Benchmarking of different molecular docking methods for protein-peptide docking

Peptides of corn oligopeptides improve Aβ1-42-injured SHSY5Y cells

BACKGROUND

There are more and more Alzheimer's patients. The formation of plaques of Aβ1-42 in the brain is one of the main causes of Alzheimer's disease. Corn oligopeptides have natural antioxidant effects. It is aimed to develop functional corn oligopeptides to prevent Alzheimer's disease through antioxidation.

METHODS

According to previous laboratory studies, peptides of corn oligopeptides were screened by biological activity score and ADMET prediction, and molecular docking technology was used to screen the peptides that had high binding energy with Aβ1-42. The protective effects of the selected peptides were evaluated against oxidative stress in Aβ1-42-injured SHSY5Y cells, and the mechanism of the effects in the protein kinase A (PKA)/cAMP-response element binding protein (CREB)/brain-derived neurotrophic factor (BDNF)-mediated signaling pathway was investigated, which is closely related to neurodegenerative and neurological diseases.

RESULTS

The results showed that three peptides (GL, FA and FQ) significantly increased the cell viability of Aβ1-42-induced cells and mitochondrial intensity and decreased extracellular lactate dehydrogenase content. They also improved intracellular oxidative stress caused by Aβ1-42, including reducing the overproduction of intracellular reactive oxygen species, and increasing the content of lipid oxidation, superoxide dismutase and glutathione peroxidase. In addition, western blot showed that treatment with GL, FA and FQ significantly increased the expression of PKA, CREB and BDNF, whereas cells injured with Aβ1-42 decreased the expression of these signaling proteins.

CONCLUSION

These results suggest that peptides of corn oligopeptides can effectively improve Aβ1-42-induced Alzheimer's disease, and may improve oxidative stress response to protective nerve cells by up-regulating the protein expression of the PKA/CREB/BDNF signaling pathway. © 2025 Society of Chemical Industry.

DINC-ensemble: A web server for docking large ligands incrementally to an ensemble of receptor conformations

Protein-ligand docking aids structure-based drug discovery by computationally modelling protein-ligand interactions. DINC (Docking INCrementally) is one approach to molecular docking that improved the docking of large ligands using a parallelized incremental meta-docking. Traditional docking tools, including DINC, explore the flexibility of the ligand in a single receptor binding pocket assuming limited flexibility of the receptor backbone. This simplifying assumption narrows down the docking search space but hinders successful docking for flexible receptors. DINC-Ensemble implicitly considers receptor backbone flexibility by running DINC docking in parallel on different receptor conformations. Inputs to DINC-Ensemble include (1) a ligand and (2) a list of different receptor conformations. For each ligand-receptor pair DINC-Ensemble performs incremental meta-docking in parallel. As a result, multiple ligand poses are generated in the binding pockets of different receptor conformations. These poses are then ranked, and the lowest scoring pose is selected. Two main outputs provided by a successful run of DINC-Ensemble are (1) the best scoring ligand poses and (2) a ranked list of selected receptor conformations. The best scoring ligand pose can be used to understand the interactions between the receptor and the ligand that influence the binding. The ranked list of receptor conformations shows the best receptor conformation fit for a given ligand and can provide insight into ligand-induced conformational selection. We provide DINC-Ensemble as a Python package and a free web server at https://dinc-ensemble.kavrakilab.rice.edu/.

Isorhamnetin inhibits cholangiocarcinoma proliferation and metastasis via PI3K/AKT signaling pathway

BACKGROUND

Cholangiocarcinoma (CCA), which is a malignant tumor originating from the epithelial cells of the bile ducts, has witnessed an increasing incidence year by year. Owing to the dearth of effective treatments, the prognosis for CCA is rather poor. Isorhamnetin is known to possess anti-tumor, anti-inflammatory and oxidative stress modulating effects; however, its role in CCA remains unclear.

METHODS

Firstly, we screened the core targets and pathways of isorhamnetin for the treatment of CCA through a network pharmacology approach. Subsequently, we verified via molecular docking that the core targets could dock stably with isorhamnetin. Finally, we verified the inhibitory effect of isorhamnetin on the malignant biological behavior of CCA in vitro and in vivo experiments.

RESULTS

Based on the network pharmacology analysis, we came to the conclusion that AKT1 might be a core target of isorhamnetin in the treatment of CCA. Molecular docking indicated that AKT1 was capable of binding stably to isorhamnetin. Subsequently, In vitro experiments demonstrated that isorhamnetin was able to suppress the proliferation and metastasis of CCA cells, and AKT1 played a pivotal role in this process. Mechanistically speaking, isorhamnetin exerts its inhibitory effect on tumor growth via the PI3K/AKT signaling pathway.

CONCLUSIONS

Our study demonstrated for the first time that isorhamnetin can inhibit the progression of CCA through PI3K/AKT, and that AKT1 may be a target of isorhamnetin for the treatment of CCA.

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

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

Algal proteins and bioactive peptides: Sustainable nutrition for human health

Animal proteins are the primary global protein source, but their production is environmentally challenging and has low conversion efficiency. This highlights the need to diversify dietary protein sources. Algal proteins provide a sustainable alternative, outperforming traditional plant and animal proteins in protein content, quality, and digestibility. Furthermore, bioactive peptides (BAPs) derived from algal proteins exhibit significant health benefits, including antihypertensive, antioxidant, antimicrobial, anticancer, and antidiabetic activities. This review comprehensively explores the nutritional benefits of algal proteins and provides an innovative summary of the production techniques for algal bioactive peptides. It also highlights the synergistic application methods of these technologies. By integrating pretreatment methods such as ultrasound-assisted extraction, pulsed electric field, and high hydrostatic pressure with enzymatic-assisted extraction, these techniques demonstrate a synergistic effect in improving protein hydrolysis efficiency while also increasing the yield of BAPs. Meanwhile, database resources related to algal proteins are integrated and the application of computer technology in the development of algal proteins is analyzed. It aims to provide new insights to optimize the development and utilization of algal proteins to help them become a sustainable source of nutrition to meet the needs of a growing global population.

MEGA PROTAC, MEGA DOCK-based PROTAC mediated ternary complex formation pipeline with sequential filtering and rank aggregation

Proteolysis-targeting chimaeras (PROTACs), which induce proteolysis by recruiting an E3 ligase to dock into a target protein, are acquiring popularity as a novel pharmacological modality because of the unique features of PROTAC, including high potency, low dosage, and effective on undruggable targets. While PROTACs are promising prospects as chemical probes and therapeutic agents, their discovery usually necessitates the synthesis of numerous analogues to explore variations on the chemical linker structure exhaustively. Without extensive trial and error, it is unknown how to link the two protein-recruiting moieties to facilitate the formation of a productive ternary complex. Although molecular docking-based and optimization pipelines have been designed to predict ternary complexes, guiding rational PROTAC design, they have suffered from limited predictive performance in the quality of the ternary structure and their ranks. Here, MEGA PROTAC has been designed to enhance the performance in quality and ranking of ternary structures. MEGA PROTAC employs MEGADOCK to execute docking for protein-protein complexes (PPCs). The docking establishes an initial exploration area for PPCs. A sequential filtration strategy combined with rank aggregation is employed to choose a subset of PPCs for grid search. Once candidate PPCs are selected, a grid search method is used separately for translation and rotation. The remaining proteins have been grouped into clusters, and MEGA PROTAC further filters these clusters based on the energy score of the proteins within each cluster. MEGA PROTAC utilises rank aggregation to choose the best clusters and then employs MEGADOCK to dock PROTAC into the selected PPCs, forming a ternary structure. Finally, MEGA PROTAC was tested on 22 cases to compare with the state-of-the-art method, Bayesian optimisation for ternary complex prediction (BOTCP). MEGA PROTAC outperformed BOTCP on 16 test cases out of 22 cases, achieving a higher maximum DockQ score with an 18% higher mean and 35% higher median. Also, MEGA PROTAC exhibited 75% superior ranks and a reduced cluster number for maximum DockQ score compared to BOTCP. Also, MEGA PROTAC outperforms BOTCP by achieving a twofold improvement in locating the first acceptable DockQ scores, with a more significant proportion of near-native structures within the detected cluster.

