Docking Algorithms Research Articles

Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope.

The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.

BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors.

Identifying small molecules that bind strongly to target proteins in rational molecular design is crucial. Machine learning techniques, such as generative adversarial networks (GAN), are now essential tools for generating such molecules. In this study, we present an enhanced method for molecule generation using objective-reinforced GANs. Specifically, we introduce BEGAN (Boltzmann-enhanced GAN), a novel approach that adjusts molecule occurrence frequencies during training based on the Boltzmann distribution exp(-ΔU/τ), where ΔU represents the estimated binding free energy derived from docking algorithms and τ is a temperature-related scaling hyperparameter. This Boltzmann reweighting process shifts the generation process toward molecules with higher binding affinities, allowing the GAN to explore molecular spaces with superior binding properties. The reweighting process can also be refined through multiple iterations without altering the overall distribution shape. To validate our approach, we apply it to the design of sex pheromone analogs targeting Spodoptera frugiperda pheromone receptor SfruOR16, illustrating that the Boltzmann reweighting significantly increases the likelihood of generating promising sex pheromone analogs with improved binding affinities to SfruOR16, further supported by atomistic molecular dynamics simulations. Furthermore, we conduct a comprehensive investigation into parameter dependencies and propose a reasonable range for the hyperparameter τ. Our method offers a promising approach for optimizing molecular generation for enhanced protein binding, potentially increasing the efficiency of molecular discovery pipelines.

In Silico Efficacy of Oleic Acid, n-Hexanoic acid, and Epiglobulol Obtained from Conocarpus Lancifolius against Class D β-lactamases

Introduction: Antimicrobial resistance poses an escalating global challenge, compounded by the sluggish pace of new antibiotic discovery. Objectives: This study undertook an in silico analysis of phytochemicals derived from Conocarpus lancifolius, a local plant in the Jazan region of Saudi Arabia, to investigate potential antibacterial agents. Methods: The 3D crystallographic structure of OXA-48 was sourced from the RCSB protein databank (PDBID: 3HBR) and pre-processed. The Lib Dock algorithm facilitated the analysis of optimal binding orientations and potential interactions between compounds and the 3HBR. Results: The docking analysis outcome revealed Oleic acid as the most active compound and the binding mode exhibited that the -OH group of the terminal carboxylic acid moiety formed hydrogen bonds with Thr197 and Ala207, and the oxygen atom showed bonding with Lys116. Moreover, N-hexanoic acid also demonstrated promising binding interactions with the 3HBR, displaying hydrogen bond interactions with Lys208, Tyr211, and Ser70, along with hydrophobic alkyl interactions with Lys208 and Met115 at the protein's active sites. Unfavourable donor-donor and acceptor-acceptor bindings were also observed with Ser70 and Arg250 residues. Conclusions: The OXA-48 and NDM-1 predominate in countries of the Arabian Peninsula. Our findings suggest oleic acid as a promising candidate for combating OXA-48 β-lactamase, pending further assessment of its atomic structure via X-ray crystallography.

Identification of novel inhibitors of plant GH3 IAA-amido synthetases through molecular docking studies.

Auxins play a critical role in several plant developmental processes and their endogenous levels are regulated at multiple levels. The enzymes of the GRETCHEN HAGEN 3 (GH3) protein family catalyze the conjugation of amino acids to indoleacetic acid (IAA), the major endogenous auxin. The GH3 proteins are encoded by multiple redundant genes in plant genomes, making it difficult to perform functional genetic studies to understand their role in auxin homeostasis. To address these challenges, we used a chemical approach that exploits the reaction mechanism of GH3 proteins to identify small molecule inhibitors of their activity from a defined chemical library. The study evaluated receptor-ligand complexes based on their binding energy and classified them accordingly. Docking algorithms were used to correct any deviations, resulting in a list of the most important inhibitory compounds for selected GH3 enzymes based on a normalized sum of energy. The study presents atomic details of protein-ligand interactions and quantifies the effect of several of the identified small molecule inhibitors on auxin-mediated root growth processes in Arabidopsis thaliana. The direct effect of these compounds on endogenous auxin levels was measured using appropriate auxin sensors and endogenous hormone measurements. Our study has identified novel compounds of the flavonoid biosynthetic pathway that are effective inhibitors of GH3 enzyme-mediated IAA conjugation. These compounds play a versatile role in hormone-regulated plant development and have potential applications in both basic research and agriculture.

