Large-Scale Supramolecular Structure in Solutions of Low Molar Mass Compounds and Mixtures of Liquids. III. Correlation with Molecular Properties and Interactions

2006 ◽  
Vol 110 (28) ◽  
pp. 13976-13984 ◽  
Author(s):  
Marián Sedlák
Keyword(s):  
Supramolecular Structure ◽  
Large Scale ◽  
Molar Mass ◽  
Molecular Properties

scholarly journals Simplified geometric representations of protein structures identify complementary interaction interfaces

2019 ◽  
Author(s):  
Caitlyn L. McCafferty ◽  
Edward M. Marcotte ◽  
David W. Taylor
Keyword(s):  
Conformational Changes ◽  
Protein Interaction ◽  
Protein Function ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Protein Complexes ◽  
Protein Structures ◽  
Molecular Properties ◽  
3D Structures ◽  
Geometric Representations

ABSTRACTProtein-protein interactions are critical to protein function, but three-dimensional (3D) arrangements of interacting proteins have proven hard to predict, even given the identities and 3D structures of the interacting partners. Specifically, identifying the relevant pairwise interaction surfaces remains difficult, often relying on shape complementarity with molecular docking while accounting for molecular motions to optimize rigid 3D translations and rotations. However, such approaches can be computationally expensive, and faster, less accurate approximations may prove useful for large-scale prediction and assembly of 3D structures of multi-protein complexes. We asked if a reduced representation of protein geometry retains enough information about molecular properties to predict pairwise protein interaction interfaces that are tolerant of limited structural rearrangements. Here, we describe a cuboid transformation of 3D protein accessible surfaces on which molecular properties such as charge, hydrophobicity, and mutation rate can be easily mapped, implemented in the MorphProt package. Pairs of surfaces are compared to rapidly assess partner-specific potential surface complementarity. On two available benchmarks of 85 overall known protein complexes, we observed F1 scores (a weighted combination of precision and recall) of 19-34% at correctly identifying protein interaction surfaces, comparable to more computationally intensive 3D docking methods in the annual Critical Assessment of PRedicted Interactions. Furthermore, we examined the effect of molecular motion through normal mode simulation on a benchmark receptor-ligand pair and observed no marked loss of predictive accuracy for distortions of up to 6 Å RMSD. Thus, a cuboid transformation of protein surfaces retains considerable information about surface complementarity, offers enhanced speed of comparison relative to more complex geometric representations, and exhibits tolerance to conformational changes.

DeepAtomicCharge: a new graph convolutional network-based architecture for accurate prediction of atomic charges

2020 ◽  
Author(s):  
Jike Wang ◽  
Dongsheng Cao ◽  
Cunchen Tang ◽  
Lei Xu ◽  
Qiaojun He ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Atomic Charge ◽  
High Accuracy ◽  
Substantial Improvement ◽  
Molecular Properties ◽  
Atomic Charges ◽  
Convolutional Network ◽  
Structure Based Drug Design ◽  
Molecular Fingerprints

Abstract Atomic charges play a very important role in drug-target recognition. However, computation of atomic charges with high-level quantum mechanics (QM) calculations is very time-consuming. A number of machine learning (ML)-based atomic charge prediction methods have been proposed to speed up the calculation of high-accuracy atomic charges in recent years. However, most of them used a set of predefined molecular properties, such as molecular fingerprints, for model construction, which is knowledge-dependent and may lead to biased predictions due to the representation preference of different molecular properties used for training. To solve the problem, we present a new architecture based on graph convolutional network (GCN) and develop a high-accuracy atomic charge prediction model named DeepAtomicCharge. The new GCN architecture is designed with only the atomic properties and the connection information between the atoms in molecules and can dynamically learn and convert molecules into appropriate atomic features without any prior knowledge of the molecules. Using the designed GCN architecture, substantial improvement is achieved for the prediction accuracy of atomic charges. The average root-mean-square error (RMSE) of DeepAtomicCharge is 0.0121 e, which is obviously more accurate than that (0.0180 e) reported by the previous benchmark study on the same two external test sets. Moreover, the new GCN architecture needs much lower storage space compared with other methods, and the predicted DDEC atomic charges can be efficiently used in large-scale structure-based drug design, thus opening a new avenue for high-performance atomic charge prediction and application.

A robust procedure for large scale synthesis of a high molar mass, unsubstituted poly(m,p-phenylene)

2015 ◽  
Vol 6 (45) ◽  
pp. 7833-7840 ◽  
Author(s):  
Bernd Deffner ◽  
A. Dieter Schlüter
Keyword(s):  
Large Scale ◽  
Molar Mass ◽  
Side Chains ◽  
High Molar Mass ◽  
Large Scale Synthesis

A high molar mass poly(m,p-phenylene) with acid cleavable side chains was synthesised. Further, the material was processed into films and subjected to a cleaving procedure yielding insoluble films of unsubstituted polyphenylene.

