Computer-aided Drug Design Techniques Research Articles

Three decades targeting falcipains to develop antiplasmodial agents: what have we learned and what can be done next?

Malaria is a devastating infectious disease that affects large swathes of human populations across the planet's tropical regions. It is caused by parasites of the genus Plasmodium, with Plasmodium falciparum being responsible for the most lethal form of the disease. During the intraerythrocytic stage in the human hosts, malaria parasites multiply and degrade hemoglobin (Hb) using a battery of proteases, which include two cysteine proteases, falcipains 2 and 3 (FP-2 and FP-3). Due to their role as major hemoglobinases, FP-2 and FP-3 have been targeted in studies aiming to discover new antimalarials and numerous inhibitors with activity against these enzymes, and parasites in culture have been identified. Nonetheless, cross-inhibition of human cysteine cathepsins remains a serious hurdle to overcome for these compounds to be used clinically. In this article, we have reviewed key functional and structural properties of FP-2/3 and described different compound series reported as inhibitors of these proteases during decades of active research in the field. Special attention is also paid to the wide range of computer-aided drug design (CADD) techniques successfully applied to discover new active compounds. Finally, we provide guidelines that, in our understanding, will help advance the rational discovery of new FP-2/3 inhibitors.

Investigating the bioactive compounds from Capsicum annum as a probable alternative therapy for prostate cancer treatment: a structure-based drug design approach

Abstract Prostate cancer remains a significant global health challenge, necessitating the exploration of novel therapeutic approaches. Androgen receptor (AR) signaling plays a critical role in prostate cancer progression and is a primary target for therapy. This study investigates the potential of phytochemicals from Capsicum annuum (Bell pepper) along with two common standand drugs (Apalutamide and Enzalutamide) as inhibitors of the human androgen receptor (AR) and prostate-specific membrane antigen (PSMA). Utilizing computer-aided drug design techniques, molecular docking studies were conducted to evaluate the binding affinities of selected ligands against AR (PDB ID: 1XOW) and PSMA (PDB ID: 2XEI), their ADMET properties, drug-likeness, oral bioavailability, and bioactivity profiles were also examined. Coumaroylquinic acid and 5-O-caffeoylquinic acid methyl-ester emerged as top-performing ligands, demonstrating strong binding affinities of −9.4 kcal/mol and −9.2 kcal/mol, respectively, against PSMA. Additionally, molecular dynamics simulations provided insights into the stability of protein-ligand complexes, with Coumaroylquinic acid exhibiting a stable binding conformation throughout the simulation. These findings suggest the potential of C. annuum phytochemicals, particularly Coumaroylquinic acid and 5-O-caffeoylquinic acid methyl-ester, as promising inhibitors of PSMA. Moreover, other ligands (Caffeoylglucoside and 1-O-galloyl-beta-d-glucose) identified in the study demonstrate interactions with AR, highlighting a multifaceted approach to prostate cancer treatment. Overall, this study underscores the potential of C. annuum phytochemicals as a source of novel therapeutic agents for prostate cancer, laying the groundwork for further lead optimization efforts.

Abstract 1815: Optimization of Lin28 inhibitors: A promising therapeutic approach for the suppression of cancer cell proliferation, stemness properties, and spheroid growth

