Protein–ligand interfaces are polarized: discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexes

Synergistic effect of horizontal transfer of antibiotic resistance genes between bacteria exposed to microplastics and per/polyfluoroalkyl substances: An explanation from theoretical methods

Microplastics (MPs) and per/polyfluoroalkyl substances (PFASs), as emerging pollutants widely present in aquatic environments, pose a significant threat to human health through the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). Molecular dynamics simulations and machine learning can accurately capture the complex interactions between molecules. This study utilized them to identify the HGT risk between bacteria under MPs and PFASs stress. This study found that MPs and PFASs significantly increase the HGT risk between bacteria, up to 1.57 and 1.59 times, respectively. Notably, long-chain PFASs and perfluoroalkyl carboxylic acids increased the HGT risk by 1.38 and 1.40 times, respectively. Additionally, MPs primarily increase the HGT risk by enhancing hydrogen bonding interaction between key proteins in the HGT pathway and "active codons". The electronegativity and polarizability of PFASs critically influence the HGT risk, acting inversely and directly proportional, respectively. The HGT risk between bacteria under the combined stress from PP-MPs and PFASs exhibits a significant synergistic effect (synergistic effect value of 27.6), which markedly increases the HGT risk. Further analysis revealed that a smaller minimum distance and sharper RDF curve peaks between key proteins and "active codons" indicate higher HGT risk. This indicates that stronger interactions lead to higher HGT risk. This study identifies the characteristics of HGT risks between bacteria in aquatic environments under the individual and combined stresses from MPs and PFASs at the molecular level. It provides a theoretical basis for mitigating ARG transfer and comprehensively assessing the health risks posed by these emerging pollutants.

Biochemical investigation and in silico analysis of the therapeutic efficacy of Ipriflavone through Tet-1 Surface-Modified-PLGA nanoparticles in Streptozotocin-Induced Alzheimer's like Disease: Reduced oxidative damage and etiological Descriptors

Ipriflavone (IPRI), an isoflavone derivative, is clinically used to prevent postmenopausal bone loss in addition to its antioxidant and cognitive benefits. However, its poor aqueous solubility retained its bioavailability. New strategies have been developed to improve the bioavailability and solubility of neurological medications to enhance their potency and limit adverse effects. This study aimed to prepare targeted IPRI-poly-lactic-co-glycolic acid (PLGA) nanoparticles coupled with Tet-1 peptide to increase the therapeutic potency of IPRI in a rat model of Alzheimer's disease (AD). Streptozotocin (STZ) exacerbates Alzheimer-related alterations by promoting central insulin resistance resulted from defective signaling pathways related to neuroinflammation and neurotoxicity. Bilateral intracerebroventricular (icv) injection of STZ was used to introduce the AD model. Icv-STZ injection significantly affected brain insulin, oxidative stress, inflammatory, and apoptotic indicators and caused behavioral abnormalities. STZ promoted the formation of amyloid β42 (Aβ42) by increasing BACE1 and reducing ADAM10 and ADAM17 expression levels. STZ also triggered the accumulation of neurofibrillary tangles and synaptic dysfunction, which are crucial for neurological impairments. Icv-STZ injection showed evident degenerative changes in the pyramidal cell layer and significantly reduced the count of viable cells in both CA1 and prefrontal cortex, indicating increased neuronal cell death. IPRI successfully ameliorated cognitive dysfunction by improving the phosphorylated forms of cAMP-response element-binding protein (pCREB) and extracellular signal-regulated kinase 1/2 (pERK1/2) related to synaptic plasticity. Targeted IPRI nanoparticles exceeded free IPRI potential in reducing oxidative stress, acetylcholinesterase/monoamine oxidase activities, Tau phosphorylation, and Aβ42 levels revealing less degenerative changes and increased viable neuron counts. IPRI-targeted nanoparticles improved the neuroprotective potential of free IPRI, making this strategy applicable to treat many neurodegenerative diseases. Finally, the in silico study predicted its ability to cross the BBB and to bind various protein targets in the brain.

Machine Learning-Driven Methods for Nanobody Affinity Prediction

Because of their high affinity, specificity, and environmental stability, nanobodies (Nbs) have continuously received attention from the field of biological research. However, it is tough work to obtain high-affinity Nbs using experimental methods. In the current study, 12 machine learning algorithms were compared in parallel to explore the potential patterns between Nb-ligand affinity and eight noncovalent interactions. After model comparison and optimization, four optimized models (SVMrB, RotFB, RFB, and C50B) and two stacked models (StackKNN and StackRF) based on nine uncorrelated (correlation coefficient <0.65) optimized models were selected. All the models showed an accuracy of around 0.70 and high specificity. Compared to the other models, RotFB and RFB were not capable of predicting nonaffinitive Nbs with lower precision (<0.44) but showed higher sensitivity at 0.6761 and 0.3521 and good model robustness (F1 score and MCC values). On the contrary, SVMrB, C50B, and StackKNN were able to effectively predict the future nonaffinitive Nbs (specificity >0.92) and reduce the number of true affinitive Nbs (precision >0.5). On the other hand, StackRF showed intermediate model performance. Furthermore, an in-depth feature analysis indicated that hydrogen bonding and aromatic-associated interactions were the key noncovalent interactions in determining Nb-ligand binding affinity. In summary, the current study provides, for the first time, a tool that can effectively predict whether there is an affinity between nanobodies and their intended ligands and explores the key factors that influence their affinity, which could improve the screening and design process of Nbs and accelerate the development of Nb drugs and applications.

