Software for molecular docking: a review

Link prediction in protein-protein interaction network: A similarity multiplied similarity algorithm with paths of length three

Protein-protein interactions (PPIs) are crucial for various biological processes, and predicting PPIs is a major challenge. To solve this issue, the most common method is link prediction. Currently, the link prediction methods based on network Paths of Length Three (L3) have been proven to be highly effective. In this paper, we propose a novel link prediction algorithm, named SMS, which is based on L3 and protein similarities. We first design a mixed similarity that combines the topological structure and attribute features of nodes. Then, we compute the predicted value by summing the product of all similarities along the L3. Furthermore, we propose the Max Similarity Multiplied Similarity (maxSMS) algorithm from the perspective of maximum impact. Our computational prediction results show that on six datasets, including S. cerevisiae, H. sapiens, and others, the maxSMS algorithm improves the precision of the top 500, area under the precision-recall curve, and normalized discounted cumulative gain by an average of 26.99%, 53.67%, and 6.7%, respectively, compared to other optimal methods.

Fluorinated benzoxazinones designed via MIA-QSAR, docking and molecular dynamics as protoporphyrinogen IX oxidase inhibitors

BACKGROUND

Fluorine plays a significant role in agrochemical science because approximately 25% of herbicides licensed worldwide contain this element. In a pool of previously synthesized benzoxazinones, some compounds contained fluorine and demonstrated inhibitory activities against protoporphyrinogen IX oxidase (PPO). Therefore, three data sets of benzoxazinone derivatives with known inhibitory activity against PPO were employed to build a multivariate image analysis applied to a quantitative structure-activity relationships (MIA-QSAR) model to identify improved analogs with at least one fluorine substituent.

RESULTS

The QSAR model was vigorously validated and demonstrated to be highly predictive (r2 = 0.85, q2 = 0.71, and r2 pred = 0.88); thus, the model can provide reliable estimations for the PPO inhibitory activity of unknown derivatives. From these compounds, a couple of N-substituted benzoxazinones that contained the -CH2CHF2 group were found with predicted pKi values larger than 8 (Ki in mol L-1) and higher lipophilicity than the most active data set compounds. In addition, we carried out a systematic investigation of the binding mode of PPO by performing computational docking followed by molecular dynamics simulations. The proposed binding mode was consistent with experimental studies, and several potential key residues were identified.

CONCLUSION

Two new proposed benzoxazinones exhibited better performance than compounds of the data set, and fluorine substituents played pivotal roles in describing the biological activities. © 2024 Society of Chemical Industry.

Insight into the inhibitory potential of metal complexes supported by (E)-2-morpholino-N-(thiophen-2-ylmethylene)ethanamine: synthesis, structural properties, biological evaluation and docking studies

A thiophene-derived Schiff base ligand (E)-2-morpholino-N-(thiophen-2-ylmethylene)ethanamine was used for the synthesis of M(II) complexes, [TEM(M)X2] (M = Co, Cu, Zn; X = Cl; M = Cd, X = Br). Structural characterization of the synthesized complexes revealed distorted tetrahedral geometry around the M(II) center. In vitro investigation of the synthesized ligand and its M(II) complexes showed considerable anti-urease and leishmanicidal potential. The synthesized complexes also exhibited a significant inhibitory effect on urease, with IC50 values in the range of 3.50-8.05 μM. In addition, the docking results were consistent with the experimental results. A preliminary study of human colorectal cancer (HCT), hepatic cancer (HepG2), and breast cancer (MCF-7) cell lines showed marked anticancer activities of these complexes.

Delineating the Antiapoptotic Property of Apigenin as an Antitumor Agent: A Computational and In Vitro Study on HeLa Cells

Apigenin, a flavonoid, is reported to have multiple health benefits including cancer prevention; this study evaluates the drug likeliness and Swiss ADME properties of apigenin. Apoptosis, which is a key hallmark of cancer, is associated with the deregulation of the balance between proapoptotic proteins and antiapoptotic proteins such as BCL-2,BCL-xl, BFL-1, BCL-w, BRAG-16, and MCL-1. The docking studies of apigenin with the mentioned proteins was performed to identify the interactions between the ligand and proteins, which suggested that apigenin was able to bind to most of the proteins similar to the inhibitory molecules of its native structure. A remarkable reduction in the total energy after energy minimization of apigenin-antiapoptotic protein complexes suggested increased stability of the docked complexes. The same complexes were found to be stable over a 10 ns period of molecular simulation at 300 K. These findings advocated the study to evaluate apigenin's potential to inhibit the HeLa cells at 5, 10, and 15 μM concentrations in the clonogenic assay. Apigenin inhibited the colony-forming ability of HeLa cells in a dose-dependent manner over a fortnight. Light microscopy of the treated cells displayed the morphological evidence characteristic of apoptosis in HeLa cells such as blebbing, spike formation, cytoplasmic oozing, and nuclear fragmentation. Thus, these results clearly indicate that apigenin may be used as a potential chemopreventive agent in cervical cancer management.

Assessment of fragment docking and scoring with the endothiapepsin model system

Fragment-based screening has become indispensable in drug discovery. Yet, the weak binding affinities of these small molecules still represent a challenge for the reliable detection of fragment hits. The extent of this issue was illustrated in the literature for the aspartic protease endothiapepsin: When seven biochemical and biophysical in vitro screening methods were applied to screen a library of 361 fragments, very poor overlap was observed between the hit fragments identified by the individual approaches, resulting in high levels of false positive and/or false negative results depending on the mutually compared methods. Here, the reported in vitro findings are juxtaposed with the results from in silico docking and scoring approaches. The docking programs GOLD and Glide were considered with the scoring functions ASP, ChemScore, ChemPLP, GoldScore, DSXCSD, and GlideScore. First, the ranking power and scoring power were assessed for the named scoring functions. Second, the capability of reproducing the crystallized fragment binding modes was tested in a structure-based redocking approach. The redocking success notably depended on the ligand efficiency of the considered fragments. Third, a blinded virtual screening approach was employed to evaluate whether in silico screening can compete with in vitro methods in the enrichment of fragment databases.

Unleashing the power of generative AI in drug discovery

Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.

