Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph

Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, mono-modal learning is inherently limited as it relies solely on a single modality of molecular representation, which restricts a comprehensive understanding of drug molecules. To overcome the limitations, we propose a multimodal fused deep learning (MMFDL) model to leverage information from different molecular representations. Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, Bidirectional Gated Recurrent Unit (BiGRU), and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, and molecular graphs, respectively. We evaluate the proposed triple-modal model using five fusion approaches on six molecule datasets, including Delaney, Llinas2020, Lipophilicity, SAMPL, BACE, and pKa from DataWarrior. The results show that the MMFDL model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in the random splitting test, outperforming mono-modal models in accuracy and reliability. Furthermore, we validate the generalization ability of our model in the prediction of binding constants for protein-ligand complex molecules, and assess the resilience capability against noise. Through analysis of feature distributions in chemical space and the assigned contribution of each modal model, we demonstrate that the MMFDL model shows the ability to acquire complementary information by using proper models and suitable fusion approaches. By leveraging diverse sources of bioinformatics information, multimodal deep learning models hold the potential for successful drug discovery.

Mining the Dynamical Properties of Substrate and FAD Binding Pockets of LSD1: Hints for New Inhibitor Design Direction

Lysine-specific demethylase 1 (LSD1), a highly sophisticated epigenetic regulator, orchestrates a range of critical cellular processes, holding promising therapeutic potential for treating diverse diseases. However, the clinical research progress targeting LSD1 is very slow. After 20 years of research, only one small-molecule drug, BEA-17, targeting the degradation of LSD1 and CoREST has been approved by the U.S. Food and Drug Administration. The primary reason for this may be the lack of abundant structural data regarding its intricate functions. To gain a deeper understanding of its conformational dynamics and guide the drug design process, we conducted molecular dynamics simulations to explore the conformational states of LSD1 in the apo state and under the influence of cofactors of flavin adenine dinucleotide (FAD) and CoREST. Our results showed that, across all states, the substrate binding pocket exhibited high flexibility, whereas the FAD binding pocket remained more stable. These distinct dynamical properties are essential for LSD1's ability to bind various substrates while maintaining efficient demethylation activity. Both pockets can be enlarged by merging with adjacent pockets, although only the substrate binding pocket can shrink into smaller pockets. These new pocket shapes can inform inhibitor design, particularly for selectively FAD-competitive inhibitors of LSD1, given the presence of numerous FAD-dependent enzymes in the human body. More interestingly, in the absence of FAD binding, the united substrate and FAD binding pocket are partitioned by the conserved residue of Tyr761, offering valuable insights for the design of inhibitors that disrupt the crucial steric role of Tyr761 and the redox role of FAD. Additionally, we identified pockets that positively or negatively correlate with the substrate and FAD binding pockets, which can be exploited for the design of allosteric or concurrent inhibitors. Our results reveal the intricate dynamical properties of LSD1 as well as multiple novel conformational states, which deepen our understanding of its sophisticated functions and aid in the rational design of new inhibitors.

Immune targeting of filarial glutaredoxin through a multi-epitope peptide-based vaccine: A reverse vaccinology approach

Despite the efforts of global programme to eliminate lymphatic filariasis (GPELF), the threat of lymphatic filariasis (LF) still looms over humanity in terms of long-term disabilities, and morbidities across the globe. In light of this situation, investigators have chosen to focus on the development of immunotherapeutics targeting the physiologically important filarial-specific proteins. Glutaredoxin (16.43 kDa) plays a pivotal role in filarial redox biology, serving as a vital contributor. In the context of the intra-host survival of filarial parasites, this antioxidant helps in mitigating the oxidative stress imposed by the host immune system. Given its significant contribution, the development of a vaccine targeting glutaredoxin holds promise as a new avenue for achieving a filaria-free world. Herein, multi-epitope-based vaccine was designed using advanced immunoinformatics approach. Initially, 4B-cell epitopes and 6 T-cell epitopes (4 MHC I and 2 MHC II) were identified from the 146 amino acid long sequence of glutaredoxin of the human filarid, Wuchereria bancrofti. Subsequent clustering of these epitopes with linker peptides finalized the vaccine structure. To boost TLR-mediated innate immunity, TLR-specific adjuvants were incorporated into the designed vaccine. After that, experimental analyses confirm the designed vaccine, Vac4 as anefficient ligand of human TLR5 to elicit protective innate immunity against filarial glutaredoxin. Immune simulation further demonstrated abundant levels of IgG and IgM as crucial contributors in triggering vaccine-induced adaptive responses in the recipients. Hence, to facilitate the validation of immunogenicity of the designed vaccine, Vac4 was cloned in silico in pET28a(+) expression vector for recombinant production. Taken together, our findings suggest that vaccine-mediated targeting of filarial glutaredoxin could be a future option for intervening LF on a global scale.

An In Vivo and In Silico Approach Reveals Possible Sodium Channel Nav1.2 Inhibitors from Ficus religiosa as a Novel Treatment for Epilepsy

Epilepsy is a neurological disease that affects approximately 50 million people worldwide. Despite an existing abundance of antiepileptic drugs, lifelong disease treatment is often required but could be improved with alternative drugs that have fewer side effects. Given that epileptic seizures stem from abnormal neuronal discharges predominately modulated by the human sodium channel Nav1.2, the quest for novel and potent Nav1.2 blockers holds promise for epilepsy management. Herein, an in vivo approach was used to detect new antiepileptic compounds using the maximum electroshock test on mice. Pre-treatment of mice with extracts from the Ficus religiosa plant ameliorated the tonic hind limb extensor phase of induced convulsions. Subsequently, an in silico approach identified potential Nav1.2 blocking compounds from F. religiosa using a combination of computational techniques, including molecular docking, prime molecular mechanics/generalized Born surface area (MM/GBSA) analysis, and molecular dynamics (MD) simulation studies. The molecular docking and MM/GBSA analysis indicated that out of 82 compounds known to be present in F. religiosa, seven exhibited relatively strong binding affinities to Nav1.2 that ranged from -6.555 to -13.476 kcal/mol; similar or with higher affinity than phenytoin (-6.660 kcal/mol), a known Na+-channel blocking antiepileptic drug. Furthermore, MD simulations revealed that two compounds: 6-C-glucosyl-8-C-arabinosyl apigenin and pelargonidin-3-rhamnoside could form stable complexes with Nav1.2 at 300 K, indicating their potential as lead antiepileptic agents. In summary, the combination of in vivo and in silico approaches supports the potential of F. religiosa phytochemicals as natural antiepileptic therapeutic agents.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review

BACKGROUND

In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment.

METHODS

The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 "CADD or virtual screening", concept 2 "anthraquinone" and concept 3 "cancer". The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023.

RESULTS

Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion.

CONCLUSION

By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.

Determination of promising inhibitors for N-SH2 domain of SHP2 tyrosine phosphatase: an in silico study

There are many genes that produce proteins related to diseases and these proteins can be targeted with drugs as a potential therapeutic approach. Recent advancement in drug discovery techniques have created new opportunities for treating variety of diseases by targeting disease-related proteins. Structure-based drug discovery is a faster and more cost-effective approach than traditional methods. SHP2 phosphatase, encoded by the PTPN11 gene, has been the focus of much attention due to its involvement in many types of diseases. The biological function of SHP2 is enabled mostly by protein-protein interaction through its SH2 domains. In this study, we report the identification of a potential small molecule inhibitor for the N-SH2 domain of SHP2 by structure-based drug discovery approach. We utilized molecular docking studies, followed by molecular dynamics simulations and MM/PBSA calculations, to analyze compounds retrieved from the Broad's Drug Repurposing Hub and ZINC15 databases. We selected 10 hit compounds with the best docking scores from the libraries and examined their binding properties in the N-SH2 domain. We found that compound CID 60838 (Irinotecan) was the most suitable compound with a binding free energy value of - 64.45 kcal/mol and significant interactions with the target residues in the domain.

