AlphaFold2 and its applications in the fields of biology and medicine

Structural Comparisons of Bifunctional Fatty Acid Dioxygenases with Allene Oxide, Epoxy Alcohol, or Diol Synthase Activities

Cyclooxygenase, lipoxygenases, and other dioxygenases (DOXs) transform fatty acids to endoperoxides or hydroperoxides, which can be isomerized by cytochromes P450 (CYPs) class III. These enzymes produce biological mediators in health and disease. Prostaglandin H2 is isomerized by thromboxane (CYP5A1) and prostacyclin synthases (CYP8A1), and hydroperoxides produced by plants by allene oxide synthases (AOS) (Cyp74A). Bifunctional DOXs belong to the peroxidase-COX superfamily and contain CYP fusion partners. They sequentially oxidize unsaturated C18 fatty acids to diols, epoxy alcohols, and allene oxides via hetero- or homolytic scissions of hydroperoxides. AlphaFold2 (AF2) models of bifunctional 8R/8S-, 9R/9S-, and 10R-DOXs illustrate the substrate recognition sites (SRSs) of the fused AOS, linoleate diol (LDS), and 10R-epoxy alcohol synthases (EAS). An Asn residue (SRS4) was positioned opposite to the heme iron of LDS, 10R-EAS, CYP8A1, and Cyp74A, but replaced at this position by Thr or nonpolar residues (Ile/Val/Ala) of 8R/9R- and 8S/9S-AOS, respectively, and by Ile of CYP5A1. Replacements of Asn and Gln residues of the SRS4 of LDSs altered the position of oxygenation and the homolytic scission of the hydroperoxide, but the amide residues were not required. The AF2 models of the CYP fusion partners illustrated the active sites in unprecedented details.

Adaptation of plateau frog peptide: From antimicrobial to angiogenic and proliferative functions

INTRODUCTION

Amphibian skin peptides, particularly defensins, play important roles in environmental adaptation, but often exhibit functional redundancy. SC17-2, a novel peptide from the high altitude frog Nanorana parkeri, exhibits unique angiogenesis and cell migration promoting activities, allowing adaptation to the extreme environment of the Tibetan Plateau with high UV radiation and low microbial diversity.

OBJECTIVES

This study aimed to investigate the adaptive role of SC17-2 in high-UV environments, its functional differences from typical defensins, and its potential biomedical applications in wound healing and angiogenesis.

METHODS

Bioinformatics analyses, including sequence alignment and ancestral reconstruction, identified positively selected amino acid sites in SC17-2. Molecular docking examined its interaction with the epidermal growth factor receptor (EGFR). In vitro and in vivo experiments, using mouse and zebrafish models, assessed its wound healing and angiogenic properties.

RESULTS

SC17-2 exhibited no antimicrobial activity, but it demonstrated antioxidant activity and potent wound healing and angiogenic properties. Molecular docking indicated that SC17-2 interacts with EGFR, potentially activating downstream signalling pathways. In vivo experiments showed that SC17-2 significantly accelerated wound healing by promoting collagen regeneration and angiogenesis, in some aspects outperforming VEGF.

CONCLUSION

SC17-2 represents a unique functional divergence in amphibian peptides, driven by ecological adaptation rather than microbial pressure. Its ability to promote angiogenesis and cell migration highlights its potential as a novel therapeutic agent for regenerative medicine, shaped by the extreme conditions of the Tibetan Plateau.

Synthesis of Cyclic Hexapeptides via the Hydrazide Method and Evaluation of Their Antibacterial Activities

Antimicrobial peptides (AMPs) have emerged as promising candidates in the fight against multidrug-resistant pathogens due to their broad-spectrum antimicrobial activity and low potential for resistance development. However, their clinical application is limited by poor stability and susceptibility to enzymatic degradation. This study aims to address these limitations by synthesizing a series of cyclic hexapeptides using the hydrazide method and evaluating their antimicrobial activity and stability. The hydrazide method facilitated the synthesis of 11 cyclic peptides through a reaction between C-terminal hydrazides and cysteine-containing peptides. Antimicrobial assays showed that Cy-f2 and Cy-f4 exhibited potent inhibitory effects against different kinds of bacteria, including E. coli, Staphylococcus aureus, and S. aureus. Hemolysis assays revealed minimal red blood cell lysis at effective antimicrobial concentrations, indicating good biocompatibility. Stability tests demonstrated improved stability of the cyclic peptides compared to linear counterparts in SGF and 80 °C. In conclusion, the cyclic hexapeptides synthesized in this study demonstrate excellent antimicrobial activity, enhanced stability, and low toxicity, suggesting their potential as new candidates for treating drug-resistant bacterial infections.

Water in drug design: pitfalls and good practices

INTRODUCTION

Structure-based drug design relies on optimizing drug-target interactions and blocking harmful pathophysiological events at the atomic level. Such events of the human body are modulated by water acting either as a medium or an individual partner in molecular interactions. A precise understanding of the modulatory mechanisms of water is essential for a successful drug design.

AREAS COVERED

The present review discusses different topographical and networking situations that result in radically different roles of water, a root of various pitfalls of drug design. The review surveys good practices for tackling the problems of determining water structure at atomic resolution. Techniques for quantifying the effects of bulk, networking, and individual water molecules on the stability of drug-target complexes are also discussed. The article is based on a literature search using the PubMed, Web of Science, and Google Scholar databases.

EXPERT OPINION

With advances in rapid computational algorithms and a better understanding of the physicochemical machinery of complex formation, theoretical approaches have resulted in elegant and cost-effective tools that fill the knowledge gaps left by the limited experimental methods. Overcoming the technical pitfalls of drug design, water transforms from a frustrating challenge into a handy tool for fine-tuning drug-target interactions.

Evolutionary insights into elongation factor G using AlphaFold and ancestral analysis

Elongation factor G (EF-G) is crucial for ribosomal translocation, a fundamental step in protein synthesis. Despite its indispensable role, the conformational dynamics and evolution of EF-G remain elusive. By integrating AlphaFold structural predictions with multiple sequence alignment (MSA)-based sequence analysis, we explored the conformational landscape, sequence-specific patterns, and evolutionary divergence of EF-G. We identified five high-confidence structural states of wild type (WT) EF-G, revealing broader conformational diversity than previously captured by experimental data. Phylogenetic analysis and MSA-embedded sequence patterns demonstrated that single-point mutations in the switch I loop modulate equilibrium between the two dominant conformational states, con1 and con2, which exhibit distinct functional specializations. Reconstructions of two ancestral EF-Gs revealed minimal GTPase activity and reduced translocase function in both forms, suggesting that robust translocase activity emerged after the divergence of con1 and con2. However, ancestral EF-Gs retained the fidelity of three-nucleotide translocation, underscoring the early evolutionary conservation of accurate mRNA movement. These findings establish a framework for understanding how conformational flexibility shapes EF-G function and specialization. Moreover, our computational pipeline can be extended to other translational GTPases, providing broader insights into the evolution of the translational machinery. This study highlights the power of AlphaFold-assisted structural analysis in revealing the mechanistic and evolutionary relationships involved in protein translation.

ABP-Xplorer: A Machine Learning Approach for Prediction of Antibacterial Peptides Targeting Mycobacterium abscessus-tRNA-Methyltransferase (TrmD)

Mycobacterium abscessus (MAB) infections pose a significant treatment challenge due to their intrinsic resistance to antibiotics, requiring prolonged multidrug regimens with limited success and frequent relapses. tRNA (m1G37) methyltransferase (TrmD), an enzyme essential for maintaining the reading frame during protein synthesis in MAB and other mycobacteria, is a potential therapeutic target for identifying new inhibitors. This study introduces ABP-Xplorer, a machine learning-based (ML) model designed to predict the antibacterial potential of peptides targeting MAB-TrmD ribosomal sites. A systematic evaluation of 26 machine learning models identified the Random Forest (RF) classifier as the most effective, achieving 96% accuracy. To address data set imbalance and enhance predictive reliability, the Synthetic Minority Oversampling Technique (SMOTE) was applied, improving model generalization and reducing bias. After that, an ABP-Xplorer streamlit was developed to predict positive and negative antibacterial peptides (ABP), enabling easy sequence input and classification based on predictive scoring. For validation, 12 positive peptides with high predictive scores were selected for molecular docking by HADDOCK. Docking analysis of selected peptides confirmed strong binding to TrmD, with P1, P7, P8, and P9 as top candidates. Notably, P1 exhibited the best interaction with a HADDOCK score of -102.2, followed by P7 (-93.6) and P8 (-91.4), indicating their potential for further development as TrmD inhibitors.Moreover, Ramachandran plot analysis validated the structural reliability. Future research should focus on the experimental validation of these peptides and optimizing their stability and bioavailability for therapeutic applications.