A comprehensive examination of ACE2 receptor and prediction of spike glycoprotein and ACE2 interaction based on in silico analysis of ACE2 receptor

The ACE2 receptor plays a vital role not only in the SARS-CoV-induced epidemic but also in various other diseases, including cardiovascular diseases and ARDS. While studies have explored the interactions between ACE2 and SARS-CoV proteins, comprehensive research utilizing bioinformatic tools on the ACE2 protein has been lacking. The one aim of present study was to extensively analyze the regions of the ACE2 protein. After utilizing all bioinformatics tools especially G104 and L108 regions on ACE2 were come forward. The results of our analysis revealed that possible mutations or deletions in the G104 and L108 regions play a critical role in both the biological functioning and the determination of the chemical-physical properties of ACE2. Additionally, these regions were found to be more susceptible to mutations or deletions compared to other regions of the ACE2 protein. Notably, the randomly selected peptide, LQQNGSSVLS (100-109), which includes G104 and L108, exhibited a crucial role in binding the RBD of the spike protein, as supported by docking scores. Furthermore, both MDs and iMODs results provided evidence that G104 and L108 influence the dynamics of ACE2-spike complexes. This study is expected to offer a new perspective on the ACE2-SARS-CoV interaction and other research areas where ACE2 plays a significant role, such as biotechnology (protein engineering, enzyme optimization), medicine (RAS, pulmonary and cardiac diseases), and basic research (structural motifs, stabilizing protein folds, or facilitating important inter molecular contacts, protein's proper structure and function).Communicated by Ramaswamy H. Sarma.

Biochemical and biophysical interaction of rare earth elements with biomacromolecules: A comprehensive review

The growing utilization of rare earth elements (REEs) in industrial and technological applications has captured global interest, leading to the development of high-performance technologies in medical diagnosis, agriculture, and other electronic industries. This accelerated utilization has also raised human exposure levels, resulting in both favourable and unfavourable impacts. However, the effects of REEs are dependent on their concentration and molecular species. Therefore, scientific interest has increased in investigating the molecular interactions of REEs with biomolecules. In this current review, particular attention was paid to the molecular mechanism of interactions of Lanthanum (La), Cerium (Ce), and Gadolinium (Gd) with biomolecules, and the biological consequences were broadly interpreted. The review involved gathering and evaluating a vast scientific collection which primarily focused on the impact associated with REEs, ranging from earlier reports to recent discoveries, including studies in human and animal models. Thus, understanding the molecular interactions of each element with biomolecules will be highly beneficial in elucidating the consequences of REEs accumulation in the living organisms.

Leveraging shape screening and molecular dynamics simulations to optimize PARP1-Specific chemo/radio-potentiators for antitumor drug design

PARP1 plays a pivotal role in DNA repair within the base excision pathway, making it a promising therapeutic target for cancers involving BRCA mutations. Current study is focused on the discovery of PARP inhibitors with enhanced selectivity for PARP1. Concurrent inhibition of PARP1 with PARP2 and PARP3 affects cellular functions, potentially causing DNA damage accumulation and disrupting immune responses. In step 1, a virtual library of 593 million compounds has been screened using a shape-based screening approach to narrow down the promising scaffolds. In step 2, hierarchical docking approach embedded in Schrödinger suite was employed to select compounds with good dock score, drug-likeness and MMGBSA score. Analysis supplemented with decomposition energy, molecular dynamics (MD) simulations and hydrogen bond frequency analysis, pinpointed that active site residues; H862, G863, R878, M890, Y896 and F897 are crucial for specific binding of ZINC001258189808 and ZINC000092332196 with PARP1 as compared to PARP2 and PARP3. The binding of ZINC000656130962, ZINC000762230673, ZINC001332491123, and ZINC000579446675 also revealed interaction involving two additional active site residues of PARP1, namely N767 and E988. Weaker or no interaction was observed for these residues with PARP2 and PARP3. This approach advances our understanding of PARP-1 specific inhibitors and their mechanisms of action, facilitating the development of targeted therapeutics.

Safranal alleviates pentetrazole-induced epileptic seizures in mice by inhibiting the NF-κB signaling pathway and mitochondrial-dependent apoptosis through GSK-3β inactivation

ETHNOPHARMACOLOGICAL RELEVANCE

Saffron, a traditional Chinese medicine, is derived from Crocus sativus L. stigmas and has been reported to possess neuroprotective properties and potentially contribute to the inhibition of apoptosis and inflammation. Safranal, a potent monothyral aldehyde, is a main component of saffron that has been reported to have antiepileptic activity. However, the specific mechanism by which safranal suppresses epileptic seizures via its antiapoptotic and anti-inflammatory properties is unclear.

AIM

To evaluate the effect of safranal on seizure severity, inflammation, and postictal neuronal apoptosis in a mouse model of pentetrazole (PTZ)-induced seizures and explore the underlying mechanism involved.

MATERIALS AND METHODS

The seizure stage and latency of stage 2 and 4 were quantified to assess the efficacy of safranal in mitigating PTZ-induced epileptic seizures in mice. Electroencephalography (EEG) was employed to monitor epileptiform afterdischarges in each experimental group. The cognitive abilities and motor functions of the mice were evaluated using the novel object recognition test and the open field test, respectively. Neurons were quantified using hematoxylin and eosin staining. Additionally, bioinformatics tools were utilized to predict the interactions between safranal and specific target proteins. Glycogen synthase kinase-3β (GSK-3β), mitochondrial apoptosis-related proteins, and inflammatory factor levels were analyzed through western blotting. Tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) concentrations in brain tissue were assessed by ELISA.

RESULTS

Safranal decreased the average seizure stage and increased the lantency of stage 2 and 4 seizures in PTZ-induced epileptic mice. Additionally, safranal exhibited neuroprotective effects on hippocampal CA1 and CA3 neurons and reduced hyperactivity caused by postictal hyperexcitability. Bioinformatics analysis revealed that safranal can bind to five specific proteins, including GSK-3β. By promoting Ser9 phosphorylation and inhibiting GSK-3β activity, safranal effectively suppressed the NF-κB signaling pathway. Moreover, the findings indicate that safranal treatment can decrease TNF-α and IL-1β levels in the cerebral tissues of epileptic mice and downregulate mitochondrial apoptosis-related proteins, including Bcl-2, Bax, Bak, Caspase 9, and Caspase 3.

CONCLUSION

Safranal can suppress the NF-κB signaling pathway and mitochondrial-dependent apoptosis through GSK-3β inactivation, suggesting that it is a promising therapeutic agent for epilepsy treatment.