Prediction of Drug-Target Affinity Using Attention Neural Network

Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational DTA prediction methods have emerged as indispensable tools for this research. In this article, we propose a novel deep learning algorithm named GRA-DTA, for DTA prediction. Specifically, we introduce Bidirectional Gated Recurrent Unit (BiGRU) combined with a soft attention mechanism to learn target representations. We employ Graph Sample and Aggregate (GraphSAGE) to learn drug representation, especially to distinguish the different features of drug and target representations and their dimensional contributions. We merge drug and target representations by an attention neural network (ANN) to learn drug-target pair representations, which are fed into fully connected layers to yield predictive DTA. The experimental results showed that GRA-DTA achieved mean squared error of 0.142 and 0.225 and concordance index reached 0.897 and 0.890 on the benchmark datasets KIBA and Davis, respectively, surpassing the most state-of-the-art DTA prediction algorithms.

Design, synthesis, biological evaluation and molecular docking studies of quinoline-anthranilic acid hybrids as potent anti-inflammatory drugs

Despite the high global prevalence, rheumatoid arthritis lacks a satisfactory treatment. Hence, the present study is undertaken to design and synthesize novel anti-inflammatory compounds. For this, quinoline and anthranilic acid, two medicinally-privileged moieties, were linked by pharmacophore hybridization, and following their computational assessments, three hybrids 5a-c were synthesized in good over all yields. The in vitro and in vivo anti-inflammatory potential of these hybrids was determined by anti-denaturation and anti-proteinase, and carrageenan-induced paw edema models. The computational studies of these hybrids revealed their drug-likeness, optimum pharmacokinetics, and less toxicity. Moreover, they demonstrated high binding affinity (-9.4 to -10.6 kcal mol-1) and suitable binding interactions for TNF-α, FLAP, and COX-II. A three-step synthetic route resulted in the hybrids 5a-c with 83-86% yield of final step. At 50 μg mL-1, the antiprotease and anti-denaturation activity of compound 5b was significantly higher than 5a and 5c. Furthermore, 5b significantly reduced the edema in the right paw of the rats that received carrageenan. The results of this study indicate the medicinal worth of the novel hybrids in treating inflammatory disorders such as rheumatoid arthritis.

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.

Deciphering the mechanisms and interactions of the endocrine disruptor bisphenol A and its analogs with the androgen receptor

Bisphenol A (BPA) and its various forms used as BPA alternatives in industries are recognized toxic compounds and antiandrogenic endocrine disruptors. These chemicals are widespread in the environment and frequently detected in biological samples. Concerns exist about their impact on hormones, disrupting natural biological processes in humans, together with their negative impacts on the environment and biotic life. This study aims to characterize the interaction between BPA analogs and the androgen receptor (AR) and the effect on the receptor's normal activity. To achieve this goal, molecular docking was conducted with BPA and its analogs and dihydrotestosterone (DHT) as a reference ligand. Four BPA analogs exhibited higher affinity (-10.2 to -8.7 kcal/mol) for AR compared to BPA (-8.6 kcal/mol), displaying distinct interaction patterns. Interestingly, DHT (-11.0 kcal/mol) shared a binding pattern with BPA. ADMET analysis of the top 10 compounds, followed by molecular dynamics simulations, revealed toxicity and dynamic behavior. Experimental studies demonstrated that only BPA disrupts DHT-induced AR dimerization, thereby affecting AR's function due to its binding nature. This similarity to DHT was observed during computational analysis. These findings emphasize the importance of targeted strategies to mitigate BPA toxicity, offering crucial insights for interventions in human health and environmental well-being.

Molecular Docking Improved with Human Spatial Perception Using Virtual Reality

Adaptive steered molecular dynamics (ASMD) is a computational biophysics method in which an external force is applied to a selected set of atoms or a specific reaction coordinate to induce a particular molecular motion. Virtual reality (VR) based methods for protein-ligand docking are beneficial for visualizing on-the-fly interactive molecular dynamics and performing promising docking trajectories. In this paper, we propose a novel method to guide ASMD with optimal trajectories collected from human experiences using interactive molecular dynamics in virtual reality (iMD-VR). We also explain the benefits of using VR as a tool for expediting the process of ligand binding, outlining an experimental protocol that enables iMD-VR users to guide Amprenavir into and out of the binding pockets of HIV-1 protease and recreate their respective crystallographic binding poses within 5 minutes. Later, we discuss our analysis of the results from iMD-VR-assisted ASMD simulation and assess its performance compared to a standard ASMD simulation. From the accuracy point of view, our proposed method calculates higher Potential Mean Force (PMF) values consistently relative to a standard ASMD simulation with an almost twofold increase in all the experiments. Finally, we describe the novelty of the research and discuss results showcasing a faster and more effective convergence of the ligand to the protein's binding site as compared to a standard molecular dynamics simulation, proving the effectiveness of VR in the field of drug discovery. Future work includes the development of an artificial intelligence algorithm capable of predicting optimal binding trajectories for many protein-ligand pairs, as well as the required force needed to steer the ligand to follow the said trajectory.

Designing of a multi-epitopes based vaccine against Haemophilius parainfluenzae and its validation through integrated computational approaches

Haemophilus parainfluenzae is a Gram-negative opportunist pathogen within the mucus of the nose and mouth without significant symptoms and has an ability to cause various infections ranging from ear, eye, and sinus to pneumonia. A concerning development is the increasing resistance of H. parainfluenzae to beta-lactam antibiotics, with the potential to cause dental infections or abscesses. The principal objective of this investigation is to utilize bioinformatics and immuno-informatic methodologies in the development of a candidate multi-epitope Vaccine. The investigation focuses on identifying potential epitopes for both B cells (B lymphocytes) and T cells (helper T lymphocytes and cytotoxic T lymphocytes) based on high non-toxic and non-allergenic characteristics. The selection process involves identifying human leukocyte antigen alleles demonstrating strong associations with recognized antigenic and overlapping epitopes. Notably, the chosen alleles aim to provide coverage for 90% of the global population. Multi-epitope constructs were designed by using suitable linker sequences. To enhance the immunological potential, an adjuvant sequence was incorporated using the EAAAK linker. The final vaccine construct, comprising 344 amino acids, was achieved after the addition of adjuvants and linkers. This multi-epitope Vaccine demonstrates notable antigenicity and possesses favorable physiochemical characteristics. The three-dimensional conformation underwent modeling and refinement, validated through in-silico methods. Additionally, a protein-protein molecular docking analysis was conducted to predict effective binding poses between the multi-epitope Vaccine and the Toll-like receptor 4 protein. The Molecular Dynamics (MD) investigation of the docked TLR4-vaccine complex demonstrated consistent stability over the simulation period, primarily attributed to electrostatic energy. The docked complex displayed minimal deformation and enhanced rigidity in the motion of residues during the dynamic simulation. Furthermore, codon translational optimization and computational cloning was performed to ensure the reliability and proper expression of the multi-Epitope Vaccine. It is crucial to emphasize that despite these computational validations, experimental research in the laboratory is imperative to demonstrate the immunogenicity and protective efficacy of the developed vaccine. This would involve practical assessments to ascertain the real-world effectiveness of the multi-epitope Vaccine.

