15 research outputs found
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 drugātarget 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><sub><i>F</i></sub>) of the
observed XBsā 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 Ć
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 DrugāTarget 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 drugātarget 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 Ļ-hole and a nucleophile. Quantum
mechanics (QM) calculation indicated that the negatively charged organohalogens
have no positively charged Ļ-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 AnionāAnion Complex: A Computational Chemistry Study
Halogen bonding is the noncovalent
interaction between the positively
charged Ļ-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 Ļ-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 anionāanion 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
anionāanion 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 proteināligand complexes from PDB by
the QM/MM method demonstrated that the anionāanion halogen
bonding could contribute to the ligandsā binding affinity up
to ā¼3 kcal/mol. Therefore, anionāanion 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 ArginineāArginine (ArgāArg) 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
arginineāarginine (ArgāArg) pairing with a carbonĀ·Ā·Ā·carbon
(CZĀ·Ā·Ā·CZ) interaction distance less than 5 Ć
.
All the ArgāArg 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 ArgāArg pairings are solvent exposed and 68.3%
ArgāArg pairings are stabilized by acidic residues, forming
ArgāArgāAsp/Glu clusters. Density functional theory
(DFT) was then employed to study the effect of environment on the
pairing structures. It was revealed that ArgāArg pairings become
thermodynamically stable (about ā1 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
ArgāArg 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 ArgāArg pairing structures to a large degree.
Energy decomposition analysis of ArgāArg pairings and ArgāArgāAsp/Glu
clusters showed that both solvation and electrostatic energies contribute
significantly to their stability. The results reported herein should
be very helpful for understanding ArgāArg 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 p38Ī±
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 p38Ī±
leads to a large change in free-energy barrier (ĪĪ<i>G</i> ā 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 p38Ī±
is also studied, which reduces the free-energy difference between
the two functional states of p38Ī± 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 p38Ī±
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 p38Ī±
leads to a large change in free-energy barrier (ĪĪ<i>G</i> ā 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 p38Ī±
is also studied, which reduces the free-energy difference between
the two functional states of p38Ī± 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