scholarly journals Improving the analysis of biological ensembles through extended similarity measures

2021 ◽  
Author(s):  
Liwei Chang ◽  
Alberto Perez ◽  
Ramon Alain Miranda-Quintana
Keyword(s):  
Molecular Dynamics ◽  
Structural Information ◽  
Similarity Measures ◽  
Structural Ensembles ◽  
New Algorithms

We present new algorithms to classify structural ensembles of macromolecules, based on the recently proposed extended similarity measures. Molecular Dynamics provides a wealth of structural information on systems of biologically...

scholarly journals Improving the analysis of biological ensembles through extended similarity measures

2021 ◽  
Author(s):  
Liwei Chang ◽  
Alberto Perez ◽  
Ramon Alain Miranda-Quintana
Keyword(s):  
Clustering Algorithm ◽  
Structural Information ◽  
Similarity Measures ◽  
Agglomerative Clustering ◽  
Excellent Performance ◽  
Biological Interest ◽  
Structural Ensembles ◽  
Similarity Indices ◽  
Hierarchical Agglomerative Clustering ◽  
New Algorithms

We present new algorithms to classify structural ensembles of macromolecules, based on the recently proposed extended similarity measures. Molecular Dynamics provides a wealth of structural information on systems of biologically interest. As computer power increases we capture larger ensembles and larger conformational transitions between states. Typically, structural clustering provides the statistical mechanics treatment of the system to identify relevant biological states. The key advantage of our approach is that the newly introduced extended similiarity indices reduce the computational complexity of assessing the similarity of a set of structures from O(N2) to O(N). Here we take advantage of this favorable cost to develop several highly efficient techniques, including a linear-scaling algorithm to determine the medoid of a set (which we effectively use to select the most representative structure of a cluster). Moreover, we use our extended similarity indices as a linkage criterion in a novel hierarchical agglomerative clustering algorithm. We apply these new metrics to analyze the ensembles of several systems of biological interest such as folding and binding of macromolecules (peptide, protein, DNA-protein). In particular, we design a new workflow that is capable of identifying the most important conformations contributing to the protein folding process. We show excellent performance in the resulting clusters (surpassing traditional linkage criteria), along with faster performance and an efficient cost-function to identify when to merge clusters.

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

2019 ◽  
Vol 25 (31) ◽  
pp. 3339-3349 ◽  
Author(s):  
Indrani Bera ◽  
Pavan V. Payghan
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Molecular Dynamics Simulations ◽  
Drug Target ◽  
Structural Information ◽  
Drug Binding ◽  
Structure Based Drug Design ◽  
Drug Designing ◽  
Target Binding ◽  
Dynamics Simulations

Background: Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process. Objective: The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design. Method: This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained. Results: This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations. Conclusion: The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

scholarly journals Explanation of the Formation of Complexes between Representatives of Oxazolidinones and HDAS-β-CD Using Molecular Modeling as a Complementary Technique to cEKC and NMR

2021 ◽  
Vol 22 (13) ◽  
pp. 7139
Author(s):  
Wojciech Bocian ◽  
Elżbieta Bednarek ◽  
Katarzyna Michalska
Keyword(s):  
Molecular Dynamics ◽  
Molecular Modeling ◽  
Computational Methods ◽  
Chiral Separation ◽  
Structural Information ◽  
Binding Constants ◽  
Nmr Studies ◽  
Thermodynamics Of Complexation ◽  
Poisson Boltzmann ◽  
Explicit Water

