Docking Performance Research Articles

Exploring therapeutic potentials of Tridax procumbens for Glioma: An in-silico approach employed network pharmacology, molecular docking, and molecular dynamics simulation

Glioma is a frequent type of malignant brain tumor, originating from glial cells. Despite advances in understanding the molecular biology and genetics of gliomas, the treatment of high-grade gliomas remains challenging. This study focuses on the identification of potentially active compounds from the medicinal plant Tridax procumbens for the treatment of glioma. Using computational approaches such as virtual screening, ADMET profiling, network pharmacology, molecular docking, and molecular dynamics, ten potential phytocompounds were identified from a total of 106 compounds. Further analysis revealed a network of 170 common genes between the phytochemicals and disease genes, comprising 161 nodes and 2083 edges. Molecular docking experiments demonstrated that luteolin, quercetin, and 6-Hydroxyluteolin 6,3′-dimethyl ether 5-rhamnoside exhibited potential inhibitory activities against two targeted proteins, HSP90 and SRC. Luteolin with HSP90 protein and Quercetin with SRC protein complexes were found to be the highest in docking performance with the least binding affinities −10 kcal mol−1 and −9.3 kcal mol−1 respectively. Molecular dynamics simulations explored Quercetin-SRC and Luteolin-HSP90 interactions over 300 nanoseconds. Quercetin-SRC binding showed dynamic and flexible behavior with an RMSD, RMSF, and radius of gyration of 0.22 nm, 0.35 nm, and 2.12 nm, respectively, whereas Luteolin-HSP90 binding was more stable and less flexible, with an RMSD of 0.14 nm, RMSF of 0.14 nm, a radius of gyration of 1.74 nm, and maintained consistent strong interactions with specific solvent accessibility. The analysis of free binding energy in Quercetin-SRC and Luteolin-HSP90 complexes indicated highly favorable formation driven by altered interactions, with delta total values of −33.74 and −29.26 kcal/mol, respectively. Both phytochemical luteolin and quercetin showed strong inhibitory potential against HSP90 and SRC proteins, indicating their potential as effective therapeutic agent for glioma treatment. This integrated approach emphasizes the significance of these compounds in advancing treatment strategies for glioma. However, pre-clinical and clinical experimentations are required to establish the potential of T. procumbens as a treatment for glioma.

The docking control system of an autonomous underwater vehicle combining intelligent object recognition and deep reinforcement learning

This study develops a visual-based docking system (VDS) for an autonomous underwater vehicle (AUV), significantly enhancing docking performance by integrating intelligent object recognition and deep reinforcement learning (DRL). The system overcomes traditional navigation limitations in complex and unpredictable environments by using a variable information dock (VID) for precise multi-sensor docking recognition in the AUV. Employing image-based visual servoing (IBVS) technology, the VDS efficiently converts 2D visual data into accurate 3D motion control commands. It integrates the YOLO (short for You Only Look Once) algorithm for object recognition and the deep deterministic policy gradient (DDPG) algorithm, improving continuous motion control, docking accuracy, and adaptability. Experimental validation at the National Cheng Kung University towing tank demonstrates that the VDS enhances control stability and operational reliability, reducing the mean absolute error (MAE) in depth control by 42.03% and pitch control by 98.02% compared to the previous method. These results confirm the VDS's reliability and its potential for transforming AUV docking.

Assessing small molecule conformational sampling methods in molecular docking.

Small molecule conformational sampling plays a pivotal role in molecular docking. Recent advancements have led to the emergence of various conformational sampling methods, each employing distinct algorithms. This study investigates the impact of different small molecule conformational sampling methods in molecular docking using UCSF DOCK 3.7. Specifically, six traditional sampling methods (Omega, BCL::Conf, CCDC Conformer Generator, ConfGenX, Conformator, RDKit ETKDGv3) and a deep learning-based model (Torsional Diffusion) for generating conformational ensembles are evaluated. These ensembles are subsequently docked against the Platinum Diverse Dataset, the PoseBusters dataset and the DUDE-Z dataset to assess binding pose reproducibility and screening power. Notably, different sampling methods exhibit varying performance due to their unique preferences, such as dihedral angle sampling ranges on rotatable bonds. Combining complementary methods may lead to further improvements in docking performance.