DiffBindFR: an SE(3) equivariant network for flexible protein-ligand docking

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate. Typical deep learning-based docking methods do not explicitly consider protein side chain conformations and fail to ensure the physical plausibility and detailed atomic interactions. In this study, we present DiffBindFR, a full-atom diffusion-based flexible docking model that operates over the product space of ligand overall movements and flexibility and pocket side chain torsion changes. We show that DiffBindFR has higher accuracy in producing native-like binding structures with physically plausible and detailed interactions than available docking methods. Furthermore, in the Apo and AlphaFold2 modeled structures, DiffBindFR demonstrates superior advantages in accurate ligand binding pose and protein binding conformation prediction, making it suitable for Apo and AlphaFold2 structure-based drug design. DiffBindFR provides a powerful flexible docking tool for modeling accurate protein-ligand binding structures.

Molecular Docking Approach for Biological Interaction of Green Synthesized Nanoparticles

In recent years, significant progress has been made in the subject of nanotechnology, with a range of methods developed to synthesize precise-sized and shaped nanoparticles according to particular requirements. Often, the nanoparticles are created by employing dangerous reducing chemicals to reduce metal ions into uncharged nanoparticles. Green synthesis or biological approaches have been used recently to circumvent this issue because biological techniques are simple, inexpensive, safe, clean, and extremely productive. Nowadays, much research is being conducted on how different kinds of nanoparticles connect to proteins and nucleic acids using molecular docking models. Therefore, this review discusses the most recent advancements in molecular docking capacity to predict the interactions between various nanoparticles (NPs), such as ZnO, CuO, Ag, Au, and Fe3O4, and biological macromolecules.

Bacterial Glycolipid Acting on Protein Transport Across Membranes

The process of protein transport across membranes involves a variety of factors and has been extensively investigated. Traditionally, proteinaceous translocons and chaperones have been recognized as crucial factors in this process. However, recent studies have highlighted the significant roles played by lipids and a glycolipid present in biological membranes in membrane protein transport. Membrane lipids can influence transport efficiency by altering the physicochemical properties of membranes. Notably, our studies have revealed that diacylglycerol (DAG) attenuates mobility in the membrane core region, leading to a dramatic suppression of membrane protein integration. Conversely, a glycolipid in Escherichia coli inner membranes, named membrane protein integrase (MPIase), enhances integration not only through the alteration of membrane properties but also via direct interactions with membrane proteins. This review explores the mechanisms of membrane protein integration mediated by membrane lipids, specifically DAG, and MPIase. Our results, along with the employed physicochemical analysis methods such as fluorescence measurements, nuclear magnetic resonance, surface plasmon resonance, and docking simulation, are presented to elucidate these mechanisms.

Phytoconstituents of Artemisia Annua as potential inhibitors of SARS CoV2 main protease: an in silico study

BACKGROUND

In November 2019, the world faced a pandemic called SARS-CoV-2, which became a major threat to humans and continues to be. To overcome this, many plants were explored to find a cure.

METHODS

Therefore, this research was planned to screen out the active constituents from Artemisia annua that can work against the viral main protease Mpro as this non-structural protein is responsible for the cleavage of replicating enzymes of the virus. Twenty-five biocompounds belonging to different classes namely alpha-pinene, beta-pinene, carvone, myrtenol, quinic acid, caffeic acid, quercetin, rutin, apigenin, chrysoplenetin, arteannunin b, artemisinin, scopoletin, scoparone, artemisinic acid, deoxyartemisnin, artemetin, casticin, sitogluside, beta-sitosterol, dihydroartemisinin, scopolin, artemether, artemotil, artesunate were selected. Virtual screening of these ligands was carried out against drug target Mpro by CB dock.

RESULTS

Quercetin, rutin, casticin, chrysoplenetin, apigenin, artemetin, artesunate, sopolin and sito-gluside were found as hit compounds. Further, ADMET screening was conducted which represented Chrysoplenetin as a lead compound. Azithromycin was used as a standard drug. The interactions were studied by PyMol and visualized in LigPlot. Furthermore, the RMSD graph shows fluctuations at various points at the start of simulation in Top1 (Azithromycin) complex system due to structural changes in the helix-coil-helix and beta-turn-beta changes at specific points resulting in increased RMSD with a time frame of 50 ns. But this change remains stable after the extension of simulation time intervals till 100 ns. On other side, the Top2 complex system remains highly stable throughout the time scale. No such structural dynamics were observed bu the ligand attached to the active site residues binds strongly.

CONCLUSION

This study facilitates researchers to develop and discover more effective and specific therapeutic agents against SARS-CoV-2 and other viral infections. Finally, chrysoplenetin was identified as a more potent drug candidate to act against the viral main protease, which in the future can be helpful.

New oxomethacrylate and acetamide: synthesis, characterization, and their computational approaches: molecular docking, molecular dynamics, and ADME analyses

The compounds 2-chloro-N-(3-methoxyphenyl)acetamide (m-acetamide) and 2-(3-methoxyphenylamino)-2-oxoethyl methacrylate (3MPAEMA) were synthesized in this study for the first time in the literature. FTIR, 1H, and 13C NMR spectroscopic techniques were used to characterize it. Subsequently, computational techniques were used to assess various ADME factors, such as drug-likeness properties, bioavailability score, and adherence to Lipinski's rule. Finally, molecular docking experiments were conducted with the human topoisomerase α2 (TOP2A) protein to verify and validate the reliability and stability of the docking procedure. The results of the docking scores, which quantify binding affinity, indicated that these derivatives exhibited a stronger affinity for TOP2A.

Discovery of Novel Aldose Reductase Inhibitors via the Integration of Ligand-Based and Structure-Based Virtual Screening with Experimental Validation

Aldose reductase plays a central role in diabetes mellitus (DM) associated complications by converting glucose to sorbitol, resulting in a harmful increase of reactive oxygen species (ROS) in various tissues, such as the heart, vasculature, neurons, eyes, and kidneys. We employed a comprehensive approach, integrating both ligand- and structure-based virtual screening followed by experimental validation. Initially, candidate compounds were extracted from extensive drug and chemical libraries using the DeepChem's GraphConvMol algorithm, leveraging its capacity for robust molecular feature representation. Subsequent refinement employed molecular docking and molecular dynamics (MD) simulations, which are crucial for understanding compound-receptor interactions and dynamic behavior in a simulated physiological environment. Finally, the candidate compounds were subjected to experimental validation of their biological activity using an aldose reductase inhibitor screening kit. The comprehensive approach led to the identification of a promising compound, demonstrating significant potential as an aldose reductase inhibitor. This comprehensive approach not only yields a potential therapeutic intervention for DM-related complications but also establishes an integrated protocol for drug development, setting a new benchmark in the field.