In silico-guided synthesis of a new, highly soluble, and anti-melanoma flavone glucoside: Skullcapflavone II-6'-O-β-glucoside

Guided by in silico analysis tools and biotransformation technology, new derivatives of natural compounds with heightened bioactivities can be explored and synthesized efficiently. In this study, in silico data mining and molecular docking analysis predicted that glucosides of skullcapflavone II (SKII) were new flavonoid compounds and had higher binding potential to oncogenic proteins than SKII. These benefits guided us to perform glycosylation of SKII by utilizing four glycoside hydrolases and five glycosyltransferases (GTs). Findings unveiled that exclusive glycosylation of SKII was achieved solely through the action of GTs, with Bacillus subtilis BsUGT489 exhibiting the highest catalytic glycosylation efficacy. Structure analysis determined the glycosylated product as a novel compound, skullcapflavone II-6'-O-β-glucoside (SKII-G). Significantly, the aqueous solubility of SKII-G exceeded its precursor, SKII, by 272-fold. Furthermore, SKII-G demonstrated noteworthy anti-melanoma activity against human A2058 cells, exhibiting an IC50 value surpassing that of SKII by 1.4-fold. Intriguingly, no substantial cytotoxic effects were observed in a murine macrophage cell line, RAW 264.7. This promising anti-melanoma activity without adverse effects on macrophages suggests that SKII-G could be a potential candidate for further preclinical and clinical studies. The in silico tool-guided synthesis of a new, highly soluble, and potent anti-melanoma glucoside, SKII-G, provides a rational design to facilitate the future discovery of new and bioactive compounds.

Distance plus attention for binding affinity prediction

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and π -stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap. Scientific Contribution StatementThis study innovatively introduces distance-based features to predict protein-ligand binding affinity, capitalizing on unique molecular interactions. Furthermore, the incorporation of protein sequence features of specific residues enhances the model's proficiency in capturing intricate binding patterns. The predictive capabilities are further strengthened through the use of a deep learning architecture with attention mechanisms, and an ensemble approach, averaging the outputs of five models, is implemented to ensure robust and reliable predictions.

In silico study of alkaloids with quercetin nucleus for inhibition of SARS-CoV-2 protease and receptor cell protease

Covid-19 disease caused by the deadly SARS-CoV-2 virus is a serious and threatening global health issue declared by the WHO as an epidemic. Researchers are studying the design and discovery of drugs to inhibit the SARS-CoV-2 virus due to its high mortality rate. The main Covid-19 virus protease (Mpro) and human transmembrane protease, serine 2 (TMPRSS2) are attractive targets for the study of antiviral drugs against SARS-2 coronavirus. Increasing consumption of herbal medicines in the community and a serious approach to these drugs have increased the demand for effective herbal substances. Alkaloids are one of the most important active ingredients in medicinal plants that have wide applications in the pharmaceutical industry. In this study, seven alkaloid ligands with Quercetin nucleus for the inhibition of Mpro and TMPRSS2 were studied using computational drug design including molecular docking and molecular dynamics simulation (MD). Auto Dock software was used to evaluate molecular binding energy. Three ligands with the most negative docking score were selected to be entered into the MD simulation procedure. To evaluate the protein conformational changes induced by tested ligands and calculate the binding energy between the ligands and target proteins, GROMACS software based on AMBER03 force field was used. The MD results showed that Phyllospadine and Dracocephin-A form stable complexes with Mpro and TMPRSS2. Prolinalin-A indicated an acceptable inhibitory effect on Mpro, whereas it resulted in some structural instability of TMPRSS2. The total binding energies between three ligands, Prolinalin-A, Phyllospadine and Dracocephin-A and two proteins MPro and TMRPSS2 are (-111.235 ± 15.877, - 75.422 ± 11.140), (-107.033 ± 9.072, -84.939 ± 10.155) and (-102.941 ± 9.477, - 92.451 ± 10.539), respectively. Since the binding energies are at a minimum, this indicates confirmation of the proper binding of the ligands to the proteins. Regardless of some Prolinalin-A-induced TMPRSS2 conformational changes, it may properly bind to TMPRSS2 binding site due to its acceptable binding energy. Therefore, these three ligands can be promising candidates for the development of drugs to treat infections caused by the SARS-CoV-2 virus.