Improving docking and virtual screening performance using AlphaFold2 multi-state modeling for kinases

Structure-based virtual screening (SBVS) is a crucial computational approach in drug discovery, but its performance is sensitive to structural variations. Kinases, which are major drug targets, exemplify this challenge due to active site conformational changes caused by different inhibitor types. Most experimentally determined kinase structures have the DFGin state, potentially biasing SBVS towards type I inhibitors and limiting the discovery of diverse scaffolds. We introduce a multi-state modeling (MSM) protocol for AlphaFold2 (AF2) kinase structures using state-specific templates to address these challenges. Our comprehensive benchmarks evaluate predicted model qualities, binding pose prediction accuracy, and hit compound identification through ensemble SBVS. Results demonstrate that MSM models exhibit comparable or improved structural accuracy compared to standard AF2 models, enhancing pose prediction accuracy and effectively capturing kinase-ligand interactions. In virtual screening experiments, our MSM approach consistently outperforms standard AF2 and AF3 modeling, particularly in identifying diverse hit compounds. This study highlights the potential of MSM in broadening kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors, offering a promising solution to the structural bias problem in kinase-targeted drug discovery.

QSAR Modeling, Molecular Docking and ADMET Study of Aryl Fluorosulfate Derivatives as Potential Anti-TB Agents

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb) infection, stands as a global infectious disease presenting substantial public health challenges due to its high incidence and mortality rates. The prolonged use of conventional anti-TB therapies has led to the emergence of severe drug resistance in Mtb, resulting in extended treatment durations, increased costs, poor patient compliance, and reduced cure rates. This phenomenon has posed a significant burden on global TB prevention and control efforts, necessitating a shift in research focus toward the exploration of novel anti-TB drugs. In this context, utilizing QSAR modeling methods, our study systematically investigated the relationship between the chemical structures of 36 aryl fluorosulfate derivatives and their inhibitory activity against Mtb. Robust and predictive Topomer CoMFA and HQSAR models were developed, featuring Topomer CoMFA model parameters: q 2 = 0.659, r 2 = 0.969, F = 102.877, N = 6, SEE = 0.138; HQSAR model parameters: q 2 = 0.705, r 2 = 0.873, SEE = 0.264, HL = 199, N = 4. Leveraging these models, structural modifications were applied to the compounds using the ZINC15 database, leading to the successful design and screening of three novel compounds with desirable inhibitory activity. Molecular docking and ADMET performance prediction results indicated that these three new compounds exhibit strong binding capabilities and promising pharmaceutical potential. This study provides valuable insights and research directions for the development of aryl fluorosulfate derivatives as potential agents for tuberculosis treatment and as novel drugs.

In-Silico exploration: Unraveling the anti-cancer potential of 8-Nitroquinoline hydrazides

The proliferation of cancer has propelled the scientific community to expedite drug discovery to nullify the threats of cancers on health segment. Addressing the adverse impact of cancer on health necessitates the effective solution. The profound activity of quinoline core appended heterocyclic derivatives against cancer proliferation impacts their significance in cancer pathology. The hydrazides of 8-nitroquinoline derivatives were synthesised, and their structures were characterized through IR, NMR and single crystal XRD techniques. Initially, the theoretical calculations like green chemistry metrics and DFT studies of the 6a-c have successfully elucidated through FMO, molecular reactivity descriptors, MEP mapping approaches. Outcomes of the studies clearly indicate that the synthesised quinoline hydrazides were highly capable to interact with the biological target entities. In-Silico studies explicit the role of organic small molecules and its efficiency to be the potent drug candidates in therapeutics. Computationally the ADMET studies and the molecular docking performance of 6a-c demonstrated to expose the pharmacokinetic behaviour and binding probability assessments with 11 different oncogenic receptors. The compound 6c could be found as favourable drug molecule adhered from its drug likeness, which comply with most of the ADMET properties. Moreover notably, methyl substituted 6c exhibits its potential binding efficiency with CDK2 and PI3K macromolecules bearing -8.7 & -7.5 kcal/mol binding affinity over 6a & 6b among the selected oncogenic proteins. The insights of in-silico studies could be validated further through in-vitro and in-vivo wet lab research works.

