ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers

Aptamer AS411 interacts with the KRAS promoter/hnRNP A1 complex and shows increased potency against drug-resistant lung cancer

G-quadruplex (G4) aptamers that can competitively binding protein with oncogene promoter G4 hold promise for cancer treatment. In this study, a neutral cytidinyl lipid, DNCA, was shown to transfect and deliver G4 aptamers (AS1411, TBA) into tumour cells, including multidrug-resistant tumour cells, and their nuclear localizations were clearly detected. Both AS1411/DNCA and TBA/DNCA showed excellent antitumour efficacies in the drug-resistant non-small cell lung cancer cell line A549/TXL at a low concentration (100 nM). Heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) was identified as a new target of AS1411 and TBA. The binding affinities were measured, and the Kd values of AS1411/hnRNP A1 and TBA/hnRNP A1 were 17.5 nM and 21.1 nM, respectively. Then the expression of KRAS mRNA in A549/TXL cells was found to be higher than that in A549 cells, and KRAS mRNA was reduced by approximately 40% after administration of AS1411 or TBA in A549/TXL cells. Further, it was confirmed for the first time that AS1411 targeted not only hnRNP A1 but also the KRAS promoter/hnRNP A1 complexes. And although TBA cannot target the KRAS promoter/hnRNP A1 complexes, the biolayer interferometry (BLI) experiment showed that TBA and AS1411 have similar effects on several key proteins in tumour cells, especially hnRNP A1. Molecular docking and molecular dynamics simulation showed that AS1411 and the KRAS promoter bound to the same domain of hnRNP A1 protein, while TBA bound to another domain.

ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

Functionality of chimeric TssA proteins in the type VI secretion system reveals sheath docking specificity within their N-terminal domains

The genome of Pseudomonas aeruginosa encodes three type VI secretion systems, each comprising a dozen distinct proteins, which deliver toxins upon T6SS sheath contraction. The least conserved T6SS component, TssA, has variations in size which influence domain organisation and structure. Here we show that the TssA Nt1 domain interacts directly with the sheath in a specific manner, while the C-terminus is essential for oligomerisation. We built chimeric TssA proteins by swapping C-termini and showed that these can be functional even when made of domains from different TssA sub-groups. Functional specificity requires the Nt1 domain, while the origin of the C-terminal domain is more permissive for T6SS function. We identify two regions in short TssA proteins, loop and hairpin, that contribute to sheath binding. We propose a docking mechanism of TssA proteins with the sheath, and a model for how sheath assembly is coordinated by TssA proteins from this position.

Codonopsis pilosula-derived glycopeptide dCP1 promotes the polarization of tumor-associated macrophage from M2-like to M1 phenotype

The majority of the immune cell population in the tumor microenvironment (TME) consists of tumor-associated macrophages (TAM), which are the main players in coordinating tumor-associated inflammation. TAM has a high plasticity and is divided into two main phenotypes, pro-inflammatory M1 type and anti-inflammatory M2 type, with tumor-suppressive and tumor-promoting functions, respectively. Considering the beneficial effects of M1 macrophages for anti-tumor and the high plasticity of macrophages, the conversion of M2 TAM to M1 TAM is feasible and positive for tumor treatment. This study sought to evaluate whether the glycopeptide derived from simulated digested Codonopsis pilosula extracts could regulate the polarization of M2-like TAM toward the M1 phenotype and the potential regulatory mechanisms. The results showed that after glycopeptide dCP1 treatment, the mRNA relative expression levels of some M2 phenotype marker genes in M2-like TAM in simulated TME were reduced, and the relative expression levels of M1 phenotype marker genes and inflammatory factor genes were increased. Analysis of RNA-Seq of M2-like TAM after glycopeptide dCP1 intervention showed that the gene sets such as glycolysis, which is associated with macrophage polarization in the M1 phenotype, were significantly up-regulated, whereas those of gene sets such as IL-6-JAK-STAT3 pathway, which is associated with polarization in the M2 phenotype, were significantly down-regulated. Moreover, PCA analysis and Pearson's correlation also indicated that M2-like TAM polarized toward the M1 phenotype at the transcriptional level after treatment with the glycopeptide dCP1. Lipid metabolomics was used to further explore the efficacy of the glycopeptide dCP1 in regulating the polarization of M2-like TAM to the M1 phenotype. It was found that the lipid metabolite profiles in dCP1-treated M2-like TAM showed M1 phenotype macrophage lipid metabolism profiles compared with blank M2-like TAM. Analysis of the key differential lipid metabolites revealed that the interconversion between phosphatidylcholine (PC) and diacylglycerol (DG) metabolites may be the central reaction of the glycopeptide dCP1 in regulating the conversion of M2-like TAM to the M1 phenotype. The above results suggest that the glycopeptide dCP1 has the efficacy to regulate the polarization of M2-like TAM to M1 phenotype in simulated TME.

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.

Fus3 Interacts with Gal83, Revealing the MAPK Crosstalk to Snf1/AMPK to Regulate Secondary Metabolic Substrates in Aspergillus flavus

Aflatoxins (AFs), highly carcinogenic natural products, are produced by the secondary metabolism of fungi such as Aspergillus flavus. Essential for the fungi to respond to environmental changes and aflatoxin synthesis, the pheromone mitogen-activated protein kinase (MAPK) is a potential regulator of aflatoxin biosynthesis. However, the mechanism by which pheromone MAPK regulates aflatoxin biosynthesis is not clear. Here, we showed Gal83, a new target of Fus3, and identified the pheromone Fus3-MAPK signaling pathway as a regulator of the Snf1/AMPK energy-sensing pathway modulating aflatoxins synthesis substrates. The screening for Fus3 target proteins identified the β subunit of Snf1/AMPK complexes using tandem affinity purification and multiomics. This subunit physically interacted with Fus3 both in vivo and in vitro and received phosphorylation from Fus3. Although the transcript levels of aflatoxin synthesis genes were not noticeably downregulated in both gal83 and fus3 deletion mutant strains, the levels of aflatoxin B1 and its synthesis substrates and gene expression levels of primary metabolizing enzymes were significantly reduced. This suggests that both the Fus3-MAPK and Snf1/AMPK pathways respond to energy signals. In conclusion, all the evidence unlocks a novel pathway of Fus3-MAPK to regulate AFs synthesis substrates by cross-talking with the Snf1/AMPK complexes.