Taking Advantage of Supramolecular Structure in Melt and Solution Electrospinning

2006 ◽  
Vol 948 ◽  
Author(s):  
Matthew T. Hunley ◽  
Matthew G. McKee ◽  
Pankaj Gupta ◽  
Garth L. Wilkes ◽  
Timothy E. Long
Keyword(s):  
Molecular Weight ◽  
Supramolecular Structure ◽  
Molar Mass ◽  
Polymer Processing ◽  
Processing Technique ◽  
Fibrous Membrane ◽  
Low Molecular Weight ◽  
Melt Electrospinning ◽  
Fibrous Membranes ◽  
Solution Electrospinning

ABSTRACTElectrospinning, a polymer processing technique to create nanofibrous membranes, has been used to fabricate fibrous membranes from solution and melt phases showing supramolecular order. Wormlike micellar phases of low molar mass amphiphiles, including the phospholipid mixture asolectin, were electrospun under normal conditions to form micron-sized fibers. From the melt, well defined phospholipid 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine was electrospun into a similar fibrous membrane. Additionally, thermoreversible physical crosslinks were used to prepare fibers from low molecular weight, star-shaped poly(D,L-lactide) under melt electrospinning conditions.

scholarly journals X-ray Structure-Based Chemoinformatic Analysis Identifies Promiscuous Ligands Binding to Proteins from Different Classes with Varying Shapes

2020 ◽  
Vol 21 (11) ◽  
pp. 3782 ◽  
Author(s):  
Christian Feldmann ◽  
Jürgen Bajorath
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Basic Research ◽  
Molecular Properties ◽  
Binding Modes ◽  
Target Structure ◽  
Computational Investigation ◽  
Activity Data ◽  
X Ray ◽  
Protein Classes

(1) Background: Compounds with multitarget activity are of interest in basic research to explore molecular foundations of promiscuous binding and in drug discovery as agents eliciting polypharmacological effects. Our study has aimed to systematically identify compounds that form complexes with proteins from distinct classes and compare their bioactive conformations and molecular properties. (2) Methods: A large-scale computational investigation was carried out that combined the analysis of complex X-ray structures, ligand binding modes, compound activity data, and various molecular properties. (3) Results: A total of 515 ligands with multitarget activity were identified that included 70 organic compounds binding to proteins from different classes. These multiclass ligands (MCLs) were often flexible and surprisingly hydrophilic. Moreover, they displayed a wide spectrum of binding modes. In different target structure environments, binding shapes of MCLs were often similar, but also distinct. (4) Conclusions: Combined structural and activity data analysis identified compounds with activity against proteins with distinct structures and functions. MCLs were found to have greatly varying shape similarity when binding to different protein classes. Hence, there were no apparent canonical binding shapes indicating multitarget activity. Rather, conformational versatility characterized MCL binding.

scholarly journals Epidermal biopolysaccharides from plant seeds enable biodegradable turbulent drag reduction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Anoop Rajappan ◽  
Gareth H. McKinley
Keyword(s):  
Turbulent Flow ◽  
Drag Reduction ◽  
Large Scale ◽  
Molar Mass ◽  
Raw Material ◽  
Large Reynolds Number ◽  
Frictional Drag ◽  
High Molar Mass ◽  
Plant Seeds ◽  
Cost Constraints

AbstractThe high cost of synthetic polymers has been a key impediment limiting the widespread adoption of polymer drag reduction techniques in large-scale engineering applications, such as marine drag reduction. To address consumable cost constraints, we investigate the use of high molar mass biopolysaccharides, present in the mucilaginous epidermis of plant seeds, as inexpensive drag reducers in large Reynolds number turbulent flows. Specifically, we study the aqueous mucilage extracted from flax seeds (Linum usitatissimum) and compare its drag reduction efficacy to that of poly(ethylene oxide) or PEO, a common synthetic polymer widely used as a drag reducing agent in aqueous flows. Macromolecular and rheological characterisation confirm the presence of high molar mass (≥2 MDa) polysaccharides in the extracted mucilage, with an acidic fraction comprising negatively charged chains. Frictional drag measurements, performed inside a bespoke Taylor-Couette apparatus, show that the as-extracted mucilage has comparable drag reduction performance under turbulent flow conditions as aqueous PEO solutions, while concurrently offering advantages in terms of raw material cost, availability, and bio-compatibility. Our results indicate that plant-sourced mucilage can potentially serve as a cost-effective and eco-friendly substitute for synthetic drag reducing polymers in large scale turbulent flow applications.

scholarly journals Supramolecular structure in the membrane ofStaphylococcus aureus

2015 ◽  
Vol 112 (51) ◽  
pp. 15725-15730 ◽  
Author(s):  
Jorge García-Lara ◽  
Felix Weihs ◽  
Xing Ma ◽  
Lucas Walker ◽  
Roy R. Chaudhuri ◽  
...  
Keyword(s):  
Supramolecular Structure ◽  
Large Scale ◽  
Cell Function ◽  
Protein Complexes ◽  
Geometric Constraints ◽  
Biological Molecules ◽  
Molecular Scaffold ◽  
Self Organized ◽  
Self Assembling ◽  
Entire Cell

All life demands the temporal and spatial control of essential biological functions. In bacteria, the recent discovery of coordinating elements provides a framework to begin to explain cell growth and division. Here we present the discovery of a supramolecular structure in the membrane of the coccal bacteriumStaphylococcus aureus, which leads to the formation of a large-scale pattern across the entire cell body; this has been unveiled by studying the distribution of essential proteins involved in lipid metabolism (PlsY and CdsA). The organization is found to require MreD, which determines morphology in rod-shaped cells. The distribution of protein complexes can be explained as a spontaneous pattern formation arising from the competition between the energy cost of bending that they impose on the membrane, their entropy of mixing, and the geometric constraints in the system. Our results provide evidence for the existence of a self-organized and nonpercolating molecular scaffold involving MreD as an organizer for optimal cell function and growth based on the intrinsic self-assembling properties of biological molecules.

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