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

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

Underestimated Halogen Bonds Forming with Protein Backbone in Protein Data Bank

Halogen bonds (XBs) are attracting increasing attention in biological systems. Protein Data Bank (PDB) archives experimentally determined XBs in biological macromolecules. However, no software for structure refinement in X-ray crystallography takes into account XBs, which might result in the weakening or even vanishing of experimentally determined XBs in PDB. In our previous study, we showed that side-chain XBs forming with protein side chains are underestimated in PDB on the basis of the phenomenon that the proportion of side-chain XBs to overall XBs decreases as structural resolution becomes lower and lower. However, whether the dominant backbone XBs forming with protein backbone are overlooked is still a mystery. Here, with the help of the ratio (<i>R</i>_<i>F</i>) of the observed XBs’ frequency of occurrence to their frequency expected at random, we demonstrated that backbone XBs are largely overlooked in PDB, too. Furthermore, three cases were discovered possessing backbone XBs in high resolution structures while losing the XBs in low resolution structures. In the last two cases, even at 1.80 Å resolution, the backbone XBs were lost, manifesting the urgent need to consider XBs in the refinement process during X-ray crystallography study

Halogen Bond: Its Role beyond Drug–Target Binding Affinity for Drug Discovery and Development

Halogen bond has attracted a great deal of attention in the past years for hit-to-lead-to-candidate optimization aiming at improving drug-target binding affinity. In general, heavy organohalogens (i.e., organochlorines, organobromines, and organoiodines) are capable of forming halogen bonds while organofluorines are not. In order to explore the possible roles that halogen bonds could play beyond improving binding affinity, we performed a detailed database survey and quantum chemistry calculation with close attention paid to (1) the change of the ratio of heavy organohalogens to organofluorines along the drug discovery and development process and (2) the halogen bonds between organohalogens and nonbiopolymers or nontarget biopolymers. Our database survey revealed that (1) an obviously increasing trend of the ratio of heavy organohalogens to organofluorines was observed along the drug discovery and development process, illustrating that more organofluorines are worn and eliminated than heavy organohalogens during the process, suggesting that heavy halogens with the capability of forming halogen bonds should have priority for lead optimization; and (2) more than 16% of the halogen bonds in PDB are formed between organohalogens and water, and nearly 20% of the halogen bonds are formed with the proteins that are involved in the ADME/T process. Our QM/MM calculations validated the contribution of the halogen bond to the binding between organohalogens and plasma transport proteins. Thus, halogen bonds could play roles not only in improving drug–target binding affinity but also in tuning ADME/T property. Therefore, we suggest that albeit halogenation is a valuable approach for improving ligand bioactivity, more attention should be paid in the future to the application of the halogen bond for ligand ADME/T property optimization

Unstable, Metastable, or Stable Halogen Bonding Interaction Involving Negatively Charged Donors? A Statistical and Computational Chemistry Study

The noncovalent halogen bonding could be attributed to the attraction between the positively charged σ-hole and a nucleophile. Quantum mechanics (QM) calculation indicated that the negatively charged organohalogens have no positively charged σ-hole on their molecular surface, leading to a postulation of repulsion between negatively charged organohalogens and nucleophiles in vacuum. However, PDB survey revealed that 24% of the ligands with halogen bonding geometry could be negatively charged. Moreover, 36% of ionizable drugs in CMC (Comprehensive Medicinal Chemistry) are possibly negatively charged at pH 7.0. QM energy scan showed that the negatively charged halogen bonding is probably metastable in vacuum. However, the QM calculated bonding energy turned negative in various solvents, suggesting that halogen bonding with negatively charged donors should be stable in reality. Indeed, QM/MM calculation on three crystal structures with negatively charged ligands revealed that the negatively charged halogen bonding was stable. Hence, we concluded that halogen bonding with negatively charged donors is unstable or metastable in vacuum but stable in protein environment, and possesses similar geometric and energetic characteristics as conventional halogen bonding. Therefore, negatively charged organohalogens are still effective halogen bonding donors for medicinal chemistry and other applications

