An iterative knowledge-based scoring function for protein-protein recognition

Assessment of Phage-Displayed Peptides Targeting Cancer Cell Surface Proteins: A Comprehensive Molecular Docking Study

Peptides binding overexpressed breast and cervical cancer cell surface proteins can be isolated by phage display technology, and their affinity to their potential receptors can be assessed by molecular docking. We isolated 44 phage clones displaying dodecapeptides with high affinity to HeLa cervical cancer and MDA-MB-231 (MDA) breast cancer cells by repeated biopanning of an MK13 phage library and explored their affinity to specific proteins by molecular docking. Six peptides appeared repeatedly during biopanning: two with affinity to HeLa (H5/H21), and four with affinity to MDA cells (M3/M7/M15/M17). Peptide pairs M3/H5 and H1/M17 had affinity to both cell lines. A systematic review identified Annexin A2, EGFR, CD44, CD146, and Integrin alpha V as potential protein targets in HeLa cells, and Vimentin, Galectin-1, and Annexins A1 and A5 in MDA cells. Via virtual screening, we selected six peptides with the highest total docking scores: H1 (-916.32), H6 (-979.21), H19 (-1093.24), M6 (-732.21), M16 (-745.5), and M19 (-739.64), and identified that docking scores were strengthened by the protein type, the interacting amino acid side chains, and the polarity of peptides. This approach facilitates the selection of relevant peptides that could be further explored for active targeting in cancer diagnosis and treatment.

The evolution and functional characterization of transcription factors E2Fs in lamprey, Lethenteron reissneri

The E2 promoter binding factors (E2Fs) are a group of transcriptional regulators that govern the cell cycle and play crucial roles in various cellular physiological processes, including proliferation and embryonic development. In this study, we identified four homologous genes-Lr-E2F3, Lr-E2F4, Lr-E2F5, and Lr-E2F8-from the lamprey (Lethenteron reissneri) genome database. Phylogenetic tree analysis was conducted to elucidate the evolutionary relationships within the E2F family across different species. Furthermore, analyses of motifs, domains, gene structures, and 3D structures reinforced the conservation of the E2F family. Notably, synteny analysis revealed that the neighboring genes of the Lr-E2Fs exhibited greater diversity compared to those in jawed vertebrates. Activity assays indicated that Lr-E2Fs may be involved in lamprey innate immunity mediated by NF-кB. Additionally, morphological observations of embryos microinjected with Cas9/sgRNA demonstrated that E2F-deficient lamprey embryos displayed embryonic lethality, suggesting that Lr-E2Fs play a significant role in lamprey embryonic development. In summary, our research not only provides new insights into the evolution of Lr-E2Fs but also offers valuable clues regarding their functional roles.

Design and Binding Affinity of Antisense Peptides for Snake Venom Neutralization

Snakebites are a significant public health problem in many tropical and subtropical regions, causing extensive morbidity and mortality. Traditional snake antivenoms face multiple challenges, including allergenicity, high production costs, and logistical difficulties, highlighting the urgent need for new therapeutic approaches. This pilot study explores the potential of oligopeptides as therapeutic inhibitors targeting the neurotoxic sites of ammodytoxin A (AtxA; PDB: 3G8G) from Vipera ammodytes. We selected two sense oligopeptides to represent critical neurotoxic regions of AtxA as targets for inhibition by complementary antisense peptides. Utilizing a heuristic antisense peptide design based on the molecular recognition theory, we modeled two antisense oligopeptides as complementary counterparts for each sense oligopeptide. The modeled sense and antisense peptides were commercially synthesized, and their binding affinities were evaluated using spectrofluorometric titrations. The determined dissociation constants (KD) were in the range of 1-10 μM for all sense-antisense pairs, revealing relatively strong binding affinities. Confirmation of sense-antisense peptide binding prompted further investigation into their potential binding to the native target protein through global docking simulations using the HPEPDOCK web server. The results highlight the applicability of molecular recognition theory in the development of antisense peptides that could change therapeutic strategies in various biomedical fields. Further studies are needed to investigate the therapeutic efficacy and broader applications of these peptides.

In silico evaluation of missense SNPs in cancer-associated Cystatin A protein and their potential to disrupt Cathepsin B interaction

Cystatin A (CSTA) functions as a cysteine protease inhibitor by forming tight complexes with the cathepsins. Pathogenic mutations in the CSTA gene can disrupt this interaction, potentially leading to physiological ailments. In this study, eight bioinformatics tools (SIFT, PolyPhen-2, PROVEAN, P-Mut, MutPred2, SNAP2, SNPs & GO, and PHD-SNP) were implemented to analyze non-synonymous SNPs from the dbSNP database. Five mutations (Y43C, Y43N, V48F, Y53H, and E94K) located in the conserved region were found to be highly deleterious and less stabilizing. The protein-protein interaction network found that Cathepsin B (CTSB) interacts highly with CSTA. Mutated CSTAs were created by homology modeling, and their altered binding with CTSB was examined through molecular docking and dynamics simulations. Among these, the Y53H (rs1448459675) and E94K (rs200394711) mutants were recognized as weaker inhibitors because they had 2.5 % and an 8 % lower binding affinity, respectively. Moreover, the E94K-CTSB complex, with a root mean square deviation (RMSD) above 5 Å, was found to be highly unstable during molecular dynamics. The root mean square fluctuation (RMSF) of the E94K mutant showed insufficient flexibility, indicating a reduced capacity to suppress CTSB. These findings suggest that the E94K mutation could affect the protein structure and cathepsin B interaction, potentially leading to pathological consequences as evidenced by colorectal adenocarcinoma patients in the COSMIC (Catalogue of Somatic Mutations in Cancer) database.

Exploring miR-34a, miR-449, and ADAM2/ADAM7 Expressions as Potential Biomarkers in Male Infertility: A Combined In Silico and Experimental Approach

miR-34a and miR-449 are key miRNAs involved in sperm function and male fertility, with their dysregulation potentially contributing to male infertility. ADAM proteins, specifically ADAM2 and ADAM7, are also implicated in sperm function. This study investigates the interactions between miR-34a, miR-449, and ADAM2/ADAM7, exploring their roles in male infertility through both experimental analyses and molecular docking. In this case-control study, 15 infertile males and 15 healthy controls were included. Gene expression levels of miR-34a, miR-449, and SOX30 were measured using real-time PCR, while protein levels of ADAM7 and ADAM2 in sperm were assessed through western blotting. Additionally, molecular docking was performed to analyze the binding affinities between miR-34a/miR-449 and ADAM2/ADAM7, with docking scores and confidence levels evaluated. Expression levels of ADAM7 and ADAM2 proteins in sperm from the infertile group showed significant differences compared with the control group (P ≤ 0.05). A significant difference was observed in the expression of miR-449, miR-34a, and SOX30 genes between the control and infertile groups (P < 0.05). A significant correlation between miR-34a expression, ADAM7 protein expression, and sperm morphology was observed. However, no statistically significant correlation was found between miR-34a expression and sperm motility, sperm count, blastocyst, or embryo rates in ICSI and IVF (P ≥ 0.05). Molecular docking and dynamics studies revealed strong interactions between miR-34a/miR-449 and ADAM proteins. The ADAM7/miR-34a complex showed the highest binding affinity with a docking score of - 372.40 and a confidence score of 0.9884, followed by ADAM7/miR-449. Hydrogen bond analysis indicated stable binding, with 9 bonds for ADAM2/miR-34a and 7 for ADAM7/miR-34a. These interactions suggest a significant role in regulating sperm morphology and function.miR-34a, miR-449, ADAM7, and ADAM2 protein expression appear to be involved in the molecular mechanisms of male infertility. These parameters show potential as biomarkers in assisted reproductive technology techniques, particularly by influencing sperm morphology and function.