A-RFP: An Adaptive Residue Flexibility Prediction Method Improving Protein-ligand Docking Based on Homologous Proteins

Background: Computational molecular docking plays an important role in determining the precise receptor-ligand conformation, which becomes a powerful tool for drug discovery. In the past 30 years, most computational docking methods have treated the receptor structure as a rigid body, although flexible docking often yields higher accuracy. The main disadvantage of flexible docking is its significantly higher computational cost. Due to the fact that different protein pocket residues exhibit different degrees of flexibility, semi-flexible docking methods, balancing rigid docking and flexible docking, have demonstrated success in predicting highly accurate conformations with a relatively low computational cost. Methods: In our study, the number of flexible pocket residues was assessed by quantitative analysis, and a novel adaptive residue flexibility prediction method, named A-RFP, was proposed to improve the docking performance. Based on the homologous information, a joint strategy is used to predict the pocket residue flexibility by combining RMSD, the distance between the residue sidechain and the ligand, and the sidechain orientation. For each receptor-ligand pair, A-RFP provides a docking conformation with the optimal affinity. Results: By analyzing the docking affinities of 3507 target-ligand pairs in 5 different values ranging from 0 to 10, we found there is a general trend that the larger number of flexible residues inevitably improves the docking results by using Autodock Vina. However, a certain number of counterexamples still exist. To validate the effectiveness of A-RFP, the experimental assessment was tested in a small-scale virtual screening on 5 proteins, which confirmed that A-RFP could enhance the docking performance. And the flexible-receptor virtual screening on a low-similarity dataset with 85 receptors validates the accuracy of residue flexibility comprehensive evaluation. Moreover, we studied three receptors with FDA-approved drugs, which further proved A-RFP can play a suitable role in ligand discovery. Conclusion: Our analysis confirms that the screening performance of the various numbers of flexible residues varies wildly across receptors. It suggests that a fine-grained docking method would offset the aforementioned deficiency. Thus, we presented A-RFP, an adaptive pocket residue flexibility prediction method based on homologous information. Without considering computational resources and time costs, A-RFP provides the optimal docking result.

Building shape-focused pharmacophore models for effective docking screening

The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins’ inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub (https://github.com/jvlehtonen/overlap-toolkit).Scientific contributionThis study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.Graphical

GalaxyDock-DL: Protein-Ligand Docking by Global Optimization and Neural Network Energy.

With the recent introduction of deep learning techniques into the prediction of biomolecular structures, structure prediction performance has significantly improved, and the potential for biomedical applications has increased considerably. The prediction of protein-ligand complex structures, applicable to the atomistic understanding of biomolecular functions and the effective design of drug molecules, has also improved with the introduction of deep learning. In this paper, it is demonstrated that docking performance can be greatly enhanced by training an energy function that encapsulates physical effects using deep learning within the framework of the traditional protein-ligand docking method. The advantage of this method, called GalaxyDock-DL, lies in its minimal overfitting to the training data compared to several existing deep learning-based protein-ligand docking methods. Unlike some recent deep learning methods, it does not use information about known binding pocket center positions. Instead, the results of this docking method show a systematic dependence on the physical properties of the target protein-ligand complexes such as atomic thermal fluctuations and binding affinity. GalaxyDock-DL utilizes the global optimization technique of the conventional protein-ligand docking method, GalaxyDock, and a neural network energy function trained to stabilize the native state compared to non-native states, just as physical free energy does. This physical principle-based approach suggests directions not only for future structure prediction involving structurally flexible biomolecular complexes but also for predicting binding affinity, thereby providing guidance for the effective design of biofunctional ligands.

Current Computational Methods for Protein-peptide Complex Structure Prediction.

Peptide-mediated protein-protein interactions (PPIs) play an important role in various biological processes. The development of peptide-based drugs to modulate PPIs has attracted increasing attention due to the advantages of high specificity and low toxicity. In the development of peptide-based drugs, one of the most important steps is to determine the interaction details between the peptide and the target protein. In addition to experimental methods, recently developed computational methods provide a cost-effective way for studying protein-peptide interactions. In this article, we carefully reviewed recently developed protein-peptide docking methods, which were classified into three groups: template-based docking, template-free docking, and hybrid method. Then, we presented available benchmarking sets and evaluation metrics for assessing protein-peptide docking performance. Furthermore, we discussed the use of molecular dynamics simulations, as well as deep learning approaches in protein-peptide complex prediction.

