Simulations and Ensemble-Based Ligand Docking

Understanding mechanisms of allosteric regulation remains elusive for the SARS-CoV-2 spike protein, despite the increasing interest and effort in discovering allosteric inhibitors of the viral activity and interactions with the host receptor ACE2. The challenges of discovering allosteric modulators of the SARS-CoV-2 spike proteins are associated with the diversity of cryptic allosteric sites and complex molecular mechanisms that can be employed by allosteric ligands, including the alteration of the conformational equilibrium of spike protein and preferential stabilization of specific functional states. In the current study, we combine conformational dynamics analysis of distinct forms of the full-length spike protein trimers and machine-learning-based binding pocket detection with the ensemble-based ligand docking and binding free energy analysis to characterize the potential allosteric binding sites and determine structural and energetic determinants of allosteric inhibition for a series of experimentally validated allosteric molecules. The results demonstrate a good agreement between computational and experimental binding affinities, providing support to the predicted binding modes and suggesting key interactions formed by the allosteric ligands to elicit the experimentally observed inhibition. We establish structural and energetic determinants of allosteric binding for the experimentally known allosteric molecules, indicating a potential mechanism of allosteric modulation by targeting the hinges of the inter-protomer movements and blocking conformational changes between the closed and open spike trimer forms. The results of this study demonstrate that combining ensemble-based ligand docking with conformational states of spike protein and rigorous binding energy analysis enables robust characterization of the ligand binding modes, the identification of allosteric binding hotspots, and the prediction of binding affinities for validated allosteric modulators, which is consistent with the experimental data. This study suggested that the conformational adaptability of the protein allosteric sites and the diversity of ligand bound conformations are both in play to enable efficient targeting of allosteric binding sites and interfere with the conformational changes.

Nutrition, phytochemical profiling, in vitro biological activities, and in silico studies of South Chinese white pitaya (Hylocereus undatus)

Background

White pitaya, a popular tropical fruit, is known for its high nutritional value. It is commercially cultivated worldwide for its potential use in the food and pharmaceutical industries. This study aims to assess the nutritional and phytochemical contents and biological potential of the South Chinese White Pitaya (SCWP) peel, flesh, and seed extracts.

Methods

Extract fractions with increasing polarity (ethyl acetate < acetone < ethanol < methanol < aqueous) were prepared. Antibacterial potential was tested against multidrug-resistant (MDR) bacteria, and antioxidant activity was determined using, 2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-Azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) radical scavenging assays, and cytotoxic activity against human keratinocyte cells using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. Pharmacological screening and molecular docking simulations were conducted to identify potential antibacterial compounds with druggable characteristics. Molecular dynamics simulation (MDS) was employed to validate the binding stability of the promising ligand-protein complexes.

Results

All parts of the fruit exhibited a substantial amount of crucial nutrients (minerals, sugars, proteins, vitamins, and fatty acids). The ethanol (ET) and acetone (AC) fractions of all samples demonstrated notable inhibitory effects against tested MDR bacteria, with MIC50 ranges of 74-925 μg/mL. Both ET and AC fractions also displayed remarkable antioxidant activity, with MIC50 ranges of 3-39 μg/mL. Cytotoxicity assays on HaCaT cells revealed no adverse effects from the crude extract fractions. LC-MS/MS analyses identified a diverse array of compounds, known and unknown, with antibacterial and antioxidant activities. Molecular docking simulations and pharmacological property screening highlighted two active compounds, baicalein (BCN) and lenticin (LTN), showing strong binding affinity with selected target proteins and adhering to pharmacological parameters. MDS indicated a stable interaction between the ligands (BCN and LTN) and the receptor proteins over a 100-ns simulation period.

Conclusion

Our study provides essential information on the nutritional profile and pharmacological potential of the peel, flesh, and seeds of SCWP. Furthermore, our findings contribute to the identification of novel antioxidants and antibacterial agents that could be capable of overcoming the resistance barrier posed by MDR bacteria.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning

BACKGROUND

Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects.