Abstract Background: The pluripotency factor known as Lin28 plays a crucial role in the regulation of cancer cell stem-like characteristics, hence facilitating the formation of cancer and the evolution of therapy-resistant tumors. The protein exerts its function by utilizing its cold shock domain and zinc knuckle domain (ZKD) to engage with the Let-7 pre-microRNA and impede the process of Let-7 production. The implementation of chemical agents to hinder the interaction between Lin28 and Let-7 has been proven as a therapeutic approach for treating aggressive tumors. Methods: Computer-aided drug design techniques were employed in the development of a novel series of small molecule inhibitors, which are derivatives of our previously identified lead compounds Ln115, Ln15, and Ln7. Fluorescent Polarization Assay (FP), and Electrophoretic Mobility Shift Assay (EMSA) were used to screen the chemicals as Lin28-Let7 interaction disruptors using Lin28A/B ZKD domain. To further screen the chemicals, the Incucyte cell proliferation assay was performed using two Lin28A and Lin28B positive cells, IGROV-1, and DuNE cells, respectively. The inhibition of Lin28 activity was subsequently evaluated by measuring Let-7 micro-RNA and Lin28 protein levels using RT-PCR and Western Blot. Then, the metabolism profile was explored using Microsomal Stability Assay. Incucyte proliferation assay and MTS assay determined the IC50 of lead compound in Lin28 positive cells. The anti-stemness properties of the lead compound were subsequently evaluated through assays for stem gene expression levels and colony formation. To evaluate the potential of the lead compound to further move into animal studies, we tested it on a newly developed 3D Neuroendocrine Prostate Cancer (NEPC) model. Results: In this study, we provide a novel set of derivatives, within which, Ln268 was chosen as the lead compound. Ln268 shows higher metabolic stability with a half-life time of 462 min and clearance rate of 5 ul/min/mg, compared to its father compound at 347 min and 46 ul/min/mg, respectively. Ln268 treatment resulted in a time-dependent inhibition of cell growth with an IC50 of 2.5 uM at 72 hrs. Ln268 reduces mRNA levels of stemness and neurodifferentiation markers such as CHGA, CHG, SYP, SCGN, OCT3/4, NANOG, ID4, FOXC1, ALDH1A2, and FOXO3. In the 3D NEPC model, Ln268 suppresses spheroid growth in a dose- and time-dependent manner. Ln268 achieved 100% spheroid growth inhibition at the concentration of 100 uM, while the current commercially available C1632 only reached 50%. Conclusion: We discovered a novel Ln15 derivative named Ln268, which has a better metabolism profile and increased inhibitory activity to distrust Lin28-Let7 interaction and cancer cell proliferation. Ln268 has the potential to be improved and used as an anti-cancer treatment for a variety of malignancies, such as prostate cancer. Citation Format: Victor M. Matias-Barrios, Mariia Radaeva, Graciella Rosellinny, Artem Cherkasov, Xuesen Dong. Optimization of Lin28 inhibitors: A promising therapeutic approach for the suppression of cancer cell proliferation, stemness properties, and spheroid growth [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1815.

Computational Molecular Docking and In-Silico, ADMET Prediction Studies of Quinoline Derivatives as EPHB4 Inhibitor

Background:: The creation and development of novel chemical entities is made possible by numerous computer-aided drug design techniques. The ability to visualize the ligand-target interaction and forecast the important holding pocket locations and affinities of ligands to their intended macromolecules is made possible by pharmacophore-based drug design and understanding in-silico methodologies. Objective:: The aim of the current investigation was to find novel 2-chloroquinoline-3-carboxamide derivatives that target the Ephrin B4 (EPHB4) receptor to treat cancer. Materials and Methods:: Chem Axon Marvin Sketch 5.11.5 was used to create derivatives of 2-chloroquinoline-3-carboxamide. The physicochemical characteristics of compounds as well as their toxicity were predicted using SwissADME& the admet SAR online software’s. Molecular docking technology was used to examine the ligand-receptor interactions of 2-chloroquinoline-3-carboxamide derivatives with the target receptor (PDB- 6FNM) using a variety of software’s, including Autodock1.1.2,Procheck, ProtParam tool, Biovia Discovery Studio Visualizer v20.1.0.19295, MGL Tools 1.5.6, PyMOL, and were all included. Results:: All developed compounds were determined to be orally bioavailable, less toxic, and have acceptable pharmacokinetic properties according to in silico studies. In comparison to the traditional medication Erdafitnib, all new compounds displayed higher docking scores. Conclusion:: The increase in binding energy and the number of H-bonds created by novel derivatives with interactions at distances below 3.40A provide a helpful starting point for formulating and synthesizing compounds that are most suitable for additional research. The application of the 2- chloroquinoline-3-carboxamide moiety as a potential new cancer treatment candidate is supported by its pharmacokinetics &toxicological profile, which may aid medicinal chemists in conducting more in-depth in vitro, in vivo chemical and pharmacological studies.