Leveraging Artificial Intelligence and Gene Expression Analysis to Identify Some Potential Bovine Coronavirus (BCoV) Receptors and Host Cell Enzymes Potentially Involved in the Viral Replication and Tissue Tropism

Bovine coronavirus (BCoV) exhibits dual tissue tropism, infecting both the respiratory and enteric tracts of cattle. Viral entry into host cells requires a coordinated interaction between viral and host proteins. However, the specific cellular receptors and co-receptors facilitating BCoV entry remain poorly understood. Similarly, the roles of host proteases such as Furin, TMPRSS2, and Cathepsin-L (CTS-L), known to assist in the replication of other coronaviruses, have not been extensively explored for BCoV. This study aims to identify novel BCoV receptors and host proteases that modulate viral replication and tissue tropism. Bovine cell lines were infected with BCoV isolates from enteric and respiratory origins, and the host cell gene expression profiles post-infection were analyzed using next-generation sequencing (NGS). Differentially expressed genes encoding potential receptors and proteases were further assessed using in-silico prediction and molecular docking analysis. These analyses focused on known coronavirus receptors, including ACE2, NRP1, DPP4, APN, AXL, and CEACAM1, to identify their potential roles in BCoV infection. Validation of these findings was performed using the qRT-PCR assays targeting individual genes. We confirmed the gene expression profiles of these receptors and enzymes in some BCoV (+/-) lung tissues. Results revealed high binding affinities of 9-O-acetylated sialic acid and NRP1 to BCoV spike (S) and hemagglutinin-esterase (HE) proteins compared to ACE2, DPP4, and CEACAM1. Additionally, Furin and TMPRSS2 were predicted to interact with the BCoV-S polybasic cleavage site (RRSRR|A), suggesting their roles in S glycoprotein activation. This is the first study to explore the interactions of BCoV with multiple host receptors and proteases. Functional studies are recommended to confirm their roles in BCoV infection and replication.

Unearthing novel and multifunctional peptides in peptidome of fermented chhurpi cheese of Indian Himalayan region

Fermented foods of the Indian Himalaya are unexplored functional resources with high nutritional potential. Chhurpi cheese, fermented by defined native proteolytic lactic acid bacteria of Sikkim was assessed for ACE inhibitory, HOCl reducing, and MPO inhibitory, activity across varying stages of gastrointestinal (GI) digestion. The enhanced bioactivity of Lactobacillus delbrueckii WS4 chhurpi was associated with the generation of bioactive and multifunctional peptides during fermentation and GI digestion. Qualitative and quantitative in silico tools were employed for prediction of ACE inhibitory activity of novel chhurpi peptides. Selected peptides, with highest predictive ACE inhibitory potential were synthesized and in vitro validation revealed the ACE inhibitory potential of peptides HPHPHLSFM and LKPTPEGDL. LKPTPEGDL showed the most potent ACE inhibitory activity (IC50 of 25.82 ± 0.26 µmol) which slightly decreased upon GI digestion. The peptides demonstrated a non-competitive type mixed ACE inhibition modality. Furthermore, the two peptides exerted observable HOCl reducing and MPO inhibitory activity, demonstrating their antioxidative potential. HPHPHLSFM exhibited superior HOCl reduction (EC50 of 0.29 ± 0.01 mmol), while LKPTPEGDL demonstrated higher MPO (IC50 of 0.29 ± 0.01 mmol) inhibition. Molecular docking of the two peptides with MPO revealed proline and aspartate near peptidyl C-terminus to bind with enzyme catalytic residues. This study presents the first peptidome analysis of chhurpi produced through controlled fermentation and identifies novel peptides with MPO and ACE inhibitory activity. Furthermore, it marks the first synthesis and in vitro bioactivity validation of bioactive peptides from chhurpi cheese, highlighting its multifunctional potential.

Characterization of Novel Plantaricin-Derived Antiviral Peptides Against Flaviviruses

Flaviviruses, including West Nile virus, Zika virus, and Dengue virus, pose global health challenges due to their distribution, pathogenicity, and lack of effective treatments or vaccines. This study investigated the antiviral activity of novel truncated peptides derived from the two-peptide plantaricins PLNC8 αβ, PlnEF, PlnJK, and PlnA. The antiviral potential was predicted using machine learning tools, followed by in vitro evaluation against the Kunjin virus using plaque reduction assays in Vero cells. Molecular docking assessed peptide interactions with KUNV and ZIKV. Full-length and truncated peptides from PlnA, PlnE, PlnF, PlnJ, and PlnK demonstrated limited antiviral efficacy against KUNV in vitro, despite in silico predictions suggesting antiviral potential for PlnA, PlnE, and PlnJ. Large discrepancies were observed between the predicted and experimentally determined activities. However, complementary two-peptide plantaricins PlnEF and PlnJK exhibited significant synergistic effects. Furthermore, the truncated peptides PLNC8 α1-15 and PLNC8 β1-20 reduced KUNV viral load by over 90%, outperforming their full-length counterparts. Molecular docking revealed interactions of PLNC8 α and PLNC8 β, and their truncated variants, with KUNV and ZIKV, suggesting a mechanism involving viral envelope disruption. These findings highlight the potential of plantaricin-derived peptides as promising antiviral candidates against flaviviruses, warranting further investigation into their mechanisms and applications.

A claudin5-binding peptide enhances the permeability of the blood-brain barrier in vitro

The blood-brain barrier (BBB) maintains brain homeostasis but also prevents most drugs from entering the brain. No paracellular diffusion of solutes is allowed because of tight junctions that are made impermeable by the expression of claudin5 (CLDN5) by brain endothelial cells. The possibility of regulating the BBB permeability in a transient and reversible fashion is in strong demand for the pharmacological treatment of brain diseases. Here, we designed and tested short BBB-active peptides, derived from the CLDN5 extracellular domains and the CLDN5-binding domain of Clostridium perfringens enterotoxin, using a robust workflow of structural modeling and in vitro validation techniques. Computational analysis at the atom level based on solubility and affinity to CLDN5 identified a CLDN5-derived peptide not reported previously called f1-C5C2, which was soluble in biological media, displayed efficient binding to CLDN5, and transiently increased BBB permeability. The peptidomimetic strategy described here may have potential applications in the pharmacological treatment of brain diseases.

