RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning

Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review

Recent breakthroughs in deep learning have revolutionized protein sequence and structure prediction. These advancements are built on decades of protein design efforts, and are overcoming traditional time and cost limitations. Diffusion models, at the forefront of these innovations, significantly enhance design efficiency by automating knowledge acquisition. In the field of de novo protein design, the goal is to create entirely novel proteins with predetermined structures. Given the arbitrary positions of proteins in 3-D space, graph representations and their properties are widely used in protein generation studies. A critical requirement in protein modelling is maintaining spatial relationships under transformations (rotations, translations, and reflections). This property, known as equivariance, ensures that predicted protein characteristics adapt seamlessly to changes in orientation or position. Equivariant graph neural networks offer a solution to this challenge. By incorporating equivariant graph neural networks to learn the score of the probability density function in diffusion models, one can generate proteins with robust 3-D structural representations. This review examines the latest deep learning advancements, specifically focusing on frameworks that combine diffusion models with equivariant graph neural networks for protein generation.

Genetic variant classification by predicted protein structure: A case study on IRF6

Next-generation genome sequencing has revolutionized genetic testing, identifying numerous rare disease-associated gene variants. However, to impute pathogenicity, computational approaches remain inadequate and functional testing of gene variant is required to provide the highest level of evidence. The emergence of AlphaFold2 has transformed the field of protein structure determination, and here we outline a strategy that leverages predicted protein structure to enhance genetic variant classification. We used the gene IRF6 as a case study due to its clinical relevance, its critical role in cleft lip/palate malformation, and the availability of experimental data on the pathogenicity of IRF6 gene variants through phenotype rescue experiments in irf6-/- zebrafish. We compared results from over 30 pathogenicity prediction tools on 37 IRF6 missense variants. IRF6 lacks an experimentally derived structure, so we used predicted structures to explore associations between mutational clustering and pathogenicity. We found that among these variants, 19 of 37 were unanimously predicted as deleterious by computational tools. Comparing in silico predictions with experimental findings, 12 variants predicted as pathogenic were experimentally determined as benign. Even with the recently published AlphaMissense model, 15/18 (83%) of the predicted pathogenic variants were experimentally determined as benign. In comparison, mapping variants to the protein revealed deleterious mutation clusters around the protein binding domain, whereas N-terminal variants tend to be benign, suggesting the importance of structural information in determining pathogenicity of mutations in this gene. In conclusion, incorporating gene-specific structural features of known pathogenic/benign mutations may provide meaningful insights into pathogenicity predictions in a gene-specific manner and facilitate the interpretation of variant pathogenicity.

Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses

Intrinsic disorder predictors were evaluated in several studies including the two large CAID experiments. However, these studies are biased towards eukaryotic proteins and focus primarily on the residue-level predictions. We provide first-of-its-kind assessment that comprehensively covers the taxonomy and evaluates predictions at the residue and disordered region levels. We curate a benchmark dataset that uniformly covers eukaryotic, archaeal, bacterial, and viral proteins. We find that predictive performance differs substantially across taxonomy, where viruses are predicted most accurately, followed by protists and higher eukaryotes, while bacterial and archaeal proteins suffer lower levels of accuracy. These trends are consistent across predictors. We also find that current tools, except for flDPnn, struggle with reproducing native distributions of the numbers and sizes of the disordered regions. Moreover, analysis of two variants of disorder predictions derived from the AlphaFold2 predicted structures reveals that they produce accurate residue-level propensities for archaea, bacteria and protists. However, they underperform for higher eukaryotes and generally struggle to accurately identify disordered regions. Our results motivate development of new predictors that target bacteria and archaea and which produce accurate results at both residue and region levels. We also stress the need to include the region-level assessments in future assessments.

The complete catalog of antimicrobial resistance secondary active transporters in Clostridioides difficile: evolution and drug resistance perspective

Secondary active transporters shuttle substrates across eukaryotic and prokaryotic membranes, utilizing different electrochemical gradients. They are recognized as one of the antimicrobial efflux pumps among pathogens. While primary active transporters within the genome of C. difficile 630 have been completely cataloged, the systematical study of secondary active transporters remains incomplete. Here, we not only identify secondary active transporters but also disclose their evolution and role in drug resistance in C. difficile 630. Our analysis reveals that C. difficile 630 carries 147 secondary active transporters belonging to 27 (super)families. Notably, 50 (34%) of them potentially contribute to antimicrobial resistance (AMR). AMR-secondary active transporters are structurally classified into five (super)families: the p-aminobenzoyl-glutamate transporter (AbgT), drug/metabolite transporter (DMT) superfamily, major facilitator (MFS) superfamily, multidrug and toxic compound extrusion (MATE) family, and resistance-nodulation-division (RND) family. Surprisingly, complete RND genes found in C. difficile 630 are likely an evolutionary leftover from the common ancestor with the diderm. Through protein structure comparisons, we have potentially identified six novel AMR-secondary active transporters from DMT, MATE, and MFS (super)families. Pangenome analysis revealed that half of the AMR-secondary transporters are accessory genes, which indicates an important role in adaptive AMR function rather than innate physiological homeostasis. Gene expression profile firmly supports their ability to respond to a wide spectrum of antibiotics. Our findings highlight the evolution of AMR-secondary active transporters and their integral role in antibiotic responses. This marks AMR-secondary active transporters as interesting therapeutic targets to synergize with other antibiotic activity.

Enhancing insight into ferroptosis mechanisms in sepsis: A genomic and pharmacological approach integrating single-cell sequencing and Mendelian randomization

This research investigated the intricate relationship between ferroptosis and sepsis by utilizing advanced genomic and pharmacological methodologies. Specifically, we obtained expression quantitative trait loci (eQTLs) for 435 genes associated with ferroptosis from the eQTLGen Consortium and detected notable cis-eQTLs for 281 of these genes. Next, we conducted a detailed analysis to assess the impact of these eQTLs on susceptibility to sepsis using Mendelian randomization (MR) with data from a cohort of 10,154 sepsis patients and 452,764 controls sourced from the UK Biobank. MR analysis revealed 16 ferroptosis-related genes that exhibited significant associations with sepsis outcomes. To bolster the robustness of these findings, sensitivity analyses were performed to assess pleiotropy and heterogeneity, thus confirming the reliability of the causal inferences. Furthermore, single-cell RNA sequencing data from sepsis patients offered a detailed examination of gene expression profiles, demonstrating varying levels of ferroptosis marker expression across different cell types. Pathway enrichment analysis utilizing gene set enrichment analysis (GSEA) further revealed the key biological pathways involved in the progression of sepsis. Additionally, the use of computational molecular docking facilitated the prediction of interactions between identified genes and potential therapeutic compounds, highlighting novel drug targets. In conclusion, our integrated approach combining genomics and pharmacology offers valuable insights into the involvement of ferroptosis in sepsis, laying the groundwork for potential therapeutic strategies targeting this cell death pathway to enhance sepsis management.