RESULTS

Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN.

CONCLUSIONS

Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention

Identifying and discovering druggable protein binding sites is an important early step in computer-aided drug discovery, but it remains a difficult task where most campaigns rely on a priori knowledge of binding sites from experiments. Here, we present a binding site prediction method called Graph Attention Site Prediction (GrASP) and re-evaluate assumptions in nearly every step in the site prediction workflow from data set preparation to model evaluation. GrASP is able to achieve state-of-the-art performance at recovering binding sites in PDB structures while maintaining a high degree of precision which will minimize wasted computation in downstream tasks such as docking and free energy perturbation.

Searching for new plastic-degrading enzymes from the plastisphere of alpine soils using a metagenomic mining approach

Plastic materials, including microplastics, accumulate in all types of ecosystems, even in remote and cold environments such as the European Alps. This pollution poses a risk for the environment and humans and needs to be addressed. Using shotgun DNA metagenomics of soils collected in the eastern Swiss Alps at about 3,000 m a.s.l., we identified genes and their proteins that potentially can degrade plastics. We screened the metagenomes of the plastisphere and the bulk soil with a differential abundance analysis, conducted similarity-based screening with specific databases dedicated to putative plastic-degrading genes, and selected those genes with a high probability of signal peptides for extracellular export and a high confidence for functional domains. This procedure resulted in a final list of nine candidate genes. The lengths of the predicted proteins were between 425 and 845 amino acids, and the predicted genera producing these proteins belonged mainly to Caballeronia and Bradyrhizobium. We applied functional validation, using heterologous expression followed by enzymatic assays of the supernatant. Five of the nine proteins tested showed significantly increased activities when we used an esterase assay, and one of these five proteins from candidate genes, a hydrolase-type esterase, clearly had the highest activity, by more than double. We performed the fluorescence assays for plastic degradation of the plastic types BI-OPL and ecovio® only with proteins from the five candidate genes that were positively active in the esterase assay, but like the negative controls, these did not show any significantly increased activity. In contrast, the activity of the positive control, which contained a PLA-degrading gene insert known from the literature, was more than 20 times higher than that of the negative controls. These findings suggest that in silico screening followed by functional validation is suitable for finding new plastic-degrading enzymes. Although we only found one new esterase enzyme, our approach has the potential to be applied to any type of soil and to plastics in various ecosystems to search rapidly and efficiently for new plastic-degrading enzymes.

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures

Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.

Enhancing the endo-activity of the thermophilic chitinase to yield chitooligosaccharides with high degrees of polymerization

Thermophilic endo-chitinases are essential for production of highly polymerized chitooligosaccharides, which are advantageous for plant immunity, animal nutrition and health. However, thermophilic endo-chitinases are scarce and the transformation from exo- to endo-activity of chitinases is still a challenging problem. In this study, to enhance the endo-activity of the thermophilic chitinase Chi304, we proposed two approaches for rational design based on comprehensive structural and evolutionary analyses. Four effective single-point mutants were identified among 28 designed mutations. The ratio of (GlcNAc)3 to (GlcNAc)2 quantity (DP3/2) in the hydrolysates of the four single-point mutants undertaking colloidal chitin degradation were 1.89, 1.65, 1.24, and 1.38 times that of Chi304, respectively. When combining to double-point mutants, the DP3/2 proportions produced by F79A/W140R, F79A/M264L, F79A/W272R, and M264L/W272R were 2.06, 1.67, 1.82, and 1.86 times that of Chi304 and all four double-point mutants exhibited enhanced endo-activity. When applied to produce chitooligosaccharides (DP ≥ 3), F79A/W140R accumulated the most (GlcNAc)4, while M264L/W272R was the best to produce (GlcNAc)3, which was 2.28 times that of Chi304. The two mutants had exposed shallower substrate-binding pockets and stronger binding abilities to shape the substrate. Overall, this research offers a practical approach to altering the cutting pattern of a chitinase to generate functional chitooligosaccharides.