Mechanism of antagonist ligand binding to REV-ERBα

REV-ERBα, a therapeutically promising nuclear hormone receptor, plays a crucial role in regulating various physiological processes such as the circadian clock, inflammation, and metabolism. However, the availability of chemical probes to investigate the pharmacology of this receptor is limited, with SR8278 being the only identified synthetic antagonist. Moreover, no X-ray crystal structures are currently available that demonstrate the binding of REV-ERBα to antagonist ligands. This lack of structural information impedes the development of targeted therapeutics. To address this issue, we employed Gaussian accelerated molecular dynamics (GaMD) simulations to investigate the binding pathway of SR8278 to REV-ERBα. For comparison, we also used GaMD to observe the ligand binding process of STL1267, for which an X-ray structure is available. GaMD simulations successfully captured the binding of both ligands to the receptor's orthosteric site and predicted the ligand binding pathway and important amino acid residues involved in the antagonist SR8278 binding. This study highlights the effectiveness of GaMD in investigating protein-ligand interactions, particularly in the context of drug recognition for nuclear hormone receptors.

An Ensemble Docking Approach for Analyzing and Designing Aptamer Heterodimers Targeting VEGF165

Vascular endothelial growth factor 165 (VEGF165) is a prominent isoform of the VEGF-A protein that plays a crucial role in various angiogenesis-related diseases. It is homodimeric, and each of its monomers is composed of two domains connected by a flexible linker. DNA aptamers, which have emerged as potent therapeutic molecules for many proteins with high specificity and affinity, can also work for VEGF165. A DNA aptamer heterodimer composed of monomers of V7t1 and del5-1 connected by a flexible linker (V7t1:del5-1) exhibits a greater binding affinity with VEGF165 compared to either of the two monomers alone. Although the structure of the complex formed between the aptamer heterodimer and VEGF165 is unknown due to the highly flexible linkers, gaining structural information will still be valuable for future developments. Toward this end of accessing structural information, we adopt an ensemble docking approach here. We first obtain an ensemble of structures for both VEGF165 and the aptamer heterodimer by considering both small- and large-scale motions. We then proceed through an extraction process based on ensemble docking, molecular dynamics simulations, and binding free energy calculations to predict the structures of the VEGF165/V7t1:del5-1 complex. Through the same procedures, we reach a new aptamer heterodimer that bears a locked nucleic acid-modified counterpart of V7t1, namely RNV66:del5-1, which also binds well with VEGF165. We apply the same protocol to the monomeric units V7t1, RNV66, and del5-1 to target VEGF165. We observe that V7t1:del5-1 and RNV66:del5-1 show higher binding affinities with VEGF165 than any of the monomers, consistent with experiments that support the notion that aptamer heterodimers are more effective anti-VEGF165 aptamers than monomeric aptamers. Among the five different aptamers studied here, the newly designed RNV66:del5-1 shows the highest binding affinity with VEGF165. We expect that our ensemble docking approach can help in de novo designs of homo/heterodimeric anti-angiogenic drugs to target the homodimeric VEGF165.

Nanomedicines for intranasal delivery: understanding the nano-bio interactions at the nasal mucus-mucosal barrier

INTRODUCTION

Intranasal administration is an effective drug delivery routes in modern pharmaceutics. However, unlike other in vivo biological barriers, the nasal mucosal barrier is characterized by high turnover and selective permeability, hindering the diffusion of both particulate drug delivery systems and drug molecules. The in vivo fate of administrated nanomedicines is often significantly affected by nano-biointeractions.

AREAS COVERED

The biological barriers that nanomedicines encounter when administered intranasally are introduced, with a discussion on the factors influencing the interaction between nanomedicines and the mucus layer/mucosal barriers. General design strategies for nanomedicines administered via the nasal route are further proposed. Furthermore, the most common methods to investigate the characteristics and the interactions of nanomedicines when in presence of the mucus layer/mucosal barrier are briefly summarized.

EXPERT OPINION

Detailed investigation of nanomedicine-mucus/mucosal interactions and exploration of their mechanisms provide solutions for designing better intranasal nanomedicines. Designing and applying nanomedicines with mucus interaction properties or non-mucosal interactions should be customized according to the therapeutic need, considering the target of the drug, i.e. brain, lung or nose. Then how to improve the precise targeting efficiency of nanomedicines becomes a difficult task for further research.

Design, Synthesis, and Molecular Docking of Novel Miscellaneous Chalcones as p38α Mitogen-Activated Protein Kinase Inhibitors

New chalcones were synthesized and evaluated to serve as p38-α type of mitogen-activated protein kinase (MAPK) inhibitors. According to the National Cancer Institute, the findings indicated that at a 10 μM dosage, compounds 3a and 6 were the most active among all the compounds examined, with mean growth inhibition% of 94.83 and 58.49, respectively. In 5-dose testing, they showed anticancer activity in the micro-molar range with GI50 in the range of 1.41-46.1 and 2.07-31.3 μM, respectively. Besides, powerful activity, especially against the leukaemia cell lines and good selectivity to cancer cells compared to normal PCS-800-017 with a selectivity index=12.41 and 23.77, respectively. Compounds 3a and 6 inhibited p38α MAPK with IC50 values of 0.1462±0.0063 and 0.4356±0.0189 μM, correspondingly. 3a showed good inhibition for HL-60(TB) cells and induced cell cycle arrest in HL-60(TB) cells at the G2/M phase. Besides, it elevated the total apoptosis by 14.68-fold and increased the caspase-3 level by 3.52-fold compared with doxorubicin, which raised it by 4.30-fold, inducing apoptosis by acting as caspase-dependent inducers. These results suggest that 3a is a promising antiproliferative and p38α MAPK inhibitor, confirmed by molecular docking with high compatibility 3a with the p38α MAPK binding site.

Tepotinib and tivantinib as potential inhibitors for the serine/threonine kinase of the mpox virus: insights from structural bioinformatics analysis

The serine/threonine kinase (STK) plays a central role as the primary kinase in poxviruses, directing phosphoryl transfer reactions. Such reactions are pivotal for the activation of certain proteins during viral replication, assembly, and maturation. Therefore, targeting this key protein is anticipated to impede virus replication. In this work, a structural bioinformatics approach was employed to evaluate the potential of drug-like kinase inhibitors in binding to the ATP-binding pocket on the STK of the Mpox virus. Virtual screening of known kinase inhibitors revealed that the top 10 inhibitors exhibited binding affinities ranging from -8.59 to -12.05 kcal/mol. The rescoring of compounds using the deep-learning default model in GNINA was performed to predict accurate binding poses. Subsequently, the top three inhibitors underwent unbiased molecular dynamics (MD) simulations for 100 ns. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis and Principal Component Analysis (PCA) suggested tepotinib as a competitive inhibitor for Mpox virus STK as evidenced by its binding free energy and the induction of similar conformational behavior of the enzyme. Nevertheless, it is sensible to experimentally test all top 10 compounds, as scoring functions and energy calculations may not consistently align with experimental findings. These insights are poised to provide an attempt to identify an effective inhibitor for the Mpox virus.Communicated by Ramaswamy H. Sarma.

