Machine learning optimization of cross docking accuracy

A Poly (Caprolactone)-Cellulose Nanocomposite Hydrogel for Transdermal Delivery of Hydrocortisone in Treating Psoriasis Vulgaris

Psoriasis vulgaris (PV) is a common chronic disease, affecting much of the population. Hydrocortisone (HCT) is currently utilized as a PV treatment; however, it is associated with undesirable side effects. The aim of this research was to create a thermo-responsive nano-hydrogel delivery system. HCT-loaded sorbitan monostearate (SMS)-polycaprolactone (PCL) nanoparticles, encapsulated with thermo-responsive hydrogel carboxymethyl cellulose (CMC), were synthesized by applying the interfacial polymer-deposition method following solvent displacement. The nanoparticles’ properties were evaluated employing Differential Scanning Colorimetry, Thermogravimetric Analysis, Fourier Transform Infrared Spectroscopy, Scanning Electron Microscopy, Zeta sizer, Ultraviolet/Visual spectroscopy, and cytotoxicity testing. The nanoparticle sizes were 110.5 nm, with polydispersity index of 0.15 and zeta potential of −58.7 mV. A drug-entrapment efficacy of 76% was attained by the HCT-loaded SMS-PCL nanoparticles and in vitro drug-release profiles showed continuous drug release over a period of 24 hrs. Keratinocyte skin cells were treated with HCT-loaded SMS-PCL nanoparticles encapsulated with CMC; the results indicated that the synthesized drug-delivery system was less toxic to the keratinocyte cells compared to HCT. The combined trials and results from the formulation of HCT-loaded SMS-PCL nanoparticles encapsulated with CMC showed evidence that this hydrogel can be utilized as a potentially invaluable formulation for transdermal drug delivery of HCT, with improved efficacy and patient conformity.

Optimization of covalent docking for organophosphates interaction with Anopheles acetylcholinesterase

Organophosphates (OPs) used as potent insecticides for malaria vector control, covalently phosphorylate the catalytic serine residue of Anopheles gambiae AChE (AgAChE) in a reaction that liberates their leaving groups. In the recent 10-year insecticide use assessment, OPs were the most frequently used World Health Organization prequalified insecticides. Molecular modelling programs are best suited to display molecular interactions between ligands and the target proteins. The docking modes that generate ligand poses closer to the binding site show high accuracy in predicting the ligand binding mode. The implicit solvation approach such as molecular mechanics-generalized born surface area (MM-GBSA) is a more reliable method to predict ligand onformations and binding affinities. Apart from covalent docking studies being scarce, current molecular docking programs do not adequately possess the covalent docking reaction algorithm to display the molecular mechanism of OPs at the AgAChE catalytic site. This results into OP docking studies commonly being conducted through noncovalent pannels. The aim of this study was to establish the optimim covalent docking system for OPs through manual customization of Schrödinger's Glide covalent docking reaction algorithm. To achieve this, a newly customized covalent reaction algorithm was assessed on a set of ligands covering aromatic, non-aromatic and hydrophobic OPs and compared to the noncovalent docking results in terms of reliability based on the reported X-ray diffraction molecular interactions and crystal poses. The study established that by virtue of omitting the well-known OP hydrolysis, noncovalent mode suggested molecular interactions that were further from the catalytic triad and could not otherwise occur when the molecule is hydrolyzed as in the customized covalent docking mode. Moreover, the MM-GBSA concurred with the optimized covalent docking in eliminating such inaccurate molecular interactions. Additionally, the covalent docking mode confined the interactions and ligand poses to the catalytic site indicating relatively high accuracy and reliability. This study reports the optimized covalent docking panel that effectively confirmed the molecular mechanisms of OPs, as well as indentifying the corresponding amino acid residues required to stabilize the aromatic, non-aromatic and hydrophobic OPs at the AgAChE catalytic site in line with the reported X-ray diffraction studies. As such, the proposed manual customization of the Schrödinger's Glide covalent docking platform can be used to reliably predict molecular interactions between OPs and AgAChE target.

