The RCSB Protein Data Bank: redesigned web site and web services

Spectroscopic (FT-IR and FT-Raman) and quantum chemical study on monomer and dimer of benznidazole from DFT and molecular docking approaches

This work presents the quantum chemical calculations of the monomer and dimer of benznidazole using density functional theory (DFT) at the B3LYP/6-311++G(d,2p) level of theory. A one-dimensional potential energy surface scan was carried out across flexible bonds to find the minimum energy structure. The structure with minimum energy was taken as a monomer and dimer is constructed based on intermolecular hydrogen bonding N-H…O. The vibrational analysis was conducted by comparing the calculated FT-IR and FT-Raman spectra of the monomer and dimer with the experimental ones. The red shift in the spectra of amide and carbonyl functional groups indicates their involvement in intermolecular hydrogen bonding in crystal packing, while the other peaks showed good agreement with the experimental result. The intra- and intermolecular interactions in the monomer and dimer were analyzed using various tools. The steric effects and van der Waals forces in the dimer were found to be more effective than the monomer. The dimer in the gaseous medium was found to have a lower Frontier molecular orbital energy (ΔEL-H) value than the monomer, suggesting that it is more reactive in a gaseous medium. The ELF value for hydrogen in monomer and dimer around the ring was found to be more which confirms that the electrons in these regions are more localized. The negative value of the overlap population density of states (OPDOS) both in monomer and dimer indicate that there are anti-bonding orbitals between the acetamide and the benzyl groups of the compound. The drug potential of benznidazole was evaluated by molecular docking with carbonic anhydrase XII, which shows the highest binding affinity of (-8.3 kcal/mol) with 6YH8, indicating that benznidazole is its potent inhibitor.

Characterization of phenolic compounds and evaluation of anti-diabetic potential in Cannabis sativa L. seeds: In vivo, in vitro, and in silico studies

Moroccan Cannabis sativa L. seeds were investigated for their phenolic profile and antidiabetic potential. Ultra-high-performance liquid chromatography with diode array detection and electrospray ionization mass spectrometry analysis revealed a rich phenolic composition, including benzoic acid, cannabisin B, genistein, and epicatechin. In vitro, the seed extract exhibited potent α-amylase inhibitory activity (half-maximal inhibitory concentration = 25.02 ± 4.03 μg/mL). In vivo studies in diabetic rats demonstrated significant hypoglycemic, hypolipidemic, hepatoprotective, and nephroprotective effects. Molecular docking studies further supported these findings, revealing strong interactions between identified phenolic and the α-amylase enzyme. These results highlight the potential of C. sativa seeds as a natural source of bioactive compounds for diabetes management.

Preclinical development of three novel CARs targeting CD79b for the treatment of non-Hodgkin's lymphoma and characterization of the loss of the target antigen

BACKGROUND

Infusion of T cells modified with a chimeric antigen receptor (CAR) targeting CD19 has achieved exceptional responses in patients with non-Hodgkin's lymphoma (NHL), which led to the approval of CAR targeting CD19 (CART19) (Axi-cel and Liso-cel) as second line of treatment for adult patients with relapsed/refractory NHL. Unfortunately, 60% of patients still relapse after CART19 due to either a loss of expression of the target antigen (CD19) in the tumor cell, observed in 27% of relapsed patients, a limited CAR-T persistence, and additional mechanisms, including the suppression of the tumor microenvironment. Clinic strategies to prevent target antigen loss include sequential treatment with CARs directed at CD20 or CD22, which have caused loss of the second antigen, suggesting targeting other antigens less prone to disappear. CD79b, expressed in NHL, is a target in patients treated with antibody-drug conjugates (ADC). However, the limited efficacy of ADC suggests that a CAR therapy targeting CD79b might improve results.

METHODS

We designed three new CARs against CD79b termed CAR for Lymphoma (CARLY)1, 2 and 3. We compared their efficacy, phenotype, and inflammatory profiles with CART19 (ARI0001) and CARTBCMA (ARI0002h), which can treat NHL. We also analyzed the target antigen's expression loss (CD79b, CD19, and B-cell maturation antigen(BCMA)).

RESULTS

We found that CARLY2 and CARLY3 had high affinity and specificity towards CD79b on B cells. In vitro, all CAR-T cells had similar anti-NHL efficacy, which was retained in an NHL model of CD19- relapse. In vivo, CARLY3 showed the highest efficacy. Analysis of the loss of the target antigen demonstrated that CARLY cells induced CD79b and CD19 downregulation on NHL cells with concomitant trogocytosis of these antigens to T cells, being most notorious in CARLY2, which had the highest affinity towards CD79b and CD19, and supporting the selection of CARLY3 to design a new treatment for patients with NHL. Finally, we created a CAR treatment based on dual targeting of CD79b and BCMA to avoid losing the target antigen. This treatment showed the highest efficacy and did not cause loss of the target antigen.

CONCLUSIONS

Based on specificity, efficacy, and loss of the target antigen, CARLY3 represents a potential novel CAR treatment for NHL.

In Silico Analysis of Curcumin's Targeted Cancer Therapy: Folate Receptor Pathways and Molecular Interaction Insights

This study explores the therapeutic potential of curcumin (CUR) in cancer therapy, specifically examining its targeted transport through folate receptors and its interaction with certain proteins in breast cancer cell lines. We employed molecular docking technique to assess the binding affinities of CUR with proteins 1H1Q, 1UOM, 4JDD, 5U2D and MCF10A normal breast epithelial cell line protein 5UGB. Out of these, the CUR-1H1Q complex exhibited the greatest binding affinity. To assess the stability of this complex in a biological setting, we conducted molecular dynamics simulations of the 1H1Q-CUR complex for a duration of 100 ns. The simulations demonstrated an extremely stable Cα-backbone, exhibiting a consistently low root mean square deviation. The radius of gyration measurements suggested a condensed structure with specific areas of flexibility. The simulation revealed a consistent hydrogen bond between CUR and 1H1Q, indicating a robust and long-lasting interaction between the two molecules. The results indicate that the cytotoxicity of curcumin on MCF7 cancer cell lines is mainly affected by its interactions with several proteins found in these cancer cells. Among the four proteins tested, 1H1Q has the greatest influence. The high affinity of these proteins for curcumin, which results in the creation of stable complexes, seems to trigger cell death. Curcumin's biocompatibility and toxicological effects were investigated in both normal and cancerous cell lines. The study revealed enhanced biocompatibility and potential toxicity in cancerous cell lines, while demonstrating reduced toxicity in normal cell lines.

Clustering one million molecular structures on GPU within seconds

Structure clustering is a general but time-consuming work in the study of life science. Up to now, most published tools do not support the clustering analysis on graphics processing unit (GPU) with root mean square deviation metric. In this work, we specially write codes to do the work. It supports multiple threads on multiple GPUs. To show the performance, we apply the program to a 33-residue fragment in protein Pin1 WW domain mutant. The dataset contains 1,400,000 snapshots, which are extracted from an enhanced sampling simulation and distribute widely in the conformational space. Various testing results present that our program is quite efficient. Particularly, with two NVIDIA RTX4090 GPUs and single precision data type, the clustering calculation on 1 million snapshots is completed in a few seconds (including the uploading time of data from memory to GPU and neglecting the reading time from hard disk). This is hundreds of times faster than central processing unit. Our program could be a powerful tool for fast extraction of representative states of a molecule among its thousands to millions of candidate structures.

Visualization of argininosuccinate synthetase by in silico analysis: novel insights into citrullinemia type I disorders

Background

Citrullinemia type I disorders (CTLN1) is a genetic metabolic disease caused by argininosuccinate synthetase (ASS1) gene mutation. To date, the human genome mutation database has documented over 100 variants of the ASS1 gene. This study reported a novel deletion-insertion variant of ASS1 gene and employed various prediction tools to determine its pathogenicity.