Discovery of New Heteroaryldihydropyrimidine Compounds as HBV Capsid Protein Inhibitors Based on QSAR, Molecular Docking and Molecular Dynamics Simulations**

AbstractHepatitis B virus (HBV) infection is a major global public health problem and a serious threat to human health. The assembly of the HBV outer shell protein is an important step in the HBV life cycle. Influencing the assembly of core proteins to block viral DNA replication has become a popular target for anti‐HBV drug development. In this study, 47 heteroarylpyrimidine (HAP) compounds were investigated by QSAR, and Topomer CoMFA and HQSAR models with strong predictive ability were developed. Eight new compounds with high activity were successfully designed by searching R groups in Topomer Search. The results of molecular docking and ADMET performance prediction showed that all these compounds have good docking scores and potential medicinal values. To further understand the possible conformations and interactions of the compounds at the protein active site, we performed molecular dynamics simulations of the four compounds with high docking scores and confirmed that these complexes have stable binding conformations by free energy mapping. The free energy calculations verified the stable binding results. These results provide an important reference and theoretical basis for the design and development of effective heteroaryldihydropyrimidine analogs as potential HBV capsid inhibitors.

Repurposing promethazine hydrochloride to inhibit biofilm formation against Burkholderia thailandensis.

Melioidosis is a severe infectious disease caused by Burkholderia pseudomallei, an intracellular pathogen with a high mortality rate and significant antibiotic resistance. The high mortality rate and resistance to antibiotics have drawn considerable attention from researchers studying melioidosis. This study evaluated the effects of various concentrations (75, 50, and 25µg/mL) of promethazine hydrochloride (PTZ), a potent antihistamine, on biofilm formation and lipase activity after 24h of exposure to B. thailandensis E264. A concentration-dependent decrease in both biofilm biomass and lipase activity was observed. RT-PCR analysis revealed that PTZ treatment not only made the biofilm structure loose but also reduced the expression of btaR1, btaR2, btaR3, and scmR. Single gene knockouts of quorum sensing (QS) receptor proteins (∆btaR1, ∆btaR2, and ∆btaR3) were successfully constructed. Deletion of btaR1 affected biofilm formation in B. thailandensis, while deletion of btaR2 and btaR3 led to reduced lipase activity. Molecular docking and biological performance results demonstrated that PTZ inhibits biofilm formation and lipase activity by suppressing the expression of QS-regulated genes. This study found that repositioning PTZ reduced biofilm formation in B. thailandensis E264, suggesting a potential new approach for combating melioidosis.

In-vitro Isolation of Serratia marcescens and Computational Analysis of Lipase–Chitinase as a Hybrid Enzyme for the Degradation of Fungicides to Sustain the Ecosystem

The application of fungicides for commercial purposes has grown in response to increased food demand and shifting disease patterns. However, there is a scarcity of studies demonstrating their proper degradation. Due to the expanding global population, there is a heightened need for more agriculture to fulfill the growing demand for food, and this necessitates safeguarding crops from pests and diseases. Disease outbreaks in crops can result in the loss of food and the depletion of valuable resources. According to the European Union, organic fungicides constitute 60% of all fungicide sales, while synthetic fungicides make up the remaining 40% of pesticide sales. Fungicide usage is indispensable but needs to be considered within the context of potential environmental risks. Nonetheless, there is a pressing need to break down fungicides to preserve the natural environment. Our research is centered on hybrid enzymes derived from [Formula: see text] marcescens, which exhibit the capability to degrade fungicides. We employed computational techniques to assess the enzymes’ effectiveness in breaking down fungicides. S. marcescens was isolated from soil and identified through 16s rRNA analysis. Chitanase and lipase, enzymes identified in S. marcescens, can individually break down fungicides, as confirmed by auto-dock vina docking simulations. Notably, the combined action of these two enzymes surpasses the performance of individual docking, a result also corroborated by auto-dock vina. To further establish the efficacy and degradability of this approach, computational techniques were employed. In the future, it may be possible to create an in vitro antifungal agent, which could be categorized as a bioactive agent.