Characterization of perceptual interactions among aroma compounds found in Rose damascena and Angelica dahurica root essential oil with threshold, S-curve, σ-τ plot and molecular docking

The study investigated the perceptual interaction between two types of Rose damascena essential oil and two types of Angelica dahurica root essential oil. Using gas chromatography-olfactometer (GC-O) and gas chromatography-mass spectrometer (GC-MS), 24 and 25 aromatic compounds in Rose damascena essential oil and Angelica dahurica root essential oil were identified and quantified, respectively. Based on flavor dilution (FD) values and odor activity values (OAVs), 10 important aroma compounds in Rose damascena essential oil and 6 in Angelica dahurica root essential oil were identified. The perceptual interactions between these aroma compounds were explored by using the threshold method, S-curve, and σ-τ plot. Additionally, molecular docking analysis revealed changes in binding energy and binding sites. Notably, when aroma compounds shared similar structures and fragrances, they exhibited additive or synergistic effects. Conversely, dissimilar compounds showed different interactions. The molecular docking results aligned with our experimental findings. Overall, our study demonstrates that the threshold method, S-curve, σ-τ plot, and molecular docking enhance our understanding of aroma compound perceptual interactions between Rose damascena essential oil and Angelica dahurica root essential oil. These insights provide a theoretical foundation and practical guidance for improving the aroma of Angelica dahurica root essential oil and studying perceptual interactions among essential oils.

Insight into Protein Engineering: From In silico Modelling to In vitro Synthesis

Protein engineering alters the polypeptide chain to obtain a novel protein with improved functional properties. This field constantly evolves with advanced in silico tools and techniques to design novel proteins and peptides. Rational incorporating mutations, unnatural amino acids, and post-translational modifications increases the applications of engineered proteins and peptides. It aids in developing drugs with maximum efficacy and minimum side effects. Currently, the engineering of peptides is gaining attention due to their high stability, binding specificity, less immunogenic, and reduced toxicity properties. Engineered peptides are potent candidates for drug development due to their high specificity and low cost of production compared with other biologics, including proteins and antibodies. Therefore, understanding the current perception of designing and engineering peptides with the help of currently available in silico tools is crucial. This review extensively studies various in silico tools available for protein engineering in the prospect of designing peptides as therapeutics, followed by in vitro aspects. Moreover, a discussion on the chemical synthesis and purification of peptides, a case study, and challenges are also incorporated.

Protocol for in silico characterization of natural-based molecules as quorum-sensing inhibitors

The search and development of new quorum-sensing (QS) inhibitors are ongoing processes for biofilm control. Here, we present a protocol for in silico characterization of natural-based molecules as QS inhibitors. We describe steps for preparing models of protein receptors for virtual screening. We then detail procedures for construction and virtual screening of phytochemical libraries and hit picking to be experimentally validated by in vitro assays. This protocol allows exploration of a broad range of potential inhibitors for a specific target. For complete details on the use and execution of this protocol, please refer to Fernandes et al.1.

RankMHC: Learning to Rank Class-I Peptide-MHC Structural Models

The binding of peptides to class-I Major Histocompability Complex (MHC) receptors and their subsequent recognition downstream by T-cell receptors are crucial processes for most multicellular organisms to be able to fight various diseases. Thus, the identification of peptide antigens that can elicit an immune response is of immense importance for developing successful therapies for bacterial and viral infections, even cancer. Recently, studies have demonstrated the importance of peptide-MHC (pMHC) structural analysis, with pMHC structural modeling methods gradually becoming more popular in peptide antigen identification workflows. Most of the pMHC structural modeling tools provide an ensemble of candidate peptide poses in the MHC-I cleft, each associated with a score stemming from a scoring function, with the top scoring pose assumed to be the most representative of the ensemble. However, identifying the binding mode, that is, the peptide pose from the ensemble that is closer to an unavailable native structure, is not trivial. Oftentimes, the peptide poses characterized as best by a protein-ligand scoring function are not the ones that are the most representative of the actual structure. In this work, we frame the peptide binding pose identification problem as a Learning-to-Rank (LTR) problem. We present RankMHC, an LTR-based pMHC binding mode identification predictor, which is specifically trained to predict the most accurate ranking of an ensemble of pMHC conformations. RankMHC outperforms classical peptide-ligand scoring functions, as well as previous Machine Learning (ML)-based binding pose predictors. We further demonstrate that RankMHC can be used with many pMHC structural modeling tools that use different structural modeling protocols.

In-silico exploration of Attukal Kizhangu L. compounds: Promising candidates for periodontitis treatment

A medicinal pteridophyte known as Attukal Kizhangu L. has been used to cure patients for centuries by administering plant parts based on conventional and common practices. Regarding its biological functions, significant use and advancement have been made. Extract of Attukal Kizhangu L. is the subject of the current study, which uses network pharmacology as its foundation. Three targeted compounds such as α-Lapachone, Dihydrochalcone, and Piperine were chosen for additional research from the 17 Phytoconstituents that were filtered out by the Coupled UPLC-HRMS study since they followed to Lipinski rule and showed no toxicity. The pharmacokinetics and physicochemical properties of these targeted compounds were analyzed by using three online web servers pkCSM, Swiss ADME, and Protox-II. This is the first in silico study to document these compound's effectiveness against the standard drug DOX in treating Periodontitis. The Swiss target prediction database was used to retrieve the targets of these compounds. DisGeNET and GeneCards were used to extract the targets of periodontitis. The top five hub genes were identified by Cytoscape utilizing the protein-protein interaction of common genes, from which two hub genes and three binding proteins of collagenase enzymes were used for further studies AA2, PGE2, PI2, TNFA, and PGP. The minimal binding energy observed in molecular docking, indicative of the optimal docking score, corresponds to the highest affinity between the protein and ligand. To corroborate the findings of the docking study, molecular dynamics (MD) simulations, and MMPBSA calculations were conducted for the complexes involving AA2-α-LPHE, AA2-DHC, and AA2-PPR. This research concluded that AA2-DHC was the most stable complex among the investigated interactions, surpassing the stability of the other complexes examined in comparison with the standard drug DOX. Overall, the findings supported the promotion of widespread use of Attukal Kizhangu L. in clinics as a potential therapeutic agent or may be employed for the treatment of acute and chronic Periodontitis.

Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery

Due to the spread of antibiotic resistance, global attention is focused on its inhibition and the expansion of effective medicinal compounds. The novel functional properties of peptides have opened up new horizons in personalized medicine. With artificial intelligence methods combined with therapeutic peptide products, pharmaceuticals and biotechnology advance drug development rapidly and reduce costs. Short-chain peptides inhibit a wide range of pathogens and have great potential for targeting diseases. To address the challenges of synthesis and sustainability, artificial intelligence methods, namely machine learning, must be integrated into their production. Learning methods can use complicated computations to select the active and toxic compounds of the drug and its metabolic activity. Through this comprehensive review, we investigated the artificial intelligence method as a potential tool for finding peptide-based drugs and providing a more accurate analysis of peptides through the introduction of predictable databases for effective selection and development.

PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model

The protein-peptide interaction plays a pivotal role in fields such as drug development, yet remains underexplored experimentally and challenging to model computationally. Herein, we introduce PepCA, a sequence-based approach for predicting peptide-binding sites on proteins. A primary obstacle in predicting peptide-protein interactions is the difficulty in acquiring precise protein structures, coupled with the uncertainty of polypeptide configurations. To address this, we first encode protein sequences using the Evolutionary Scale Modeling 2 (ESM-2) pre-trained model to extract latent structural information. Additionally, we have developed a multi-input coattention mechanism to concurrently update the encoding of both peptide and protein residues. PepCA integrates this module within an encoder-decoder structure. This model's high precision in identifying binding sites significantly advances the field of computational biology, offering vital insights for peptide drug development and protein science.

MSC-extracellular vesicle microRNAs target host cell-entry receptors in COVID-19: in silico modeling for in vivo validation

BACKGROUND

Coronavirus disease 2019 (COVID-19) has created a global pandemic with significant morbidity and mortality. SARS-CoV-2 primarily infects the lungs and is associated with various organ complications. Therapeutic approaches to combat COVID-19, including convalescent plasma and vaccination, have been developed. However, the high mutation rate of SARS-CoV-2 and its ability to inhibit host T-cell activity pose challenges for effective treatment. Mesenchymal stem cells (MSCs) and their extracellular vesicles (MSCs-EVs) have shown promise in COVID-19 therapy because of their immunomodulatory and regenerative properties. MicroRNAs (miRNAs) play crucial regulatory roles in various biological processes and can be manipulated for therapeutic purposes.

OBJECTIVE

We aimed to investigate the role of lyophilized MSC-EVs and their microRNAs in targeting the receptors involved in SARS-CoV-2 entry into host cells as a strategy to limit infection. In silico microRNA prediction, structural predictions of the microRNA-mRNA duplex, and molecular docking with the Argonaut protein were performed.

METHODS

Male Syrian hamsters infected with SARS-CoV-2 were treated with human Wharton's jelly-derived Mesenchymal Stem cell-derived lyophilized exosomes (Bioluga Company)via intraperitoneal injection, and viral shedding was assessed. The potential therapeutic effects of MSCs-EVs were measured via histopathology of lung tissues and PCR for microRNAs.

RESULTS

The results revealed strong binding potential between miRNA‒mRNA duplexes and the AGO protein via molecular docking. MSCs-EVs reduced inflammation markers and normalized blood indices via the suppression of viral entry by regulating ACE2 and TMPRSS2 expression. MSCs-EVs alleviated histopathological aberrations. They improved lung histology and reduced collagen fiber deposition in infected lungs.

CONCLUSION

We demonstrated that MSCs-EVs are a potential therapeutic option for treating COVID-19 by preventing viral entry into host cells.

Molecular mechanism of Spatholobi Caulis treatment for cholangiocarcinoma based on network pharmacology, molecular docking, and molecular dynamics simulation

Cholangiocarcinoma (CCA) is a type of malignant tumor originating from the intrahepatic, periportal, or distal biliary system. The treatment means for CCA is limited, and its prognosis is poor. Spatholobi Caulis (SC) is reported to have effects on anti-inflammatory and anti-tumor, but its role in CCA is unclear. First, the potential molecular mechanism of SC for CCA treatment was explored based on network pharmacology, and the core targets were verified by molecular docking and molecular dynamics simulation. Then, we explored the inhibitory effect of SC on the malignant biological behavior of CCA in vitro and in vivo and also explored the related signaling pathways. The effect of combination therapy of SC and cisplatin (DDP) in CCA was also explored. Finally, we conducted a network pharmacological study and simple experimental verification on luteolin, one of the main components of SC. Network pharmacology analysis showed that the core targets of SC on CCA were AKT1, CASP3, MYC, TP53, and VEGFA. Molecular docking and molecular dynamics simulation indicated a good combination between the core target protein and the corresponding active ingredients. In vitro, SC inhibited proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) of CCA cells. In vivo experiments, the results were consistent with in vitro experiments, and there was no significant hepatorenal toxicity of SC at our dosage. Based on KEGG enrichment analysis, we found PI3K/AKT signaling pathway might be the main signaling pathway of SC action on CCA by using AKT agonist SC79. To explore whether SC was related to the chemotherapy sensitivity of CCA, we found that SC combined with DDP could more effectively inhibit the progression of cholangiocarcinoma. Finally, we found luteolin may inhibit the proliferation and invasion of CCA cells. Our study demonstrates for the first time that SC inhibits the progression of CCA by suppressing EMT through the PI3K-AKT signaling pathway, and SC could enhance the effectiveness of cisplatin therapy for CCA.

SepM mutation in Streptococcus mutans clinical isolates and related function analysis

BACKGROUND

Streptococcus mutans (S. mutans) is an important pathogenic bacterium that causes dental caries, while Streptococcus gordonii (S. gordonii) is a non-cariogenic bacterium that inhibits the growth of S. mutans. The SepM protein can promote the inhibitory ability of S. mutans against S. gordonii by cleaving CSP-21 and activating the ComDE two-component system. This study was designed to explore sepM mutation in S. mutans clinical isolates and related function in the regulation of interactions with S. gordonii.

METHODS

The S. mutans clinical strains that can inhibit the growth of S. gordonii constitute the inhibitory group. 286 C-serotype S. mutans strains were categorized into S. gordonii inhibitory (n = 114) and non-inhibitory bacteria (n = 172). We detected sanger sequencing of sepM gene, the expression levels of related genes and proteins in clinical isolates, obtained prokaryotic expression and purification of mutated proteins, and analyzed the effect of the target mutations on the binding between SepM and CSP-21.

RESULTS

We found that C482T, G533A, and G661A missense mutations were presented at significantly higher frequency in the inhibitory group relative to the non-inhibitory group. There was no significant difference in the expression of the sepM gene between selected clinical isolates harboring the G533A mutation and the control group. The expression levels of SepM, phosphorylated ComD, and ComE in the mutation group were significantly higher than those in the control group. SepM_control and SepM_D221N (G661A at the gene level) were found to contain two residues close to the active center while SepM_G178D (G533A at the gene level) contained three residues close to the active center. At 25 °C and a pH of 5.5, SepM_D221N (G661A) exhibited higher affinity for CSP-21 (KD = 8.25 µM) than did the SepM control (KD = 33.1 µM), and at 25 °C and a pH of 7.5, SepM_G178D (G533A) exhibited higher affinity (KD = 3.02 µM) than the SepM control (KD = 15.9 µM). It means that it is pH dependent.

CONCLUSIONS

Our data suggest that increased cleavage of CSP-21 by the the mutant SepM may be a reason for the higher inhibitory effect of S. mutans on S. gordonii .