Molecular modeling (MM) results for tedizolid and radezolid with heptakis-(2,3-diacetyl-6-sulfo)-β-cyclodextrin (HDAS-β-CD) are presented and compared with the results previously obtained for linezolid and sutezolid. The mechanism of interaction of chiral oxazolidinone ligands belonging to a new class of antibacterial agents, such as linezolid, tedizolid, radezolid, and sutezolid, with HDAS-β-CD based on capillary electrokinetic chromatography (cEKC), nuclear magnetic resonance (NMR) spectroscopy, and MM methods was described. Principles of chiral separation of oxazolidinone analogues using charged single isomer derivatives of cyclodextrin by the cEKC method were presented, including the selection of the optimal chiral selector and separation conditions, complex stoichiometry, and binding constants, which provided a comprehensive basis for MM studies. In turn, NMR provided, where possible, direct information on the geometry of the inclusion complexes and also provided the necessary structural information to validate the MM calculations. Consequently, MM contributed to the understanding of the structure of diastereomeric complexes, the thermodynamics of complexation, and the visualization of their structures. The most probable mean geometries of the studied supramolecular complexes and their dynamics (geometry changes over time) were determined by molecular dynamics methods. Oxazolidinone ligands have been shown to complex mainly the inner part of cyclodextrin, while the external binding is less privileged, which is consistent with the conclusions of the NMR studies. Enthalpy values of binding of complexes were calculated using long-term molecular dynamics in explicit water as well as using molecular mechanics, the Poisson–Boltzmann or generalized Born, and surface area continuum solvation (MM/PBSA and MM/GBSA) methods. Computational methods predicted the effect of changes in pH and composition of the solution on the strength and complexation process, and it adapted the conditions selected as optimal during the cEKC study. By changing the dielectric constant in the MM/PBSA and MM/GBSA calculations, the effect of changing the solution to methanol/acetonitrile was investigated. A fairly successful attempt was made to predict the chiral separation of the oxazolidinones using the modified cyclodextrin by computational methods.

scholarly journals Predicting and Experimentally Validating Hot-Spot Residues at Protein-Protein Interfaces

2019 ◽  
Author(s):  
Amaurys Ibarra ◽  
Gail J. Bartlett ◽  
Zsofia Hegedus ◽  
Som Dutt ◽  
Fruzsina Hobor ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Comparative Analysis ◽  
Hot Spots ◽  
Hot Spot ◽  
Accurate Prediction ◽  
Protein Interface ◽  
Alanine Scanning ◽  
Protein Interfaces ◽  
Structural Ensembles ◽  
Α Helix

Here we describe a comparative analysis of multiple CAS methods, which highlights effective approaches to improve the accuracy of predicting hot-spot residues. Alongside this, we introduce a new method, BUDE Alanine Scanning, which can be applied to single structures from crystallography, and to structural ensembles from NMR or molecular dynamics data. The comparative analyses facilitate accurate prediction of hot-spots that we validate experimentally with three diverse targets: NOXA-B/MCL-1 (an α helix-mediated PPI), SIMS/SUMO and GKAP/SHANK-PDZ (both β strand-mediated interactions). Finally, the approach is applied to the accurate prediction of hot-residues at a topographically novel Affimer/BCL-xL protein-protein interface.

scholarly journals Flexibility of Short-Strand RNA in Aqueous Solution as Revealed by Molecular Dynamics Simulation: Are A-RNA and A´-RNA Distinct Conformational Structures?

2009 ◽  
Vol 62 (9) ◽  
pp. 1054 ◽  
Author(s):  
Defang Ouyang ◽  
Hong Zhang ◽  
Dirk-Peter Herten ◽  
Harendra S. Parekh ◽  
Sean C. Smith
Keyword(s):  
Molecular Dynamics ◽  
Aqueous Solution ◽  
Structural Information ◽  
Structural Parameters ◽  
Biological Molecules ◽  
Dynamics Simulation ◽  
Viral Gene ◽  
Conformational Structure ◽  
Crystal Forms ◽  
Gene Delivery Vectors

We use molecular dynamics simulations to compare the conformational structure and dynamics of a 21-base pair RNA sequence initially constructed according to the canonical A-RNA and A′-RNA forms in the presence of counterions and explicit water. Our study aims to add a dynamical perspective to the solid-state structural information that has been derived from X-ray data for these two characteristic forms of RNA. Analysis of the three main structural descriptors commonly used to differentiate between the two forms of RNA – namely major groove width, inclination and the number of base pairs in a helical twist – over a 30 ns simulation period reveals a flexible structure in aqueous solution with fluctuations in the values of these structural parameters encompassing the range between the two crystal forms and more. This provides evidence to suggest that the identification of distinct A-RNA and A′-RNA structures, while relevant in the crystalline form, may not be generally relevant in the context of RNA in the aqueous phase. The apparent structural flexibility observed in our simulations is likely to bear ramifications for the interactions of RNA with biological molecules (e.g. proteins) and non-biological molecules (e.g. non-viral gene delivery vectors).

scholarly journals Molecular Dynamics Modellization and Simulation of Water Diffusion through Plant Cutin

1999 ◽  
Vol 54 (11) ◽  
pp. 896-902 ◽  
Author(s):  
Antonio Matas ◽  
Antonio Heredia
Keyword(s):  
Experimental Data ◽  
Molecular Dynamics ◽  
Structural Information ◽  
Md Simulations ◽  
Water Molecules ◽  
Plant Cuticle ◽  
X Ray Diffraction ◽  
Cross Link ◽  
X Ray ◽  
Good Agreement

