A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts

This paper presents a multi-robot system for manufacturing personalized medical stent grafts. The proposed system adopts a modular design, which includes: a (personalized) mandrel module, a bimanual sewing module, and a vision module. The mandrel module incorporates the personalized geometry of patients, while the bimanual sewing module adopts a learning-by-demonstration approach to transfer human hand-sewing skills to the robots. The human demonstrations were firstly observed by the vision module and then encoded using a statistical model to generate the reference motion trajectories. During autonomous robot sewing, the vision module plays the role of coordinating multi-robot collaboration. Experiment results show that the robots can adapt to generalized stent designs. The proposed system can also be used for other manipulation tasks, especially for flexible production of customized products and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial Informatics, Key words: modularity, medical device customization, multi-robot system, robot learning, visual servoing, robot sewin

Effective Conformational Sampling in Explicit Solvent with Gaussian Biased Accelerated Molecular Dynamics

In this Article, a user-friendly Gaussian biased accelerated molecular dynamics (GbAMD) method is presented that uses a sum of Gaussians of potential energies as the biased force to accelerate the conformational sampling. The easy parameter setting of GbAMD is demonstrated in a variety of simulation tests for the conformational transitions of proteins with various complexity including the folding of Trpcage, GB1p, and HP35 peptides as well as the functional conformational changes of nCaM and HIV-1 PR proteins. Additionally, the ability of GbAMD in conformational sampling and free-energy evaluation is quantitatively assessed through the comparison of GbAMD simulations on the folding of α-helical Trpcage and β-hairpin GB1p with the accompanying standard dual boost AMD and conventional MD (cMD) simulations. While GbAMD can fold both peptides into their native structures repeatedly in individual trajectories, AMD can only fold Trpcage and cMD fails the folding in both cases. As a result, only GbAMD can quantitatively measure the properties of the equilibrium conformational ensemble of protein folding consistent with experimental data. Also notable is that the structural properties of the indispensable unfolded and transition states in the folding pathways of Trpcage and GB1p characterized by GbAMD simulations are in great agreement with previous simulations on the two peptides. In summary, GbAMD has an effective conformational sampling ability that provides a convenient and effective access for simulating the structural dynamics of biomolecular systems

How Well Can Implicit Solvent Simulations Explore Folding Pathways? A Quantitative Analysis of α‑Helix Bundle Proteins

Protein folding has been posing challenges for molecular simulation for decades. Implicit solvent models are sought as routes to increase the capability of simulation, with trade-offs between computational speed and accuracy. Here, we systematically investigate the folding of a variety of α-helix bundle proteins ranging in size from 46 to 102 amino acids using a state-of-the-art force field and an implicit solvent model. The accurate all-atom simulated folding is enabled for six proteins, including for the first time a successful folding of protein with >100 amino acids in implicit solvent. The detailed free-energy landscape analysis sheds light on a set of general principles underlying the folding of α-helix bundle proteins, suggesting a hybrid framework/nucleation-condensation mechanism favorably adopted in implicit solvent condition. The similarities and discrepancies of the folding pathways measured among the present implicit solvent simulations and previously reported experiments and explicit solvent simulations are deeply analyzed, providing quantitative assessment for the availability and limitation of implicit solvent simulation in exploring the folding transition of large-size proteins

The root-mean-square deviation (RMSD) of the backbone atoms relative to their crystal structure as a function of time for 8CA (black), F8A(red), I4A(blue) and unbound A-FABP (dark cyan).

<p>The root-mean-square deviation (RMSD) of the backbone atoms relative to their crystal structure as a function of time for 8CA (black), F8A(red), I4A(blue) and unbound A-FABP (dark cyan).</p

Binding free energies of wild-type and mutant A-FABP to inhibitors calculated by the SIE method^a.

a<p>All energies are in kcal·mol⁻¹,</p>b<p>ΔEnergy = Energy^complex–Energy^A-FABP–Energy^inhibitor,</p><p>ΔG^exp were derived from the experimental values in Ref (Barf et al. 2009) using the equation ΔG≈–RTlnIC50,</p><p>ΔΔG_bind = ΔG^mutant–ΔG^complex.</p

Determining Protein Folding Pathway and Associated Energetics through Partitioned Integrated-Tempering-Sampling Simulation

Replica exchange molecular dynamics (REMD) and integrated-tempering-sampling (ITS) are two representative enhanced sampling methods which utilize parallel and integrated tempering approaches, respectively. In this work, a partitioned integrated-tempering-sampling (P-ITS) method is proposed which takes advantage of the benefits of both parallel and integrated tempering approaches. Using P-ITS, the folding pathways of a series of proteins with diverse native structures are explored on multidimensional free-energy landscapes, and the associated thermodynamics are evaluated. In comparison to the original form of ITS, P-ITS improves the sampling efficiency and measures the folding/unfolding thermodynamic quantities more consistently with experimental data. In comparison to REMD, P-ITS significantly reduces the requirement of computational resources and meanwhile achieves similar simulation results. The observed structural characterizations of transition and intermediate states of the proteins under study are in good agreement with previous experimental and simulation studies on the same proteins and homologues. Therefore, the P-ITS method has great potential in simulating the structural dynamics of complex biomolecular systems

The Underestimated Halogen Bonds Forming with Protein Side Chains in Drug Discovery and Design

Halogen bonds (XBs) have been attracting increasing attention in biological systems, especially in drug discovery and design, for their advantages of both improving drug–target binding affinity and tuning ADME/T properties. After a comprehensive literature survey in drug discovery and design, we found that most of the studies on XBs between ligands and proteins have focused on the protein backbone. Meanwhile, we also noticed that the proportion of side-chain XBs to overall XBs decreases as structural resolution becomes lower and lower. We postulated that protein side chains are more flexible in comparison with backbone structures, leading to more unclear electron density and lower resolution of the side chains. As the classic force field used to refine protein structures from diffraction data cannot handle XBs correctly, some of the interactions are lost during the refinement. On the contrary, there is no change in the corresponding ratio of hydrogen bonds (HBs) during structural resolution because HBs can be handled well with the classic force field. Further analysis revealed that Thr and Gln account for a large part of the decreasing XB trend, which could be partly attributed to the misidentified N, C, or O atoms. In addition, the lost XBs might be recovered after the atoms are reassigned, e.g., by flipping Thr side chains. In summary, formation of XBs with protein side chains is underestimated, and more attention should be paid to the potential formation of XBs between organohalogens and protein side chains during X-ray crystallography studies

Molecular structures of the three inhibitors 8CA (A), F8A(B) and I4A(C).

<p>The structural difference is labeled by red circle.</p

Interactions of key residues in A-FABP with the inhibitor 8CA.

<p>Fig. A represents frequency distribution of the H atom…acceptor distance, Fig. B depicts the position of inhibitor 8CA relative to key residues, Fig. C shows the hydrophobic contacts as a function of the simulation time.</p

Hydrogen bonding energy calculated based on an empirical equation.

<p>Hydrogen bonding energy calculated based on an empirical equation.</p