Identification of novel potential cathepsin-B inhibitors through pharmacophore-based virtual screening, molecular docking, and dynamics simulation studies for the treatment of Alzheimer's disease

Cathepsin B is a cysteine protease lysosomal enzyme involved in several physiological functions. Overexpression of the enzyme enhances its proteolytic activity and causes the breakdown of amyloid precursor protein (APP) into neurotoxic amyloid β (Aβ), a characteristic hallmark of Alzheimer's disease (AD). Therefore, inhibition of the enzyme is a crucial therapeutic aspect for treating the disease. Combined structure and ligand-based drug design strategies were employed in the current study to identify the novel potential cathepsin B inhibitors. Five different pharmacophore models were developed and used for the screening of the ZINC-15 database. The obtained hits were analyzed for the presence of duplicates, interfering PAINS moieties, and structural similarities based on Tanimoto's coefficient. The molecular docking study was performed to screen hits with better target binding affinity. The top seven hits were selected and were further evaluated based on their predicted ADME properties. The resulting best hits, ZINC827855702, ZINC123282431, and ZINC95386847, were finally subjected to molecular dynamics simulation studies to determine the stability of the protein-ligand complex during the run. ZINC123282431 was obtained as the virtual lead compound for cathepsin B inhibition and may be a promising novel anti-Alzheimer agent.

Identification of phytocompounds as potent inhibitors of sodium/glucose cotransporter-2 leading to diabetes treatment

Type-II diabetes, a major metabolic disorder has threatened the very existence of a healthy life since long ago. Commercially available antidiabetic drugs are known for several adverse effects. The present study attempted to identify potential phytocompounds as inhibitors of sodium/glucose cotransporter-2 (SGLT2), a major protein that helps in glucose re-absorption from renal tubules. A total of 28 phytocompounds were collected based on the literature survey. 3D co-ordinates of phytocompounds were collected from PubChem database. Molecular docking was carried out with SGLT2 protein and the best 3 docking complexes were subjected to molecular dynamics simulation for 100 ns. Free energy changes were also analyzed using MM/PBSA analysis. Phytocompounds were also analyzed for their drug-likeness and ADMET properties. Docking study observed a strong binding affinity of phytocompounds (> -7.0 kcal/mol). More than 10 phytocompounds showed better binding affinity compared to reference drugs. Further analysis of three best docking complexes when analyzed by MD simulation showed better stability and compactness of the complexes compared to reference drug, empagliflozin. MM/PBSA analysis also revealed that van der Waals force and electrostatic energy are the major binding energy involved in the complex formation. Like docking energy, free energy analysis also observed stronger binding energies (ΔGGAS) in SGLT2-phytocompound complexes compared to empagliflozin complex. All the phytocompounds showed drug-likeness and considerable ADMET properties. The study, therefore, suggests that Trifolirhizin-6'-monoacetate, Aspalathin, and Quercetin-3-glucoside could be a possible inhibitor of SGLT2 protein. However, further studies need to be carried out to reveal the exact mode of activity.Communicated by Ramaswamy H. Sarma.

Structural studies of catalytic peptides using molecular dynamics simulations

Many self-assembling peptides can form amyloid like structures with different sizes and morphologies. Driven by non-covalent interactions, their aggregation can occur through distinct pathways. Additionally, they can bind metal ions to create enzyme like active sites that allow them to catalyze diverse reactions. Due to the non-crystalline nature of amyloids, it is quite challenging to elucidate their structures using experimental spectroscopic techniques. In this aspect, molecular dynamics (MD) simulations provide a useful tool to derive structures of these macromolecules in solution. They can be further validated by comparing with experimentally measured structural parameters. However, these simulations require a multi-step process starting from the selection of the initial structure to the analysis of MD trajectories. There are multiple force fields, parametrization protocols, equilibration processes, software and analysis tools available for this process. Therefore, it is complicated for non-experts to select the most relevant tools and perform these simulations effectively. In this chapter, a systematic methodology that covers all major aspects of modeling of catalytic peptides is provided in a user-friendly manner. It will be helpful for researchers in this critical area of research.

Predicting Surface pH in Unbuffered Conditions for Acids, Bases, and Their Salts - A Review of Modeling Approaches and Their Performance

Dissolution of ionizable drugs and their salts is a function of drug surface solubility driven by the surface pH, i.e., the microenvironmental pH at the solid/liquid interface, which will deviate from bulk pH when there is an acid-base reaction occurring at the solid/liquid interface. In this work, we first present a brief overview of the modeling approaches available in the literature, classified according to the rate-determining step assumed in the dissolution process. In the second part, we present and evaluate the prediction performance of two different modeling approaches for surface pH. The first method relies only on thermodynamic equilibria, while the second method accounts for transport phenomena of charged compounds through the diffusional boundary layer using the Nernst - Planck equation. Model outcomes are compared with experimental data taken from the literature and obtained during this work. In terms of surface pH predictions, the models provide identical values for weak acids or weak bases. The models' outcomes for bases are in good agreement with experimental data in acidic conditions (bulk pH 1-4), while overpredictions are observed in the 5-7 bulk pH range in a system-dependent manner. Deviations can be related to the effect of surface dissolution (also referred to as surface reaction), which may become a controlling mechanism and slow the replenishment of the unionized drug at the surface of the crystal. Surface pH predictions for acids are generally in good agreement with experiments, with a slight underestimation for some drug examples, which could be related to errors in intrinsic solubility determination or to the assumption of thermodynamic equilibrium at the surface of the drug. A good agreement is also observed for salts with the thermodynamic model except for mesylate salts, suggesting that other phenomena, not currently included in the thermodynamic equilibrium model, may determine the surface pH.

Development of multi-targetable chalcone derivatives bearing N-aryl piperazine moiety for the treatment of Alzheimer's disease

The multi-target directed ligand (MTDL) discovery has been gaining immense attention in the development of therapeutics for Alzheimer's disease (AD). The strategy has been evolved as an auspicious approach suitable to combat the heterogeneity and the multifactorial nature of AD. Therefore, multi-targetable chalcone derivatives bearing N-aryl piperazine moiety were designed, synthesized, and evaluated for the treatment of AD. All the synthesized compounds were screened for thein vitro activityagainst acetylcholinesterase (AChE), butylcholinesterase (BuChE), β-secretase-1 (BACE-1), and inhibition of amyloid β (Aβ) aggregation. Amongst all the tested derivatives, compound 41bearing unsubstituted benzylpiperazine fragment and para-bromo substitution at the chalcone scaffold exhibited balanced inhibitory profile against the selected targets. Compound 41 elicited favourable permeation across the blood-brain barrier in the PAMPA assay. The molecular docking and dynamics simulation studies revealed the binding mode analysis and protein-ligand stability ofthe compound with AChE and BACE-1. Furthermore,itameliorated cognitive dysfunctions and signified memory improvement in thein-vivobehavioural studies (scopolamine-induced amnesia model). Theex vivobiochemical analysis of mice brain homogenates established the reduced AChE and increased ACh levels. The antioxidant activity of compound 41 was accessed with the determination of catalase (CAT) and malondialdehyde (MDA) levels. The findings suggested thatcompound 41, containing a privileged chalcone scaffold, can act as a lead molecule for developing AD therapeutics.

Combined in vitro/in silico approaches, molecular dynamics simulations and safety assessment of the multifunctional properties of thymol and carvacrol: A comparative insight

Bioactive compounds derived from medicinal plants have acquired immense attentiveness in drug discovery and development. The present study investigated in vitro and predicted in silico the antibacterial, antifungal, and antiviral properties of thymol and carvacrol, and assessed their safety. The performed microbiological assays against Pseudomonas aeruginosa, Escherichia coli, Salmonella enterica Typhimurium revealed that the minimal inhibitory concentration values ranged from (0.078 to 0.312 mg/mL) and the minimal fungicidal concentration against Candida albicans was 0.625 mg/mL. Molecular docking simulations, stipulated that these compounds could inhibit bacterial replication and transcription functions by targeting DNA and RNA polymerases receptors with docking scores varying between (-5.1 to -6.9 kcal/mol). Studied hydroxylated monoterpenes could hinder C. albicans growth by impeding lanosterol 14α-demethylase enzyme and showed a (ΔG=-6.2 and -6.3 kcal/mol). Computational studies revealed that thymol and carvacrol could target the SARS-Cov-2 spike protein of the Omicron variant RBD domain. Molecular dynamics simulations disclosed that these compounds have a stable dynamic behavior over 100 ns as compared to remdesivir. Chemo-computational toxicity prediction using Protox II webserver indicated that thymol and carvacrol could be safely and effectively used as drug candidates to tackle bacterial, fungal, and viral infections as compared to chemical medication.