Antipruritic effect of ursolic acid through MRGPRX2/MrgprB2-dependent inhibition of mast cell degranulation and reduced TSLP production

Ursolic acid (UA), a pentacyclic triterpene, exhibits diverse pharmacological effects, including potential treatment for allergic diseases. It downregulates thymic stromal lymphopoietin (TSLP) and disrupts mast cell signaling pathways. However, the exact molecular mechanism by which UA interferes with mast cell action remains unclear. Therefore, the current study aimed to uncover molecular entities underlying the effect of UA on mast cells and its potential antipruritic effect, specifically investigating its modulation of key molecules such as TRPV4, PAR2, and MRGPRX2, which are involved in TSLP regulation and sensation. Calcium imaging experiments revealed that UA pretreatment significantly suppressed MRGPRX2 activation (and its mouse orthologue MrgprB2), a G protein-coupled receptor predominantly expressed in mast cells. Molecular docking predictions suggested potential interactions between UA and MRGPRX2/MrgprB2. UA pretreatment also reduced mast cell degranulation through MRGPRX2 and MrgprB2-dependent mechanisms. In a dry skin mouse model, UA administration decreased tryptase and TSLP production in the skin, and diminished TSLP response in the sensory neurons. While PAR2 and TRPV4 activation enhances TSLP production, UA did not inhibit their activity. Notably, UA attenuated compound 48/80-induced scratching behaviors in mice and suppressed spontaneous scratching in a dry skin model. The present study confirms the effective inhibition of UA on MRGPRX2/MrgprB2, leading to reduced mast cell degranulation and suppressed scratching behaviors. These findings highlight the potential of UA as an antipruritic agent for managing various allergy- or itch-related conditions.

Artificial Intelligence Learns Protein Prediction

From AlphaGO over StableDiffusion to ChatGPT, the recent decade of exponential advances in artificial intelligence (AI) has been altering life. In parallel, advances in computational biology are beginning to decode the language of life: AlphaFold2 leaped forward in protein structure prediction, and protein language models (pLMs) replaced expertise and evolutionary information from multiple sequence alignments with information learned from reoccurring patterns in databases of billions of proteins without experimental annotations other than the amino acid sequences. None of those tools could have been developed 10 years ago; all will increase the wealth of experimental data and speed up the cycle from idea to proof. AI is affecting molecular and medical biology at giant steps, and the most important might be the leap toward more powerful protein design.

Exploring protein functions from structural flexibility using CABS-flex modeling

Understanding protein function often necessitates characterizing the flexibility of protein structures. However, simulating protein flexibility poses significant challenges due to the complex dynamics of protein systems, requiring extensive computational resources and accurate modeling techniques. In response to these challenges, the CABS-flex method has been developed as an efficient modeling tool that combines coarse-grained simulations with all-atom detail. Available both as a web server and a standalone package, CABS-flex is dedicated to a wide range of users. The web server version offers an accessible interface for straightforward tasks, while the standalone command-line program is designed for advanced users, providing additional features, analytical tools, and support for handling large systems. This paper examines the application of CABS-flex across various structure-function studies, facilitating investigations into the interplay among protein structure, dynamics, and function in diverse research fields. We present an overview of the current status of the CABS-flex methodology, highlighting its recent advancements, practical applications, and forthcoming challenges.

B. glomerulata promotes neuroprotection against ischemic stroke by inhibiting apoptosis through the activation of PI3K/AKT/mTOR pathway

BACKGROUND

Brassaiopsis glomerulata (Blum) Regel (B.glomerulata) is recognized as a traditional Chinese medicine (TCM) primarily used for promoting blood circulation and removing stasis. It is frequently utilized in the treatment of injuries resulting from falls and bumps.

PURPOSE

Despite its effective use in clinical treatment for ischemic stroke (IS), there are currently no reports on its composition and mechanism of action, which affects its promotion. The study investigated the chemical components and molecular mechanisms of B.glomerulata, with the following components: UPLC-Q-TOF-MS, network pharmacology Analysis and experimental verification in vivo and vitro.

METHODS

The effect of B.glomerulata on interfering with ischemic stroke was assessed on MCAO/R rats and ORD cell model. Then the compositional analysis was conducted using UPLC-Q-TOF-MS. Furthermore, network pharmacology and molecular docking techniques were explored to identify potential targets and pathways. The predicted mechanisms of action were ultimately confirmed by immunohistochemistry and protein blotting.

RESULTS

B. glomerulata exhibited neuroprotective effects in MCAO/R rats by reductions in hippocampal and cortical neuronal damage, brain infarction, and cerebral edema. Both in vivo and in vitro experiments demonstrated that it decreased ROS and MDA levels, increased SOD and GSH levels, thereby inhibiting oxidative stress. Moreover, the improvements in neuronal morphology and the modulation of Nissl bodies suggested a potential mechanism underlying its neuroprotective action. Additionally, B.glomerulata exhibited concentration-dependent reductions in Bax and Caspase-3 expressions, along with increases in GFAP, Bcl2/Bax ratio, p-PI3K, p-AKT, and p-mTOR levels.

CONCLUSION

B.glomerulata exhibited neuroprotective effects against cerebral ischemia-reperfusion injury both in vivo and in vitro. It prevented oxidative stress damage and inhibited apoptosis of ischemic stroke through the PI3K/AKT/mTOR pathway.

Mendelian randomization reveals potential causal relationships between cellular senescence-related genes and multiple cancer risks

Cellular senescence is widely acknowledged as having strong associations with cancer. However, the intricate relationships between cellular senescence-related (CSR) genes and cancer risk remain poorly explored, with insights on causality remaining elusive. In this study, Mendelian Randomization (MR) analyses were used to draw causal inferences from 866 CSR genes as exposures and summary statistics for 18 common cancers as outcomes. We focused on genetic variants affecting gene expression, DNA methylation, and protein expression quantitative trait loci (cis-eQTL, cis-mQTL, and cis-pQTL, respectively), which were strongly linked to CSR genes alterations. Variants were selected as instrumental variables (IVs) and analyzed for causality with cancer using both summary-data-based MR (SMR) and two-sample MR (TSMR) approaches. Bayesian colocalization was used to unravel potential regulatory mechanisms underpinning risk variants in cancer, and further validate the robustness of MR results. We identified five CSR genes (CNOT6, DNMT3B, MAP2K1, TBPL1, and SREBF1), 18 DNA methylation genes, and LAYN protein expression which were all causally associated with different cancer types. Beyond causality, a comprehensive analysis of gene function, pathways, and druggability values was also conducted. These findings provide a robust foundation for unravelling CSR genes molecular mechanisms and promoting clinical drug development for cancer.

Using augmented reality in molecular case studies to enhance biomolecular structure-function explorations in undergraduate classrooms

Molecular case studies (MCSs) are open educational resources that use a storytelling approach to engage students in biomolecular structure-function explorations, at the interface of biology and chemistry. Although MCSs are developed for a particular target audience with specific learning goals, they are suitable for implementation in multiple disciplinary course contexts. Detailed teaching notes included in the case study help instructors plan and prepare for their implementation in diverse contexts. A newly developed MCS was simultaneously implemented in a biochemistry and a molecular parasitology course at two different institutions. Instructors participating in this cross-institutional and multidisciplinary implementation collaboratively identified the need for quick and effective ways to bridge the gap between the MCS authors' vision and the implementing instructor's interpretation of the case-related molecular structure-function discussions. Augmented reality (AR) is an interactive and engaging experience that has been used effectively in teaching molecular sciences. Its accessibility and ease-of-use with smart devices (e.g., phones and tablets) make it an attractive option for expediting and improving both instructor preparation and classroom implementation of MCSs. In this work, we report the incorporation of ready-to-use AR objects as checkpoints in the MCS. Interacting with these AR objects facilitated instructor preparation, reduced students' cognitive load, and provided clear expectations for their learning. Based on our classroom observations, we propose that the incorporation of AR in MCSs can facilitate its successful implementation, improve the classroom experience for educators and students, and make MCSs more broadly accessible in diverse curricular settings.

