The Protein Data Bank Archive

Differential molecular interactions between iberiotoxin and human SLO3 and SLO1 potassium channels

CONTEXT

SLO1and SLO3 are similar voltage-gated K + channels. However, SLO3 expression is sperm specific and plays an important role in the hyperpolarization of the sperm membrane potential that is crucial for sperm fertilization. This makes SLO3 an excellent molecular target for the development of male contraceptives, and computational methods can facilitate structural insights for this drug development. Here, we evaluated the differential molecular interactions between the human SLO3 (hSLO3) and SLO1 (hSLO1) potassium channels and iberiotoxin (IbTX), a toxin that selectively blocks SLO channels. To do this, molecular docking and dynamics were implemented on the channel-toxin complexes to help elucidate atomistic details of their interaction and binding energy. Our analysis found that IbTX has a similar binding energy to both channels but interacts in a distinct manner with them. Particularly, Trp14 and Arg25 residues of IbTX diverges in their interaction with the residues Val283 and Asn260 residues of hSLO3 and the corresponding residues Tyr359 and Ala336 of hSLO1. Knowledge of key residues in the molecular interface of IbTX blockage can help guide and hasten non-hormonal contraceptive development. Our results encourage the use of toxins as scaffolds for specific SLO3 blockers.

METHODS

Atomistic molecular dynamics were implemented on the channel-toxin complexes. To generate the complexes, IbTX was docked to the channels using HADDOCK. CHARMM-GUI was used to generate simulation systems. GROMACS v2023.1 was used to run the simulations for 500 ns in an NPT ensemble at 297.26 K employing the CHARMM36 force field. Binding energy was evaluated by molecular mechanics generalized born surface area (MM/GBSA) with gmxMMPBGBSA.py.

Investigating the Effect and Mechanism of 3-Methyladenine Against Diabetic Encephalopathy by Network Pharmacology, Molecular Docking, and Experimental Validation

Background/Objectives: Diabetic encephalopathy (DE), a severe neurological complication of diabetes mellitus (DM), is characterized by cognitive dysfunction. 3-Methyladenine (3-MA), a methylated adenine derivative, acts as a biomarker for DNA methylation and exhibits hypoglycemic and neuroprotective properties. However, the pharmacological mechanisms underlying 3-MA's therapeutic effects on diabetic microvascular complications remain incompletely understood, owing to the intricate and multifactorial pathogenesis of DE. Methods: This study employed network pharmacology and molecular docking techniques to predict potential targets and signaling pathways of 3-MA against DE, with subsequent validation through animal experiments to elucidate the molecular mechanisms of 3-MA in DE treatment. Results: Network pharmacological analysis identified two key targets of 3-MA in DE modulation: AKT and GSK3β. Molecular docking confirmed a strong binding affinity between 3-MA and AKT/GSK3β. In animal experiments, 3-MA significantly reduced blood glucose levels in diabetic mice, ameliorated learning and memory deficits, and preserved hippocampal neuronal integrity. Furthermore, we found that 3-MA inhibited apoptosis by regulating the expression of Bax and BCL-2. Notably, 3-MA also downregulated the expression of amyloid precursor protein (APP) and Tau while enhancing the expression of phosphorylated AKT and GSK-3β. Conclusions: Our findings may contribute to elucidating the therapeutic mechanisms of 3-MA in diabetic microangiopathy and provide potential therapeutic targets through activation of the AKT/GSK-3β pathway.

Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs

Intrinsically Disordered Proteins (IDPs) challenge traditional structure-function paradigms by existing as dynamic ensembles rather than stable tertiary structures. Capturing these ensembles is critical to understanding their biological roles, yet Molecular Dynamics (MD) simulations, though accurate and widely used, are computationally expensive and struggle to sample rare, transient states. Artificial intelligence (AI) offers a transformative alternative, with deep learning (DL) enabling efficient and scalable conformational sampling. They leverage large-scale datasets to learn complex, non-linear, sequence-to-structure relationships, allowing for the modeling of conformational ensembles in IDPs without the constraints of traditional physics-based approaches. Such DL approaches have been shown to outperform MD in generating diverse ensembles with comparable accuracy. Most models rely primarily on simulated data for training and experimental data serves a critical role in validation, aligning the generated conformational ensembles with observable physical and biochemical properties. However, challenges remain, including dependence on data quality, limited interpretability, and scalability for larger proteins. Hybrid approaches combining AI and MD can bridge the gaps by integrating statistical learning with thermodynamic feasibility. Future directions include incorporating physics-based constraints and learning experimental observables into DL frameworks to refine predictions and enhance applicability. AI-driven methods hold significant promise in IDP research, offering novel insights into protein dynamics and therapeutic targeting while overcoming the limitations of traditional MD simulations.

Integrated network pharmacology, molecular docking, and animal experiments to reveal the potential mechanism of hesperetin on COPD

Hesperetin (HE), a natural flavonoid exhibiting anti-inflammatory and antioxidant properties, holds significant potential in treating chronic obstructive pulmonary disease (COPD). Nonetheless, the precise mechanisms underlying its effects are yet to be fully elucidated. In this study, we aim to explore the role and potential mechanism of HE in treating COPD using network pharmacology, molecular docking and experimental validation. We screened for HE and COPD-related targets from public databases, and then imported potential targets into a STRING database to establish a protein-protein interaction network. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes enrichment analysis were performed to obtain key signaling pathways. We then predicted the binding interactions between HE and core targets using molecular docking. The animal model of COPD was established through lipopolysaccharide and cigarette smoke induction in mice to observe lung function, inflammatory factors, pathology, and the expression of related proteins. Network pharmacology findings unveiled that HE and COPD shared 105 common targets. MAPKs and NF-κB signaling pathways were selected for further validation. In animal experiment, HE enhanced lung function and histopathological morphology, while reducing inflammation levels. The results of Western blot tests indicated that HE treatment considerably inhibited the expression of MAPKs and NF-κB. HE effectively reduced lung inflammation and improved lung function in mice. This mechanism may be achieved by inhibition of MAPKs and NF-κB signaling pathways.

Discover the Power of Lithospermic Acid as Human Carbonic Anhydrase VA and Pancreatic Lipase Inhibitor Through In Silico and In Vitro Studies

Obesity remains a significant global health concern, with limited pharmacological options that balance efficacy and safety. In this study, we identified lithospermic acid (LTS0059529) from Salvia miltiorrhiza as a potential dual inhibitor of pancreatic lipase (PL) and human carbonic anhydrase VA (hCA VA), two key enzymes in lipid metabolism. Using molecular docking and dynamics simulations, we observed that lithospermic acid interacts with Zn²⁺ in hCA VA via its benzofuran carboxylate moiety and forms stable complexes with PL through hydrogen bonding with ASP 205 and π-stacking interactions with PHE 77 and PHE 215. Experimental validation confirmed its inhibitory activity, with Ki values of 33.1 ± 1.6 μM for PL and 0.69 ± 0.01 μM for hCA VA. While its inhibition of hCA VA is not isoform-specific, lithospermic acid demonstrates significant potential as a dual inhibitor, targeting complementary pathways in obesity management. This study is the first to explore its dual action on PL and hCA VA, highlighting a promising strategy for future antiobesity therapies. Further research will focus on optimizing selectivity and potency to develop safer and more effective treatments.