Computational evaluation and benchmark study of 342 crystallographic holo-structures of SARS-CoV-2 Mpro enzyme

The coronavirus disease 19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a global health crisis with millions of confirmed cases and related deaths. The main protease (Mpro) of SARS-CoV-2 is crucial for viral replication and presents an attractive target for drug development. Despite the approval of some drugs, the search for effective treatments continues. In this study, we systematically evaluated 342 holo-crystal structures of Mpro to identify optimal conformations for structure-based virtual screening (SBVS). Our analysis revealed limited structural flexibility among the structures. Three docking programs, AutoDock Vina, rDock, and Glide were employed to assess the efficiency of virtual screening, revealing diverse performances across selected Mpro structures. We found that the structures 5RHE, 7DDC, and 7DPU (PDB Ids) consistently displayed the lowest EF, AUC, and BEDROCK scores. Furthermore, these structures demonstrated the worst pose prediction results in all docking programs. Two structural differences contribute to variations in docking performance: the absence of the S1 subsite in 7DDC and 7DPU, and the presence of a subpocket in the S2 subsite of 7DDC, 7DPU, and 5RHE. These findings underscore the importance of selecting appropriate Mpro conformations for SBVS, providing valuable insights for advancing drug discovery efforts.

Effect of dapagliflozin on ferroptosis through the gut microbiota metabolite TMAO during myocardial ischemia–reperfusion injury in diabetes mellitus rats

Dapagliflozin (DAPA) demonstrates promise in the management of diabetic mellitus (DM) and cardiomyopathy. Trimethylamine N-oxide (TMAO) is synthesized by the gut microbiota through the metabolic conversion of choline and phosphatidylcholine. Ferroptosis may offer novel therapeutic avenues for the management of diabetes and myocardial ischemia–reperfusion injury (IRI). However, the precise mechanism underlying ferroptosis in cardiomyocytes and the specific role of TMAO generated by gut microbiota in the therapeutic approach for DM and myocardial IRI utilizing DAPA need to be further explored. Nine male SD rats with specific pathogen-free (SPF) status were randomly divided equally into the normal group, the DM + IRI (DIR) group, and the DAPA group. The diversity of the gut microbiota was analyzed using 16S rRNA gene sequencing. Additionally, the Wekell technique was employed to measure the levels of TMAO in the three groups. Application of network pharmacology to search for intersection targets of DAPA, DIR, and ferroptosis, and RT-PCR experimental verification. Ultimately, the overlapping targets that were acquired were subjected to molecular docking analysis with TMAO. The changes of Bacteroidetes and Firmicutes in the gut microbiota of DIR rats were most significantly affected by DAPA. Escherichia-Shigella and Prevotella_9 within the phylum Bacteroidetes could be identified as the primary effects of DAPA on DIR. Compared with the normal group, the TMAO content in the DIR group was significantly increased, while the TMAO content in the DAPA group was decreased compared to the DIR group. For the network pharmacology analysis, DAPA and DIR generated 43 intersecting target genes, and then further intersected with ferroptosis-related genes, resulting in 11 overlapping target genes. The mRNA expression of ALB, HMOX1, PPARG, CBS, LCN2, and PPARA decreased in the DIR group through reverse transcription polymerase chain reaction (RT-PCR) validation, while the opposite trend was observed in the DAPA group. The docking score between TMAO and DPP4 was − 5.44, and the MM-GBSA result of − 22.02 kcal/mol. It epitomizes the finest docking performance among all the target genes with the lowest score. DAPA could reduce the levels of metabolite TMAO produced by gut microbiota, thereby regulating related target genes to decrease ferroptosis in DIR cardiomyocytes.

Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction.

Structure-based virtual screening utilizes molecular docking to explore and analyze ligand-macromolecule interactions, crucial for identifying and developing potential drug candidates. Although there is availability of several widely used docking programs, the accurate prediction of binding affinity and binding mode still presents challenges. In this study, we introduced a novel protocol that combines our in-house geometry optimization algorithm, the conjugate gradient with backtracking line search (CG-BS), which is capable of restraining and constraining rotatable torsional angles and other geometric parameters with a highly accurate machine learning potential, ANI-2x, renowned for its precise molecular energy predictions reassembling the wB97X/6-31G(d) model. By integrating this protocol with binding pose prediction using the Glide, we conducted additional structural optimization and potential energy prediction on 11 small molecule-macromolecule and 12 peptide-macromolecule systems. We observed that ANI-2x/CG-BS greatly improved the docking power, not only optimizing binding poses more effectively, particularly when the RMSD of the predicted binding pose by Glide exceeded around 5 Å, but also achieving a 26% higher success rate in identifying those native-like binding poses at the top rank compared to Glide docking. As for the scoring and ranking powers, ANI-2x/CG-BS demonstrated an enhanced performance in predicting and ranking hundreds or thousands of ligands over Glide docking. For example, Pearson's and Spearman's correlation coefficients remarkedly increased from 0.24 and 0.14 with Glide docking to 0.85 and 0.69, respectively, with the addition of ANI-2x/CG-BS for optimizing and ranking small molecules binding to the bacterial ribosomal aminoacyl-tRNA receptor. These results suggest that ANI-2x/CG-BS holds considerable potential for being integrated into virtual screening pipelines due to its enhanced docking performance.

