Future Clinical Application of β-galactosidase Stabilized by Magnesium oxide Nanoparticles

The present study demonstrates the application of freshly prepared neem leaf extract as a reducing agent for synthesizing magnesium oxide nanoparticles (MgO-NPs). In silico interaction of Aspergillus oryzae β-galactosidase with MgO-NPs was observed by using molecular docking program Dock v.6.5 while the visual analyses and illustration of protein–ligand complex were investigated by utilizing chimera v.1.6.2 and PyMOL v.1.3 softwares. The prepared nanomatrix provided 83% immobilization yield, and broadened the biocatalytic activity of immobilized β-galactosidase at higher pH and temperature ranges. Immobilized β-galactosidase exhibited greater activity even at 5.0% galactose concentration as compared to the soluble enzyme under similar experimental conditions. Hence, the use of green nanotechnology makes the process inexpensive, and therefore, immobilization of these enzymes on such nanoparticles can help to recover the enzyme, which ultimately decreases the cost of process.

Deep Learning Model of Dock by Dock Process Significantly Accelerate the Process of Docking-Based Virtual Screening

Docking-based virtual screening (VS process) selects ligands with potential pharmacological activities from millions of molecules using computational docking methods, which greatly could reduce the number of compounds for experimental screening, shorten the research period and save the research cost. Howerver, a majority of compouds with low docking scores could waste most of the computational resources. Herein, we report a novel and practical docking-based machine learning method called MLDDM (Machince Learning Docking-by-Docking Models). It is composed of a regression model and a classification model that simulates a classical docking by docking protocol ususally applied in many virtual screening projects. MLDDM could quickly eliminate compounds with low docking scores and the retained compounds with potential high docking scores would be examined for further real docking program. We demonstrated that MLDDM has a good ability to identify active compounds in the case studies for 10 specific protein targets. Compared to pure docking by docking based VS protocol, the VS process with MLDDM can achieve an over 120 times speed increment on average and the consistency rate with corresponding docking by docking VS protocol is above 0.8. Therefore, it would be promising to be used for examing ultra-large compound libraries in the current big data era.

Advanced Molecular Dynamics Approaches to Model a Tertiary Complex APRIL/TACI with Long Glycosaminoglycans

Glycosaminoglycans (GAGs) are linear anionic periodic polysaccharides participating in a number of biologically relevant processes in the extracellular matrix via interactions with their protein targets. Due to their periodicity, conformational flexibility, pseudo-symmetry of the sulfation pattern, and the key role of electrostatics, these molecules are challenging for both experimental and theoretical approaches. In particular, conventional molecular docking applied for GAGs longer than 10-mer experiences severe difficulties. In this work, for the first time, 24- and 48-meric GAGs were docked using all-atomic repulsive-scaling Hamiltonian replica exchange molecular dynamics (RS-REMD), a novel methodology based on replicas with van der Waals radii of interacting molecules being scaled. This approach performed well for proteins complexed with oligomeric GAGs and is independent of their length, which distinguishes it from other molecular docking approaches. We built a model of long GAGs in complex with a proliferation-inducing ligand (APRIL) prebound to its receptors, the B cell maturation antigen and the transmembrane activator and calcium modulator and cyclophilin ligand interactor (TACI). Furthermore, the prediction power of the RS-REMD for this tertiary complex was evaluated. We conclude that the TACI–GAG interaction could be potentially amplified by TACI’s binding to APRIL. RS-REMD outperformed Autodock3, the docking program previously proven the best for short GAGs.

Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro

It has been demonstrated earlier that the neural network based program AlphaFold2 can be used to dock proteins given the two sequences separated by a gap as the input. The protocol presented here combines AlphaFold2 with the physics based docking program ClusPro. The monomers of the model generated by AlphaFold2 are separated, re-docked using ClusPro, and the resulting 10 models are refined by AlphaFold2. Finally, the five original AlphaFold2 models are added to the 10 AlphaFold2 refined ClusPro models, and the 15 models are ranked by their predicted aligned error (PAE) values obtained by AlphaFold2. The protocol is applied to two benchmark sets of complexes, the first based on the established protein-protein docking benchmark, and the second consisting of only structures released after May 2018, the cut-off date for training AlphaFold2. It is shown that the quality of the initial AlphaFold2 models improves with each additional step of the protocol. In particular, adding the AlphaFold2 refined ClusPro models to the AlphaFold2 models increases the success rate by 23% in the top 5 predictions, whereas considering the 10 models obtained by the combined protocol increases the success rate to close to 40%. The improvement is similar for the second benchmark that includes only complexes distinct from the proteins used for training the neural network.

LRET-derived HADDOCK structural models describe the conformational heterogeneity required for processivity of the Mre11-Rad50 DNA damage repair complex

The Mre11-Rad50-Nbs1 protein complex is one of the first responders to DNA double strand breaks. Studies have shown that the catalytic activities of the evolutionarily conserved Mre11-Rad50 (MR) core complex depend on an ATP-dependent global conformational change that takes the macromolecule from an open, extended structure in the absence of ATP to a closed, globular structure when ATP is bound. We have previously identified an additional 'partially open' conformation using Luminescence Resonance Energy Transfer (LRET) experiments. Here, a combination of LRET and the molecular docking program HADDOCK was used to further investigate this partially open state and identify three conformations of ATP-bound MR in solution: closed, partially open, and open, which are in addition to the extended, apo conformation. These models are supported with mutagenesis and SAXS data that corroborate the presence of these three states and suggest a mechanism for the processivity of the MR complex along the DNA.