In Vitro Evaluation of the Antiviral Activity of Polyphenol (-)-Epigallocatechin-3-Gallate (EGCG) Against Mayaro Virus

The Mayaro virus (MAYV), Togaviridae family, genus Alphavirus, has caused several sporadic outbreaks, affecting countries in the Americas. Currently, there are no licensed drugs against MAYV, requiring the search for effective antiviral compounds. Thus, this study aimed to evaluate the antiviral potential of polyphenol (-)-epigallocatechin-3-gallate (EGCG) against MAYV infection, in vitro. Antiviral assays against MAYV were performed in BHK-21 and Vero E6 cells. In addition, molecular docking was performed with EGCG and the MAYV non-structural and structural proteins. EGCG showed a significant protective effect against MAYV infection in both cell lines. The virucidal assay showed an effect on extracellular viral particles at the entry stage into BHK-21 cells. Finally, it also showed significant inhibition in the post-entry stages of the MAYV replication cycle, acting on the replication of the genetic material and late stages, such as assembly and release. In addition, the MAYV proteins E1 and nsP1 were significantly inhibited by the EGCG treatment in BHK-21 cells. Molecular docking analysis also showed that EGCG could interact with MAYV Capsid and Envelope proteins (E1 and E2). Therefore, this study shows the potential of EGCG as a promising antiviral against MAYV, as it acts on different stages of the MAYV replication cycle.

Genes related to neural tube defects and glioblastoma

There are many similarities between early embryonic development and tumorigenesis. The occurrence of neural tube defects (NTDs) and glioblastoma (GBM) are both related to the abnormal development of neuroectodermal cells. To obtain genes related to both NTDs and GBM, as well as small molecule drugs with potential clinical application value. We performed bioinformatics analysis on transcriptome sequencing data of retinoic acid (RA)-induced NTDs mice, human NTDs samples and GBM samples. RT-qPCR, Western blot, and immunohistochemistry were used to validate the expression of candidate genes. Our results indicated that two genes at mRNA and protein levels have been well verified in both NTDs mouse and GBM human samples, namely, Poli and Fgf1. Molecular docking and validating in vitro were performed for FGF1 against pazopanib by using Autodock and Biacore. Cytological experiments showed that pazopanib significantly inhibited the proliferation of GBM tumor cells and mouse neural cells, promoted apoptosis, and had no effect on GBM tumor cells migration. Overall, our results demonstrated that Fgf1 abnormally expressed at different developmental stages, it may be a potentially prenatal biomarker for NTDs and potential therapeutic target for GBM. Pazopanib may be a new drug for the treatment of GBM tumors.

Advances and Mechanisms of RNA-Ligand Interaction Predictions

The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA's specific recognition of other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA-ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA-ligand complexes by examining RNA's sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA-ligand interaction prediction tools.

TRIM103 activates the RLRs pathway to enhance antiviral response by targeting VP5 and VP7

Grass carp (Ctenopharyngodon idella), crucial to global inland aquaculture with a production of 5.8 million tones in 2020, faces significant challenges from hemorrhagic disease caused by grass carp reovirus (GCRV). Rapid mutations compromise current vaccines, underscoring the need for a deeper understanding of antiviral mechanisms to enhance molecular marker-assisted selection. This study investigates the role of Tripartite Motif (TRIM) family in the innate immune response of grass carp, focusing on TRIM103 from Ctenopharyngodon Idella (CiTRIM103), a member of the TRIM-B30.2 family, which includes proteins with the B30.2 domain at the N-terminus, known for antiviral properties in teleosts. CiTRIM103 bind to the outer coat proteins VP5 and VP7 of GCRV. This binding is theorized to strengthen the function of the RIG-I-like Receptor (RLR) signaling pathway, crucial for antiviral responses. Demonstrations using overexpression and RNA interference (RNAi) techniques have shown that CiTRIM103 effectively inhibits GCRV replication. Moreover, molecular docking and pulldown assays suggest potential binding interactions of CiTRIM103's B30.2 domain with GCRV outer coat proteins VP5 and VP7. These interactions impede viral replication, enhance RLR receptor expression, and activate key transcription factors to induce type I interferons (IFNs). These findings elucidate the antiviral mechanisms of CiTRIM103, provide a foundation for future Molecular genetic breeding in grass carp.

A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction

The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence and secondary structure features and neglect small molecule characteristics, and resulting in poor performance on unknown small molecule testing. To overcome this limitation, we developed a double-layer stacking-based machine learning model called ZHMol-RLinter. This approach more effectively predicts RNA-small molecule binding preferences by learning RNA and small molecule features to capture their interaction information. ZHMol-RLinter also combines sequence and secondary structural features with structural geometric and physicochemical environment information to capture the specificity of RNA spatial conformations in recognizing small molecules. Our results demonstrate that ZHMol-RLinter has a success rate of 90.8% on the published RL98 testing set, representing a significant improvement over existing methods. Additionally, ZHMol-RLinter achieved a success rate of 77.1% on the unknown small molecule UNK96 testing set, showing substantial improvement over the existing methods. The evaluation of predicted structures confirms that ZHMol-RLinter is reliable and accurate for predicting RNA-small molecule binding preferences, even for challenging unknown small molecule testing. Predicting RNA-small molecule binding preferences can help in the understanding of RNA-small molecule interactions and promote the design of RNA-related drugs for biological and medical applications.

Preparation and Characterization of Cumin Essential Oil Nanoemulsion (CEONE) as an Antibacterial Agent and Growth Promoter in Broilers: A Study on Efficacy, Safety, and Health Impact

This research characterized and explored the effect of cumin essential oil nanoemulsion (CEONE) on broiler growth performance, serum biochemistry, hematological parameters, and cecal microbial count. Day-old (n = 96) broilers (Ross 308) were randomly assigned to six treatments with five replicates of three broilers each. The dietary treatments consisted of negative control (only basal diet), positive control (basal diet + 200 µL of enrofloxacin), 25 µL (basal diet + 25 µL of CEONE), 50 µL (basal diet + 50 µL of CEONE), 75 µL (basal diet + 75 µL of CEONE), and 100 µL (basal diet + 100 µL of CEONE). The broiler's body weight gain (BWG) after 42 days of treatment exhibited increased weight in the CEONE group (976.47 ± 11.82-1116.22 ± 29.04). The gain in weight was further evidenced by the beneficial microbe load (107 log) compared to the pathogenic strain. All the biochemical parameters were observed in the normal range, except for a higher level of HDL and a lower LDL value. This safety has been validated by pKCSM toxicity analysis showing a safe and highly tolerable dose of cuminaldehyde. In conclusion, this research observed the potential of CEONE as a multifunctional agent. It is a valuable candidate for further application in combating bacterial infections and enhancing animal health and growth.