In-silico analysis of deleterious non-synonymous SNPs in the human AVPR1a gene linked to autism

Single nucleotide polymorphisms are the most prevalent type of DNA variation occurring at a single nucleotide within the genomic sequence. The AVPR1a gene exhibits genetic polymorphism and is linked to neurological and developmental problems, including autism spectrum disorder. Due to the difficulties of studying all non-synonymous single nucleotide polymorphisms (nsSNPs) of the AVPR1a gene in the general population, our goal is to use a computational approach to identify the most detrimental nsSNPs of the AVPR1a gene. We employed several bioinformatics tools, such as SNPnexus, PROVEAN, PANTHER, PhD-SNP, SNP & GO, and I-Mutant2.0, to detect the 23 most detrimental mutants (R85H, D202N, E54G, H92P, D148Y, C203G, V297M, D148V, S182N, Q108L, R149C, G212V, M145T, G212S, Y140S, F207V, Q108H, W219G, R284W, L93F, P156R, F136C, P107L). Later, we used other bioinformatics tools to perform domain and conservation analysis. We analyzed the consequences of high‑risk nsSNPs on active sites, post-translational modification (PTM) sites, and their functional effects on protein stability. 3D modeling, structure validation, protein-ligand binding affinity prediction, and Protein-protein docking were conducted to verify the presence of five significant substitutions (R284W, Y140S, P107L, R149C, and F207V) and explore the modifications induced due to these mutants. These non-synonymous single nucleotide polymorphisms can potentially be the focus of future investigations into various illnesses caused by AVPR1a malfunction. Employing in-silico methodologies to evaluate AVPR1a gene variants will facilitate the coordination of extensive investigations and the formulation of specific therapeutic approaches for diseases associated with these variations.

AlphaFold-Guided Bespoke Gene Editing Enhances Field-Grown Soybean Oil Contents

Enhancing the oil or protein content of soybean, a major crop for oil and protein production is highly desirable. GmSWEET10a encodes a sugar transporter that is strongly selected during domestication and breeding, increasing seed size and oil content. GmSWEET10b is functionally similar to GmSWEET10a, yet has not been artificially selected. Here, AlphaFold is used to find that C-terminal variants of GmSWEET10a can endow enhanced or reduced transport activity. Guided by AlphaFold, the functionality is improved for GmSWEET10a in terms of oil content through gene editing. Furthermore, novel GmSWEET10b haplotypes possessing strengthened or weakened sugar-transport capabilities that are absent in nature are engineered. Consequently, soybean oil content or protein content in independent GmSWEET10b gene-edited lines during multi-year and multi-site field trials is consistently increased, without negatively affecting yield. The study demonstrates that the combination of AlphaFold-guided protein design and gene editing has the potential to generate novel beneficial alleles, which can optimize protein function in the context of crop breeding.

Natural products for anti-fibrotic therapy in idiopathic pulmonary fibrosis: marine and terrestrial insights

Idiopathic Pulmonary Fibrosis (IPF) is a chronic fibrotic interstitial lung disease (ILD) of unknown etiology, characterized by increasing incidence and intricate pathogenesis. Current FDA-approved drugs suffer from significant side effects and limited efficacy, highlighting the urgent need for innovative therapeutic agents for IPF. Natural products (NPs), with their multi-target and multifaceted properties, present promising candidates for new drug development. This review delineates the anti-fibrotic pathways and targets of various natural products based on the established pathological mechanisms of IPF. It encompasses over 20 compounds, including flavonoids, saponins, polyphenols, terpenoids, natural polysaccharides, cyclic peptides, deep-sea fungal alkaloids, and algal proteins, sourced from both terrestrial and marine environments. The review explores their potential roles in mitigating pulmonary fibrosis, such as inhibiting inflammatory responses, protecting against lipid peroxidation damage, suppressing mesenchymal cell activation and proliferation, inhibiting fibroblast migration, influencing the synthesis and secretion of pro-fibrotic factors, and regulating extracellular matrix (ECM) synthesis and degradation. Additionally, it covers various in vivo and in vitro disease models, methodologies for analyzing marker expression and signaling pathways, and identifies potential new therapeutic targets informed by the latest research on IPF pathogenesis, as well as challenges in bioavailability and clinical translation. This review aims to provide essential theoretical and technical insights for the advancement of novel anti-pulmonary fibrosis drugs.

In silico prediction of GRP78-CRIPTO binding sites to improve therapeutic targeting in glioblastoma

Glioblastoma multiforme (GBM) is one of the most malignant tumors in central nervous system (CNS) tumors. The glucose-regulated protein 78 (GRP78) and CRIPTO (Cripto-1), a protein that belongs to the EGF-CFC (epidermal growth factor cripto-1 FRL-1 cryptic) family, are overexpressed in GBM. A complex between GRP78 SBDβ (substrate binding domain beta) and CRIPTO CFC domain was reported in previous studies. This complex activates MAPK/AKT signaling, Src/PI3K/AKT, and Smad2/3 pathways which is a reason for tumor proliferation. In this work, we study how the two proteins form the complex figuring out binding sites between GRP78 and CRIPTO utilizing computational biophysics and bioinformatics tools, such as protein-protein docking, molecular dynamics simulation and MMGBSA calculations. Haddock web server results of 4 regions from the CFC domain (region1 (- 70.4), region2 (- 78.7), region3 (- 74.2), region4 (- 86.8)) with selected residues of the SBDβ are then simulated for 100 ns MDS then MMGBSA were calculated for the four complexes. The results reveal the stability of the complexes with binding free energy (complex1 (- 15.07 kcal/mol), complex2 (- 59.78 kcal/mol), complex3 (- 81.92 kcal/mol), complex4 (- 126.26 kcal/mol). All these findings ensure that GRP78 SBDβ associates with the CRIPTO CFC domain, and the binding sites suggested make stable interactions between the proteins.

Multifunctional cytochrome P450 orchestrates radical cleavage and non-radical cyclization in 5-oxaindolizidine biosynthesis

Penicilactam A (1), a fungal alkaloid featuring a rare 5-oxaindolizidine scaffold, has long eluded biosynthetic characterization despite recent advances in microbial genomics. Through retro-biosynthetic analysis of Penicillium citrinum HDN11-186, we identified the pnlt gene cluster governing its production. This pathway ultilizes a hybrid polyketide synthase-nonribosomal peptide synthetase (PKS-NRPS) system to assemble the prolinol-containing precursor scalusamide A (2). The multifunctional cytochrome P450 enzyme PnltC then orchestrates two mechanistically distinct reactions: radical-mediated C-C bond cleavage followed by iminium-driven cyclization. Combined structural and computational analyses unveil PnltC's unprecedented catalytic logic, merging radical oxidation with non-radical cyclization within a single active site, which challenges existing paradigms of P450 enzymology. Our findings expand the functional repertoire of oxygenases in natural products (NPs) biosynthesis, revealing nature's sophisticated strategies for constructing complex nitrogen heterocycles.