Opioid/Dopamine Receptor Binding Studies, NMR and Molecular Dynamics Simulation of LENART01 Chimera, an Opioid-Bombesin-like Peptide

The design and development of hybrid compounds as a new class of drug candidates remains an excellent opportunity to improve the pharmacological properties of drugs (including enzymatic stability, efficacy and pharmacokinetic and pharmacodynamic profiles). In addition, considering various complex diseases and/or disorders, the conjugate chemistry approach is highly acceptable and justified. Opioids have long been recognized as the most potent analgesics and serve as the basic pharmacophore for potent hybrid compounds that may be useful in pain management. However, a risk of tolerance and physical dependence exists. Since dopamine receptors have been implicated in the aforementioned adverse effects of opioids, the construction of a hybrid with dual action at opioid and dopamine receptors is of interest. Herein, we present nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics simulation results for LENART01, an opioid-ranatensin hybrid peptide. Apart from molecular docking, protein-ligand interactions were also assessed in vitro using a receptor binding assay, which proved LENART01 to be bound to mu-opioid and dopamine receptors, respectively.

Conformational features and interaction mechanisms of VH H antibodies with β-hairpin CDR3: A case of Nb8-HigB2 interaction

The β-hairpin conformation is regarded as an important basic motif to form and regulate protein-protein interactions. Single-domain VH H antibodies are potential therapeutic and diagnostic tools, and the third complementarity-determining regions of the heavy chains (CDR3s) of these antibodies are critical for antigen recognition. Although the sequences and conformations of the CDR3s are diverse, CDR3s sometimes adopt β-hairpin conformations. However, characteristic features and interaction mechanisms of β-hairpin CDR3s remain to be fully elucidated. In this study, we investigated the molecular recognition of the anti-HigB2 VH H antibody Nb8, which has a CDR3 that forms a β-hairpin conformation. The interaction was analyzed by evaluation of alanine-scanning mutants, molecular dynamics simulations, and hydrogen/deuterium exchange mass spectrometry. These experiments demonstrated that positions 93 and 94 (Chothia numbering) in framework region 3, which is right outside CDR3 by definition, play pivotal roles in maintaining structural stability and binding properties of Nb8. These findings will facilitate the design and optimization of single-domain antibodies.

Anti-COVID-19, Anti-Inflammatory, and Anti-Osteoarthritis Activities of Sesamin from Sesamum indicum L

During the COVID-19 (coronavirus disease 2019) outbreak, many people were infected, and the symptoms may persist for several weeks or months for recovering patients. This is also known as "long COVID" and includes symptoms such as fatigue, joint pain, muscle pain, et cetera. The COVID-19 virus may trigger hyper-inflammation associated with cytokine levels in the body. COVID-19 can trigger inflammation in the joints, which can lead to osteoarthritis (OA), while long-term COVID-19 symptoms may lead to joint damage and other inflammation problems. According to several studies, sesame has potent anti-inflammatory properties due to its major constituent, sesamin. This study examined sesamin's anti-inflammatory, anti-osteoarthritis, and anti-COVID-19 effects. Moreover, in vivo and in vitro assays were used to determine sesamin's anti-inflammatory activity against the RAW264.7 and SW1353 cell lines. Sesamin had a dose-dependent effect (20 mg/kg) in a monoiodoacetic acid (MIA)-induced osteoarthritis rat model. Sesamin reduced paw swelling and joint discomfort. In addition, the findings indicated that sesamin suppressed the expression of iNOS (inducible nitric oxide synthase) and COX-2 (cyclooxygenase-2) in the RAW264.7 cell line within the concentration range of 6.25-50 μM. Furthermore, sesamin also had a suppressive effect on MMP (matrix metalloproteinase) expression in chondrocytes and the SW1353 cell line within the same concentration range of 6.25-50 μM. To examine the anti-viral activity, an in silico analysis was performed to evaluate sesamin's binding affinity with SARS-CoV-2 RdRp (severe acute respiratory syndrome coronavirus 2 RNA-dependent RNA polymerase) and human ACE2 (angiotensin-converting enzyme 2). Compared to the controls, sesamin exhibited strong binding affinities towards SARS-CoV-2 RdRp and human ACE2. Furthermore, sesamin had a higher binding affinity for the ACE2 target protein. This study suggests that sesamin shows potential anti-SARS-CoV-2 activity for drug development.

Deciphering Houttuynia cordata extract as electron shuttles with anti-COVID-19 activity and its performance in microbial fuel cells

Background

Traditional herbal medicines usually contain electron shuttle (ES)-like structures compounds which are potential candidates for antiviral compounds selection. Houttuynia cordata is applied as a biomaterial to decipher its potential applications in bioenergy extraction in microbial fuel cells (MFCs) and anti-COVID-19 via molecular docking evaluation.

Methods

H. cordata leaves extracts by water and 60% ethanol solvent were analyzed for total polyphenols, antioxidant activity, cyclic voltammetry (CV), and MFCs. The bioactive compounds of H. cordata leaves extracts were assayed via LC/MS analysis. Identification of the marker substances for potential antiviral activity using a molecular docking model was provided.