Methods

We reported a case of early-onset CTLN1. Whole exome sequencing was conducted to identify genetic mutations. We employed various structure prediction tools to generate accurate 3D models and utilized computational biology tools to elucidate the disparities between the wild-type and mutant proteins.

Results

The patient was characterized by severe clinical manifestations, including poor responsiveness, lethargy, convulsions, and cardiac arrest. Notably, the patient exhibited significantly elevated blood ammonia levels (655 μmol/L; normal reference: 10-30 μmol/L) and increased citrulline concentrations (936 μmol/L; normal reference: 5-25 μmol/L). Whole exome sequencing revealed a in-frame deletion-insertion mutation c.1128_1134delinsG in the ASS1 gene of unknown significance, which has not been previously reported. Our finding indicated that the C- terminal helix domain of the mutant protein structure, which was an important structure for ASS1 protein to form protein tetramers, was indeed more unstable than that of the wild-type protein structure.

Conclusion

Through conducting an in silico analysis on this unique in-frame deletion-insertion variant of ASS1, our aim was to enhance understanding regarding its structure-function relationship as well as unraveling the molecular mechanism underlying CTLN1.

Large-scale annotation of biochemically relevant pockets and tunnels in cognate enzyme-ligand complexes

Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme-ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75% of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50% of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles.Scientific contributionsThe pipeline introduced in this work allows for the detailed analysis of a large set of protein-ligand complexes, focusing on transport pathways. We are introducing a novel predictor for determining the relevance of binding pockets for tunnel calculation. For the first time in the field, we present a high-throughput energetic analysis of ligand binding and unbinding, showing that approximate methods for these simulations can identify additional mutagenesis hotspots in enzymes compared to purely geometrical methods. The predictor is included in the supplementary material and can also be accessed at https://github.com/Faranehhad/Large-Scale-Pocket-Tunnel-Annotation.git . The tunnel data calculated in this study has been made publicly available as part of the ChannelsDB 2.0 database, accessible at https://channelsdb2.biodata.ceitec.cz/ .

DrugReAlign: a multisource prompt framework for drug repurposing based on large language models

Drug repurposing is a promising approach in the field of drug discovery owing to its efficiency and cost-effectiveness. Most current drug repurposing models rely on specific datasets for training, which limits their predictive accuracy and scope. The number of both market-approved and experimental drugs is vast, forming an extensive molecular space. Due to limitations in parameter size and data volume, traditional drug-target interaction (DTI) prediction models struggle to generalize well within such a broad space. In contrast, large language models (LLMs), with their vast parameter sizes and extensive training data, demonstrate certain advantages in drug repurposing tasks. In our research, we introduce a novel drug repurposing framework, DrugReAlign, based on LLMs and multi-source prompt techniques, designed to fully exploit the potential of existing drugs efficiently. Leveraging LLMs, the DrugReAlign framework acquires general knowledge about targets and drugs from extensive human knowledge bases, overcoming the data availability limitations of traditional approaches. Furthermore, we collected target summaries and target-drug space interaction data from databases as multi-source prompts, substantially improving LLM performance in drug repurposing. We validated the efficiency and reliability of the proposed framework through molecular docking and DTI datasets. Significantly, our findings suggest a direct correlation between the accuracy of LLMs' target analysis and the quality of prediction outcomes. These findings signify that the proposed framework holds the promise of inaugurating a new paradigm in drug repurposing.

TCRcost: a deep learning model utilizing TCR 3D structure for enhanced of TCR-peptide binding

Introduction

Predicting TCR-peptide binding is a complex and significant computational problem in systems immunology. During the past decade, a series of computational methods have been developed for better predicting TCR-peptide binding from amino acid sequences. However, the performance of sequence-based methods appears to have hit a bottleneck. Considering the 3D structures of TCR-peptide complexes, which provide much more information, could potentially lead to better prediction outcomes.

Methods

In this study, we developed TCRcost, a deep learning method, to predict TCR-peptide binding by incorporating 3D structures. TCRcost overcomes two significant challenges: acquiring a sufficient number of high-quality TCR-peptide structures and effectively extracting information from these structures for binding prediction. TCRcost corrects TCR 3D structures generated by protein structure tools, significantly extending the available datasets. The main and side chains of a TCR structure are separately corrected using a long short-term memory (LSTM) model. This approach prevents interference between the chains and accurately extracts interactions among both adjacent and global atoms. A 3D convolutional neural network (CNN) is designed to extract the atomic features relevant to TCR-peptide binding. The spatial features extracted by the 3DCNN are then processed through a fully connected layer to estimate the probability of TCR-peptide binding.

Results

Test results demonstrated that predicting TCR-peptide binding from 3D TCR structures is both efficient and highly accurate with an average accuracy of 0.974 on precise structures. Furthermore, the average accuracy on corrected structures was 0.762, significantly higher than the average accuracy of 0.375 on uncorrected original structures. Additionally, the average root mean square distance (RMSD) to precise structures was significantly reduced from 12.753 Å for predicted structures to 8.785 Å for corrected structures.

Discussion

Thus, utilizing structural information of TCR-peptide complexes is a promising approach to improve the accuracy of binding predictions.

Antimicrobial and Hepatoprotective Properties of Pods of Acacia nilotica (L.) Willd. ex Delile: In Vivo and In Silico Approaches

Background

Acacia nilotica is a multipurpose plant known for its remedial properties but the antimicrobial and hepatoprotective activity of its pods remained unexplored.

Objective

This study aimed to evaluate the antimicrobial and hepatoprotective activity of n-hexane (ANPH) and methanol (ANPM) extracts of pods to scientifically validate their medicinal claims.

Methods

After the pharmacognostic evaluation of pods, in vitro tests were carried out to estimate phenolic and flavonoid content and antimicrobial potential. In vivo experiments involved testing of both extracts (250 and 500 mg/kg) paracetamol (PCM)-induced hepatotoxicity model in rats. The molecular docking studies explored insights into the potential binding capabilities of the ligands with the specific target proteins.

Results

ANPH and ANPM were enriched with phenols and flavonoids and showed antimicrobial effects. In the hepatoprotective test, the rats chronically treated with extracts had a dose-dependent hepatoprotection as markers of liver functionality were notably reduced (P < 0.05). The in silico studies revealed strong binding interactions of ergost-5-en-3-ol and oxiranyl methyl ester 9-octadecenoic acid with target proteins for antibacterial activity and hepatoprotective activity, respectively.

Conclusion

The antimicrobial and hepatoprotective potential of pods might be due to their phenols and flavonoids. The Pyrogallol, Ergost-5-en-3-and 9-octadecenoic acid might be bringing these remedial benefits through antioxidant and anti-inflammatory effects.