Exploring drug repositioning possibilities of kinase inhibitors via molecular simulation

Kinases, a class of enzymes controlling various substrates phosphorylation, are pivotal in both physiological and pathological processes. Although their conserved ATP binding pockets pose challenges for achieving selectivity, this feature offers opportunities for drug repositioning of kinase inhibitors (KIs). This study presents a cost-effective in silico prediction of KIs drug repositioning via analyzing cross-docking results. We established the KIs database (278 unique KIs, 1834 bioactivity data points) and kinases database (357 kinase structures categorized by the DFG motif) for carrying out cross-docking. Comparative analysis of the docking scores and reported experimental bioactivity revealed that the Atypical, TK, and TKL superfamilies are suitable for drug repositioning. Among these kinase superfamilies, Olverematinib, Lapatinib, and Abemaciclib displayed enzymatic activity in our focused AKT-PI3K-mTOR pathway with IC50 values of 3.3, 3.2 and 5.8 μM. Further cell assays showed IC50 values of 0.2, 1.2 and 0.6 μM in tumor cells. The consistent result between prediction and validation demonstrated that repositioning KIs via in silico method is feasible.

EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models

Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing distinct NMR signals in different chemical environments. Apprehending chemical shifts from NMR signals can be challenging since having an NMR structure does not necessarily provide all the required chemical shift information, making predictive models essential for accurately deducing chemical shifts, either from protein structures or, more ideally, directly from amino acid sequences. Here, we present EFG-CS, a web server that specializes in chemical shift prediction. EFG-CS employs a machine learning-based transfer prediction model for backbone atom chemical shift prediction, using ESMFold-predicted protein structures. Additionally, ESG-CS incorporates a graph neural network-based model to provide comprehensive side-chain atom chemical shift predictions. Our method demonstrated reliable performance in backbone atom prediction, achieving comparable accuracy levels with root mean square errors (RMSE) of 0.30 ppm for H, 0.22 ppm for Hα, 0.89 ppm for C, 0.89 ppm for Cα, 0.84 ppm for Cβ, and 1.69 ppm for N. Moreover, our approach also showed predictive capabilities in side-chain atom chemical shift prediction achieving RMSE values of 0.71 ppm for Hβ, 0.74-1.15 ppm for Hδ, and 0.58-0.94 ppm for Hγ, solely utilizing amino acid sequences without homology or feature curation. This work shows for the first time that generative AI protein models can predict NMR shifts nearly comparable to experimental models. This web server is freely available at https://biosig.lab.uq.edu.au/efg_cs, and the chemical shift prediction results can be downloaded in tabular format and visualized in 3D format.

Protein research in millets: current status and way forward

MAIN CONCLUSION

Millets' protein studies are lagging behind those of major cereals. Current status and future insights into the investigation of millet proteins are discussed. Millets are important small-seeded cereals majorly grown and consumed by people in Asia and Africa and are considered crops of future food security. Although millets possess excellent climate resilience and nutrient supplementation properties, their research advancements have been lagging behind major cereals. Although considerable genomic resources have been developed in recent years, research on millet proteins and proteomes is currently limited, highlighting a need for further investigation in this area. This review provides the current status of protein research in millets and provides insights to understand protein responses for climate resilience and nutrient supplementation in millets. The reference proteome data is available for sorghum, foxtail millet, and proso millet to date; other millets, such as pearl millet, finger millet, barnyard millet, kodo millet, tef, and browntop millet, do not have any reference proteome data. Many studies were reported on stress-responsive protein identification in foxtail millet, with most studies on the identification of proteins under drought-stress conditions. Pearl millet has a few reports on protein identification under drought and saline stress. Finger millet is the only other millet to have a report on stress-responsive (drought) protein identification in the leaf. For protein localization studies, foxtail millet has a few reports. Sorghum has the highest number of 40 experimentally proven crystal structures, and other millets have fewer or no experimentally proven structures. Further proteomics studies will help dissect the specific proteins involved in climate resilience and nutrient supplementation and aid in breeding better crops to conserve food security.

Synthesis of novel carbazole hydrazine-carbothioamide scaffold as potent antioxidant, anticancer and antimicrobial agents

BACKGROUND

Carbazole-based molecules containing thiosemicarbazide functional groups are recognized for their diverse biological activities, particularly in enhancing therapeutic anticancer effects through inhibiting crucial pathways. These derivatives also exhibit noteworthy antioxidant properties.

OBJECTIVES

This study aims to synthesize, characterize, and evaluate the antioxidant and anticancer activities of 18 novel carbazole derivatives.

METHODS

The radical scavenging capabilities of the compounds were assessed using the 2,2-diphenyl-1-picrylhydrazyl assay. Antiproliferative activities were evaluated on MCF-7 cancer cell lines through viability assays. Additionally, the modulation of the PI3K/Akt/mTOR pathway, apoptosis/necrosis induction, and cell cycle analysis were conducted for the most promising anticancer agents.

RESULTS

nine compounds showed potent antioxidant activities with IC50 values lower than the positive control acarbose, with compounds 4 h and 4y exhibiting the highest potency (IC50 values of 0.73 and 0.38 µM, respectively). Furthermore, compounds 4o and 4r displayed significant anticancer effects, with IC50 values of 2.02 and 4.99 µM, respectively. Compound 4o, in particular, exhibited promising activity by targeting the PI3K/Akt/mTOR signaling pathway, inhibiting tumor survival, inducing apoptosis, and causing cell cycle arrest in MCF-7 cell lines. Furthermore, compound 4o was showed significant antimicrobial activities against S. aureus and E. coli, and antifungal effect against C. albicans. Its potential to overcome drug resistance through this pathway inhibition highlights its promise as an anticancer agent. Molecular docking simulations supported these findings, revealing favorable binding profiles and interactions within the active sites of the enzymes PI3K, AKT1, and mTOR. Moreover, assessing the druggability of the newly synthesized thiosemicarbazide derivatives demonstrated optimal physicochemical properties, further endorsing their potential as drug candidates.

The Histone Deacetylase Family: Structural Features and Application of Combined Computational Methods

Histone deacetylases (HDACs) are crucial in gene transcription, removing acetyl groups from histones. They also influence the deacetylation of non-histone proteins, contributing to the regulation of various biological processes. Thus, HDACs play pivotal roles in various diseases, including cancer, neurodegenerative disorders, and inflammatory conditions, highlighting their potential as therapeutic targets. This paper reviews the structure and function of the four classes of human HDACs. While four HDAC inhibitors are currently available for treating hematological malignancies, numerous others are undergoing clinical trials. However, their non-selective toxicity necessitates ongoing research into safer and more efficient class-selective or isoform-selective inhibitors. Computational techniques have greatly facilitated the discovery of HDAC inhibitors that achieve the desired potency and selectivity. These techniques encompass ligand-based strategies such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure–activity relationships (3D-QSAR), and structure-based virtual screening (molecular docking). Additionally, advancements in molecular dynamics simulations, along with Poisson–Boltzmann/molecular mechanics generalized Born surface area (PB/MM-GBSA) methods, have enhanced the accuracy of predicting ligand binding affinity. In this review, we delve into the ways in which these methods have contributed to designing and identifying HDAC inhibitors.

RCSB Protein Data Bank: supporting research and education worldwide through explorations of experimentally determined and computationally predicted atomic level 3D biostructures

The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules (e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.

In silico drug design strategies for discovering novel tuberculosis therapeutics

INTRODUCTION

Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments.

AREAS COVERED

In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis.

EXPERT OPINION

These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.