An overview of aptamer: Design strategy, prominent applications, and potential challenge in plants

Aptamers, serving as highly efficient molecular recognition and biotechnology tools, have garnered increasing interest in the realm of plant science in recent years. Aptamers are synthetic single-stranded short nucleotides or peptides, that bind targets with high specificity and affinity, triggering precise biological responses. As an alternative to antibodies, aptamers present promising avenues for advancement in biological researches. Aptamers function in a range of fields, encompassing cell signaling, drug development, biosensor technology, as well as botany, agricultural and forestry sciences. In this review, we introduce classifications and screening methods of aptamers, as well as aptamer-based technologies, highlighting their significant contributions to recent advancements. With their powerful functionality and ability to bind targets with high specificity and affinity, aptamers offer promising opportunities for breakthroughs in plant research.

Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches

Biomedical research has long relied on animal models to unravel the intricacies of human physiology and pathology. However, concerns surrounding ethics, expenses, and inherent species differences have catalyzed the exploration of alternative avenues. The contemporary alternatives to traditional animal models in biomedical research delve into three main categories of alternative approaches: in vitro models, in vertebrate models, and in silico models. This unique approach to artificial intelligence and machine learning has been a keen interest to be used in different biomedical research. The main goal of this review is to serve as a guide to researchers seeking novel avenues for their investigations and underscores the importance of considering alternative models in the pursuit of scientific knowledge and medical breakthroughs, including showcasing the broad spectrum of modern approaches that are revolutionizing biomedical research and leading the way toward a more ethical, efficient, and innovative future. Models can insight into cellular processes, developmental biology, drug interaction, assessing toxicology, and understanding molecular mechanisms.

Physics-Informed Generative Model for Drug-like Molecule Conformers

We present a diffusion-based generative model for conformer generation. Our model is focused on the reproduction of the bonded structure and is constructed from the associated terms traditionally found in classical force fields to ensure a physically relevant representation. Techniques in deep learning are used to infer atom typing and geometric parameters from a training set. Conformer sampling is achieved by taking advantage of recent advancements in diffusion-based generation. By training on large, synthetic data sets of diverse, drug-like molecules optimized with the semiempirical GFN2-xTB method, high accuracy is achieved for bonded parameters, exceeding that of conventional, knowledge-based methods. Results are also compared to experimental structures from the Protein Databank and the Cambridge Structural Database.

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

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

Homology modeling and molecular docking studies to decrease glutamine affinity of Yarrowia lipolytica L-asparaginase

L-Asparaginase is a key component in the treatment of leukemias and lymphomas. However, the glutamine affinity of this therapeutic enzyme is an off-target activity that causes several side effects. The modeling and molecular docking study of Yarrowia lipolytica L-asparaginase (YL-ASNase) to reduce its l-glutamine affinity and increase its stability was the aim of this study. Protein-ligand interactions of wild-type and different mutants of YL-ASNase against L-asparagine compared to l-glutamine were assessed using AutoDock Vina tools because the crystal structure of YL-ASNase does not exist in the protein data banks. The results showed that three mutants, T171S, T171S-N60A, and T171A-T223A, caused a considerable increase in L-asparagine affinity and a decrease in l-glutamine affinity as compared to the wild-type and other mutants. Then, molecular dynamics simulation and MM/GBSA free energy were applied to assess the stability of protein structure and its interaction with ligands. The three mutated proteins, especially T171S-N60A, had higher stability and interactions with L-asparagine than l-glutamine in comparison with the wild-type. The YL-ASNase mutants could be introduced as appropriate therapeutic candidates that might cause lower side effects. However, the functional properties of these mutated enzymes need to be confirmed by genetic manipulation and in vitro and in vivo studies.

Electronic tongue, proton-transfer-reaction mass spectrometry, spectral analysis, and molecular docking characterization for determining the effect of α-amylase on flavor perception

The effects of α-amylase on of flavor perception were investigated via spectrum analysis, electronic tongue, on-line mass spectrometry, and molecular docking. Aroma release results showed that α-amylase exhibited variable release patterns of different aroma compounds. Electronic tongue analysis showed that the perception of bitterness, sweetness, sour, and saltiness was subtly increased and that of umami was significantly increased (p < 0.01) along with the increasing enzyme activity of α-amylase. Ultraviolet absorption and fluorescence spectroscopy analyses showed that static quenching occurred between α-amylase and eight flavor compounds and their interaction effects were spontaneous. One binding pocket was confirmed between the α-amylase and flavor compounds, and molecular docking simulation results showed that the hydrogen, electrostatic, and hydrophobic bonds were the main force interactions. The TYP82, TRP83, LEU173, HIS80, HIS122, ASP297, ASP206, and ARG344 were the key α-amylase amino acid residues that interacted with the eight flavor compounds.

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.

Deep learning in structural bioinformatics: current applications and future perspectives

In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL-from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.

Identifying Biomarkers and Therapeutic Targets by Multiomic Analysis for HNSCC: Precision Medicine and Healthcare Management

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the major types of cancer, with 900,000 cases and over 400,000 deaths annually. It constitutes 3-4% of all cancers in Europe and western countries. As early diagnosis is the key to treating the disease, reliable biomarkers play an important role in the precision medicine of HNSCC. Despite treatments, the survival rate of cancer patients remains unchanged, and this is mainly due to the failure to detect the disease early. Thus, the objective of this study is to identify reliable biomarkers for head and neck cancers for better healthcare management. Methods: In this study, all available, curated human genes were screened for their expression against HNSCC TCGA patient samples using genomic and proteomic data by various bioinformatic approaches and datamining. Docking studies were performed using AutoDock or online virtual screening tools for identifying potential ligands. Results: Sixty genes were short-listed, and most of them show a consistently higher expression in head and neck patient samples at both the mRNA and the protein level. Irrespective of human papillomavirus (HPV) status, all of them show a higher expression in cancer samples. The higher expression of 30 genes shows adverse effects on patient survival. Out of the 60 genes, 12 genes have crystal structures and druggable potential. We show that genes such as GTF2H4, HAUS7, MSN, and MNDA could be targets of Pembrolizumab and Nivolumab, which are approved monoclonal antibodies for HNSCC. Conclusion: Sixty genes are identified as potential biomarkers for head and neck cancers based on their consistent and statistically significantly higher expression in patient samples. Four proteins have been identified as potential drug targets based on their crystal structure. However, the utility of these candidate genes has to be further tested using patient samples.