Ligand- and Structure-Based Virtual Screening Identifies New Inhibitors of the Interaction of the SARS-CoV-2 Spike Protein with the ACE2 Host Receptor

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a fast-spreading viral pathogen and poses a serious threat to human health. New SARS-CoV-2 variants have been arising worldwide; therefore, is necessary to explore more therapeutic options. The interaction of the viral spike (S) protein with the angiotensin-converting enzyme 2 (ACE2) host receptor is an attractive drug target to prevent the infection via the inhibition of virus cell entry. In this study, Ligand- and Structure-Based Virtual Screening (LBVS and SBVS) was performed to propose potential inhibitors capable of blocking the S receptor-binding domain (RBD) and ACE2 interaction. The best five lead compounds were confirmed as inhibitors through ELISA-based enzyme assays. The docking studies and molecular dynamic (MD) simulations of the selected compounds maintained the molecular interaction and stability (RMSD fluctuations less than 5 Å) with key residues of the S protein. The compounds DRI-1, DRI-2, DRI-3, DRI-4, and DRI-5 efficiently block the interaction between the SARS-CoV-2 spike protein and receptor ACE2 (from 69.90 to 99.65% of inhibition) at 50 µM. The most potent inhibitors were DRI-2 (IC50 = 8.8 µM) and DRI-3 (IC50 = 2.1 µM) and have an acceptable profile of cytotoxicity (CC50 > 90 µM). Therefore, these compounds could be good candidates for further SARS-CoV-2 preclinical experiments.

Synergistic effects of microbial transglutaminase and apple pectin on the gelation properties of pea protein isolate and its application to probiotic encapsulation

The low gelation capacity of pea protein isolate (PPI) limits their use in food industry. Therefore, microbial transglutaminase (MTG) and apple pectin (AP) were combined to modify PPI to enhance its gelling characteristics, and the mechanism of MTG-induced PPI-AP composite gel generation was investigated. PPI (10 wt%) could not form a gel at 40 °C, while MTG-treated PPI (10 wt%) formed a self-supporting gel at 40 °C. Subsequently, the addition of AP further promoted the crosslinking of PPI and significantly improved the water holding capacity, rheology, and strength of PPI gels, which was attributed to both hydrogen and isopeptide bonds in the composite gel. Additionally, the PPI-AP composite gel showed excellent protection ability, and the survival rate of probiotics could reach over 90%, which could be used as an effective delivery system. This study verified that MTG and AP were efficient in enhancing the functional quality of PPI gels.

Chimerism of avian IgY-scFv and truncated IgG-Fc: A novel strategy in cross-species antibody generation and enhancement

Chicken single-chain fragment variable (IgY-scFv) is a functional fragment and an emerging development in genetically engineered antibodies with a wide range of biomedical applications. However, scFvs have considerably shorter serum half-life due to the absence of antibody Fc region compared with the full-length antibody, and usually requires continuous intravenous administration for efficacy. A promising approach to overcome this limitation is to fuse scFv with immunoglobulin G (IgG) Fc region, for better recognition and mediation by the neonatal Fc receptor (FcRn) in the host. In this study, engineered mammalian ΔFc domains (CH2, CH3, and intact Fc region) were fused with anti-canine parvovirus-like particles avian IgY-scFv to produce chimeric antibodies and expressed in the HEK293 cell expression system. The obtained scFv-CH2, scFv-CH3, and scFv-Fc can bind with antigen specifically and dose-dependently. Surface plasmon resonance investigation confirmed that scFv-CH2, scFv-CH3, and scFv-Fc had different degrees of binding to FcRn, with scFv-Fc showing the highest affinity. scFv-Fc had a significantly longer half-life in mice compared with the unfused scFv. The identified ΔFcs are promising for the development of engineered Fc-based therapeutic antibodies and proteins with longer half-lives. The avian IgY-scFv-mammalian IgG Fc region opens up new avenues for antibody engineering, and it is a novel strategy to enhance the rapid development and screening of functional antibodies in veterinary and human medicine.

Targeting c-Myc transactivation by LMNA inhibits tRNA processing essential for malate-aspartate shuttle and tumour progression

BACKGROUND

A series of studies have demonstrated the emerging involvement of transfer RNA (tRNA) processing during the progression of tumours. Nevertheless, the roles and regulating mechanisms of tRNA processing genes in neuroblastoma (NB), the prevalent malignant tumour outside the brain in children, are yet unknown.

METHODS

Analysis of multi-omics results was conducted to identify crucial regulators of downstream tRNA processing genes. Co-immunoprecipitation and mass spectrometry methods were utilised to measure interaction between proteins. The impact of transcriptional regulators on expression of downstream genes was measured by dual-luciferase reporter, chromatin immunoprecipitation, western blotting and real-time quantitative reverse transcription-polymerase chain reaction (RT-PCR) methods. Studies have been conducted to reveal impact and mechanisms of transcriptional regulators on biological processes of NB. Survival differences were analysed using the log-rank test.

RESULTS

c-Myc was identified as a transcription factor driving tRNA processing gene expression and subsequent malate-aspartate shuttle (MAS) in NB cells. Mechanistically, c-Myc directly promoted the expression of glutamyl-prolyl-tRNA synthetase (EPRS) and leucyl-tRNA synthetase (LARS), resulting in translational up-regulation of glutamic-oxaloacetic transaminase 1 (GOT1) as well as malate dehydrogenase 1 (MDH1) via inhibiting general control nonrepressed 2 or activating mechanistic target of rapamycin signalling. Meanwhile, lamin A (LMNA) inhibited c-Myc transactivation via physical interaction, leading to suppression of MAS, aerobic glycolysis, tumourigenesis and aggressiveness. Pre-clinically, lobeline was discovered as a LMNA-binding compound to facilitate its interaction with c-Myc, which inhibited aminoacyl-tRNA synthetase expression, MAS and tumour progression of NB, as well as growth of organoid derived from c-Myc knock-in mice. Low levels of LMNA or elevated expression of c-Myc, EPRS, LARS, GOT1 or MDH1 were linked to a worse outcome and a shorter survival time of clinical NB patients.

CONCLUSIONS

These results suggest that targeting c-Myc transactivation by LMNA inhibits tRNA processing essential for MAS and tumour progression.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

Targeted drug delivery to the thrombus by fusing streptokinase with a fibrin-binding peptide (CREKA): an in silico study

Aim: Streptokinase has poor selectivity and provokes the immune response. In this study, we used in silico studies to design a fusion protein to achieve targeted delivery to the thrombus. Materials & methods: Streptokinase was analyzed computationally for mapping. The fusion protein modeling and quality assessment were carried out on several servers. The enzymatic activity and the stability of the fusion protein and its complex with plasminogen were assessed through molecular docking analysis and molecular dynamics simulation respectively. Results: Physicochemical properties analysis, protein quality assessments, protein-protein docking and molecular dynamics simulations predicted that the designed fusion protein is functionally active. Conclusion: Our results showed that this fusion protein might be a prospective candidate as a novel thrombolytic agent with better selectivity.