Structure-based prediction of protein-protein interaction network in rice

Comprehensive protein-protein interaction (PPI) maps are critical for understanding the functional organization of the proteome, but challenging to produce experimentally. Here, we developed a computational method for predicting PPIs based on protein docking. Evaluation of performance on benchmark sets demonstrated the ability of the docking-based method to accurately identify PPIs using predicted protein structures. By employing the docking-based method, we constructed a structurally resolved PPI network consisting of 24,653 interactions between 2,131 proteins, which greatly extends the current knowledge on the rice protein-protein interactome. Moreover, we mapped the trait-associated single nucleotide polymorphisms (SNPs) to the structural interactome, and computationally identified 14 SNPs that had significant consequences on PPI network. The protein structural interactome map provided a resource to facilitate functional investigation of PPI-perturbing alleles associated with agronomically important traits in rice.

Structure-aware deep model for MHC-II peptide binding affinity prediction

The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Experiments on three datasets show that the introduction of position-coding information can further improve the performance built upon the existing model. The idea of introducing positional encoding to this field can provide important reference significance for the optimization of the deep network structure in the future.

Study on the anti-mitochondrial apoptosis mechanism of Erigeron breviscapus injection based on UPLC-Q-TOF-MS metabolomics and molecular docking in rats with cerebral ischemia-reperfusion injury

ETHNOPHARMACOLOGICAL RELEVANCE

Erigeron breviscapus is a common medicine of eight ethnic minorities, including Miao, Naxi, and Yi. As early as the Ming Dynasty (AD 1368-1644), Lanmao's Materia Medica of Southern Yunnan (AD 1436) recorded that the medicine is used for the treatment of "Zuo tan you huan." In modern pharmacological research, Erigeron breviscapus injection is the most commonly used preparation in the treatment of ischemic stroke caused by acute cerebral infarction, but its mechanism of action in the treatment of ischemic stroke is not well understood.

AIM OF THE STUDY

In this study, a metabonomics study based on ultraperformance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS) was used in investigating the effect of a traditional Chinese medicine preparation Erigeron breviscapus injection on the rat model of focal cerebral ischemia-reperfusion and the affinity of its main components with the targets of mitochondrial apoptotic pathways.

MATERIALS AND METHODS

This study used molecular docking technology to verify the effective binding ability of main effective components of Erigeron breviscapus injection to target proteins related to mitochondrial apoptosis pathway. This study developed a metabonomics method based on the ultra-performance liquid chromatography combined with quadrupole time-of-flight tandem mass spectrometry (UPLC Q-TOF MS) to evaluate the efficacy and study the mechanism of traditional Chinese medicine preparation. With pattern recognition analysis (principal component analysis and partial least squares-discriminate analysis) of urinary metabolites, a clear separation of focal cerebral ischemia-reperfusion model group and healthy control group was achieved.

RESULTS

Erigeron breviscapus injection can significantly reduce the area of cerebral infarction, improve tissue morphological lesion in rats, and can increase the number of Nissl bodies. It may be a promoting factor by inhibiting hippocampal nerve cell apoptosis and Bax protein expression and by exerting effects against ischemia reperfusion after the induction of apoptosis. Thus, it plays a role in brain protection. Moreover, it can considerably promote the recovery of neurological deficiency signs in advance. Meanwhile, Erigeron breviscapus decreased malondialdehyde content and T-NOS activity. Its curative effect from strong to weak order: low dose > high dose > medium dose. The representative components of Erigeron breviscapus have good affinity with the active sites of mitochondrial apoptosis-related proteins. Metabolomics found that the potential biomarkers regulated by breviscapine are kynurequinolinic acid, succinylornithine, and leucine proline. It is speculated that it may participate in TRP-kynurequinolinic acid and succinylornithine-urea cycle-NO metabolic pathways.

CONCLUSIONS

This paper revealed the potential biomarkers and metabolic pathways regulated by Erigeron breviscapus. It was speculated that the mechanism is related to its inhibition of mitochondrion-mediated apoptosis. Erigeron breviscapus could restore the metabolic profiles of the model animals to normal animal levels. The mechanism may be related to the potential biomarkers of quinolinic acid, succinylornithine, and leucine proline and the metabolic pathways involved. However, the exact mechanism by which Erigeron breviscapus inhibits mitochondrion-mediated apoptosis remains to be further explored.

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences

The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.

Research on the Regulatory Mechanism of Ginseng on the Tumor Microenvironment of Colorectal Cancer based on Network Pharmacology and Bioinformatics Validation

BACKGROUND

A network pharmacology study on the biological action of ginseng in the treatment of colorectal cancer (CRC) by regulating the tumor microenvironment (TME).

OBJECTIVES

To investigate the potential mechanism of action of ginseng in the treatment of CRC by regulating TME.

METHODS

This research employed network pharmacology, molecular docking techniques, and bioinformatics validation. Firstly, the active ingredients and the corresponding targets of ginseng were retrieved using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), the Traditional Chinese Medicine Integrated Database (TCMID), and the Traditional Chinese Medicine Database@Taiwan (TCM Database@Taiwan). Secondly, the targets related to CRC were retrieved using Genecards, Therapeutic Target Database (TTD), and Online Mendelian Inheritance in Man (OMIM). Tertiary, the targets related to TME were derived from screening the GeneCards and National Center for Biotechnology Information (NCBI)-Gene. Then the common targets of ginseng, CRC, and TME were obtained by Venn diagram. Afterward, the Protein-protein interaction (PPI) network was constructed in the STRING 11.5 database, intersecting targets identified by PPI analysis were introduced into Cytoscape 3.8.2 software cytoHubba plugin, and the final determination of core targets was based on degree value. The OmicShare Tools platform was used to analyze the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the core targets. Autodock and PyMOL were used for molecular docking verification and visual data analysis of docking results. Finally, we verified the core targets by Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases in bioinformatics.

RESULTS

A total of 22 active ingredients and 202 targets were identified to be closely related to the TME of CRC. PPI network mapping identified SRC, STAT3, PIK3R1, HSP90AA1, and AKT1 as possible core targets. Go enrichment analysis showed that it was mainly involved in T cell co-stimulation, lymphocyte co-stimulation, growth hormone response, protein input, and other biological processes; KEGG pathway analysis found 123 related signal pathways, including EGFR tyrosine kinase inhibitor resistance, chemokine signaling pathway, VEGF signaling pathway, ErbB signaling pathway, PD-L1 expression and PD-1 checkpoint pathway in cancer, etc. The molecular docking results showed that the main chemical components of ginseng have a stable binding activity to the core targets. The results of the GEPIA database showed that the mRNA levels of PIK3R1 were significantly lowly expressed and HSP90AA1 was significantly highly expressed in CRC tissues. Analysis of the relationship between core target mRNA levels and the pathological stage of CRC showed that the levels of SRC changed significantly with the pathological stage. The HPA database results showed that the expression levels of SRC were increased in CRC tissues, while the expression of STAT3, PIK3R1, HSP90AA1, and AKT1 were decreased in CRC tissues.

CONCLUSION

Ginseng may act on SRC, STAT3, PIK3R1, HSP90AA1, and AKT1 to regulate T cell costimulation, lymphocyte costimulation, growth hormone response, protein input as a molecular mechanism regulating TME for CRC. It reflects the multi-target and multi-pathway role of ginseng in modulating TME for CRC, which provides new ideas to further reveal its pharmacological basis, mechanism of action and new drug design and development.

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.