Exploring Lead-Like Molecules of Traditional Chinese Medicine for Treatment Quest against Aliarcobacter butzleri: In Silico Toxicity Assessment, Dynamics Simulation, and Pharmacokinetic Profiling

Background. Aliarcobacter butzleri is a Gram-negative, curved or spiral-shaped, microaerophilic bacterium and causes human infections, specifically diarrhea, fever, and sepsis. The research objective of this study was to employ computer-aided drug design techniques to identify potential natural product inhibitors of a vital enzyme in this bacterium. The pyrimidine biosynthesis pathway in its core genome fraction is crucial for its survival and presents a potential target for novel therapeutics. Hence, novel small molecule inhibitors were identified (from traditional Chinese medicinal (TCM) compound library) against it, which may be used for possible curbing of infection by A. butzleri. Methods. A comprehensive subtractive genomics approach was utilized to identify a key enzyme (orotidine-5′-phosphate decarboxylase) cluster conserved in the core genome fraction of A. butzleri. It was selected for inhibitor screening due to its vital role in pyrimidine biosynthesis. TCM library (n>36,000 compounds) was screened against it using pharmacophore model based on orotidylic acid (control), and the obtained lead-like molecules were subjected to structural docking using AutoDock Vina. The top-scoring compounds, ZINC70454134, ZINC85632684, and ZINC85632721, underwent further scrutiny via a combination of physiological-based pharmacokinetics, toxicity assessment, and atomic-scale dynamics simulations (100 ns). Results. Among the screened compounds, ZINC70454134 displayed the most favorable characteristics in terms of binding, stability, absorption, and safety parameters. Overall, traditional Chinese medicine (TCM) compounds exhibited high bioavailability, but in diseased states (cirrhosis, renal impairment, and steatosis), there was a significant decrease in absorption, Cmax, and AUC of the compounds compared to the healthy state. Furthermore, MD simulation demonstrated that the ODCase-ZINC70454134 complex had a superior overall binding affinity, supported by PCA proportion of variance and eigenvalue rank analysis. These favorable characteristics underscore its potential as a promising drug candidate. Conclusion. The computer-aided drug design approach employed for this study helped expedite the discovery of antibacterial compounds against A. butzleri, offering a cost-effective and efficient approach to address infection by it. It is recommended that ZINC70454134 should be considered for further experimental analysis due to its indication as a potential therapeutic agent for combating A. butzleri infections. This study provides valuable insights into the molecular basis of biophysical inhibition of A. butzleri through TCM compounds.

New Drug Design to Suppress Nonalcoholic Steatohepatitis

A de novo designed biomolecule called INASHD was utilized through computer-aided drug design techniques to specifically target β2-spectrin, effectively suppressing and preventing NASH disease. Advanced computational software tools concerning the technologies of molecular docking and molecular dynamics (MD), were employed to showcase the drug's remarkable ability to efficiently suppress and control the α-helical topology of β2-spectrin. This protein is a vital component within the disease pathway. We successfully devised an effective design suppressing β2-spectrin, exhibiting an inhibition score surpassing any other molecule documented in scientific literature. With robust support from validated computational software, this bioorganic structure holds significant value and can be applied for a patent due to its innovative design. It shows promising potential for delivering positive outcomes in various stages, including in vitro, in vivo, ex vivo, and human phase studies.

Characterization of prevalent tyrosine kinase inhibitors and their challenges in glioblastoma treatment.

Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non-allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients' survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future.

