Software for molecular docking: a review

Virtual Screening and Validation of Affinity DNA Functional Ligands for IgG Fc Segment

The effective attachment of antibodies to the immune sensing interface is a crucial factor that determines the detection performance of immunosensors. Therefore, this study aims to investigate a novel antibody immobilization material with low molecular weight, high stability, and excellent directional immobilization effect. In this study, we employed molecular docking technology based on the ZDOCK algorithm to virtually screen DNA functional ligands (DNAFL) for the Fc segment of antibodies. Through a comprehensive analysis of the key binding sites and contact propensities at the interface between DNAFL and IgG antibody, we have gained valuable insights into the affinity relationship, as well as the principles governing amino acid and nucleotide interactions at this interface. Furthermore, molecular affinity experiments and competitive binding experiments were conducted to validate both the binding ability of DNAFL to IgG antibody and its actual binding site. Through affinity experiments using multi-base sequences, we identified bases that significantly influence antibody-DNAFL binding and successfully obtained DNAFL with an enhanced affinity towards the IgG Fc segment. These findings provide a theoretical foundation for the targeted design of higher-affinity DNAFLs while also presenting a new technical approach for immunosensor preparation with potential applications in biodetection.

Molecular interaction analysis of ferulic acid (4-hydroxy-3-methoxycinnamic acid) as main bioactive compound from palm oil waste against MCF-7 receptors: An in silico study

Ferulic acid (4-hydroxy-3-methoxycinnamic acid) is a phytochemical compound that is commonly found in conjugated forms within mono-, di-, polysaccharides and other organic compounds in cell walls of grain, fruits, and vegetables. This compound is highly abundant in the palm oil waste. The aim of the study was to predict the anticancer activity of ferulic acid against the breast cancer cell lines (MCF-7) receptors through a computational analysis. MCF-7 receptors with PDB IDs of 1R5K, 2IOG, 4IV2, 4IW6, 5DUE, 5T92, and 5U2B were selected based on the Simplified Molecular Input Line Entry System (SMILES) similarity of the native ligand. Thereafter, the protein was prepared on Chimera 1.16 and docked with ferulic acid on Autodock Vina 1.2.5. The ligand-protein complex interaction was validated by computing the root mean square fluctuation (RMSF) and radius of gyration (Rg) through molecular dynamic simulation. In addition, an absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction was performed on ferulic acid using the pkCSM platform. The molecular docking revealed that the ferulic acid could interact with all receptors as indicated by the affinity energy <-5 kcal/mol. The compound had the most optimum interaction with receptor 2IOG (affinity energy=-6.96 kcal/mol), involving hydrophobic interaction (n=12) and polar hydrogen interaction (n=4). The molecular dynamic simulation revealed that the complex had an RMSF of 1.713 Å with a fluctuation of Rg value around 1.000 Å. The ADMET properties of ferulic acid suggested that the compound is an ideal drug candidate. In conclusion, this study suggested that ferulic acid, which can be isolated from palm oil waste, has the potential to interact with MCF-7 receptors.

Exploring the Potential of Oleanolic Acid Dimers-Cytostatic and Antioxidant Activities, Molecular Docking, and ADMETox Profile

The presented work aimed to explore the potential of oleanolic acid dimers (OADs): their cytostatic and antioxidant activities, molecular docking, pharmacokinetics, and ADMETox profile. The cytostatic properties of oleanolic acid (1) and its 14 synthesised dimers (2a-2n) were evaluated against 10 tumour types and expressed as IC50 values. Molecular docking was performed with the CB-Dock2 server. Antioxidant properties were evaluated with the CUPRAC method. ADMETox properties were evaluated with the ADMETlab Manual (2.0) database. The results indicate that the obtained OADs can be effective cytostatic agents, for which the IC50 not exceeded 10.00 for many tested cancer cell lines. All OADs were much more active against all cell lines than the mother compound (1). All dimers can inhibit the interaction between the 1MP8 protein and cellular proteins with the best results for compounds 2f and 2g with unsaturated bonds within the linker. An additional advantage of the tested OADs was a high level of antioxidant activity, with Trolox equivalent for OADs 2c, 2d, 2g-2j, 2l, and 2m of approximately 0.04 mg/mL, and beneficial pharmacokinetics and ADMETox properties. The differences in the DPPH and CUPRAC assay results obtained for OADs may indicate that these compounds may be effective antioxidants against different radicals.