Structural insights into the mechanism of resistance to bicalutamide by the clinical mutations in androgen receptor in chemo-treatment resistant prostate cancer

Despite advanced diagnosis and detection technologies, prostate cancer (PCa) is the most prevalent neoplasms in males. Dysregulation of the androgen receptor (AR) is centrally involved in the tumorigenesis of PCa cells. Acquisition of drug resistance due to modifications in AR leads to therapeutic failure and relapse in PCa. An overhaul of comprehensive catalogues of cancer-causing mutations and their juxta positioning on 3D protein can help in guiding the exploration of small drug molecules. Among several well-studied PCa-specific mutations, T877A, T877S and H874Y are the most common substitutions in the ligand-binding domain (LBD) of the AR. In this study, we combined structure as well as dynamics-based in silico approaches to infer the mechanistic effect of amino acid substitutions on the structural stability of LBD. Molecular dynamics simulations allowed us to unveil a possible drug resistance mechanism that acts through structural alteration and changes in the molecular motions of LBD. Our findings suggest that the resistance to bicalutamide is partially due to increased flexibility in the H12 helix, which disturbs the compactness, thereby reducing the affinity for bicalutamide. In conclusion, the current study helps in understanding the structural changes caused by mutations and could assist in the drug development process.Communicated by Ramaswamy H. Sarma.

De Novo Drug Design Using Transformer-Based Machine Translation and Reinforcement Learning of an Adaptive Monte Carlo Tree Search

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder-Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder-Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder-Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases.

Lysine-Specific Demethylase 1 Inhibitors: A Comprehensive Review Utilizing Computer-Aided Drug Design Technologies

Lysine-specific demethylase 1 (LSD1/KDM1A) has emerged as a promising therapeutic target for treating various cancers (such as breast cancer, liver cancer, etc.) and other diseases (blood diseases, cardiovascular diseases, etc.), owing to its observed overexpression, thereby presenting significant opportunities in drug development. Since its discovery in 2004, extensive research has been conducted on LSD1 inhibitors, with notable contributions from computational approaches. This review systematically summarizes LSD1 inhibitors investigated through computer-aided drug design (CADD) technologies since 2010, showcasing a diverse range of chemical scaffolds, including phenelzine derivatives, tranylcypromine (abbreviated as TCP or 2-PCPA) derivatives, nitrogen-containing heterocyclic (pyridine, pyrimidine, azole, thieno[3,2-b]pyrrole, indole, quinoline and benzoxazole) derivatives, natural products (including sanguinarine, phenolic compounds and resveratrol derivatives, flavonoids and other natural products) and others (including thiourea compounds, Fenoldopam and Raloxifene, (4-cyanophenyl)glycine derivatives, propargylamine and benzohydrazide derivatives and inhibitors discovered through AI techniques). Computational techniques, such as virtual screening, molecular docking and 3D-QSAR models, have played a pivotal role in elucidating the interactions between these inhibitors and LSD1. Moreover, the integration of cutting-edge technologies such as artificial intelligence holds promise in facilitating the discovery of novel LSD1 inhibitors. The comprehensive insights presented in this review aim to provide valuable information for advancing further research on LSD1 inhibitors.

Recent developments in molecular modeling tools and applications related to pharmaceutical and biomedical research

In modern pharmaceutical and biomedical research, molecular modeling represents a useful tool to explore processes and their mechanistic bases at the molecular level. Integrating experimental and virtual analysis is a fruitful approach to study ligand-receptor interaction in chemical, biochemical and biological environments. In these fields, molecular docking and molecular dynamics are considered privileged techniques for modeling (bio)macromolecules and related complexes. This review aims to present the current landscape of molecular modeling in pharmaceutical and biomedical research by examining selected representative applications published in the last years and highlighting current topics and trends of this field. Thus, a systematic compilation of all published literature has not been attempted herein. After a brief overview of the main theoretical and computational tools used to investigate mechanisms at molecular level, recent applications of molecular modeling in drug discovery, ligand binding and for studying protein conformation and function will be discussed. Furthermore, specific sections will be devoted to the application of molecular modeling for unravelling enantioselective mechanisms underlying the enantioseparation of chiral compounds of pharmaceutical and biomedical interest as well as for studying new forms of noncovalent interactivity identified in biochemical and biological environments. The general aim of this review is to provide the reader with a modern overview of the topic, highlighting advancements and outlooks as well as drawbacks and pitfalls still affecting the applicability of theoretical and computational methods in the field of pharmaceutical and biomedical research.

Unraveling the Behavior of Intrinsically Disordered Protein c-Myc: A Study Utilizing Gaussian-Accelerated Molecular Dynamics

The protein c-Myc is a transcription factor that remains largely intrinsically disordered and is known to be involved in various biological processes and is overexpressed in various cancers, making it an attractive drug target. However, intrinsically disordered proteins such as c-Myc do not show funnel-like basins in their free-energy landscapes; this makes their druggability a challenge. For the first time, we propose a heterodimer model of c-Myc/Max in full length in this work. We used Gaussian-accelerated molecular dynamics (GaMD) simulations to explore the behavior of c-Myc and its various regions, including the transactivation domain (TAD) and the basic helix-loop-helix-leucine-zipper (bHLH-Zipper) motif in three different conformational states: (a) monomeric c-Myc, (b) c-Myc when bound to its partner protein, Max, and (c) when Max was removed after binding. We analyzed the GaMD trajectories using root-mean-square deviation (RMSD), radius of gyration, root-mean-square fluctuation, and free-energy landscape (FEL) calculations to elaborate the behaviors of these regions. The results showed that the monomeric c-Myc structure showed a higher RMSD fluctuation as compared with the c-Myc/Max heterodimer in the bHLH-Zipper motif. This indicated that the bHLH-Zipper motif of c-Myc is more stable when it is bound to Max. The TAD region in both monomeric and Max-bound states showed similar plasticity in terms of RMSD. We also conducted residue decomposition calculations and showed that the c-Myc and Max interaction could be driven mainly by electrostatic interactions and the residues Arg299, Ile403, and Leu420 seemed to play important roles in the interaction. Our work provides insights into the behavior of c-Myc and its regions that could support the development of drugs that target c-Myc and other intrinsically disordered proteins.

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

Structural and Dynamical Basis of VP35-RBD Inhibition by Marine Fungi Compounds to Combat Marburg Virus Infection

The Marburg virus (MBV), a deadly pathogen, poses a serious threat to world health due to the lack of effective treatments, calling for an immediate search for targeted and efficient treatments. In this study, we focused on compounds originating from marine fungi in order to identify possible inhibitory compounds against the Marburg virus (MBV) VP35-RNA binding domain (VP35-RBD) using a computational approach. We started with a virtual screening procedure using the Lipinski filter as a guide. Based on their docking scores, 42 potential candidates were found. Four of these compounds-CMNPD17596, CMNPD22144, CMNPD25994, and CMNPD17598-as well as myricetin, the control compound, were chosen for re-docking analysis. Re-docking revealed that these particular compounds had a higher affinity for MBV VP35-RBD in comparison to the control. Analyzing the chemical interactions revealed unique binding properties for every compound, identified by a range of Pi-cation interactions and hydrogen bond types. We were able to learn more about the dynamic behaviors and stability of the protein-ligand complexes through a 200-nanosecond molecular dynamics simulation, as demonstrated by the compounds' consistent RMSD and RMSF values. The multidimensional nature of the data was clarified by the application of principal component analysis, which suggested stable conformations in the complexes with little modification. Further insight into the energy profiles and stability states of these complexes was also obtained by an examination of the free energy landscape. Our findings underscore the effectiveness of computational strategies in identifying and analyzing potential inhibitors for MBV VP35-RBD, offering promising paths for further experimental investigations and possible therapeutic development against the MBV.