Unveiling the multitargeted potency of Sodium Danshensu against cervical cancer: a multitargeted docking-based, structural fingerprinting and molecular dynamics simulation study

Cervical Cancer (CC) is one of the most common types of cancer in women worldwide, with a significant number of deaths reported yearly. Despite the various treatment options available, the high mortality rate associated with CC highlights the need to develop new and effective therapeutic agents. In this study, we have screened the complete prepared FDA library against the Mitotic kinesin-like protein 1, Cyclin B1, DNA polymerase, and MCM10-ID using three glide-based molecular docking algorithms: HTVS, SP and XP to produce a robust calculation. All four proteins are crucial proteins that actively participate in CC development, and inhibiting them together can be a game-changer step for multitargeted drug designing. Our multitargeted screening identified Sodium (Na) Danshensu, a natural FDA-approved phenolic compound of caffeic acid derivatives isolated from Salvia miltiorrhiza. The docking score ranges from -5.892 to -13.103 Kcal/mol, and the screening study was evaluated with the pharmacokinetics and interaction fingerprinting to identify the pattern of interactions that revealed that the compound has bound to the best site it can be fitted to where maximum bonds were created to make the complex stable. The molecular dynamics simulations for 100 ns were then extended to validate the stability of the protein-ligand complexes. The results provide insight into the repurposing, and Na-danshensu exhibited strong binding affinity and stable complex formation with the target proteins, indicating its potential as a multitargeted drug against CC.Communicated by Ramaswamy H. Sarma.

pyGROMODS: a Python package for the generation of input files for molecular dynamic simulation with GROMACS

The pyGROMODS, an easy-to-use cross-platform python-based package, with a graphical user interface, for the generation of molecular dynamic (MD) input files and running MD simulation (MDS) of proteins, peptides, and protein-ligand complex using GROMACS, is here presented. Four routes, with underlining Python scripts, are implemented in pyGROMODS for the generation of MD input files. They are 'RLmulti' for processing multi-ligand protein complex, 'RLmany' for processing multiple ligands against a single protein target, 'RLsingle' for processing multiple pairs of proteins and ligands, and 'PPmore' for processing peptides or proteins without ligands or non-standard residues. In addition, using the package, the generated input files or appropriate input files from other sources can be uploaded to run MDS with GROMACS. The pyGROMODS is implemented with a unique ability to search the host machine systems for the installation of the required software, update and/or install required Python packages, allow the user to pre-define working directory, and generate unique workflow organization with well-defined folders and files in a well-organized manner. The pyGROMODS, which is released under the MIT License, is freely available for download via the GitHub (https://github.com/Dankem/pyGROMODS) and Zenodo (https://doi.org/10.5281/zenodo.7912747) repositories. The precompiled executables can also be downloaded from Zenodo (https://doi.org/10.5281/zenodo.8087090), and a video tutorial can be downloaded from https://youtu.be/I4OKc6uVx1M.Communicated by Ramaswamy H. Sarma.

Reverse Vaccinology and Immunoinformatic Approach for Designing a Bivalent Vaccine Candidate Against Hepatitis A and Hepatitis B Viruses

Hepatitis A and B are two crucial viral infections that still dramatically affect public health worldwide. Hepatitis A Virus (HAV) is the main cause of acute hepatitis, whereas Hepatitis B Virus (HBV) leads to the chronic form of the disease, possibly cirrhosis or liver failure. Therefore, vaccination has always been considered the most effective preventive method against pathogens. At this moment, we aimed at the immunoinformatic analysis of HAV-Viral Protein 1 (VP1) as the major capsid protein to come up with the most conserved immunogenic truncated protein to be fused by HBV surface antigen (HBs Ag) to achieve a bivalent vaccine against HAV and HBV using an AAY linker. Various computational approaches were employed to predict highly conserved regions and the most immunogenic B-cell and T-cell epitopes of HAV-VP1 capsid protein in both humans and BALB/c. Moreover, the predicted fusion protein was analyzed regarding primary and secondary structures and also homology validation. Afterward, the three-dimensional structure of vaccine constructs docked with various toll-like receptors (TLR) 2, 4 and 7. According to the bioinformatics tools, the region of 99-259 amino acids of VP1 was selected with high immunogenicity and conserved epitopes. T-cell epitope prediction showed that this region contains 32 antigenic peptides for Human leukocyte antigen (HLA) class I and 20 antigenic peptides in terms of HLA class II which are almost fully conserved in the Iranian population. The vaccine design includes 5 linear and 4 conformational B-cell lymphocyte (BCL) epitopes to induce humoral immune responses. The designed VP1-AAY-HBsAg fusion protein has the potency to be constructed and expressed to achieve a bivalent vaccine candidate, especially in the Iranian population. These findings led us to claim that the designed vaccine candidate provides potential pathways for creating an exploratory vaccine against Hepatitis A and Hepatitis B Viruses with high confidence for the identified strains.

Molecules targeting a novel homotrimer cavity of Spike protein attenuate replication of SARS-CoV-2

The SARS-CoV-2 Spike glycoprotein (S) utilizes a unique trimeric conformation to interact with the ACE2 receptor on host cells, making it a prime target for inhibitors that block viral entry. We have previously identified a novel proteinaceous cavity within the Spike protein homotrimer that could serve as a binding site for small molecules. However, it is not known whether these molecules would inhibit, stimulate, or have no effect on viral replication. To address this, we employed structural-based screening to identify small molecules that dock into the trimer cavity and assessed their impact on viral replication. Our findings show that a cohort of identified small molecules binding to the Spike trimer cavity effectively reduces the replication of various SARS-CoV-2 variants. These molecules exhibited inhibitory effects on B.1 (European original, D614G, EDB2) and B.1.617.2 (δ) variants, while showing moderate activity against the B.1.1.7 (α) variant. We further categorized these molecules into distinct groups based on their structural similarities. Our experiments demonstrated a dose-dependent viral replication inhibitory activity of these compounds, with some, like BCC0040453 exhibiting no adverse effects on cell viability even at high concentrations. Further investigation revealed that pre-incubating virions with compounds like BCC0031216 at different temperatures significantly inhibited viral replication, suggesting their specificity towards the S protein. Overall, our study highlights the inhibitory impact of a diverse set of chemical molecules on the biological activity of the Spike protein. These findings provide valuable insights into the role of the trimer cavity in the viral replication cycle and aid drug discovery programs aimed at targeting the coronavirus family.

Proscan: a structure-based proline design web server

The ability to control protein conformations and dynamics through structure-based design has been useful in various scenarios, including engineering of viral antigens for vaccines. One effective design strategy is the substitution of residues to proline amino acids, which due to its unique cyclic side chain can favor and rigidify key backbone conformations. To provide the community with a means to readily identify and explore proline designs for target proteins of interest, we developed the Proscan web server. Proscan provides assessment of backbone angles, energetic and deep learning-based favorability scores, and other parameters for proline substitutions at each position of an input structure, along with interactive visualization of backbone angles and candidate substitution sites on structures. It identifies known favorable proline substitutions for viral antigens, and was benchmarked against datasets of proline substitution stability effects from deep mutational scanning and thermodynamic measurements. This tool can enable researchers to identify and prioritize designs for prospective vaccine antigen targets, or other designs to favor stability of key protein conformations. Proscan is available at: https://proscan.ibbr.umd.edu.