Mechanism Analysis of Antimicrobial Peptide NoPv1 Related to Potato Late Blight through a Computer-Aided Study

Phytophthora infestans (Mont.) de Bary, the oomycotic pathogen responsible for potato late blight, is the most devastating disease of potato production. The primary pesticides used to control oomycosis are phenyl amide fungicides, which cause environmental pollution and toxic residues harmful to both human and animal health. To address this, an antimicrobial peptide, NoPv1, has been screened to target Plasmopara viticola cellulose synthase 2 (PvCesA2) to inhibit the growth of Phytophthora infestans (P. infestans). In this study, we employed AlphaFold2 to predict the three-dimensional structure of PvCesA2 along with NoPv peptides. Subsequently, utilizing computational methods, we dissected the interaction mechanism between PvCesA2 and these peptides. Based on this analysis, we performed a saturation mutation of NoPv1 and successfully obtained the double mutants DP1 and DP2 with a higher affinity for PvCesA2. Meanwhile, dynamics simulations revealed that both DP1 and DP2 utilize a mechanism akin to the barrel-stave model for penetrating the cell membrane. Furthermore, the predicted results showed that the antimicrobial activity of DP1 was superior to that of NoPv1 without being toxic to human cells. These findings may offer insights for advancing the development of eco-friendly pesticides targeting various oomycete diseases, including late blight.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Extracellular vesicles for developing targeted hearing loss therapy

Substantial efforts have been made for local administration of small molecules or biologics in treating hearing loss diseases caused by either trauma, genetic mutations, or drug ototoxicity. Recently, extracellular vesicles (EVs) naturally secreted from cells have drawn increasing attention on attenuating hearing impairment from both preclinical studies and clinical studies. Highly emerging field utilizing diverse bioengineering technologies for developing EVs as the bioderived therapeutic materials, along with artificial intelligence (AI)-based targeting toolkits, shed the light on the unique properties of EVs specific to inner ear delivery. This review will illuminate such exciting research field from fundamentals of hearing protective functions of EVs to biotechnology advancement and potential clinical translation of functionalized EVs. Specifically, the advancements in assessing targeting ligands using AI algorithms are systematically discussed. The overall translational potential of EVs is reviewed in the context of auditory sensing system for developing next generation gene therapy.

Extended Metadynamics Protocol for Binding/Unbinding Free Energies of Peptide Ligands to Class A G-Protein-Coupled Receptors

A metadynamics protocol is presented to characterize the binding and unbinding of peptide ligands to class A G-protein-coupled receptors (GPCRs). The protocol expands on the one previously presented for binding and unbinding small-molecule ligands to class A GPCRs and accounts for the more demanding nature of the peptide binding-unbinding process. It applies to almost all class A GPCRs. Exemplary simulations are described for subtypes Y1R, Y2R, and Y4R of the neuropeptide Y receptor family, vasopressin binding to the vasopressin V2 receptor (V2R), and oxytocin binding to the oxytocin receptor (OTR). Binding free energies and the positions of alternative binding sites are presented and, where possible, compared with the experiment.

Approaches to Backbone Flexibility in Protein-Protein Docking

Proteins are the fundamental organic macromolecules in living systems that play a key role in a variety of biological functions including immunological detection, intracellular trafficking, and signal transduction. The docking of proteins has greatly advanced during recent decades and has become a crucial complement to experimental methods. Protein-protein docking is a helpful method for simulating protein complexes whose structures have not yet been solved experimentally. This chapter focuses on major search tactics along with various docking programs used in protein-protein docking algorithms, which include: direct search, exhaustive global search, local shape feature matching, randomized search, and broad category of post-docking approaches. As backbone flexibility predictions and interactions in high-resolution protein-protein docking remain important issues in the overall optimization context, we have put forward several methods and solutions used to handle backbone flexibility. In addition, various docking methods that are utilized for flexible backbone docking, including ATTRACT, FlexDock, FLIPDock, HADDOCK, RosettaDock, FiberDock, etc., along with their scoring functions, algorithms, advantages, and limitations are discussed. Moreover, what progress in search technology is expected, including not only the creation of new search algorithms but also the enhancement of existing ones, has been debated. As conformational flexibility is one of the most crucial factors affecting docking success, more work should be put into evaluating the conformational flexibility upon binding for a particular case in addition to developing new algorithms to replace the rigid body docking and scoring approach.

Introducing the Automated Ligand Searcher

The Automated Ligand Searcher (ALISE) is designed as an automated computational drug discovery tool. To approximate the binding free energy of ligands to a receptor, ALISE includes a three-stage workflow, with each stage involving an increasingly sophisticated computational method: molecular docking, molecular dynamics, and free energy perturbation, respectively. To narrow the number of potential ligands, poorly performing ligands are gradually segregated out. The performance and usability of ALISE are benchmarked for a case study containing known active ligands and decoys for the HIV protease. The example illustrates that ALISE filters the decoys successfully and demonstrates that the automation, comprehensiveness, and user-friendliness of the software make it a valuable tool for improved and faster drug development workflows.

Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes

Being able to accurately predict the three-dimensional structure of an Ab can facilitate Ab characterization and epitope prediction, with important diagnostic and clinical implications. In this study, we evaluated the ability of AlphaFold to predict the structures of 222 recently published, high-resolution Fab H and L chain structures of Abs from different species directed against different Ags. We show that although the overall Ab prediction quality is in line with the results of CASP14, regions such as the complementarity-determining regions (CDRs) of the H chain, which are prone to higher variation, are predicted less accurately. Moreover, we discovered that AlphaFold mispredicts the bending angles between the variable and constant domains. To evaluate the ability of AlphaFold to model Ab-Ag interactions based only on sequence, we used AlphaFold-Multimer in combination with ZDOCK to predict the structures of 26 known Ab-Ag complexes. ZDOCK, which was applied on bound components of both the Ab and the Ag, succeeded in assembling 11 complexes, whereas AlphaFold succeeded in predicting only 2 of 26 models, with significant deviations in the docking contacts predicted in the rest of the molecules. Within the 11 complexes that were successfully predicted by ZDOCK, 9 involved short-peptide Ags (18-mer or less), whereas only 2 were complexes of Ab with a full-length protein. Docking of modeled unbound Ab and Ag was unsuccessful. In summary, our study provides important information about the abilities and limitations of using AlphaFold to predict Ab-Ag interactions and suggests areas for possible improvement.

Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ

Polo-like kinase 1 (PLK1) and p38γ mitogen-activated protein kinase (p38γ) play important roles in cancer pathogenesis by controlling cell cycle progression and are therefore attractive cancer targets. The design of multitarget inhibitors may offer synergistic inhibition of distinct targets and reduce the risk of drug-drug interactions to improve the balance between therapeutic efficacy and safety. We combined deep-learning-based quantitative structure-activity relationship (QSAR) modeling and hybrid-based consensus scoring to screen for inhibitors with potential activity against the targeted proteins. Using this combination strategy, we identified a potent PLK1 inhibitor (compound 4) that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Next, we deployed both our QSAR models for PLK1 and p38γ on the Enamine compound library to identify dual-targeting inhibitors against PLK1 and p38γ. Likewise, the identified hits were subsequently subjected to hybrid-based consensus scoring. Using this method, we identified a promising compound (compound 14) that could inhibit both PLK1 and p38γ activities. At nanomolar concentrations, compound 14 inhibited the growth of human hepatocellular carcinoma and hepatoblastoma cells in vitro. This study demonstrates the combined screening strategy to identify novel potential inhibitors for existing targets.