Quantum Molecular Docking with a Quantum-Inspired Algorithm

Molecular docking (MD) is a crucial task in drug design, which predicts the position, orientation, and conformation of the ligand when it is bound to a target protein. It can be interpreted as a combinatorial optimization problem, where quantum annealing (QA) has shown a promising advantage for solving combinatorial optimization. In this work, we propose a novel quantum molecular docking (QMD) approach based on a QA-inspired algorithm. We construct two binary encoding methods to efficiently discretize the degrees of freedom with an exponentially reduced number of bits and propose a smoothing filter to rescale the rugged objective function. We propose a new quantum-inspired algorithm, hopscotch simulated bifurcation (hSB), showing great advantages in optimizing over extremely rugged energy landscapes. This hSB can be applied to any formulation of an objective function under binary variables. An adaptive local continuous search is also introduced for further optimization of the discretized solution from hSB. Concerning the stability of docking, we propose a perturbation detection method to help rank the candidate poses. We demonstrate our approach on a typical data set. QMD has shown advantages over the search-based Autodock Vina and the deep-learning DIFFDOCK in both redocking and self-docking scenarios. These results indicate that quantum-inspired algorithms can be applied to solve practical problems in drug discovery even before quantum hardware become mature.

Advances in Parkinson's disease research - A computational network pharmacological approach

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, is projected to see a significant rise in incidence over the next three decades. The precise treatment of PD remains a formidable challenge, prompting ongoing research into early diagnostic methodologies. Network pharmacology, a burgeoning field grounded in systems biology, examines the intricate networks of biological systems to identify critical signal nodes, facilitating the development of multi-target therapeutic molecules. This approach systematically maps the components of Parkinson's disease, thereby reducing its complexity. In this review, we explore the application of network pharmacology workflows in PD, discuss the techniques employed in this field, and evaluate the current advancements and status of network pharmacology in the context of Parkinson's disease. The comprehensive insights will pave newer paths to explore early disease biomarkers and to develop diagnosis with a holistic in silico, in vitro, in vivo and clinical studies.

Hybrid Virtual Screening Approach to Predict Novel Natural Compounds against HIV-1 CCR5

BACKGROUND

Human immunodeficiency virus (HIV) infection continues to pose a major global health challenge. HIV entry into host cells via membrane fusion mediated by the viral envelope glycoprotein gp120/gp41 is a key step in the HIV life cycle. CCR5, expressed on CD4+ T cells and macrophages, acts as a coreceptor facilitating HIV-1 entry. The CCR5 antagonist maraviroc is used to treat HIV infection. However, it can cause adverse effects and has limitations such as only inhibiting CCR5-tropic viruses. There remains a need to develop alternative CCR5 inhibitors with improved safety profiles.

PROBLEM STATEMENT

Natural products may offer advantages over synthetic inhibitors including higher bioavailability, binding affinity, effectiveness, lower toxicity, and molecular diversity. However, screening the vast chemical space of natural compounds to identify novel CCR5 inhibitors presents challenges. This study aimed to address this gap through a hybrid ligand-based pharmacophore modeling and molecular docking approach to virtually screen large natural product databases.

METHODS

A reliable pharmacophore model was developed based on 311 known CCR5 antagonists and validated against an external data set. Five natural product databases containing over 306,000 compounds were filtered based on drug-likeness rules. The validated pharmacophore model screened the databases to identify 611 hits. Key residues of the CCR5 receptor crystal structure were identified for docking. The top hits were docked, and interactions were analyzed. Molecular dynamics simulations were conducted to examine complex stability. Computational prediction evaluated pharmacokinetic properties.

RESULTS

Three compounds exhibited similar interactions and binding energies to maraviroc. MD simulations demonstrated complex stability comparable to maraviroc. One compound showed optimal predicted absorption, minimal metabolism, and a lower likelihood of interactions than maraviroc.

CONCLUSION

This computational screening workflow identified three natural compounds with promising CCR5 inhibition and favorable pharmacokinetic profiles. One compound emerged as a lead based on bioavailability potential and minimal interaction risk. These findings present opportunities for developing alternative CCR5 antagonists and warrant further experimental investigation. Overall, the hybrid virtual screening approach proved effective for mining large natural product spaces to discover novel molecular entities with drug-like properties.

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.