Q-BioLiP: A Comprehensive Resource for Quaternary Structure-based Protein-ligand Interactions

Since its establishment in 2013, BioLiP has become one of the widely used resources for protein-ligand interactions. Nevertheless, several known issues occurred with it over the past decade. For example, the protein-ligand interactions are represented in the form of single chain-based tertiary structures, which may be inappropriate as many interactions involve multiple protein chains (known as quaternary structures). We sought to address these issues, resulting in Q-BioLiP, a comprehensive resource for quaternary structure-based protein-ligand interactions. The major features of Q-BioLiP include: (1) representing protein structures in the form of quaternary structures rather than single chain-based tertiary structures; (2) pairing DNA/RNA chains properly rather than separation; (3) providing both experimental and predicted binding affinities; (4) retaining both biologically relevant and irrelevant interactions to alleviate the wrong justification of ligands' biological relevance; and (5) developing a new quaternary structure-based algorithm for the modelling of protein-ligand complex structure. With these new features, Q-BioLiP is expected to be a valuable resource for studying biomolecule interactions, including protein-small molecule interaction, protein-metal ion interaction, protein-peptide interaction, protein-protein interaction, protein-DNA/RNA interaction, and RNA-small molecule interaction. Q-BioLiP is freely available at https://yanglab.qd.sdu.edu.cn/Q-BioLiP/.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Multisampling-based docking reveals Imidazolidinyl urea as a multitargeted inhibitor for lung cancer: an optimisation followed multi-simulation and in-vitro study

Lung Cancer is one of the deadliest cancers, responsible for more than 1.80 million deaths annually worldwide, and it is on the priority list of WHO. In the current scenario, when cancer cells become resistant to the drug, making it less effective leaves the patient in vulnerable conditions. To overcome this situation, researchers are constantly working on new drugs and medications that can help fight drug resistance and improve patients' outcomes. In this study, we have taken five main proteins of lung cancer, namely RSK4 N-terminal kinase, guanylate kinase, cyclin-dependent kinase 2, kinase CK2 holoenzyme, tumour necrosis factor-alpha and screened the prepared Drug Bank library with 1,55,888 compounds against all using three Glide-based docking algorithms namely HTVS, standard precision and extra precise with a docking score ranging from -5.422 to -8.432 Kcal/mol. The poses were filtered with the MM\GBSA calculations, which helped to identify Imidazolidinyl urea C11H16N8O8 (DB14075) as a multitargeted inhibitor for lung cancer, validated with advanced computations like ADMET, interaction pattern fingerprints, and optimised the compound with Jaguar, producing satisfied relative energy. All five complexes were performed with MD Simulation for 100 ns with NPT ensemble class, producing cumulative deviation and fluctuations < 2 Å and a web of intermolecular interaction, making the complexes stable. Further, the in-vitro analysis for morphological imaging, Annexin V/PI FACS assay, ROS and MMP analysis caspase3//7 activity were performed on the A549 cell line producing promising results and can be an option to treat lung cancer at a significantly cheaper state.Communicated by Ramaswamy H. Sarma.

Structure-Based In Silico Approaches Reveal IRESSA as a Multitargeted Breast Cancer Regulatory, Signalling, and Receptor Protein Inhibitor

Breast cancer begins in the breast cells, mainly impacting women. It starts in the cells that line the milk ducts or lobules responsible for producing milk and can spread to nearby tissues and other body parts. In 2020, around 2.3 million women across the globe received a diagnosis, with an estimated 685,000 deaths. Additionally, 7.8 million women were living with breast cancer, making it the fifth leading cause of cancer-related deaths among women. The mutational changes, overexpression of drug efflux pumps, activation of alternative signalling pathways, tumour microenvironment, and cancer stem cells are causing higher levels of drug resistance, and one of the major solutions is to identify multitargeted drugs. In our research, we conducted a comprehensive screening using HTVS, SP, and XP, followed by an MM/GBSA computation of human-approved drugs targeting HER2/neu, BRCA1, PIK3CA, and ESR1. Our analysis pinpointed IRESSA (Gefitinib-DB00317) as a multitargeted inhibitor for these proteins, revealing docking scores ranging from -4.527 to -8.809 Kcal/mol and MM/GBSA scores between -49.09 and -61.74 Kcal/mol. We selected interacting residues as fingerprints, pinpointing 8LEU, 6VAL, 6LYS, 6ASN, 5ILE, and 5GLU as the most prevalent in interactions. Subsequently, we analysed the ADMET properties and compared them with the standard values of QikProp. We extended our study for DFT computations with Jaguar and plotted the electrostatic potential, HOMO and LUMO regions, and electron density, followed by a molecular dynamics simulation for 100 ns in water, showing an utterly stable performance, making it a suitable drug candidate. IRESSA is FDA-approved for lung cancer, which shares some pathways with breast cancers, clearing the hurdles of multitargeted drugs against breast and lung cancer. This has the potential to be groundbreaking; however, more studies are needed to concreate IRESSA's role.

NanoLAS: a comprehensive nanobody database with data integration, consolidation and application

Nanobodies, a unique subclass of antibodies first discovered in camelid animals, are composed solely of a single heavy chain's variable region. Their significantly reduced molecular weight, in comparison to conventional antibodies, confers numerous advantages in the treatment of various diseases. As research and applications involving nanobodies expand, the quantity of identified nanobodies is also rapidly growing. However, the existing antibody databases are deficient in type and coverage, failing to satisfy the comprehensive needs of researchers and thus impeding progress in nanobody research. In response to this, we have amalgamated data from multiple sources to successfully assemble a new and comprehensive nanobody database. This database has currently included the latest nanobody data and provides researchers with an excellent search and data display interface, thus facilitating the progression of nanobody research and their application in disease treatment. In summary, the newly constructed Nanobody Library and Archive System may significantly enhance the retrieval efficiency and application potential of nanobodies. We envision that Nanobody Library and Archive System will serve as an accessible, robust and efficient tool for nanobody research and development, propelling advancements in the field of biomedicine. Database URL: https://www.nanolas.cloud.

Drug Repositioning Using Computer-aided Drug Design (CADD)

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

In silico study reveals unconventional interactions between MDC1 of DDR and Beclin-1 of autophagy

DNA damage response (DDR) and autophagy are concerned with maintaining cellular homeostasis and dysregulation of these two pathways lead to pathologic conditions including tumorigenesis. Autophagy is activated as a protective mechanism during DDR which is indicative of their functional cooperativity but the molecular mechanism leading to the convergence of these two pathways during genotoxic stress remains elusive. In this study, through in silico analysis, we have shown an interaction between the Mediator of DNA damage checkpoint 1 (MDC1), an important DDR-associated protein, and Beclin-1, an autophagy inducer. MDC1 is an adaptor or scaffold protein known to regulate DDR, apoptosis, and cell cycle progression. While, Beclin-1 is involved in autophagosome nucleation and exhibits affinity for binding to Fork-head-associated domain (FHA) containing proteins. The FHA domain is commonly conserved in DDR-related proteins including MDC1. Through molecular docking, we have predicted the modeled complex between the MDC1 FHA domain and the Beclin-1 Coiled coil domain (CCD). The docking complex was modeled using ClusPro2.0, based on the crystal structure for the dimerized MDC1 FHA domain and Beclin-1 CCD. The complex stability and binding affinities were assessed using a Ramachandran plot, MD simulation, MM/GBSA, and PRODIGY webserver. Finally, the hot-spot residues at the interface were determined using computational alanine scanning by the DrugScorePPI webserver. Our analysis unveils significant interaction between MDC1 and Beclin-1, involving hydrogen bonds, non-bonded contacts, and salt bridges and indicates MDC1 possibly recruits Beclin-1 to the DSBs, as a consequence of which Beclin-1 is able to modulate DDR.

Investigating the effect of the inhibitory peptide on L.monocytogenes cell invasion: an in silico and in vitro study

AIMS

L.monocytogenes monocytogenes is an omnipresent bacterium that causes a fatal food-borne illness, listeriosis. The connection of this bacterium to E-cadherin through internalin A plays a significant role in the internalization of the bacteria. In this study, this interaction has been investigated for the design of an inhibitory peptide.

METHODS

The interaction of the proteins involved in the entry of bacteria was evaluated by molecular docking. According to their interactions, an inhibitory peptide was designed to bind to internalin A by server peptiderive. Its effects on L.monocytogenes invasion on the Caco-2 cell line and biofilm formation were also assessed.

FINDINGS

Docking results showed that the peptide has a high affinity for binding to Internalin A. The synthesized peptide at a concentration of 64 µg/ml inhibited 80% of the invasion of L.monocytogenes into the Caco-2 cell line. Furthermore, the studied peptide at the highest concentration had a slight inhibitory effect on biofilm formation.