Regulation of FLC nuclear import by coordinated action of the NUP62-subcomplex and importin β SAD2

Flowering locus C (FLC) is a central transcriptional repressor that controls flowering time. However, how FLC is imported into the nucleus is unknown. Here, we report that Arabidopsis nucleoporins 62 (NUP62), NUP58, and NUP54 composed NUP62-subcomplex modulates FLC nuclear import during floral transition in an importin α-independent manner, via direct interaction. NUP62 recruits FLC to the cytoplasmic filaments and imports it into the nucleus through the NUP62-subcomplex composed central channel. Importin β supersensitive to ABA and drought 2 (SAD2), a carrier protein, is critical for FLC nuclear import and flower transition, which facilitates FLC import into the nucleus mainly through the NUP62-subcomplex. Proteomics, RNA-seq, and cell biological analyses indicate that the NUP62-subcomplex mainly mediates the nuclear import of cargos with unconventional nuclear localization sequences (NLSs), such as FLC. Our findings illustrate the mechanisms of the NUP62-subcomplex and SAD2 on FLC nuclear import process and floral transition, and provide insights into the role of NUP62-subcomplex and SAD2 in protein nucleocytoplasmic transport in plants.

Antioxidant, Collagenase Inhibitory, and Antibacterial Effects of Bioactive Peptides Derived from Enzymatic Hydrolysate of Ulva australis

The protein extract of Ulva australis hydrolyzed with Alcalase and Flavourzyme was found to have multi-functional properties, including total antioxidant capacity (TAC), collagenase inhibitory, and antibacterial activities. The #5 fraction (SP5) and #7 fraction (SP7) of U. australis hydrolysate from cation-exchange chromatography displayed significantly high TAC, collagenase inhibitory, and antibacterial effects against Propionibacterium acnes, and only the Q3 fraction from anion-exchange chromatography displayed high multi-functional activities. Eight of 42 peptides identified by MALDI-TOF/MS and Q-TOF/MS/MS were selected from the results for screening with molecular docking on target proteins and were then synthesized. Thr-Gly-Thr-Trp (TGTW) displayed ABTS [2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid)] radical scavenging activity. The effect of TAC as Trolox equivalence was dependent on the concentration of TGTW. Asn-Arg-Asp-Tyr (NRDY) and Arg-Asp-Arg-Phe (RDRF) exhibited collagenase inhibitory activity, which increased according to the increase in concentration, and their IC50 values were 0.95 mM and 0.84 mM, respectively. Peptides RDRF and His-Ala-Val-Tyr (HAVY) displayed anti-P. Acnes effects, with IC50 values of 8.57 mM and 13.23 mM, respectively. These results suggest that the U. australis hydrolysate could be a resource for the application of effective nutraceuticals and cosmetics.

Fluoroquinolone Analogs, SAR Analysis, and the Antimicrobial Evaluation of 7-Benzimidazol-1-yl-fluoroquinolone in In Vitro, In Silico, and In Vivo Models

Structure-activity relationship (SAR) studies allow the evaluation of the relationship between structural chemical changes and biological activity. Fluoroquinolones have chemical characteristics that allow their structure to be modified and new analogs with different therapeutic properties to be generated. The objective of this research is to identify and select the C-7 heterocycle fluoroquinolone analog (FQH 1-5) with antibacterial activity similar to the reference fluoroquinolone through in vitro, in silico, and in vivo evaluations. First, SAR analysis was conducted on the FQH 1-5, using an in vitro antimicrobial sensibility model in order to select the best compound. Then, an in silico model mechanism of action analysis was carried out by molecular docking. The non-bacterial cell cytotoxicity was evaluated, and finally, the antimicrobial potential was determined by an in vivo model of topical infection in mice. The results showed antimicrobial differences between the FQH 1-5 and Gram-positive and Gram-negative bacteria, identifying the 7-benzimidazol-1-yl-fluoroquinolone (FQH-2) as the most active against S. aureus. Suggesting the same mechanism of action as the other fluoroquinolones; no cytotoxic effects on non-bacterial cells were found. FQH-2 was demonstrated to decrease the amount of bacteria in infected wound tissue.

Accelerating the Screening of Small Peptide Ligands by Combining Peptide-Protein Docking and Machine Learning

This research introduces a novel pipeline that couples machine learning (ML), and molecular docking for accelerating the process of small peptide ligand screening through the prediction of peptide-protein docking. Eight ML algorithms were analyzed for their potential. Notably, Light Gradient Boosting Machine (LightGBM), despite having comparable F1-score and accuracy to its counterparts, showcased superior computational efficiency. LightGBM was used to classify peptide-protein docking performance of the entire tetrapeptide library of 160,000 peptide ligands against four viral envelope proteins. The library was classified into two groups, 'better performers' and 'worse performers'. By training the LightGBM algorithm on just 1% of the tetrapeptide library, we successfully classified the remaining 99%with an accuracy range of 0.81-0.85 and an F1-score between 0.58-0.67. Three different molecular docking software were used to prove that the process is not software dependent. With an adjustable probability threshold (from 0.5 to 0.95), the process could be accelerated by a factor of at least 10-fold and still get 90-95% concurrence with the method without ML. This study validates the efficiency of machine learning coupled to molecular docking in rapidly identifying top peptides without relying on high-performance computing power, making it an effective tool for screening potential bioactive compounds.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

Peptide-based therapeutics are increasingly pushing to the forefront of biomedicine with their promise of high specificity and low toxicity. Although noncanonical residues can always be used, employing only the natural 20 residues restricts the chemical space to a finite dimension allowing for comprehensive in silico screening. Towards this goal, the dataset comprising all possible di-, tri-, and tetra-peptide combinations of the canonical residues has been previously reported. However, with increasing computational power, the comprehensive set of pentapeptides is now also feasible for screening as the comprehensive set of cyclic peptides comprising four or five residues. Here, we provide both the complete and prefiltered libraries of all di-, tri-, tetra-, and penta-peptide sequences from 20 canonical amino acids and their homodetic (N-to-C-terminal) cyclic homologues. The FASTA, simplified molecular-input line-entry system (SMILES), and structure-data file (SDF)-three dimension (3D) libraries can be readily used for screening against protein targets. We also provide a simple method and tool for conducting identity-based filtering. Access to this dataset will accelerate small peptide screening workflows and encourage their use in drug discovery campaigns. As a case study, the developed library was screened against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease to identify potential small peptide inhibitors.

Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies

Protein kinase p38γ is an attractive target against cancer because it plays a pivotal role in cancer cell proliferation by phosphorylating the retinoblastoma tumour suppressor protein. Therefore, inhibition of p38γ with active small molecules represents an attractive alternative for developing anti-cancer drugs. In this work, we present a rigorous and systematic virtual screening framework to identify potential p38γ inhibitors against cancer. We combined the use of machine learning-based quantitative structure activity relationship modelling with conventional computer-aided drug discovery techniques, namely molecular docking and ligand-based methods, to identify potential p38γ inhibitors. The hit compounds were filtered using negative design techniques and then assessed for their binding stability with p38γ through molecular dynamics simulations. To this end, we identified a promising compound that inhibits p38γ activity at nanomolar concentrations and hepatocellular carcinoma cell growth in vitro in the low micromolar range. This hit compound could serve as a potential scaffold for further development of a potent p38γ inhibitor against cancer.