DATABASE ENRICHMENTS OF MAO-B THROUGH ENSEMBLE DOCKING

Objective: The recent growth of highly resoluted crystallographic structures, together with the continuous improvements of the computing power, has established molecular docking as a leading drug design technique. However, the problems concerning the receptor flexibility and the lowered ability of docking software to correctly score the occurred interactions in some receptors are still relevant. Methods: Recently, several research groups have reported an enhancement in enrichment values when ensemble docking has been applied. Therefore, we utilized the latest technique for a dataset of Monoamine Oxidase–B (MAO-B) inhibitors. The docking program GOLD 5.3 was used in our study. Several docking parameters (grid space, scoring functions and ligand flexibility) were altered in order to achieve the optimal docking protocol. Results: The results of 200 000+docking simulations are represented in a modest table. The ensembled simulations demonstrated low ability of the docking software to correctly score the actives seeded in the dataset. However, the superimposed complex-1S3B-1OJA-1OJC, achieved a moderate enrichment value equaled to 9. No significant improvements were noted when five complexed receptors were employed. Conclusion: As a conclusion, it should be noted that in some cases the ensemble docking enhanced the database enrichments, however overall the value is not suitable for future virtual screening. Further investigations in that area should be considered.

The role of A268V exon-7 polymorphism of PPARA in development of axial spondyloarthritis

Objectives Axial spondyloarthritis (axSpA) is a chronic inflammatory disease that mainly affects the axial skeleton. Peroxisome proliferator activated receptor alpha (PPARA) is an intracellular transcription factor, which play a role in inflammation and osteoblasting activity. This study is designed to investigate the relationship of NG_012204.2:p.Ala268Val polymorphism of PPARA with axSpA risk and its role in disease development. Methods This study was conducted with 168 patients and 181 controls. Genotyping was done with MALDITOF. Gene expression level was analyzed by quantitative real time PCR (RT-qPCR). The protein homology models of PPARA were created with ProMod3. Ligand binding dynamics were tested using the AutoDock4 docking program. Statistical evaluations were made with SPSS (ver24) and GeneGlobe. Results Our results showed that C>T polymorphism causing NG_012204.2:p.Ala268Val change was associated with disease risk (p=0.024) and T allele increased disease risk 1.7 times (95% CI=1.070–2.594). PPARA expression decreased (p<0.05) in individuals carrying the T allele. We determined that the ligand entry pocket was opened 1.1 Å in the polymorphic PPARA. Polymorphic change caused a decrease in the ligand binding affinity. Conclusions Our results provide an important contribution to elucidating the development of axSpA and demonstrate the potential of PPARA as a marker for the diagnosis of axSpA.

VirtualFlow Ants—Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization

The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and the inclusion of experimental constraints. Here, we add support of PLANTS to VirtualFlow (VirtualFlow Ants), which adds a valuable method for primary virtual screenings and rescoring procedures. Furthermore, we have added support of ligand libraries in the MOL2 format, as well as on the fly conversion of ligand libraries which are in the PDBQT format to the MOL2 format to endow VirtualFlow Ants with an increased flexibility regarding the ligand libraries. The on the fly conversion is carried out with Open Babel and the program SPORES. We applied VirtualFlow Ants to a test system involving KEAP1 on the Google Cloud up to 128,000 CPUs, and the observed scaling behavior is approximately linear. Furthermore, we have adjusted several central docking parameters of PLANTS (such as the speed parameter or the number of ants) and screened 10 million compounds for each of the 10 resulting docking scenarios. We analyzed their docking scores and average docking times, which are key factors in virtual screenings. The possibility of carrying out ultra-large virtual screening with PLANTS via VirtualFlow Ants opens new avenues in computational drug discovery.

Binding free energy predictions in host-guest systems using Autodock4. A retrospective analysis on SAMPL6, SAMPL7 and SAMPL8 challenges

We systematically tested the Autodock4 docking program for absolute binding free energy predictions using the host-guest systems from the recent SAMPL6, SAMPL7 and SAMPL8 challenges. We found that Autodock4 behaves surprisingly well, outperforming in many instances expensive molecular dynamics or quantum chemistry techniques, with an extremely favorable benefit-cost ratio. Some interesting features of Autodock4 predictions are revealed, yielding valuable hints on the overall reliability of docking screening campaigns in drug discovery projects.

Alantolactone inhibits cervical cancer progression by downregulating BMI1

Cervical cancer is the second most common cancer in women. Despite advances in cervical cancer therapy, tumor recurrence and metastasis remain the leading causes of mortality. High expression of BMI1 is significantly associated with poor tumor differentiation, high clinical grade, and poor prognosis of cervical cancer, and is an independent prognostic factor in cervical carcinoma. Alantolactone (AL), a sesquiterpene lactone, exhibits potent anti-inflammatory and anticancer activities. In this paper, we investigated the mechanism of AL in reducing the proliferation, migration, and invasion of HeLa and SiHa cervical cancer cells as well as its promotion of mitochondrial damage and autophagy. BMI1 silencing decreased epithelial-mesenchymal transformation-associated proteins and increased autophagy-associated proteins in HeLa cells. These effects were reversed by overexpression of BMI1 in HeLa cells. Thus, BMI1 expression is positively correlated with invasion and negatively correlated with autophagy in HeLa cells. Importantly, AL decreased the weight, volume, and BMI1 expression in HeLa xenograft tumors. Furthermore, the structure of BMI1 and target interaction of AL were virtually screened using the molecular docking program Autodock Vina; AL decreased the expression of N-cadherin, vimentin, and P62 and increased the expression of LC3B and Beclin-1 in xenograft tumors. Finally, expression of BMI1 increased the phosphorylation of STAT3, which is important for cell proliferation, survival, migration, and invasion. Therefore, we suggest that AL plays a pivotal role in inhibiting BMI1 in the tumorigenesis of cervical cancer and is a potential therapeutic agent for cervical cancer.