Abstract A theoretical molecular modelling study has been conducted for cutin, the biopolyester that forms the main structural component of the plant cuticle. Molecular dynamics (MD) simulations, extended over several ten picoseconds, suggests that cutin is a moderately flexible netting with motional constraints mainly located at the cross-link sites of functional ester groups. This study also gives structural information essentially in accordance with previously reported experimental data, obtained from X -ray diffraction and nuclear magnetic resonance experiments. MD calculations were also performed to simulate the diffusion of water mole­cules through the cutin biopolymer. The theoretical analysis gives evidence that water perme­ation proceedes by a “hopping mechanism”. Coefficients for the diffusion of the water molecules in cutin were obtained from their mean-square displacements yielding values in good agreement with experimental data.

Content Based Image Retrieval Using Active-Nets

2011 ◽  
pp. 85-114 ◽  
Author(s):  
David García Pérez ◽  
Antonio Mosquera ◽  
Stefano Berretti ◽  
Alberto Del Bimbo
Keyword(s):  
Image Retrieval ◽  
Structural Information ◽  
Similarity Measures ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Objects ◽  
Image Retrieval System ◽  
Deformable Structure ◽  
Retrieval Problem ◽  
Active Research

Content-based image retrieval has been an active research area in past years. Many different solutions have been proposed to improve performance of retrieval, but the large part of these works have focused on sub-parts of the retrieval problem, providing targeted solutions only for individual aspects (i.e., feature extraction, similarity measures, indexing, etc). In this chapter, we first shortly review some of the main practiced solutions for content-based image retrieval evidencing some of the main issues. Then, we propose an original approach for the extraction of relevant image objects and their matching for retrieval applications, and present a complete image retrieval system which uses this approach (including similarity measures and image indexing). In particular, image objects are represented by a two-dimensional deformable structure, referred to as “active net.” Active net is capable of adapting to relevant image regions according to chromatic and edge information. Extension of the active nets has been defined, which permits the nets to break themselves, thus increasing their capability to adapt to objects with complex topological structure. The resulting representation allows a joint description of color, shape, and structural information of extracted objects. A similarity measure between active nets has also been defined and used to combine the retrieval with an efficient indexing structure. The proposed system has been experimented on two large and publicly available objects databases, namely, the ETH-80 and the ALOI.

Characterization of the structural ensembles of p53 TAD2 by molecular dynamics simulations with different force fields

2018 ◽  
Vol 20 (13) ◽  
pp. 8676-8684 ◽  
Author(s):  
Yanhua Ouyang ◽  
Likun Zhao ◽  
Zhuqing Zhang
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Force Fields ◽  
Structural Ensembles ◽  
Dynamics Simulations

The conformations of p53 TAD2 in complexes and sampled in simulations with five force fields.

scholarly journals Knowledge-driven graph similarity for text classification

2020 ◽  
Author(s):  
Niloofer Shanavas ◽  
Hui Wang ◽  
Zhiwei Lin ◽  
Glenn Hawe
Keyword(s):  
Text Classification ◽  
Structural Information ◽  
Similarity Measures ◽  
Semantic Knowledge ◽  
Exact Matching ◽  
Text Documents ◽  
Graph Kernel ◽  
Word Similarity ◽  
Graph Similarity ◽  
Automatic Text Classification

AbstractAutomatic text classification using machine learning is significantly affected by the text representation model. The structural information in text is necessary for natural language understanding, which is usually ignored in vector-based representations. In this paper, we present a graph kernel-based text classification framework which utilises the structural information in text effectively through the weighting and enrichment of a graph-based representation. We introduce weighted co-occurrence graphs to represent text documents, which weight the terms and their dependencies based on their relevance to text classification. We propose a novel method to automatically enrich the weighted graphs using semantic knowledge in the form of a word similarity matrix. The similarity between enriched graphs, knowledge-driven graph similarity, is calculated using a graph kernel. The semantic knowledge in the enriched graphs ensures that the graph kernel goes beyond exact matching of terms and patterns to compute the semantic similarity of documents. In the experiments on sentiment classification and topic classification tasks, our knowledge-driven similarity measure significantly outperforms the baseline text similarity measures on five benchmark text classification datasets.

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