Molecular interactions of paraben family of pollutants with embryonic neuronal proteins of Danio rerio: A step ahead in computational toxicity towards adverse outcome pathway

The paraben family of endocrine disruptors exhibit persistent behaviours in aquatic matrices, having bio-accumulative effects and necessitating toxicity analysis and safe use, as well as prevention of food web penetration. In this study, the toxicity effects of 9 different parabens (Methyl, Ethyl, Propyl, Butyl, Heptyl, Isopropyl, Isobutyl, benzyl parabens and p-hydroxybenzoic acid) were studied against 17 neuronal proteins (Neurog1, Ascl1a, DLA, Syn2a, Ntn1a, Pitx2, and SoxB1, Her/Hes, Zic family) expressed during the early embryonic developmental stage of Danio rerio. The neuronal genes were selected as a biomarker to study the inhibitory effects on the cascade of genes expressed in the early developmental stage. The study uses trRossetta software to predict protein structures of neuronal genes, followed by structural refinement, energy minimisation, and active site prediction, evaluated using energy value, RC plot and ERRAT scores of PROCHECK and ERRAT programs. Compared to raw structures, highly confident predicted structures and quality scores were observed for refined protein with few exceptions. Based on the polarity and charge of the aminoacids, the probable pockets were identified using active site prediction, which were then used for molecular docking analysis. Further, the ADMET analysis, ligand likeliness and toxicological test revealed the paraben family of compounds as one of the most susceptible toxic and mutagenic compounds. The molecular docking results showed an interesting pattern of increasing binding affinity with increase in the carbon chains of paraben molecules. Benzyl Paraben showed higher binding affinities across all 17 neuronal proteins. Finally, gene co-occurrence/co-expression and protein-protein interaction studies using the STRING database depict that all proteins are functionally related and play essential roles in standard biological processes or pathways, conserved and expressed in diverse organisms. The interaction between paraben compounds and neuronal genes indicates high risks of inhibiting reactions in embryonic stages, emphasising the need for effective treatment measures and strict regulations.

Enhanced Antioxidant and Neuroprotective Properties of Pterostilbene (Resveratrol Derivative) in Amorphous Solid Dispersions

In this study, amorphous solid dispersions (ASDs) of pterostilbene (PTR) with polyvinylpyrrolidone polymers (PVP K30 and VA64) were prepared through milling, affirming the amorphous dispersion of PTR via X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC). Subsequent analysis of DSC thermograms, augmented using mathematical equations such as the Gordon-Taylor and Couchman-Karasz equations, facilitated the determination of predicted values for glass transition (Tg), PTR's miscibility with PVP, and the strength of PTR's interaction with the polymers. Fourier-transform infrared (FTIR) analysis validated interactions maintaining PTR's amorphous state and identified involved functional groups, namely, the 4'-OH and/or -CH groups of PTR and the C=O group of PVP. The study culminated in evaluating the impact of amorphization on water solubility, the release profile in pH 6.8, and in vitro permeability (PAMPA-GIT and BBB methods). In addition, it was determined how improving water solubility affects the increase in antioxidant (ABTS, DPPH, CUPRAC, and FRAP assays) and neuroprotective (inhibition of cholinesterases: AChE and BChE) properties. The apparent solubility of the pure PTR was ~4.0 µg·mL-1 and showed no activity in the considered assays. For obtained ASDs (PTR-PVP30/PTR-PVPVA64, respectively) improvements in apparent solubility (410.8 and 383.2 µg·mL-1), release profile, permeability, antioxidant properties (ABTS: IC50 = 52.37/52.99 μg·mL-1, DPPH: IC50 = 163.43/173.96 μg·mL-1, CUPRAC: IC0.5 = 122.27/129.59 μg·mL-1, FRAP: IC0.5 = 95.69/98.57 μg·mL-1), and neuroprotective effects (AChE: 39.1%/36.2%, BChE: 76.9%/73.2%) were confirmed.