Arabidopsis HSFA9 Acts as a Regulator of Heat Response Gene Expression and the Acquisition of Thermotolerance and Seed Longevity

Heat-shock transcription factors (HSFs) are crucial for regulating plant responses to heat and various stresses, as well as for maintaining normal cellular functions and plant development. HSFA9 and HSFA2 are two of the Arabidopsis class A HSFs and their expressions are dramatically induced in response to heat shock (HS) stress among all 21 Arabidopsis HSFs. However, the detailed biological roles of their cooperation have not been fully characterized. In this study, we employed an integrated approach that combined bioinformatics, molecular genetics and computational analysis to identify and validate the molecular mechanism that controls seed longevity and thermotolerance in Arabidopsis. The acquisition of tolerance to deterioration was accompanied by a significant transcriptional switch that involved the induction of primary metabolism, reactive oxygen species and unfolded protein response, as well as the regulation of genes involved in response to dehydration, heat and hypoxia. In addition, the cis-regulatory motif analysis in normal stored and controlled deterioration treatment (CDT) seeds confirmed the CDT-repressed genes with heat-shock element (HSE) in their promoters. Using a yeast two-hybrid and molecular dynamic interaction assay, it is shown that HSFA9 acted as a potential regulator that can interact with HSFA2. Moreover, the knock-out mutants of both HSFA9 and HSFA2 displayed a significant reduction in seed longevity. These novel findings link HSF transcription factors with seed deterioration tolerance and longevity.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

The PTM profiling of CTCF reveals the regulation of 3D chromatin structure by O-GlcNAcylation

CCCTC-binding factor (CTCF), a ubiquitously expressed and highly conserved protein, is known to play a critical role in chromatin structure. Post-translational modifications (PTMs) diversify the functions of protein to regulate numerous cellular processes. However, the effects of PTMs on the genome-wide binding of CTCF and the organization of three-dimensional (3D) chromatin structure have not been fully understood. In this study, we uncovered the PTM profiling of CTCF and demonstrated that CTCF can be O-GlcNAcylated and arginine methylated. Functionally, we demonstrated that O-GlcNAcylation inhibits CTCF binding to chromatin. Meanwhile, deficiency of CTCF O-GlcNAcylation results in the disruption of loop domains and the alteration of chromatin loops associated with cellular development. Furthermore, the deficiency of CTCF O-GlcNAcylation increases the expression of developmental genes and negatively regulates maintenance and establishment of stem cell pluripotency. In conclusion, these results provide key insights into the role of PTMs for the 3D chromatin structure.

Enhancing cancer immunotherapy with Anti-NKG2D/IL-15(N72D)/Sushi fusion protein: Targeting cytotoxic immune cells and boosting IL-15 efficacy

BACKGROUND

There are a number of distinct challenges and complexities associated with administering IL-15 for cancer immunotherapy that must be taken into consideration.

OBJECTIVE

The purpose of this study was to design a fusion protein for targeting cytotoxic immune cells and enhance IL-15 efficiency.

METHODS

A fusokine that contains IL-15(N72D), a Sushi domain, and anti-NKG2D scFv was designed. The fusion protein was in-silico modeled using the Swiss model server, followed by docking and molecular dynamics simulations. The in-vitro purified fusokine was evaluated using dot blot and Western blot. Then, flow cytometry was employed to evaluate biological properties such as proliferation, cytotoxicity, and degranulation.

RESULTS

Fusokine and IL-15(N72D)/Sushi, which had molecular weights of about 52 kDa and 26 kDa, respectively, were expressed in CHO-K1 cells. The fusokine binds 69.6 % of the CHO-NKG2D+ cells that express 83.1 % NKG2D. Both the fusokine and the IL-15(N72D)/Sushi significantly stimulate the proliferation of lymphocytes. After 14 days of growth, the vitality of untreated cells decreased to about 17.5 %, but 82.2 % and 56.6 % of cells were still alive when fusokine and IL-15(N72D)/Sushi were present. Furthermore, administration of fusokine was associated with the highest rates of target tumor cell cytotoxicity. Additionally, although it was not statistically significant, fusokine increased the expression of CD107a and granzyme B by 1.25 times and 2.4 times, respectively.

CONCLUSION

The fusokine possesses the capability to stimulate the survival and multiplication of lymphocytes, as well as their ability to eliminate tumors. These characteristics have led to its consideration as a potential treatment for immunotherapy.

The scorpion toxin BeKm-1 blocks hERG cardiac potassium channels using an indispensable arginine residue

BeKm-1 is a peptide toxin from scorpion venom that blocks the pore of the potassium channel hERG (Kv11.1) in the human heart. Although individual protein structures have been resolved, the structure of the complex between hERG and BeKm-1 is unknown. Here, we used molecular dynamics and ensemble docking, guided by previous double-mutant cycle analysis data, to obtain an in silico model of the hERG-BeKm-1 complex. Adding to the previous mutagenesis study of BeKm-1, our model uncovers the key role of residue Arg20, which forms three interactions (a salt bridge and hydrogen bonds) with the channel vestibule simultaneously. Replacement of this residue even by lysine weakens the interactions significantly. In accordance, the recombinantly produced BeKm-1R20K mutant exhibited dramatically decreased activity on hERG. Our model may be useful for future drug design attempts.

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.

Identification and functional analysis of a serine protease inhibitor using machine learning strategy

In the intricate realm of animal biology, a multitude of vital processes heavily rely on precisely orchestrated proteinase cascades, but the potential for havoc makes proteinase inhibitors indispensable, with serine proteinase inhibitors (serpins) at the forefront, serving as custodians of homeostasis and participating in various critical biological processes. Importantly, there are still many unexplored facets of serpin functionality. In this study, we focused on the serpin family proteins from Marsupenaeus japonicus, utilizing a fine-tuned pretrained protein language model. This approach led to the identification and evolutionary validation of 28 serpins, one of which, referred to as Mjserpin-1, was both computationally and experimentally demonstrated to show potential as an antiviral and apoptosis inhibitor. Our research unveils exciting prospects for the fusion of state-of-the-art artificial intelligence and rich bioinformatics, holding the promise of significant discoveries that could pave the way for future therapeutic advancements.