Applications of Molecular Dynamics Simulations in Drug Discovery

In the current drug development process, molecular dynamics (MD) simulations have proven to be very useful. This chapter provides an overview of the current applications of MD simulations in drug discovery, from detecting protein druggable sites and validating drug docking outcomes to exploring protein conformations and investigating the influence of mutations on its structure and functions. In addition, this chapter emphasizes various strategies to improve the conformational sampling efficiency in molecular dynamics simulations. With a growing computer power and developments in the production of force fields and MD techniques, the importance of MD simulations in helping the drug development process is projected to rise significantly in the future.

Molecular insight into CREBBP and TANGO2 variants causing intellectual disability

BACKGROUND

Intellectual disability (ID) can be associated with different syndromes such as Rubinstein-Taybi syndrome (RSTS) and can also be related to conditions such as metabolic encephalomyopathic crises, recurrent,with rhabdomyolysis, cardiac arrhythmias and neurodegeneration. Rare congenital RSTS1 (OMIM 180849) is characterized by mental and growth retardation, significant and duplicated distal phalanges of thumbs and halluces, facial dysmorphisms, and an elevated risk of malignancies. Microdeletions and point mutations in the CREB-binding protein (CREBBP) gene, located at 16p13.3, have been reported to cause RSTS. By contrast, TANGO2-related metabolic encephalopathy and arrhythmia (TRMEA) is a rare metabolic condition that causes repeated metabolic crises, hypoglycemia, lactic acidosis, rhabdomyolysis, arrhythmias and encephalopathy with cognitive decline. Clinicians need more clinical and genetic evidence to detect and comprehend the phenotypic spectrum of this disorder.

METHODS

Exome sequencing was used to identify the disease-causing variants in two affected families A and B from District Kohat and District Karak, Khyber Pakhtunkhwa. Affected individuals from both families presented symptoms of ID, developmental delay and behavioral abnormalities. The validation and co-segregation analysis of the filtered variant was carried out using Sanger sequencing.

RESULTS

In the present study, two families (A and B) exhibiting various forms of IDs were enrolled. In Family A, exome sequencing revealed a novel missense variant (NM 004380.3: c.4571A>G; NP_004371.2: p.Lys1524Arg) in the CREBBP gene, whereas, in Family B, a splice site variant (NM 152906.7: c.605 + 1G>A) in the TANGO2 gene was identified. Sanger sequencing of both variants confirmed their segregation with ID in both families. The in silico tools verified the aberrant changes in the CREBBP protein structure. Wild-type and mutant CREBBP protein structures were superimposed and conformational changes were observed likely altering the protein function.

CONCLUSIONS

RSTS and TRMEA are exceedingly rare disorders for which specific clinical characteristics have been clearly established, but more investigations are underway and required. Multicenter studies are needed to increase our understanding of the clinical phenotypes, mainly showing the genotype-phenotype associations.

In silico Screening of Food and Drug Administration-approved Compounds against Trehalose 2-sulfotransferase (Rv0295c) in Mycobacterium tuberculosis: Insights from Molecular Docking and Dynamics Simulations

BACKGROUND

Tuberculosis (TB) remains a prominent global health challenge, distinguished by substantial occurrences of infection and death. The upsurge of drug-resistant TB strains underscores the urgency to identify novel therapeutic targets and repurpose existing compounds. Rv0295c is a potentially druggable enzyme involved in cell wall biosynthesis and virulence. We evaluated the inhibitory activity of Food and Drug Administration (FDA)-approved compounds against Rv0295c of Mycobacterium tuberculosis, employing molecular docking, ADME evaluation, and dynamics simulations.

METHODS

The study screened 1800 FDA-approved compounds and selected the top five compounds with the highest docking scores. Following this, we subjected the initially screened ligands to ADME analysis based on their dock scores. In addition, the compound exhibited the highest binding affinity chosen for molecular dynamics (MD) simulation to investigate the dynamic behavior of the ligand-receptor complex.

RESULTS

Dihydroergotamine (CHEMBL1732) exhibited the highest binding affinity (-12.8 kcal/mol) for Rv0295c within this set of compounds. We evaluated the stability and binding modes of the complex over extended simulation trajectories.

CONCLUSION

Our in silico analysis demonstrates that FDA-approved drugs can serve as potential Rv0295c inhibitors through repurposing. The combination of molecular docking and MD simulation offers a comprehensive understanding of the interactions between ligands and the protein target, providing valuable guidance for further experimental validation. Identifying Rv0295c inhibitors may contribute to new anti-TB drugs.

Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach

Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).

LSTM-SAGDTA: Predicting Drug-target Binding Affinity with an Attention Graph Neural Network and LSTM Approach

INTRODUCTION

Drug development is a challenging and costly process, yet it plays a crucial role in improving healthcare outcomes. Drug development requires extensive research and testing to meet the demands for economic efficiency, cures, and pain relief.

METHODS

Drug development is a vital research area that necessitates innovation and collaboration to achieve significant breakthroughs. Computer-aided drug design provides a promising avenue for drug discovery and development by reducing costs and improving the efficiency of drug design and testing.

RESULTS

In this study, a novel model, namely LSTM-SAGDTA, capable of accurately predicting drug-target binding affinity, was developed. We employed SeqVec for characterizing the protein and utilized the graph neural networks to capture information on drug molecules. By introducing self-attentive graph pooling, the model achieved greater accuracy and efficiency in predicting drug-target binding affinity.

CONCLUSION

Moreover, LSTM-SAGDTA obtained superior accuracy over current state-of-the-art methods only by using less training time. The results of experiments suggest that this method represents a highprecision solution for the DTA predictor.

Exploration of human pancreatic alpha-amylase inhibitors from Physalis peruviana for the treatment of type 2 diabetes

Type 2 Diabetes (T2D), a chronic metabolic disorder characterized by persistent hyperglycemia, accounts for ∼90% of all types of diabetes. Pancreatic α-amylase is a potential drug target for preventing postprandial hyperglycemia and inhibiting T2D in humans. Although many synthetic drugs have been identified against pancreatic α-amylase, however, reported several side effects, and plant-derived natural products are less explored against T2D. This study tested 34 flavonoids derived from the plant Physalis peruviana against the human pancreatic α-amylase (HPA) using in silico computational approaches such as molecular docking and molecular dynamics simulation approaches. Schrödinger, a drug discovery package with modules applicable for molecular docking, protein-ligand interaction analysis, molecular dynamics, post-dynamics simulation, and binding free energy calculation, was employed for all computational studies. Four flavonoids, namely, Chlorogenic acid, Withaperuvin F, Withaperuvin H, and Rutin, were picked based on their docking score ranging between -7.03 kcal/mol and -11.35 kcal/mol compared to the docking score -7.3 kcal/mol of reference ligand, i.e. Myricetin. The molecular dynamics analysis suggested that all flavonoids showed considerable stability within the protein's catalytic pocket, except chlorogenic acid, which showed high deviation during the last 15 ns. However, the interactions observed in initial docking and extracted from the simulation trajectory involved > 90% identical residues, indicating the affinity and stability of the docked flavonoids with the protein. Therefore, all four compounds identified in this study are proposed as promising antidiabetic candidates and should be further considered for their in vitro and in vivo validation.Communicated by Ramaswamy H. Sarma.