Identification of phytocompounds as potent inhibitors of sodium/glucose cotransporter-2 leading to diabetes treatment

Type-II diabetes, a major metabolic disorder has threatened the very existence of a healthy life since long ago. Commercially available antidiabetic drugs are known for several adverse effects. The present study attempted to identify potential phytocompounds as inhibitors of sodium/glucose cotransporter-2 (SGLT2), a major protein that helps in glucose re-absorption from renal tubules. A total of 28 phytocompounds were collected based on the literature survey. 3D co-ordinates of phytocompounds were collected from PubChem database. Molecular docking was carried out with SGLT2 protein and the best 3 docking complexes were subjected to molecular dynamics simulation for 100 ns. Free energy changes were also analyzed using MM/PBSA analysis. Phytocompounds were also analyzed for their drug-likeness and ADMET properties. Docking study observed a strong binding affinity of phytocompounds (> -7.0 kcal/mol). More than 10 phytocompounds showed better binding affinity compared to reference drugs. Further analysis of three best docking complexes when analyzed by MD simulation showed better stability and compactness of the complexes compared to reference drug, empagliflozin. MM/PBSA analysis also revealed that van der Waals force and electrostatic energy are the major binding energy involved in the complex formation. Like docking energy, free energy analysis also observed stronger binding energies (ΔGGAS) in SGLT2-phytocompound complexes compared to empagliflozin complex. All the phytocompounds showed drug-likeness and considerable ADMET properties. The study, therefore, suggests that Trifolirhizin-6'-monoacetate, Aspalathin, and Quercetin-3-glucoside could be a possible inhibitor of SGLT2 protein. However, further studies need to be carried out to reveal the exact mode of activity.Communicated by Ramaswamy H. Sarma.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Identification, Biochemical Characterization, and In Vivo Detection of a Zn-Metalloprotease with Collagenase Activity from Mannheimia haemolytica A2

Respiratory diseases in ruminants are a main cause of economic losses to farmers worldwide. Approximately 25% of ruminants experience at least one episode of respiratory disease during the first year of life. Mannheimia haemolytica is the main etiological bacterial agent in the ruminant respiratory disease complex. M. haemolytica can secrete several virulence factors, such as leukotoxin, lipopolysaccharide, and proteases, that can be targeted to treat infections. At present, little information has been reported on the secretion of M. haemolytica A2 proteases and their host protein targets. Here, we obtained evidence that M. haemolytica A2 proteases promote the degradation of hemoglobin, holo-lactoferrin, albumin, and fibrinogen. Additionally, we performed biochemical characterization for a specific 110 kDa Zn-dependent metalloprotease (110-Mh metalloprotease). This metalloprotease was purified through ion exchange chromatography and characterized using denaturing and chaotropic agents and through zymography assays. Furthermore, mass spectrometry identification and 3D modeling were performed. Then, antibodies against the 110 kDa-Mh metalloprotease were produced, which achieved great inhibition of proteolytic activity. Finally, the antibodies were used to perform immunohistochemical tests on postmortem lung samples from sheep with suggestive histology data of pneumonic mannheimiosis. Taken together, our results strongly suggest that the 110-Mh metalloprotease participates as a virulence mechanism that promotes damage to host tissues.