Performance of Hybrid Strategies Combining MDockPP and AlphaFold2 in CAPRI Rounds 47-55

CAPRI challenges offer a range of blind tests for biomolecule interaction prediction. This study evaluates the performance of our prediction protocols for the human group Zou and the server group MDockPP in CAPRI rounds 47-55, highlighting the impact of AlphaFold2 (AF2) and the effectiveness of massive sampling approaches. Prior to AlphaFold2's release, our methods relied on homology modeling and docking-based protocols, achieving limited accuracy due to constraints in structural templates and inherent docking limitations. After AlphaFold2's public release, which demonstrated breakthrough accuracy in protein structure prediction, we integrated its multimer models and massive sampling techniques into our protocols. This integration significantly improved prediction accuracy, with human predictions increasing from 1 correct interface of 19 pre-AlphaFold2 to 4 of 8 post-AlphaFold2. The massive sampling approach further enhanced performance, particularly for targets T231 and T233, yielding medium-quality models that default parameters could not achieve.

Beclin 1 prevents ISG15-mediated cytokine storms to secure fetal hematopoiesis and survival

Proper control of inflammatory responses is essential for embryonic development, but the underlying mechanism is poorly understood. Here, we show that under physiological conditions, inactivation of ISG15, an inflammation amplifier, is associated with the interaction of Beclin 1 (Becn1), via its evolutionarily conserved domain, with STAT3 in the major fetal hematopoietic organ of mice. Conditional loss of Becn1 caused sequential dysfunction and exhaustion of fetal liver hematopoietic stem cells, leading to lethal inflammatory cell-biased hematopoiesis in the fetus. Molecularly, the absence of Becn1 resulted in the release of STAT3 from Becn1 tethering and subsequent phosphorylation and translocation to the nucleus, which in turn directly activated the transcription of ISG15 in fetal liver hematopoietic cells, coupled with increased ISGylation and production of inflammatory cytokines, whereas inactivating STAT3 reduced ISG15 transcription and inflammation but improved hematopoiesis potential, and further silencing ISG15 mitigated the above collapse in the Becn1-null hematopoietic lineage. The Becn1/STAT3/ISG15 axis remains functional in autophagy-disrupted fetal hematopoietic organs. These results suggest that Becn1, in an autophagy-independent manner, secures hematopoiesis and survival of the fetus by directly inhibiting STAT3/ISG15 activation to prevent cytokine storms. Our findings highlight a previously undocumented role of Becn1 in governing ISG15 to safeguard the fetus.

Pharmacological Dissection Identifies Retatrutide Overcomes the Therapeutic Barrier of Obese TNBC Treatments through Suppressing the Interplay between Glycosylation and Ubiquitylation of YAP

Triple-negative breast cancer (TNBC) in obese patients remains challenging. Recent studies have linked obesity to an increased risk of TNBC and malignancies. Through multiomic analysis and experimental validation, a dysfunctional Eukaryotic Translation Initiation Factor 3 Subunit H (EIF3H)/Yes-associated protein (YAP) proteolytic axis is identified as a pivotal junction mediating the interplay between cancer-associated adipocytes and the response to anti-cancer drugs in TNBC. Mechanistically, cancer-associated adipocytes drive metabolic reprogramming resulting in an upregulated hexosamine biosynthetic pathway (HBP). This aberrant upregulation of HBP promotes YAP O-GlcNAcylation and the subsequent recruitment of EIF3H deubiquitinase, which stabilizes YAP, thus promoting tumor growth and chemotherapy resistance. It is found that Retatrutide, an anti-obesity agent, inhibits HBP and YAP O-GlcNAcylation leading to increased YAP degradation through the deprivation of EIF3H-mediated deubiquitylation of YAP. In preclinical models of obese TNBC, Retatrutide downregulates HBP, decreases YAP protein levels, and consequently decreases tumor size and enhances chemotherapy efficacy. This effect is particularly pronounced in obese mice bearing TNBC tumors. Overall, these findings reveal a critical interplay between adipocyte-mediated metabolic reprogramming and EIF3H-mediated YAP proteolytic control, offering promising therapeutic strategies to mitigate the adverse effects of obesity on TNBC progression.

Irisin-mediated KEAP1 degradation alleviates oxidative stress and ameliorates pancreatitis

Oxidative stress (OS) injury is pivotal in acute pancreatitis (AP) pathogenesis, contributing to inflammatory cascades. Irisin, a ubiquitous cytokine, exhibits antioxidant properties. However, the role of irisin in AP remains inconclusive. Our study aims to elucidate irisin expression in AP patients and investigate its mechanism of action to propose a novel treatment strategy for AP. Serum irisin levels in 65 AP patients were quantified using an enzyme-linked immunosorbent assay and correlated with disease severity scores. Core genes implicated in AP-related oxidative stress were identified and screened via bioinformatics analysis. The therapeutic efficacy of irisin in AP was confirmed using a murine cerulein-induced AP model. The intrinsic mechanism of irisin's antioxidative stress action was investigated and verified in pancreatic AR42J cells (Supplementary Fig. 1). Common targets shared by irisin and AP were further validated using a molecular docking model which was constructed for virtual docking analysis. This study investigated alterations in redox status in AP and found a significant reduction in serum irisin levels, correlating inversely with AP severity. In a murine AP model, we showed that irisin triggers an antioxidative stress program via the KEAP1 gene; this process helps reestablish redox balance by decreasing the buildup of reactive oxygen species (ROS) and suppressing the secretion of inflammatory mediators within pancreatic tissues Notably, increased KEAP1 expression counteracted the antioxidative effects of irisin. Our findings unveil a novel therapeutic mechanism for AP, wherein irisin inhibits KEAP1 to alleviate OS. Increasing irisin levels in vivo presents a promising strategy for AP treatment.

Synthesis and Antifungal Activity of Fmoc-Protected 1,2,4-Triazolyl-α-Amino Acids and Their Dipeptides Against Aspergillus Species

In recent years, fungal infections have emerged as a significant health concern across veterinary species, especially in livestock such as cattle, where fungal diseases can result in considerable economic losses, as well as in humans. In particular, Aspergillus species, notably Aspergillus flavus and Aspergillus versicolor, are opportunistic pathogens that pose a threat to both animals and humans. This study focuses on the synthesis and antifungal evaluation of novel 9-fluorenylmethoxycarbonyl (Fmoc)-protected 1,2,4-triazolyl-α-amino acids and their dipeptides, designed to combat fungal pathogens. More in detail, we evaluated their antifungal activity against various species, including Aspergillus versicolor (ATCC 12134) and Aspergillus flavus (ATCC 10567). The results indicated that dipeptide 7a exhibited promising antifungal activity against Aspergillus versicolor with an IC50 value of 169.94 µM, demonstrating greater potency than fluconazole, a standard treatment for fungal infections, which showed an IC50 of 254.01 µM. Notably, dipeptide 7a showed slightly enhanced antifungal efficacy compared to fluconazole also in Aspergillus flavus (IC50 176.69 µM vs. 184.64 µM), suggesting that this dipeptide might be more potent even against this strain. Remarkably, 3a and 7a are also more potent than fluconazole against A. candidus 10711. On the other hand, the protected amino acid 3a demonstrated consistent inhibition across all tested Aspergillus strains, but with an IC50 value of 267.86 µM for Aspergillus flavus, it was less potent than fluconazole (IC50 184.64 µM), still showing some potential as a good antifungal molecule. Overall, our findings indicate that the synthesized 1,2,4-triazolyl derivatives 3a and 7a hold significant promise as potential antifungal agents in treating Aspergillus-induced diseases in cattle, as well as for broader applications in human health. Our mechanistic studies based on molecular docking revealed that compounds 3a and 7a bind to the same region of the sterol 14-α demethylase as fluconazole. Given the rising concerns about antifungal resistance, these amino acid derivatives, with their unique bioactive structures, could serve as a novel class of therapeutic agents. Further research into their in vivo efficacy and safety profiles is warranted to fully realize their potential as antifungal drugs in clinical and agricultural settings.