Production and characterization of bioactive peptides in fermented soybean meal produced using proteolytic Bacillus species isolated from kinema

The study aims to enhance the functional properties of soybean meal (SBM) using potent proteolytic Bacillus strains isolated from kinema, a traditional fermented soybean product of Sikkim Himalaya. Selected Bacillus species; Bacillus licheniformis KN1G, B. amyloliquifaciens KN2G, B. subtilis KN36D, B. subtilis KN2B, and B. subtilis KN36D were employed for solid state fermentation (SSF) of SBM samples. The water and methanol extracts of SBM hydrolysates presented a significant increase in antioxidant activity. The water-soluble extracts of B. subtilis KN2B fermented SBM exhibited the best DPPH radical scavenging activity of 2.30 mg/mL. In contrast, the methanol-soluble extract of B. licheniformis KN1G fermented SBM showed scavenging activity of 0.51 mg/mL. Proteomic analysis of fermented soybean meal revealed several common and unique peptides produced by applying different starter cultures. Unique antioxidant peptides (HFDSEVVFF and VVDMNEGALFLPH) were identified from FSBM via LC/MS. B. subtilis KN36D showed the highest diversity of peptides produced during fermentation. The results indicate the importance of specific strains for fermentation to upgrade the nutritional value of raw fermented biomass.

Potential targets of severe acute respiratory syndrome coronavirus 2 of clinical drug fluvoxamine: Docking and molecular dynamics studies to elucidate viral action

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued evolving for survival and adaptation by mutating itself into different variants of concern, including omicron. Several studies and clinical trials found fluvoxamine, an Food and Drug Administration-approved antidepressant drug, to be effective at preventing mild coronavirus disease 2019 (COVID-19) from progressing to severe diseases. However, the mechanism of fluvoxamine's direct antiviral action against COVID-19 is still unknown. Fluvoxamine was docked with 11 SARS-CoV-2 targets and subjected to stability, conformational changes, and binding free energy analyses to explore its mode of action. Of the targets, nonstructural protein 14 (NSP14), main protease (Mpro), and papain-like protease (PLpro) had the best docking scores with fluvoxamine. Consistent with the docking results, it was confirmed by molecular dynamics simulations that the NSP14 N7-MTase ((N7-guanine)-methyltransferase)-fluvoxamine, Mpro-fluvoxamine, and PLpro-fluvoxamine complexes are stable, with the lowest binding free energies of -105.1, -82.7, and - 38.5 kJ/mol, respectively. A number of hotspot residues involved in the interaction were also identified. These include Glu166, Asp187, His41, and Cys145 in Mpro, Gly163 and Arg166 in PLpro, and Glu302, Gly333, and Phe426 in NSP14, which could aid in the development of better antivirals against SARS-CoV-2.

Disruption of 3CLpro protease self-association by short peptides as a potential route to broad spectrum coronavirus inhibitors

Coronaviruses have posed a persistent threat to human health over the last two decades. Despite the accumulated knowledge about coronavirus-related pathogens, development of an effective treatment for its new variant COVID-19 is highly challenging. For the highly-conserved and main coronavirus protease 3CLpro, dimerization is known to be essential for its catalytic activity and thereby for virus proliferation. Here, we assess the potential of short peptide segments to disrupt dimerization of the 3CLpro protease as a route to block COVID-19 proliferation. Based on the X-ray structure of the 3CLpro dimer, we identified the SPSGVY₁₂₆QCAMRP dodecapeptide segment as overlapping the hotspot regions on the 3CLpro dimer interface. Using computational blind docking of the peptide to the 3CLpro monomer, we found that the SPSGVY₁₂₆QCAMRP peptide has favourable thermodynamic binding (ΔG= -5.93 kcal/mol) to the hotspot regions at the 3CLpro dimer interface. Importantly, the peptide was also found to preferentially bind to the hotspot regions compared to other potential binding sites lying away from the dimer interface (ΔΔG=-1.31 kcal/mol). Docking of peptides corresponding to systematic mutation of the V125 and Y126 residues led to the identification of seven peptides, SPSGHAQCAMRP, SPSGVTQCAMRP, SPSGKPQCAMRP, SPSGATQCAMRP, SPSGWLQCAMRP, SPSGAPQCAMRP and SPSGHPQCAMRP, that outperform the wild-type SPSGVY₁₂₆QCAMRP peptide in terms of preferential binding to the 3CLpro dimer interface. These peptides have the potential to disrupt 3CLpro dimerization and therefore could provide lead structures for the development of broad spectrum COVID-19 inhibitors.Communicated by Ramaswamy H. Sarma.

Fragmented blind docking: a novel protein-ligand binding prediction protocol

In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein-ligand bound state. The FRAD protocol has been tested on 116 protein-ligand complexes of the Heat Shock Protein 90 - alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein-ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma.

ACP_MS: prediction of anticancer peptides based on feature extraction

Anticancer peptides (ACPs) are bioactive peptides with antitumor activity and have become the most promising drugs in the treatment of cancer. Therefore, the accurate prediction of ACPs is of great significance to the research of cancer diseases. In the paper, we developed a more efficient prediction model called ACP_MS. Firstly, the monoMonoKGap method is used to extract the characteristic of anticancer peptide sequences and form the digital features. Then, the AdaBoost model is used to select the most discriminating features from the digital features. Finally, a stochastic gradient descent algorithm is introduced to identify anticancer peptide sequences. We adopt 7-fold cross-validation and independent test set validation, and the final accuracy of the main dataset reached 92.653% and 91.597%, respectively. The accuracy of the alternate dataset reached 98.678% and 98.317%, respectively. Compared with other advanced prediction models, the ACP_MS model improves the identification ability of anticancer peptide sequences. The data of this model can be downloaded from the public website for free https://github.com/Zhoucaimao1998/Zc.

Supercritical CO2 assisted extraction of essential oil and naringin from Citrus grandis peel: in vitro antimicrobial activity and docking study

The extraction of bioactive compounds, including essential oils and flavonoids, using organic solvents is a significant environmental concern. In this work, waste C. grandis peel was the ingredient used to extract essential oil and naringin by conducting a supercritical CO2 technique with a two stage process. In the first stage, the extraction with only supercritical CO2 solvent showed a significant enhancement of the d-limonene component, up to 95.66% compared with the hydro-distillation extraction (87.60%). The extraction of naringin using supercritical CO2 and ethanol as a co-solvent was done in the second stage of the process, followed by evaluating in vitro antimicrobial activity of both the essential oil and naringin. The essential oil indicated significant activity against M. catarrhalis (0.25 mg ml-1), S. pyogenes (1.0 mg ml-1), S. pneumoniae (1.0 mg ml-1). Whilst naringin gave good inhibition towards all tested microbial strains with MIC values in the range of 6.25-25.0 μM. In particular, naringin exhibited high antifungal activity against T. rubrum, T. mentagrophytes, and M. gypseum. The molecular docking study also confirmed that d-limonene inhibited bacterium M. catarrhalis well and that naringin possessed potential ligand interactions that proved the inhibition effective against fungi. Molecular dynamics simulations of naringin demonstrated the best docking model using Gromacs during simulation up to 100 ns to explore the stability of the complex naringin and crystal structure of enzyme 2VF5: PDB.