PlastiCRISPR: Genome Editing-Based Plastic Waste Management with Implications in Polyethylene Terephthalate (PET) Degradation

Plastic pollution has become a significant environmental issue worldwide, with global plastic production expected to reach 1800 million tons by 2050. One of the most commonly used plastics in the world is polyethylene terephthalate (PET), a synthetic polymer that is extremely durable but difficult to degrade. Thus, PET is dangerous to human health. To address this crisis, innovative approaches are being developed, including genome editing technologies. One of the recently advanced genome editing technologies is PlastiCRISPR, a novel concept that applies CRISPR-based genome editing to transform plastic waste management. PlastiCRISPR utilizes microorganisms to degrade plastic, generating valuable bioproducts like biofuels and biochemicals. Thus, this technology offers a sustainable solution because of its simple design, adequacy, and low cost, which can be integrated into existing waste management systems. Importantly, this review focuses on the PlastiCRISPR-based management of PET because it could drastically lower plastic waste, sustain natural resources by decreasing the requirement for plastic production, minimize energy intake, etc. Overall, this review provides an overview of the principles, applications, challenges, and future prospects of PlastiCRISPR in combating plastic pollution and shaping a more sustainable future.

Structure/activity relationships of two alginate lyases from Flavobacterium spp. and their potential application in detergents

Alginate is one of the most abundant marine polysaccharides and is found primarily in brown algae. Enzymes like alginate lyases have been extensively used to depolymerize alginate, breaking the glycosidic bonds through a β-elimination mechanism, and facilitate the utilisation of alginate by algae-associated bacteria to obtain valuable alginate oligosaccharides. Two alginate lyases, Aly40 and Aly30 from the family Polysaccharide Lyase 7 (PL7), were found in a commercial alginate lyase extract from Flavobacterium spp. The species of origin was determined to be Flavobacterium quisquiliarum using proteomics, and both enzymes were expressed in Escherichia coli BL21(DE3) for analysis of their kinetics and substrate specificity. Aly40 is a polyG-specific lyase, whereas Aly30 is a bifunctional lyases active against all the monomeric units of alginate, thus enabling the organism to break down a broad range of alginates. The molecular dynamic simulation of Aly30 in complex with the pentamannuronate indicate the possible dual role of Tyr238 acting as both the Brønsted base and acid to break the scissile β-1,4 glycosidic bond through a syn-β-elimination mechanism. The application of the alginate lyases in detergent was evaluated; Aly40 is a suitable detergent additive and increased stain removal of algal alginate-containing chocolate mousse stains by 52 %, whereas Aly30 did not improve stain removal although could offer other benefits to cleaning compositions by targeting alginates of microbial origin.

VirulentHunter: deep learning-based virulence factor predictor illuminates pathogenicity in diverse microbial contexts

Virulence factors (VFs) are critical determinants of bacterial pathogenicity, but current homology-based identification methods often miss novel or divergent VFs, and many machine learning approaches neglect functional classification. Here, we present VirulentHunter, a novel deep learning framework that enable simultaneous VF identification and classification directly from protein sequences by leveraging the crucial step of fine-tuning pretrained protein language model. We curate a comprehensive VF database by integrating diverse public resources and expanding VF category annotations. Our benchmarking results demonstrate that VirulentHunter outperforms existing methods, particularly in identifying VFs lacking detectable homologs. Additionally, strain-level analysis using VirulentHunter highlights distinct pathogenicity profiles between Mycobacterium tuberculosis and Mycobacterium avium, revealing enrichment in VFs related to adherence, effector delivery systems, and immune modulation in M. tuberculosis, compared to biofilm formation and motility in M. avium. Furthermore, metagenomic profiling of gut microbiota from inflammatory bowel disease patient reveals a depletion of VFs associated with immune homeostasis. These results underscore the versatility of VirulentHunter as a powerful tool for VF analysis across diverse applications. To facilitate broader accessibility, we provide a freely accessible web service for VF prediction (http://www.unimd.org/VirulentHunter), accommodating protein sequences, genomes, and metagenomic data.

AI and omics technologies in biobanking: Applications and challenges for public health

OBJECTIVES

Considering the growing intersection of biobanks, artificial intelligence (AI) and omics research, and their critical impact on public health, this study aimed to explore the current and future public health implications and challenges of AI and omics-driven innovations in biobanking.

STUDY DESIGN

Narrative literature review.

METHODS

A structured literature search was conducted in Scopus, PubMed, Web of Science and IEEExplore databases using relevant search terms. Additional references were identified through backward and forward citation chaining. Key themes were aggregated and analysed through thematic analysis.

RESULTS

Thirty-seven studies were selected for analysis, leading to the identification and categorisation of key developments. Several key technical, ethical and implementation challenges were also identified, including AI model selection, data accessibility, variability and quality issues, lack of robust and standardised validation methods, explainability, accountability, lack of transparency, algorithmic bias, privacy, security and fairness issues, and governance model selection. Based on these results, potential future scenarios of AI and omics integration in biobanking and their related public health implications were considered.

CONCLUSIONS

While AI and omics-driven innovations in biobanking offer specific transformative public health benefits, addressing their technical, ethical and implementation challenges is crucial. Robust regulatory frameworks, feasible governance models, access to quality data, interdisciplinary collaboration, and transparent and validated AI systems are essential to maximise benefits and mitigate risks. Further research and policy development are needed to support the responsible integration of these technologies in biobanking and public health.

Computational modeling of the anti-inflammatory complexes of IL37

Interleukin (IL) 37 is an anti-inflammatory cytokine belonging to the IL1 protein family. Owing to its pivotal role in modulating immune responses, elucidating the IL37 complex structures holds substantial therapeutic promise for various autoimmune disorders and cancers. However, none of the structures of IL37 complexes have been experimentally characterized. This computational study aims to address this gap through molecular modeling and classical molecular dynamics simulations. We modeled all protein-protein complexes of IL37 using a range of methods from homology modeling to AlphaFold2 multimer predictions. Models that successfully recapitulated experimental features underwent further analysis through molecular dynamics simulations. As positive controls, binary and ternary complexes of IL18 from PDB were included for comparison. Several key findings emerged from the comparative analysis of IL37 and IL18 complexes. IL37 complexes exhibited higher mobility than the IL18 complexes. Simulations of the IL37-IL18Rα complex revealed altered receptor conformations capable of accommodating a dimeric IL37, with the N-terminal loop of IL37 contributing significantly to complex mobility. Additionally, the glycosyl chain on N297 of IL18Rα, which contours one edge of the cytokine binding surface, acted as a steric block against the N-terminal loop of IL37. Further, investigations into interactions between IL37 and IL18BP suggested that a binding mode homologous to IL18 was unstable for IL37, indicating an alternative binding mechanism. Altogether, this study accesses to the structure and dynamics of IL37 complexes, revealing the structural underpinnings of the IL37's modulatory effect on the IL18 signaling pathway.

Leveraging AlphaFold2 and residual dipolar couplings for side-chain methyl group assignment: A case study with S. cerevisiae Xrs2

Side-chain methyl group NMR spectroscopy provides invaluable insights into macromolecular structure, dynamics, and function, particularly for large biomolecular complexes. Accurate assignment of methyl group resonances in two-dimensional spectra is essential for structural and dynamics studies. Traditional methyl group assignment strategies rely on either transferring assignments from backbone resonance data or NOESY data and high-resolution experimental structures; however, these methods are often limited by molecular size or availability of structural information, respectively. Here, we describe the use of AlphaFold2 structural models as a basis for the manual, distance-based assignment of side-chain methyl group resonances in the folded domains of S. cerevisiae Xrs2. While AlphaFold2 models facilitated initial assignments for the methyl resonances, inaccuracies in the side-chain coordinates highlighted the need for improved structural models. By generating >500 ColabFold-derived models and filtering with methyl residual dipolar couplings (RDCs), we identified structural models with superior agreement to experimental data. These refined models enabled additional methyl group assignments while suggesting an iterative approach to simultaneously improve structure prediction and resonance assignment. Our findings outline a workflow that integrates machine learning-based structural predictions with experimental NMR data, offering a pathway for advancing methyl group assignment in systems lacking high-resolution experimental structures.

