AutoDock-IPS: An Automated Docking for Mobile Robot Based on Indoor Positioning System

Mobile robots are proven to be reliable in supporting the human tasks by using a computerized system that minimizes human errors. However, recharging the battery in these robots is still performed manually by the user. Therefore, to extend their lifetime, an indoor automatic docking system ‘AutoDock-IPS’ is created for mobile robot to charge its battery automatically. The automatic docking system determines the location of the docks (i.e., charging stations) so that, prototype can immediately navigate to them. Experiments were carried out to validate the docking method by utilizing a compass module as a direction sensor and a rotary encoder as a displacement indicator. These sensors are combined into a robust indoor positioning system. The results show that the prototype can find the fastest route to the docking station to perform battery charging procedure.

Development of a novel 6-DOF hybrid serial-parallel mechanism for pose adjustment of large-volume components

In this paper, a novel 6-degrees-of-freedom (DOF) hybrid mechanism is proposed to realize position and posture adjusting for large-volume equipment. The designed hybrid manipulator is composed of the lower and upper modules, namely, a 3-DOF redundant spatial parallel mechanism (SPM) and a 3-DOF planar parallel mechanism (PPM), which has three rotational and three translational DOFs. According to the step-by-step pose adjusting strategy, the kinematics analyses of the lower and upper modules have been carried out systematically. For the whole hybrid mechanism, a complete kinematic model has been established; and visualized workspace of the kinematic model with regular shape and large volume demonstrates profound application prospects in engineering. In order to evaluate the performance of the proposed mechanism, experimental tests have been conducted in an automated docking system for pose adjustment of large and heavy components. The analysis results demonstrate the effectiveness and practicability of the new mechanism.

Concerns Raised on Blood Group Determinants in Plasma Membrane Interaction of the SARS-CoV-2

The SARS-CoV-2 pandemic has resulted in the generation of evolutionary-related variants. The S-protein of the B.1.1.7 variant (deletion N-terminal domain (NTD) His69Val70Tyr144) may contribute to altered infectivity. These mutations may have been presaged by animal mutations in minks housed in mink farms that according to the present analysis by modelling of protein ligand docking altered a high affinity binding site in the S-protein NTD. These mutants likely occurred only sporadically in humans. Tissue-adaptations and the size of the mink relative to the infected human population size back then may have comparatively increased the relative mutation rate. Simple, multi-threaded automated docking that is widely available, assigns increased binding of the blood type II A antigen to the SARS-Cov-2 S-protein NTD of B.1.1.7 with an overall increased docking interaction of blood group A harbouring glycolipids relative to group B or H (H, p=0.04). The top scoring glycan is identified as a DSGG (also classified as sialosyl-MSGG or disialosyl-Gb5) that may compete with heparin, which is similar to heparan sulfate linked to proteinaceous receptors on the tissue surface. Other glycolipids are found to interact with lower affinity, except long ligands that have suitable ligand binding poses to match the curved binding pocket.

Concerns Raised on Blood Group Determinants in Plasma Membrane Interaction of the SARS-CoV-2

The SARS-CoV-2 pandemic has resulted in the generation of evolutionary-related variants. The S-protein of the B.1.1.7 variant (deletion N-terminal domain (NTD) His69Val70Tyr144) may contribute to altered infectivity. These mutations may have been presaged by animal mutations in minks housed in mink farms that according to the present analysis by modelling of protein ligand docking altered a high affinity binding site in the S-protein NTD. These mutants likely occurred only sporadically in humans. Tissue-adaptations and the size of the mink relative to the infected human population size back then may have comparatively increased the relative mutation rate. Simple, multi-threaded automated docking that is widely available, assigns increased binding of the blood type II A antigen to the SARS-Cov-2 S-protein NTD of B.1.1.7 with an overall increased docking interaction of blood group A harbouring glycolipids relative to group B or H (H, p=0.04). The top scoring glycan is identified as a DSGG (also classified as sialosyl-MSGG or disialosyl-Gb5) that may compete with heparin, which is similar to heparan sulfate linked to proteinaceous receptors on the tissue surface. Other glycolipids are found to interact with lower affinity, except long ligands that have suitable ligand binding poses to match the curved binding pocket.

Concerns Raised on Blood Group Determinants in Plasma Membrane Interaction of the SARS-CoV-2

The SARS-CoV-2 pandemic has resulted in the generation of evolutionary-related variants. The S-protein of the B.1.1.7 variant (deletion N-terminal domain (NTD) His69Val70Tyr144) may contribute to altered infectivity. These mutations may have been presaged by animal mutations in minks housed in mink farms that according to the present analysis by modelling of protein ligand docking altered a high affinity binding site in the S-protein NTD. These mutants likely occurred only sporadically in humans. Tissue-adaptations and the size of the mink relative to the infected human population size back then may have comparatively increased the relative mutation rate. Simple, multi-threaded automated docking that is widely available, assigns increased binding of the blood type II A antigen to the SARS-Cov-2 S-protein NTD of B.1.1.7 with an overall increased docking interaction of blood group A harbouring glycolipids relative to group B or H (H, p=0.04). The top scoring glycan is identified as a DSGG (also classified as sialosyl-MSGG or disialosyl-Gb5) that may compete with heparin, which is similar to heparan sulfate linked to proteinaceous receptors on the tissue surface. Other glycolipids are found to interact with lower affinity, except long ligands that have suitable ligand binding poses to match the curved binding pocket.

Molecular Modeling Approach for the Discovery of Escherichia coli O157:H7 Interaction Inhibitors of Natural Product Compounds

The aim of this study is to report the development of Escherichia coli O157:H7 template for structure-based drug design. This template was validated by redocking with crystal ligand I. The results showed a good matching of docked and the crystallographic binding orientations with root mean square deviation (RMSD) less than 2.0 Å. Moreover, the developed template was applied to predict binding mode of 19 known E. coli inhibitors and 40 natural products. The results showed that the binding energy of almost E. coli inhibitors was related to their biological activity. The use of developed E. coli O157:H7 template in automated docking could speed up the process of novel drug discovery by allowing designed inhibitors to be tested computationally before the compounds are synthesized.

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

1SummaryDocking calculations can be used to accelerate drug discovery by providing predictions of the poses of candidate ligands bound to a targeted protein. However, studies in the literature use varied docking methods, and it is not clear which work best, either in general or for specific protein targets. In addition, a complete docking calculation requires components beyond the docking algorithm itself, such as preparation of the protein and ligand for calculations, and it is difficult to isolate which aspects of a method are most in need of improvement. To address such issues, we have developed the Continuous Evaluation of Ligand Protein Predictions (CELPP), a weekly blinded challenge for automated docking workflows. Participants in CELPP create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new (never before released) protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow’s predictions and posts the scores online. CELPP is a new cyberinfrastructure resource to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.