CONCLUSION

These results reveal that short polypeptides can impede the invasion of target cells by L. monocytogenes in vitro and could be advantageous as restoring agents in vivo.

DIPS-Plus: The enhanced database of interacting protein structures for interface prediction

In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.

In silico identification of novel stilbenes analogs for potential multi-targeted drugs against Alzheimer's disease

CONTEXT

Alzheimer's disease (AD) is a chronic progressive neurodegenerative syndrome, which adversely disturbs cognitive abilities as well as intellectual processes and frequently occurs in the elderly. Inhibition of cholinesterase is a valuable approach to upsurge acetylcholine concentrations in the brain and persuades the development of multi-targeted ligands against cholinesterases.

METHODS

The current study aims to determine the binding potential accompanied by antioxidant and anti-inflammatory activities of stilbenes-designed analogs against both cholinesterases (Acetylcholinesterase and butyrylcholinesterase) and neurotrophin targets for effective AD therapeutics. Docking results have shown that the WS6 compound exhibited the least binding energy - 10.1 kcal/mol with Acetylcholinesterase and - 7.8 kcal/mol with butyrylcholinesterase. The WS6 also showed a better binding potential with neurotrophin targets that are Brain-derived Neurotrophic Factor, Neurotrophin 4, Nerve Growth Factor, and Neurotrophin 3. The tested compounds particularly WS6 revealed significant antioxidant and anti-inflammatory activities through the comparative docking analysis with Fluorouracil and Melatonin as control drugs of antioxidants while Celecoxib and Anakinra as anti-inflammatory. The bioinformatics approaches including molecular docking calculations followed by the pharmacokinetics analysis and molecular dynamic simulations were accomplished to explore the capabilities of designed stilbenes as effective and potential leads. Root mean square deviation, root mean square fluctuations, and MM-GBSA calculations were performed through molecular dynamic simulations to extract the structural and residual variations and binding free energies through the 50-ns time scale.

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning

The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the structural basis of TCRs and their engagement of peptide-MHCs can provide major insights into normal and aberrant immunity, and can help guide the design of vaccines and immunotherapeutics. Given the limited amount of experimentally determined TCR-peptide-MHC structures and the vast amount of TCRs within each individual as well as antigenic targets, accurate computational modeling approaches are needed. Here, we report a major update to our web server, TCRmodel, which was originally developed to model unbound TCRs from sequence, to now model TCR-peptide-MHC complexes from sequence, utilizing several adaptations of AlphaFold. This method, named TCRmodel2, allows users to submit sequences through an easy-to-use interface and shows similar or greater accuracy than AlphaFold and other methods to model TCR-peptide-MHC complexes based on benchmarking. It can generate models of complexes in 15 minutes, and output models are provided with confidence scores and an integrated molecular viewer. TCRmodel2 is available at https://tcrmodel.ibbr.umd.edu.

Network Pharmacology and Molecular Modeling to Elucidate the Potential Mechanism of Neem Oil against Acne vulgaris

Acne vulgaris is a common skin disorder with a complicated etiology. Papules, lesions, comedones, blackheads, and other skin lesions are common physical manifestations of Acne vulgaris, but the individual who has it also regularly has psychological repercussions. Natural oils are being utilized more and more to treat skin conditions since they have fewer negative effects and are expected to provide benefits. Using network pharmacology, this study aims to ascertain if neem oil has any anti-acne benefits and, if so, to speculate on probable mechanisms of action for such effects. The neem leaves (Azadirachta indica) were collected, verified, authenticated, and assigned a voucher number. After steam distillation was used to extract the neem oil, the phytochemical components of the oil were examined using gas chromatography-mass spectrometry (GC-MS). The components of the oil were computationally examined for drug-likeness using Lipinski's criteria. The Pharm Mapper service was used to anticipate the targets. Prior to pathway and protein-protein interaction investigations, molecular docking was performed to predict binding affinity. Neem oil was discovered to be a potential target for STAT1, CSK, CRABP2, and SYK genes in the treatment of Acne vulgaris. In conclusion, it was discovered that the neem oil components with PubChem IDs: ID_610088 (2-(1-adamantyl)-N-methylacetamide), ID_600826 (N-benzyl-2-(2-methyl-5-phenyl-3H-1,3,4-thiadiazol-2-yl)acetamide), and ID_16451547 (N-(3-methoxyphenyl)-2-(1-phenyltetrazol-5-yl)sulfanylpropanamide) have strong affinities for these drug targets and may thus be used as therapeutic agents in the treatment of acne.

CM2D3: Furnishing the human interactome with structural models of protein complexes derived by comparative modeling and docking

The human interactome is composed of around half a million interactions according to recent estimations and it is only for a small fraction of those that three-dimensional structural information is available. Indeed, the structural coverage of the human interactome is very low and given the complexity and time-consuming requirements of solving protein structures this problem will remain for the foreseeable future. Structural models, or predictions, of protein complexes can provide valuable information when the experimentally determined 3D structures are not available. Here we present CM2D3, a relational database containing structural models of the whole human interactome derived both from comparative modeling and data-driven docking. Starting from a consensus interactome derived from integrating several interactomics databases, a strategy was devised to derive structural models by computational means. Currently, CM2D3 includes 33338 structural models of which 5121 derived from comparative modeling and the remaining from docking. Of the latter, the structures of 14554 complexes were derived from monomers modeled by M4T while the rest were modeled with structures as predicted by AlphaFold2. Lastly, CM2D3 complements existing resources by focusing on models derived from both free-docking, as opposed to template-based docking, and hence expanding the available structural information on protein complexes to the scientific community. Database URL:http://www.bioinsilico.org/CM2D3.

Constructing discriminative feature space for LncRNA-protein interaction based on deep autoencoder and marginal fisher analysis

Long non-coding RNAs (lncRNAs) play important roles by regulating proteins in many biological processes and life activities. To uncover molecular mechanisms of lncRNA, it is very necessary to identify interactions of lncRNA with proteins. Recently, some machine learning methods were proposed to detect lncRNA-protein interactions according to the distribution of known interactions. The performances of these methods were largely dependent upon: (1) how exactly the distribution of known interactions was characterized by feature space; (2) how discriminative the feature space was for distinguishing lncRNA-protein interactions. Because the known interactions may be multiple and complex model, it remains a challenge to construct discriminative feature space for lncRNA-protein interactions. To resolve this problem, a novel method named DFRPI was developed based on deep autoencoder and marginal fisher analysis in this paper. Firstly, some initial features of lncRNA-protein interactions were extracted from the primary sequences and secondary structures of lncRNA and protein. Secondly, a deep autoencoder was exploited to learn encode parameters of the initial features to describe the known interactions precisely. Next, the marginal fisher analysis was employed to optimize the encode parameters of features to characterize a discriminative feature space of the lncRNA-protein interactions. Finally, a random forest-based predictor was trained on the discriminative feature space to detect lncRNA-protein interactions. Verified by a series of experiments, the results showed that our predictor achieved the precision of 0.920, recall of 0.916, accuracy of 0.918, MCC of 0.836, specificity of 0.920, sensitivity of 0.916 and AUC of 0.906 respectively, which outperforms the concerned methods for predicting lncRNA-protein interaction. It may be suggested that the proposed method can generate a reasonable and effective feature space for distinguishing lncRNA-protein interactions accurately. The code and data are available on https://github.com/D0ub1e-D/DFRPI.