Manganese(II) and Zinc(II) metal complexes of novel bidentate formamide-based Schiff base ligand: synthesis, structural characterization, antioxidant, antibacterial, and in-silico molecular docking study

A new bidentate Schiff base ligand (C16H16Cl2N4), condensation product of ethylene diamine and 4-chloro N-phenyl formamide, and its metal complexes [M(C16H16Cl2N4)2(OAc)2] (where M = Mn(II) and Zn(II)) were synthesized and characterized using various analytical and spectral techniques, including high-resolution mass spectrometry (HRMS), elemental analysis, ultraviolet-visible (UV-vis), Fourier-transform infrared (FTIR) spectroscopy, AAS, molar conductance, 1H NMR, and powder XRD. All the compounds were non-electrolytes and nanocrystalline. The synthesized compounds were assessed for antioxidant potential by DPPH radical scavenging and FRAP assay, with BHT serving as the positive control. Inhibitory concentration at 50% inhibition (IC50) values were calculated and used for comparative analysis. Furthermore, the prepared compounds were screened for antibacterial activity against two Gram-negative bacteria (Staphylococcus aureus and Bacillus subtilis) and two Gram-positive bacteria (Escherichia coli and Salmonella typhi) using disk-diffusion methods, with amikacin employed as the standard reference. The comparison of inhibition zones revealed that the complexes showed better antibacterial activity than the ligand. To gain insights into the molecular interactions underlying the antibacterial activity, the ligand and complexes were analyzed for their binding affinity with S. aureus tyrosyl-tRNA synthetase (PDB ID: 1JIL) and S. typhi cell membrane protein OmpF complex (PDB ID: 4KR4). These analyses revealed robust interactions, validating the observed antibacterial effects against the tested bacterial strains.

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.

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.

Efficient molecular conformation generation with quantum-inspired algorithm

CONTEXT

Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5Å), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware becomes mature.

METHODS

The objective function of MU is defined as the sum of all internal distances between atoms in the molecule, which is a high-order unconstrained binary optimization (HUBO) problem. The degree of freedom of variables is discretized and encoded with binary variables by the phase encoding method. We employ the quantum-inspired simulated bifurcation algorithm for optimization. The public QM9 dataset is generated by DFT. The simulation experiment of quantum computation is implemented by MindQuantum using QAOA.

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.

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.

Drug repurposing for respiratory infections

Respiratory infections such as Coronavirus disease 2019 are a substantial worldwide health challenge, frequently resulting in severe sickness and death, especially in susceptible groups. Conventional drug development for respiratory infections faces obstacles such as extended timescales, substantial expenses, and the rise of resistance to current treatments. Drug repurposing is a potential method that has evolved to quickly find and reuse existing medications for treating respiratory infections. Drug repurposing utilizes medications previously approved for different purposes, providing a cost-effective and time-efficient method to tackle pressing medical needs. This chapter summarizes current progress and obstacles in repurposing medications for respiratory infections, focusing on notable examples of repurposed pharmaceuticals and their probable modes of action. The text also explores the significance of computational approaches, high-throughput screening, and preclinical investigations in identifying potential candidates for repurposing. The text delves into the significance of regulatory factors, clinical trial structure, and actual data in confirming the effectiveness and safety of repurposed medications for respiratory infections. Drug repurposing is a valuable technique for quickly increasing the range of treatments for respiratory infections, leading to better patient outcomes and decreasing the worldwide disease burden.

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.

Bioinformatics Approaches in the Development of Antifungal Therapeutics and Vaccines

Fungal infections are considered a great threat to human life and are associated with high mortality and morbidity, especially in immunocompromised individuals. Fungal pathogens employ various defense mechanisms to evade the host immune system, which causes severe infections. The available repertoire of drugs for the treatment of fungal infections includes azoles, allylamines, polyenes, echinocandins, and antimetabolites. However, the development of multidrug and pandrug resistance to available antimycotic drugs increases the need to develop better treatment approaches. In this new era of -omics, bioinformatics has expanded options for treating fungal infections. This review emphasizes how bioinformatics complements the emerging strategies, including advancements in drug delivery systems, combination therapies, drug repurposing, epitope-based vaccine design, RNA-based therapeutics, and the role of gut-microbiome interactions to combat anti-fungal resistance. In particular, we focused on computational methods that can be useful to obtain potent hits, and that too in a short period.

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.

Exploring the potential of drug repurposing for treating depression

Researchers are interested in drug repurposing or drug repositioning of existing pharmaceuticals because of rising costs and slower rates of new medication development. Other investigations that authorized these treatments used data from experimental research and off-label drug use. More research into the causes of depression could lead to more effective pharmaceutical repurposing efforts. In addition to the loss of neurotransmitters like serotonin and adrenaline, inflammation, inadequate blood flow, and neurotoxins are now thought to be plausible mechanisms. Because of these other mechanisms, repurposing drugs has resulted for treatment-resistant depression. This chapter focuses on therapeutic alternatives and their effectiveness in drug repositioning. Atypical antipsychotics, central nervous system stimulants, and neurotransmitter antagonists have investigated for possible repurposing. Nonetheless, extensive research is required to ensure their formulation, effectiveness, and regulatory compliance.

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.