Multi-Modal Design, Synthesis, and Biological Evaluation of Novel Fusidic Acid Derivatives

Fusidic acid (FA), a tetracyclic triterpenoid, has been approved to treat methicillin-resistant Staphylococcus aureus (MRSA) infections. However, there are few reports about FA derivatives with high efficacy superior to FA, manifesting the difficulty of discovering the derivatives based on experience-based drug design. In this study, we employed a stepwise method to discover novel FA derivatives. First, molecular dynamics (MD) simulations were performed to identify the molecular mechanism of FA against elongation factor G (EF-G) and drug resistance. Then, we utilized a scaffold decorator to design novel FA derivatives at the 3- and 21-positions of FA. The ligand-based and structure-based screening models, including Chemprop and RTMScore, were employed to identify promising hits from the generated set. Ten generated FA derivatives with high efficacy in the Chemprop and RTMScore models were synthesized for in vitro testing. Compounds 4 and 10 demonstrated a 2-fold increase in potency against MRSA strains compared to FA. This study highlights the significant impact of AI-based methods on the design of novel FA derivatives with drug efficacy, which provides a new approach for drug discovery.

Proteomics of Bacterial and Mouse Extracellular Vesicles Released in the Gastrointestinal Tracts of Nutrient-Stressed Animals Reveals an Interplay Between Microbial Serine Proteases and Mammalian Serine Protease Inhibitors

Bacterial extracellular vesicles (BEVs) produced by members of the intestinal microbiota can not only contribute to digestion but also mediate microbe-host cell communication via the transfer of functional biomolecules to mammalian host cells. An unresolved question is which host factors and conditions influence BEV cargo and how they impact host cell function. To address this question, we analysed and compared the proteomes of BEVs released by the major human gastrointestinal tract (GIT) symbiont Bacteroides thetaiotaomicron (Bt) in vivo in fed versus fasted animals using nano-liquid chromatography with tandem mass spectrometry (LC-MSMS). Among the proteins whose abundance was negatively affected by fasting, nine of ten proteins of the serine protease family, including the regulatory protein dipeptidyl peptidase-4 (DPP-4), were significantly decreased in BEVs produced in the GITs of fasted animals. Strikingly, in extracellular vesicles produced by the intestinal epithelia of the same fasted mice, the proteins with the most increased abundance were serine protease inhibitors (serpins). Together, these findings suggest a dynamic interaction between GI bacteria and the host. Additionally, they indicate a regulatory role for the host in determining the balance between bacterial serine proteases and host serpins exported in bacterial and host extracellular vesicles.

Functional assessment of genetic variants in thrombomodulin detected in patients with bleeding and thrombosis

ABSTRACT

Thrombomodulin (TM) expressed on endothelial cells regulates coagulation. Specific nonsense variants in the TM gene, THBD, result in high soluble TM levels causing rare bleeding disorders. In contrast, although THBD variants have been associated with venous thromboembolism, this association remains controversial. A multigene panel was used to diagnose 601 patients with inherited bleeding or thrombotic disorders. This resulted in the identification of 8 THBD variants for 6 patients with a thrombotic (C175S, A282P, L433P, P501L, G502R, and P508L) and 2 patients with a bleeding (P260A and T478I) phenotype. These were all classified as variants of uncertain significance, and we here aimed to assess their functional role in coagulation. For this purpose, soluble and cell membrane-bound recombinant TM were produced in Expi293F cells. L433P TM showed a marked decrease in the inhibition of thrombin generation and complete inhibition of protein C and thrombin activatable fibrinolysis inhibitor (TAFI) activation. Soluble C175S TM showed decreased inhibition of thrombin generation and protein C activation, whereas no effect was observed for cell membrane-bound recombinant TM. For the other TM variants, no effect on thrombin generation, protein C, or TAFI activation could be observed. Surface plasmon resonance analysis showed no thrombin-TM binding in the presence of L433P because this residue is located at their interaction site. In conclusion, our study shows the functional effects of L433P TM and potentially C175S TM, which are compatible with an increased thrombosis risk. THBD variants are rare but can be relevant to both bleeding and thrombosis. Functional assays for these variants are critical to understand their roles.

Bioinformatics analysis, immunogenicity, and therapeutic efficacy evaluation of a novel multi-stage, multi-epitope DNA vaccine for tuberculosis

BACKGROUND

The global tuberculosis (TB) epidemic remains severe. We aimed to develop a therapeutic DNA vaccine as an adjunct to TB treatment to improve efficacy.

METHODS

The W545 DNA vaccine was constructed using the M. tuberculosis (MTB) antigens Ag85A and Rv1419, integrated with epitopes from the Ag85B, Rv3407, and Rv2628. Bioinformatics tools were used to predict and analyze the physicochemical properties, structure modelling and molecular docking, epitopes (HTL, CTL, and B-cell), safety, population coverage, and simulated immunization of the W545 vaccine protein. Animal studies were then performed to evaluate the vaccine's immunogenicity by measuring Th1-type immune responses (IFN-γ, IL-2) and IgG antibody levels, as well as its therapeutic efficacy in reducing lung inflammation and pathological damage in a murine TB model.

RESULTS

The vaccine protein is a 70 kDa hydrophilic protein with a half-life of 30 h, an instability index of 43.33, and strong affinity to Toll-like receptor (TLR) 2 and TLR4. It contains 397 helper T cell (HTL) epitopes, 248 cytotoxic T cell (CTL) epitopes, and 27 B cell epitopes, with broad population coverage (global: 99.7 %, Chinese: 97.6 %). The W545 vaccine significantly induced a Th1-type immune response, producing high levels of IFN-γ (5.38 pg/ml ± 0.89 pg/ml) and IgG antibodies (OD450: 0.13 ± 0.06). It also reduced the lung weight index, tissue lesions, and severity in the murine TB model.

CONCLUSION

The W545 DNA vaccine effectively induces a Th1-type immune response, alleviates pathological damage, and demonstrates potential as an immunotherapeutic agent. Bioinformatics analysis provides valuable guidance for vaccine design and optimization.

An integrated AI knowledge graph framework of bacterial enzymology and metabolism

The study of bacterial metabolism holds immense significance for improving human health and advancing agricultural practices. The prospective applications of genomically encoded bacterial metabolism present a compelling opportunity, particularly in the light of the rapid expansion of genomic sequencing data. Current metabolic inference tools face challenges in scaling with large datasets, leading to increased computational demands, and often exhibit limited inter-relatability and interoperability. Here, we introduce the Integrated Biosynthetic Inference Suite (IBIS), which employs deep learning models and a knowledge graph to facilitate rapid, scalable bacterial metabolic inference. This system leverages a series of Transformer based models to generate high quality, meaningful embeddings for individual enzymes, biosynthetic domains, and metabolic pathways. These embedded representations enable rapid, large-scale comparisons of metabolic proteins and pathways, surpassing the capabilities of conventional methodologies. The examination of evolutionary and functionally conserved metabolites across diverse bacterial species is facilitated by integrating the predictive capabilities of IBIS into a graph database enriched with comprehensive metadata. The consideration of both primary and specialized metabolism, combined with an embedding logic for enzyme discovery, uniquely positions IBIS to identify potential novel metabolic pathways. With the expansion of genomic data necessitating transformative approaches to advance molecular metabolism research, IBIS delivers an AI-driven holistic investigation of bacterial metabolism.

AlphaFold3: An Overview of Applications and Performance Insights

AlphaFold3, the latest release of AlphaFold developed by Google DeepMind and Isomorphic Labs, was designed to predict protein structures with remarkable accuracy. AlphaFold3 enhances our ability to model not only single protein structures but also complex biomolecular interactions, including protein-protein interactions, protein-ligand docking, and protein-nucleic acid complexes. Herein, we provide a detailed examination of AlphaFold3's capabilities, emphasizing its applications across diverse biological fields and its effectiveness in complex biological systems. The strengths of the new AI model are also highlighted, including its ability to predict protein structures in dynamic systems, multi-chain assemblies, and complicated biomolecular complexes that were previously challenging to depict. We explore its role in advancing drug discovery, epitope prediction, and the study of disease-related mutations. Despite its significant improvements, the present review also addresses ongoing obstacles, particularly in modeling disordered regions, alternative protein folds, and multi-state conformations. The limitations and future directions of AlphaFold3 are discussed as well, with an emphasis on its potential integration with experimental techniques to further refine predictions. Lastly, the work underscores the transformative contribution of the new model to computational biology, providing new insights into molecular interactions and revolutionizing the fields of accelerated drug design and genomic research.