Emergence of methicillin-resistant Staphylococcus aureus (MRSA) RdJ clone (CC5-ST105-SCCmecII-t002) in Santa Catarina, Brazil

The emergence of highly successful genetic lineages of methicillin-resistant Staphylococcus aureus (MRSA) poses a challenge in human healthcare due to increased morbidity and mortality rates. The RdJ clone (CC5-ST105-SCCmecII-t002 lineage), previously identified in Rio de Janeiro, Brazil, was linked to bloodstream infections and features a mutation in the aur gene (encoding aureolysin). Additionally, clinical isolates derived from this clone were more effective at evading monocytic immune responses. This study aimed to detect the RdJ clone among clinical MRSA isolated in Santa Catarina (SC) and examine its antimicrobial resistance and phagocytosis evasion capabilities. Our findings revealed the RdJ clone in 20 % of MRSA isolates, all exhibiting multiresistance. RdJ clone isolates from SC did not demonstrate a decreased rate of phagocytosis compared to CC5 non-RdJ isolates. Structural analysis suggests that the aur mutation is unlikely to significantly impact aureolysin activity. Genomic analysis of one isolate unveiled a genetic variant of the RdJ clone, sharing lineage and gene distribution but lacking the aur mutation. This study enhances the understanding of the clinical and epidemiologic risks associated with the RdJ clone and the biological mechanisms underlying its spreading in SC.

An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits

Modern nanotechnology has generated numerous datasets from in vitro and in vivo studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, we report a nanoinformatics platform that accurately annotates nanostructures into machine-readable data files and provides modeling toolkits. This platform, accessible to the public at https://vinas-toolbox.com/, has annotated nanostructures of 14 material types. The associated nanodescriptor data and assay test results are appropriate for modeling purposes. The modeling toolkits enable data standardization, data visualization, and machine learning model development to predict properties and bioactivities of new nanomaterials. Moreover, a library of virtual nanostructures with their predicted properties and bioactivities is available, directing the synthesis of new nanomaterials. This platform provides a data-driven computational modeling platform for the nanoscience community, significantly aiding in the development of safe and effective nanomaterials.

In silico analysis of balsaminol as anti-viral agents targeting SARS-CoV-2 main protease, spike receptor binding domain and papain-like protease receptors

Plant-derived phytochemicals from medicinal plants are becoming increasingly attractive natural sources of antimicrobial and antiviral agents due to their therapeutic value, mechanism of action, level of toxicity and bioavailability. The continued emergence of more immune-evasive strains and the rate of resistance to current antiviral drugs have created a need to identify new antiviral agents against SARS-CoV-2. This study investigated the antiviral potential of balsaminol, a bioactive compound from Momordica balsamina, and its inhibitory activities against SARS-CoV-2 receptor proteins. In this study, three Food and Drug Administration (FDA) COVID-19 approved drugs namely; nirmatrelvir, ritonavir and remdesivir were used as positive control. Molecular docking was performed to determine the predominant binding mode (most negative Gibbs free energy of binding/ΔG) and inhibitory activity of balsaminol against SARS-CoV-2 receptor proteins. The pharmacokinetics, toxicity, physicochemical and drug-like properties of balsaminol were evaluated to determine its potential as an active oral drug candidate as well as its non-toxicity in humans. The results show that balsaminol E has the highest binding affinity to the SARS CoV-2 papain-like protease (7CMD) with a free binding energy of - 8.7 kcal/mol, followed by balsaminol A interacting with the spike receptor binding domain (6VW1) with - 8.5 kcal/mol and balsaminol C had a binding energy of - 8.1 kcal/mol with the main protease (6LU7) comparable to the standard drugs namely ritonavir, nirmatrelvir and remdesivir. However, the ADMET and drug-like profile of balsaminol F favours it as a better potential drug candidate and inhibitor of the docked SARS-CoV-2 receptor proteins. Further preclinical studies are therefore recommended.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00241-0.

Extracellular ISG15 triggers ISGylation via a type-I interferon independent non-canonical mechanism to regulate host response during virus infection

Type-I interferons (IFN) induce cellular proteins with antiviral activity. One such protein is Interferon Stimulated Gene 15 (ISG15). ISG15 is conjugated to proteins during ISGylation to confer antiviral activity and regulate cellular activities associated with inflammatory and neurodegenerative diseases and cancer. Apart from ISGylation, unconjugated free ISG15 is also released from cells during various conditions, including virus infection. The role of extracellular ISG15 during virus infection was unknown. We show that extracellular ISG15 triggers ISGylation and acts as a soluble antiviral factor to restrict virus infection via an IFN-independent mechanism. Specifically, extracellular ISG15 acts post-translationally to markedly enhance the stability of basal intracellular ISG15 protein levels to support ISGylation. Furthermore, extracellular ISG15 interacts with cell surface integrin (α5β1 integrins) molecules via its RGD-like motif to activate the integrin-FAK (Focal Adhesion Kinase) pathway resulting in IFN-independent ISGylation. Thus, our studies have identified extracellular ISG15 protein as a new soluble antiviral factor that confers IFN-independent non-canonical ISGylation via the integrin-FAK pathway by post-translational stabilization of intracellular ISG15 protein.

A New Renieramycin T Right-Half Analog as a Small Molecule Degrader of STAT3

Constitutive activation of STAT3 contributes to tumor development and metastasis, making it a promising target for cancer therapy. (1R,4R,5S)-10-hydroxy-9-methoxy-8,11-dimethyl-3-(naphthalen-2-ylmethyl)-1,2,3,4,5,6-hexahydro-1,5-epiminobenzo[d]azocine-4-carbonitrile, DH_31, a new derivative of the marine natural product Renieramycin T, showed potent activity against H292 and H460 cells, with IC50 values of 5.54 ± 1.04 µM and 2.9 ± 0.58 µM, respectively. Structure-activity relationship (SAR) analysis suggests that adding a naphthalene ring with methyl linkers to ring C and a hydroxyl group to ring E enhances the cytotoxic effect of DH_31. At 1-2.5 µM, DH_31 significantly inhibited EMT phenotypes such as migration, and sensitized cells to anoikis. Consistent with the upregulation of ZO1 and the downregulation of Snail, Slug, N-cadherin, and Vimentin at both mRNA and protein levels, in silico prediction identified STAT3 as a target, validated by protein analysis showing that DH_31 significantly decreases STAT3 levels through ubiquitin-proteasomal degradation. Immunofluorescence and Western blot analysis confirmed that DH_31 significantly decreased STAT3 and EMT markers. Additionally, molecular docking suggests a covalent interaction between the cyano group of DH_31 and Cys-468 in the DNA-binding domain of STAT3 (binding affinity = -7.630 kcal/mol), leading to destabilization thereafter. In conclusion, DH_31, a novel RT derivative, demonstrates potential as a STAT3-targeting drug that significantly contribute to understanding of the development of new targeted therapy.