Computational screening of coumarin derivatives as inhibitors of the NACHT domain of NLRP3 inflammasome for the treatment of Alzheimer's disease

The nucleotide-binding oligomerization domain (NOD)-like receptor (NLR), leucine-rich-repeat (LRR), and pyrin domain containing 3 (NLRP3) is one of the key players in neuroinflammation, which is a major pathological hallmark of Alzheimer's Disease (AD). Activated NLRP3 causes release of pro-inflammatory molecules that aggravate neurodegeneration. Thus, pharmacologically inhibiting the NLRP3 inflammasome has the potential to alleviate the inflammatory injury to the neurons. Coumarin is a multifunctional nucleus with potent anti-inflammatory properties and can be utilized to develop novel drugs for the treatment and management of AD. In the present study, we have explored the NLRP3-inhibitory activities of a library of coumarin derivatives through a computational drug discovery approach. Drug-like, PAINS free, and potentially BBB permeable compounds were screened out and subjected to molecular docking and in silico ADMET studies, resulting in three virtual hits, i.e. MolPort-050-872-358, MolPort-050-884-068, and MolPort-051-135-630. The hits exhibited better NLRP3-binding affinity than MCC950, a selective inhibitor of NLRP3. Further, molecular dynamics (MD) simulations, post-MD simulation analyses, and binding free energy calculations of the hits established their potential as promising virtual leads with a common coumarin scaffold for the inhibition of NLRP3 inflammasome.Communicated by Ramaswamy H. Sarma.

A polymeric nanocarrier that eradicates breast cancer stem cells and delivers chemotherapeutic drugs

BACKGROUND

Drug nanocarriers can markedly reduce the toxicities and side effects of encapsulated chemotherapeutic drugs in the clinic. However, these drug nanocarriers have little effect on eradicating breast cancer stem cells (BCSCs). Although compounds that can inhibit BCSCs have been reported, these compounds are difficult to use as carriers for the widespread delivery of conventional chemotherapeutic drugs.

METHODS

Herein, we synthesize a polymeric nanocarrier, hyaluronic acid-block-poly (curcumin-dithiodipropionic acid) (HA-b-PCDA), and explore the use of HA-b-PCDA to simultaneously deliver chemotherapeutic drugs and eradicate BCSCs.

RESULTS

Based on molecular docking and molecular dynamics studies, HA-b-PCDA delivers 35 clinical chemotherapeutic drugs. To further verify the drug deliver ability of HA-b-PCDA, doxorubicin, paclitaxel, docetaxel, gemcitabine and camptothecin are employed as model drugs to prepare nanoparticles. These drug-loaded HA-b-PCDA nanoparticles significantly inhibit the proliferation and stemness of BCSC-enriched 4T1 mammospheres. Moreover, doxorubicin-loaded HA-b-PCDA nanoparticles efficiently inhibit tumor growth and eradicate approximately 95% of BCSCs fraction in vivo. Finally, HA-b-PCDA eradicates BCSCs by activating Hippo and inhibiting the JAK2/STAT3 pathway.

CONCLUSION

HA-b-PCDA is a polymeric nanocarrier that eradicates BCSCs and potentially delivers numerous clinical chemotherapeutic drugs.

Unraveling the therapeutic potential of Senna singueana phytochemicals to attenuate pancreatic cancer using protein-protein interactions, molecular docking, and MD simulation

Pancreatic cancer (PC) presents challenges due to limited treatment options and patients seek complementary therapies alongside conventional treatments to improve well-being. This study uses computational drug discovery approaches to find potential phytochemicals from S. singueana for PC treatment. Among the 38 phytochemicals screened from S. singueana, specific inhibitors against PC were selected. Protein-protein interaction (PPI) network analysis highlighted key targets with high degrees, including PTEN (8) and PTK2 (7) genes, along with their respective proteins 5BZX and 3BZ3, which were employed for molecular docking studies. 1-methylchrysene and 3-methyl-1,8,9-anthracenetriol showed strong binding affinities of - 9.2 and - 8.1 Kcal/mol, respectively. Molecular dynamics simulations lasting 300 ns assessed structural stability and interaction energy of compound-target dockings: 1-methylchrysene-PTEN and 3-methyl-1,8,9-anthracenetriol-PTK2. In molecular dynamics simulations, the 3-methyl-1,8,9-anthracenetriol-PTK2 complex showed lower RMSD, RMSF, radius of gyration, solvent-accessible surface area, and more hydrogen bonds than the 1-methylchrysene-PTEN complex. The 3-methyl-1,8,9-anthracenetriol-PTK2 complex exhibited significantly stronger binding with a binding free energy (ΔG) of - 21.92 kcal/mol compared to the less favourable ΔG of - 10.65 kcal/mol for the 1-methylchrysene-PTEN complex. The consistent and stable binding interaction observed in the 3-methyl-1,8,9-anthracenetriol-PTK2 complex highlights its potential as a potent inhibitor of Focal Adhesion Kinase 1. Consequently, it emerges as a promising lead compound for the development of pancreatic cancer therapeutics. Conversely, the fluctuations observed in the 1-methylchrysene-PTEN complex indicate a less stable binding interaction. This indicates the potential of 3-methyl-1,8,9-anthracenetriol as a primary candidate for pancreatic cancer treatment. These findings improve our grasp of S. singueana's multi-target effects and its promise in addressing pancreatic cancer. Nevertheless, additional in-vivo and in-vitro studies are required to validate their effectiveness and therapeutic potential.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00179-9.

Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.

In silico drug repurposing carvedilol and its metabolites against SARS-CoV-2 infection using molecular docking and molecular dynamic simulation approaches

The pandemic of coronavirus disease 2019 (COVID-19) caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a significant impact on the economy and public health worldwide. Therapeutic options such as drugs and vaccines for this newly emerged disease are eagerly desired due to the high mortality. Using the U.S. Food and Drug Administration (FDA) approved drugs to treat a new disease or entirely different diseases, in terms of drug repurposing, minimizes the time and cost of drug development compared to the de novo design of a new drug. Drug repurposing also has some other advantages such as reducing safety evaluation to accelerate drug application on time. Carvedilol, a non-selective beta-adrenergic blocker originally designed to treat high blood pressure and manage heart disease, has been shown to impact SARS-CoV-2 infection in clinical observation and basic studies. Here, we applied computer-aided approaches to investigate the possibility of repurposing carvedilol to combat SARS-CoV-2 infection. The molecular mechanisms and potential molecular targets of carvedilol were identified by evaluating the interactions of carvedilol with viral proteins. Additionally, the binding affinities of in vivo metabolites of carvedilol with selected targets were evaluated. The docking scores for carvedilol and its metabolites with RdRp were - 10.0 kcal/mol, - 9.8 kcal/mol (1-hydroxyl carvedilol), - 9.7 kcal/mol (3-hydroxyl carvedilol), - 9.8 kcal/mol (4-hydroxyl carvedilol), - 9.7 kcal/mol (5-hydroxyl carvedilol), - 10.0 kcal/mol (8-hydroxyl carvedilol), and - 10.1 kcal/mol (O-desmethyl carvedilol), respectively. Using the molecular dynamics simulation (100 ns) method, we further confirmed the stability of formed complexes of RNA-dependent RNA polymerase (RdRp) and carvedilol or its metabolites. Finally, the drug-target interaction mechanisms that contribute to the complex were investigated. Overall, this study provides the molecular targets and mechanisms of carvedilol and its metabolites as repurposed drugs to fight against SARS-CoV-2 infection.