Bacillus subtilis Utilizes Decarboxylated S-Adenosylmethionine for the Biosynthesis of Tandem Aminopropylated Microcin C, a Potent Inhibitor of Bacterial Aspartyl-tRNA Synthetase

The biosynthetic pathways of natural products involve unusual biochemical reactions catalyzed by unique enzymes. Aminopropylation, although apparently simple, is an extremely rare modification outside polyamine biosynthesis. The canonical pathway used in the biosynthesis of peptide-adenylate antibiotic microcin C of E. coli (Eco-McC) entails alkylation by the S-adenosyl-methionine-derived 3-amino-3-carboxypropyl group of the adenylate moiety and subsequent decarboxylation to yield the bioactive aminopropylated compound. Here, we report the structure and biosynthesis of a new member of the microcin C family of antibiotics, Bsu-McC, produced by Bacillus subtilis MG27, which employs an alternative aminopropylation pathway. Like Eco-McC, Bsu-McC consists of a peptide moiety that facilitates prodrug import into susceptible bacteria and a warhead, a nonhydrolyzable modified isoasparaginyl-adenylate, which, when released into the cytoplasm, binds aspartyl-tRNA synthetase (AspRS) inhibiting translation. In contrast to the Eco-McC, whose warhead carries a single aminopropyl group attached to the phosphate moiety of isoasparaginyl-adenylate, the warhead of Bsu-McC is decorated with a tandem of two aminopropyl groups. Our in silico docking of the Bsu-McC warhead to the AspRS-tRNA complex suggests that two aminopropyl groups form extended interactions with the enzyme and tRNA, stabilizing the enzyme-inhibitor complex. We show that tandem aminopropylation results in a 32-fold increase in the biological activity of peptidyl-adenylate. We also show that B. subtilis adopted an alternative pathway for aminopropylation in which two homologous 3-aminopropyltransferases utilize decarboxylated S-adenosylmethionine as a substrate. Additionally, Bsu-McC biosynthesis alters the social behavior of the B. subtilis producer strain, resulting in a sharp decrease in their ability to form biofilms.

Multiscale Simulations and Profiling of Human Thymidine Phosphorylase Mutations: Insights into Structural, Dynamics, and Functional Impacts in Mitochondrial Neurogastrointestinal Encephalopathy

Mitochondrial neurogastrointestinal encephalopathy (MNGIE) is a rare metabolic disorder caused by missense mutations in the TYMP gene, leading to the loss of human thymidine phosphorylase (HTP) activity and subsequent mitochondrial dysfunction. Despite its well-characterized biochemical basis, the molecular mechanisms by which MNGIE-associated mutations alter HTP's structural stability, dynamics, and substrate (thymidine) binding remain unclear. In this study, we employ a multiscale computational approach, integrating AlphaFold2-based structural modeling, all-atom and coarse-grained molecular dynamics (MD) simulations, protein-ligand (HTP-thymidine) docking, HTP-thymidine complex simulations, binding free-energy landscape analysis, and systematic mutational profiling to investigate the impact of key MNGIE-associated mutations (R44Q, G145R, G153S, K222S, and E289A) on HTP function. Analyses of our long-duration multiscale simulations (comprising 9 μs coarse-grained, 1.2 μs all-atom apo HTP, and 1.2 μs HTP-thymidine complex MD simulations) and physicochemical properties reveal that while wild-type HTP maintains structural integrity and strong thymidine-binding affinity, MNGIE-associated mutations induce substantial destabilization, increased flexibility, and reduced enzymatic efficiency. Free-energy landscape analysis highlights a shift toward less stable conformational states in mutant HTPs, further supporting their functional impairment. Additionally, the G145R mutation introduces steric hindrance at the active site, preventing thymidine binding and causing off-site interactions. These findings not only provide fundamental insights into the physicochemical and dynamic alterations underlying HTP dysfunction in MNGIE but also establish a computational framework for guiding future experimental studies and the rational design of therapeutic interventions aimed at restoring HTP function.

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

The structure of the R2T complex reveals a different architecture from the related HSP90 cochaperone R2TP

The R2TP complex is a specialized HSP90 cochaperone essential for the maturation of macromolecular complexes such as RNAPII and TORC1. R2TP is formed by a hetero-hexameric ring of AAA-ATPases RuvBL1 and RuvBL2, which interact with RPAP3 and PIH1D1. Several R2TP-like complexes have been described, but these are less well characterized. Here, we identified, characterized and determined the cryo-electron microscopy (cryo-EM) structure of R2T from Arabidopsis thaliana, which lacks PIH1D1 and is probably the only form of the complex in seed plants. In contrast to R2TP, R2T is organized as two rings of AtRuvBL1-AtRuvBL2a interacting back-to-back, with one AtRPAP3 anchored per ring. AtRPAP3 has no effect on the ATPase activity of AtRuvBL1-AtRuvBL2a and binds with a different stoichiometry than in human R2TP. We show that the interaction of AtRPAP3 with AtRuvBL2a and AtHSP90 occurs via a conserved mechanism. However, the distinct architectures of R2T and R2TP suggest differences in their functions and mechanisms.

Predicting the effects of single pathological mutations in hemophilia A and type 2N von Willebrand diseases using AlphaFold2-multimer and AlphaFold3

Most factor VIII (FVIII) in circulation exists in a complex with von Willebrand factor (vWF). The interaction between FVIII and vWF is vital for normal hemostatic function, and disruptions in this interaction can lead to bleeding disorders such as von Willebrand disease or hemophilia. However, the impact of pathological mutations on the binding between FVIII and vWF remains largely uncharacterized. In the current study, we used AlphaFold2-multimer and AlphaFold3 to predict the complex involving FVIII and vWF. Additionally, we explored how known mutations in FVIII or vWF, which can result in mild to severe forms of hemophilia and type 2N von Willebrand disease, affect this complex. Our predictions confirm that AlphaFold2 and AlphaFold3 can accurately model the FVIII/vWF complex in a manner consistent with existing cryogenic electron microscopy structures. However, the single pathological mutations can generally disrupt the complex interface predicted by AlphaFold2-multimer but not AlphaFold3. Molecular dynamic simulations showed that the flexibility of several common regions was affected by single pathological mutations. We further designed a new FVIII construct using AlphaFold2, which holds promise as a more effective therapeutic agent with reduced autoimmune responses. In summary, our findings suggest that in combination with molecular dynamics, AlphaFold2 is a valuable tool for swiftly assessing the impact of both known and novel mutations on hemophilia, with potential applications in precision medicine and the development of novel therapeutic interventions. SIGNIFICANCE STATEMENT: This study provides novel insights into the protein structure of the factor VIII and von Willebrand factor complex. This research demonstrates that AlphaFold2-multimer rather than AlphaFold3 can better predict the variations in the complex corresponding to clinical observations of disease severity. These findings not only deepen our comprehension of hemostatic mechanisms but also establish AlphaFold2 in combination with molecular dynamics as a useful tool for hemophilia research, with potential applications in precision medicine and the development of novel therapeutic interventions.

Genetic and Epigenetic Adaptation Mechanisms of Sheep Under Multi-Environmental Stress Environment

Sheep (Ovis aries), domesticated from wild Asian mouflon ~10,000 years ago, are an important livestock species adapted to various ecological environments. Recent advancements in high-throughput sequencing and global environmental databases have facilitated the exploration of genetic-environmental associations, uncovering the genetic and epigenetic mechanisms behind sheep's adaptation to multiple environments. Studies show that HIF-1α and EPAS1 enhance high-altitude adaptation via hypoxic stress regulation; UCP1 contributes to cold adaptation through non-shivering thermogenesis; SLC4A4 and GPX3 increase drought resistance by regulating renal water reabsorption; and SOCS2 likely plays a role in metabolic and stress response regulation. Additionally, sheep adapt to temperature, drought, and environmental stress through DNA methylation, transcriptional regulation (e.g., SOD1, GPX4), heat shock proteins (e.g., HSP70), and metabolic pathways (e.g., UCP1). These findings offer valuable insights for improving sheep breeding and genetic enhancement. This review summarizes the mechanisms of adaptation to high altitude, cold, heat, drought, and comprehensive climate stress.