Clinical, genetic and structural delineation of RPL13-related spondyloepimetaphyseal dysplasia suggest extra-ribosomal functions of eL13

Spondyloepimetaphyseal dysplasia with severe short stature, RPL13-related (SEMD-RPL13), MIM#618728), is a rare autosomal dominant disorder characterized by short stature and skeletal changes such as mild spondylar and epimetaphyseal dysplasia affecting primarily the lower limbs. The genetic cause was first reported in 2019 by Le Caignec et al., and six disease-causing variants in the gene coding for a ribosomal protein, RPL13 (NM_000977.3) have been identified to date. This study presents clinical and radiographic data from 12 affected individuals aged 2-64 years from seven unrelated families, showing highly variable manifestations. The affected individuals showed a range from mild to severe short stature, retaining the same radiographic pattern of spondylar- and epi-metaphyseal dysplasia, but with varying severity of the hip and knee deformities. Two new missense variants, c.548 G>A, p.(Arg183His) and c.569 G>T, p.(Arg190Leu), and a previously known splice variant c.477+1G>A were identified, confirming mutational clustering in a highly specific RNA binding motif. Structural analysis and interpretation of the variants' impact on the protein suggests that disruption of extra-ribosomal functions of the protein through binding of mRNA may play a role in the skeletal phenotype of SEMD-RPL13. In addition, we present gonadal and somatic mosaicism for the condition.

Genome-wide identification and expression analysis of Hsp70 family genes in Cassava (Manihot esculenta Crantz)

Hsp70 proteins function as molecular chaperones, regulating various cellular processes in plants. In this study, a genome-wide analysis led to the identification of 22 Hsp70 (MeHsp70) genes in cassava. Phylogenetic relationship studies with other Malpighiales genomes (Populus trichocarpa, Ricinus communis and Salix purpurea) classified MeHsp70 proteins into eight groups (Ia, Ib, Ic, Id, Ie, If, IIa and IIb). Promoter analysis of MeHsp70 genes revealed the presence of tissue-specific, light, biotic and abiotic stress-responsive cis-regulatory elements showing their functional importance in cassava. Meta-analysis of publically available RNA-seq transcriptome datasets showed constitutive, tissue-specific, biotic and abiotic stress-specific expression patterns among MeHsp70s in cassava. Among 22 Hsp70, six MeHsp70s viz., MecHsp70-3, MecHsp70-6, MeBiP-1, MeBiP-2, MeBiP-3 and MecpHsp70-2 displayed constitutive expression, while three MecHsp70s were induced under both drought and cold stress conditions. Five MeHsp70s, MecHsp70-7, MecHsp70-11, MecHsp70-12, MecHsp70-13, and MecHsp70-14 were induced under drought stress conditions. We predicted that 19 MeHsp70 genes are under the regulation of 24 miRNAs. This comprehensive genome-wide analysis of the Hsp70 gene family in cassava provided valuable insights into their functional roles and identified various potential Hsp70 genes associated with stress tolerance and adaptation to environmental stimuli.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03760-3.

EzMechanism: an automated tool to propose catalytic mechanisms of enzyme reactions

Over the years, hundreds of enzyme reaction mechanisms have been studied using experimental and simulation methods. This rich literature on biological catalysis is now ripe for use as the foundation of new knowledge-based approaches to investigate enzyme mechanisms. Here, we present a tool able to automatically infer mechanistic paths for a given three-dimensional active site and enzyme reaction, based on a set of catalytic rules compiled from the Mechanism and Catalytic Site Atlas, a database of enzyme mechanisms. EzMechanism (pronounced as 'Easy' Mechanism) is available to everyone through a web user interface. When studying a mechanism, EzMechanism facilitates and improves the generation of hypotheses, by making sure that relevant information is considered, as derived from the literature on both related and unrelated enzymes. We validated EzMechanism on a set of 62 enzymes and have identified paths for further improvement, including the need for additional and more generic catalytic rules.

Interaction of Vanadium Complexes with Proteins: Revisiting the Reported Structures in the Protein Data Bank (PDB) since 2015

The structural determination and characterization of molecules, namely proteins and enzymes, is crucial to gaining a better understanding of their role in different chemical and biological processes. The continuous technical developments in the experimental and computational resources of X-ray diffraction (XRD) and, more recently, cryogenic Electron Microscopy (cryo-EM) led to an enormous growth in the number of structures deposited in the Protein Data Bank (PDB). Bioinorganic chemistry arose as a relevant discipline in biology and therapeutics, with a massive number of studies reporting the effects of metal complexes on biological systems, with vanadium complexes being one of the relevant systems addressed. In this review, we focus on the interactions of vanadium compounds (VCs) with proteins. Several types of binding are established between VCs and proteins/enzymes. Considering that the V-species that bind may differ from those initially added, the mentioned structural techniques are pivotal to clarifying the nature and variety of interactions of VCs with proteins and to proposing the mechanisms involved either in enzymatic inhibition or catalysis. As such, we provide an account of the available structural information of VCs bound to proteins obtained by both XRD and/or cryo-EM, mainly exploring the more recent structures, particularly those containing organic-based vanadium complexes.

Analysis of the Structural Dynamics of Proteins in the Ligand-Unbound and -Bound States by Diffracted X-ray Tracking

Although many protein structures have been determined at atomic resolution, the majority of them are static and represent only the most stable or averaged structures in solution. When a protein binds to its ligand, it usually undergoes fluctuation and changes its conformation. One attractive method for obtaining an accurate view of proteins in solution, which is required for applications such as the rational design of proteins and structure-based drug design, is diffracted X-ray tracking (DXT). DXT can detect the protein structural dynamics on a timeline via gold nanocrystals attached to the protein. Here, the structure dynamics of single-chain Fv antibodies, helix bundle-forming de novo designed proteins, and DNA-binding proteins in both ligand-unbound and ligand-bound states were analyzed using the DXT method. The resultant mean square angular displacements (MSD) curves in both the tilting and twisting directions clearly demonstrated that structural fluctuations were suppressed upon ligand binding, and the binding energies determined using the angular diffusion coefficients from the MSD agreed well with the binding thermodynamics determined using isothermal titration calorimetry. In addition, the size of gold nanocrystals is discussed, which is one of the technical concerns of DXT.

The impact of AlphaFold Protein Structure Database on the fields of life sciences

Arguably, 2020 was the year of high-accuracy protein structure predictions, with AlphaFold 2.0 achieving previously unseen accuracy in the Critical Assessment of Protein Structure Prediction (CASP). In 2021, DeepMind and EMBL-EBI developed the AlphaFold Protein Structure Database to make an unprecedented number of reliable protein structure predictions easily accessible to the broad scientific community. We provide a brief overview and describe the latest developments in the AlphaFold database. We highlight how the fields of data services, bioinformatics, structural biology, and drug discovery are directly affected by the influx of protein structure data. We also show examples of cutting-edge research that took advantage of the AlphaFold database. It is apparent that connections between various fields through protein structures are now possible, but the amount of data poses new challenges. Finally, we give an outlook regarding the future direction of the database, both in terms of data sets and new functionalities.

Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.

Bioinformatics-based construction of prognosis-related methylation prediction model for pancreatic cancer patients and its application value

Objective: Pancreatic adenocarcinoma (PAAD) is a highly malignant gastrointestinal tumor with almost similar morbidity and mortality. In this study, based on bioinformatics, we investigated the role of gene methylation in PAAD, evaluated relevant factors affecting patient prognosis, screened potential anti-cancer small molecule drugs, and constructed a prediction model to assess the prognosis of PAAD.