Significant findings

60% ethanol extract exhibits the highest total polyphenols and antioxidant activity compared with water extracts. Bioenergy extraction in MFCs showed that 60% ethanol extracts could give 1.76-fold more power generation compared to the blank. Flavonoids and their sugar-to-glycan ratios increased after CV scanning and they are expected to be effective ES substances. Quercitrin, from the H. cordata extract that shares an ES-like structure, was found to exhibit strong binding affinities towards ACE2 and RdRp. This indicated the potential of H. cordata leaves as a promising antiviral herb.

Trio-pharmacophore DNA-encoded chemical library for simultaneous selection of fragments and linkers

The split-and-pool method has been widely used to synthesize chemical libraries of a large size for early drug discovery, albeit without the possibility of meaningful quality control. In contrast, a self-assembled DNA-encoded chemical library (DEL) allows us to construct an m x n-member library by mixing an m-member and an n-member pre-purified sub-library. Herein, we report a trio-pharmacophore DEL (T-DEL) of m x l x n members through assembling three pre-purified and validated sub-libraries. The middle sub-library is synthesized using DNA-templated synthesis with different reaction mechanisms and designed as a linkage connecting the fragments displayed on the flanking two sub-libraries. Despite assembling three fragments, the resulting compounds do not exceed the up-to-date standard of molecular weight regarding drug-likeness. We demonstrate the utility of T-DEL in linker optimization for known binding fragments against trypsin and carbonic anhydrase II and by de novo selections against matrix metalloprotease-2 and -9.

Interaction of the nanobio-based reagent with sodium fluorescein and lipids via bioinformatics for forensic fingerprint visualisations

Being commonly found at crime scenes, fingerprints are crucial for human identification, attributable to their uniqueness, persistence and systematic classification of ridge patterns. In addition to latent fingerprints being invisible to the naked eye, the escalating trends of disposing forensic evidence bearing such prints in watery bodies would further complicate criminal investigations. Taking into account the toxicity of small particle reagent (SPR) commonly used in visualising latent fingerprints on wet and non-porous objects, a greener alternative using the nanobio-based reagent (NBR) has been suggested. However, NBR only applies to white and/or relatively light-coloured objects. Thus, conjugation of sodium fluorescein dye with NBR (f-NBR) may be beneficial for increasing the contrast of fingerprint on multi-colored objects. Hence, this study was aimed at investigating the possibility of such conjugation (i.e., f-NBR) as well as proposing suitable interactions between the f-NBR and lipid constituents of fingerprints (tetra-, hexa- and octadecanoic acids) via molecular docking and molecular dynamics simulations. The binding energies between CRL with its ligands were observed at -8.1, -5.0, -4.9 and -3.6 kcal/mole for sodium fluorescein, tetra-, hexa- and octadecanoic acids, respectively. Besides, the formations of hydrogen bonds observed in all complexes (ranged between 2.6 and 3.4 Å), further supported by the stabilized root mean square deviation (RMSDs) plots in MD simulations. In short, the conjugation of f-NBR was computationally feasible, and thereby merits further investigations in the laboratory.Communicated by Ramaswamy H. Sarma.

Interactive deciphering electron-shuttling characteristics of Coffea arabica leaves and potential bioenergy-steered anti-SARS-CoV-2 RdRp inhibitor via microbial fuel cells

Due to the pandemics of COVID-19, herbal medicine has recently been explored for possible antiviral treatment and prevention via novel platform of microbial fuel cells. It was revealed that Coffea arabica leaves was very appropriate for anti-COVID-19 drug development. Antioxidant and anti-inflammatory tests exhibited the most promising activities for C. arabica ethanol extracts and drying approaches were implemented on the leaf samples prior to ethanol extraction. Ethanol extracts of C. arabica leaves were applied to bioenergy evaluation via DC-MFCs, clearly revealing that air-dried leaves (CA-A-EtOH) exhibited the highest bioenergy-stimulating capabilities (ca. 2.72 fold of power amplification to the blank). Furthermore, molecular docking analysis was implemented to decipher the potential of C. arabica leaves metabolites. Chlorogenic acid (-6.5 kcal/mol) owned the highest binding affinity with RdRp of SARS-CoV-2, showing a much lower average RMSF value than an apoprotein. This study suggested C. arabica leaves as an encouraging medicinal herb against SARS-CoV-2.

Targeting COVID-19 pandemic: in silico evaluation of 2-hydroxy-1, 2-diphenylethanone N(4)-methyl-N(4)-phenylthiosemicarbazone as a potential inhibitor of SARS-CoV-2

The global spread of the COVID-19 pandemic caused by the etiological agent, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), triggered researchers to identify and develop novel antiviral therapeutics. Herein, we report a new molecule 2-hydroxy-1,2-diphenylethanone N(4)-methyl-N(4)-phenyl thiosemicarbazone (BMPTSC), as a potential inhibitor of SARS-CoV-2. BMPTSC was synthesized, characterized by IR and NMR studies, and the structural parameters were analyzed computationally by B3LYP/cc-pVDZ method. Molecular docking studies were performed to get insights into the energetics and compatibility of BMPTSC against various SARS-CoV-2 drug targets. The best docking poses of target protein-BMPTSC complex structures were further subjected to molecular dynamics (MD) simulations. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations on the binding of BMPTSC with the target proteins viz. spike glycoprotein and ACE-2 protein showed energy values of -179.87 and -145.61 kJ/mol, respectively. Moreover, BMPTSC obeys Lipinski's rule, and further in silico assessment of oral bioavailability, bioactivity scores, ADME, drug-likeness, and medicinal chemistry friendliness suggests that this molecule is a promising candidate for the COVID-19 drug discovery process.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11224-022-02033-8.