Comparative Study of Docking Tools for Evaluation of Potential Copper Metallodrugs and Their Interaction with TMPRSS2

COVID-19 has caused over seven million deaths globally due to its high transmission rate. The virus responsible for the disease requires a transmembrane protease serine type II (TMPRSS2-7MEQ) to infiltrate host cells and has been linked to several cancers, particularly prostate cancer. To investigate COVID-19 potential therapies, a series of Casiopeina-like copper complexes containing 1,10-Phenanthroline and amino acids were investigated as TMPRSS2 inhibitors. The molecular structures of twelve Phenanthroline copper complexes were calculated, and their global reactivity indices were analyzed using DFT and conceptual DFT methods. Three molecular docking algorithms were employed to identify the most effective inhibitors by examining their interactions with amino acid residues in the target protein’s catalytic activity triad (Asp345, His296, and Ser441). All complexes are docked above the catalytic site, blocking the interaction with substrates. The Phenanthroline complexes showed better interactions than the Bipyridine complexes, likely due to increased hydrophobic contacts. Analogs’ cationic nature and amino acids’ basic side chains bring them near the active site by interacting with Asp435. The top complexes in this study contain Ornithine, Lysine, and Arginine, making them promising alternatives for researching new drugs for COVID-19 and cancers like prostate cancer.

P70S6K as a Potential Anti-COVID-19 Target: Insights from Wet Bench and In Silico Studies.

The onset of SARS-CoV-2 infection in 2019 sparked a global COVID-19 pandemic. This infection is marked by a significant rise in both viral and host kinase activity. Our primary objective was to identify a pivotal host kinase essential for COVID-19 infection and the associated phenomenon of the cytokine storm, which may lead to long-term COVID-19 complications irrespective of viral genetic variations. To achieve this, our study tracked kinase phosphorylation dynamics in RAW264.7 macrophages following SPIKE transfection over time. Among the kinases surveyed, p70S6K (RPS6KB1) exhibited a 3.5-fold increase in phosphorylation at S418. This significant change prompted the selection of p70S6K for in silico investigation, utilizing its structure bound to M2698 (PDB: 7N93). M2698, an oral dual Akt/p70S6K inhibitor with an IC50 of 1.1 nM, exhibited psychosis side effects in phase I clinical trials, potentially linked to its interaction with Akt2. Our secondary objective was to discover a small-molecule analogue of M2698 that exhibits a distinct binding preference for p70S6K over Akt2 through computational modeling and analysis. The in silico part of our project began with validating the prediction accuracy of the docking algorithm, followed by an OCA analysis pinpointing specific atoms on M2698 that could be modified to enhance selectivity. Subsequently, our investigation led to the identification of an analog of M2698, designated as S34, that showed a superior docking score towards p70S6K compared to Akt2. To further assess the stability of S34 in its protein-ligand (PL) complexes with p70S6K and Akt2, MD simulations were conducted. These simulations suggest that S34, on average, forms two hydrogen bond interactions with p70S6K, whereas it only forms one hydrogen bond interaction with Akt2. This difference in hydrogen bond interactions likely contributed to the observed larger root mean square deviation (RMSD) of 0.3 nm in the S34-Akt2 complex, compared to 0.1 nm in the S34-p70S6K complex. Additionally, we calculated free binding energy to predict the strength of the binding interactions of S34 to p70S6K and Akt2, which showed ~2-fold favorable binding affinity of S34 in the p70S6K binding pocket compared to that in the Akt2 binding pocket. These observations may suggest that the S34-p70S6K complex is more stable than the S34-Akt2 complex. Our work focused on identifying a host kinase target and predicting the binding affinity of a novel small molecule to accelerate the development of effective treatments. The wet bench results specifically highlight p70S6K as a compelling anti-COVID-19 target. Meanwhile, our in silico investigations address the known off-target effects associated with M2698 by identifying a close analog called S34. In conclusion, this study presents novel and intriguing findings that could potentially lead to clinical applications with further investigations.