Methods: Clinical and genomic data of PAAD were collected from the Tumor Genome Atlas Project (TCGA) database and gene expression profiles were obtained from the GTEX database. Analysis of differentially methylated genes (DMGs) and significantly differentially expressed genes (DEGs) was performed on tumorous samples with KRAS wild-type and normal samples using the “limma” package and combined analysis. We selected factors significantly associated with survival from the significantly differentially methylated and expressed genes (DMEGs), and their fitting into a relatively streamlined prognostic model was validated separately from the internal training and test sets and the external ICGC database to show the robustness of the model.

Results: In the TCGA database, 2,630 DMGs were identified, with the largest gap between DMGs in the gene body and TSS200 region. 318 DEGs were screened, and the enrichment analysis of DMGs and DEGs was taken to intersect DMEGs, showing that the DMEGs were mainly related to Olfactory transduction, natural killer cell mediated cytotoxicity pathway, and Cytokine -cytokine receptor interaction. DMEGs were able to distinguish well between PAAD and paraneoplastic tissues. Through techniques such as drug database and molecular docking, we screened a total of 10 potential oncogenic small molecule compounds, among which felbamate was the most likely target drug for PAAD. We constructed a risk model through combining three DMEGs (S100P, LY6D, and WFDC13) with clinical factors significantly associated with prognosis, and confirmed the model robustness using external and internal validation.

Conclusion: The classification model based on DMEGs was able to accurately separate normal samples from tumor samples and find potential anti-PAAD drugs by performing gene-drug interactions on DrugBank.

Role of N-Terminal Extensional Long α-Helix in the Arylesterase from Lacticaseibacillus rhamnosus GG on Catalysis and Stability

In the α/β hydrolases superfamily, the extra module modulated enzymatic activity, substrate specificity, and stability. The functional role of N-terminal extensional long α-helix (Ala2-Glu29, designated as NEL-helix) acting as the extra module in the arylesterase LggEst from Lacticaseibacillus rhamnosus GG had been systemically investigated by deletion mutagenesis, biochemical characterization, and biophysical methods. The deletion of the NEL-helix did not change the overall structure of this arylesterase. The deletion of the NEL-helix led to the shifting of optimal pH into the acidity and the loss of thermophilic activity. The deletion of the NEL-helix produced a 10.6-fold drop in catalytic activity towards the best substrate pNPC10. NEL-Helix was crucial for the thermostability, chemical resistance, and organic solvents tolerance. The deletion of the NEL-helix did not change the overall rigidity of enzyme structure and only reduced the local rigidity of the active site. Sodium deoxycholate might partially replenish the loss of activity caused by the deletion of the NEL-helix. Our research further enriched the functional role of the extra module on catalysis and stability in the α/β hydrolase fold superfamily.

Investigation of the interaction behavior between quercetin and pepsin by spectroscopy and MD simulation methods

The ability of a therapeutic compound to bind to proteins is critical for characterizing its therapeutic impacts. We have selected quercetin (Qu), a most common flavonoid found in plants and vegetables among therapeutic molecules that are known to have anti-inflammatory, antioxidant, anti-genotoxic, and anti-cancer effects. The current study aimed to see how quercetin interacts with pepsin in an aqueous environment under physiological conditions. Absorbance and emission spectroscopy, circular dichroism (CD), and kinetic methods, as well as molecular dynamic (MD) simulation and docking, were applied to study the effects of Qu on the structure, dynamics, and kinetics of pepsin. Stern-Volmer (K_sv) constants were computed for the pepsin-quercetin complex at three temperatures, showing that Qu reduces enzyme emission spectra using a static quenching. With Qu binding, the V_max and the k_cat/K_m values decreased. UV-vis absorption spectra, fluorescence emission spectroscopy, and CD result indicated that Qu binding to pepsin leads to microenvironmental changes around the enzyme, which can alter the enzyme's secondary structure. Therefore, quercetin caused alterations in the function and structure of pepsin. Thermodynamic parameters, MD binding, and docking simulation analysis showed that non-covalent reactions, including the hydrophobic forces, played a key role in the interaction of Qu with pepsin. The findings conclude of spectroscopic experiments were supported by molecular dynamics simulations and molecular docking results.

Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile

This paper describes a method Pprint2, which is an improved version of Pprint developed for predicting RNA-interacting residues in a protein. Training and independent/validation datasets used in this study comprises of 545 and 161 non-redundant RNA-binding proteins, respectively. All models were trained on training dataset and evaluated on the validation dataset. The preliminary analysis reveals that positively charged amino acids such as H, R and K, are more prominent in the RNA-interacting residues. Initially, machine learning based models have been developed using binary profile and obtain maximum area under curve (AUC) 0.68 on validation dataset. The performance of this model improved significantly from AUC 0.68 to 0.76, when evolutionary profile is used instead of binary profile. The performance of our evolutionary profile-based model improved further from AUC 0.76 to 0.82, when convolutional neural network has been used for developing model. Our final model based on convolutional neural network using evolutionary information achieved AUC 0.82 with Matthews correlation coefficient of 0.49 on the validation dataset. Our best model outperforms existing methods when evaluated on the independent/validation dataset. A user-friendly standalone software and web-based server named 'Pprint2' has been developed for predicting RNA-interacting residues (https://webs.iiitd.edu.in/raghava/pprint2 and https://github.com/raghavagps/pprint2).

Annotation of biologically relevant ligands in UniProtKB using ChEBI

MOTIVATION

To provide high quality, computationally tractable annotation of binding sites for biologically relevant (cognate) ligands in UniProtKB using the chemical ontology ChEBI (Chemical Entities of Biological Interest), to better support efforts to study and predict functionally relevant interactions between protein sequences and structures and small molecule ligands.

RESULTS

We structured the data model for cognate ligand binding site annotations in UniProtKB and performed a complete reannotation of all cognate ligand binding sites using stable unique identifiers from ChEBI, which we now use as the reference vocabulary for all such annotations. We developed improved search and query facilities for cognate ligands in the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that ChEBI provides.

AVAILABILITY AND IMPLEMENTATION

Binding site annotations for cognate ligands described using ChEBI are available for UniProtKB protein sequence records in several formats (text, XML and RDF) and are freely available to query and download through the UniProt website (www.uniprot.org), REST API (www.uniprot.org/help/api), SPARQL endpoint (sparql.uniprot.org/) and FTP site (https://ftp.uniprot.org/pub/databases/uniprot/).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Fragment-based virtual screening identifies a first-in-class preclinical drug candidate for Huntington's disease

Currently, there are no therapies available to modify the disease progression of Huntington's disease (HD). Recent clinical trial failures of antisense oligonucleotide candidates in HD have demonstrated the need for new therapeutic approaches. Here, we developed a novel in-silico fragment scanning approach across the surface of mutant huntingtin (mHTT) polyQ and predicted four hit compounds. Two rounds of compound analoging using a strategy of testing structurally similar compounds in an affinity assay rapidly identified GLYN122. In vitro, GLYN122 directly binds and reduces mHTT and induces autophagy in neurons. In vivo, our results confirm that GLYN122 can reduce mHTT in the cortex and striatum of the R/2 mouse model of Huntington's disease and subsequently improve motor symptoms. Thus, the in-vivo pharmacology profile of GLYN122 is a potential new preclinical candidate for the treatment of HD.