Computational representations of protein-ligand interfaces for structure-based virtual screening

Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.

A recursive molecular docking coupled with energy-based pose-rescoring and MD simulations to identify hsGC βH-NOX allosteric modulators for cardiovascular dysfunctions

Modulating the activity of human soluble guanylate cyclase (hsGC) through allosteric regulation of the βH-NOX domain has been considered as an immediate treatment for cardiovascular disorder (CVDs). Currently available βH-NOX domain-specific agonists including cinaciguat are unable to deal with the conundrum raised due to oxidative stress in the case of CVDs and their associated comorbidities. Therefore, the idea of investigating novel compounds for allosteric regulation of hsGC activation has been rekindled to circumvent CVDs. Current study aims to identify novel βH-NOX domain-specific compounds that can selectively turn on sGC functions by modulating the conformational dynamics of the target protein. Through a comprehensive computational drug-discovery approach, we first executed a target-based performance assessment of multiple docking (PLANTS, QVina, LeDock, Vinardo, Smina) scoring functions based on multiple performance metrices. QVina showed the highest capability of selecting true-positive ligands over false positives thus, used to screen 4.8 million ZINC15 compounds against βH-NOX domain. The docked ligands were further probed in terms of contact footprint and pose reassessment through clustering analysis and PLANTS docking, respectively. Subsequently, energy-based AMBER rescoring of top 100 low-energy complexes, per-residue energy decomposition analysis, and ADME-Tox analysis yielded the top three compounds i.e. ZINC000098973660, ZINC001354120371, and ZINC000096022607. The impact of three selected ligands on the internal structural dynamics of the βH-NOX domain was also investigated through molecular dynamics simulations. The study revealed potential electrostatic interactions for better conformational dialogue between βH-NOX domain and allosteric ligands that are critical for the activation of hsGC as compared to the reference compound.

DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina

Motivation: Bringing a new drug to the market is expensive and time-consuming. To cut the costs and time, computer-aided drug design (CADD) approaches have been increasingly included in the drug discovery pipeline. However, despite traditional docking tools show a good conformational space sampling ability, they are still unable to produce accurate binding affinity predictions. This work presents a novel scoring function for molecular docking seamlessly integrated into DockingApp, a user-friendly graphical interface for AutoDock Vina. The proposed function is based on a random forest model and a selection of specific features to overcome the existing limits of Vina’s original scoring mechanism. A novel version of DockingApp, named DockingApp RF, has been developed to host the proposed scoring function and to automatize the rescoring procedure of the output of AutoDock Vina, even to nonexpert users. Results: By coupling intermolecular interaction, solvent accessible surface area features and Vina’s energy terms, DockingApp RF’s new scoring function is able to improve the binding affinity prediction of AutoDock Vina. Furthermore, comparison tests carried out on the CASF-2013 and CASF-2016 datasets demonstrate that DockingApp RF’s performance is comparable to other state-of-the-art machine-learning- and deep-learning-based scoring functions. The new scoring function thus represents a significant advancement in terms of the reliability and effectiveness of docking compared to AutoDock Vina’s scoring function. At the same time, the characteristics that made DockingApp appealing to a wide range of users are retained in this new version and have been complemented with additional features.

Higher Accuracy Achieved for Protein-Ligand Binding Pose Prediction by Elastic Network Model-Based Ensemble Docking