Synthesis, characterization, anti-inflammatory activity and molecular docking studies of aromatic aldehydes substituted oxazine derivatives derived from 3-methoxy chalcone

In this study, a novel synthesis series (R4, D1-D10) of 4-[2-Amino-4-(3‑methoxy-phenyl)-6H-[1,3]oxazin-6-yl]-phenol derivatives was synthesised by the condensation of substituted aromatic aldehydes. Structures of the synthesised compounds were characterised using 1H and 13C nuclear magnetic resonance (NMR), fourier transform infrared spectroscopy (FT-IR), and mass spectrometry (MS) spectroscopic data. Further, the compounds were screened for in vivo anti-inflammatory activity by the carrageenan-induced paw edema method. Tested compounds have shown mild-to-moderate anti-inflammatory activity. The COX-2 selective compounds anti-inflammatory of five-oxazine derivatives (D7, D9, D5, D10, and D2) was modest. In comparison to the reference drug parecoxib (% edema inhibition = 29.2, 1 h; 23.30, 3 h; 21, 6 h), the most active compounds demonstrated exceptional In vivo anti-inflammatory action (% edema inhibition = 40–52, 1 h; 40.70–60.40, 3 h; 30–47.80, 6 h). Also, compounds 4-{6-(3-Methoxy-phenyl)-2-[(4-nitro-benzylidene)-amino]-6H-[1,3]oxazin-4-yl}-phenol (D7) and 4-[2-[(3-Fluoro-benzylidene)-amino]-6-(3-methoxy-phenyl)-6H-[1,3]oxazin-4-yl]-phenol (D9) have shown excellent anti-inflammatory action (% edema inhibition = 52–59.3 & 46.7–60.40). The molecular docking studies were also carried out at the active sites of COX-1 and COX-2 enzymes (PDB ID: 4O1Z, 4M11) using the docking algorithm offered by AutoDock Vina. The compounds D7, D9, and D5 exhibited good docking scores of −10.30, −10.20, and −10.10 onto the active site of COX-2 and least dock scores of −10.10, −9.80, and −10.00 on COX-1 enzymes and were comparable with standard COX-2 inhibitor parecoxib. A significant correlation was observed between the in silico and the in vivo studies. The anti-inflammatory and docking results highlight the fact that the synthesised compounds D7, D9, and D5 could be considered as possible hits as therapeutic agents.

Mutations in Plasmodium falciparum Kelch13 (PfK13) dysregulates PI3K/AKT signalling pathway in artemisinin resistance

BackgroundPfK13 protein mutations are associated with the emergence of artemisinin resistance in Plasmodium falciparum. PfK13 protein is essential for mediating ubiquitination and controlling the PI3K/AKT pathway. Mutant PfK13 variations can interfere with substrate binding, especially with PfPI3K, which raises PfPI3K levels. MethodsDUET, DynaMut2, mCSM, iStable 2.0, I-Mutant 2.0, and MuPro were utilized to study the protein stability and protein-substrate binding was studied using HADDOCK 2.4 docking algorithm between Wild-type and mutant PfK13 with the helical and catalytic domain of PfPI3K. Resultsi-Stable server analysis predicted that seven, out of the nine mutations associated with artemisinin resistance (F446I, Y493H, R539T, I543T, P553L, R561H, C580Y) reduced the protein stability. HADDOCK scores of the catalytic domain underscores the significant impact of the reported mutations on the binding affinity of the PfK13 protein. Further validation through the MM_GBSA technique, the binding free energy (DDG) between the wild-type and the mutant PfK13 protein analysis revealed a loss of interactions resulting from mutations in PfK13. ConclusionThe study finding suggest that mutations in the PfK13 cause destabilization in the protein structure and affects the binding of PfPI3K. Although the findings remain preliminary and require further validation, it provides the basis for further research considering the importance of the interaction of PfK13 and PfPI3K to overcome the impact of artemisinin resistance.