Structural insights into the fusion of annexin A5 and fluorescent proteins generating hundredfold differentiated binding affinities to phosphatidylserine

Fluorescent proteins (FPs) are an indispensable part of modern biology. Numerous studies utilize FPs for protein labeling and cell tracking purposes. They are commonly fused with proteins to aid in their visualization. It is generally assumed that these FP tags have minimal impact on the properties of the fusion proteins. Do the FP types affect the function and characteristics of target proteins on earth? So far, there is no definite answer. Fluorescent annexin A5 (AnxA5) has been extensively employed as apoptosis probes. However, except for chemically labeled AnxA5, there are few developed FP-based AnxA5 probes. Therefore, it is essential to screen out suitable FPs for developing high-affinity AnxA5 probes. Here, various fusion proteins (AnxA5-FPs) were developed. The fusion of AnxA5 did not change the chromophore environments of FPs, while the fusion of FPs led to over a 100-fold difference in AnxA5's affinity for phosphatidylserine (PS). We found that polymeric AnxA5-FPs had higher PS-affinity. Remarkably, although the structures of FPs were similar, they fused with AnxA5 in different modes, generating fusion proteins with different spatial conformations. The difference in conformation resulted in variations in the PS-binding pattern of AnxA5, leading to differing levels of PS-affinity. More importantly, we found five high-affinity (Kd > 10-7 M) FP-based AnxA5 probes with different excitation wavelengths. Together, these observations suggested that differences in the fusion modes of AnxA5 and FPs provided a robust mechanism for modulating PS-affinity of AnxA5. We anticipate that our findings can provide a guideline to develop highly sensitive AnxA5 probes.

Progress in the Identification and Design of Novel Antimicrobial Peptides Against Pathogenic Microorganisms

The occurrence and spread of antimicrobial resistance (AMR) pose a looming threat to human health around the world. Novel antibiotics are urgently needed to address the AMR crisis. In recent years, antimicrobial peptides (AMPs) have gained increasing attention as potential alternatives to conventional antibiotics due to their abundant sources, structural diversity, broad-spectrum antimicrobial activity, and ease of production. Given its significance, there has been a tremendous advancement in the research and development of AMPs. Numerous AMPs have been identified from various natural sources (e.g., plant, animal, human, microorganism) based on either well-established isolation or bioinformatic pipelines. Moreover, computer-assisted strategies (e.g., machine learning (ML) and deep learning (DL)) have emerged as a powerful and promising technology for the accurate prediction and design of new AMPs. It may overcome some of the shortcomings of traditional antibiotic discovery and contribute to the rapid development and translation of AMPs. In these cases, this review aims to appraise the latest advances in identifying and designing AMPs and their significant antimicrobial activities against a wide range of bacterial pathogens. The review also highlights the critical challenges in discovering and applying AMPs.

Advancements in protein structure prediction: A comparative overview of AlphaFold and its derivatives

This review provides a comprehensive analysis of AlphaFold (AF) and its derivatives (AF2 and AF3) in protein structure prediction. These tools have revolutionized structural biology with their highly accurate predictions, driving progress in protein modeling, drug discovery, and the study of protein dynamics. Its exceptional accuracy has redefined our understanding of protein folding, which enables groundbreaking advancements in protein design, disease research and discusses future integration with experimental techniques. In addition, their achievement features, architectures, important case studies, and noteworthy effects in the field of biology and medicine were evaluated. In consideration of the fact that AF2 is a relatively recent innovation, it has already been taken into account in many studies that highlight its applications in many ways. Moreover, the limitations of AF2 that directed to the introduction of AF3 are also reported, which is a great improvement as it provides precise predictions of the structures and interactions of proteins, DNA, RNA, and ligands, thereby aiding in the understanding of the molecular level. Addressing current challenges and forecasting future developments, this work underscores the lasting significance of AF in reshaping the scientific landscape of protein research.

Applications of Artificial Intelligence in Drug Repurposing

Drug repurposing identifies new therapeutic uses for the existing drugs originally developed for different indications, aiming at capitalizing on the established safety and efficacy profiles of known drugs. Thus, it is beneficial to bypass of early stages of drug development, and to reduction of the time and cost associated with bringing new therapies to market. Traditional experimental methods are often time-consuming and expensive, making artificial intelligence (AI) a promising alternative due to its lower cost, computational advantages, and ability to uncover hidden patterns. This review focuses on the availability of AI algorithms in drug development, and their positive and specific roles in revealing repurposing of the existing drugs, especially being integrated with virtual screening. It is shown that the existing AI algorithms excel at analyzing large-scale datasets, identifying the complicated patterns of drug responses from these datasets, and making predictions for potential drug repurposing. Building on these insights, challenges remain in developing efficient AI algorithms and future research, including integrating drug-related data across databases for better repurposing, enhancing AI computational efficiency, and advancing personalized medicine.

AlphaFold3 versus experimental structures: assessment of the accuracy in ligand-bound G protein-coupled receptors

G protein-coupled receptors (GPCRs) are critical drug targets involved in numerous physiological processes, yet many of their structures remain unresolved due to inherent flexibility and diverse ligand interactions. This study systematically evaluates the accuracy of AlphaFold3-predicted GPCR structures compared to experimentally determined structures, with a primary focus on ligand-bound states. Our analysis reveals that while AlphaFold3 shows improved performance over AlphaFold2 in predicting overall GPCR backbone architecture, significant discrepancies persist in ligand-binding poses, particularly for ions, peptides, and proteins. Despite advancements, these limitations constrain the utility of AlphaFold3 models in functional studies and structure-based drug design, where high-resolution details of ligand interactions are crucial. We assess the accuracy of predicted structures across various ligand types, quantifying deviations in binding pocket geometries and ligand orientations. Our findings highlight specific challenges in the computational prediction of ligand-bound GPCR structures, emphasizing areas where further refinement is needed. This study provides valuable insights for researchers using AlphaFold3 in GPCR studies, underscores the ongoing necessity for experimental structure determination, and offers direction for improving protein-ligand interaction predictions in future computational models.

Advances in designed bionanomolecular assemblies for biotechnological and biomedical applications

Recent advances in protein engineering have revolutionized the design of bionanomolecular assemblies for functional therapeutic and biotechnological applications. This review highlights the progress in creating complex protein architectures, encompassing both finite and extended assemblies. AI tools, including AlphaFold, RFDiffusion, and ProteinMPNN, have significantly enhanced the scalability and success of de novo designs. Finite assemblies, like nanocages and coiled-coil-based structures, enable precise molecular encapsulation or functional protein domain presentation. Extended assemblies, including filaments and 2D/3D lattices, offer unparalleled structural versatility for applications such as vaccine development, responsive biomaterials, and engineered cellular scaffolds. The convergence of artificial intelligence-driven design and experimental validation promises strong acceleration of the development of tailored protein assemblies, offering new opportunities in synthetic biology, materials science, biotechnology, and biomedicine.

Mining Highly Active Oleate Hydratases by Structure Clustering, Sequence Clustering, and Ancestral Sequence Reconstruction

Oleate hydratases (Ohys) catalyze the conversion of oleic acid (OA) to 10-(R)-hydroxystearic acid (10-HSA), a compound widely used in the chemical industry. However, the limited activity of Ohys has hindered their broader applications. To address this limitation, we propose a novel strategy for mining highly active Ohys through structure clustering, sequence clustering, and ancestral sequence reconstruction (SSA strategy). Structure clustering via AI-driven protein structure prediction followed by classification enhanced the ability to mine target Ohys. Ancestral enzyme reconstruction was carried out based on mining results from both structure and sequence clustering. This strategy significantly reduces the time and cost of the discovery process. Among the 1304 Ohys screened via SSA, 13 candidates were selected. Seven candidates demonstrated high activity. Ohy 64, identified through structure clustering, exhibited the highest activity. Ancestral enzymes that were reconstructed from structure clustering targets were 3 times more likely to exhibit high catalytic activity than those identified through sequence clustering. Four critical, hydrophobic residues were identified through structure and sequence comparisons between StOhy and targets mined by SSA. Site-directed mutagenesis revealed that these hydrophobic residues conferred varying levels of enzyme activity, confirming that increased hydrophobicity at these positions enhances cofactor FAD binding, thus improving enzyme catalytic efficiency.