Nanostructural Modulation of G-Quadruplex DNA in Neurodegeneration: Orotate Interaction Revealed Through Experimental and Computational Approaches

The natural compound orotic acid and its anionic form, orotate, play a pivotal role in various biological processes, serving as essential intermediates in pyrimidine de novo synthesis, with demonstrated connections to dietary, supplement, and neurodrug applications. A novel perspective on biomolecular aggregation at the nanoscale, particularly pertinent to neurodegeneration, challenges the established paradigm positing that peptide (amyloid beta) and protein (tau) aggregation mainly govern the molecular events underlying prevalent neuropathologies. Emerging biological evidence indicates a notable role for G-quadruplex (G4) DNA aggregation in neurodegenerative processes affecting neuronal cells, particularly in the presence of extended (G4C2)n repeats in nuclear DNA sequences. Our study concerns d[(GGGGCC)3GGGG], a G4-forming DNA model featuring G4C2 repeats that is in correlation with neurodegeneration. Through different investigations utilizing spectroscopic techniques (CD, UV, and thermal denaturations), PAGE electrophoresis, and molecular docking, the study explores the influence of orotate on the aggregation of this neurodegeneration-associated DNA. A computational approach was employed to construct an in silico model of the DNA aggregate, which involved the docking of multiple G4 units and subsequent integration of the ligand into both the DNA monomer and its in silico aggregated model. The convergence of computational analyses and empirical data collectively supports the hypothesis that orotate possesses the capability to modulate the aggregation of neurodegeneration-related DNA. Notably, the findings suggest the potential utility of orotate as a neurodrug, especially for the therapy of amyotrophic lateral sclerosis (ALS) and Frontotemporal Dementia (FTD), with its current status as a dietary supplement indicating minimal safety concerns. Additionally, orotate demonstrated a slight increase in mitochondrial dehydrogenase activity as assessed by the MTT assay, which is beneficial for a neurodrug as it suggests a potential role in enhancing mitochondrial function and supporting neuronal health.

Insights into electron transfer and bifurcation of the Synechocystis sp. PCC6803 hydrogenase reductase module

The NAD+-reducing soluble [NiFe] hydrogenase (SH) is the key enzyme for production and consumption of molecular hydrogen (H2) in Synechocystis sp. PCC6803. In this study, we focused on the reductase module of the SynSH and investigated the structural and functional aspects of its subunits, particularly the so far elusive role of HoxE. We demonstrated the importance of HoxE for enzyme functionality, suggesting a regulatory role in maintaining enzyme activity and electron supply. Spectroscopic analysis confirmed that HoxE and HoxF each contain one [2Fe2S] cluster with an almost identical electronic structure. Structure predictions, alongside experimental evidence for ferredoxin interactions, revealed a remarkable similarity between SynSH and bifurcating hydrogenases, suggesting a related functional mechanism. Our study unveiled the subunit arrangement and cofactor composition essential for biological electron transfer. These findings enhance our understanding of NAD+-reducing [NiFe] hydrogenases in terms of their physiological function and structural requirements for biotechnologically relevant modifications.

P-type pilus PapG protein elicits toll-like receptor 2-mediated immune activation during cancer immunotherapy

The immune activation ability of FimH, an adhesion protein in pili of Escherichia coli (E. coli), has been recently reported. However, studies on the immune activity of PapG, another major pili terminal protein, have not been well explored. In this study, the immune stimulatory effect of purified recombinant PapG was evaluated. PapG treatment promoted dramatic changes in dendritic morphology of the bone marrow-derived dendritic cells (BMDCs) and induced upregulation of co-stimulatory molecule levels, major histocompatibility complex (MHC) I and II expression, and pro-inflammatory cytokine production in BMDCs. To identify the stimulatory receptor of PapG, an in silico study was performed. PapG exhibited strong binding affinity with murine toll-like receptor 2 (TLR2). In addition, PapG-induced activation of splenic DC and its subsets was unsuccessful in TLR2-knock out mice. Combination of PapG and ovalbumin (OVA) elicited OVA-specific T cell proliferation and cytokine production and cytotoxicity that consequently promoted anti-cancer immune responses against OVA-expressing B16 melanoma. Furthermore, PapG treatment induced activation of peripheral blood DCs and its subsets in humans in a TLR2 dependent manner. PapG-stimulated human conventional DC2 promoted syngeneic T cell proliferation and activation. The findings of this study demonstrated that PapG could be a useful immune stimulator for immunotherapy against cancer.

Deciphering the mechanism underlying poor aqueous solubility of extracted quinoa proteins

This study aimed to decipher the mechanisms underlying poor solubility of quinoa proteins by investigating the form of quinoa proteins dispersed in water and how protein-protein interactions influenced the kinetic stability of proteins in the dispersions. Specifically, the relative solubility and the forms of quinoa proteins in 1-5 w/w% protein dispersions were determined by separating proteins via centrifugation and/or ultrafiltration. The kinetic stability of quinoa proteins in the supernatants over a 3-week storage period was characterized by determining the changes of concentration, composition and physicochemical properties of quinoa proteins and predicting protein-protein interactions. The results showed that quinoa proteins existed mainly as differently-sized protein aggregates in the dispersions, leading to low relative solubility. The coagulation of protein aggregates in the supernatants caused severe precipitation during the first week of storage whereas they were disassociated simultaneously. With further storage, the remaining proteins in the supernatants reached kinetic stability, which was contributed by stronger electrostatic repulsion and lower surface hydrophobicity. Moreover, 11S globulin and 2S albumin were precipitated and solubilized together during storage, which was ascribed to intermolecular interactions driven by multiple sites between 11S globulin and/or 2S albumin. This study lays a foundation for extensive utilization of quinoa proteins.

A potent antimicrobial glycolipopeptide GLIP and its promising combined antimicrobial effect

Here, the glycolipopeptide GLIP was obtained by coupling IL-C8 and the monosaccharide molecule D-(+)-glucosamine to the N-terminal and C-terminal of the peptide P, which was designed on the basis of the biological characteristics of the antimicrobial peptides. In vitro bioactivity and physicochemical properties assays confirmed that GLIP had excellent antimicrobial activity against Gram-negative E. coli ATCC 25922 and Gram-positive S. aureus ATCC 29213, as well as good stability in serum and trypsin, low hemolysis, and good bacterial membrane-disrupting ability. In addition, the glycolipopeptide GLIP could self-assembly in aqueous solution to form spherical nano-aggregates, which could encapsulate the small molecule antibiotic TC to form the nanomedicine GLIP@TC and release the TC continuously and slowly in a sustained-release manner, exerting the combined antimicrobial effect of both. The results of animal experiments demonstrated the excellent in vivo antimicrobial activities of GLIP and nanomedicine GLIP@TC. Finally, molecular docking experiment showed that the GLIP could effectively bind to penicillin-binding protein 5 (PBP5) of E. coli and possibly inhibit its D-Ala carboxypeptidase (CPase) activity. All these results may imply the great potential of GLIP for clinical application against bacterial drug resistance.

Diarylpropionitrile-stimulated ERβ nuclear accumulation promotes MyoD-induced muscle regeneration in mdx mice by interacting with FOXO3A

Duchenne muscular dystrophy (DMD) is an X-linked recessive progressive degenerative disease of skeletal muscle, characterized by intramuscular inflammation, muscle regeneration disorder and replacement of muscle with fibroadipose tissue. DMD is caused by the absence of normal dystrophy. Impaired self-renew ability and limited differentiation capacity of satellite cells are proved as main reasons for muscle regeneration failure. The deficiency of estrogen impedes the process of muscle regeneration. However, the role of estrogen receptor β (ERβ) in muscle regeneration is still unclear. This study aims to investigate the role and the pharmacological effect of ERβ activation on muscle regeneration in mdx mice. This study showed that mRNA levels of ERβ and myogenic-related genes both witnessed increasing trends in dystrophic context. Our results revealed that treatment with selective ERβ agonist (DPN, diarylpropionitrile) significantly increased myogenic differentiation 1 (MyoD-1) level and promoted muscle regeneration in mdx mice. Similarly, in mdx mice with muscle-specific estrogen receptor α (ERα) ablation, DPN treatment still promoted muscle regeneration. Moreover, we demonstrated that myoblasts differentiation was accompanied by raised nuclear accumulation of ERβ. DPN treatment augmented the nuclear accumulation of ERβ and, thus, contributed to myotubes formation. One important finding was that forkhead box O3A (FOXO3A), as a pivotal transcription factor in Myod-1 transcription, participated in the ERβ-promoted muscle regeneration. Overall, we offered an interesting explanation about the crucial role of ERβ during myogenesis.