Identification of mycobacterial Thymidylate kinase inhibitors: a comprehensive pharmacophore, machine learning, molecular docking, and molecular dynamics simulation studies

Thymidylate kinase (TMK) is a pivotal enzyme in Mycobacterium tuberculosis (Mtb), crucial for phosphorylating thymidine monophosphate (dTMP) to thymidine diphosphate (dTDP), thereby playing a critical role in DNA biosynthesis. Dysregulation or inhibition of TMK activity disrupts DNA replication and cell division, making it an attractive target for anti-tuberculosis drug development. In this study, the statistically validated pharmacophore mode was developed from a set of known TMK inhibitors. Further, the robust pharmacophore was considered for screening the Enamine database. The chemical space was reduced through multiple molecular docking approaches, pharmacokinetics, and absolute binding energy estimation. Two different molecular docking algorithms favor the strong binding affinity of the proposed molecules towards TMK. Machine learning-based absolute binding energy also showed the potentiality of the proposed molecules. The binding interactions analysis exposed the strong binding affinity between the proposed molecules and active site amino residues of TMK. Several statistical parameters from all atoms MD simulation explained the stability between proposed molecules and TMK in the dynamic states. The MM-GBSA approach also found a strong binding affinity for each proposed molecule. Therefore, the proposed molecules might be crucial TMK inhibitors for managing Mtb inhibition subjected to in vitro/in vivo validations.

Purification of Potential Antimicrobial Metabolites from Endophytic Fusarium oxysporum Isolated from Myrtus communis.

The rise of microbial resistance and emerging infections pose significant health threats. Natural products from endophytic fungi offer a promising source of novel compounds with the potential as major drug leads. This research aims to screen Myrtus communis and Moringa oleifera for endophytic fungi and screen their metabolites for antibacterial and antifungal potential. Six endophytic fungal strains were isolated using a potato dextrose agar (PDA) medium. The M. communis isolates were designated MC1, MC2, and MC3, and the M. oleifera isolates were named MO1, MO2, and MO3. Preliminary bioactivity testing revealed that the MC3 isolate exhibited significant growth inhibition against multidrug-resistant bacterial and fungal pathogens, including Staphylococcus aureus, Enterococcus faecalis, Bacillus subtilis, Pseudomonas aeruginosa, Escherichia coli, Candida albicans, and Candida glabrata. The MC3 isolate was identified as Fusarium oxysporum through morphological and microscopic methods. For metabolite production, the fungal strain was cultured in potato dextrose broth (PDB) medium at 28°C for 14days in a shaking incubator. The metabolites were purified using various chromatographic techniques, HPLC and GC-MS. The GC-MS analysis of the bioactive compound containing fungal strain (F. oxysporum) revealed multiple compounds at different retention times using the NIST-20 Library. Based on RSI values and probability indices, two compounds were targeted for further purification. Structure elucidation was performed using 1D and 2D nuclear magnetic resonance (NMR) experiments on a Varian 500 NMR machine. The compounds identified were ethyl iso-allocholate (C26H44O5, exact mass 436.32) and 1-monolinoleoyl glycerol trimethylsilyl ether (C27H56O4Si2, exact mass 500.37). The MS (NIST-20) library facilitated the investigation of the in silico antimicrobial activity of these compounds against the elastase virulence protein of P. aeruginosa and protease Sapp1p from C. parapsilosis. Both the compounds were docked with druggable proteins using the Glide induced fit docking (IFD) algorithm. The ethyl iso-allocholate and 1-monolinoleoyl glycerol trimethylsilyl ether compounds showed binding scores - 10.07kcalmol-1 and - 7.47kcalmol-1 against elastase, and - 8.16kcalmol-1 and - 6.89kcalmol-1 against aspartic protease, respectively. In vitro studies confirmed the inhibitory activity of these compounds against multidrug-resistant P. aeruginosa and E. faecalis. Ethyl iso-allocholate exhibited higher bioactivity against P. aeruginosa with inhibition rates of 41%, 27%, and 35% at concentrations of 1000, 500, and 250µgmL-1, respectively. These results suggest that bioactive compounds from F. oxysporum have the potential as antimicrobial agents, warranting further research.