In-silico Binding, Stability, Pharmacokinetics, and Toxicity Studies on Natural (-)-ambrox Metabolites as Binding Ligands to Luminal B and Triple- Negative/basal-like Proteins for Breast Cancer Therapy

Background:: Breast cancer is the most prevalent malignant tumour in women of all races and is the second largest cause of cancer-related death in the majority of races. Based on the pattern of gene expression, five intrinsic or molecular classifications for breast tumours are frequently used. Our research, which is presently being utilized to treat breast cancer and has the potential to significantly change the course of the illness, is focused on two of them: luminal B breast cancer and triplenegative/ basal-like breast cancer. Methods:: Screening a database containing millions of drug molecules or phytochemicals has become rapid and simple due to computer-aided drug design (CADD) techniques. In the current work, nine natural compounds were screened for ambrox from a sperm whale using docking research. Results:: Following docking studies, nine substances were discovered to interact with basal-like and luminal B breast cancer proteins. All nine metabolites, however, adhered to Lipinski's rule of five and had sufficient oral bioavailability. The greatest binding affinities were demonstrated by 13,14,15,16-tetranorlabdane-3-oxo-8,12-diol, 6-β-hydroxy ambrox, 1-α-hydroxy-3-oxoambrox, and 2-α-3-β-dihydroxy ambrox. Conclusion:: Therefore, it can be concluded that research on molecular docking and pharmacological mimics may hasten the discovery of new medications. The use of ambrox metabolites in the treatment of breast cancer also requires future perspectives on their therapeutic use.

Molecular dynamics exploration of Lupenone: therapeutic implications for glioblastoma multiforme and alzheimer's amyloid beta pathogenesis.

Neuro-oncological and neurodegenerative disorders, represented paradigmatically by glioblastoma and Alzheimer's disease, respectively, persist as formidable challenges in the biomedical realm. The interconnected molecular underpinnings of these conditions necessitate rigorous and novel therapeutic examinations. This comprehensive research was anchored on the premise of unveiling the therapeutic potential and specificity of Lupenone, a potent phytoconstituent, in targeting the molecular pathways underpinning both glioblastoma and Alzheimer's amyloid beta pathology. This was gauged through its interactions with key protein structures, 5H08 and 2ZHV. An integrative approach was adopted, marrying advanced proteomics and modern computer-aided drug design techniques. Molecular docking of Lupenone with 5H08 and 2ZHV was meticulously executed, with subsequent molecular dynamics simulations providing insights into the stability, viability, and intricacies of these interactions. Lupenone demonstrated profound binding affinities, evidenced by robust docking scores of -9.54kcal/mol for 5H08 and -10.59kcal/mol for 2ZHV. These interactions underscored Lupenone's eminent therapeutic potential in mitigating glioblastoma and modulating the amyloid beta pathology inherent to Alzheimer's. The introduction of Proteolysis Targeting Chimeras (PROTACs) further magnified the therapeutic prospects, accentuating Lupenone's efficacy. The findings of this study not only underscore the therapeutic acumen of Lupenone in addressing the challenges posed by glioblastoma and Alzheimer's but also lay a strong foundation for its consideration as a leading candidate in future neuro-oncological and neurodegenerative research endeavors. Given the compelling in-silico data, a clarion call is made for its empirical validation in holistic in-vivo settings, potentially pioneering a new therapeutic epoch in both glioblastoma and Alzheimer's interventions.

Computational 3D Modeling-Based Identification of Inhibitors Targeting Cysteine Covalent Bond Catalysts for JAK3 and CYP3A4 Enzymes in the Treatment of Rheumatoid Arthritis.

This work aimed to find new inhibitors of the CYP3A4 and JAK3 enzymes, which are significant players in autoimmune diseases such as rheumatoid arthritis. Advanced computer-aided drug design techniques, such as pharmacophore and 3D-QSAR modeling, were used. Two strong 3D-QSAR models were created, and their predictive power was validated by the strong correlation (R2 values > 80%) between the predicted and experimental activity. With an ROC value of 0.9, a pharmacophore model grounded in the DHRRR hypothesis likewise demonstrated strong predictive ability. Eight possible inhibitors were found, and six new inhibitors were designed in silico using these computational models. The pharmacokinetic and safety characteristics of these candidates were thoroughly assessed. The possible interactions between the inhibitors and the target enzymes were made clear via molecular docking. Furthermore, MM/GBSA computations and molecular dynamics simulations offered insightful information about the stability of the binding between inhibitors and CYP3A4 or JAK3. Through the integration of various computational approaches, this study successfully identified potential inhibitor candidates for additional investigation and efficiently screened compounds. The findings contribute to our knowledge of enzyme-inhibitor interactions and may help us create more effective treatments for autoimmune conditions like rheumatoid arthritis.