CAPRI-Q: The CAPRI resource evaluating the quality of predicted structures of protein complexes

Protein interactions are essential for cellular processes. In recent years there has been significant progress in computational prediction of 3D structures of individual protein chains, with the best-performing algorithms reaching sub-Ångström accuracy. These techniques are now finding their way into the prediction of protein interactions, adding to the existing modeling approaches. The community-wide Critical Assessment of Predicted Interactions (CAPRI) has been a catalyst for the development of procedures for the structural modeling of protein assemblies by organizing blind prediction experiments. The predicted structures are assessed against unpublished experimentally determined structures using a set of metrics with proven robustness that have been established in the CAPRI community. In addition, several advanced benchmarking databases provide targets against which users can test docking and assembly modeling software. These include the Protein-Protein Docking Benchmark, the CAPRI Scoreset, and the Dockground database, all developed by members of the CAPRI community. Here we present CAPRI-Q, a stand-alone model quality assessment tool, which can be freely downloaded or used via a publicly available web server. This tool applies the CAPRI metrics to assess the quality of query structures against given target structures, along with other popular quality metrics such as DockQ, TM-score and l-DDT, and classifies the models according to the CAPRI model quality criteria. The tool can handle a variety of protein complex types including those involving peptides, nucleic acids, and oligosaccharides. The source code is freely available from https://gitlab.in2p3.fr/cmsb-public/CAPRI-Q and its web interface through the Dockground resource at https://dockground.compbio.ku.edu/assessment/.

QPH-FR: A Novel Quinoa Peptide Enhances Chemosensitivity by Targeting Leucine-Rich Repeat-Containing G Protein-Coupled Receptor 5 in Colorectal Cancer

Chemoresistance is one of the difficulties in the treatment of colorectal cancer (CRC), and the enhanced stemness of tumor cells is the underlying contributing factor. Leucine-rich repeat-containing G-protein-coupled receptor 5 (LGR5) is a classical marker of CRC stem cells and can be an important potential target for CRC chemotherapy. Quinoa, a protein-rich plant, offers potential as a source of high-quality active peptides. Novelly, the study obtained quinoa protein hydrolysate (QPH) from whole quinoa grains by simulated digestion. In vivo experiments revealed that the tumor volume in the 5-FU+QPH group decreased from 145.90 ± 13.35 to 94.49 ± 13.05 mm3 in the 5-FU group, suggesting that QPH enhances the chemosensitivity of CRC. Further, the most effective peptide QPH-FR from 631 peptides in QPH was screened by activity prediction, molecular docking, and experimental validation. Mechanistically, QPH-FR competitively suppressed the formation of the LGR5/RSPO1 complex by binding to LGR5, causing RNF43/ZNRF3 to ubiquitinate the FZD receptor, thereby suppressing the Wnt/β-catenin signaling pathway and exerting stemness inhibition. In summary, the study proposes that a novel peptide QPH-FR from quinoa elucidates the mechanism by which QPH-FR targets LGR5 to enhance chemosensitivity, providing theoretical support for the development of chemotherapeutic adjuvant drugs based on plant peptides.

Development of a novel multi-epitope vaccine for brucellosis prevention

Brucellosis, a zoonotic disease caused by Brucella, presents a significant threat to both animal and human health. In animals, the disease can lead to infertility, miscarriage, and high fever, while in humans, symptoms may include recurrent fever, fatigue, sweating, hepatosplenomegaly, and joint and muscle pain following infection. Treatment often involves long-term antibiotic therapy, placing a substantial psychological and financial burden on patients. While vaccination is crucial for prevention, current animal vaccines have drawbacks such as residual virulence, and a safe and effective human vaccine is lacking. Hence, the development of a vaccine for brucellosis is imperative. In this study, we utilized bioinformatics methods to design a multi-epitope vaccine targeting Brucella. Targeting Heme transporter BhuA and polysaccharide export protein, we identified antigenic epitopes, including six cytotoxic T lymphocyte (CTL) dominant epitopes, six helper T lymphocyte (HTL) dominant epitopes, one conformation B cell dominant epitope, and three linear B cell dominant epitopes. By linking these epitopes with appropriate linkers and incorporating a Toll-like receptor (TLR) agonist (human beta-defensin-2) and an auxiliary peptide (Pan HLA-DR epitopes), we constructed the multi-epitope vaccine (MEV). The MEV demonstrated high antigenicity, non-toxicity, non-allergenicity, non-human homology, stability, and solubility. Molecular docking analysis and molecular dynamics simulations confirmed the interaction and stability of the MEV with receptors (MHCI, MHCII, TLR4). Codon optimization and in silico cloning validated the translation efficiency and successful expression of MEV in Escherichia coli. Immunological simulations further demonstrated the efficacy of MEV in inducing robust immune responses. In conclusion, our findings suggest that the engineered MEVs have the potential to stimulate both humoral and cellular immune responses, offering valuable insights for the future development of safe and efficient Brucella vaccines.

Discovery of a Novel Dual Targeting Peptide for Human Glioma: From In Silico Simulation to Acting as Targeting Ligand

Purpose

Receptor-mediated transcytosis (RMT) is a more specific, highly efficient, and reliable approach to crossing the blood-brain-barrier (BBB) and releasing the therapeutic cargos into the brain parenchyma.

Methods

Here, we introduced and characterized a human/mouse-specific novel leptin-derived peptide using in silico, in vitro and in vivo experiments.

Results

Based on the bioinformatics analysis and molecular dynamics (MD) simulation, a 14 amino acid peptide sequence (LDP 14) was introduced and its interaction with leptin-receptor (ObR) was analyzed in comparison with an well known leptin-derived peptide, Lep 30. MD simulation data revealed a significant stable interaction between ligand binding domains (LBD) of ObR with LDP 14. Analyses demonstrated suitable cellular uptake of LDP 14 alone and its derivatives (LDP 14-modified G4 PAMAM dendrimer and LDP 14-modified G4 PAMAM/pEGFP-N1 plasmid complexes) via ObR, energy and species dependent manner (preferred uptake by human/mouse cell lines compared to rat cell line). Importantly, our findings illustrated that the entry of LDP 14-modified dendrimers in hBCEC-D3 cells not only is not affected by protein corona (PC) formation, as the main reason for diminishing the cellular uptake, but also PC per se can enhance uptake rate. Finally, fluorescein labeled LDP 14-modified G4 PAMAM dendrimers efficiently accumulated in the mice brain with lower biodistribution in other organs, in our in vivo study.

Conclusion

LDP 14 introduced as a novel and highly efficient ligand, which can be used for drugs/genes delivery to brain tissue in different central nervous system (CNS) disorders.