Insight into molecular basis and dynamics of full-length CRaf kinase in cellular signaling mechanisms

Raf kinases play key roles in signal transduction in cells for regulating proliferation, differentiation, and survival. Despite decades of research into functions and dynamics of Raf kinases with respect to other cytosolic proteins, understanding Raf kinases is limited by the lack of their full-length structures at the atomic resolution. Here, we present the first model of the full-length CRaf kinase obtained from artificial intelligence/machine learning algorithms with a converging ensemble of structures simulated by large-scale temperature replica exchange simulations. Our model is validated by comparing simulated structures with the latest cryo-EM structure detailing close contacts among three key domains and regions of the CRaf. Our simulations identify potentially new epitopes of intramolecule interactions within the CRaf and reveal a dynamical nature of CRaf kinases, in which the three domains can move back and forth relative to each other for regulatory dynamics. The dynamic conformations are then used in a docking algorithm to shed insight into the paradoxical effect caused by vemurafenib in comparison with a paradox breaker PLX7904. We propose a model of Raf-heterodimer/KRas-dimer as a signalosome based on the dynamics of the full-length CRaf.

An Accurate and Rapid Docking Algorithm for Four-Way Shuttle in High-Density 3D Warehousing Environment

High-density 3D warehousing is the future cornerstone of modern logistics storage. It aims to efficiently manage and store various types of goods by utilizing 3D space and automation technology. The accurate and fast docking algorithm of the four-way shuttles, as a core method of carrying the goods in high-density 3D warehousing, has become an increasingly important technical challenge. Inaccurate docking will result in a failure to change direction, while long docking time will affect the operation efficiency of the four-way shuttle. To overcome these obstacles, this paper presents an accurate and rapid docking algorithm for four-way shuttles. Firstly, the deceleration and jerk of a four-way shuttle in the braking stage are initialized according to the motion parameter set calculated by the docking motion model. Then, the four-way shuttle starts to move until the stop signal has been detected. If the system fails to detect the stop signal, the distance compensation is needed to ensure that the four-way shuttle can arrive at the stop point. Finally, the four-way shuttle stops immediately by way of amplifying the deceleration when reaching the stop point. The result shows that the accuracy of rapid docking is superior to that of direct docking. Compared with crawling docking, rapid docking reduces the braking time by 3.99s and stops at a speed lower than the crawling speed. The rapid docking algorithm not only enhances the throughput of high-density 3D warehousing but also improves the service life of four-way shuttles.