Stability and Characteristics of the Halogen Bonding Interaction in an Anion–Anion Complex: A Computational Chemistry Study

Halogen bonding is the noncovalent interaction between the positively charged σ-hole of organohalogens and nucleophiles. In reality, both the organohalogen and nucleophile could be deprotonated to form anions, which may lead to the vanishing of the σ-hole and possible repulsion between the two anions. However, our database survey in this study revealed that there are halogen bonding-like interactions between two anions. Quantum mechanics calculations with small model complexes composed of halobenzoates and propiolate indicated that the anion–anion halogen bonding is unstable in vacuum but attractive in solvents. Impressively, the QM optimized halogen bonding distance between the two anions is shorter than that in a neutral system, indicating a possibly stronger halogen bonding interaction, which is verified by the calculated binding energies. Furthermore, natural bond orbital and quantum theory of atoms in molecule analyses also suggested stronger anion–anion halogen bonding than that of the neutral one. Energy decomposition by symmetry adapted perturbation theory revealed that the strong binding might be attributed to large induction energy. The calculations on 4 protein–ligand complexes from PDB by the QM/MM method demonstrated that the anion–anion halogen bonding could contribute to the ligands’ binding affinity up to ∼3 kcal/mol. Therefore, anion–anion halogen bonding is stable and applicable in reality

D3Rings: A Fast and Accurate Method for Ring System Identification and Deep Generation of Drug-Like Cyclic Compounds

Continuous exploration of the chemical space of molecules to find ligands with high affinity and specificity for specific targets is an important topic in drug discovery. A focus on cyclic compounds, particularly natural compounds with diverse scaffolds, provides important insights into novel molecular structures for drug design. However, the complexity of their ring structures has hindered the applicability of widely accepted methods and software for the systematic identification and classification of cyclic compounds. Herein, we successfully developed a new method, D3Rings, to identify acyclic, monocyclic, spiro ring, fused and bridged ring, and cage ring compounds, as well as macrocyclic compounds. By using D3Rings, we completed the statistics of cyclic compounds in three different databases, e.g., ChEMBL, DrugBank, and COCONUT. The results demonstrated the richness of ring structures in natural products, especially spiro, macrocycles, and fused and bridged rings. Based on this, three deep generative models, namely, VAE, AAE, and CharRNN, were trained and used to construct two data sets similar to DrugBank and COCONUT but 10 times larger than them. The enlarged data sets were then used to explore the molecular chemical space, focusing on complex ring structures, for novel drug discovery and development. Docking experiments with the newly generated COCONUT-like data set against three SARS-CoV-2 target proteins revealed that an expanded compound database improves molecular docking results. Cyclic structures exhibited the best docking scores among the top-ranked docking molecules. These results suggest the importance of exploring the chemical space of structurally novel cyclic compounds and continuous expansion of the library of drug-like compounds to facilitate the discovery of potent ligands with high binding affinity to specific targets. D3Rings is now freely available at http://www.d3pharma.com/D3Rings/

The Stabilization Effect of Dielectric Constant and Acidic Amino Acids on Arginine–Arginine (Arg–Arg) Pairings: Database Survey and Computational Studies