CB1 as a novel target for Ginkgo biloba's terpene trilactone for controlling chemotherapy-induced peripheral neuropathy (CIPN)

The application of antineoplastic chemotherapeutic agents causes a common side effect known as chemotherapy-induced peripheral neuropathy (CIPN) that leads to reducing the quality of patient's life. This research involves the performance of molecular docking and molecular dynamic (MD) simulation studies to explore the impact of terpenoids of Ginkgo biloba on the targets (CB-1, TLR4, FAAH-1, COX-1, COX-2) that can significantly affect the controlling of CIPN's symptoms. According to the in-vitro and in-vivo investigations, terpenoids, particularly ginkgolides B, A, and bilobalide, can cause significant effects on neuropathic pain. The molecular docking results disclosed the tendency of our ligands to interact with mainly CB1 and FAAH-1, as well as partly with TLR4, throughout their interactions with targets. Terpene trilactone can exhibit a lower rate of binding energy than CB1's inhibitor (7dy), while being precisely located in the CB1's active site and capable of inducing stable interactions by forming hydrogen bonds. The analyses of MD simulation proved that ginkgolide B was a more suitable activator and inhibitor for CB1 and TLR4, respectively, when compared to bilobalide and ginkgolide A. Moreover, bilobalide is capable of inhibiting FAAH-1 more effectively than the two other ligands. According to the analyses of ADME, every three ligands followed the Lipinski's rule of five. Considering these facts, the exertion of three ligands is recommended for their anti-inflammatory, neuroprotective, and anti-nociception influences caused by primarily activating CB1 and inhibiting FAAH-1 and TLR4; in this regard, these compounds can stand as potential candidates for the control and treatment of CIPN's symptoms.

Inhibition of GSK_3β by Iridoid Glycosides of Snowberry (Symphoricarpos albus) Effective in the Treatment of Alzheimer's Disease Using Computational Drug Design Methods

The inhibition of glycogen synthase kinase-3β (GSK-3β) activity prevents tau hyperphosphorylation and binds it to the microtubule network. Therefore, a GSK-3β inhibitor may be a recommended drug for Alzheimer's treatment. In silico methods are currently considered as one of the fastest and most cost-effective available alternatives for drug/design discovery in the field of treatment. In this study, computational drug design was conducted to introduce compounds that play an effective role in inhibiting the GSK-3β enzyme by molecular docking and molecular dynamics simulation. The iridoid glycosides of the common snowberry (Symphoricarpos albus), including loganin, secologanin, and loganetin, are compounds that have an effect on improving memory and cognitive impairment and the results of which on Alzheimer's have been studied as well. In this study, in the molecular docking phase, loganin was considered a more potent inhibitor of this protein by establishing a hydrogen bond with the ATP-binding site of GSK-3β protein and the most negative binding energy to secologanin and loganetin. Moreover, by molecular dynamics simulation of these ligands and GSK-3β protein, all structures were found to be stable during the simulation. In addition, the protein structure represented no change and remained stable by binding ligands to GSK-3β protein. Furthermore, loganin and loganetin have higher binding free energy than secologanin; thus, these compounds could effectively bind to the active site of GSK-3β protein. Hence, loganin and loganetin as iridoid glycosides can be effective in Alzheimer's prevention and treatment, and thus, further in vitro and in vivo studies can focus on these iridoid glycosides as an alternative treatment.

Atomistic insights into the selective therapeutic activity of 6-(2,4-difluorophenoxy)-5-((ethylmethyl)pyridine-3-yl)-8-methylpyrrolo[1,2-a]pyrazin-1(2H)-one towards bromodomain-containing proteins