Covalent Flexible Peptide Docking in Drug Discovery: Current Challenges and Potential Interventions

Recently, there has been a significant increase in the search for new covalent inhibitors through drug discovery platforms, which has led to the development and implementation of new computational tools, including covalent docking methods to predict the binding mode and affinity of covalent ligands. Since the discovery of insulin nearly a century ago, more than 80 peptide medications have hit the market to treat a wide range of diseases, including diabetes, cancer, osteoporosis, multiple sclerosis, HIV infection, and chronic pain. Electrophilic peptides that form covalent bonds with target proteins have great potential for binding targets that have been previously considered undruggable. Despite the recent advancements in computational performance and docking algorithms, covalent docking still poses a number of challenges. Peptides covalent docking presents additional challenges, the main ones being the choice of the optimal peptide sequence, incorporation of electrophilic warheads, the inherent flexibility of peptide structures, and the fact that, unlike small molecules, peptides do fold. In this review, we present a brief overview of the current state of peptide therapeutics in drug discovery, covalent docking in general, covalent peptide binders, and-with particular emphasis-the difficulties that covalent peptide-protein docking is currently facing and some potential solutions.

A New Optimized Hybridization Approach for in silico High Throughput Molecular Docking on FPGA Platform.

The development process of a new drug should be a subject of continuous evolution and rapid improvement as drugs are essential to treat a wide range of diseases of which many are life-threatening. The advances in technology resulted in a novel track in drug discovery and development known as in silico drug design. The molecular docking phase plays a vital role in in silico drug development process. In this phase, thousands of 3D conformations of both the ligand and receptor are generated and the best conformations that create the most stable drug-receptor complex are determined. The speed in finding accurate and high-quality complexes depends on the efficiency of the search function in the molecular docking procedure. The objective of this research is to propose and implement a novel hybrid approach called hABCDE to replace the EMC searching part inside the BUDE docking algorithm. This helps in reaching the best solution in a much accelerated time and higher solution quality compared to using the ABC and DE algorithms separately. In this work, we have employed a new approach of hybridization between the Artificial Bee Colony (ABC) algorithm and the Differential Evolution (DE) algorithm as an alternative searching part of the Bristol University Docking Engine (BUDE) in order to accelerate the search for higher quality solutions. Moreover, the proposed docking approach was implemented on Field Programmable Gate Array (FPGA) parallel platform using Vivado High-Level Synthesis Tool (HLST) in order to optimize and enhance the execution time and overall efficiency. The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our approach. The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our approach. The results showed that the execution time for the BUDE with the new proposed hybridization approach was improved by 9,236 times. Our novel approach was significantly effective to improve the functionality of docking algorithms (Bristol University Docking Engine (BUDE)).

In silico analysis of the wild-type and mutant-type of BRCA2 gene

BackgroundThe aim of this study was to conduct an in silico analysis of a novel compound heterozygous variant in breast cancer susceptibility gene 2 (BRCA2) to clarify its structure–function relationship and elucidate the molecular mechanisms underlying triple-negative breast cancer (TNBC).MethodsA tumor biopsy sample was obtained from a 42-year-old Chinese woman during surgery, and a maxBRCA™ test was conducted using the patient’s whole blood. We obtained an experimentally determined 3D structure (1mje.pdb) of the BRCA2 protein from the Protein Data Bank (PDB) as a relatively reliable reference. Subsequently, the wild-type and mutant structures were predicted using SWISS-MODEL and AlphaFold, and the accuracy of these predictions was assessed through the SAVES online server. Furthermore, we utilized a high ambiguity-driven protein–protein docking (HADDOCK) algorithm and protein–ligand interaction profiler (PLIP) to predict the pathogenicity of the mutations and elucidate pathogenic mechanisms that potentially underlies TNBC.ResultsHistological examination revealed that the tumor biopsy sample exhibited classical pathological characteristics of TNBC. Furthermore, the maxBRCA™ test revealed two compound heterozygous BRCA2 gene mutations (c.7670 C > T.pA2557V and c.8356G > A.pA2786T). Through performing in silico structural analyses and constructing of 3D models of the mutants, we established that the mutant amino acids valine and threonine were located in the helical domain and oligonucleotide binding 1 (OB1), regions that interact with DSS1.ConclusionOur analysis revealed that substituting valine and threonine in the helical domain region alters the structure and function of BRCA2 proteins. This mutation potentially affects the binding of proteins and DNA fragments and disrupts interactions between the helical domain region and OB1 with DSS1, potentially leading to the development of TNBC. Our findings suggest that the identified compound heterozygous mutation contributes to the clinical presentation of TNBC, providing new insights into the pathogenesis of TNBC and the influence of compound heterozygous mutations in BRCA2.