Database survey in this study revealed that about one-third of the protein structures deposited in the Protein Data Bank (PDB) contain arginine–arginine (Arg–Arg) pairing with a carbon···carbon (CZ···CZ) interaction distance less than 5 Å. All the Arg–Arg pairings were found to bury in a polar environment composed of acidic residues, water molecules, and strong polarizable or negatively charged moieties from binding site or bound ligand. Most of the Arg–Arg pairings are solvent exposed and 68.3% Arg–Arg pairings are stabilized by acidic residues, forming Arg–Arg–Asp/Glu clusters. Density functional theory (DFT) was then employed to study the effect of environment on the pairing structures. It was revealed that Arg–Arg pairings become thermodynamically stable (about −1 kcal/mol) as the dielectric constant increases to 46.8 (DMSO), in good agreement with the results of the PDB survey. DFT calculations also demonstrated that perpendicular Arg–Arg pairing structures are favorable in low dielectric constant environment, while in high dielectric constant environment parallel structures are favorable. Additionally, the acidic residues can stabilize the Arg–Arg pairing structures to a large degree. Energy decomposition analysis of Arg–Arg pairings and Arg–Arg–Asp/Glu clusters showed that both solvation and electrostatic energies contribute significantly to their stability. The results reported herein should be very helpful for understanding Arg–Arg pairing and its application in drug design

Exploring Transition Pathway and Free-Energy Profile of Large-Scale Protein Conformational Change by Combining Normal Mode Analysis and Umbrella Sampling Molecular Dynamics

Large-scale conformational changes of proteins are usually associated with the binding of ligands. Because the conformational changes are often related to the biological functions of proteins, understanding the molecular mechanisms of these motions and the effects of ligand binding becomes very necessary. In the present study, we use the combination of normal-mode analysis and umbrella sampling molecular dynamics simulation to delineate the atomically detailed conformational transition pathways and the associated free-energy landscapes for three well-known protein systems, viz., adenylate kinase (AdK), calmodulin (CaM), and p38α kinase in the absence and presence of respective ligands. For each protein under study, the transient conformations along the conformational transition pathway and thermodynamic observables are in agreement with experimentally and computationally determined ones. The calculated free-energy profiles reveal that AdK and CaM are intrinsically flexible in structures without obvious energy barrier, and their ligand binding shifts the equilibrium from the ligand-free to ligand-bound conformation (population shift mechanism). In contrast, the ligand binding to p38α leads to a large change in free-energy barrier (ΔΔ<i>G</i> ≈ 7 kcal/mol), promoting the transition from DFG-in to DFG-out conformation (induced fit mechanism). Moreover, the effect of the protonation of D168 on the conformational change of p38α is also studied, which reduces the free-energy difference between the two functional states of p38α and thus further facilitates the conformational interconversion. Therefore, the present study suggests that the detailed mechanism of ligand binding and the associated conformational transition is not uniform for all kinds of proteins but correlated to their respective biological functions

Exploring Transition Pathway and Free-Energy Profile of Large-Scale Protein Conformational Change by Combining Normal Mode Analysis and Umbrella Sampling Molecular Dynamics

Large-scale conformational changes of proteins are usually associated with the binding of ligands. Because the conformational changes are often related to the biological functions of proteins, understanding the molecular mechanisms of these motions and the effects of ligand binding becomes very necessary. In the present study, we use the combination of normal-mode analysis and umbrella sampling molecular dynamics simulation to delineate the atomically detailed conformational transition pathways and the associated free-energy landscapes for three well-known protein systems, viz., adenylate kinase (AdK), calmodulin (CaM), and p38α kinase in the absence and presence of respective ligands. For each protein under study, the transient conformations along the conformational transition pathway and thermodynamic observables are in agreement with experimentally and computationally determined ones. The calculated free-energy profiles reveal that AdK and CaM are intrinsically flexible in structures without obvious energy barrier, and their ligand binding shifts the equilibrium from the ligand-free to ligand-bound conformation (population shift mechanism). In contrast, the ligand binding to p38α leads to a large change in free-energy barrier (ΔΔ<i>G</i> ≈ 7 kcal/mol), promoting the transition from DFG-in to DFG-out conformation (induced fit mechanism). Moreover, the effect of the protonation of D168 on the conformational change of p38α is also studied, which reduces the free-energy difference between the two functional states of p38α and thus further facilitates the conformational interconversion. Therefore, the present study suggests that the detailed mechanism of ligand binding and the associated conformational transition is not uniform for all kinds of proteins but correlated to their respective biological functions