Development of a virtual reality simulator replacingthe check valve on christmas tree

The article describes the process of designing and creating a software environment that allows in automatic mode to create a realistic landscape. A review of existing approaches to landscape generation is carried out, which have a set of disadvantages taken into account when developing a software environment. A diagram of components and main classes is described. The developed subroutine that implements the polygon mesh generation algorithm provides an interface for creating and editing a mesh of hexagons on a plane, used for simplified work with biomes, as well as detailing the boundaries of polygons to give the landscape elements of randomness and, as a result, realism. The process uses the Diamond Square noise generation algorithm. The docking algorithm is designed to reduce the gaps between the heights of different biomes. The erosion algorithm uses particles generated on a height mapto carry soil particles in accordance with physical laws. The user interface of the application and the results of the algorithms are presented.

Improved protein docking by predicted interface residues.

Scoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today’s best scoring functions can significantly increase the number of top-ranked models but still fails for most targets. Here, we examine the possibility of utilising predicted residues on a protein-protein interface to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the portions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. Different interface prediction methods are systematically tested for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that BIPSPI is the best method to identify interface amino acids and score docking solutions. Further, using BIPSPI provides better docking results than state of the art scoring functions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. We also discussed several limitations for the adoption of interface predictions as constraints in a docking protocol.

The Complementarity Principle—One More Step towards Analytical Docking on the Example of Dihydrofolate Reductase Complexes

New approaches to assessing the “enzyme–ligand” complementarity, taking into account hydrogens, have been proposed. The approaches are based on the calculation of three-dimensional maps of the electron density of the receptor–ligand complexes. The action of complementarity factors, first proposed in this article, has been demonstrated on complexes of human dihydrofolate reductase (DHFR) with ligands. We found that high complementarity is ensured by the formation of the most effective intermolecular contacts, which are provided due to predominantly paired atomic–atomic interactions, while interactions of the bifurcate and more disoriented type are minimized. An analytical docking algorithm based on the proposed receptor–ligand complementarity factors is proposed.

Assessing the affinity of oxazolidinedione/hydantoin derivatives for the human 5HT1A/2A receptor through homology modeling and docking studies

A flexible docking of a series of heteroaryl compounds to the binding site of a model of human 5-HT1A/2A receptor was exercised using GLIDE docking methods. The resultant docking scores were used to correlate the in vivo affinity data. The GLIDE docking algorithm when used with a homology model of 5HT1A/2A was based on β2- adrenergic receptor template. The influence of structure and hydrophobic properties of aryl moiety on binding affinities was discussed and a model for ligand binding in the hydrophobic part of the binding site was proposed.

Improved protein docking by predicted interface residues

ABSTRACTScoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today’s best scoring functions can significantly increase the number of top-ranked models but still fails for most targets. Here, we examine the possibility of utilising predicted residues on a protein-protein interface to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the portions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. Different interface prediction methods are systematically tested for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that BIPSPI is the best method to identify interface amino acids and score docking solutions. Further, using BIPSPI provides better docking results than state of the art scoring functions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. We also discussed several limitations for the adoption of interface predictions as constraints in a docking protocol.

In Silico Comparison of Separate or Combinatorial Effects of Potential Inhibitors of the SARS-CoV-2 Binding Site of ACE2

Background: The COVID-19 is a pandemic viral infection with a high morbidity rate, leading to many worldwide deaths since the end of 2019. The RBD (Receptor Binding Domain) of SARS-CoV-2 through its spike utilizes several host molecules to enter host cells. One of the most important ones is the angiotensin-converting enzyme 2 (ACE2), an enzyme normally engaged in renin angiotensin pathway and is responsible for hypertension regulation. As different articles have analyzed separate compounds which can bind ACE2 as the potential virus entry blockers, and each one with a different molecular docking algorithm, in this study we compared all candidate compounds individually as well as their combinations using a unique validated software to introduce most promising ones. Methods: We collected and prepared a list of all available compounds which potentially can inhibit RBD binding site of the ACE2 from different studies and then reanalyzed and compared them using the Patchdock (ver. 1.3) as a suitable molecular docking algorithm for analysis of separate compounds or their combinations. Results: Saikosaponin A (e.g. in Bupleurum chinense), Baicalin (e.g. in several species in the genus Scutellaria), Glycyrrhizin (Glycyrrhiza glabra), MLN-4760 and Umifenovir better occupied ACE2 to inhibit viral RBD binding and are suggested as the top five inhibitors of the SARS-CoV-2 binding site of ACE2. Their combinatory effects were also inspiring concurrent ACE2 blockade. Conclusion: The results propose greatest compounds and their combinatory anti-SARS-CoV-2 effects in order to decrease the time and expenses required for further experimental designs.

Exploring Molecular Docking algorithm for Lung Cancer Drug Discovery – A Case Study with RET Protein

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related deaths across the globe.1.33% of all NSCLC cases occur due to an alteration in RET protein. Commonly occurring RET fusion partners include KIF5B, CCDC6, NCOA4, and TRIM33. Numerous multikinase inhibitors are active against rearranged RET. However, mutations in the RET-fusion protein can result in adverse effects in terms of drug resistance against NSCLC. In this context, molecular docking algorithm is certainly important to support the drug discovery pipelines. However, availability of huge number of algorithms in the literature limits the researchers to proceed further in drug discovery development. Thus, the present study focuses on finding the best docking algorithm among ArgusLab, PatchDock, AutoDock 4.0 and AutoDock Vina for drug discovery process against RET fusion cancers using Pearson’s correlation coefficient. We believe that our study will be a valuable source of information for carrying out further computational studies on RET fusion cancer, both mutant and wild type.