Natural product rhynchophylline prevents stress-induced hair graying by preserving melanocyte stem cells via the β2 adrenergic pathway suppression

Norepinephrine (NA), a stress hormone, can accelerate hair graying by binding to β2 adrenergic receptors (β2AR) on melanocyte stem cells (McSCs). From this, NA-β2AR axis could be a potential target for preventing the stress effect. However, identifying selective blockers for β2AR has been a key challenge. Therefore, in this study, advanced computer-aided drug design (CADD) techniques were harnessed to screen natural molecules, leading to the discovery of rhynchophylline as a promising compound. Rhynchophylline exhibited strong and stable binding within the active site of β2AR, as verified by molecular docking and dynamic simulation assays. When administered to cells, rhynchophylline effectively inhibited NA-β2AR signaling. This intervention resulted in a significant reduction of hair graying in a stress-induced mouse model, from 28.5% to 8.2%. To gain a deeper understanding of the underlying mechanisms, transcriptome sequencing was employed, which revealed that NA might disrupt melanogenesis by affecting intracellular calcium balance and promoting cell apoptosis. Importantly, rhynchophylline acted as a potent inhibitor of these downstream pathways. In conclusion, the study demonstrated that rhynchophylline has the potential to mitigate the negative impact of NA on melanogenesis by targeting β2AR, thus offering a promising solution for preventing stress-induced hair graying.Graphical

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.Graphical abstract

Design of Novel Imidazopyrazine Derivative against Breast Cancer via Targeted NPY1R Antagonist.

Breast cancer is the most frequent malignancy in women with more than one in ten new cancer diagnoses each year. Synthetic products are a key source for the identification of new anticancer medicines and drug leads. Imidazopyrazine is a highly favored skeleton for the design of new anticancer drugs. In silico designed derivatives were screened using computer aided drug design techniques and validated using MTT assay. A template-based methodology was used in the current work to create novel Imidazopyrazine derivatives, targeting the NPY1R protein. Molecular docking, Diffusion docking, MD simulation, MM-GBSA and meta-dynamics techniques were followed. MTT assay was performed to validate the activity of principal compound. A docking score of -6.660 and MMGBSA value of -108.008 (+/-) 9.14 kcal/mol was obtained from the investigations conducted. In addition, molecular dynamics simulation was carried out for 500 ns, yielding a stable RMSD and value of 5.6 Å, thus providing insights on the stability of the protein conformation on interaction with the principal compound. Furthermore, the in vivo validation studies conducted via MTT assay showed an IC50 value of 73.45 (+/-) 0.45 μg /mL. The research has produced encouraging findings and can be applied as a model for precise enumerations in the future. It also encourages the study of novel synthetic compounds with potential anti-cancer properties.

Editorial: Hot trends in computer-aided drug design techniques

Editorial: Hot trends in computer-aided drug design techniques

HIV-1/HBV Coinfection Accurate Multitarget Prediction Using a Graph Neural Network-Based Ensemble Predicting Model.

HIV and HBV infection are both serious public health challenges. There are more than approximately 4 million patients coinfected with HIV and HBV worldwide, and approximately 5% to 15% of those infected with HIV are coinfected with HBV. Disease progression is more rapid in patients with coinfection, which significantly increases the likelihood of patients progressing from chronic hepatitis to cirrhosis, end-stage liver disease, and hepatocellular carcinoma. HIV treatment is complicated by drug interactions, antiretroviral (ARV) hepatotoxicity, and HBV-related immune reconditioning and inflammatory syndromes. Drug development is a highly costly and time-consuming procedure with traditional experimental methods. With the development of computer-aided drug design techniques, both machine learning and deep learning have been successfully used to facilitate rapid innovations in the virtual screening of candidate drugs. In this study, we proposed a graph neural network-based molecular feature extraction model by integrating one optimal supervised learner to replace the output layer of the GNN to accurately predict the potential multitargets of HIV-1/HBV coinfections. The experimental results strongly suggested that DMPNN + GBDT may greatly improve the accuracy of binary-target predictions and efficiently identify the potential multiple targets of HIV-1 and HBV simultaneously.