Tire additives: Evaluation of joint toxicity, design of new derivatives and mechanism analysis of free radical oxidation

N-(1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) is one of the most widely used antioxidant agents in tire additives. Its ozonation by-product 6PPD-quinone has recently been recognized as inducing acute mortality in aquatic organisms such as coho salmon. In this study, we aimed to develop an in-silico method to design environmentally friendly 6PPD derivatives and evaluate the joint toxicity of 6PPD with other commonly used tire additives on coho salmon through full factorial design-molecular docking and molecular dynamic simulation. The toxicity mentioned in this study is represented by the binding energy of chemical(s) binding to the coho salmon growth hormone. The recommended formula for tire additives with relatively low toxicity was then proposed. To further reduce the toxicity of 6PPD, 129 6PPD derivatives were designed based on the N-H bond dissociation reaction, and three of these derivatives showed improved antioxidant activity and 6PPD-106 was finally screened as the optimum alternative with lower toxicity to coho salmon. Besides, the mechanism of free radical oxidation (i.e., antioxidation and ozonation metabolic pathway) for 6PPD-106 was also analyzed and found that after ozonation, the toxicity of 6PPD-106's by-products is much lower than that of 6PPD's by-products. This study provided a molecular modelling-based examination of 6PPD, which comprehensively advanced the understanding of 6PPD's environmental behaviors and provided more environmentally friendly 6PPD alternatives with desired functional property and lower ecological risks.

The potential of phytochemicals against epidermal growth factor receptor tyrosine kinase (EGFRK): an insight from molecular dynamic simulations

Cancer is an umbrella term used to define various diseases with abnormal cell proliferation at the focal point. According to the WHO, cancer is the leading cause of death worldwide, with lung cancer being the second most common perpetrator after breast cancer. There are several proteins acting in harmony that lead to cancer. EGFR has been identified as one of the proteins that is linked to cell division, even when it is cancerous in nature. Cancer can be treated using therapeutic agents that target EGFR or their signaling networks. Available drugs that could inhibit EGFR have acquired resistance in most cases and multiple side effects on the human body. That is why phytochemicals are being studied for their role in this case. Around 8000 compounds were retrieved from our previously created phytochemdb database for their drug activity, and the 3D protein structure was collected from the protein data bank. The selected dataset of ligands was virtually screened through HTVS, SP, and XP to retain the top 4 hits. Molecular dynamics revealed the stability and flexibility of protein-(selected)ligand interactions. The non-bond interactions of each of the compounds with EGFR, such as Gossypetin interacting with active site MET769 and ASP831; Muxiangrine III interacting with MET769 and ASP831; Quercetagetin showing non-bonded interactions with GLU738, GLN767, and MET769 for >100% of the simulation timeframe These findings suggest further research into these compounds, which can yield a potential phytochemical drug against cancer.Communicated by Ramaswamy H. Sarma.

Exploring the mechanism of interaction between TBG and halogenated thiophenols: Insights from fluorescence analysis and molecular simulation

Thyroxine-binding globulin (TBG) plays a vital role in regulating metabolism, growth, organ differentiation, and energy homeostasis, exerting significant effects in various key metabolic pathways. Halogenated thiophenols (HTPs) exhibit high toxicity and harmfulness to organisms, and numerous studies have demonstrated their thyroid-disrupting effects. To understand the mechanism of action of HTPs on TBG, a combination of competitive binding experiments, multiple fluorescence spectroscopy techniques, molecular docking, and molecular simulations was employed to investigate the binding mechanism and identify the binding site. The competition binding assay between HTPs and ANS confirmed the competition of HTPs with thyroid hormone T4 for the active site of TBG, resulting in changes in the TBG microenvironment upon the binding of HTPs to the active site. Key amino acid residues involved in the binding process of HTPs and TBG were further investigated through residue energy decomposition. The distribution of high-energy contributing residues was determined. Analysis of root-mean-square deviation (RMSD) demonstrated the stability of the HTPs-TBG complex. These findings confirm the toxic mechanism of HTPs in thyroid disruption, providing a fundamental reference for accurately assessing the ecological risk of pollutants and human health. Providing mechanistic insights into how HTPS causes thyroid diseases.

Dissecting the Interaction Fingerprints and Binding Affinity of BYL719 Analogs Targeting PI3Kα

Phosphatidylinositol-3-kinase Alpha (PI3Kα) is a lipid kinase which regulates signaling pathways involved in cell proliferation. Dysregulation of these pathways promotes several human cancers, pushing for the development of anticancer drugs to target PI3Kα. One such medicinal chemistry campaign at Novartis led to the discovery of BYL719 (Piqray, Alpelicib), a PI3Kα inhibitor approved by the FDA in 2019 for treatment of HR+/HER2-advanced breast cancer with a PIK3CA mutation. Structure-based drug design played a key role in compound design and optimization throughout the discovery process. However, further characterization of potency drivers via structural dynamics and energetic analyses can be advantageous for ensuing PI3Kα programs. Here, our goal is to employ various in-silico techniques, including molecular simulations and machine learning, to characterize 14 ligands from the BYL719 analogs and predict their binding affinities. The structural insights from molecular simulations suggest that although the ligand-hinge interaction is the primary driver of ligand stability at the pocket, the R group positioning at C2 or C6 of pyridine/pyrimidine also plays a major role. Binding affinities predicted via thermodynamic integration (TI) are in good agreement with previously reported IC50s. Yet, computationally demanding techniques such as TI might not always be the most efficient approach for affinity prediction, as in our case study, fast high-throughput techniques were capable of classifying compounds as active or inactive, and one docking approach showed accuracy comparable to TI.

Elucidation of protein-ligand interactions by multiple trajectory analysis methods

The identification of interaction between protein and ligand including binding positions and strength plays a critical role in drug discovery. Molecular docking and molecular dynamics (MD) techniques have been widely applied to predict binding positions and binding affinity. However, there are few works that describe the systematic exploration of the MD trajectory evolution in this context, potentially leaving out important information. To address the problem, we build a framework, Moira (molecular dynamics trajectory analysis), which enables automating the whole process ranging from docking, MD simulations and various analyses as well as visualizations. We utilized Moira to analyze 400 MD simulations in terms of their geometric features (root mean square deviation and protein-ligand interaction profiler) and energetics (molecular mechanics Poisson-Boltzmann surface area) for these trajectories. Finally, we demonstrate the performance of different analysis techniques in distinguishing native poses among four poses.