Gliotoxin triggers cell death through multifaceted targeting of cancer-inducing genes in breast cancer therapy

Fungal secondary metabolites have a long history of contributing to pharmaceuticals, notably in the development of antibiotics and immunosuppressants. Harnessing their potent bioactivities, these compounds are now being explored for cancer therapy, by targeting and disrupting the genes that induce cancer progression. The current study explores the anticancer potential of gliotoxin, a fungal secondary metabolite, which encompasses a multi-faceted approach integrating computational predictions, molecular dynamics simulations, and comprehensive experimental validations. In-silico studies have identified potential gliotoxin targets, including MAPK1, NFKB1, HIF1A, TDP1, TRIM24, and CTSD which are involved in critical pathways in cancer such as the NF-κB signaling pathway, MAPK/ERK signaling pathway, hypoxia signaling pathway, Wnt/β-catenin pathway, and other essential cellular processes. The gene expression analysis results indicated all the identified targets are overexpressed in various breast cancer subtypes. Subsequent molecular docking and dynamics simulations have revealed stable binding of gliotoxin with TDP1 and HIF1A. Cell viability assays exhibited a dose-dependent decreasing pattern with its remarkable IC50 values of 0.32, 0.14, and 0.53 μM for MDA-MB-231, MDA-MB-468, and MCF-7 cells, respectively. Likewise, in 3D tumor spheroids, gliotoxin exhibited a notable decrease in viability indicating its effectiveness against solid tumors. Furthermore, gene expression studies using Real-time PCR revealed a reduction of expression of cancer-inducing genes, MAPK1, HIF1A, TDP1, and TRIM24 upon gliotoxin treatment. These findings collectively underscore the promising anticancer potential of gliotoxin through multi-targeting cancer-promoting genes, positioning it as a promising therapeutic option for breast cancer.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

The potential of Chlorella spp. as antiviral source against African swine fever virus through a virtual screening pipeline

African swine fever (ASF) causes high mortality in pigs and threatens global swine production. There is still a lack of therapeutics available, with two vaccines under scrutiny and no approved small-molecule drugs. Eleven (11) viral proteins were used to identify potential antivirals in in silico screening of secondary metabolites (127) from Chlorella spp. The metabolites were screened for affinity and binding selectivity. High-scoring compounds were assessed through in silico ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) predictions, compared to structurally similar drugs, and checked for off-target docking with prepared swine receptors. Molecular dynamics (MD) simulations determined binding stability while binding energy was measured in Molecular Mechanics - Generalized Born Surface Area (MMGBSA) or Poisson-Boltzmann Surface Area (MMPBSA). Only six (6) compounds passed until MD analyses, of which five (5) were stable after 100 ns of MD runs. Of these five compounds, only three had binding affinities that were comparable to or stronger than controls. Specifically, phytosterols 24,25-dihydrolanosterol and CID 4206521 that interact with the RNA capping enzyme (pNP868R), and ergosterol which bound to the Erv-like thioreductase (pB119L). The compounds identified in this study can be used as a theoretical basis for in vitro screening to develop potent antiviral drugs against ASFV.

Transposition with Tn3-family elements occurs through interaction with the host β-sliding clamp processivity factor

Tn3 family transposons are a widespread group of replicative transposons, notorious for contributing to the dissemination of antibiotic resistance, particularly the global prevalence of carbapenem resistance. The transposase (TnpA) of these elements catalyzes DNA breakage and rejoining reactions required for transposition. However, the molecular mechanism for target site selection with these elements remains unclear. Here, we identify a QLxxLR motif in N-terminal of Tn3 TnpAs and demonstrate that this motif allows interaction between TnpA of Tn3 family transposon Tn1721 and the host β-sliding clamp (DnaN), the major processivity factor of the DNA replication machinery. The TnpA-DnaN interaction is essential for Tn1721 transposition. Our work unveils a mechanism whereby Tn3 family transposons can bias transposition into certain replisomes through an interaction with the host replication machinery. This study further expands the diversity of mobile elements that use interaction with the host replication machinery to bias integration.

Overview and limitations of database in global traditional medicines: A narrative review

The study of traditional medicine has garnered significant interest, resulting in various research areas including chemical composition analysis, pharmacological research, clinical application, and quality control. The abundance of available data has made databases increasingly essential for researchers to manage the vast amount of information and explore new drugs. In this article we provide a comprehensive overview and summary of 182 databases that are relevant to traditional medicine research, including 73 databases for chemical component analysis, 70 for pharmacology research, and 39 for clinical application and quality control from published literature (2000-2023). The review categorizes the databases by functionality, offering detailed information on websites and capacities to facilitate easier access. Moreover, this article outlines the primary function of each database, supplemented by case studies to aid in database selection. A practical test was conducted on 68 frequently used databases using keywords and functionalities, resulting in the identification of highlighted databases. This review serves as a reference for traditional medicine researchers to choose appropriate databases and also provides insights and considerations for the function and content design of future databases.

Rationally seeded computational protein design of ɑ-helical barrels

Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

De novo variants in the RNU4-2 snRNA cause a frequent neurodevelopmental syndrome

Around 60% of individuals with neurodevelopmental disorders (NDD) remain undiagnosed after comprehensive genetic testing, primarily of protein-coding genes1. Large genome-sequenced cohorts are improving our ability to discover new diagnoses in the non-coding genome. Here we identify the non-coding RNA RNU4-2 as a syndromic NDD gene. RNU4-2 encodes the U4 small nuclear RNA (snRNA), which is a critical component of the U4/U6.U5 tri-snRNP complex of the major spliceosome2. We identify an 18 base pair region of RNU4-2 mapping to two structural elements in the U4/U6 snRNA duplex (the T-loop and stem III) that is severely depleted of variation in the general population, but in which we identify heterozygous variants in 115 individuals with NDD. Most individuals (77.4%) have the same highly recurrent single base insertion (n.64_65insT). In 54 individuals in whom it could be determined, the de novo variants were all on the maternal allele. We demonstrate that RNU4-2 is highly expressed in the developing human brain, in contrast to RNU4-1 and other U4 homologues. Using RNA sequencing, we show how 5' splice-site use is systematically disrupted in individuals with RNU4-2 variants, consistent with the known role of this region during spliceosome activation. Finally, we estimate that variants in this 18 base pair region explain 0.4% of individuals with NDD. This work underscores the importance of non-coding genes in rare disorders and will provide a diagnosis to thousands of individuals with NDD worldwide.