Rational design of antibody-like peptides for targeting the human complement fragment protein C5a

Human complement fragment 5a (C5a) is one of the most potent glycoproteins generated downstream of C3a and C4a during late-stage activation of the complement signaling cascade. C5a recruits receptors like C5aR1 and C5aR2 and is established to play a critical role in complement-mediated inflammation. Thus, excessive C5a in the plasma due to aberrant activation of the complement contributes to the pathophysiology of several chronic inflammatory diseases. Therefore, restricting the excessive interaction of C5a with its receptors by neutralizing C5a has been one of the most effective therapeutic strategies for the management of inflammatory diseases. Indeed, antibodies targeting C5 (Eculizumab), the precursor of C5a, and C5a (Vilobelimab) have already been approved by the FDA. Still, small designer peptides that work like antibodies and can target and stop C5a from interacting with its receptors seem to be a possible therapeutic alternative to antibodies because they are smaller, cheaper to make, more specific to their target, and can get through membrane barriers. As a proof-of-principle, the current study describes the computational design and evaluation of a pair of peptides that are able to form stable high-affinity complexes with the epitope regions of C5a that are important for the recruitment of C5aR1 and C5aR2. The computational data further supports the potential of designer peptides for mimicking the function of antibodies targeting C5a. However, further experimental studies will be required to establish the structure-function relationship of the designer peptides and also to establish the hypothesis of antibody-like peptides targeting C5a.

HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses

BACKGROUND

The RNA-Recognition motif (RRM) is a protein domain that binds single-stranded RNA (ssRNA) and is present in as much as 2% of the human genome. Despite this important role in biology, RRM-ssRNA interactions are very challenging to study on the structural level because of the remarkable flexibility of ssRNA. In the absence of atomic-level experimental data, the only method able to predict the 3D structure of protein-ssRNA complexes with any degree of accuracy is ssRNA'TTRACT, an ssRNA fragment-based docking approach using ATTRACT. However, since ATTRACT parameters are not ssRNA-specific and were determined in 2010, there is substantial opportunity for enhancement.

RESULTS

Here we present HIPPO, a composite RRM-ssRNA scoring potential derived analytically from contact frequencies in near-native versus non-native docking models. HIPPO consists of a consensus of four distinct potentials, each extracted from a distinct reference pool of protein-trinucleotide docking decoys. To score a docking pose with one potential, for each pair of RNA-protein coarse-grained bead types, each contact is awarded or penalised according to the relative frequencies of this contact distance range among the correct and incorrect poses of the reference pool. Validated on a fragment-based docking benchmark of 57 experimentally solved RRM-ssRNA complexes, HIPPO achieved a threefold or higher enrichment for half of the fragments, versus only a quarter with the ATTRACT scoring function. In particular, HIPPO drastically improved the chance of very high enrichment (12-fold or higher), a scenario where the incremental modelling of entire ssRNA chains from fragments becomes viable. However, for the latter result, more research is needed to make it directly practically applicable. Regardless, our approach already improves upon the state of the art in RRM-ssRNA modelling and is in principle extendable to other types of protein-nucleic acid interactions.

FASN Inhibition Decreases MHC-I Degradation and Synergizes with PD-L1 Checkpoint Blockade in Hepatocellular Carcinoma

Immune checkpoint inhibitors (ICI) transformed the treatment landscape of hepatocellular carcinoma (HCC). Unfortunately, patients with attenuated MHC-I expression remain refractory to ICIs, and druggable targets for upregulating MHC-I are limited. Here, we found that genetic or pharmacologic inhibition of fatty acid synthase (FASN) increased MHC-I levels in HCC cells, promoting antigen presentation and stimulating antigen-specific CD8+ T-cell cytotoxicity. Mechanistically, FASN inhibition reduced palmitoylation of MHC-I that led to its lysosomal degradation. The palmitoyltransferase DHHC3 directly bound MHC-I and negatively regulated MHC-I protein levels. In an orthotopic HCC mouse model, Fasn deficiency enhanced MHC-I levels and promoted cancer cell killing by tumor-infiltrating CD8+ T cells. Moreover, the combination of two different FASN inhibitors, orlistat and TVB-2640, with anti-PD-L1 antibody robustly suppressed tumor growth in vivo. Multiplex IHC of human HCC samples and bioinformatic analysis of The Cancer Genome Atlas data further illustrated that lower expression of FASN was correlated with a higher percentage of cytotoxic CD8+ T cells. The identification of FASN as a negative regulator of MHC-I provides the rationale for combining FASN inhibitors and immunotherapy for treating HCC.

SIGNIFICANCE

Inhibition of FASN increases MHC-I protein levels by suppressing its palmitoylation and lysosomal degradation, which stimulates immune activity against hepatocellular carcinoma and enhances the efficacy of immune checkpoint inhibition.

Endothelial β-catenin upregulation and Y142 phosphorylation drive diabetic angiogenesis via upregulating KDR/HDAC9

BACKGROUND

Diabetic angiogenesis is closely associated with disabilities and death caused by diabetic microvascular complications. Advanced glycation end products (AGEs) are abnormally accumulated in diabetic patients and are a key pathogenic factor for diabetic angiogenesis. The present study focuses on understanding the mechanisms underlying diabetic angiogenesis and identifying therapeutic targets based on these mechanisms.

METHODS

In this study, AGE-induced angiogenesis serves as a model to investigate the mechanisms underlying diabetic angiogensis. Mouse aortic rings, matrigel plugs, and HUVECs or 293T cells were employed as research objects to explore this pathological process by using transcriptomics, gene promoter reporter assays, virtual screening and so on.

RESULTS

Here, we found that AGEs activated Wnt/β-catenin signaling pathway and enhanced the β-catenin protein level by affecting the expression of β-catenin degradation-related genes, such as FZDs (Frizzled receptors), LRPs (LDL Receptor Related Proteins), and AXIN1. AGEs could also mediate β-catenin Y142 phosphorylation through VEGFR1 isoform5. These dual effects of AGEs elevated the nuclear translocation of β-catenin and sequentially induced the expression of KDR (Kinase Insert Domain Receptor) and HDAC9 (Histone Deacetylase 9) by POU5F1 and NANOG, respectively, thus mediating angiogenesis. Finally, through virtual screening, Bioymifi, an inhibitor that blocks VEGFR1 isoform5-β-catenin complex interaction and alleviates AGE-induced angiogenesis, was identified.

CONCLUSION

Collectively, this study offers insight into the pathophysiological functions of β-catenin in diabetic angiogenesis.