Three prime repair exonuclease 1 preferentially degrades the integration-incompetent HIV-1 DNA through favorable kinetics, thermodynamic, structural, and conformational properties

HIV-1 integration into the human genome is dependent on 3'-processing of the viral DNA. Recently, we reported that the cellular Three Prime Repair Exonuclease 1 (TREX1) enhances HIV-1 integration by degrading the unprocessed viral DNA, while the integration-competent 3'-processed DNA remained resistant. Here, we describe the mechanism by which the 3'-processed HIV-1 DNA resists TREX1-mediated degradation. Our kinetic studies revealed that the rate of cleavage (kcat) of the 3'-processed DNA was significantly lower (approximately 2-2.5-fold) than the unprocessed HIV-1 DNA by TREX1. The kcat values of human TREX1 for the processed U5 and U3 DNA substrates were 3.8 s-1 and 4.5 s-1, respectively. In contrast, the unprocessed U5 and U3 substrates were cleaved at 10.2 s-1 and 9.8 s-1, respectively. The efficiency of degradation (kcat/Km) of the 3'-processed DNA (U5-70.2 and U3-28.05 pM-1s-1) was also significantly lower than the unprocessed DNA (U5-103.1 and U3-65.3 pM-1s-1). Furthermore, the binding affinity (Kd) of TREX1 was markedly lower (∼2-fold) for the 3'-processed DNA than the unprocessed DNA. Molecular docking and dynamics studies revealed distinct conformational binding modes of TREX1 with the 3'-processed and unprocessed HIV-1 DNA. Particularly, the unprocessed DNA was favorably positioned in the active site with polar interactions with the catalytic residues of TREX1. Additionally, a stable complex was formed between TREX1 and the unprocessed DNA compared the 3'-processed DNA. These results pinpoint the mechanism by which TREX1 preferentially degrades the integration-incompetent HIV-1 DNA and reveal the unique structural and conformational properties of the integration-competent 3'-processed HIV-1 DNA.

In silico discovery of potential PPI inhibitors for anti-lung cancer activity by targeting the CCND1-CDK4 complex via the P21 inhibition mechanism

Non-Small Cell Lung Cancer (NSCLC) is a prevalent and deadly form of lung cancer worldwide with a low 5-year survival rate. Current treatments have limitations, particularly for advanced-stage patients. P21, a protein that inhibits the CCND1-CDK4 complex, plays a crucial role in cell proliferation. Computer-Aided Drug Design (CADD) based on pharmacophores can screen and design PPI inhibitors targeting the CCND1-CDK4 complex. By analyzing known inhibitors, key pharmacophores are identified, and computational methods are used to screen potential PPI inhibitors. Molecular docking, pharmacophore matching, and structure-activity relationship studies optimize the inhibitors. This approach accelerates the discovery of CCND1-CDK4 PPI inhibitors for NSCLC treatment. Molecular dynamics simulations of CCND1-CDK4-P21 and CCND1-CDK4 complexes showed stable behavior, comprehensive sampling, and P21's impact on complex stability and hydrogen bond formation. A pharmacophore model facilitated virtual screening, identifying compounds with favorable binding affinities. Further simulations confirmed the stability and interactions of selected compounds, including 513457. This study demonstrates the potential of CADD in optimizing PPI inhibitors targeting the CCND1-CDK4 complex for NSCLC treatment. Extended simulations and experimental validations are necessary to assess their efficacy and safety.

Unveiling the Ro60-Ro52 complex

The coexistence within a subcellular complex of inter-cellular proteins Ro60, responsible for preserving ncRNA quality, and Ro52, involved in intracellular proteolysis, has been a subject of ongoing debate. Employing molecular docking in tandem with experimental methods like Quartz Crystal Microbalance with Dissipation (QCM-D), Proximity Ligation Assay (PLA), and Indirect Immunofluorescence (IIF), we reveal the presence of Ro60 associating with Ro52 within the cytoplasm. This result unveils the formation of a weak transient complex with a Ka ≈ (3.7 ± 0.3) x 106 M-1, where the toroid-shaped Ro60 structure interacts with the Ro52's Fc receptor, aligning horizontally within the PRY-SPRY domains of the Ro52's homodimer. The stability of this complex relies on the interaction between Ro52 chain A and specific Ro60 residues, such as K133, W177, or L185, vital in the Ro60-YRNA bond. These findings bridge the role of Ro60 in YRNA management with Ro52's function in intracellular proteolysis, emphasizing the potential impact of transient complexes on cellular pathways. See also the graphical abstract(Fig. 1).

Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity in cis-regulatory elements

Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ∼25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. We introduce a structure-based learning approach to predict the binding preferences of TFs and the automated modelling of TF regulatory complexes. We show the advantage of using our approach over the classical nearest-neighbor prediction in the limits of remote homology. Starting from a TF sequence or structure, we predict binding preferences in the form of motifs that are then used to scan a DNA sequence for occurrences. The best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA. Co-operativity is modelled by: (i) the co-localization of TFs and (ii) the structural modeling of protein-protein interactions between TFs and with co-factors. We have applied our approach to automatically model the interferon-β enhanceosome and the pioneering complexes of OCT4, SOX2 (or SOX11) and KLF4 with a nucleosome, which are compared with the experimentally known structures.

SurfPro-NN: A 3D point cloud neural network for the scoring of protein-protein docking models based on surfaces features and protein language models

Protein-protein interactions (PPI) play a crucial role in numerous key biological processes, and the structure of protein complexes provides valuable clues for in-depth exploration of molecular-level biological processes. Protein-protein docking technology is widely used to simulate the spatial structure of proteins. However, there are still challenges in selecting candidate decoys that closely resemble the native structure from protein-protein docking simulations. In this study, we introduce a docking evaluation method based on three-dimensional point cloud neural networks named SurfPro-NN, which represents protein structures as point clouds and learns interaction information from protein interfaces by applying a point cloud neural network. With the continuous advancement of deep learning in the field of biology, a series of knowledge-rich pre-trained models have emerged. We incorporate protein surface representation models and language models into our approach, greatly enhancing feature representation capabilities and achieving superior performance in protein docking model scoring tasks. Through comprehensive testing on public datasets, we find that our method outperforms state-of-the-art deep learning approaches in protein-protein docking model scoring. Not only does it significantly improve performance, but it also greatly accelerates training speed. This study demonstrates the potential of our approach in addressing protein interaction assessment problems, providing strong support for future research and applications in the field of biology.

A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models

The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies, data and feature construction. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.

The Truncated Peptide AtPEP1(9-23) Has the Same Function as AtPEP1(1-23) in Inhibiting Primary Root Growth and Triggering of ROS Burst

Currently, the widely used active form of plant elicitor peptide 1 (PEP1) from Arabidopsis thaliana is composed of 23 amino acids, hereafter AtPEP1(1-23), serving as an immune elicitor. The relatively less conserved N-terminal region in AtPEP family indicates that the amino acids in this region may be unrelated to the function and activity of AtPEP peptides. Consequently, we conducted an investigation to determine the necessity of the nonconserved amino acids in AtPEP1(1-23) peptide for its functional properties. By assessing the primary root growth and the burst of reactive oxygen species (ROS), we discovered that the first eight N-terminal amino acids of AtPEP1(1-23) are not crucial for its functionality, whereas the conserved C-terminal aspartic acid plays a significant role in its functionality. In this study, we identified a truncated peptide, AtPEP1(9-23), which exhibits comparable activity to AtPEP1(1-23) in inhibiting primary root growth and inducing ROS burst. Additionally, the truncated peptide AtPEP1(13-23) shows similar ability to induce ROS burst as AtPEP1(1-23), but its inhibitory effect on primary roots is significantly reduced. These findings are significant as they provide a novel approach to explore and understand the functionality of the AtPEP1(1-23) peptide. Moreover, exogenous application of AtPEP1(13-23) may enhance plant resistance to pathogens without affecting their growth and development. Therefore, AtPEP1(13-23) holds promise for development as a potentially applicable biopesticides.

MD investigation on the differences in the dynamic interactions between the specific ligand azamulin and two CYP3A isoforms, 3A4 and 3A5

The unmarked potential drug molecule azamulin has been found to be a specific inhibitor of CYP3A4 and CYP3A5 in recent years, but this molecule also shows different binding ability and affinity to the two CYP3A isoforms. In order to explore the microscopic mechanism, conventional molecular dynamics (MD) simulation methods were performed to study the dynamic interactions between two isoforms and azamulin. The simulation results show that the binding of the ligand leads to different structural properties of two CYP3A proteins. First of all, compared with apo-CYP3A4, the binding of the ligand azamulin can lead to changes in the structural flexibility of CYP3A4, i.e., holo-CYP3A4 is more flexible than apo-CYP3A4. The structural changes of CYP3A5 are just the opposite. The ligand binding increases the rigidity of CYP3A5. Furthermore, the representative structures of the production phase in the MD simulation were in details analyzed to obtain the microscopic interactions between the ligand azamulin and two CYP3A isoforms at the atomic level. It is speculated that the difference of composition and interaction of the active sites is the fundamental cause of the change of structural properties of the two proteins.Communicated by Ramaswamy H. Sarma.

Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.

Experimental and computational evidence that Calpain-10 binds to the carboxy terminus of NaV1.2 and NaV1.6

Voltage-gated sodium channels (NaV) are pivotal proteins responsible for initiating and transmitting action potentials. Emerging evidence suggests that proteolytic cleavage of sodium channels by calpains is pivotal in diverse physiological scenarios, including ischemia, brain injury, and neuropathic pain associated with diabetes. Despite this significance, the precise mechanism by which calpains recognize sodium channels, especially given the multiple calpain isoforms expressed in neurons, remains elusive. In this work, we show the interaction of Calpain-10 with NaV's C-terminus through a yeast 2-hybrid assay screening of a mouse brain cDNA library and in vitro by GST-pulldown. Later, we also obtained a structural and dynamic hypothesis of this interaction by modeling, docking, and molecular dynamics simulation. These results indicate that Calpain-10 interacts differentially with the C-terminus of NaV1.2 and NaV1.6. Calpain-10 interacts with NaV1.2 through domains III and T in a stable manner. In contrast, its interaction with NaV1.6 involves domains II and III, which could promote proteolysis through the Cys-catalytic site and C2 motifs.

Three Prime Repair Exonuclease 1 preferentially degrades the integration-incompetent HIV-1 DNA through favorable kinetics, thermodynamic, structural and conformational properties

HIV-1 integration into the human genome is dependent on 3'-processing of the reverse transcribed viral DNA. Recently, we reported that the cellular Three Prime Repair Exonuclease 1 (TREX1) enhances HIV-1 integration by degrading the unprocessed viral DNA, while the integration-competent 3'-processed DNA remained resistant. Here, we describe the mechanism by which the 3'-processed HIV-1 DNA resists TREX1-mediated degradation. Our kinetic studies revealed that the rate of cleavage (kcat) of the 3'-processed DNA was significantly lower than the unprocessed HIV-1 DNA by TREX1. The efficiency of degradation (kcat/KM) of the 3'-processed DNA was also significantly lower than the unprocessed DNA. Furthermore, the binding affinity (Kd) of TREX1 was markedly lower to the 3'-processed DNA compared to the unprocessed DNA. Molecular docking and dynamics studies revealed distinct conformational binding modes of TREX1 with the 3'-processed and unprocessed HIV-1 DNA. Particularly, the unprocessed DNA was favorably positioned in the active site with polar interactions with the catalytic residues of TREX1. Additionally, a stable complex was formed between TREX1 and the unprocessed DNA compared the 3'-processed DNA. These results pinpoint the biochemical mechanism by which TREX1 preferentially degrades the integration-incompetent HIV-1 DNA and reveal the unique structural and conformational properties of the integration-competent 3'-processed HIV-1 DNA.

OTUD4 promotes the progression of glioblastoma by deubiquitinating CDK1 and activating MAPK signaling pathway

Glioblastoma, IDH-Wild type (GBM, CNS WHO Grade 4) is a highly heterogeneous and aggressive primary malignant brain tumor with high morbidity, high mortality, and poor patient prognosis. The global burden of GBM is increasing notably due to limited treatment options, drug delivery problems, and the lack of characteristic molecular targets. OTU deubiquitinase 4 (OTUD4) is a potential predictive factor for several cancers such as breast cancer, liver cancer, and lung cancer. However, its function in GBM remains unknown. In this study, we found that high expression of OTUD4 is positively associated with poor prognosis in GBM patients. Moreover, we provided in vitro and in vivo evidence that OTUD4 promotes the proliferation and invasion of GBM cells. Mechanism studies showed that, on the one hand, OTUD4 directly interacts with cyclin-dependent kinase 1 (CDK1) and stabilizes CDK1 by removing its K11, K29, and K33-linked polyubiquitination. On the other hand, OTUD4 binds to fibroblast growth factor receptor 1 (FGFR1) and reduces FGFR1's K6 and K27-linked polyubiquitination, thereby indirectly stabilizing CDK1, ultimately influencing the activation of the downstream MAPK signaling pathway. Collectively, our results revealed that OTUD4 promotes GBM progression via OTUD4-CDK1-MAPK axis, and may be a prospective therapeutic target for GBM treatment.

Antimicrobial Peptide Recognition Motif of the Substrate Binding Protein SapA from Nontypeable Haemophilus influenzae

Nontypeable Haemophilus influenzae (NTHi) is an opportunistic pathogen associated with respiratory diseases, including otitis media and exacerbations of chronic obstructive pulmonary disease. NTHi exhibits resistance to killing by host antimicrobial peptides (AMPs) mediated by SapA, the substrate binding protein of the sensitivity to antimicrobial peptides (Sap) transporter. However, the specific mechanisms by which SapA selectively binds various AMPs such as defensins and cathelicidin are unknown. In this study, we report mutational analyses of both defensin AMPs and the SapA binding pocket to define the specificity of AMP recognition. Bactericidal assays revealed that NTHi lacking SapA are more susceptible to human beta defensins and LL-37, while remaining highly resistant to a human alpha defensin. In contrast to homologues, our research underscores the distinct specificity of NTHi SapA, which selectively recognizes and binds to peptides containing the charged-hydrophobic motif PKE and RRY. These findings provide valuable insight into the divergence of SapA among bacterial species and NTHi SapA's ability to selectively interact with specific AMPs to mediate resistance.

Syringaresinol promotes the recovery of spinal cord injury by inhibiting neuron apoptosis via activating the ubiquitination factor E4B/AKT Serine/Threonine kinase signal pathway

Spinal cord injury (SCI) is a serious traumatic disease with no effective treatment. This study aimed to explore the therapeutic effect of syringaresinol on SCI. First, the potential targets and associated signaling pathways of syringaresinol were predicted by bioinformatics analysis and molecular docking. Second, MTT was employed to evaluate cell proliferation rate, Western blot was performed to detect protein expression, RT-qPCR was conducted to detect mRNA expression levels, flow cytometry and 5-ethynyl-2'-deoxyuridine (EDU) staining were used to determine cell apoptosis, and immunofluorescence and immunohistochemistry were used to estimate the expression of RNA binding fox-1 homolog 3 and clipped caspase 3. Basso-Beattie-Bresnahan scores and inclined plate tests were conducted to analyze hindlimb locomotor function. Results showed that syringaresinol could inhibit the apoptosis of glutamate-treated SHSY5Y cells by upregulating the expression of ubiquitination factor E4B (UBE4B) and activating the AKT serine/threonine kinase (AKT) signaling pathway. This effect can be rescued by UBE4B knockdown or AKT pathway inhibition. Syringaresinol remarkably improved locomotor function and increased neuronal survival in SCI rats. Our results suggested that syringaresinol could promote locomotor functional recovery by reducing neuronal apoptosis by activating the UBE4B/AKT signaling pathway.

Genomic Expedition: Deciphering Human Adenovirus Strains from the 2023 Outbreak in West Bengal, India: Insights into Viral Evolution and Molecular Epidemiology

Understanding the genetic dynamics of circulating Human Adenovirus (HAdV) types is pivotal for effectively managing outbreaks and devising targeted interventions. During the West Bengal outbreak of 2022-2023, an investigation into the genetic characteristics and outbreak potential of circulating HAdV types was conducted. Twenty-four randomly selected samples underwent whole-genome sequencing. Analysis revealed a prevalent recombinant strain, merging type 3 and type 7 of human mastadenovirus B1 (HAd-B1) species, indicating the emergence of recent strains of species B in India. Furthermore, distinctions in VA-RNAs and the E3 region suggested that current circulating strains of human mastadenovirus B1 (HAd-B1) possess the capacity to evade host immunity, endure longer within hosts, and cause severe respiratory infections. This study underscores the significance of evaluating the complete genome sequence of HAdV isolates to glean insights into their outbreak potential and the severity of associated illnesses.