Addressing Suboptimal Poses in Nonequilibrium Alchemical Calculations

Alchemical transformations can be used to quantitatively estimate absolute binding free energies at a reasonable computational cost. However, most of the approaches currently in use require knowledge of the correct (crystallographic) pose. In this paper, we present a combined Hamiltonian replica exchange nonequilibrium alchemical method that allows us to reliably calculate absolute binding free energies, even when starting from suboptimal initial binding poses. Performing a preliminary Hamiltonian replica exchange enhances the sampling of slow degrees of freedom of the ligand and the target, allowing the system to populate the correct binding pose when starting from an approximate docking pose. We apply the method on 6 ligands of the first bromodomain of the BRD4 bromodomain-containing protein. For each ligand, we start nonequilibrium alchemical transformations from both the crystallographic pose and the top-scoring docked pose that are often significantly different. We show that the method produces statistically equivalent binding free energies, making it a useful tool for computational drug discovery pipelines.

In silico approaches to investigate enzyme immobilization: a comprehensive systematic review

Enzymes are popular catalysts with many applications, especially in industry. Biocatalyst usage on a large scale is facing some limitations, such as low operational stability, low recyclability, and high enzyme cost. Enzyme immobilization is a beneficial strategy to solve these problems. Bioinformatics tools can often correctly predict immobilization outcomes, resulting in a cost-effective experimental phase with the least time consumed. This study provides an overview of in silico methods predicting immobilization processes via a comprehensive systematic review of published articles till 11 December 2022. It also mentions the strengths and weaknesses of the processes and explains the computational analyses in each method that are required for immobilization assessment. In this regard, Web of Science and Scopus databases were screened to gain relevant publications. After screening the gathered documents (n = 3873), 60 articles were selected for the review. The selected papers have applied in silico procedures including only molecular dynamics (MD) simulations (n = 20), parallel tempering Monte Carlo (PTMC) and MD simulations (n = 3), MD and docking (n = 1), density functional theory (DFT) and MD (n = 1), only docking (n = 11), metal ion binding site prediction (MIB) server and docking (n = 2), docking and DFT (n = 1), docking and analysis of enzyme surfaces (n = 1), only DFT (n = 1), only MIB server (n = 2), analysis of an enzyme structure and surface (n = 12), rational design of immobilized derivatives (RDID) software (n = 3), and dissipative particle dynamics (DPD; n = 2). In most included studies (n = 51), enzyme immobilization was investigated experimentally in addition to in silico evaluation.

MucLiPred: Multi-Level Contrastive Learning for Predicting Nucleic Acid Binding Residues of Proteins

Protein-molecule interactions play a crucial role in various biological functions, with their accurate prediction being pivotal for drug discovery and design processes. Traditional methods for predicting protein-molecule interactions are limited. Some can only predict interactions with a specific molecule, restricting their applicability, while others target multiple molecule types but fail to efficiently process diverse interaction information, leading to complexity and inefficiency. This study presents a novel deep learning model, MucLiPred, equipped with a dual contrastive learning mechanism aimed at improving the prediction of multiple molecule-protein interactions and the identification of potential molecule-binding residues. The residue-level paradigm focuses on differentiating binding from non-binding residues, illuminating detailed local interactions. The type-level paradigm, meanwhile, analyzes overarching contexts of molecule types, like DNA or RNA, ensuring that representations of identical molecule types gravitate closer in the representational space, bolstering the model's proficiency in discerning interaction motifs. This dual approach enables comprehensive multi-molecule predictions, elucidating the relationships among different molecule types and strengthening precise protein-molecule interaction predictions. Empirical evidence demonstrates MucLiPred's superiority over existing models in robustness and prediction accuracy. The integration of dual contrastive learning techniques amplifies its capability to detect potential molecule-binding residues with precision. Further optimization, separating representational and classification tasks, has markedly improved its performance. MucLiPred thus represents a significant advancement in protein-molecule interaction prediction, setting a new precedent for future research in this field.

TopoFormer: Multiscale Topology-enabled Structure-to-Sequence Transformer for Protein-Ligand Interaction Predictions

Pre-trained deep Transformers have had tremendous success in a wide variety of disciplines. However, in computational biology, essentially all Transformers are built upon the biological sequences, which ignores vital stereochemical information and may result in crucial errors in downstream predictions. On the other hand, three-dimensional (3D) molecular structures are incompatible with the sequential architecture of Transformer and natural language processing (NLP) models in general. This work addresses this foundational challenge by a topological Transformer (TopoFormer). TopoFormer is built by integrating NLP and a multiscale topology techniques, the persistent topological hyperdigraph Laplacian (PTHL), which systematically converts intricate 3D protein-ligand complexes at various spatial scales into a NLP-admissible sequence of topological invariants and homotopic shapes. Element-specific PTHLs are further developed to embed crucial physical, chemical, and biological interactions into topological sequences. TopoFormer surges ahead of conventional algorithms and recent deep learning variants and gives rise to exemplary scoring accuracy and superior performance in ranking, docking, and screening tasks in a number of benchmark datasets. The proposed topological sequences can be extracted from all kinds of structural data in data science to facilitate various NLP models, heralding a new era in AI-driven discovery.

Herbicide Safeners: From Molecular Structure Design to Safener Activity

Herbicide safeners, highly effective antidotes, find widespread application in fields for alleviating the phytotoxicity of herbicides to crops. Designing new herbicide safeners remains a notable issue in pesticide research. This review focuses on discussing and summarizing the structure-activity relationships, molecular structures, physicochemical properties, and molecular docking of herbicide safeners in order to explore how different structures affect the safener activities of target compounds. It also provides insights into the application prospects of computer-aided drug design for designing and synthesizing new safeners in the future.

Potent inhibitors targeting cyclin-dependent kinase 9 discovered via virtual high-throughput screening and absolute binding free energy calculations

Due to the crucial regulatory mechanism of cyclin-dependent kinase 9 (CDK9) in mRNA transcription, the development of kinase inhibitors targeting CDK9 holds promise as a potential treatment strategy for cancer. A structure-based virtual screening approach has been employed for the discovery of potential novel CDK9 inhibitors. First, compounds with kinase inhibitor characteristics were identified from the ZINC15 database via virtual high-throughput screening. Next, the predicted binding modes were optimized by molecular dynamics simulations, followed by precise estimation of binding affinities using absolute binding free energy calculations based on the free energy perturbation scheme. The binding mode of molecule 006 underwent an inward-to-outward flipping, and the new binding mode exhibited binding affinity comparable to the small molecule T6Q in the crystal structure (PDB ID: 4BCF), highlighting the essential role of molecular dynamics simulation in capturing a plausible binding pose bridging docking and absolute binding free energy calculations. Finally, structural modifications based on these findings further enhanced the binding affinity with CDK9. The results revealed that enhancing the molecule's rigidity through ring formation, while maintaining the major interactions, reduced the entropy loss during the binding process and, thus, enhanced binding affinities.