Development of Shark Single Domain Antibodies Specific for Human α-Fetoprotein and the Multimerization Strategy in Serum Detection

Sensitive detection of cancer biomarkers can contribute to the timely diagnosis and treatment of diseases. In this study, the whitespotted bamboo sharks were immunized with human α-fetoprotein (AFP), and a phage-displayed variable new antigen receptor (VNAR) single domain antibody library was constructed. Then four unique VNARs (VNAR1, VNAR11, VNAR21, and VNAR25) against AFP were isolated from the library by biopanning for the first time. All of the sequences belong to type II of VNAR, and the VNAR11 was much different from the rest of the three sequences. Then VNAR1 and VNAR11 were selected to fuse with the C4-binding protein α chain (C4bpα) sequence and efficiently expressed in the Escherichia coli system. Furthermore, a VNAR-C4bpα-mediated sandwich chemiluminescence immunoassay (VSCLIA) was developed for the detection of AFP in human serum samples. After optimization, the VSCLIA showed a limit of detection of 0.74 ng/mL with good selectivity and accuracy. Moreover, the results of clinical serum samples detected by the VSCLIA were confirmed by an automatic immunoanalyzer in the hospital, indicating its practical application in actual samples. In conclusion, the novel antibody element VNAR exhibits great potential for immunodiagnosis, and this study also provides a new direction and experimental basis for AFP detection.

MEK-mediated CHPF2 phosphorylation promotes colorectal cancer cell proliferation and metastasis by activating NF-κB signaling

The cytokine tumor necrosis factor (TNF) plays a crucial role in the proliferation and metastasis of colorectal cancer (CRC) cells, but the underlying mechanisms remain poorly understood. Here, we report that chondroitin polymerizing factor 2 (CHPF2) promotes CRC cell proliferation and metastasis mediated by TNF, independently of its enzymatic activity. CHPF2 is highly expressed in CRC, and its elevated expression is associated with poor prognosis of CRC patients. Mechanistically, upon TNF stimulation, CHPF2 is phosphorylated at the T588 residue by MEK, enabling CHPF2 to interact with both TAK1 and IKKα. This interaction enhances the binding of TAK1 and IKKα, leading to increased phosphorylation of the IKK complex and activation of NF-κB signaling. As a result, the expression of early growth factors (EGR1) is upregulated to promote CRC cell proliferation and metastasis. In contrast, introduction of a phospho-deficient T588A mutation in CHPF2 weakened the interaction between CHPF2 and TAK1, thus impairing NF-κB signaling. CHPF2 T588A mutation reduced the ability of CHPF2 to promote the proliferation and metastasis of CRC in vitro and in vivo. Furthermore, the NF-κB RELA subunit promotes CHPF2 expression, further amplifying TNF-induced NF-κB signaling activation. These findings identify a moonlighting function of CHPF2 in promoting tumor cell proliferation and metastasis and provide insights into the mechanism by which CHPF2 amplifies TNF-mediated NF-κB signaling activation. Our study provides a molecular basic for the development of therapeutic strategies for CRC treatment.

A dual computational and experimental strategy to enhance TSLP antibody affinity for improved asthma treatment

Thymic stromal lymphopoietin is a key cytokine involved in the pathogenesis of asthma and other allergic diseases. Targeting TSLP and its signaling pathways is increasingly recognized as an effective strategy for asthma treatment. This study focused on enhancing the affinity of the T6 antibody, which specifically targets TSLP, by integrating computational and experimental methods. The initial affinity of the T6 antibody for TSLP was lower than the benchmark antibody AMG157. To improve this, we utilized alanine scanning, molecular docking, and computational tools including mCSM-PPI2 and GEO-PPI to identify critical amino acid residues for site-directed mutagenesis. Subsequent mutations and experimental validations resulted in an antibody with significantly enhanced blocking capacity against TSLP. Our findings demonstrate the potential of computer-assisted techniques in expediting antibody affinity maturation, thereby reducing both the time and cost of experiments. The integration of computational methods with experimental approaches holds great promise for the development of targeted therapeutic antibodies for TSLP-related diseases.

CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score >0.7) 72% of the complexes among the top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding Protein Data Bank entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.

Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis

Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve intercellular communication analysis. Meantime, due to lack of a "gold standard" dataset, it remains a challenge to evaluate LRI-mediated intercellular communication results. Here, we introduce CellGiQ, a high-confident LRI prediction framework for intercellular communication analysis. Highly confident LRIs are first inferred by LRI feature extraction with BioTriangle, LRI selection using LightGBM, and LRI classification based on ensemble of gradient boosted neural network and interpretable boosting machine. Subsequently, known and identified high-confident LRIs are filtered by combining single-cell RNA sequencing (scRNA-seq) data and further applied to intercellular communication inference through a quartile scoring strategy. To validation the predictions, CellGiQ exploited several evaluation strategies: using AUC and AUPR, it surpassed six competing LRI prediction models on four LRI datasets; through Venn diagrams and molecular docking, its predicted LRIs were validated by five other popular intercellular communication inference methods; based on the overlapping LRIs, it computed high Jaccard index with six other state-of-the-art intercellular communication prediction tools within human HNSCC tissues; by comparing with classical models and literature retrieve, its inferred HNSCC-related intercellular communication results was further validated. The novelty of this study is to identify high-confident LRIs based on machine learning as well as design several LRI validation ways, providing reference for computational LRI prediction. CellGiQ provides an open-source and useful tool to decompose LRI-mediated intercellular communication at single cell resolution. CellGiQ is freely available at https://github.com/plhhnu/CellGiQ.

B7-H1 agonists suppress the PI3K/AKT/mtor pathway by degrading p110γ and independently induce cell death

The biological role of B7-H1 intrinsic signal is reportedly diverse and controversial, its signal pathway remains unclear. Although B7-H1 blocking antibodies were found to have agonist capacity, their binding features and agonist mechanisms need further investigation. Here, by constructing cell strains with full-length or truncated B7-H1, we found that B7-H1 functioned as a receptor to transmit cell death signal from PD-1 protein or anti-B7-H1s through its cytoplasmic domain. Specific binding to the IgV-like domain of B7-H1 was required for the downstream signal. Upon agonists interaction, B7-H1 regulated the degradation of phosphoinositide 3-kinases (PI3Ks) subunit p110γ, subsequently inhibited the PI3K/AKT/mTOR pathway, and significantly increased autophagy. Moreover, B7-H1 agonists also suppressed ubiquitylation in B7-H1+cells by reducing ubiquitin-activating enzyme (E1), eventually leading to cell death. Finally, we validated the receptor role of B7-H1 in multiple tumor cells and demonstrated that B7-H1 agonists could suppress tumor progression independent of T cells in vivo. Our findings revealed that B7-H1 agonists functions as a PI3K inhibitor and may offer new strategies for PI3K targeting therapy.