Molecular docking plays an indispensable role in predicting the receptor-ligand interactions in which the protein receptor is usually kept rigid, whereas the ligand is treated as being flexible. Because of the inherent flexibility of proteins, the binding pocket of apo receptors might undergo significant conformational rearrangement upon ligand binding, which limits the prediction accuracy of docking. Here, we present an iterative anisotropic network model (iterANM)-based ensemble docking approach, which generates multiple holo-like receptor structures starting from the apo receptor and incorporates protein flexibility into docking. In a validation data set consisting of 233 chemically diverse cyclin-dependent kinase 2 (CDK2) inhibitors, the iterANM-based ensemble docking achieves higher capacity to reproduce native-like binding poses compared with those using single apo receptor conformation or conformational ensemble from molecular dynamics simulations. The prediction success rate within the top5-ranked binding poses produced by the iterANM can further be improved through reranking with the molecular mechanics-Poisson-Boltzmann surface area method. In a smaller data set with 58 CDK2 inhibitors, the iterANM-based ensemble shows a higher success rate compared with the flexible receptor-based docking procedure AutoDockFR and other receptor conformation generation approaches. Further, an additional docking test consisting of 10 diverse receptor-ligand combinations shows that the iterANM is robustly applicable for different receptor structures. These results suggest the iterANM-based ensemble docking as an accurate, efficient, and practical framework to predict the binding mode of a ligand for receptors with flexibility.

Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach

We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard docking procedure and fed into a dual-graph architecture that includes separate subnetworks for the ligand bonded topology and the ligand-protein contact map. Recent work has indicated that data set bias drives many past promising results derived from combining deep learning and docking. Our dual-graph network allows contributions from ligand identity that give rise to such biases to be distinguished from effects of protein-ligand interactions on classification. We show that our neural network is capable of learning from protein structural information when, as in the case of binding mode prediction, an unbiased data set is constructed. We next develop a deep learning model for binding mode prediction that uses docking ranking as input in combination with docking structures. This strategy mirrors past consensus models and outperforms a baseline docking program (AutoDock Vina) in a variety of tests, including on cross-docking data sets that mimic real-world docking use cases. Furthermore, the magnitudes of network predictions serve as reliable measures of model confidence.

Searching new structural scaffolds for BRAF inhibitors. An integrative study using theoretical and experimental techniques

The identification of the V600E activating mutation in the protein kinase BRAF in around 50% of melanoma patients has driven the development of highly potent small inhibitors (BRAFi) of the mutated protein. To date, Dabrafenib and Vemurafenib, two specific BRAFi, have been clinically approved for the treatment of metastatic melanoma. Unfortunately, after the initial response, tumors become resistant and patients develop a progressive and lethal disease, making imperative the development of new therapeutic options. The main objective of this work was to find new BRAF inhibitors with different structural scaffolds than those of the known inhibitors. Our study was carried out in different stages; in the first step we performed a virtual screening that allowed us to identify potential new inhibitors. In the second step, we synthesized and tested the inhibitory activity of the novel compounds founded. Finally, we conducted a molecular modelling study that allowed us to understand interactions at the molecular level that stabilize the formation of the different molecular complexes. Our theoretical and experimental study allowed the identification of four new structural scaffolds, which could be used as starting structures for the design and development of new inhibitors of BRAF. Our experimental data indicate that the most active compounds reduced significantly ERK½ phosphorylation, a measure of BRAF inhibition, and cell viability. Thus, from our theoretical and experimental results, we propose new substituted hydroxynaphthalenecarboxamides, N-(hetero)aryl-piperazinylhydroxyalkylphenylcarbamates, substituted piperazinylethanols and substituted piperazinylpropandiols as initial structures for the development of new inhibitors for BRAF. Moreover, by performing QTAIM analysis, we are able to describe in detail the molecular interactions that stabilize the different Ligand-Receptor complexes. Such analysis indicates which portion of the different molecules must be changed in order to obtain an increase in the binding affinity of these new ligands.

An Efficient ABC_DE_Based Hybrid Algorithm for Protein-Ligand Docking

Protein–ligand docking is a process of searching for the optimal binding conformation between the receptor and the ligand. Automated docking plays an important role in drug design, and an efficient search algorithm is needed to tackle the docking problem. To tackle the protein–ligand docking problem more efficiently, An ABC_DE_based hybrid algorithm (ADHDOCK), integrating artificial bee colony (ABC) algorithm and differential evolution (DE) algorithm, is proposed in the article. ADHDOCK applies an adaptive population partition (APP) mechanism to reasonably allocate the computational resources of the population in each iteration process, which helps the novel method make better use of the advantages of ABC and DE. The experiment tested fifty protein–ligand docking problems to compare the performance of ADHDOCK, ABC, DE, Lamarckian genetic algorithm (LGA), running history information guided genetic algorithm (HIGA), and swarm optimization for highly flexible protein–ligand docking (SODOCK). The results clearly exhibit the capability of ADHDOCK toward finding the lowest energy and the smallest root-mean-square deviation (RMSD) on most of the protein–ligand docking problems with respect to the other five algorithms.