In-silico Validation of Pyrazolone Derivatives as the Potent Scaff old for Modulating Protein Abnormalities Associated with Parkinson’s Disease

Aim: We explored pyrazolone derivatives for anti-Parkinson’s activity using in-silico tools to identify novel lead hits against Parkinson’s disease. Background: Parkinson’s disease is the most predominant neuronal degenerative disorder, caused by protein aggregation and dopamine imbalance. The available therapeutic agents on prolonged exposure lead to severe adverse eff ects and provide only symptomatic relief. Therefore, it is a need for novel drug molecules that would modulate the disease condition. Objectives: To identify novel hit molecule for a sequence of molecules designed using pyrazolone as a parent moiety by computer-aided drug design techniques. Materials and Methods: Derivatives are generated by various substituted functional groups and further, pharmacokinetic profi le, the biological spectrum, adverse drug eff ect, molecular docking, molecular dynamic, and MMPBSA evaluation was performed. Results: Thirty-four compounds follow a pharmacokinetic profi le. 15 compounds were predicted to possess a positive central nervous system activity score. The compounds C13, C12, C14, A1, C9, and C7 possessed the highest binding affi nity of -7.81, -3.15, -8.49, -3.43, -6.09, and -2.77 kcal/mol with various targets involved in Parkinson’s disease. Compound C13 exhibit ed highest binding score of -9.78 kcal/mol formono amino oxidase-B. Conclusion: From our investigations, we hope that novel substituted pyrazolone derivatives can act as an assuring virtual hit molecule for developing anti-Parkinson’s agents. These predictions obtained via in-silico techniques could aid the development of pharmacological inhibitors for Parkinson’s disease.

Designing Bcl-2 protein binding aptamers using screened Coulomb interaction method & regulate apoptosis.

Cancer cells develop different physiological abnormalities. One of them is overexpression of Bcl-2 protein, an anti-apoptotic biomolecule helping cancer cells to sustain. We planned to regulate the apoptosis by inhibiting the Bcl-2 protein's functions in cancer cells. We used computer aided drug design techniques to make a nucleic acid-based aptamer drug that targets Bcl-2 proteins. We applied a theoretical biophysics technique ‘screened Coulomb interactions approach (SCIA)’ for designing aptamers. This technique was patented (US10916330B1) and found suitable for designing novel aptamers to target biomoleculesin physiological conditions. Physicochemical analysis of Bcl-2 protein helps detect drug binding site(s) and charge distribution inamino acid structures. We made a judicious choice to select amino acid arginine as our target molecule in Bcl-2 structure. We constructed a universal drug design platform by applying SCIA, developed our algorithms and applied in Mathematica 13 to perform numerical computations (NCs) addressing the drug-target binding energetics. The NC results show that nucleotide guanine has the lowest binding energy over other nucleotides while interacting with arginine. Selection of other subsequent nucleotides as aptamer building blocks at various screening order is being made using their preferred SCI binding energetics with arginine. Thus, the discovery of a few aptamers to target arginine (specifically) and Bcl-2 protein (in general) has been made. Based on target binding energetic preferences we rank the aptamers. We are now working on the following two things: (i) perform 100 nanosecond molecular dynamics (MD) simulations to address drug-target binding kinetics and (ii) perform in vitro protein binding assays using top selected aptamers. Thus, we shall be able to finalize a few candidate drugs as optimal therapeutic agents capable of binding with Bcl-2 proteins and thus inhibit the protein functions. This research will lead us to discovering a few aptamer candidates for future chemotherapy applications.

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs.