Artificial intelligence methods enhance the discovery of RNA interactions

Understanding how RNAs interact with proteins, RNAs, or other molecules remains a challenge of main interest in biology, given the importance of these complexes in both normal and pathological cellular processes. Since experimental datasets are starting to be available for hundreds of functional interactions between RNAs and other biomolecules, several machine learning and deep learning algorithms have been proposed for predicting RNA-RNA or RNA-protein interactions. However, most of these approaches were evaluated on a single dataset, making performance comparisons difficult. With this review, we aim to summarize recent computational methods, developed in this broad research area, highlighting feature encoding and machine learning strategies adopted. Given the magnitude of the effect that dataset size and quality have on performance, we explored the characteristics of these datasets. Additionally, we discuss multiple approaches to generate datasets of negative examples for training. Finally, we describe the best-performing methods to predict interactions between proteins and specific classes of RNA molecules, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), and methods to predict RNA-RNA or RNA-RBP interactions independently of the RNA type.

In silico identification of the rare-coding pathogenic mutations and structural modeling of human NNAT gene associated with anorexia nervosa

PURPOSE

Increased susceptibility towards anorexia nervosa (AN) was reported with reduced levels of neuronatin (NNAT) gene. We sought to investigate the most pathogenic rare-coding missense mutations, non-synonymous single-nucleotide polymorphisms (nsSNPs) of NNAT and their potential damaging impact on protein function through transcript level sequence and structure based in silico approaches.

METHODS

Gene sequence, single nucleotide polymorphisms (SNPs) of NNAT was retrieved from public databases and the putative post-translational modification (PTM) sites were analyzed. Distinctive in silico algorithms were recruited for transcript level SNPs analyses and to characterized high-risk rare-coding nsSNPs along with their impact on protein stability function. Ab initio 3D-modeling of wild-type, alternate model prediction for most deleterious nsSNP, validation and recognition of druggable binding pockets were also performed. AN 3D therapeutic compounds that followed rule of drug-likeness were docked with most pathogenic variant of NNAT to estimate the drugs' binding free energies.

RESULTS

Conclusively, 10 transcript (201-205)-based nsSNPs from 3 rare-coding missense variants, i.e., rs539681368, rs542858994, rs560845323 out of 840 exonic SNPs were identified. Transcript-based functional impact analyses predicted rs539681368 (C30Y) from NNAT-204 as the high-risk rare-coding pathogenic nsSNP, deviating protein functions. The 3D-modeling analysis of AN drugs' binding energies indicated lowest binding free energy (ΔG) and significant inhibition constant (K_i) with mutant models C30Y.

CONCLUSIONS

Mutant model (C30Y) exhibiting significant drug binding affinity and the commonest interaction observed at the acetylation site K59. Thus, based on these findings, we concluded that the identified nsSNP may serve as potential targets for various studies, diagnosis and therapeutic interventions.

LEVEL OF EVIDENCE

No level of evidence-open access bioinformatics research.

A comprehensive analysis of the mutational landscape of the newly emerging Omicron (B.1.1.529) variant and comparison of mutations with VOCs and VOIs

The Omicron variant is spreading rapidly throughout several countries. Thus, we comprehensively analyzed Omicron's mutational landscape and compared mutations with VOC/VOI. We analyzed SNVs throughout the genome, and AA variants (NSP and SP) in VOC/VOI, including Omicron. We generated heat maps to illustrate the AA variants with high mutation prevalence (> 75% frequency) of Omicron, which demonstrated eight mutations with > 90% prevalence in ORF1a and 29 mutations with > 75% prevalence in S-glycoprotein. A scatter plot for Omicron and VOC/VOI's cluster evaluation was computed. We performed a risk analysis of the antibody-binding risk among four mutations (L452, F490, P681, D614) and observed three mutations (L452R, F490S, D614G) destabilized antibody interactions. Our comparative study evaluated the properties of 28 emerging mutations of the S-glycoprotein of Omicron, and the ΔΔG values. Our results showed K417N with minimum and Q954H with maximum ΔΔG value. Furthermore, six important RBD mutations (G339D, S371L, N440K, G446S, T478K, Q498R) were chosen for comprehensive analysis for stabilizing/destabilizing properties and molecular flexibility. The G339D, S371L, N440K, and T478K were noted as stable mutations with 0.019 kcal/mol, 0.127 kcal/mol, 0.064 kcal/mol, and 1.009 kcal/mol. While, G446S and Q498R mutations showed destabilizing results. Simultaneously, among six RBD mutations, G339D, G446S, and Q498R mutations increased the molecular flexibility of S-glycoprotein. This study depicts the comparative mutational pattern of Omicron and other VOC/VOI, which will help researchers to design and deploy novel vaccines and therapeutic antibodies to fight against VOC/VOI, including Omicron.

The New Microtubule-Targeting Agent SIX2G Induces Immunogenic Cell Death in Multiple Myeloma

Microtubule-targeting agents (MTAs) are effective drugs for cancer treatment. A novel diaryl [1,2]oxazole class of compounds binding the colchicine site was synthesized as cis-restricted-combretastatin-A-4-analogue and then chemically modified to have improved solubility and a wider therapeutic index as compared to vinca alkaloids and taxanes. On these bases, a new class of tricyclic compounds, containing the [1,2]oxazole ring and an isoindole moiety, has been synthetized, among which SIX2G emerged as improved MTA. Several findings highlighted the ability of some chemotherapeutics to induce immunogenic cell death (ICD), which is defined by the cell surface translocation of Calreticulin (CALR) via dissociation of the PP1/GADD34 complex. In this regard, we computationally predicted the ability of SIX2G to induce CALR exposure by interacting with the PP1 RVxF domain. We then assessed both the potential cytotoxic and immunogenic activity of SIX2G on in vitro models of multiple myeloma (MM), which is an incurable hematological malignancy characterized by an immunosuppressive milieu. We found that the treatment with SIX2G inhibited cell viability by inducing G2/M phase cell cycle arrest and apoptosis. Moreover, we observed the increase of hallmarks of ICD such as CALR exposure, ATP release and phospho-eIF2α protein level. Through co-culture experiments with immune cells, we demonstrated the increase of (i) CD86 maturation marker on dendritic cells, (ii) CD69 activation marker on cytotoxic T cells, and (iii) phagocytosis of tumor cells following treatment with SIX2G, confirming the onset of an immunogenic cascade. In conclusion, our findings provide a framework for further development of SIX2G as a new potential anti-MM agent.

Qingfei Jiedu decoction inhibits PD-L1 expression in lung adenocarcinoma based on network pharmacology analysis, molecular docking and experimental verification

Objective: We aim at investigating the molecular mechanisms through which the Qingfei Jiedu decoction (QFJDD) regulates PD-L1 expression in lung adenocarcinoma (LUAD).