HIGA: A Running History Information Guided Genetic Algorithm for Protein-Ligand Docking

Protein-ligand docking is an essential part of computer-aided drug design, and it identifies the binding patterns of proteins and ligands by computer simulation. Though Lamarckian genetic algorithm (LGA) has demonstrated excellent performance in terms of protein-ligand docking problems, it can not memorize the history information that it has accessed, rendering it effort-consuming to discover some promising solutions. This article illustrates a novel optimization algorithm (HIGA), which is based on LGA for solving the protein-ligand docking problems with an aim to overcome the drawback mentioned above. A running history information guided model, which includes CE crossover, ED mutation, and BSP tree, is applied in the method. The novel algorithm is more efficient to find the lowest energy of protein-ligand docking. We evaluate the performance of HIGA in comparison with GA, LGA, EDGA, CEPGA, SODOCK, and ABC, the results of which indicate that HIGA outperforms other search algorithms.

The Performance of Several Docking Programs at Reproducing Protein-Macrolide-Like Crystal Structures

The accuracy of five docking programs at reproducing crystallographic structures of complexes of 8 macrolides and 12 related macrocyclic structures, all with their corresponding receptors, was evaluated. Self-docking calculations indicated excellent performance in all cases (mean RMSD values ≤ 1.0) and confirmed the speed of AutoDock Vina. Afterwards, the lowest-energy conformer of each molecule and all the conformers lying 0–10 kcal/mol above it (as given by Macrocycle, from MacroModel 10.0) were subjected to standard docking calculations. While each docking method has its own merits, the observed speed of the programs was as follows: Glide 6.6 > AutoDock Vina 1.1.2 > DOCK 6.5 >> AutoDock 4.2.6 > AutoDock 3.0.5. For most of the complexes, the five methods predicted quite correct poses of ligands at the binding sites, but the lower RMSD values for the poses of highest affinity were in the order: Glide 6.6 ≈ AutoDock Vina ≈ DOCK 6.5 > AutoDock 4.2.6 >> AutoDock 3.0.5. By choosing the poses closest to the crystal structure the order was: AutoDock Vina > Glide 6.6 ≈ DOCK 6.5 ≥ AutoDock 4.2.6 >> AutoDock 3.0.5. Re-scoring (AutoDock 4.2.6//AutoDock Vina, Amber Score and MM-GBSA) improved the agreement between the calculated and experimental data. For all intents and purposes, these three methods are equally reliable.

Interaction with specific HSP90 residues as a scoring function: validation in the D3R Grand Challenge 2015

Here is reported the development of a novel scoring function that performs remarkably well at identifying the native binding pose of a subset of HSP90 inhibitors containing aminopyrimidine or resorcinol based scaffolds. This scoring function is called PocketScore, and consists of the interaction energy between a ligand and three residues in the binding pocket: Asp93, Thr184 and a water molecule. We integrated PocketScore into a molecular docking workflow, and used it to participate in the Drug Design Data Resource (D3R) Grand Challenge 2015 (GC2015). PocketScore was able to rank 180 molecules of the GC2015 according to their binding affinity with satisfactory performance. These results indicate that the specific residues considered by PocketScore are determinant to properly model the interaction between HSP90 and its subset of inhibitors containing aminopyrimidine or resorcinol based scaffolds. Moreover, the development of PocketScore aimed at improving docking power while neglecting the prediction of binding affinities, suggesting that accurate identification of native binding poses is a determinant factor for the performance of virtual screens.