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

Obstructing Salmonella typhi's virulence in eukaryotic cells through design of its SipB protein antagonists

ObjectiveTyphoid fever, a disease caused by Salmonella typhi, is a leading cause of morbidity and mortality, particularly in developing nations. The evolution of resistance mechanisms to existing antibiotics has necessitated a search for newer drug candidates. This study used computer aided drug design techniques to design novel antibiotics that function by antagonizing SipB, an effector protein of the bacterium that is responsible for its pathogenicity and virulence in eukaryotic host cells. MethodsA data set of 32 bioactive molecules with established antibacterial activity against S. typhi was retrieved from the PubChem data base; optimized through a DFT approach in Spartan 14 software; and further subjected to QSAR modeling in BIOVIA-Accelrys Material Studio. The validated model (R2 = 0.80, R2Adj = 0.78, Q2LOO = 0.74, R2pred = 0.54, lack of fit = 0.07) revealed the dominant influence of MATS6c and E3p descriptors on the observed antibacterial activity of the compounds. Information from the model was used to optimize the structures of selected lead compounds in the data set, thus leading to the design of a highly potent set of novel analogues denoted D-1, D-2 and D-3. ResultsThe predicted MIC values of D-1, D-2 and D-3 were 1.03, 0.73 and 0.30 μg/mL, respectively. Furthermore, molecular docking studies on these novel ligands against the active sites of SipB revealed binding energy values of −8.0, −7.7 and −7.7 kcal/mol for D-1, D-2 and D-3, respectively. These values were better than the −7.0 kcal/mol recorded for ciprofloxacin, the reference antibiotic used herein for quality assurance. In addition, drug-likeness and ADMET evaluation of the designed compounds revealed that they are orally bioavailable and exhibit excellent pharmacokinetic and toxicological profiles. ConclusionThe current findings may provide a roadmap for the discovery of more potent antibiotics against S. typhi.

In silico investigation of acyclovir derivatives potency against herpes simplex virus

Herpes simplex virus (HSV) is a major human pathogen, sub-divided into two types: HSV-1 which causes oral and perioral infections and HSV-2 which causes genital herpes. The global disease burden is high with 67% and 11% of the World's population being infected with HSV-1 and HSV-2 respectively. Acyclovir, an FDA approved drug, is a synthetic purine nucleoside analogue used for the treatment of HSV infections. The drug acts via the viral encoded thymidine kinase and competitively inhibits viral DNA polymerase. Emerging HSV resistance to acyclovir and lack of approved vaccines necessitates need for effective strategies to circumvent the viral infection. Derivatives of acyclovir were modelled using computer aided drug design techniques through Chemsketch and drugability determined based on Lipinski's Ro5 where a Python filtering code was created and derivatives with zero non-violations were selected. Putative biological targets were determined through network pharmacology and validated through gene ontology. Molecular docking with known and putative targets was done to determine the binding affinities of the acyclovir derivatives. Based on enzyme inhibition scores, pharmacokinetics prediction was done on Molinspiration while pharmacodynamics predictions were done on AdmetSAR based on bioavailability scores. Thirty acyclovir derivatives were modelled of which 22 had zero non-violations. 2-[(3, 6-dihydro-9H-purin-9-yl)methoxy]ethan-1-ol) had the highest enzyme inhibition score at 1.0 compared to 0.84 for acyclovir. The molecule was further optimized generating (2‐[(6‐methyl‐6,9‐dihydro‐3H‐purin‐9‐yl)methoxy]ethan‐1‐ol which had a better bioavailability and a higher blood brain barrier value of 0.9880 indicating possible better patient tolerance. 2‐[(6‐methyl‐6,9‐dihydro‐3H‐purin‐9‐yl)methoxy]ethan‐1‐ol had an enzyme inhibition score of 0.90 and binding energies of -5.1 kcal/mol when docked against thymidine kinase. The intra-viral HSV PPI network was analysed and DNA replication helicase (UL5) and serine /threonine-protein kinase (US3) were selected due to functional similarity with thymidine kinase with kappa statistics value of 0.30 and 0.24 respectively. Gene enrichment analysis indicated that the biological targets UL5 and US3 had a significant P value of 0.62 based on Benjamin Correction at P<0.05. The study recommends in vitro and in vivo validation of this novel compound and molecular validation of the two new potential biological targets for possible development of new anti-herpes therapeutic compounds.