Using Euf1 transcription factor as a titrator of erythritol-inducible promoters in Yarrowia lipolytica; insight into the structure, splicing, and regulation mechanism

Controllable regulatory elements, like inducible, titratable promoters, are highly desired in synthetic biology toolboxes. A set of previously developed erythritol-inducible promoters along with an engineered Yarrowia lipolytica host strain were shown to be a very potent expression platform. In this study, we push the previously encountered limits of the synthetic promoters' titratability (by the number of upstream motifs) by using a compatible transcription factor, Euf1, as the promoter titrator. Overexpression of spliced EUF1 turned out to be very efficient in promoting expression from the compatible promoter, however, the erythritol-inducible character of the promoter was then lost. Analysis of the EUF1's splicing pattern suggests that the intron removal is promoted in the presence of erythritol, but is not dependent on it. The 3D structures of spliced versus unspliced Euf1 were modeled, and ligand-binding strength was calculated and compared. Furthermore, the EUF1-dependent expression profile under different chemical stimulants was investigated. Depletion of carbon source was identified as the significant factor upregulating the expression from the Euf1-dependent promoter (2-10-fold). Considering these findings and transcriptomics data, a new mechanism of the Euf1-regulated promoter action is proposed, involving a 'catabolite repression' transcription factor-Adr1, both acting on the same ERY-inducible promoter.

TTD: Therapeutic Target Database describing target druggability information

Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.

Identification of novel inhibitors against VP40 protein of Marburg virus by integrating molecular modeling and dynamics approaches

Marburg virus (MV) is a highly etiological agent of haemorrhagic fever in humans and has spread across the world. Its outbreaks caused a 23-90% human death rate. However, there are currently no authorized preventive or curative measures yet. VP40 is the MV matrix protein, which builds protein shell underneath the viral envelope and confers hallmark filamentous. VP40 alone is able to induce assembly and budding of filamentous virus-like particles (VLPs), which resemble authentic virions. As a result, this research is credited with clarifying the function of VP40 and leading to the discovery of new therapeutic targets effective in combating MV disease (MVD). Virtual screening, molecular docking and molecular dynamics (MD) simulation were used to find the putative active chemicals based on a 3D pharmacophore model of the protein's active site cavity. Initially, andrographidine-C, a potent inhibitor was selected for the development of the pharmacophore model. Later, a library of 30,000 compounds along with the andrographidine-C was docked against VP40 protein. Three best hits including avanafil, diuvaretin and macrourone were subjected to further MD simulation analysis, as these compounds had better binding affinities as compared to andrographidine-C. Furthermore, throughout the 100 ns simulations, the back bone of VP40 protein in presence of avanafil, diuvaretin and macrourone remained stable which was further validated by MM-PBSA analysis. Additionally, all of these compounds depict maximum drug-like properties. The predicted drugs based on the ligand, avanafil, diuvaretin and macrourone could be exploited and developed as an alternative or complementary therapy for the treatment of MVD.Communicated by Ramaswamy H. Sarma.

Pleiotropic Potential of Evernia prunastri Extracts and Their Main Compounds Evernic Acid and Atranorin: In Vitro and In Silico Studies

Evernia prunastri is a lichen widely distributed in the Northern Hemisphere. Its biological properties still need to be discovered. Therefore, our paper focuses on studies of E. prunastri extracts, including its main metabolites evernic acid (EA) or atranorin (ATR). Phytochemical profiles using chromatographic analysis were confirmed. The antioxidant activity was evaluated using in vitro chemical tests and in vitro enzymatic cells-free tests, namely superoxide dismutase (SOD), glutathione peroxidase (GPx), glutathione reductase (GR), and catalase (CAT). The anti-inflammatory potential using cyclooxygenase-2 (COX-2) and hyaluronidase were determined. The neuroprotective potential using acetylcholinesterase, (AChE), butyrylcholinesterase (BChE), and tyrosinase (Tyr) was estimated. The hypoglycemic activity was also confirmed (α-glucosidase). Principal component analysis was performed to determine the relationship between the biological activity of extracts. The inhibitory effect of EA and ATR on COX-2 AChE, BChE, Tyr, and α-glucosidase was evaluated using molecular docking techniques and confirmed for EA and ATR (besides α-glucosidase). The penetration of EA and ATR from extracts through the blood-brain barrier was confirmed using the parallel artificial membrane permeability assay blood-brain barrier test. In conclusion, depending on chemical surroundings and the concentration, the E. prunastri extracts, EA or ATR, showed attractive pleiotropic properties, which should be further investigated.