Identification of renal protective gut microbiome derived-metabolites in diabetic chronic kidney disease: An integrated approach using network pharmacology and molecular docking

Metabolites from the gut microbiota define molecules in the gut-kidney cross talks. However, the mechanistic pathway by which the kidneys actively sense gut metabolites and their impact on diabetic chronic kidney disease (DCKD) remains unclear. This study is an attempt to investigate the gut microbiome metabolites, their host targeting genes, and their mechanistic action against DCKD. Gut microbiome, metabolites, and host targets were extracted from the gutMgene database and metabolites from the PubChem database. DCKD targets were identified from DisGeNET, GeneCard, NCBI, and OMIM databases. Computational examination such as protein-protein interaction networks, enrichment pathway, identification of metabolites for potential targets using molecular docking, hubgene-microbes-metabolite-samplesource-substrate (HMMSS) network architecture were executed using Network analyst, ShinyGo, GeneMania, Cytoscape, Autodock tools. There were 574 microbial metabolites, 2861 DCKD targets, and 222 microbes targeting host genes. After screening, we obtained 27 final targets, which are used for computational examination. From enrichment analysis, we found NF-ΚB1, AKT1, EGFR, JUN, and RELA as the main regulators in the DCKD development through mitogen activated protein kinase (MAPK) pathway signalling. The (HMMSS) network analysis found F.prausnitzi, B.adolescentis, and B.distasonis probiotic bacteria that are found in the intestinal epithelium, colonic region, metabolize the substrates like tryptophan, other unknown substrates might have direct interaction with the NF-kB1 and epidermal growth factor receptor (EGFR) targets. On docking of these target proteins with 3- Indole propionic acid (IPA) showed high binding energy affinity of -5.9 kcal/mol and -7.4kcal/mol. From this study we identified, the 3 IPA produced by F. prausnitzi A2-165 was found to have renal sensing properties inhibiting MAPK/NF-KB1 inflammatory pathway and would be useful in treating CKD in diabetics.

Identification of the potential Pan-CDK antagonists: tracing the path of virtual screening and inhibitory activity on lung cancer cells

Cyclin-dependent kinases (CDKs) are overexpressed in tumor cells, and their aberrant activation can promote the progression of non-small-cell lung cancer (NSCLC). We utilized structure-based virtual screening and experimental validation to screen for potential CDKs antagonists among TargetMol natural products. Molecular docking and molecular dynamics simulation results indicate that Dolastatin 10 exhibits strong interactions with multiple subtypes of CDKs (CDK1, CDK2, CDK3, CDK4, and CDK6), forming stable CDKs-Dolastatin 10 complex compounds. Furthermore, in vitro experiments demonstrate that Dolastatin 10 significantly inhibits the viability, migration, and invasion of H1299 cells in a concentration-dependent manner, arresting the cell cycle at the G2/M phase by inducing cell senescence. These findings suggest that Dolastatin 10 may serve as a potential CDKs antagonist deserving further investigation.

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

ZMPY3D: accelerating protein structure volume analysis through vectorized 3D Zernike moments and Python-based GPU integration

Motivation

Volumetric 3D object analyses are being applied in research fields such as structural bioinformatics, biophysics, and structural biology, with potential integration of artificial intelligence/machine learning (AI/ML) techniques. One such method, 3D Zernike moments, has proven valuable in analyzing protein structures (e.g., protein fold classification, protein-protein interaction analysis, and molecular dynamics simulations). Their compactness and efficiency make them amenable to large-scale analyses. Established methods for deriving 3D Zernike moments, however, can be inefficient, particularly when higher order terms are required, hindering broader applications. As the volume of experimental and computationally-predicted protein structure information continues to increase, structural biology has become a "big data" science requiring more efficient analysis tools.

Results

This application note presents a Python-based software package, ZMPY3D, to accelerate computation of 3D Zernike moments by vectorizing the mathematical formulae and using graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow together with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithm development. The ZMPY3D package can be installed via PyPI, and the source code is available from GitHub. Volumetric-based protein 3D structural similarity scores and transform matrix of superposition functionalities have both been implemented, creating a powerful computational tool that will allow the research community to amalgamate 3D Zernike moments with existing AI/ML tools, to advance research and education in protein structure bioinformatics.

Availability and implementation

ZMPY3D, implemented in Python, is available on GitHub (https://github.com/tawssie/ZMPY3D) and PyPI, released under the GPL License.

VOGDB-Database of Virus Orthologous Groups

Computational models of homologous protein groups are essential in sequence bioinformatics. Due to the diversity and rapid evolution of viruses, the grouping of protein sequences from virus genomes is particularly challenging. The low sequence similarities of homologous genes in viruses require specific approaches for sequence- and structure-based clustering. Furthermore, the annotation of virus genomes in public databases is not as consistent and up to date as for many cellular genomes. To tackle these problems, we have developed VOGDB, which is a database of virus orthologous groups. VOGDB is a multi-layer database that progressively groups viral genes into groups connected by increasingly remote similarity. The first layer is based on pair-wise sequence similarities, the second layer is based on the sequence profile alignments, and the third layer uses predicted protein structures to find the most remote similarity. VOGDB groups allow for more sensitive homology searches of novel genes and increase the chance of predicting annotations or inferring phylogeny. VOGD B uses all virus genomes from RefSeq and partially reannotates them. VOGDB is updated with every RefSeq release. The unique feature of VOGDB is the inclusion of both prokaryotic and eukaryotic viruses in the same clustering process, which makes it possible to explore old evolutionary relationships of the two groups. VOGDB is freely available at vogdb.org under the CC BY 4.0 license.

Computational Assessment of the Phytochemicals of Panax ginseng C.A. Meyer Against Dopamine Receptor D1 for Early Huntington's Disease Prophylactics

A herb, Panax ginseng C.A. Meyer has been used traditionally for the treatment of various diseases. In this work, its chemical components have been explored by computational methods for the possibility of therapeutic potential against early Huntington's disease. The molecular docking calculations against dopamine receptor D1 (PDB ID: 7X2F) involved in pathogenesis of early Huntington's disease gave the binding affinities (kcal/mol) of schizandrin (-10.530), ergosterol (-10.124), protopanaxadiol (-9.650), panaxydol (-9.399), diphenhydramine (-9.358), and panasenoside (-9.358). The values for native ligand (-7.748) and some selected drugs, Nefazodone (-9.880), Risperidone (-9.752), and Haloperidol (-9.712) were higher revealing weaker interactions. The stability assessment of top protein-ligand adducts in terms of various geometrical and thermodynamical parameters extracted from 200 ns molecular dynamics simulations pointed to schizandrin, protopanaxadiol, and panasenoside as hit molecules. The minimal translational and rotational motion of the docked ligands at orthosteric pocket of the receptor at near physiological conditions hinted at the probability of it restricting or inhibiting over-activation of DRD1. The sustained thermodynamic spontaneity of complex formation reaction augmented the inferences derived from spatial results. The phytochemicals from Panax ginseng could be used in the prophylactics of early Huntington's disease and recommendation is made for further evaluation by experimental work.

Identification and Validation of Diagnostic Model Based on Angiogenesis- and Epithelial Mesenchymal Transition-Related Genes in Myocardial Infarction

Background

Myocardial infarction (MI) is a chronic cardiovascular disease. This study aims to discern potentially angiogenesis- and epithelial mesenchymal transition (EMT)-related genes as biomarkers for MI diagnosis through bioinformatics.