Cross-target effect has been one of the major mechanisms of drug toxicity, this has necessitated the design of inhibitors that are specifically tailored to target particular biomolecules. 6-(2,4-difluorophenoxy)-5-((ethylmethyl)pyridine-3-yl)-8-methylpyrrolo[1,2-a] pyrazin-1(2H)-one (Cpd38) is an inhibitor possessing high inhibition rate and tailored specificity towards bromodomain-containing protein 4 (BRD4). In this research, we used an array of computational techniques to provide insight at the atomistic level the specific targeting of BRD4 by Cpd38 relative to the binding of Cpd38 with E1A binding protein P300 (EP300); another bromodomain-containing protein (BCP). Comparatively, binding of Cpd38 improved the conformational stability and compactness of BRD4 protein when compared to the Cpd38 bound EP300. Also, Cpd38 induced a conformational change in the active site of BRD4 that facilitated a complementary pose between Cpd38 and BRD4 suitable for effective atomistic interactions. Expectedly, thermodynamic calculations revealed that the Cpd38-BRD4 system had higher binding energy (-36.11 Kcal/mol) than the Cpd38-EP300 system with a free binding energy of -15.86 Kcal/mol. Noteworthy is the opposing role Trp81 (acting as hydrogen bond acceptor) and Pro1074 (acting as hydrogen bond donor) found on the WPF and LPF loops respectively play in maintaining Cpd38 stability. Furthermore, the hydrogen bond acceptor/donator ratio was approximately 4:1 in Cpd38-BRD4 system compared with 2:1 in Cpd38-EP300 system. Taken together, atomistic insights and structural perspectives detailed in this report supplements the experimental report supporting the improved selectivity of Cpd38 for BRD4 ahead of other BCPs while providing leeway for the future design of BET selective agents with better pharmacological profile.

Structural basis of an epitope tagging system derived from Haloarcula marismortui bacteriorhodopsin I D94N and its monoclonal antibody GD-26

Specific antibody interactions with short peptides have made epitope tagging systems a vital tool employed in virtually all fields of biological research. Here, we present a novel epitope tagging system comprised of a monoclonal antibody named GD‐26, which recognises the TD peptide (GTGATPADD) derived from Haloarcula marismortui bacteriorhodopsin I (HmBRI) D94N mutant. The crystal structure of the antigen‐binding fragment (Fab) of GD‐26 complexed with the TD peptide was determined to a resolution of 1.45 Å. The TD peptide was found to adopt a 3₁₀ helix conformation within the binding cleft, providing a characteristic peptide structure for recognition by GD‐26 Fab. Based on the structure information, polar and nonpolar forces collectively contribute to the strong binding. Attempts to engineer the TD peptide show that the proline residue is crucial for the formation of the 3₁₀ helix in order to fit into the binding cleft. Isothermal calorimetry (ITC) reported a dissociation constant K_D of 12 ± 2.8 nm, indicating a strong interaction between the TD peptide and GD‐26 Fab. High specificity of GD‐26 IgG to the TD peptide was demonstrated by western blotting, ELISA and immunofluorescence as only TD‐tagged proteins were detected, suggesting the effectiveness of the GD‐26/TD peptide tagging system. In addition to already‐existing epitope tags such as the FLAG tag and the ALFA tag adopting either extended or α‐helix conformations, the unique 3₁₀ helix conformation of the TD peptide together with the corresponding monoclonal antibody GD‐26 offers a novel tagging option for research.

Insilico drug repurposing using FDA approved drugs against Membrane protein of SARS-CoV-2

The novel coronavirus (SARS-CoV-2) outbreak has started taking away the millions of lives worldwide. Identification of known and approved drugs against novel coronavirus disease (COVID-19) seems to be an urgent need for the repurposing of the existing drugs. So, here we examined a safe strategy of using approved drugs of SuperDRUG2 database against modeled membrane protein (M-protein) of SARS-CoV-2 which is essential for virus assembly by using molecular docking-based virtual screening. A total of 3639 drugs from SuperDRUG2 database and additionally 14 potential drugs reported against COVID-19 proteins were selected. Molecular docking analyses revealed that nine drugs can bind the active site of M-protein with desirable molecular interactions. We therefore applied molecular dynamics simulations and binding free energy calculation using MM-PBSA to analyze the stability of the compounds. The complexes of M-protein with the selected drugs were simulated for 50 ns and ranked according to their binding free energies. The binding mode of the drugs with M-protein was analyzed and it was observed that Colchicine, Remdesivir, Bafilomycin A1 from COVID-19 suggested drugs and Temozolomide from SuperDRUG2 database displayed desirable molecular interactions and higher binding affinity towards M-protein. Interestingly, Colchicine was found as the top most binder among tested drugs against M-protein. We therefore additionally identified four Colchicine derivatives which can bind efficiently with M-protein and have better pharmacokinetic properties. We recommend that these drugs can be tested further through in vitro studies against SARS-CoV-2 M-protein.

In silico structure-based discovery of a SARS-CoV-2 main protease inhibitor

The Coronavirus Disease 2019 (COVID-19) pandemic caused by the novel lineage B betacoroanvirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in significant mortality, morbidity, and socioeconomic disruptions worldwide. Effective antivirals are urgently needed for COVID-19. The main protease (M^pro) of SARS-CoV-2 is an attractive antiviral target because of its essential role in the cleavage of the viral polypeptide. In this study, we performed an in silico structure-based screening of a large chemical library to identify potential SARS-CoV-2 M^pro inhibitors. Among 8,820 compounds in the library, our screening identified trichostatin A, a histone deacetylase inhibitor and an antifungal compound, as an inhibitor of SARS-CoV-2 M^pro activity and replication. The half maximal effective concentration of trichostatin A against SARS-CoV-2 replication was 1.5 to 2.7µM, which was markedly below its 50% effective cytotoxic concentration (75.7µM) and peak serum concentration (132µM). Further drug compound optimization to develop more stable analogues with longer half-lives should be performed. This structure-based drug discovery platform should facilitate the identification of additional enzyme inhibitors of SARS-CoV-2.