Computational Analysis of Plasmodium falciparum DNA Damage Inducible Protein 1 (PfDdi1): Insights into Binding of Artemisinin and its Derivatives and Implications for Antimalarial Drug Design

Human malaria remains a global health challenge, with Plasmodium falciparum responsible for the most severe cases. Despite global efforts, eradicating malaria has proven difficult, mainly because of the rise in drug resistance, particularly against artemisinin and its derivatives. One possible cause of this resistance is the activation of the unfolded protein response (UPR), which helps maintain cellular balance under stress. In P. falciparum, the UPR operates through the ubiquitin-proteasome system (UPS), which involves proteins such as Dsk2, Rad23, and Ddi1. Among these, Plasmodium falciparum DNA-damage-inducible protein 1 (PfDdi1) plays a crucial role in DNA repair and is present throughout the parasite life cycle, making it an attractive drug target. However, there is limited research on PfDdi1 as a therapeutic target. Recent in vitro studies have indicated that artemisinin (ART) and dihydroartemisinin (DHA) inhibit PfDdi1 activity. Building on this, we investigated whether ART and its derivatives could serve as inhibitors of PfDdi1 using computational modeling. Our study included clinically relevant ART derivatives such as artemether (ARM), arteether (AET), artemiside (AMD), and artesunate (ATS). All these compounds showed strong binding to PfDdi1, with free binding energies ranging from -20.75 kcal/mol for AET to -34.24 kcal/mol for ATS. ARM increased PfDdi1's structural rigidity and hydrophobic stability, whereas AMD improved its kinetic stability, resulting in the least residue motion. Unlike AET and AMD, the other ligands destabilize the PfDdi1 structure. Importantly, three key binding regions-Loop 1 (GLN 266 - ILE 269), Loop 2 (ILE 323 - TYR 326), and Loop 3 (ALA 292 - GLY 294)-were identified as potential targets for new antimalarial drugs against PfDdi1. This study highlights the potential of ART derivatives as PfDdi1 inhibitors, paving the way for further experimental validation.

Neutrophil elastase binds at the central domain of extracellular Toll-like receptor 4: AI prediction, docking, and validation in disease model

The interaction between Neutrophil Elastase (NE) and Toll-like receptor 4 (TLR4) has attracted substantial scientific attention, particularly regarding its potential role in cardiovascular diseases. Employing AlphaFold2, biomolecular docking, and MMGBSA calculation we aimed to predict their binding and validated the results through a co-immunoprecipitation study in a rat model with isoproterenol (ISO) -induced cardiac hypertrophy. Our findings strongly suggest a specific and plausible interaction between rat NE and rat TLR4, distinct from other neutrophil-derived serine proteases. Notably, AlphaFold2's precision was confirmed through cross-validation with known protein crystal structures, while Consurf analysis emphasized the evolutionary variable to conserve the rat NE - rat TLR4 binding site. HADDOCK, RosettaDock, ZDOCK, MD simulation, MMGBSA calculations, and superimposition with the stabilized structure complex all predicted strong binding between rat NE and rat TLR4. Our animal experiments revealed elevated NE and TLR4 expression in the hypertrophied myocardium following ISO infusion, with data confirming the physical interaction between NE and TLR4. Overall, this study sheds light on the intricate molecular association between NE and TLR4, underlining their potential significance in cardiovascular pathophysiology. Furthermore, it underscores AlphaFold2's reliability as a robust tool for predicting protein-protein interactions and complex structures.

Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery

Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, protein structures have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy. While these methods are effective and are considered the gold standard, they are very resource-intensive and time-consuming, ultimately limiting their scalability. However, with recent developments in computational biology and artificial intelligence (AI), the field of protein prediction has been revolutionized. Innovations like AlphaFold and RoseTTAFold enable protein structure predictions to be made directly from amino acid sequences with remarkable speed and accuracy. Despite the enormous enthusiasm associated with these newly developed AI-approaches, their true potential in structure-based drug discovery remains uncertain. In fact, although these algorithms generally predict overall protein structures well, essential details for computational ligand docking, such as the exact location of amino acid side chains within the binding pocket, are not predicted with the necessary accuracy. Additionally, docking methodologies are considered more as a hypothesis generator rather than a precise predictor of ligand-target interactions, and thus, usually identify many false-positive hits among only a few correctly predicted interactions. In this paper, we are reviewing the latest development in this cutting-edge field with emphasis on the GPCR target class to assess the potential role of AI approaches in structure-based drug discovery.

MedKG: enabling drug discovery through a unified biomedical knowledge graph

Biomedical knowledge graphs have emerged as powerful tools for drug discovery, but existing platforms often suffer from outdated information, limited accessibility, and insufficient integration of complex data. This study presents MedKG, a comprehensive and continuously updated knowledge graph designed to address these challenges in precision medicine and drug discovery. MedKG integrates data from 35 authoritative sources, encompassing 34 node types and 79 relationships. A Continuous Integration/Continuous Update pipeline ensures MedKG remains current, addressing a critical limitation of static knowledge bases. The integration of molecular embeddings enhances semantic analysis capabilities, bridging the gap between chemical structures and biological entities. To demonstrate MedKG's utility, a novel hybrid Relational Graph Convolutional Network for disease-drug link prediction, MedLINK was developed and used in case studies on clinical trial data for disease drug link prediction. Furthermore, a web-based application with user-friendly APIs and visualization tools was built, making MedKG accessible to both technical and non-technical users, which is freely available at http://pitools.niper.ac.in/medkg/.

Binding Free Energy Analysis of Colicin D, E3 and E8 to Their Respective Cognate Immunity Proteins Using Computational Simulations

Colicins are antimicrobial proteins produced by bacteria for the purpose of destroying neighboring bacteria. Colicin activity is neutralized by a specific cognate immunity protein in order to protect the host. This study investigates the structural and binding mechanisms underlying the interaction of colicin-D, -E3 and -E8 to their respective immunity proteins (ImD, Im3 and Im8) using structure prediction, molecular dynamics (MD) simulations and MM-PBSA approach of free energy calculations. High-confidence colicin-immunity (Col-Im) complex structures predicted using AlphaFold2 were subjected to MD simulations of 150 ns with GROMACS and were analyzed for the binding free energy calculation using gmx_MMPBSA. Results showed that the complex of Col_E3-Im3 exhibited the most favorable binding free energy, driven by strong van der Waals and electrostatic interactions. Col_D-ImD and Col_E8-Im8 also showed the favorable binding. Electrostatics and hydrogen bonding emerged as a key factor driving binding and stability, while polar solvation acted as a destabilizing factor across all systems. These outcomes provide an understanding of the molecular mechanisms of Col-Im systems, with potential applications for developing natural antimicrobials for food safety.

Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues

Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregates formation. Despite the many advancements made so far, the performance of docking protocols is deeply dependent on their capability to identify binding regions. From this, the importance of developing low-cost and computationally efficient methods in this field. We present an integrated novel protocol mainly based on compact modeling of protein surface patches via sets of orthogonal polynomials to identify regions of high shape/electrostatic complementarity. By incorporating both hydrophilic and hydrophobic contributions, we define new binding matrices, which serve as effective inputs for training a neural network. In this work, we propose a new Neural Network (NN)-based architecture, Core Interacting Residues Network (CIRNet), which achieves a performance in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC) of approximately 0.87 in identifying pairs of core interacting residues on a balanced data set. In a blind search for core interacting residues, CIRNet distinguishes them from random decoys with an ROC AUC of 0.72. We test this protocol to enhance docking algorithms by filtering the proposed poses, addressing one of the still open problems in computational biology. Notably, when applied to the top ten models from three widely used docking servers, CIRNet improves docking outcomes, significantly reducing the average RMSD between the selected poses and the native state. Compared to another state-of-the-art tool for rescaling docking poses, CIRNet more efficiently identified the worst poses generated by the three docking servers under consideration and achieved superior rescaling performance in two cases.