Methods

All datasets and angiogenesis- and EMT-related genes were collected from the public database. The differentially expressed genes (DEGs) of MI and MI-related genes were acquired. DEGs, MI-related genes, and angiogenesis- and EMT-related genes were intersected to obtain hub genes. Functional enrichment, immune microenvironment, and transcription factors (TFs)-hub genes regulatory network analysis were performed. The diagnostic markers and models were developed and validated. Drug prediction and molecular docking were performed. Finally, diagnostic markers expressions were validated using RT-qPCR.

Results

A total of 224 angiogenesis- and EMT-related genes, 2,897 DEGs, 1,217 MI-related genes, and 9 hub genes were acquired. The immune infiltration levels of plasma cells, T cells CD4 memory activated, monocytes, macrophages M0, mast cells resting, and neutrophils were higher in patients with MI. LRPAP1, COLGALT1, QSOX1, THBD, VCAN, PLOD1, and PLAUR as the diagnostic markers were identified and used to construct diagnostic models, which can distinguish MI from controls well. Then, 9 drugs were screened, and the binding energies ranged from -7.08 to -5.21 kcal/mol. RT-qPCR results showed that the expression of LRPAP1, PLAUR, and PLOD1 was significantly increased in the MI group.

Conclusion

The 7 diagnostic markers may play potential roles in MI and could contribute to improved future diagnostics.

Influence of the Water Model on the Structure and Interactions of the GPR40 Protein with the Lipid Membrane and the Solvent: Rigid versus Flexible Water Models

G protein-coupled receptors (GPCR) are responsible for modulating various physiological functions and are thus related to the pathophysiology of different diseases. Being potential therapeutic targets, multiple computational methodologies have been developed to analyze their behavior and interactions with other species. The solvent, on the other hand, has received much less attention. In this work, we analyzed the effect of four explicit water models on the structure and interactions of the GPR40 receptor in its apo form. We employed the rigid SPC/E and TIP4P models, and their flexible versions, the FBA/ϵ and TIP4P/ϵflex. We explored the structural changes and their correlation with some bulk dynamic properties of water. Our results showed an adverse effect on the conservation of the secondary structure of the receptor with all the models due to the breaking of the intramolecular hydrogen bond network, being more evident for the TIP4P models. Notably, all four models brought the receptor to states similar to the active one, modifying the intracellular part of the TM5 and TM6 domains in a "hinge" type movement, allowing the opening of the structure. Regarding the dynamic properties, the rigid models showed results comparable to those obtained in other studies on membrane systems. However, flexible models exhibit disparities in the molecular representation of systems. Surprisingly, the FBA/ϵ model improves the molecular picture of several properties, even though their agreement with bulk diffusion is poorer. These findings reinforce our idea that exploring other water models or improving the current ones, to better represent the membrane interface, can lead to a positive impact on the description of the signal transduction mechanisms and the search of new drugs by targeting these receptors.

Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions

Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web-based tools due to intellectual property concerns. We focus on reproducing the RNA structure existing in RNA-small molecule complexes, particularly on the ability to model ligand binding sites. Using a comprehensive set of RNA structures from the PDB, which includes diverse structural elements, we found that machine learning (ML)-based methods effectively predict global RNA folds but are less accurate with local interactions. Conversely, non-ML-based methods demonstrate higher precision in modeling intramolecular interactions, particularly with secondary structure restraints. Importantly, ligand-binding site accuracy can remain sufficiently high for practical use, even if the overall model quality is not optimal. With the recent release of AlphaFold 3, we included this advanced method in our tests. Benchmark subsets containing new structures, not used in the training of the tested ML methods, show that AlphaFold 3's performance was comparable to other ML-based methods, albeit with some challenges in accurately modeling ligand binding sites. This study underscores the importance of enhancing binding site prediction accuracy and the challenges in modeling RNA-ligand interactions accurately.

A Bioinformatic Assay of Quercetin in Gastric Cancer

Gastric cancer (GC) remains a significant global health challenge, with high mortality rates, especially in developing countries. Current treatments are invasive and have considerable risks, necessitating the exploration of safer alternatives. Quercetin (QRC), a flavonoid present in various plants and foods, has demonstrated multiple health benefits, including anticancer properties. This study investigated the therapeutic potential of QRC in the treatment of GC. We utilized advanced molecular techniques to assess the impact of QRC on GC cells, examining its effects on cellular pathways and gene expression. Our findings indicate that QRC significantly inhibits GC cell proliferation and induces apoptosis, suggesting its potential as a safer therapeutic option for GC treatment. Further research is required to validate these results and explore the clinical applications of QRC in cancer therapy.

Genomic and computational-aided integrative drug repositioning strategy for EGFR and ROS1 mutated NSCLC

Non-small cell lung cancer (NSCLC) has been marked as the major cause of death in lung cancer patients. Due to tumor heterogeneity, mutation burden, and emerging resistance against the available therapies in NSCLC, it has been posing potential challenges in the therapy development. Hence, identification of cancer-driving mutations and their effective inhibition have been advocated as a potential approach in NSCLC treatment. Thereof, this study aims to employ the genomic and computational-aided integrative drug repositioning strategy to identify the potential mutations in the selected molecular targets and repurpose FDA-approved drugs against them. Accordingly, molecular targets and their mutations, i.e., EGFR (V843L, L858R, L861Q, and P1019L) and ROS1 (G1969E, F2046Y, Y2092C, and V2144I), were identified based on TCGA dataset analysis. Following, virtual screening and redocking analysis, Elbasvir, Ledipasvir, and Lomitapide drugs for EGFR mutants (>-10.8 kcal/mol) while Indinavir, Ledipasvir, Lomitapide, Monteleukast, and Isavuconazonium for ROS1 mutants (>-8.8 kcal/mol) were found as putative inhibitors. Furthermore, classical molecular dynamics simulation and endpoint binding energy calculation support the considerable stability of the selected docked complexes aided by substantial hydrogen bonding and hydrophobic interactions in comparison to the respective control complexes. Conclusively, the repositioned FDA-approved drugs might be beneficial alone or in synergy to overcome acquired resistance to EGFR and ROS1-positive lung cancers.

Synthesis, Crystal Structure, Hirshfeld Surface Analysis, and Computational Approach of a New Pyrazolo[3,4-g]isoquinoline Derivative as Potent against Leucine-Rich Repeat Kinase 2 (LRRK2)

Ethyl-2-((8-cyano-3,5,9a-trimethyl-1-(4-oxo-4,5-dihydrothiazol-2-yl)-4-phenyl-3a,4,9,9a-tetrahydro-1H-pyrazolo[3,4-g]isoquinolin-7-yl)thio)acetate (5) was synthesized, and its structure was characterized by IR, MS, and NMR (1H and 13C) and verified by a single-crystal X-ray structure determination. Compound 5 adopts a "pincer" conformation. In the crystal, the hydrogen bonds of -H···O, C-H···O, and O-H···S form thick layers of molecules that are parallel to (101). The layers are linked by C-H···π(ring) interactions. The Hirshfeld surface analysis shows that intermolecular hydrogen bonding plays a more important role than both intramolecular hydrogen bonding and π···π stacking in the crystal. The intramolecular noncovalent interactions in 5 were studied by QTAIM, NCI, and DFT-NBO calculations. Based on structural activity relationship studies, leucine-rich repeat kinase 2 (LRRK2) was found to bind 5 and was further subjected to molecular docking studies, molecular dynamics, and ADMET analysis to probe potential drug candidacy.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Inhibitors and PROTACs of CDK2: challenges and opportunities

INTRODUCTION

Abundant evidence suggests that the overexpression of CDK2-cyclin A/E complex disrupts normal cell cycle regulation, leading to uncontrolled proliferation of cancer cells. Thus, CDK2 has become a promising therapeutic target for cancer treatment. In recent years, insights into the structures of the CDK2 catalytic site and allosteric pockets have provided notable opportunities for developing more effective clinical candidates of CDK2 inhibitors.