Assessing Molecular Docking Tools to Guide the Design of Polymeric Materials Formulations: A Case Study of Canola and Soybean Protein

After more than 40 years of biopolymer development, the current research is still based on conventional laboratory techniques, which require a large number of experiments. Therefore, finding new research methods are required to accelerate and power the future of biopolymeric development. In this study, promising biopolymer-additive ranking was described using an integrated computer-aided molecular design platform. In this perspective, a set of 21 different additives with plant canola and soy proteins were initially examined by predicting the molecular interactions scores and mode of molecule interactions within the binding site using AutoDock Vina, Molecular Operating Environment (MOE), and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA). The findings of the investigated additives highlighted differences in their binding energy, binding sites, pockets, types, and distance of bonds formed that play crucial roles in protein-additive interactions. Therefore, the molecular docking approach can be used to rank the optimal additive among a set of candidates by predicting their binding affinities. Furthermore, specific molecular-level insights behind protein-additives interactions were provided to explain the ranking results. The highlighted results can provide a set of guidelines for the design of high-performance polymeric materials at the molecular level. As a result, we suggest that the implementation of molecular modeling can serve as a fast and straightforward tool in protein-based bioplastics design, where the correct ranking of additives among sets of candidates is often emphasized. Moreover, these approaches may open new ways for the discovery of new additives and serve as a starting point for more in-depth investigations into this area.

PepNN: a deep attention model for the identification of peptide binding sites

Protein-peptide interactions play a fundamental role in many cellular processes, but remain underexplored experimentally and difficult to model computationally. Here, we present PepNN-Struct and PepNN-Seq, structure and sequence-based approaches for the prediction of peptide binding sites on a protein. A main difficulty for the prediction of peptide-protein interactions is the flexibility of peptides and their tendency to undergo conformational changes upon binding. Motivated by this, we developed reciprocal attention to simultaneously update the encodings of peptide and protein residues while enforcing symmetry, allowing for information flow between the two inputs. PepNN integrates this module with modern graph neural network layers and a series of transfer learning steps are used during training to compensate for the scarcity of peptide-protein complex information. We show that PepNN-Struct achieves consistently high performance across different benchmark datasets. We also show that PepNN makes reasonable peptide-agnostic predictions, allowing for the identification of novel peptide binding proteins.

Biophysical Insight into the SARS-CoV2 Spike-ACE2 Interaction and Its Modulation by Hepcidin through a Multifaceted Computational Approach

At the center of the SARS-CoV2 infection, the spike protein and its interaction with the human receptor ACE2 play a central role in the molecular machinery of SARS-CoV2 infection of human cells. Vaccine therapies are a valuable barrier to the worst effects of the virus and to its diffusion, but the need of purposed drugs is emerging as a core target of the fight against COVID19. In this respect, the repurposing of drugs has already led to discovery of drugs thought to reduce the effects of the cytokine storm, but still a drug targeting the spike protein, in the infection stage, is missing. In this work, we present a multifaceted computational approach strongly grounded on a biophysical modeling of biological systems, so to disclose the interaction of the SARS-CoV2 spike protein with ACE2 with a special focus to an allosteric regulation of the spike-ACE2 interaction. Our approach includes the following methodologies: Protein Contact Networks and Network Clustering, Targeted Molecular Dynamics, Elastic Network Modeling, Perturbation Response Scanning, and a computational analysis of energy flow and SEPAS as a protein-softness and monomer-based affinity predictor. We applied this approach to free (closed and open) states of spike protein and spike-ACE2 complexes. Eventually, we analyzed the interactions of free and bound forms of spike with hepcidin (HPC), the major hormone in iron regulation, recently addressed as a central player in the COVID19 pathogenesis, with a special emphasis to the most severe outcomes. Our results demonstrate that, compared with closed and open states, the spike protein in the ACE2-bound state shows higher allosteric potential. The correspondence between hinge sites and the Allosteric Modulation Region (AMR) in the S-ACE complex suggests a molecular basis for hepcidin involvement in COVID19 pathogenesis. We verify the importance of AMR in different states of spike and then study its interactions with HPC and the consequence of the HPC-AMR interaction on spike dynamics and its affinity for ACE2. We propose two complementary mechanisms for HPC effects on spike of SARS-CoV-2; (a) HPC acts as a competitive inhibitor when spike is in a preinfection state (open and with no ACE2), (b) the HPC-AMR interaction pushes the spike structure into the safer closed state. These findings need clear molecular in vivo verification beside clinical observations.

Bioactive Peptides from Algae: Traditional and Novel Generation Strategies, Structure-Function Relationships, and Bioinformatics as Predictive Tools for Bioactivity

Over the last decade, algae have been explored as alternative and sustainable protein sources for a balanced diet and more recently, as a potential source of algal-derived bioactive peptides with potential health benefits. This review will focus on the emerging processes for the generation and isolation of bioactive peptides or cryptides from algae, including: (1) pre-treatments of algae for the extraction of protein by physical and biochemical methods; and (2) methods for the generation of bioactive including enzymatic hydrolysis and other emerging methods. To date, the main biological properties of the peptides identified from algae, including anti-hypertensive, antioxidant and anti-proliferative/cytotoxic effects (for this review, anti-proliferative/cytotoxic will be referred to by the term anti-cancer), assayed in vitro and/or in vivo, will also be summarized emphasizing the structure–function relationship and mechanism of action of these peptides. Moreover, the use of in silico methods, such as quantitative structural activity relationships (QSAR) and molecular docking for the identification of specific peptides of bioactive interest from hydrolysates will be described in detail together with the main challenges and opportunities to exploit algae as a source of bioactive peptides.

PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes

Deeper understanding of T-cell-mediated adaptive immune responses is important for the design of cancer immunotherapies and antiviral vaccines against pandemic outbreaks. T-cells are activated when they recognize foreign peptides that are presented on the cell surface by Major Histocompatibility Complexes (MHC), forming peptide:MHC (pMHC) complexes. 3D structures of pMHC complexes provide fundamental insight into T-cell recognition mechanism and aids immunotherapy design. High MHC and peptide diversities necessitate efficient computational modelling to enable whole proteome structural analysis. We developed PANDORA, a generic modelling pipeline for pMHC class I and II (pMHC-I and pMHC-II), and present its performance on pMHC-I here. Given a query, PANDORA searches for structural templates in its extensive database and then applies anchor restraints to the modelling process. This restrained energy minimization ensures one of the fastest pMHC modelling pipelines so far. On a set of 835 pMHC-I complexes over 78 MHC types, PANDORA generated models with a median RMSD of 0.70 Å and achieved a 93% success rate in top 10 models. PANDORA performs competitively with three pMHC-I modelling state-of-the-art approaches and outperforms AlphaFold2 in terms of accuracy while being superior to it in speed. PANDORA is a modularized and user-configurable python package with easy installation. We envision PANDORA to fuel deep learning algorithms with large-scale high-quality 3D models to tackle long-standing immunology challenges.