Multi-scale structural similarity embedding search across entire proteomes

The rapid expansion of three-dimensional (3D) biomolecular structure information, driven by breakthroughs in artificial intelligence/deep learning (AI/DL)-based structure predictions, has created an urgent need for scalable and efficient structure similarity search methods. Traditional alignment-based approaches, such as structural superposition tools, are computationally expensive and challenging to scale with the vast number of available macromolecular structures. Herein, we present a scalable structure similarity search strategy designed to navigate extensive repositories of experimentally determined structures and computed structure models predicted using AI/DL methods. Our approach leverages protein language models and a deep neural network architecture to transform 3D structures into fixed-length vectors, enabling efficient large-scale comparisons. Although trained to predict TM-scores between single-domain structures, our model generalizes beyond the domain level, accurately identifying 3D similarity for full-length polypeptide chains and multimeric assemblies. By integrating vector databases, our method facilitates efficient large-scale structure retrieval, addressing the growing challenges posed by the expanding volume of 3D biostructure information.

AlphaFold2-Guided Functional Screens Reveal a Conserved Antioxidant Protein at ER Membranes

Oxidative protein folding in the endoplasmic reticulum (ER) is essential for all eukaryotic cells yet generates hydrogen peroxide (H2O2), a reactive oxygen species (ROS). The ER-transmembrane protein that provides reducing equivalents to ER and guards the cytosol for antioxidant defense remains unidentified. Here we combine AlphaFold2-based and functional reporter screens in C. elegans to discover a previously uncharacterized and evolutionarily conserved protein ERGU-1 that fulfills these roles. Deleting C. elegans ERGU-1 causes excessive H2O2 and transcriptional gene up-regulation through SKN-1, homolog of mammalian antioxidant master regulator NRF2. ERGU-1 deficiency also impairs organismal reproduction and behavioral responses to H2O2. Both C. elegans and human ERGU-1 proteins localize to ER membranes and form network reticulum structures. Human and Drosophila homologs of ERGU-1 can rescue C. elegans mutant phenotypes, demonstrating evolutionarily ancient and conserved functions. In addition, purified ERGU-1 and human homolog TMEM161B exhibit redox-modulated oligomeric states. Together, our results reveal an ER-membrane-specific protein machinery for peroxide detoxification and suggest a previously unknown and conserved mechanisms for antioxidant defense in animal cells.

Progress of machine learning in the application of small molecule druggability prediction

Machine learning (ML) has become an important tool for predicting the pharmaceutical properties of small molecules. Recent advancements in ML algorithms enable the rapid and accurate evaluation of solubility, activity, toxicity, pharmacokinetics, and other molecular properties through ML-based models. By conducting virtual screening of drug targets and elucidating drug-target protein interactions, researchers can conduct preliminary evaluations of the activity and safety of compounds from the ultra-large drug compound libraries, thereby accelerating the screening process for lead compounds. Moreover, ML leverages existing experimental data to train and generate new datasets, addressing the challenge of limited compounds and protein target data. This review provided a concise overview of ML applications in predicting small molecule properties, focusing on model construction principles, molecular feature selection, and other essential aspects. It also discussed the potential applications of ML in the screening of pharmaceutical small molecules.

Enhanced thermostability of nattokinase by rational design of disulfide bond

Nattokinase, the thrombolytically active substance in the health food natto, nevertheless, its lower thermostability restricts its use in food and pharmaceutical applications. In this study, two heat-resistant variants of nattokinase, designated 50-109 (M1) and 15-271 (M2), were successfully obtained by introducing a disulfide bonding strategy. Their half-lives at 55℃ were found to be 2.50-fold and 5.17-fold higher, respectively, than that of the wild type. Furthermore, the specific enzyme activities of the variants, M1 and M2, were also increased by 2.37 and 1.66-fold, respectively. Meanwhile, the combination of two mutants increased the thermostability of nattokinase by 8.0-fold. Bioinformatics analyses indicated that the enhanced thermostability of the M1 and M2 variants was due to the increased rigidity and structural contraction of the overall structure. Finally, the fermentation process of mutant M1 was optimized to increase the expression of nattokinase. Study provides substantial molecular and theoretical support for the industrial production and application of nattokinase.

Experimental approaches to investigate biophysical interactions between homeodomain transcription factors and DNA

Homeodomain transcription factors (TFs) bind to specific DNA sequences to regulate the expression of target genes. Structural work has provided insight into molecular identities and aided in unraveling structural features of these TFs. However, the detailed affinity and specificity by which these TFs bind to DNA sequences is still largely unknown. Qualitative methods, such as DNA footprinting, Electrophoretic Mobility Shift Assays (EMSAs), Systematic Evolution of Ligands by Exponential Enrichment (SELEX), Bacterial One Hybrid (B1H) systems, Surface Plasmon Resonance (SPR), and Protein Binding Microarrays (PBMs) have been widely used to investigate the biochemical characteristics of TF-DNA binding events. In addition to these qualitative methods, bioinformatic approaches have also assisted in TF binding site discovery. Here we discuss the advantages and limitations of these different approaches, as well as the benefits of utilizing more quantitative approaches, such as Mechanically Induced Trapping of Molecular Interactions (MITOMI), Microscale Thermophoresis (MST) and Isothermal Titration Calorimetry (ITC), in determining the biophysical basis of binding specificity of TF-DNA complexes and improving upon existing computational approaches aimed at affinity predictions.

Rapid Evolution in Action: Environmental Filtering Supports Coral Adaptation to a Hot, Acidic, and Deoxygenated Extreme Habitat

The semienclosed Bouraké lagoon in New Caledonia is a natural system that enables observation of evolution in action with respect to stress tolerance in marine organisms, a topic directly relevant to understanding the consequences of global climate change. Corals inhabiting the Bouraké lagoon endure extreme conditions of elevated temperature (> 33°C), acidification (7.2 pH units), and deoxygenation (2.28 mg O2 L-1), which fluctuate with the tide due to the lagoon's geomorphology. To investigate the underlying bases of the apparent stress tolerance of these corals, we combined whole genome resequencing of the coral host and ITS2 metabarcoding of the photosymbionts from 90 Acropora tenuis colonies from three localities along the steep environmental gradient from Bouraké to two nearby control reefs. Our results highlight the importance of coral flexibility to associate with different photosymbionts in facilitating stress tolerance of the holobiont; but, perhaps more significantly, strong selective effects were detected at specific loci in the host genome. Fifty-seven genes contained SNPs highly associated with the extreme environment of Bouraké and were enriched in functions related to sphingolipid metabolism. Within these genes, the conserved sensor of noxious stimuli TRPA1 and the ABCC4 transporter stood out due to the high number of environmentally selected SNPs that they contained. Protein 3D structure predictions suggest that a single-point mutation causes the rotation of the main regulatory domain of TRPA1, which may be behind this case of natural selection through environmental filtering. While the corals of the Bouraké lagoon provide a striking example of rapid adaptation to extreme conditions, overall, our results highlight the need to preserve the current standing genetic variation of coral populations to safeguard their adaptive potential to ongoing rapid environmental change.

RoseTTAFold diffusion-guided short peptide design: a case study of binders against Keap1/Nrf2

In this study, we proposed a novel comprehensive computational framework that combines deep generative modeling with in silico peptide optimization to expedite the discovery of bioactive compounds. Our methodology utilizes RFdiffusion, a variation of the RoseTTAFold model for protein design, in tandem with ProteinMPNN, a deep neural network for protein sequence optimization, to provide short candidate peptides for targeted binding interactions. As a proof-of-concept, we focused on Keap1 (Kelch-like ECH-associated protein 1), a key regulator in the Keap1/Nrf2 antioxidant pathway. To achieve this, we designed peptide sequences that would interact with specific binding subpockets within its Kelch domain. We integrated machine learning models to forecast essential peptide properties, including toxicity, stability, and allergenicity, thus enhancing the selection of prospective candidates. Our in silico screening identified eight top candidates that exhibited strong binding affinity and good biophysical characteristics. The candidates underwent additional validation via comprehensive molecular dynamics simulations, which confirmed their strong binding contacts and structural stability over time. This integrated framework offers a scalable and adaptable platform for the rapid design of therapeutic peptides, merging breakthrough computational techniques with focused case studies. Furthermore, our modular methodology facilitates its straightforward adaptation to alternative protein targets, hence considerably enhancing its potential influence in drug development and discovery.