Piecing the fragments together: Dynamical insights into the enhancement of BRD4-BD1 (BET protein) druggability in cancer chemotherapy using novel 8-methyl-pyrrolo[1,2-a]pyrazin-1(2H)-one derivatives

BACKGROUND

Fragment-based drug discovery in recent times has been explored in the design of highly potent therapeutics.

METHODS

In this study, we explored the inhibitory dynamics of Compound 38 (Cpd38), a newly synthesized Bromodomain-containing protein 4 bromodomain 1 (BRD4-BD1) protein inhibitor derived from the synthetic coupling of Fragment 47 (Fgt47) into ABBV-075 scaffold. Using dynamic simulation methods, we unraveled the augmentative effects of chemical fragmentation on improved BRD4-BD1 inhibition.

RESULTS

Findings from this study revealed that although Fgt47 exhibited a considerable ΔGbind, its incorporation into the difluoro-phenoxy pyridine scaffold (Cpd38) notably enhanced the binding affinity. Time-based analyses of interaction dynamics further revealed that the bulkiness of Cpd38 favored its interaction at the BRD4-BD1 active site relative to the fragment. Strikingly, when compared to Fgt47, Cpd38 demonstrated high mobility, which could have enabled it to bind optimally and complementarily with key residues of the active site such as Ile146, Asn140, Cys136, Tyr98, Leu94, Val87, Phe83 and Trp81.

DISCUSSION

On the contrary, majority of these interactions were gradually lost in Fgt47 which could further indicate the essence of coupling it with the difluoro-phenoxy pyridine scaffold. Furthermore, Cpd38 had a more altering effect on BRD4-BDI relative to Fgt47 which could also be as a result of its higher inhibitory activity.

CONCLUSION

Conclusively, the design of highly potent therapeutics could be facilitated by the incorporation of pharmacologically active small molecule fragments into the scaffold of existing drugs.

Format chain exchange (FORCE) for high-throughput generation of bispecific antibodies in combinatorial binder-format matrices

Generation of bispecific antibodies (bsAbs) requires a combination of compatible binders in formats that support desired functionalities. Here, we report that bsAb-matrices can be generated by Format Chain Exchange (FORCE), enabling screening of combinatorial binder/format spaces. Input molecules for generation of bi/multi-valent bsAbs are monospecific entities similar to knob-into-hole half-antibodies, yet with complementary CH3-interface-modulated and affinity-tagged dummy-chains. These contain mutations that lead to limited interface repulsions without compromising expression or biophysical properties of educts. Mild reduction of combinations of educts triggers spontaneous chain-exchange reactions driven by partially flawed CH3-educt interfaces resolving to perfect complementarity. This generates large bsAb matrices harboring different binders in multiple formats. Benign biophysical properties and good expression yields of educts, combined with simplicity of purification enables process automation. Examples that demonstrate the relevance of screening binder/format combinations are provided as a matrix of bsAbs that simultaneously bind Her1/Her2 and DR5 without encountering binder or format-inflicted interferences.

Bispecific antibodies have been generated in many different formats and it is becoming clear that rational design alone cannot create optimal functionalities. Here the authors introduce the high throughput methodology, Format Chain Exchange (FORCE), to enable combinatorial generation of bispecific antibodies.

Screening of potential drug for Alzheimer's disease: a computational study with GSK-3 β inhibition through virtual screening, docking, and molecular dynamics simulation

The global impact of Alzheimer's disease (AD) necessitates intensive research to find appropriate and effective drugs. Many studies in AD suggested beta-amyloid plaques and neurofibrillary tangles-associated tau protein as the key targets for drug development. On the other hand, it is proved that triggering of Glycogen Synthase Kinase-3β (GSK-3β) also cause AD, therefore, GSK-3β is a potential drug target to combat AD. We, in this study, investigated the ability of small molecules to inhibit GSK-3β through virtual screening, Absorption, Distribution, Metabolism, and Excretion (ADME), induced-fit docking (IFD), molecular dynamics simulation, and binding free energy calculation. Besides, molecular docking was performed to reveal the binding and interaction of the ligand at the active site of GSK-3β. We found two compounds such as 6961 and 6966, which exhibited steady-state interaction with GSK-3β for 30 ns in molecular dynamics simulation. The compounds (6961 and 6966) also achieved a docking score of -9.05 kcal/mol and -8.11 kcal/mol, respectively, which is relatively higher than the GSK-3β II inhibitor (-6.73 kcal/mol). The molecular dynamics simulation revealed that the compounds have a stable state during overall simulation time, and lesser root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) values compared with co-crystal. In conclusion, we suggest the two compounds (6966 and 6961) as potential leads that could be utilized as effective inhibitors of GSK-3β to combat AD.

Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition

In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery.

Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

The Binding of Drug Molecules to Serum Albumin: The Effect of Drug Hydrophobicity on Binding Strength and Protein Desolvation

In this work, dual polarization interferometry (DPI) and quartz crystal microgravimetry with dissipation monitoring (QCM-D) were used to examine the binding characteristics and structure-activity relationships of 12 common drugs on a model bovine serum albumin (BSA) film. By taking advantage of the different hydration sensitivities of DPI and QCM-D, we were able to quantify changes in the solvent state upon drug binding to BSA. Quantifying the changes in water mass within binding pockets and upon drug-protein binding allows for a more complete understanding of binding phenomena between drug molecules and serum proteins. For the drugs tested, a quantitative structure-activity relationship (QSAR) was used to establish a correlation between drug binding (K_D) and hydrophobicity (ClogP), with the latter being related to the drug's ability to desolvate the BSA upon binding. Understanding these relationships provides insight into the role of water at the protein-ligand interface and is of particular importance in the area of ligand binding within the field of drug design. This study underscores the importance of hydrophobicity to drug binding kinetics and may be used to further understand and improve drug design and delivery protocols.

SmoPSI: Analysis and Prediction of Small Molecule Binding Sites Based on Protein Sequence Information

The analysis and prediction of small molecule binding sites is very important for drug discovery and drug design. The traditional experimental methods for detecting small molecule binding sites are usually expensive and time consuming, and the tools for single species small molecule research are equally inefficient. In recent years, some algorithms for predicting binding sites of protein-small molecules have been developed based on the geometric and sequence characteristics of proteins. In this paper, we have proposed SmoPSI, a classification model based on the XGBoost algorithm for predicting the binding sites of small molecules, using protein sequence information. The model achieved better results with an AUC of 0.918 and an ACC of 0.913. The experimental results demonstrate that our method achieves high performances and outperforms many existing predictors. In addition, we also analyzed the binding residues and nonbinding residues and finally found the PSSM; hydrophilicity, hydrophobicity, charge, and hydrogen bonding have obviously different effects on the binding-site predictions.

Automated discovery of GPCR bioactive ligands

While G-protein-coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Because of the involvement of GPCRs in various signaling pathways and physiological roles, the identification of endogenous ligands as well as designing novel drugs is of high interest to the research and medical communities. Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances for automating the discovery of bioactive ligands using machine learning. Machine learning is centered around the development and applications of algorithms that can learn from data automatically. Such an approach offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies. This review describes the most recent and successful applications of machine learning for bioactive ligand discovery, concluding with an outlook on deep learning methods that are capable of automatically extracting salient information from structural data as a promising future direction for rapid and efficient bioactive ligand discovery.

DUckCov: a Dynamic Undocking-Based Virtual Screening Protocol for Covalent Binders

Thanks to recent guidelines, the design of safe and effective covalent drugs has gained significant interest. Other than targeting non‐conserved nucleophilic residues, optimizing the noncovalent binding framework is important to improve potency and selectivity of covalent binders toward the desired target. Significant efforts have been made in extending the computational toolkits to include a covalent mechanism of protein targeting, like in the development of covalent docking methods for binding mode prediction. To highlight the value of the noncovalent complex in the covalent binding process, here we describe a new protocol using tethered and constrained docking in combination with Dynamic Undocking (DUck) as a tool to privilege strong protein binders for the identification of novel covalent inhibitors. At the end of the protocol, dedicated covalent docking methods were used to rank and select the virtual hits based on the predicted binding mode. By validating the method on JAK3 and KRas, we demonstrate how this fast iterative protocol can be applied to explore a wide chemical space and identify potent targeted covalent inhibitors.

Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals

Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.

Binding Affinity via Docking: Fact and Fiction

In 1982, Kuntz et al. published an article with the title “A Geometric Approach to Macromolecule-Ligand Interactions”, where they described a method “to explore geometrically feasible alignment of ligands and receptors of known structure”. Since then, small molecule docking has been employed as a fast way to estimate the binding pose of a given compound within a specific target protein and also to predict binding affinity. Remarkably, the first docking method suggested by Kuntz and colleagues aimed to predict binding poses but very little was specified about binding affinity. This raises the question as to whether docking is the right tool to estimate binding affinity. The short answer is no, and this has been concluded in several comprehensive analyses. However, in this opinion paper we discuss several critical aspects that need to be reconsidered before a reliable binding affinity prediction through docking is realistic. These are not the only issues that need to be considered, but they are perhaps the most critical ones. We also consider that in spite of the huge efforts to enhance scoring functions, the accuracy of binding affinity predictions is perhaps only as good as it was 10–20 years ago. There are several underlying reasons for this poor performance and these are analyzed. In particular, we focus on the role of the solvent (water), the poor description of H-bonding and the lack of the systems’ true dynamics. We hope to provide readers with potential insights and tools to overcome the challenging issues related to binding affinity prediction via docking.