Assessment of Protein-Protein Docking Models Using Deep Learning

Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes have been determined by biophysical experimental methods, such as X-ray crystallography and cryogenic electron microscopy. However, as experimental methods are costly in resources, many computational methods have been developed that model protein complex structures. One of the difficulties in computational protein complex modeling (protein docking) is to select the most accurate models among many models that are usually generated by a docking method. This article reviews advances in protein docking model assessment methods, focusing on recent developments that apply deep learning to several network architectures.

Docking Foundations: From Rigid to Flexible Docking

Despite the development of methods for the experimental determination of protein structures, the dissonance between the number of known sequences and their solved structures is still enormous. This is particularly evident in protein-protein complexes. To fill this gap, diverse technologies have been developed to study protein-protein interactions (PPIs) in a cellular context including a range of biological and computational methods. The latter derive from techniques originally published and applied almost half a century ago and are based on interdisciplinary knowledge from the nexus of the fields of biology, chemistry, and physics about protein sequences, structures, and their folding. Protein-protein docking, the main protagonist of this chapter, is routinely treated as an integral part of protein research. Herein, we describe the basic foundations of the whole process in general terms, but step by step from protein representations through docking methods and evaluation of complexes to their final validation.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

Repurposing immune boosting and anti-viral efficacy of Parkia bioactive entities as multi-target directed therapeutic approach for SARS-CoV-2: exploration of lead drugs by drug likeness, molecular docking and molecular dynamics simulation methods

The COVID-19 pandemic has caused adverse health (severe respiratory, enteric and systemic infections) and environmental impacts that have threatened public health and the economy worldwide. Drug repurposing and small molecule multi-target directed herbal medicine therapeutic approaches are the most appropriate exploration strategies for SARS-CoV-2 drug discovery. This study identified potential multi-target-directed Parkia bioactive entities against SARS-CoV-2 receptors (S-protein, ACE2, TMPRSS2, RBD/ACE2, RdRp, MPro, and PLPro) using ADMET, drug-likeness, molecular docking (AutoDock, FireDock and HDOCK), molecular dynamics simulation and MM-PBSA tools. One thousand Parkia bioactive entities were screened out by virtual screening and forty-five bioactive phytomolecules were selected based on favorable binding affinity and acceptable pharmacokinetic and pharmacodynamics properties. The binding affinity values of Parkia phyto-ligands (AutoDock: -6.00--10.40 kcal/mol; FireDock: -31.00--62.02 kcal/mol; and HDOCK: -150.0--294.93 kcal/mol) were observed to be higher than the reference antiviral drugs (AutoDock: -5.90--9.10 kcal/mol; FireDock: -35.64--59.35 kcal/mol; and HDOCK: -132.82--211.87 kcal/mol), suggesting a potent modulatory action of Parkia bioactive entities against the SARS-CoV-2. Didymin, rutin, epigallocatechin gallate, epicatechin-3-0-gallate, hyperin, ursolic acid, lupeol, stigmasta-5,24(28)-diene-3-ol, ellagic acid, apigenin, stigmasterol, and campesterol strongly bound with the multiple targets of the SARS-CoV-2 receptors, inhibiting viral entry, attachment, binding, replication, transcription, maturation, packaging and spread. Furthermore, ACE2, TMPRSS2, and MPro receptors possess significant molecular dynamic properties, including stability, compactness, flexibility and total binding energy. Residues GLU-589, and LEU-95 of ACE2, GLN-350, HIS-186, and ASP-257 of TMPRSS2, and GLU-14, MET-49, and GLN-189 of MPro receptors contributed to the formation of hydrogen bonds and binding interactions, playing vital roles in inhibiting the activity of the receptors. Promising results were achieved by developing multi-targeted antiviral Parkia bioactive entities as lead and prospective candidates under a small molecule strategy against SARS-CoV-2 pathogenesis. The antiviral activity of Parkia bioactive entities needs to be further validated by pre-clinical and clinical trials.

Sampling and Scoring in Protein-Protein Docking

Protein-protein docking is considered one of the most important techniques supporting experimental proteomics. Recent developments in the field of computer science helped to improve this computational technique so that it better handles the complexity of protein nature. Sampling algorithms are responsible for the generation of numerous protein-protein ensembles. Unfortunately, a primary docking output comprises a set of both near-native poses and decoys. Application of the efficient scoring function helps to differentiate poses with the most favorable properties from those that are very unlikely to represent a natural state of the complex. This chapter explains the importance of sampling and scoring in the process of protein-protein docking. Moreover, it summarizes advances in the field.

Assessing Protein-Protein Docking Protocols: Case Studies of G-Protein-Coupled Receptor Interactions

The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

Evaluation of DNA-protein complex structures using the deep learning method

Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental methods for this purpose are costly and technically demanding. Consequently, there is an urgent need for computational techniques to identify the structures of DNA-protein complexes. Despite technological advancements, accurately identifying DNA-protein complexes through computational methods still poses a challenge. Our team has developed a cutting-edge deep-learning approach called DDPScore that assesses DNA-protein complex structures. DDPScore utilizes a 4D convolutional neural network to overcome limited training data. This approach effectively captures local and global features while comprehensively considering the conformational changes arising from the flexibility during the DNA-protein docking process. DDPScore consistently outperformed the available methods in comprehensive DNA-protein complex docking evaluations, even for the flexible docking challenges. DDPScore has a wide range of applications in predicting and designing structures of DNA-protein complexes.

Phylogenetic and ecophysiological novelty of subsurface mercury methylators in mangrove sediments

Mangrove sediment is a crucial component in the global mercury (Hg) cycling and acts as a hotspot for methylmercury (MeHg) production. Early evidence has documented the ubiquity of well-studied Hg methylators in mangrove superficial sediments; however, their diversity and metabolic adaptation in the more anoxic and highly reduced subsurface sediments are lacking. Through MeHg biogeochemical assay and metagenomic sequencing, we found that mangrove subsurface sediments (20-100 cm) showed a less hgcA gene abundance but higher diversity of Hg methylators than superficial sediments (0-20 cm). Regional-scale investigation of mangrove subsurface sediments spanning over 1500 km demonstrated a prevalence and family-level novelty of Hg-methylating microbial lineages (i.e., those affiliated to Anaerolineae, Phycisphaerae, and Desulfobacterales). We proposed the candidate phylum Zixibacteria lineage with sulfate-reducing capacity as a currently understudied Hg methylator across anoxic environments. Unlike other Hg methylators, the Zixibacteria lineage does not use the Wood-Ljungdahl pathway but has unique capabilities of performing methionine synthesis to donate methyl groups. The absence of cobalamin biosynthesis pathway suggests that this Hg-methylating lineage may depend on its syntrophic partners (i.e., Syntrophobacterales members) for energy in subsurface sediments. Our results expand the diversity of subsurface Hg methylators and uncover their unique ecophysiological adaptations in mangrove sediments.

Dronabinol as an answer to flavivirus infections: an in-silico investigation

Flavivirus infections are common in several parts of the world. Two major types of flaviviruses are dengue and zika viruses. Both these two viral infections have caused many fatalities around the world. There is an absence of a vaccine and an effective medication against these viruses. In this study, we analyzed the ability of dronabinol to act as a potential cure against these viral infections. We performed the docking of dronabinol with several viral proteins followed by molecular dynamics simulation, MM/PBSA and PCA analysis. We checked the ability of the polyphenol dronabinol to interfere with the binding of viral helicases to their cellular targets. We performed 2 D-QSAR studies, drug likeliness, ADMET and target prediction studies. From our study, we observed that dronabinol had the best docking ability against the helicase proteins of dengue and zika. Molecular dynamics simulation and MM/PBSA investigation confirmed the stability of the binding while PCA investigation showed a lowering of molecular motions in response to dronabinol docking to the helicases. Dronabinol interfered in the binding of the helicases to RNA. 2 D QSAR studies revealed a low IC50 value for dronabinol. Dronabinol showed favorable drug-likeness, ADMET properties and target prediction results. Thus we propose dronabinol be further investigated in-vitro as a cure against dengue and zika virus infections.Communicated by Ramaswamy H. Sarma.