A Targetable Self-association Surface of the Huntingtin exon1 Helical Tetramer Required for Assembly of Amyloid Pre-nucleation Oligomers

Polyglutamine (polyQ) sequences undergo repeat-length dependent formation of disease-associated, amyloid-like cross-β core structures with kinetics and aggregate morphologies often influenced by the flanking sequences. In Huntington’s disease (HD), the httNT segment on the polyQ’s N-terminal flank enhances aggregation rates by changing amyloid nucleation from a classical homogeneous mechanism to a two-step process requiring an ɑ-helix-rich oligomeric intermediate. A folded, helix-rich httNT tetrameric structure suggested to be this critical intermediate was recently reported. Here we employ single alanine replacements along the httNT sequence to assess this proposed structure and refine the mechanistic model. We find that Ala replacement of hydrophobic residues within simple httNT peptides greatly suppresses helicity, supporting the tetramer model. These same helix-disruptive replacements in the httNT segment of an exon-1 analog greatly reduce aggregation kinetics, suggesting that an ɑ-helix rich multimer – either the tetramer or a larger multimer – plays an on-pathway role in nucleation. Surprisingly, several other Ala replacements actually enhance helicity and/or amyloid aggregation. The spatial localization of these residues on the tetramer surface suggests a self-association interface responsible for formation of the octomers and higher-order multimers most likely required for polyQ amyloid nucleation. Multimer docking of the tetramer, using the protein–protein docking algorithm ClusPro, predicts this symmetric surface to be a viable tetramer dimerization interface. Intriguingly, octomer formation brings the emerging polyQ chains into closer proximity at this tetramer-tetramer interface. Further supporting the potential importance of tetramer super-assembly, computational docking with a known exon-1 aggregation inhibitor predicts ligand contacts with residues at this interface.

Enhancing plant-based cheese formulation through molecular docking and dynamic simulation of tocopherol and retinol complexes with zein, soy and almond proteins via SVM-machine learning integration

The current study addresses the growing demand for sustainable plant-based cheese alternatives by employing molecular docking and deep learning algorithms to optimize protein-ligand interactions. Focusing on key proteins (zein, soy, and almond protein) along with tocopherol and retinol, the goal was to improve texture, nutritional value, and flavor characteristics via dynamic simulations. The findings demonstrated that the docking analysis presented high accuracy in predicting conformational changes. Flexible docking algorithms provided insights into dynamic interactions, while analysis of energetics revealed variations in binding strengths. Tocopherol exhibited stronger affinity (−5.8Kcal/mol) to zein compared to retinol (−4.1Kcal/mol). Molecular dynamics simulations offered comprehensive insights into stability and behavior over time. The integration of machine learning algorithms improved the classification and the prediction accuracy, achieving a rate of 71.59%. This study underscores the significance of molecular understanding in driving innovation in the plant-based cheese industry, facilitating the development of sustainable alternatives to traditional dairy products.

Sparus Docking Station: A current aware docking station system for a non‐holonomic AUV

AbstractThis paper presents the design and development of a funnel‐shaped Sparus Docking Station intended for the non‐holonomic torpedo‐shaped Sparus II Autonomous Underwater Vehicle. The Sparus Docking Station is equipped with sensors and batteries, allowing for a stand‐alone long‐term deployment of the vehicle. An inverted Ultra Short Base‐Line system is used to locate the Docking Station as well as to provide long‐term drift‐less vehicle navigation. The Sparus Docking Station is able to observe the ocean currents using a Doppler Velocity Log, being motorized to allow its self‐alignment with the current. Moreover, a docking algorithm accounting for the current is used to guide the robot during the docking maneuver. The paper reports consecutive successful experimental results of the docking maneuver in sea trials in two different countries.