Methods: Bioactive compounds and targets of QFJDD were screened from TCMSP, BATMAN-TCM, and literature. Then, GeneCard, OMIM, PharmGKB, Therapeutic Target, and DrugBank databases were used to identify LUAD-related genes. The protein-protein interaction (PPI) network was constructed using overlapping targets of bioactive compounds in LUAD with the Cytoscape software and STRING database. The potential functions and pathways in which the hub genes were enriched by GO, KEGG, and DAVID pathway analyses. Molecular docking of bioactive compounds and key genes was executed via AutoDock Vina. Qualitative and quantitative analyses of QFJDD were performed using UPLC-Q-TOF-MS and UPLC. Expressions of key genes were determined by qRT-PCR, immunoreactivity score (IRS) of PD-L1 was assessed by immunohistochemistry (IHC), while the CD8⁺PD-1⁺T% derived from spleen tissues of Lewis lung cancer (LLC) bearing-mice was calculated using flow cytometry (FCM).

Results: A total of 53 bioactive compounds and 288 targets of QFJDD as well as 8151 LUAD associated genes were obtained. Further, six bioactive compounds, including quercetin, luteolin, kaempferol, wogonin, baicalein, and acacetin, and 22 hub genes were identified. The GO analysis showed that the hub genes were mainly enriched in DNA or RNA transcription. KEGG and DAVID pathway analyses revealed that 20 hub genes were primarily enriched in virus, cancer, immune, endocrine, and cardiovascular pathways. The EGFR, JUN, RELA, HIF1A, NFKBIA, AKT1, MAPK1, and MAPK14 hub genes were identified as key genes in PD-L1 expression and PD-1 checkpoint pathway. Moreover, ideal affinity and regions were identified between core compounds and key genes. Notably, QFJDD downregulated EGFR, JUN, RELA, HIF1A, NFKBIA, and CD274 expressions (p < 0.05), while it upregulated AKT1 and MAPK1 (p < 0.05) levels in A549 cells. The PD-L1 IRS of LLC tissue in the QFJDD high dose (H_d) group was lower than model group (p < 0.01). CD8⁺PD-1⁺T% was higher in the QFJDD H_d group than in normal and model groups (p < 0.05).

Conclusion: QFJDD downregulates PD-L1 expression and increases CD8⁺PD-1⁺T% via regulating HIF-1, EGFR, JUN and NFκB signaling pathways. Therefore, QFJDD is a potential treatment option for LUAD.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems

Environmental health sciences (EHS) span many diverse disciplines. Within the EHS community, the National Institute of Environmental Health Sciences Superfund Research Program (SRP) funds multidisciplinary research aimed to address pressing and complex issues on how people are exposed to hazardous substances and their related health consequences with the goal of identifying strategies to reduce exposures and protect human health. While disentangling the interrelationships that contribute to environmental exposures and their effects on human health over the course of life remains difficult, advances in data science and data sharing offer a path forward to explore data across disciplines to reveal new insights. Multidisciplinary SRP-funded teams are well-positioned to examine how to best integrate EHS data across diverse research domains to address multifaceted environmental health problems. As such, SRP supported collaborative research projects designed to foster and enhance the interoperability and reuse of diverse and complex data streams. This perspective synthesizes those experiences as a landscape view of the challenges identified while working to increase the FAIR-ness (Findable, Accessible, Interoperable, and Reusable) of EHS data and opportunities to address them.

Insight Into Biological Targets and Molecular Mechanisms in the Treatment of Arsenic-Related Dermatitis With Vitamin A via Integrated in silico Approach

Exposure to arsenic (As), an inorganic poison, may lead to skin lesions, including dermatitis. Vitamin A (VA), a fat-soluble vitamin essential for mucous membrane integrity, plays a key role in skin protection. Although the beneficial actions of VA are known, the anti-As-related dermatitis effects of VA action remain unclear. Hence, in this study, we aimed to interpret and identify the core target genes and therapeutic mechanisms of VA action in the treatment of As-related dermatitis through integrated in silico approaches of network pharmacology and molecular docking. We integrated the key VA-biological target-signaling pathway-As-related dermatitis networks for identifying core drug targets and interaction pathways associated with VA action. The network pharmacology data indicated that VA may possess potential activity for treating As-related dermatitis through the effective regulation of core target genes. An enrichment analysis in biological processes further revealed multiple immunoregulation-associated functions, including interferon-gamma production and negative regulation of T-cell activation and production of molecular mediator of immune response. An enrichment analysis in molecular pathways mainly uncovered multiple biological signaling, including natural killer cell mediated cytotoxicity, autophagy, apoptosis, necroptosis, platelet activation involved in cell fate, and immunity regulations. Molecular docking study was used to identify docked well core target proteins with VA, including Jun, tumor protein p53 (TP53), mitogen-activated protein kinase-3 (MAPK3), MAPK1, and MAPK14. In conclusion, the potential use of VA may suppress the inflammatory stress and enhance the immunity against As-related dermatitis. In the future, VA might be useful in the treatment of dermatitis associated with As through multi-targets and multi-pathways in clinical practice.

Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0

Cryo-electron microscopy (cryoEM) has become a well established technique with the potential to produce structures of large and dynamic supramolecular complexes that are not amenable to traditional approaches for studying structure and dynamics. The size and low resolution of such molecular systems often make structural modelling and molecular dynamics simulations challenging and computationally expensive. This, together with the growing wealth of structural data arising from cryoEM and other structural biology methods, has driven a trend in the computational biophysics community towards the development of new pipelines for analysing global dynamics using coarse-grained models and methods. At the centre of this trend has been a return to elastic network models, normal mode analysis (NMA) and ensemble analyses such as principal component analysis, and the growth of hybrid simulation methodologies that make use of them. Here, this field is reviewed with a focus on ProDy, the Python application programming interface for protein dynamics, which has been developed over the last decade. Two key developments in this area are highlighted: (i) ensemble NMA towards extracting and comparing the signature dynamics of homologous structures, aided by the recent SignDy pipeline, and (ii) pseudoatom fitting for more efficient global dynamics analyses of large and low-resolution supramolecular assemblies from cryoEM, revisited in the CryoDy pipeline. It is believed that such a renewal and extension of old models and methods in new pipelines will be critical for driving the field forward into the next cryoEM revolution.

Omicron variant (B.1.1.529) of SARS-CoV-2: understanding mutations in the genome, S-glycoprotein, and antibody-binding regions

The Omicron variant has been detected in nearly 150 countries. We analyzed the mutational landscape of Omicron throughout the genome, focusing the S-glycoprotein. We also evaluated mutations in the antibody-binding regions and observed some important mutations overlapping those of previous variants including N501Y, D614G, H655Y, N679K, and P681H. Various new receptor-binding domain mutations were detected, including Q493K, G496S, Q498R, S477N, G466S, N440K, and Y505H. New mutations were found in the NTD (Δ143-145, A67V, T95I, L212I, and Δ211) including one new mutation in fusion peptide (D796Y). There are several mutations in the antibody-binding region including K417N, E484A, Q493K, Q498R, N501Y, and Y505H and several near the antibody-binding region (S477N, T478K, G496S, G446S, and N440K). The impact of mutations in regions important for the affinity between spike proteins and neutralizing antibodies was evaluated. Furthermore, we examined the effect of significant antibody-binding mutations (K417N, T478K, E484A, and N501Y) on antibody affinity, stability to ACE2 interaction, and possibility of amino acid substitution. All the four mutations destabilize the antibody-binding affinity. This study reveals future directions for developing neutralizing antibodies against the Omicron variant.