ComDock: A novel approach for protein-protein docking with an efficient fusing strategy

Protein-protein interaction plays an important role in studying the mechanism of protein functions from the structural perspective. Molecular docking is a powerful approach to detect protein-protein complexes using computational tools, due to the high cost and time-consuming of the traditional experimental methods. Among existing technologies, the template-based method utilizes the structural information of known homologous 3D complexes as available and reliable templates to achieve high accuracy and low computational complexity. However, the performance of the template-based method depends on the quality and quantity of templates. When insufficient or even no templates, the ab initio docking method is necessary and largely enriches the docking conformations. Therefore, it's a feasible strategy to fuse the effectivity of the template-based model and the universality of ab initio model to improve the docking performance. In this study, we construct a new, diverse, comprehensive template library derived from PDB, containing 77,685 complexes. We propose a template-based method (named TemDock), which retrieves the evolutionary relationship between the target sequence and samples in the template library and transfers similar structural information. Then, the target structure is built by superposing on the homologous template complex with TM-align. Moreover, we develop a consensus-based method (named ComDock) to integrate our TemDock and an existing ab initio method (ZDOCK). On 105 targets with templates from Benchmark 5.0, the TemDock and ComDock achieve a success rate of 68.57 % and 71.43 % in the top 10 conformations, respectively. Compared with the HDOCK, ComDock obtains better I-RMSD of hit configurations on 9 targets and more hit models in the top 100 conformations. As an efficient method for protein-protein docking, the ComDock is expected to study protein-protein recognition and reveal the various biological passways that are critical for developing drug discovery. The final results are stored at https://github.com/guofei-tju/mqz_ComDock_docking.

Stereoselective Voltammetric Biosensor for Myo-Inositol and D-Chiro-Inositol Recognition

This paper describes the development of a simple voltammetric biosensor for the stereoselective discrimination of myo-inositol (myo-Ins) and D-chiro-inositol (D-chiro-Ins) by means of bovine serum albumin (BSA) adsorption onto a multi-walled carbon nanotube (MWCNT) graphite screen-printed electrode (MWCNT-GSPE), previously functionalized by the electropolymerization of methylene blue (MB). After a morphological characterization, the enantioselective biosensor platform was electrochemically characterized after each modification step by differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS). The results show that the binding affinity between myo-Ins and BSA was higher than that between D-chiro-Ins and BSA, confirming the different interactions exhibited by the novel BSA/MB/MWCNT/GSPE platform towards the two diastereoisomers. The biosensor showed a linear response towards both stereoisomers in the range of 2-100 μM, with LODs of 0.5 and 1 μM for myo-Ins and D-chiro-Ins, respectively. Moreover, a stereoselectivity coefficient α of 1.6 was found, with association constants of 0.90 and 0.79, for the two stereoisomers, respectively. Lastly, the proposed biosensor allowed for the determination of the stereoisomeric composition of myo-/D-chiro-Ins mixtures in commercial pharmaceutical preparations, and thus, it is expected to be successfully applied in the chiral analysis of pharmaceuticals and illicit drugs of forensic interest.

Assessing Genetic Algorithm-Based Docking Protocols for Prediction of Heparin Oligosaccharide Binding Geometries onto Proteins

Although molecular docking has evolved dramatically over the years, its application to glycosaminoglycans (GAGs) has remained challenging because of their intrinsic flexibility, highly anionic character and rather ill-defined site of binding on proteins. GAGs have been treated as either fully "rigid" or fully "flexible" in molecular docking. We reasoned that an intermediate semi-rigid docking (SRD) protocol may be better for the recapitulation of native heparin/heparan sulfate (Hp/HS) topologies. Herein, we study 18 Hp/HS-protein co-complexes containing chains from disaccharide to decasaccharide using genetic algorithm-based docking with rigid, semi-rigid, and flexible docking protocols. Our work reveals that rigid and semi-rigid protocols recapitulate native poses for longer chains (5→10 mers) significantly better than the flexible protocol, while 2→4-mer poses are better predicted using the semi-rigid approach. More importantly, the semi-rigid docking protocol is likely to perform better when no crystal structure information is available. We also present a new parameter for parsing selective versus non-selective GAG-protein systems, which relies on two computational parameters including consistency of binding (i.e., RMSD) and docking score (i.e., GOLD Score). The new semi-rigid protocol in combination with the new computational parameter is expected to be particularly useful in high-throughput screening of GAG sequences for identifying promising druggable targets as well as drug-like Hp/HS sequences.

Iterative Knowledge-Based Scoring Function for Protein-Ligand Interactions by Considering Binding Affinity Information

Scoring functions for protein-ligand interactions play a critical role in structure-based drug design. Owing to the good balance between general applicability and computational efficiency, knowledge-based scoring functions have obtained significant advancements and achieved many successes. Nevertheless, knowledge-based scoring functions face a challenge in utilizing the experimental affinity data and thus may not perform well in binding affinity prediction. Addressing the challenge, we have proposed an improved version of the iterative knowledge-based scoring function ITScore by considering binding affinity information, which is referred to as ITScoreAff, based on a large training set of 6216 protein-ligand complexes with both structures and affinity data. ITScoreAff was extensively evaluated and compared with ITScore, 33 traditional, and 6 machine learning scoring functions in terms of docking power, ranking power, and screening power on the independent CASF-2016 benchmark. It was shown that ITScoreAff obtained an overall better performance than the other 40 scoring functions and gave an average success rate of 85.3% in docking power, a correlation coefficient of 0.723 in scoring power, and an average rank correlation coefficient of 0.668 in ranking power. In addition, ITScoreAff also achieved the overall best screening power when the top 10% of the ranked database were considered. These results demonstrated the robustness of ITScoreAff and its improvement over existing scoring functions.

Computational design of novel nanobodies targeting the receptor binding domain of variants of concern of SARS-CoV-2

The COVID-19 pandemic has created an urgent need for effective therapeutic and diagnostic strategies to manage the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the emergence of numerous variants of concern (VOCs) has made it challenging to develop targeted therapies that are broadly specific in neutralizing the virus. In this study, we aimed to develop neutralizing nanobodies (Nbs) using computational techniques that can effectively neutralize the receptor-binding domain (RBD) of SARS-CoV-2 VOCs. We evaluated the performance of different protein-protein docking programs and identified HDOCK as the most suitable program for Nb/RBD docking with high accuracy. Using this approach, we designed 14 novel Nbs with high binding affinity to the VOC RBDs. The Nbs were engineered with mutated amino acids that interacted with key amino acids of the RBDs, resulting in higher binding affinity than human angiotensin-converting enzyme 2 (ACE2) and other viral RBDs or haemagglutinins (HAs). The successful development of these Nbs demonstrates the potential of molecular modeling as a low-cost and time-efficient method for engineering effective Nbs against SARS-CoV-2. The engineered Nbs have the potential to be employed in RBD-neutralizing assays, facilitating the identification of novel treatment, prevention, and diagnostic strategies against SARS-CoV-2.

Developing a peptide to disrupt cohesin head domain interactions

Cohesin mediates the 3-D structure of chromatin and is involved in maintaining genome stability and function. The cohesin core comprises Smc1 and Smc3, elongated-shaped proteins that dimerize through globular domains at their edges, called head and hinge. ATP binding to the Smc heads induces their dimerization and the formation of two active sites, while ATP hydrolysis results in head disengagement. This ATPase cycle is essential for driving cohesin activity. We report on the development of the first cohesin-inhibiting peptide (CIP). The CIP binds Smc3 in vitro and inhibits the ATPase activity of the holocomplex. Treating yeast cells with the CIP prevents cohesin's tethering activity and, interestingly, leads to the accumulation of cohesin on chromatin. CIP3 also affects cohesin activity in human cells. Altogether, we demonstrate the power of peptides to inhibit cohesin in cells and discuss the potential application of CIPs as a therapeutic approach.