Identifying OGN as a Biomarker Covering Multiple Pathogenic Pathways for Diagnosing Heart Failure: From Machine Learning to Mechanism Interpretation

BACKGROUND

The pathophysiologic heterogeneity of heart failure (HF) necessitates a more detailed identification of diagnostic biomarkers that can reflect its diverse pathogenic pathways.

METHODS

We conducted weighted gene and multiscale embedded gene co-expression network analysis on differentially expressed genes obtained from HF and non-HF specimens. We employed a machine learning integration framework and protein-protein interaction network to identify diagnostic biomarkers. Additionally, we integrated gene set variation analysis, gene set enrichment analysis (GSEA), and transcription factor (TF)-target analysis to unravel the biomarker-dominant pathways. Leveraging single-sample GSEA and molecular docking, we predicted immune cells and therapeutic drugs related to biomarkers. Quantitative polymerase chain reaction validated the expressions of biomarkers in the plasma of HF patients. A two-sample Mendelian randomization analysis was implemented to investigate the causal impact of biomarkers on HF.

RESULTS

We first identified COL14A1, OGN, MFAP4, and SFRP4 as candidate biomarkers with robust diagnostic performance. We revealed that regulating biomarkers in HF pathogenesis involves TFs (BNC2, MEOX2) and pathways (cell adhesion molecules, chemokine signaling pathway, cytokine-cytokine receptor interaction, oxidative phosphorylation). Moreover, we observed the elevated infiltration of effector memory CD4+ T cells in HF, which was highly related to biomarkers and could impact immune pathways. Captopril, aldosterone antagonist, cyclopenthiazide, estradiol, tolazoline, and genistein were predicted as therapeutic drugs alleviating HF via interactions with biomarkers. In vitro study confirmed the up-regulation of OGN as a plasma biomarker of HF. Mendelian randomization analysis suggested that genetic predisposition toward higher plasma OGN promoted the risk of HF.

CONCLUSIONS

We propose OGN as a diagnostic biomarker for HF, which may advance our understanding of the diagnosis and pathogenesis of HF.

Computational drug development for membrane protein targets

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

Recent advances in chemometric modelling of inhibitors against SARS-CoV-2

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused great harm to all countries worldwide. This disease can be prevented by vaccination and managed using various treatment methods, including injections, oral medications, or aerosol therapies. However, the selection of suitable compounds for the research and development of anti-SARS-CoV-2 drugs is a daunting task because of the vast databases of available compounds. The traditional process of drug research and development is time-consuming, labour-intensive, and costly. The application of chemometrics can significantly expedite drug R&D. This is particularly necessary and important for drug development against pandemic public emergency diseases, such as COVID-19. Through various chemometric techniques, such as quantitative structure-activity relationship (QSAR) modelling, molecular docking, and molecular dynamics (MD) simulations, compounds with inhibitory activity against SARS-CoV-2 can be quickly screened, allowing researchers to focus on the few prioritised candidates. In addition, the ADMET properties of the screened candidate compounds should be further explored to promote the successful discovery of anti-SARS-CoV-2 drugs. In this case, considerable time and economic costs can be saved while minimising the need for extensive animal experiments, in line with the 3R principles. This paper focuses on recent advances in chemometric modelling studies of COVID-19-related inhibitors, highlights current limitations, and outlines potential future directions for development.

Comparison of cytotoxicity of Miltefosine and its niosomal form on chick embryo model

Various drugs have been used for the treatment of leishmaniasis, but they often have adverse effects on the body's organs. In this study, we aimed to explore the effects of one type of drug, Miltefosine (MIL), and its analogue or modifier, liposomal Miltefosine (NMIL), on several fetal organs using both in silico analysis and practical tests on chicken embryos. Our in silico approach involved predicting the affinities of MIL and NMIL to critical proteins involved in leishmaniasis, including Vascular Endothelial Growth Factor A (VEGF-A), the Kinase insert domain receptor (KDR1), and apoptotic-regulator proteins (Bcl-2-associate). We then validated and supported these predictions through in vivo investigations, analyzing gene expression and pathological changes in angiogenesis and apoptotic mediators in MIL- and NMIL-treated chicken embryos. The results showed that NMIL had a more effective action towards VEGF-A and KDR1 in leishmaniasis, making it a better candidate for potential operative treatment during pregnancy than MIL alone. In vivo, studies also showed that chicken embryos under MIL treatment displayed less vascular mass and more degenerative and apoptotic changes than those treated with NMIL. These results suggest that NMIL could be a better treatment option for leishmaniasis during pregnancy.

Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modeling, molecular docking, MM-GBSA calculations and molecular dynamics studies

Pin1 is a pivotal player in interactions with a diverse array of phosphorylated proteins closely linked to critical processes such as carcinogenesis and tumor suppression. Its axial role in cancer initiation and progression, coupled with its overexpression and activation in various cancers render it a potential candidate for the development of targeted therapeutics. While several known Pin1 inhibitors possess favorable enzymatic profiles, their cellular efficacy often falls short. Consequently, the pursuit of novel Pin1 inhibitors has gained considerable attention in the field of medicinal chemistry. In this study, we employed the Phase tool from Schrödinger to construct a structure-based pharmacophore model. Subsequently, 449,008 natural products (NPs) from the SN3 database underwent screening to identify compounds sharing pharmacophoric features with the native ligand. This resulted in 650 compounds, which then underwent molecular docking and binding free energy calculations. Among them, SN0021307, SN0449787 and SN0079231 showed better docking scores with values of -9.891, -7.579 and -7.097 kcal/mol, respectively than the reference compound (-6.064 kcal/mol). Also, SN0021307, SN0449787 and SN0079231 exhibited lower free binding energies (-57.12, -49.81 and -46.05 kcal/mol, respectively) than the reference ligand (-37.75 kcal/mol). Based on these studies, SN0021307, SN0449787, and SN0079231 showed better binding affinity that the reference compound. Further the validation of these findings, molecular dynamics simulations confirmed the stability of the ligand-receptor complex for 100 ns with RMSD ranging from 0.6 to 1.8 Å. Based on these promising results, these three phytochemicals emerge as promising lead compounds warranting comprehensive biological screening in future investigations. These compounds hold great potential for further exploration regarding their efficacy and safety as Pin1 inhibitors, which could usher in new avenues for combating cancer.

GPCR-IPL score: multilevel featurization of GPCR-ligand interaction patterns and prediction of ligand functions from selectivity to biased activation

G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure-activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR-ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR-ligand complexes. As a result, we developed GPCR-ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out 'residue level' (for flexible transmembrane helices of GPCRs), (2) zoomed-in 'pocket level' (for sophisticated mode of action) and (3) 'atom level' (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and β-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.