An efficient and universal In silico screening strategy for acquisition of high-affinity Aptamer and its application in analytical utility

Numerous aptamers against various targets have been identified through the technology of systematic evolution of ligands by exponential enrichment (SELEX), but the affinity of these aptamers are often insufficient due to the limitations of SELEX. Therefore, a more rational in silico screening strategy (ISS) was developed for efficient screening of high affinity aptamers, which took shape complementarity and thermodynamic stability into consideration. Neuron specific enolase (NSE), a tumor marker, was selected as the target molecule. In the screening process, three aptamer candidates with good shape complementarity, lower ΔG values, and higher ZDOCK scores were produced. The dissociation constant (Kd) of these candidates to NSE was determined to be 10.13 nM, 14.82 nM, and 2.76 nM, respectively. Each of them exhibited higher affinity to NSE than the parent aptamer (Kd = 23.83 nM). Finally, an antibody-free fluorescence aptasensor assay, based on the aptamer with the highest affinity, P-5C8G, was conducted, resulting in a limit of detection (LOD) value of 1.8 nM, which was much lower than the parental aptamer (P, LOD = 12.6 nM). The proposed ISS approach provided an efficient and universal strategy to improve the aptamer to have a high affinity and good analytical utility.

RGS6 drives cardiomyocyte death following nucleolar stress by suppressing Nucleolin/miRNA-21

BACKGROUND

Prior evidence demonstrated that Regulator of G protein Signaling 6 (RGS6) translocates to the nucleolus in response to cytotoxic stress though the functional significance of this phenomenon remains unknown.

METHODS

Utilizing in vivo gene manipulations in mice, primary murine cardiac cells, human cell lines and human patient samples we dissect the participation of a RGS6-nucleolin complex in chemotherapy-dependent cardiotoxicity.

RESULTS

Here we demonstrate that RGS6 binds to a key nucleolar protein, Nucleolin, and controls its expression and activity in cardiomyocytes. In the human myocyte AC-16 cell line, induced pluripotent stem cell derived cardiomyocytes, primary murine cardiomyocytes, and the intact murine myocardium tuning RGS6 levels via overexpression or knockdown resulted in diametrically opposed impacts on Nucleolin mRNA, protein, and phosphorylation.RGS6 depletion provided marked protection against nucleolar stress-mediated cell death in vitro, and, conversely, RGS6 overexpression suppressed ribosomal RNA production, a key output of the nucleolus, and triggered death of myocytes. Importantly, overexpression of either Nucleolin or Nucleolin effector miRNA-21 counteracted the pro-apoptotic effects of RGS6. In both human and murine heart tissue, exposure to the genotoxic stressor doxorubicin was associated with an increase in the ratio of RGS6/Nucleolin. Preventing RGS6 induction via introduction of RGS6-directed shRNA via intracardiac injection proved cardioprotective in mice and was accompanied by restored Nucleolin/miRNA-21 expression, decreased nucleolar stress, and decreased expression of pro-apoptotic, hypertrophy, and oxidative stress markers in heart.

CONCLUSION

Together, these data implicate RGS6 as a driver of nucleolar stress-dependent cell death in cardiomyocytes via its ability to modulate Nucleolin. This work represents the first demonstration of a functional role for an RGS protein in the nucleolus and identifies the RGS6/Nucleolin interaction as a possible new therapeutic target in the prevention of cardiotoxicity.

Phage Display Screening of Anchor Peptides for Red Blood Cell-Derived Extracellular Vesicles

Extracellular vesicles (EVs) are increasingly used for disease diagnosis and treatment. Among them, red blood cell-derived EVs (RBC-EVs) have attracted great attention due to their abundant sources and low risks of gene transfer (RBC-EVs lack nuclear and mitochondrial DNA). Here, we first revealed the high expression level of membrane protein solute carrier family 4 member 1 (SLC4A1) in RBC-EVs through proteomic analysis. We then identified several binding peptides with high affinity for the SLC4A1 extracellular domain (SLC4A1-EC) from phage display library screening. A high affinity of SLC4A1-EC and the three peptides (XRB2, XRE4, and XRH7) were assessed in vitro using surface plasmon resonance analysis and SDS-polyacrylamide gel electrophoresis (SDS-PAGE). The binding sites of SLC4A1-EC and polypeptides were further predicted by LigPlot + analysis, and the results showed that these three polypeptides could bind to part of the hydrophobic residues of SLC4A1-EC. The binding efficiency of the anchor peptides to the RBC-EVs was further verified by flow cytometry and fluorescence imaging. In conclusion, we successfully screened three specific RBC-EV-targeting peptides which could potentially be utilized for isolating RBC-derived EVs from serum samples. More importantly, this peptide could be coupled with targeting peptides to modify RBC-EVs for drug delivery. Our work will provide a viable method for optimizing the function of RBC-EVs.

Elucidation of conserved multi-epitope vaccine against Leishmania donovani using reverse vaccinology

Visceral leishmaniasis (VL) is a tropical disease that causes severe public health problems in humans when untreated. As no licensed vaccine exists against VL, we aimed to formulate a potential MHC-restricted chimeric vaccine construct against this dreadful parasitic disease. Amastin-like protein derived from L. donovani is considered to be stable, immunogenic and non-allergic. A comprehensive established framework was used to explore the set of immunogenic epitopes with estimated population coverage of 96.08% worldwide. The rigorous assessment revealed 6 promiscuous T-epitopes which can plausibly be presented by more than 66 diverse HLA alleles. Further docking and simulation study of peptide receptor complexes identified a strong and stable binding interaction with better structural compactness. The predicted epitopes were combined with appropriate linkers and adjuvant molecules and their translation efficiency was evaluated in pET28+(a), an bacterial expression vector using in-silico cloning. Molecular docking followed by MD simulation study revealed a stable interaction between chimeric vaccine construct with TLRs. Immune simulation of the chimeric vaccine constructs showed an elevated Th1 immune response against both B and T epitopes. With this, the detailed computational analysis suggested that the chimeric vaccine construct can evoke a robust immune response against Leishmania donovani infection. Future studies are required to validate the role of amastin as a promising vaccine target.Communicated by Ramaswamy H. Sarma.