Network Pharmacology Approach to Identify the Calotropis Phytoconstituents' Potential Epileptic Targets and Evaluation of Molecular Docking, MD Simulation, and MM-PBSA Performance.

Epilepsy originates from unusual electrical rhythm within brain cells, causes seizures. Calotropis species have been utilized to treat a wide spectrum of ailments since antiquity. Despite chemical and biological investigations, there have been minimal studies on their anticonvulsant activity, and the molecular targets of this plant constituents are unexplored. This study aimed to investigate the plausible epileptic targets of Calotropis phytoconstituents through network pharmacology, and to evaluate their binding strength and stability with the identified targets. In detail, 125 phytoconstituents of the Calotropis plant (C. procera and C. gigantea) were assessed for their drug-likeness (DL), blood-brain-barrier (BBB) permeability and oral bioavailability (OB). Network analysis revealed that targets PTGS2 and PPAR-γ were ranked first and fourth, respectively, among the top ten hub genes significantly linked with antiepileptic drug targets. Additionally, docking, molecular dynamic (MD) simulation, and Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) were employed to validate the compound-gene interactions. Docking studies suggested ergost-5-en-3-ol, stigmasterol and β-sitosterol exhibit stronger binding affinity and favorable interactions than co-crystallized ligands with both the targets. Furthermore, both MD simulations and MM-PBSA calculations substantiated the docking results. Combined data revealed that Calotropis phytoconstituents ergost-5-en-3-ol, stigmasterol, and β-sitosterol might be the best inhibitors of both PTGS2 and PPAR-γ.

Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures

Decades of drug development research have explored a vast chemical space for highly active compounds. The exponential growth of virtual libraries enables easy access to billions of synthesizable molecules. Computational modeling, particularly molecular docking, utilizes physics-based calculations to prioritize molecules for synthesis and testing. Nevertheless, the molecular docking process often yields docking poses with favorable scores that prove to be inaccurate with experimental testing. To address these issues, several approaches using machine learning (ML) have been proposed to filter incorrect poses based on the crystal structures. However, most of the methods are limited by the availability of structure data. Here, we propose a new pose classification approach, PECAN2 (Pose Classification with 3D Atomic Network 2), without the need for crystal structures, based on a 3D atomic neural network with Point Cloud Network (PCN). The new approach uses the correlation between docking scores and experimental data to assign labels, instead of relying on the crystal structures. We validate the proposed classifier on multiple datasets including human mu, delta, and kappa opioid receptors and SARS-CoV-2 Mpro. Our results demonstrate that leveraging the correlation between docking scores and experimental data alone enhances molecular docking performance by filtering out false positives and false negatives.

DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model

While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they’re computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.

Efficient Dye Degradation and Antimicrobial Behavior with Molecular Docking Performance of Silver and Polyvinylpyrrolidone-Doped Zn-Fe Layered Double Hydroxide.

Zn-Fe layered double hydroxide (LDH) was synthesized through the low-temperature-based coprecipitation method. Various concentrations of Ag (1, 3, and 5 wt %) with a fixed amount (5 wt %) of polyvinylpyrrolidone (PVP) were doped into LDH nanocomposites. This research aims to improve the bactericidal properties and catalytic activities of doping-dependent nanocomposites. Adding Ag and PVP to LDH enhanced oxygen vacancies, which increased the amount of hydroxide adsorption sites and the number of active sites. The doped LDH was employed to degrade rhodamine-B dye in the presence of a reducing agent (NaBH4), and the obtained results showed maximum dye degradation in a basic medium compared to acidic and neutral. The bactericidal efficacy of doped Zn-Fe (5 wt %) showed a considerably greater inhibition zone of 3.65 mm against Gram-negative (G-ve) or Escherichia coli (E. coli). Furthermore, molecular docking was used to decipher the mystery behind the microbicidal action of Ag-doped PVP/Zn-Fe LDH and to propose an inhibition mechanism of β-ketoacyl-acyl carrier protein synthase IIE.coli (FabH) and deoxyribonucleic acid gyrase E.coli behind in vitro results.