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.

Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics

Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursuit of leveraging MI tools in automation and artificial intelligence (AI) to predict material properties in vitro and in vivo. However, the scarcity of large, standardized, and labeled materials data for connecting structure-function relationships represents one of the largest hurdles to overcome. In this Highlight, focus is brought to emerging developments in polymer-based therapeutic delivery platforms, where teams generate large experimental datasets around specific therapeutics and successfully establish a design-to-deployment cycle of specialized nanocarriers. Three select collaborations demonstrate how custom-built polymers protect and deliver small molecules, nucleic acids, and proteins, representing ideal use-cases for machine learning to understand how molecular-level interactions impact drug stabilization and release. We conclude with our perspectives on how MI innovations in automation efficiencies and digitalization of data-coupled with fundamental insight and creativity from the polymer science community-can accelerate translation of more gene therapies into lifesaving medicines.

A Multifaceted Computational Approach to Understanding the MERS-CoV Main Protease and Brown Algae Compounds' Interaction

Middle East Respiratory Syndrome (MERS) is a viral respiratory disease caused b a special type of coronavirus called MERS-CoV. In the search for effective substances against the MERS-CoV main protease, we looked into compounds from brown algae, known for their medicinal benefits. From a set of 1212 such compounds, our computer-based screening highlighted four-CMNPD27819, CMNPD1843, CMNPD4184, and CMNPD3156. These showed good potential in how they might attach to the MERS-CoV protease, comparable to a known inhibitor. We confirmed these results with multiple computer tests. Studies on the dynamics and steadiness of these compounds with the MERS-CoV protease were performed using molecular dynamics (MD) simulations. Metrics like RMSD and RMSF showed their stability. We also studied how these compounds and the protease interact in detail. An analysis technique, PCA, showed changes in atomic positions over time. Overall, our computer studies suggest brown algae compounds could be valuable in fighting MERS. However, experimental validation is needed to prove their real-world effectiveness.

A novel synthetic peptide analog enhanced antibacterial activity of the frog-derived skin peptide wuchuanin-A1

In recent years, there has been a growing focus on the development of novel antibacterial compounds for clinical applications, such as antimicrobial peptide (AMP). Among the developed AMP, wuchuanin-A1, a coil-shaped bioactive peptide derived from Odorrana wuchuanensis frog skin, has been reported to exhibit antibacterial, antifungal, and antioxidant activity, but there are limited studies on its potential as an antibacterial agent. Therefore, this study aims to molecularly modify the sequence of wuchuanin-A1 to enhance its antibacterial properties. The interaction of both the native and analog peptide with bacterial inner membranes was initially assessed using computational methods. Specific amino acid substitutions were then used to enhance the modified peptide's antibacterial efficacy, followed by several preliminary tests to evaluate its activity. This study bridges the gap in exploring the potential of wuchuanin-A1 for antibacterial purposes, providing insights into the design of effective antimicrobial agents.Communicated by Ramaswamy H. Sarma.

Synthesis, biological evaluation, and molecular docking study of xanthene-linked thiosemicarbazones as cholinesterase inhibitors

This study delineates the design and synthesis of a series of xanthene-based thiosemicarbazones that show low μM inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), crucial enzymes associated with, among others, Alzheimer's Disease (AD) pathology. Despite FDA-approved AChE inhibitors being frontline treatments for AD, there remains a need for agents exhibiting improved efficacy and selectivity. Our synthesized series demonstrate meaningful inhibition against AChE (IC50 ranging from 4.2 to 62 μM). These compounds exhibit comparatively lower potency against BChE (IC50 values between 64 and 315 μM), showcasing a pronounced AChE selectivity compared to physostigmine. The selectivity index for the compounds between the two targets does vary between 0.02 and 0.75 highlighting that even minor structural differences can have drastic effects on protein interactions. Molecular docking insights further substantiated these observations, revealing the importance of the xanthene scaffold for AChE-binding and the aryl R2 moiety for BChE interactions. Notably, some compounds demonstrated dual enzyme targeting, emphasizing their interactions could be exploited for developing monotherapies against cholinesterase-associated neurodegenerative afflictions like AD. Collectively, these findings suggest that xanthene-based thiosemicarbazones are a promising and highly accessible scaffold that deserve further investigative exploration in the cholinesterase inhibitor therapeutic landscape.Communicated by Ramaswamy H. Sarma.

Identification of natural antiviral drug candidates against Tilapia Lake Virus: Computational drug design approaches

Tilapia Lake Virus (TiLV) is a disease that affects tilapia fish, causing a high rate of sudden death at any stage in their life cycle. Unfortunately, there are currently no effective antiviral drugs or vaccines to prevent or control the progression of this disease. Researchers have discovered that the CRM1 protein plays a critical function in the development and spreading of animal viruses. By inhibiting CRM1, the virus's spread in commercial fish farms can be suppressed. With this in mind, this study intended to identify potential antiviral drugs from two different tropical mangrove plants from tropical regions: Heritiera fomes and Ceriops candolleana. To identify promising compounds that target the CRM1 protein, a computer-aided drug discovery approach is employed containing molecular docking, ADME (absorption, distribution, metabolism and excretion) analysis, toxicity assessment as well as molecular dynamics (MD) simulation. To estimate binding affinities of all phytochemicals, molecular docking is used and the top three candidate compounds with the highest docking scores were selected, which are CID107876 (-8.3 Kcal/mol), CID12795736 (-8.2 Kcal/mol), and CID12303662 (-7.9 Kcal/mol). We also evaluated the ADME and toxicity properties of these compounds. Finally, MD simulation was conducted to analyze the stability of the protein-ligand complex structures and confirm the suitability of these compounds. The computational study demonstrated that the phytochemicals found in H. fomes and C. candolleana could potentially serve as important inhibitors of TiLV, offering practical utility. However, further in vivo investigations are necessary to investigate and potentially confirm the effectiveness of these compounds as antiviral drugs against the virus TiLV.

The temperature-dependence of host-guest binding thermodynamics: experimental and simulation studies

The thermodynamic parameters of host-guest binding can be used to describe, understand, and predict molecular recognition events in aqueous systems. However, interpreting binding thermodynamics remains challenging, even for these relatively simple molecules, as they are determined by both direct and solvent-mediated host-guest interactions. In this contribution, we focus on the contributions of water to binding by studying binding thermodynamics, both experimentally and computationally, for a series of nearly rigid, electrically neutral host-guest systems and report the temperature-dependent thermodynamic binding contributions ΔGb(T), ΔHb(T), ΔSb(T), and ΔCp,b. Combining isothermal titration calorimetry (ITC) measurements with molecular dynamics (MD) simulations, we provide insight into the binding forces at play for the macrocyclic hosts cucurbit[n]uril (CBn, n = 7-8) and β-cyclodextrin (β-CD) with a range of guest molecules. We find consistently negative changes in heat capacity on binding (ΔCp,b) for all systems studied herein - as well as for literature host-guest systems - indicating increased enthalpic driving forces for binding at higher temperatures. We ascribe these trends to solvation effects, as the solvent properties of water deteriorate as temperature rises. Unlike the entropic and enthalpic contributions to binding, with their differing signs and magnitudes for the classical and non-classical hydrophobic effect, heat capacity changes appear to be a unifying and more general feature of host-guest complex formation in water. This work has implications for understanding protein-ligand interactions and other complex systems in aqueous environments.