Biochemical characterization and key catalytic residue identification of a novel alpha-agarase with CBM2 domain

Agarooligosaccharides have great potential in food industry because of their various bio-activities, while the limited availability and diversity of α-agarases hinder agarooligosaccharides' broader application. To overcome this limitation, a computer-assisted method was used to screen and identify novel agarases. Firstly, one novel α-agarase, AgaB, with an N-terminal CBM2 domain (the first report of this domain in agarases), was discovered. Purified agarases only exhibited activity against agarose, with optimum activity at 40℃ and pH 8.0. Analysis of hydrolysis products indicated that AgaB is an endo-type α-agarase, producing agarotetraose and agarohexaose. Secondly, AgaB truncated CBM2 showed increased Km values, suggesting that CBM2 aids in substrate binding. Thirdly, E468 and D333 are possibly catalytic amino acids, which was supported by molecular docking results and mutants. Biochemical characterization of first reported CBM2-containing agarase and catalytic mechanism study lay the foundation for the exploration and development of α-agarases in the future.

PoSSuM v.3: A Major Expansion of the PoSSuM Database for Finding Similar Binding Sites of Proteins

Information on structures of protein-ligand complexes, including comparisons of known and putative protein-ligand-binding pockets, is valuable for protein annotation and drug discovery and development. To facilitate biomedical and pharmaceutical research, we developed PoSSuM (https://possum.cbrc.pj.aist.go.jp/PoSSuM/), a database for identifying similar binding pockets in proteins. The current PoSSuM database includes 191 million similar pairs among almost 10 million identified pockets. PoSSuM drug search (PoSSuMds) is a resource for investigating ligand and receptor diversity among a set of pockets that can bind to an approved drug compound. The enhanced PoSSuMds covers pockets associated with both approved drugs and drug candidates in clinical trials from the latest release of ChEMBL. Additionally, we developed two new databases: PoSSuMAg for investigating antibody-antigen interactions and PoSSuMAF to simplify exploring putative pockets in AlphaFold human protein models.

Identification of Potential Modulators of a Pathogenic G Protein-Gated Inwardly Rectifying K+ Channel 4 Mutant: In Silico Investigation in the Context of Drug Discovery for Hypertension

Genetic abnormalities have been associated with primary aldosteronism, a major cause of secondary hypertension. This includes mutations in the KCNJ5 gene, which encodes G protein-gated inwardly rectifying K+ channel 4 (GIRK4). For example, the substitution of glycine with glutamic acid gives rise to the pathogenic GIRK4G151E mutation, which alters channel selectivity, making it more permeable to Na+ and Ca2+. While tertiapin and tertiapin-Q are well-known peptide inhibitors of the GIRK4WT channel, clinically, there is a need for the development of selective modulators of mutated channels, including GIRK4G151E. Using in silico methods, including homology modeling, protein-peptide docking, ligand-binding site prediction, and molecular docking, we aimed to explore potential modulators of GIRK4WT and GIRK4G151E. Firstly, protein-peptide docking was performed to characterize the binding site of tertiapin and its derivative to the GIRK4 channels. In accordance with previous studies, the peptide inhibitors preferentially bind to the GIRK4WT channel selectivity filter compared to GIRK4G151E. A ligand-binding site analysis was subsequently performed, resulting in the identification of two potential regions of interest: the central cavity and G-loop gate. Utilizing curated chemical libraries, we screened over 700 small molecules against the central cavity of the GIRK4 channels. Flavonoids, including luteolin-7-O-rutinoside and rutin, and the macrolides rapamycin and troleandomycin bound strongly to the GIRK4 channels. Similarly, xanthophylls, particularly luteoxanthin, bound to the central cavity with a strong preference towards the mutated GIRK4G151E channel compared to GIRK4WT. Overall, our findings suggest potential lead compounds for further investigation, particularly luteoxanthin, that may selectively modulate GIRK4 channels.