Investigation of Potential Drug Targets Involved in Inflammation Contributing to Alzheimer's Disease Progression

Alzheimer's disease has become a major public health issue. While extensive research has been conducted in the last few decades, few drugs have been approved by the FDA to treat Alzheimer's disease. There is still an urgent need for understanding the disease pathogenesis, as well as identifying new drug targets for further drug discovery. Alzheimer's disease is known to arise from a build-up of amyloid beta (Aβ) plaques as well as tangles of tau proteins. Along similar lines to Alzheimer's disease, inflammation in the brain is known to stem from the degeneration of tissue and build-up of insoluble materials. A minireview was conducted in this work assessing the genes, proteins, reactions, and pathways that link brain inflammation and Alzheimer's disease. Existing tools in Systems Biology were implemented to build protein interaction networks, mainly for the classical complement pathway and G protein-coupled receptors (GPCRs), to rank the protein targets according to their interactions. The top 10 protein targets were mainly from the classical complement pathway. With the consideration of existing clinical trials and crystal structures, proteins C5AR1 and GARBG1 were identified as the best targets for further drug discovery, through computational approaches like ligand-protein docking techniques.

Exploring the impact of alignment media on RDC analysis of phosphorus-containing compounds: a molecular docking approach

Residual dipolar couplings (RDCs) are employed in NMR analysis when conventional methods, such as J-couplings and nuclear Overhauser effects (NOEs) fail. Low-energy (optimized) conformers are often used as input structures in RDC analysis programs. However, these low-energy structures do not necessarily resemble conformations found in anisotropic environments due to interactions with the alignment medium, especially if the analyte molecules are flexible. Considering interactions with alignment media in RDC analysis, we developed and evaluated a molecular docking-based approach to generate more accurate conformer ensembles for compounds in the presence of the poly-γ-benzyl-L-glutamate alignment medium. We designed chiral phosphorus-containing compounds that enabled us to utilize 31P NMR parameters for the stereochemical analysis. Using P3D/PALES software to evaluate diastereomer discrimination, we found that our conformer ensembles outperform moderately the standard, low-energy conformers in RDC analysis. To further improve our results, we (i) averaged the experimental values of the molecular docking-based conformers by applying the Boltzmann distribution and (ii) optimized the structures through normal mode relaxation, thereby enhancing the Pearson correlation factor R and even diastereomer discrimination in some cases. Nevertheless, we presume that significant differences between J-couplings in isotropic and in anisotropic environments may preclude RDC measurements for flexible molecules. Therefore, generating conformer ensembles based on molecular docking enhances RDC analysis for mildly flexible systems while flexible molecules may require applying more advanced approaches, in particular approaches including dynamical effects.

Phomopsterone B Alleviates Liver Fibrosis through mTOR-Mediated Autophagy and Apoptosis Pathway

Liver fibrosis is the initial pathological process of many chronic liver diseases. Targeting hepatic stellate cell (HSC) activation is an available strategy for the therapy of liver fibrosis. We aimed to explore the anti-liver fibrosis activity and potential mechanism of phomopsterone B (PB) in human HSCs. The results showed that PB effectively attenuated the proliferation of TGF-β1-stimulated LX-2 cells in a concentration-dependent manner at doses of 1, 2, and 4 μM. Quantitative real-time PCR and Western blot assays displayed that PB significantly reduced the expression levels of α-SMA and collagen I/III. AO/EB and Hoechst33342 staining and flow cytometry assays exhibited that PB promoted the cells' apoptosis. Meanwhile, PB diminished the number of autophagic vesicles and vacuolated structures, and the LC3B fluorescent spots indicated that PB could effectively inhibit the accretion of autophagosomes in LX-2 cells. Moreover, rapamycin and MHY1485 were utilized to further investigate the effect of mTOR in autophagy and apoptosis. The results demonstrated that PB regulated autophagy and apoptosis via the mTOR-dependent pathway in LX-2 cells. In summary, this is the first evidence that PB effectively alleviates liver fibrosis in TGF-β1-stimulated LX-2 cells, and PB may be a promising candidate for the prevention of liver fibrosis.

Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

Redocking the PDB

Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

DOCK 6: Incorporating hierarchical traversal through precomputed ligand conformations to enable large-scale docking

To allow DOCK 6 access to unprecedented chemical space for screening billions of small molecules, we have implemented features from DOCK 3.7 into DOCK 6, including a search routine that traverses precomputed ligand conformations stored in a hierarchical database. We tested them on the DUDE-Z and SB2012 test sets. The hierarchical database search routine is 16 times faster than anchor-and-grow. However, the ability of hierarchical database search to reproduce the experimental pose is 16% worse than that of anchor-and-grow. The enrichment performance is on average similar, but DOCK 3.7 has better enrichment than DOCK 6, and DOCK 6 is on average 1.7 times slower. However, with post-docking torsion minimization, DOCK 6 surpasses DOCK 3.7. A large-scale virtual screen is performed with DOCK 6 on 23 million fragment molecules. We use current features in DOCK 6 to complement hierarchical database calculations, including torsion minimization, which is not available in DOCK 3.7.

Structure-based, deep-learning models for protein-ligand binding affinity prediction

The launch of AlphaFold series has brought deep-learning techniques into the molecular structural science. As another crucial problem, structure-based prediction of protein-ligand binding affinity urgently calls for advanced computational techniques. Is deep learning ready to decode this problem? Here we review mainstream structure-based, deep-learning approaches for this problem, focusing on molecular representations, learning architectures and model interpretability. A model taxonomy has been generated. To compensate for the lack of valid comparisons among those models, we realized and evaluated representatives from a uniform basis, with the advantages and shortcomings discussed. This review will potentially benefit structure-based drug discovery and related areas.

Repurposing dye ligands as antivirals via a docking approach on viral membrane and globular proteins - SARS-CoV-2 and HPV-16

A series of dye ligands are docked to three different proteins, E and 3a of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) and E6 of human papilloma virus type 16 (HPV-16) using three different software. A four-level selection algorithm is used based on nonparametric statistics of numerical key values such as the "rank" derived from (i) averaged estimated binding energies (EBEs) and (ii) absolute EBE value of each of the software, (iii) frequency of ranking and (iv) rank of the area-under-curve values (AUCs) from decoy docking. A series of repurposing drugs and known antivirals used in experimental studies are docked for comparison. One dye ligand is ranked best for all proteins using the selection algorithm levels i - iii. Another three dye ligands are ranked top for the proteins individually when using all four levels.