Individual transmembrane domains of SfABCC2 from Spodoptera frugiperda do not serve as functional Cry1F receptors

The fall armyworm (Spodoptera frugiperda) is a major global pest causing severe damage to various crops, especially corn. Transgenic corn producing the Cry1F pesticidal protein from the bacterium Bacillus thuringiensis (Cry1F corn) showed effectiveness in controlling this pest until S. frugiperda populations at locations in North and South America evolved practical resistance. The mechanism for practical resistance involved disruptive mutations in an ATP binding cassette transporter subfamily C2 gene (SfABCC2), which serves as a functional Cry1F receptor in the midgut cells of susceptible S. frugiperda. The SfABCC2 protein contains two transmembrane domains (TMD1 and TMD2), each with a cytosolic nucleotide (ATP) binding domain (NBD1 and NBD2, respectively). Previous reports have demonstrated that disruptive mutations in TMD2 were linked with resistance to Cry1F, yet whether the complete SfABCC2 structure is needed for receptor functionality or if a single TMD-NBD protein can serve as functional Cry1F receptor remains unknown. In the present study, we separately expressed TMD1 and TMD2 with their corresponding NBDs in cultured insect cells and tested their Cry1F receptor functionality. Our results show that the complete SfABCC2 structure is required for Cry1F receptor functionality. Moreover, binding competition assays revealed that Cry1F specifically bound to SfABCC2, whereas neither SfTMD1-NBD1 nor SfTMD2-NBD2 exhibited any significant binding. These results provide insights into the molecular mechanism of Cry1F recognition by SfABCC2 in S. frugiperda, which could facilitate the development of more effective insecticidal proteins.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

Computational Design of Peptide Assemblies

With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expanding exponentially. Consequently, attempting to explore all functional sequences comprehensively through experience and experiments alone has become impractical. By utilizing computational methods, especially artificial intelligence enhanced molecular dynamics (MD) simulation and de novo peptide design, there has been a significant expansion in the exploration of sequence space. Through these methods, a variety of supramolecular functional materials, including fibers, two-dimensional arrays, nanocages, etc., have been designed by meticulously controlling the inter- and intramolecular interactions. In this review, we first provide a brief overview of the current main computational methods and then focus on the computational design methods for various self-assembled peptide materials. Additionally, we introduce some representative protein self-assemblies to offer guidance for the design of self-assembling peptides.

ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody-antigen computational docking

Accurate prediction of antibody-antigen complex structures is pivotal in drug discovery, vaccine design and disease treatment and can facilitate the development of more effective therapies and diagnostics. In this work, we first review the antibody-antigen docking (ABAG-docking) datasets. Then, we present the creation and characterization of a comprehensive benchmark dataset of antibody-antigen complexes. We categorize the dataset based on docking difficulty, interface properties and structural characteristics, to provide a diverse set of cases for rigorous evaluation. Compared with Docking Benchmark 5.5, we have added 112 cases, including 14 single-domain antibody (sdAb) cases and 98 monoclonal antibody (mAb) cases, and also increased the proportion of Difficult cases. Our dataset contains diverse cases, including human/humanized antibodies, sdAbs, rodent antibodies and other types, opening the door to better algorithm development. Furthermore, we provide details on the process of building the benchmark dataset and introduce a pipeline for periodic updates to keep it up to date. We also utilize multiple complex prediction methods including ZDOCK, ClusPro, HDOCK and AlphaFold-Multimer for testing and analyzing this dataset. This benchmark serves as a valuable resource for evaluating and advancing docking computational methods in the analysis of antibody-antigen interaction, enabling researchers to develop more accurate and effective tools for predicting and designing antibody-antigen complexes. The non-redundant ABAG-docking structure benchmark dataset is available at https://github.com/Zhaonan99/Antibody-antigen-complex-structure-benchmark-dataset.

circGlis3 promotes β-cell dysfunction by binding to heterogeneous nuclear ribonucleoprotein F and encoding Glis3-348aa protein

Circular RNAs (circRNAs) are crucial regulators of β-cell function and are involved in lipotoxicity-induced β-cell damage in type 2 diabetes mellitus (T2DM). We previously identified that circGlis3, a circRNA derived from exon 4 of the diabetes susceptibility gene Glis3, was upregulated in lipotoxic β cells. However, the functional role and molecular mechanism of circGlis3 in β cells remain largely unknown. Here, we revealed that the splicing factor CUGBP Elav-Like Family Member 1 (CELF1) facilitated the biogenesis of circGlis3. Moreover, we established a transgenic mouse model and confirmed that the overexpression of circGlis3 impaired β-cell function. Mechanistically, circGlis3 bound to heterogeneous nuclear ribonucleoprotein F (hnRNPF) and blocked its nuclear translocation, thereby reducing Sirt1 levels. Additionally, circGlis3 encoded a 348aa protein that interacted with GLIS3 and inhibited its transcriptional activity. Our data uncover a critical role of circGlis3 in β-cell dysfunction, suggesting that circGlis3 may be a potential therapeutic target for T2DM.

VDAC1, as a downstream molecule of MLKL, participates in OGD/R-induced necroptosis by inducing mitochondrial damage

Ischemia-reperfusion (I/R) injury constitutes a significant risk factor for a range of diseases, including ischemic stroke, myocardial infarction, and trauma. Following the restoration of blood flow post-tissue ischemia, oxidative stress can lead to various forms of cell death, including necrosis, apoptosis, autophagy, and necroptosis. Recent evidence has highlighted the crucial role of mitochondrial dysfunction in I/R injury. Nevertheless, there remains much to be explored regarding the molecular signaling network governing cell death under conditions of oxidative stress. Voltage-dependent anion channel 1 (VDAC1), a major component in the outer mitochondrial membrane, is closely involved in the regulation of cell death. In a cellular model of oxygen-glucose deprivation and reoxygenation (OGD/R), which effectively simulates I/R injury in vitro, our study reveals that OGD/R induces VDAC1 oligomerization, consequently exacerbating cell death. Furthermore, we have revealed the translocation of mixed lineage kinase domain-like protein (MLKL) to the mitochondria, where it interacts with VDAC1 following OGD/R injury, leading to an increased mitochondrial membrane permeability. Notably, the inhibition of MLKL by necrosulfonamide hinders the binding of MLKL to VDAC1, primarily by affecting the membrane translocation of MLKL, and reduces OGD/R-induced VDAC1 oligomerization. Collectively, our findings provide preliminary evidence of the functional association between MLKL and VDAC1 in the regulation of necroptosis.

Rationally Designed Self-Assembling Nanovaccines Elicit Robust Mucosal and Systemic Immunity against Rhabdovirus

Viral diseases have constantly caused great threats to global public health, resulting in an urgent need for effective vaccines. However, the current viral vaccines often show low immunogenicity. To counter this, we report a smart strategy of a well-designed modular nanoparticle (LSG-TDH) that recapitulates the dominant antigen SG, low-molecular-weight protamine, and tetralysine-modified H-chain apoferritin (TDH). The constructed LSG-TDH nanovaccine could self-assemble into a nanocage structure, which confers excellent mucus-penetrating, cellular affinity, and uptake ability. Studies demonstrate that the LSG-TDH nanovaccine could strongly activate both mucosal and systemic immune responses. Importantly, by immunizing wild-type and TLR2 knockout (TLR2-KO) zebrafish, we found that TLR2 could mediate LSG-TDH-induced adaptive mucosal and systemic immune responses by activating antigen-presenting cells. Collectively, our findings offer new insights into rational viral vaccine design and provide additional evidence of the vital role of TLR2 in regulating adaptive immunity.