AREA COVERED

This article reviews the latest CDK2 inhibitors that have entered clinical trials and discusses the design and discovery of the most promising new preclinical CDK2 inhibitors in recent years. Additionally, it summarizes the development of allosteric CDK2 inhibitors and CDK2-targeting PROTACs. The review encompasses strategies for inhibitor and PROTAC design, structure-activity relationships, as well as in vitro and in vivo biological assessments.

EXPERT OPINION

Despite considerable effort, no CDK2 inhibitor has yet received FDA approval for marketing due to poor selectivity and observed toxicity in clinical settings. Future research must prioritize the optimization of the selectivity, potency, and pharmacokinetics of CDK2 inhibitors and PROTACs. Moreover, exploring combination therapies incorporating CDK2 inhibitors with other targeted agents, or the design of multi-target inhibitors, presents significant promise for advancing cancer treatment strategies.

AlPaCas: allele-specific CRISPR gene editing through a protospacer-adjacent-motif (PAM) approach

Gene therapy of dominantly inherited genetic diseases requires either the selective disruption of the mutant allele or the editing of the specific mutation. The CRISPR-Cas system holds great potential for the genetic correction of single nucleotide variants (SNVs), including dominant mutations. However, distinguishing between single-nucleotide variations in a pathogenic genomic context remains challenging. The presence of a PAM in the disease-causing allele can guide its precise targeting, preserving the functionality of the wild-type allele. The AlPaCas (Aligning Patients to Cas) webserver is an automated pipeline for sequence-based identification and structural analysis of SNV-derived PAMs that satisfy this demand. When provided with a gene/SNV input, AlPaCas can: (i) identify SNV-derived PAMs; (ii) provide a list of available Cas enzymes recognizing the SNV (s); (iii) propose mutational Cas-engineering to enhance the selectivity towards the SNV-derived PAM. With its ability to identify allele-specific genetic variants that can be targeted using already available or engineered Cas enzymes, AlPaCas is at the forefront of advancements in genome editing. AlPaCas is open to all users without a login requirement and is freely available at https://schubert.bio.uniroma1.it/alpacas.

AlphaFind: discover structure similarity across the proteome in AlphaFold DB

AlphaFind is a web-based search engine that provides fast structure-based retrieval in the entire set of AlphaFold DB structures. Unlike other protein processing tools, AlphaFind is focused entirely on tertiary structure, automatically extracting the main 3D features of each protein chain and using a machine learning model to find the most similar structures. This indexing approach and the 3D feature extraction method used by AlphaFind have both demonstrated remarkable scalability to large datasets as well as to large protein structures. The web application itself has been designed with a focus on clarity and ease of use. The searcher accepts any valid UniProt ID, Protein Data Bank ID or gene symbol as input, and returns a set of similar protein chains from AlphaFold DB, including various similarity metrics between the query and each of the retrieved results. In addition to the main search functionality, the application provides 3D visualizations of protein structure superpositions in order to allow researchers to instantly analyze the structural similarity of the retrieved results. The AlphaFind web application is available online for free and without any registration at https://alphafind.fi.muni.cz.

Deep learning for the PSIPRED Protein Analysis Workbench

The PSIRED Workbench is a long established and popular bioinformatics web service offering a wide range of machine learning based analyses for characterizing protein structure and function. In this paper we provide an update of the recent additions and developments to the webserver, with a focus on new Deep Learning based methods. We briefly discuss some trends in server usage since the publication of AlphaFold2 and we give an overview of some upcoming developments for the service. The PSIPRED Workbench is available at http://bioinf.cs.ucl.ac.uk/psipred.

The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction

In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design.

Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins

Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein "unknownme". This large knowledge gap prevents the biological community from fully leveraging the plethora of genomic data that is now available. Machine-learning approaches are showing some promise in propagating functional knowledge from experimentally characterized proteins to the correct set of isofunctional orthologs. However, they largely fail to predict enzymatic functions unseen in the training set, as shown by dissecting the predictions made for 450 enzymes of unknown function from the model bacteria Escherichia coli using the DeepECTransformer platform. Lessons from these failures can help the community develop machine-learning methods that assist domain experts in making testable functional predictions for more members of the uncharacterized proteome.

Describing and Sharing Molecular Visualizations Using the MolViewSpec Toolkit

With the ever-expanding toolkit of molecular viewers, the ability to visualize macromolecular structures has never been more accessible. Yet, the idiosyncratic technical intricacies across tools and the integration complexities associated with handling structure annotation data present significant barriers to seamless interoperability and steep learning curves for many users. The necessity for reproducible data visualizations is at the forefront of the current challenges. Recently, we introduced MolViewSpec (homepage: https://molstar.org/mol-view-spec/, GitHub project: https://github.com/molstar/mol-view-spec), a specification approach that defines molecular visualizations, decoupling them from the varying implementation details of different molecular viewers. Through the protocols presented herein, we demonstrate how to use MolViewSpec and its 3D view-building Python library for creating sophisticated, customized 3D views covering all standard molecular visualizations. MolViewSpec supports representations like cartoon and ball-and-stick with coloring, labeling, and applying complex transformations such as superposition to any macromolecular structure file in mmCIF, BinaryCIF, and PDB formats. These examples showcase progress towards reusability and interoperability of molecular 3D visualization in an era when handling molecular structures at scale is a timely and pressing matter in structural bioinformatics as well as research and education across the life sciences. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating a MolViewSpec view using the MolViewSpec Python package Basic Protocol 2: Creating a MolViewSpec view with reference to MolViewSpec annotation files Basic Protocol 3: Creating a MolViewSpec view with labels and other advanced features Support Protocol 1: Computing rotation and translation vectors Support Protocol 2: Creating a MolViewSpec annotation file.