Combined computational and synthetic strategies for the development of potent pyrazolo-pyridine derivatives as anticancer agents

PI3K comprise a group of lipid kinases mainly made up of three classes and have a particular role in signal transduction. The aberration of class IA PI3K is most frequently shown in cancer. The computation techniques have been applied for the identification of potent compounds. This involved the generation of a library of novel compounds (2700) through R-group enumeration study. The screening process utilized a combination of pharmacophore-based virtual screening and various docking algorithms, leading to the discovery of ten novel compounds. The docking studies against PI3K kinase highlighted MSA6 and MSA7 as the most promising compounds, with docking scores of -9.03 and -9.0 kcal/mol, respectively. Results of docking studies of all derivatives confirmed satisfactory binding mode of the compounds within the active site of the target protein PI3K kinase. The screened pyrazolo-pyridine derivatives (MSA1-MSA10) were synthesized and evaluated as anti-cancer agents against NSCLC using A549 cell line. MSA6 showed the dose dependent enhancement in ROS production at concentration of 10 and 20 µM in A549 cells. MSA6 triggered caspase 3 cleavage in lung cancer cells in a dose-dependent manner. MSA6 treatment at 10 and 20 µM levels boosted NF-κβ expression in the cytoplasm while decreasing it in the nucleus, as demonstrated by flow cytometry with an anti-NF-κβ antibody. The modulation of PI3K activity have been observed at 10 µM and 20 µM concentration of MSA6. A549 cells treated with MSA6 with 20 µM showed significant reduction in wound healing in cancerous cell. A549 cells treated with 20 µM showed significantly reduction in number of colonies compared to control group. Compound MSA6 having a methyl group at the para position of phenyl ring at imine carbon and phenyl ring substitution at nitrogen of pyrazole ring exhibited remarkable growth inhibitory activity with IC50 value of 49.23 μM. Based on the above results, pyrazolo-pyridine derivatives can be used for the further development of novel PI3K kinase inhibitors as anticancer agents.

Molecular modeling of some commercially available antiviral drugs and their derivatives against SARS-CoV-2 infection.

Numerous prior studies have identified therapeutic targets that could effectively combat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, including the angiotensin-converting enzyme 2 (ACE2) receptor, RNA-dependent RNA polymerase (RdRp), and Main protease (Mpro). In parallel, antiviral compounds like abacavir, acyclovir, adefovir, amantadine, amprenavir, darunavir, didanosine, oseltamivir, penciclovir, and tenofovir are under investigation for their potential in drug repurposing to address this infection. The aim of the study was to determine the effect of modifying the functional groups of the aforementioned antivirals in silico. Using the genetic optimization for ligand docking algorithm on software Maestro (version 11.1), the modified antivirals were docked onto ACE2 receptor, RdRp, and Mpro. Using QuickProp (Maestro v11.1), PASS (prediction of activity spectra for the substances), and altogether with SwissADME, the ADMET (absorption, distribution, metabolism, excretion, and toxicity) of the modified antivirals, as well as their bioavailability and the predicted activity spectra, were determined. Discovery studio software was used to undertake post-docking analysis. Among the 10 antivirals, N(CH3)2 derivative of darunavir, N(CH3)2 derivative of amprenavir and NCH3 derivative of darunavir exhibited best binding affinities with ACE2 receptor (docking scores: -10.333, -9.527 and -9.695 kJ/mol, respectively). Moreover, NCH3 derivative of abacavir (-6.506 kJ/mol), NO2 derivative of didanosine (-6.877 kJ/mol), NCH3 derivative of darunavir (-7.618 kJ/mol) exerted promising affinity to Mpro. In conclusion, the results of the in silico screenings can serve as a useful information for future experimental works.