Effects of LDLR variants rs5928, rs750518671 and rs879254797 on protein structure and functional activity in HepG2 cells transfected with CRISPR/Cas9 constructs

Familial Hypercholesterolemia (FH) is a genetic disorder associated with premature atherosclerosis and increased risk of cardiovascular diseases. LDLR deleterious mutations are associated with FH, however the role of some missense variants in FH pathogenicity remains to be elucidated. This study explored the predictive impact of LDLR missense variants on protein structure and investigated their functional effects on LDLR expression in HepG2 cells transfected with CRISPR/Cas9 constructs. FH (n = 287) and non-FH patients (n = 45) were selected, and lipid profile was obtained from medical records. LDLR variants were identified using an exon-targeted gene sequencing strategy, considering its cost-effective to increase accuracy in the identification step of the most likely FH-related variants in a less laborious process. LDLR variants were selected based on conflicting pathogenicity results found in Clinvar, in silico prediction tools, affected LDLR domains, and less common variants considering minor allele frequency < 0.05. Molecular modeling studies were used to predict the effects of LDLR missense variants on protein structure. Recombinant LDLR variants were constructed using CRISPR/Cas9 system and were used to transfect HepG2 cells. Functional assays in transfected cells were performed to assess LDLR expression using flow cytometry and western blotting, and LDLR activity using flow cytometry and confocal microscopy. The variants rs121908039 (c.551G>A, p.C184Y), rs879254797 (c.1118G>A, p.G373D), rs28941776 (c.1646G>A, p.G549D), rs750518671 (c.2389G>C, p.V797L), rs5928 (c.2441G>A, p.R814Q) and rs137853964 (c.2479G>A, p.V827I) were selected for molecular docking analysis. The p.C184Y exhibited a favorable energy change for protein stability due to its interaction with EGF-A/EGF-B regions; p.G373D and p.G549D displayed intermediate energy changes; and p.R814Q and p.V827I showed smaller energy changes. The results of functional assays showed that p.G373D, p.V797L and p.R814Q reduced LDLR expression and activity (p < 0.05). Microscopic analysis of the p.V797L and p.G373D variants revealed altered lipid localization and accumulation in transfected HepG2 cells. Carriers of p.G549D, p.V797L and p.R814Q had higher LDL cholesterol levels than non-FH group, and (p < 0.05). p.G373D and p.G549D were associated with clinical manifestations of FH. In conclusion, the p.C184Y, p.G373D, p.G549D and p.R814Q variants alter protein stability and intramolecular interactions, while p.V797L has a minimal impact on protein stability, and p.V827I has no significant intramolecular interactions. p.G373D, p.V767L and p.R814Q are associated with impaired LDLR expression and activity.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Isolation and Characterization of Neutralizing Monoclonal Antibodies from a Large Panel of Murine Antibodies against RBD of the SARS-CoV-2 Spike Protein

The COVID-19 pandemic, once a global crisis, is now largely under control, a testament to the extraordinary global efforts involving vaccination and public health measures. However, the relentless evolution of SARS-CoV-2, leading to the emergence of new variants, continues to underscore the importance of remaining vigilant and adaptable. Monoclonal antibodies (mAbs) have stood out as a powerful and immediate therapeutic response to COVID-19. Despite the success of mAbs, the evolution of SARS-CoV-2 continues to pose challenges and the available antibodies are no longer effective. New variants require the ongoing development of effective antibodies. In the present study, we describe the generation and characterization of neutralizing mAbs against the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein by combining plasmid DNA and recombinant protein vaccination. By integrating genetic immunization for rapid antibody production and the potent immune stimulation enabled by protein vaccination, we produced a rich pool of antibodies, each with unique binding and neutralizing specificities, tested with the ELISA, BLI and FACS assays and the pseudovirus assay, respectively. Here, we present a panel of mAbs effective against the SARS-CoV-2 variants up to Omicron BA.1 and BA.5, with the flexibility to target emerging variants. This approach ensures the preparedness principle is in place to address SARS-CoV-2 actual and future infections.

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.

Software and Databases for Protein-Protein Docking

Protein-protein interactions (PPIs) provide valuable insights for understanding the principles of biological systems and for elucidating causes of incurable diseases. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. This chapter is a summary of software and databases used for protein-protein docking.

GRAMM Web Server for Protein Docking

Prediction of the structure of protein complexes by docking methods is a well-established research field. The intermolecular energy landscapes in protein-protein interactions can be used to refine docking predictions and to detect macro-characteristics, such as the binding funnel. A new GRAMM web server for protein docking predicts a spectrum of docking poses that characterize the intermolecular energy landscape in protein interaction. A user-friendly interface provides options to choose free or template-based docking, as well as other advanced features, such as clustering of the docking poses, and interactive visualization of the docked models.

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.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

Structural basis for the role of C-terminus acidic tail of Saccharomyces cerevisiae ubiquitin-conjugating enzyme (Rad6) in E3 ligase (Bre1) mediated recognition of histones

Ubiquitination of histone H2B on chromatin is key to gene regulation. E3 ligase Bre1 and E2 Rad6 in Saccharomyces cerevisiae associate together to catalyze mono-ubiquitination at histone H2BK123. Prior studies identified the role of a highly dynamic C-terminal acidic tail of Rad6 indispensable for H2BK123 mono-ubiquitination. However, the mechanistic basis for the Rad6-acidic tail role remained elusive. Using different structural and biophysical approaches, this study for the first time uncovers the direct role of Rad6-acidic tail in interaction with the Bre1 Rad6-Binding Domain (RBD) and recognition of histones surface to facilitate histone H2B mono-ubiquitination. A combination of NMR, SAXS, ITC, site-directed mutagenesis and molecular dynamics studies reveal that RBD domain of Bre1 interacts with Rad6 to stabilize the dynamics of acidic tail. This Bre1-RBD mediated stability in acidic tail of Rad6 could be one of the key factors for facilitating correct recognition of histone surface and ubiquitin-transfer at H2BK123. We provide biophysical evidence that Rad6-acidic tail and a positivity charged surface on histone H2B are involved in recognition of E2:Histones. Taken together, this study uncovers the mechanistic basis for the role of Rad6-acidic in Bre1-RBD mediated recognition of histone surface that ensure the histone H2B mono-ubiquitination.