Impact of structural biology and the protein data bank on us fda new drug approvals of low molecular weight antineoplastic agents 2019-2023

Open access to three-dimensional atomic-level biostructure information from the Protein Data Bank (PDB) facilitated discovery/development of 100% of the 34 new low molecular weight, protein-targeted, antineoplastic agents approved by the US FDA 2019-2023. Analyses of PDB holdings, the scientific literature, and related documents for each drug-target combination revealed that the impact of structural biologists and public-domain 3D biostructure data was broad and substantial, ranging from understanding target biology (100% of all drug targets), to identifying a given target as likely druggable (100% of all targets), to structure-guided drug discovery (>80% of all new small-molecule drugs, made up of 50% confirmed and >30% probable cases). In addition to aggregate impact assessments, illustrative case studies are presented for six first-in-class small-molecule anti-cancer drugs, including a selective inhibitor of nuclear export targeting Exportin 1 (selinexor, Xpovio), an ATP-competitive CSF-1R receptor tyrosine kinase inhibitor (pexidartinib,Turalia), a non-ATP-competitive inhibitor of the BCR-Abl fusion protein targeting the myristoyl binding pocket within the kinase catalytic domain of Abl (asciminib, Scemblix), a covalently-acting G12C KRAS inhibitor (sotorasib, Lumakras or Lumykras), an EZH2 methyltransferase inhibitor (tazemostat, Tazverik), and an agent targeting the basic-Helix-Loop-Helix transcription factor HIF-2α (belzutifan, Welireg).

High-Speed Atomic Force Microscopy for Filming Protein Molecules in Dynamic Action

Structural biology is currently undergoing a transformation into dynamic structural biology, which reveals the dynamic structure of proteins during their functional activity to better elucidate how they function. Among the various approaches in dynamic structural biology, high-speed atomic force microscopy (HS-AFM) is unique in the ability to film individual molecules in dynamic action, although only topographical information is acquirable. This review provides a guide to the use of HS-AFM for biomolecular imaging and showcases several examples, as well as providing information on up-to-date progress in HS-AFM technology. Finally, we discuss the future prospects of HS-AFM in the context of dynamic structural biology in the upcoming era.

MolPLA: a molecular pretraining framework for learning cores, R-groups and their linker joints

MOTIVATION

Molecular core structures and R-groups are essential concepts in drug development. Integration of these concepts with conventional graph pre-training approaches can promote deeper understanding in molecules. We propose MolPLA, a novel pre-training framework that employs masked graph contrastive learning in understanding the underlying decomposable parts in molecules that implicate their core structure and peripheral R-groups. Furthermore, we formulate an additional framework that grants MolPLA the ability to help chemists find replaceable R-groups in lead optimization scenarios.

RESULTS

Experimental results on molecular property prediction show that MolPLA exhibits predictability comparable to current state-of-the-art models. Qualitative analysis implicate that MolPLA is capable of distinguishing core and R-group sub-structures, identifying decomposable regions in molecules and contributing to lead optimization scenarios by rationally suggesting R-group replacements given various query core templates.

AVAILABILITY AND IMPLEMENTATION

The code implementation for MolPLA and its pre-trained model checkpoint is available at https://github.com/dmis-lab/MolPLA.

PPInterface: A Comprehensive Dataset of 3D Protein-Protein Interface Structures

The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein-protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein-protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset's utility and accessibility. The dataset and web server can be found at https://3dpath.ku.edu.tr/PPInt/.

CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks

We present a novel and interpretable approach for assessing small-molecule binding using context explanation networks. Given the specific structure of a protein/ligand complex, our CENsible scoring function uses a deep convolutional neural network to predict the contributions of precalculated terms to the overall binding affinity. We show that CENsible can effectively distinguish active vs inactive compounds for many systems. Its primary benefit over related machine-learning scoring functions, however, is that it retains interpretability, allowing researchers to identify the contribution of each precalculated term to the final affinity prediction, with implications for subsequent lead optimization.

ACE inhibitors from Suaeda salsa: 3D-QSAR modeling, metabolomics, molecular docking and molecular dynamics simulations

Inhibition of ACE is considered as one of the main strategies to reduce hypertension. ACE inhibitors derived from Suaeda salsa (S. salsa) present a novel antihypertensive agent source. This study employed 3D-QSAR pharmacophore, metabolomics, docking-based screening, and molecular dynamics simulations to identify ACE inhibitors from S. salsa. A set of 53 known molecules was chemically diverse to construct a 3D-QSAR model for predictive purposes. S. salsa was characterized using UPLC-QqQ-MS/MS and UPLC-Q-TOF-LC-MS techniques, 211 and 586 kinds of bioactive metabolites were identified, respectively. A total of 680 compounds were collected for database construction and virtual screening. An ADMET assessment was conducted to evaluate drug-likeness and pharmacokinetics parameters. Moreover, molecular docking results show that six top hit compounds bind to ACE tightly. Specially, diosmin could interact with ACE by hydrogen bond, Pi-cation bond, and metal bond. Molecular dynamics (MD) simulation and MMPBSA calculations were subsequently employed to elucidate complex stability and the interaction between diosmin and ACE, indicating it a strong ACE inhibitory activity. In conclusion, this study suggests that S.salsa represents a potential source of antihypertensive agents.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00233-0.

Integrated component identification, network pharmacology, and experimental verification revealed mechanism of Dendrobium officinale Kimura et Migo against lung cancer

BACKGROUND

Dendrobium officinale Kimura et Migo (DO), a valuable Chinese herbal medicine, has been reported to exhibit potential effects in the prevention and treatment of lung cancer. However, its material basis and mechanism of action have not been comprehensively analyzed.

PURPOSE

The objective of this study was to preliminarily elucidate the active components and pharmacological mechanisms of DO in treating lung cancer, according to UPLC-Q/TOF-MS, HPAEC-PAD, network pharmacology, molecular docking, and experimental verification.

METHODS

The chemical components of DO were identified via UPLC-Q/TOF-MS, while the monosaccharide composition of Dendrobium officinale polysaccharide (DOP) was determined by HPAEC-PAD. The prospective active constituents of DO as well as their respective targets were predicted in the combined database of Swiss ADME and Swiss Target Prediction. Relevant disease targets for lung cancer were searched in OMIM, TTD, and Genecards databases. Further, the active compounds and potential core targets of DO against lung cancer were found by the C-T-D network and the PPI network, respectively. The core targets were then subjected to enrichment analysis in the Metascape database. The main active compounds were molecularly docked to the core targets and visualized. Finally, the viability of A549 cells and the relative quantity of associated proteins within the major signaling pathway were detected.

RESULTS

249 ingredients were identified from DO, including 39 flavonoids, 39 bibenzyls, 50 organic acids, 8 phenanthrenes, 27 phenylpropanoids, 17 alkaloids, 17 amino acids and their derivatives, 7 monosaccharides, and 45 others. Here, 50 main active compounds with high degree values were attained through the C-T-D network, mainly consisting of bibenzyls and monosaccharides. Based on the PPI network analysis, 10 core targets were further predicted, including HSP90AA1, SRC, ESR1, CREBBP, MAPK3, AKT1, PIK3R1, PIK3CA, HIF1A, and HDAC1. The results of the enrichment analysis and molecular docking indicated a close association between the therapeutic mechanism of DO and the PI3K-Akt signaling pathway. It was confirmed that the bibenzyl extract and erianin could inhibit the multiplication of A549 cells in vitro. Furthermore, erianin was found to down-regulate the relative expressions of p-AKT and p-PI3K proteins within the PI3K-Akt signaling pathway.

CONCLUSIONS

This study predicted that DO could treat lung cancer through various components, multiple targets, and diverse pathways. Bibenzyls from DO might exert anti-lung cancer activity by inhibiting cancer cell proliferation and modulating the PI3K-Akt signaling pathway. A fundamental reference for further studies and clinical therapy was given by the above data.