Network pharmacology- and molecular simulation-based exploration of therapeutic targets and mechanisms of heparin for the treatment of sepsis/COVID-19

Critically infected patients with COVID-19 (coronavirus disease 2019) are prone to develop sepsis-related coagulopathy as a result of a robust immune response. The mechanism underlying the relationship between sepsis and COVID-19 is largely unknown. LMWH (low molecular weight heparin) exhibits both anti-inflammatory and anti-coagulating properties that result in a better prognosis of severely ill patients with COVID-19 co-associated with sepsis-induced coagulopathy or with a higher D-dimer value. Heparin-associated molecular targets and their mechanism of action in sepsis/COVID-19 are not well understood. In this work, we characterize the pharmacological targets, biological functions and therapeutic actions of heparin in sepsis/COVID-19 from the perspective of network pharmacology. A total of 38 potential targets for heparin action against sepsis/COVID-19 and 8 core pharmacological targets were identified, including IL6, KNG1, CXCL8, ALB, VEGFA, F2, IL10 and TNF. Moreover, enrichment analysis showed that heparin could help in treating sepsis/COVID-19 through immunomodulation, inhibition of the inflammatory response, regulation of angiogenesis and antiviral activity. The pharmacological effects of heparin against these targets were further confirmed by molecular docking and simulation analysis, suggesting that heparin exerts effective binding capacity by targeting the essential residues in sepsis/COVID-19. Prospective clinical practice evaluations may consider the use of these key prognostic indicators for the treatment of sepsis/COVID-19.Communicated by Ramaswamy H. Sarma.

Targeted Protein-Specific Multi-Epitope-Based Vaccine Designing against Human Cytomegalovirus by Using Immunoinformatics Approaches

Cytomegaloviruses are emerging pathogenic agents known to cause congenital disorders in humans. In this study, immune epitopes (CTL, B cell and HTL) were screened for highly antigenic target proteins of the Human Cytomegalovirus. These shortlisted epitopes were then joined together through suitable linkers to construct multi epitope-based vaccine constructs (MEVCs). The functionality of each vaccine construct was evaluated through tertiary vaccine structure modelling and validations. Furthermore, physio-chemical properties including allergenicity, antigenicity molecular weight and many others were also predicted. The vaccine designs were also docked with the human TLR-4 receptor to demonstrate the receptor specific affinity and formed interactions. The vaccine peptides sequences were also subjected to codon optimization to confirm the potential vaccines expression in E. coli hosts. Additionally, all the MEVCs were also evaluated for immune response (IgG and IgM) induction. However, further in vivo tests are needed to ensure the efficacy of these vaccine designs.

β-glucan protects against necrotizing enterocolitis in mice by inhibiting intestinal inflammation, improving the gut barrier, and modulating gut microbiota

BACKGROUND

Necrotizing enterocolitis (NEC) is a devastating gastrointestinal disease with high morbidity and mortality, affecting preterm infants especially those with very low and extremely low birth weight. β-glucan has manifested multiple biological effects including anti-inflammatory, regulation of gut microbiota, and immunomodulatory activities. This study aimed to investigate the effects of β-glucan on NEC.

METHODS

Neonatal C57BL/6 mice were randomly divided into three groups: Control group, NEC group and β-glucan group. Newborn 3-day-old mice were gavaged with either 1 mg/ml β-glucan or phosphate buffer saline at 0.03 ml/g for 7 consecutive days before NEC induction and a NEC model was established with hypoxia combined with cold exposure and formula feeding. All the pups were killed after 72-h modeling. Hematoxylin-eosin staining was performed to assess the pathological injury to the intestines. The mRNA expression levels of inflammatory factors in intestinal tissues were determined using quantitative real-time PCR. The protein levels of TLR4, NF-κB and tight junction proteins in intestinal tissues were evaluated using western blotting and immunohistochemistry. 16S rRNA sequencing was performed to determine the structure of the gut microbiota.

RESULTS

β-glucan administration ameliorated intestinal injury of NEC mice; reduced the intestinal expression of TLR4, NF-κB, IL-1β, IL-6, and TNF-α; increased the intestinal expression of IL-10; and improved the expression of ZO-1, Occludin and Claudin-1 within the intestinal barrier. Pre-treatment with β-glucan also increased the proportion of Actinobacteria, Clostridium butyricum, Lactobacillus johnsonii, Lactobacillus murinus, and Lachnospiraceae bacterium mt14 and reduced the proportion of Klebsiella oxytoca g Klebsiella in the NEC model.

CONCLUSION

β-glucan intervention prevents against NEC in neonatal mice, possibly by suppressing the TLR4-NF-κB signaling pathway, improving intestinal barrier function, and partially regulating intestinal microbiota.

Computational Vaccine Design for Poxviridae Family Viruses

The computational approach to designing vaccines has several useful characteristics over traditional vaccine development, such as being highly specific, less time-consuming and less expensive. Thus, this chapter describes an immunoinformatics workflow to design a vaccine against a member of the Poxviridae family known as Monkeypox virus. The immunoinformatics approach uses several online servers to select highly antigenic and non-allergenic CTL, HTL, and B cell epitopes. Then, it links the predicted epitopes through linkers and submit them for 3D structure modeling. Afterward, the modeled vaccine is docked with TLRs to check the induction of the immune system. Finally, immune simulations are performed to check the level of several immune factors like IgG, IgM, cytokines and interleukins, among others, upon the injection of the constructed vaccine. This approach can be used to successfully design novel and effective vaccine candidates against emerging species from the Poxviridae family.

CPInformer for Efficient and Robust Compound-Protein Interaction Prediction

Recently, deep learning has become the mainstream methodology for Compound-Protein Interaction (CPI) prediction. However, the existing compound-protein feature extraction methods have some issues that limit their performance. First, graph networks are widely used for structural compound feature extraction, but the chemical properties of a compound depend on functional groups rather than graphic structure. Besides, the existing methods lack capabilities in extracting rich and discriminative protein features. Last, the compound-protein features are usually simply combined for CPI prediction, without considering information redundancy and effective feature mining. To address the above issues, we propose a novel CPInformer method. Specifically, we extract heterogeneous compound features, including structural graph features and functional class fingerprints, to reduce prediction errors caused by similar structural compounds. Then, we combine local and global features using dense connections to obtain multi-scale protein features. Last, we apply ProbSparse self-attention to protein features, under the guidance of compound features, to eliminate information redundancy, and to improve the accuracy of CPInformer. More importantly, the proposed method identifies the activated local regions that link a CPI, providing a good visualisation for the CPI state. The results obtained on five benchmarks demonstrate the merits and superiority of CPInformer over the state-of-the-art approaches.

An in-silico investigation of fluoride ions impact on pancreatic lipase

Fluorine is a halogen beneficial to teeth and bones at a lower concentration. But in excess, it is a toxin and causes adverse effects. Fluoride is toxic to enzymes generally when it inhibits the enzyme activity involved in metabolic pathways. Here we study invitro and invivo findings on the interaction of fluoride on the enzymes Aconitase, Adenylyl cyclase, Arginase, Cytochrome-c-oxidase, Glucose-6-phosphatase, Protein phosphatase, Succinate dehydrogenase from liver and lipase from pancreas by using molecular docking and simulations to gain insights into the mechanism by which fluoride modifies the activity of pancreatic lipase. our molecular modeling and docking studies identified that lipase is the most strongly inhibited enzyme compared to other enzymes mentioned above with -0.42 Kcal/mol binding energy and 495.78 milli molar of predicted IC50 value with interaction with Phe227 residue. To further validate this, we have taken the lipase enzyme in presence of fluoride ions for molecular dynamic simulations of 100 ns. To analyze the impact of fluoride ions on the lipase dynamics, two different simulations of 100 ns each were performed. In one simulation, we have simulated lipase in its apo form in the aqueous environment without any fluoride ions and in another simulation lipase in its apo form was kept in the presence of randomly placed fluoride ions countered with sodium ions to maintain the pH as neutral. The simulation analysis revealed that major fluctuations in lipase was observed between 230 and 300 residues in presence of fluoride ions. Interestingly, this is the exact location of the "lid" like acting loop of residues responsible for the inward/outward movement of the substrate to lipase catalytically active site containing catalytic triad of residues Leu153, His263, and Pro177. His263 residue random flip is believed to be the critical incident that causes the substrate's inward/outward movement at the catalytically active site coordinated by "lid" opening, providing enough space for the substrate.