EpiMed Coronabank Chemical Collection: Compound selection, ADMET analysis, and utilisation in the context of potential SARS-CoV-2 antivirals

Antiviral drugs are important for the coronavirus disease 2019 (COVID-19) response, as vaccines and antibodies may have reduced efficacy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Antiviral drugs that have been made available for use, albeit with questionable efficacy, include remdesivir (Veklury®), nirmatrelvir-ritonavir (Paxlovid™), and molnupiravir (Lagevrio®). To expand the options available for COVID-19 and prepare for future pandemics, there is a need to investigate new uses for existing drugs and design novel compounds. To support these efforts, we have created a comprehensive library of 750 molecules that have been sourced from in vitro, in vivo, and in silico studies. It is publicly available at our dedicated website (https://epimedlab.org/crl/). The EpiMed Coronabank Chemical Collection consists of compounds that have been divided into 10 main classes based on antiviral properties, as well as the potential to be used for the management, prevention, or treatment of COVID-19 related complications. A detailed description of each compound is provided, along with the molecular formula, canonical SMILES, and U.S. Food and Drug Administration approval status. The chemical structures have been obtained and are available for download. Moreover, the pharmacokinetic properties of the ligands have been characterised. To demonstrate an application of the EpiMed Coronabank Chemical Collection, molecular docking was used to evaluate the binding characteristics of ligands against SARS-CoV-2 nonstructural and accessory proteins. Overall, our database can be used to aid the drug repositioning process, and for gaining further insight into the molecular mechanisms of action of potential compounds of interest.

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.

Comparative Analysis of Conformational Dynamics and Systematic Characterization of Cryptic Pockets in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 Spike Complexes with the ACE2 Host Receptor: Confluence of Binding and Structural Plasticity in Mediating Networks of Conserved Allosteric Sites

In the current study, we explore coarse-grained simulations and atomistic molecular dynamics together with binding energetics scanning and cryptic pocket detection in a comparative examination of conformational landscapes and systematic characterization of allosteric binding sites in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 spike full-length trimer complexes with the host receptor ACE2. Microsecond simulations, Markov state models and mutational scanning of binding energies of the SARS-CoV-2 BA.2 and BA.2.75 receptor binding domain complexes revealed the increased thermodynamic stabilization of the BA.2.75 variant and significant dynamic differences between these Omicron variants. Molecular simulations of the SARS-CoV-2 Omicron spike full-length trimer complexes with the ACE2 receptor complemented atomistic studies and enabled an in-depth analysis of mutational and binding effects on conformational dynamic and functional adaptability of the Omicron variants. Despite considerable structural similarities, Omicron variants BA.2, BA.2.75 and XBB.1 can induce unique conformational dynamic signatures and specific distributions of the conformational states. Using conformational ensembles of the SARS-CoV-2 Omicron spike trimer complexes with ACE2, we conducted a comprehensive cryptic pocket screening to examine the role of Omicron mutations and ACE2 binding on the distribution and functional mechanisms of the emerging allosteric binding sites. This analysis captured all experimentally known allosteric sites and discovered networks of inter-connected and functionally relevant allosteric sites that are governed by variant-sensitive conformational adaptability of the SARS-CoV-2 spike structures. The results detailed how ACE2 binding and Omicron mutations in the BA.2, BA.2.75 and XBB.1 spike complexes modulate the distribution of conserved and druggable allosteric pockets harboring functionally important regions. The results are significant for understanding the functional roles of druggable cryptic pockets that can be used for allostery-mediated therapeutic intervention targeting conformational states of the Omicron variants.