Synthesis and Cytotoxicity Evaluation of Novel Coumarin-Palladium(II) Complexes against Human Cancer Cell Lines

Two newly synthesized coumarin-palladium(II) complexes (C1 and C2) were characterized using elemental analysis, spectroscopy (IR and ¹H-¹³C NMR), and DFT methods at the B3LYP-D3BJ/6-311+G(d,p) level of theory. The in vitro and in silico cytotoxicity of coumarin ligands and their corresponding Pd(II) complexes was examined. For in vitro testing, five cell lines were selected, namely human cervical adenocarcinoma (HeLa), the melanoma cell line (FemX), epithelial lung carcinoma (A549), the somatic umbilical vein endothelial cell line (EA.hi926), and pancreatic ductal adenocarcinoma (Panc-1). In order to examine the in silico inhibitory potential and estimate inhibitory constants and binding energies, molecular docking studies were performed. The inhibitory activity of C1 and C2 was investigated towards epidermal growth factor receptor (EGFR), receptor tyrosine kinase (RTK), and B-cell lymphoma 2 (BCL-2). According to the results obtained from the molecular docking simulations, the inhibitory activity of the investigated complexes towards all the investigated proteins is equivalent or superior in comparison with current therapeutical options. Moreover, because of the low binding energies and the high correlation rate with experimentally obtained results, it was shown that, out of the three, the inhibition of RTK is the most probable mechanism of the cytotoxic activity of the investigated compounds.

Computer Aided Development of Nucleic Acid Applications in Nanotechnologies

Utilization of nucleic acids (NAs) in nanotechnologies and nanotechnology-related applications is a growing field with broad application potential, ranging from biosensing up to targeted cell delivery. Computer simulations are useful techniques that can aid design and speed up development in this field. This review focuses on computer simulations of hybrid nanomaterials composed of NAs and other components. Current state-of-the-art molecular dynamics simulations, empirical force fields (FFs), and coarse-grained approaches for the description of deoxyribonucleic acid and ribonucleic acid are critically discussed. Challenges in combining biomacromolecular and nanomaterial FFs are emphasized. Recent applications of simulations for modeling NAs and their interactions with nano- and biomaterials are overviewed in the fields of sensing applications, targeted delivery, and NA templated materials. Future perspectives of development are also highlighted.

Synthesis, biological evaluation, and molecular docking of novel hydroxyzine derivatives as potential AR antagonists

Prostate cancer (PCa) is a malignant tumor with a higher mortality rate in the male reproductive system. In this study, the hydroxyazine derivatives were synthesized with different structure from traditional anti-prostate cancer drugs. In the evaluation of in vitro cytotoxicity and antagonistic activity of PC-3, LNCaP, DU145 and androgen receptor, it was found that the mono-substituted derivatives on the phenyl group (4, 6, 7, and 9) displayed strong cytotoxic activities, and compounds 11–16 showed relatively strong antagonistic potency against AR (Inhibition% &gt;55). Docking analysis showed that compounds 11 and 12 mainly bind to AR receptor through hydrogen bonds and hydrophobic bonds, and the structure-activity relationship was discussed based on activity data. These results suggested that these compounds may have instructive implications for drug structural modification in prostate cancer.

Sequence-structure functional implications and molecular simulation of high deleterious nonsynonymous substitutions in IDH1 revealed the mechanism of drug resistance in glioma

In the past few years, various somatic point mutations of isocitrate dehydrogenase (IDH) encoding genes (IDH1 and IDH2) have been identified in a broad range of cancers, including glioma. Despite the important function of IDH1 in tumorigenesis and its very polymorphic nature, it is not yet clear how different nsSNPs affect the structure and function of IDH1. In the present study, we employed different machine learning algorithms to screen nsSNPs in the IDH1 gene that are highly deleterious. From a total of 207 SNPs, all of the servers classified 80 mutations as deleterious. Among the 80 deleterious mutations, 14 were reported to be highly destabilizing using structure-based prediction methods. Three highly destabilizing mutations G15E, W92G, and I333S were further subjected to molecular docking and simulation validation. The docking results and molecular simulation analysis further displayed variation in dynamics features. The results from molecular docking and binding free energy demonstrated reduced binding of the drug in contrast to the wild type. This, consequently, shows the impact of these deleterious substitutions on the binding of the small molecule. PCA (principal component analysis) and FEL (free energy landscape) analysis revealed that these mutations had caused different arrangements to bind small molecules than the wild type where the total internal motion is decreased, thus consequently producing minimal binding effects. This study is the first extensive in silico analysis of the IDH1 gene that can narrow down the candidate mutations for further validation and targeting for therapeutic purposes.

Influence of cation-π interactions to the structural stability of phycocyanin proteins: A computational study

The influences of cation-π interactions in phycocyanin proteins and their environmental preferences were analyzed. The number of interactions formed by arginine showed to be higher than those formed by the lysine in the cationic group, while histidine is comparatively higher than phenylalanine and N-terminal residue in the π group. Arg-Tyr and Arg-Phe interacting pairs are predominant among the various pairs analyzed. Cation-π interactions are distance-dependent and can be realized above a wider area above the π ring. We analyzed the energy contribution resulting from cation-π interactions using ab initio calculations. The energy contribution resulting from the most frequent cation-π interactions was in the lower range of strong hydrogen bonds. The results showed that, while most of their interaction energies lay ranged from - 2 to - 8 kcal/mol, those energies could be up to -12- 12 kcal/mol. Stabilization centers for these proteins showed that all residues found in cation-π interactions are important in locating one or more of such centers. In the cation-π interacting residues, 54% of the amino acid residues involved in these interactions might be conserved in phycocyanins. From this study, we infer that cation-π forming residues play an important role in the stability of the multiply commercially used phycocyanin proteins and could help structural biologists and medicinal chemists to design better and safer drugs.

Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants

High-resolution experimental structural determination of protein-protein interactions has led to valuable mechanistic insights, yet due to the massive number of interactions and experimental limitations there is a need for computational methods that can accurately model their structures. Here we explore the use of the recently developed deep learning method, AlphaFold, to predict structures of protein complexes from sequence. With a benchmark of 152 diverse heterodimeric protein complexes, multiple implementations and parameters of AlphaFold were tested for accuracy. Remarkably, many cases (43%) had near-native models (medium or high critical assessment of predicted interactions accuracy) generated as top-ranked predictions by AlphaFold, greatly surpassing the performance of unbound protein-protein docking (9% success rate for near-native top-ranked models), however AlphaFold modeling of antibody-antigen complexes within our set was unsuccessful. We identified sequence and structural features associated with lack of AlphaFold success, and we also investigated the impact of multiple sequence alignment input. Benchmarking of a multimer-optimized version of AlphaFold (AlphaFold-Multimer) with a set of recently released antibody-antigen structures confirmed a low rate of success for antibody-antigen complexes (11% success), and we found that T cell receptor-antigen complexes are likewise not accurately modeled by that algorithm, showing that adaptive immune recognition poses a challenge for the current AlphaFold algorithm and model. Overall, our study demonstrates that end-to-end deep learning can accurately model many transient protein complexes, and highlights areas of improvement for future developments to reliably model any protein-protein interaction of interest.

Structural Features of Antibody-Peptide Recognition

Antibody recognition of antigens is a critical element of adaptive immunity. One key class of antibody-antigen complexes is comprised of antibodies targeting linear epitopes of proteins, which in some cases are conserved elements of viruses and pathogens of relevance for vaccine design and immunotherapy. Here we report a detailed analysis of the structural and interface features of this class of complexes, based on a set of nearly 200 nonredundant high resolution antibody-peptide complex structures that were assembled from the Protein Data Bank. We found that antibody-bound peptides adopt a broad range of conformations, often displaying limited secondary structure, and that the same peptide sequence bound by different antibodies can in many cases exhibit varying conformations. Propensities of contacts with antibody loops and extent of antibody binding conformational changes were found to be broadly similar to those for antibodies in complex with larger protein antigens. However, antibody-peptide interfaces showed lower buried surface areas and fewer hydrogen bonds than antibody-protein antigen complexes, while calculated binding energy per buried interface area was found to be higher on average for antibody-peptide interfaces, likely due in part to a greater proportion of buried hydrophobic residues and higher shape complementarity. This dataset and these observations can be of use for future studies focused on this class of interactions, including predictive computational modeling efforts and the design of antibodies or epitope-based vaccine immunogens.

Self-derived peptides from the SARS-CoV-2 spike glycoprotein disrupting shaping and stability of the homotrimer unit

The structural spike (S) protein from the SARS-CoV-2 β-coronavirus is shown to make different pre- and post-fusion conformations within its homotrimer unit. To support the ongoing novel vaccine design and development strategies, we report the structure-based design approach to develop self-derived S peptides. A dataset of crucial regions from the S protein were transformed into linear motifs that could act as the blockers or stabilizers for the S protein homotrimer unit. Among these distinct S peptides, the pep02 (537-QQFGRDIAD-545) and pep07 (821-RDLICAQKFNGLTVLPPLLTDE-842) were found making stable folded binding with the S protein (550-750 and 950-1050 regions). Upon inserting SARS-CoV-2 S variants in the peptide destabilized the complexed S protein structure, resulting an allosteric effect in different functional regions of the protein. Particularly, the molecular dynamics revealed that A544D mutation in the pep02 peptide induced instability for the complexed S protein, whereas the N943K variant from pep09 exhibited an opposite behavior. An increased protein-peptide binding affinity and the stable structural folding were observed in mutated systems, compared to that of the wild type systems. The presence of mutation has induced an "up" active conformation of the spike (RBD) domain, responsible for interacting the host cell receptor. Among the lower affinity peptide datasets (e.g., pep01), the S1 and S2 subunit in the protein formed an "open" conformation, whereas with higher affinity peptides (e.g., pep07) these domains gained a "closed" conformation. These findings propose that our designed self-derived S peptides could replace a single S protein monomer, blocking the homotrimer formation or inducing stability.

Repositioning of experimentally validated anti-breast cancer peptides to target FAK-PAX complex to halt the breast cancer progression: a biomolecular simulation approach

FAK (focal adhesin kinase), a tyrosine kinase, plays an imperative role in cell-cell communication, particularly in cell signaling systems. It is a multi-functional signaling protein, which integrates and transduces signals into cancer cells through growth factor receptors or integrin and its interaction with Paxillin (PAX). The molecular processes by which FAK promotes the development and progression of cancer have progressively established the possible relationship between FAK-PAX complex in many types of cancer. The interaction of FAX and PAX is very important in breast cancer and thus acts as an essential biomarker for drugs, vaccines or peptide inhibitor designing. In this regard, computational approaches, particularly peptide designing to target the binding interface of the interacting partners, would greatly assist the design of peptide inhibitors against various cancer. Accordingly, in this present study, we screened 236 experimentally validated anti-breast cancer peptides using computational drugs repositioning approach to design peptides targeting the FAK-PAX complex. Using protein-peptide docking the binding site for the HP1 was confirmed and a total of 236 anti-breast cancer peptides were screened. Among the 236, only 12 peptides reported a docking score better than the control. From these 12, Magainin with the docking score - 103.8 ± 10.3 kcal/mol, NRC-07 with the docking score - 100.8 ± 16.5 kcal/mol, and Indolicidin with the docking score - 101.7 ± 3.9 kcal/mol, peptides potentially inhibit the FAX-PAX binding. Calculation of protein's motion and FEL revealed the binding and inhibitory behavior. Moreover, binding free energy (MM/GBSA) confirmed that Magainin exhibited the total binding energy - 53.28 kcal/mol, NRC-07 possessed the TBE - 44.16 kcal/mol, and Indolicidin reported the TBE of - 40.48 kcal/mol, thus explaining the inhibitory potential of these peptides. In conclusion, these peptides exhibit strong inhibitory potential and could abrogate the FAK-PAX complex in in vitro models and thus may relieve the burden of breast cancer.

IMGT/3Dstructure-DB: T-Cell Receptor TR Paratope and Peptide/Major Histocompatibility pMH Contact Sites and Epitope

T-cell receptors (TR), the antigen receptors of T cells, specifically recognize peptides presented by the major histocompatibility (MH) proteins, as peptide/MH (pMH), on the cell surface. The structure characterization of the trimolecular TR/pMH complexes is crucial to the fields of immunology, vaccination, and immunotherapy. IMGT/3Dstructure-DB is the three-dimensional (3-D) structure database of IMGT^®, the international ImMunoGenetics information system^®. By its creation, IMGT^® marks the advent of immunoinformatics, which emerged at the interface between immunogenetics and bioinformatics. The IMGT^® immunoglobulin (IG) and TR gene and allele nomenclature (CLASSIFICATION axiom) and the IMGT unique numbering and IMGT/Collier-de-Perles (NUMEROTATION axiom) are the two founding breakthroughs of immunoinformatics. IMGT-ONTOLOGY concepts and IMGT Scientific chart rules generated from these axioms allowed IMGT^® bridging genes, structures, and functions. IMGT/3Dstructure-DB contains 3-D structures of IG or antibodies, TR and MH proteins of the adaptive immune responses of jawed vertebrates (gnathostomata), IG or TR complexes with antigens (IG/Ag, TR/pMH), related proteins of the immune system of any species belonging to the IG and MH superfamilies, and fusion proteins for immune applications. The focus of this chapter is on the TR V domains and MH G domains and the contact analysis comparison in TR/pMH interactions. Standardized molecular characterization includes "IMGT pMH contact sites" for peptide and MH groove interactions and "IMGT paratopes and epitopes" for TR/pMH complexes. Data are available in the IMGT/3Dstructure database, at the IMGT Home page http://www.imgt.org .

Predicting epitopes for vaccine development using bioinformatics tools

Epitope-based DNA vaccine development is one application of bioinformatics or in silico studies, that is, computational methods, including mathematical, chemical, and biological approaches, which are widely used in drug development. Many in silico studies have been conducted to analyze the efficacy, safety, toxicity effects, and interactions of drugs. In the vaccine design process, in silico studies are performed to predict epitopes that could trigger T-cell and B-cell reactions that would produce both cellular and humoral immune responses. Immunoinformatics is the branch of bioinformatics used to study the relationship between immune responses and predicted epitopes. Progress in immunoinformatics has been rapid and has led to the development of a variety of tools that are used for the prediction of epitopes recognized by B cells or T cells as well as the antigenic responses. However, the in silico approach to vaccine design is still relatively new; thus, this review is aimed at increasing understanding of the importance of in silico studies in the design of vaccines and thereby facilitating future research in this field.