scholarly journals Molecular Skin Surface-Based Transformation Visualization between Biological Macromolecules

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Ke Yan ◽  
Bing Wang ◽  
Holun Cheng ◽  
Zhiwei Ji ◽  
Jing Huang ◽  
...  
Keyword(s):  
Protein Structures ◽  
Computational Cost ◽  
Data Bank ◽  
Skin Surface ◽  
Transformation Process ◽  
Molecular Surface ◽  
Biological Macromolecules ◽  
Dynamic Simulations ◽  
Macromolecular Dynamics ◽  
Union Of Balls

Molecular skin surface (MSS), proposed by Edelsbrunner, is a C2 continuous smooth surface modeling approach of biological macromolecules. Compared to the traditional methods of molecular surface representations (e.g., the solvent exclusive surface), MSS has distinctive advantages including having no self-intersection and being decomposable and transformable. For further promoting MSS to the field of bioinformatics, transformation between different MSS representations mimicking the macromolecular dynamics is demanded. The transformation process helps biologists understand the macromolecular dynamics processes visually in the atomic level, which is important in studying the protein structures and binding sites for optimizing drug design. However, modeling the transformation between different MSSs suffers from high computational cost while the traditional approaches reconstruct every intermediate MSS from respective intermediate union of balls. In this study, we propose a novel computational framework named general MSS transformation framework (GMSSTF) between two MSSs without the assistance of union of balls. To evaluate the effectiveness of GMSSTF, we applied it on a popular public database PDB (Protein Data Bank) and compared the existing MSS algorithms with and without GMSSTF. The simulation results show that the proposed GMSSTF effectively improves the computational efficiency and is potentially useful for macromolecular dynamic simulations.

ACMS: a database of alternate conformations found in the atoms of main and side chains of protein structures

2019 ◽  
Vol 52 (4) ◽  
pp. 910-913 ◽  
Author(s):  
R. Santhosh ◽  
P. Chandrasekaran ◽  
Daliah Michael ◽  
K. Rangachari ◽  
Namrata Bankoti ◽  
...  
Keyword(s):  
High Resolution ◽  
Knowledge Base ◽  
Crystal Structures ◽  
Protein Structures ◽  
Protein Molecule ◽  
Data Bank ◽  
Side Chain ◽  
Biological Macromolecules ◽  
Conformational Ensembles ◽  
User Friendly

Proteins are usually dynamic biological macromolecules, thereby exhibiting a large number of conformational ensembles which influence the association with their neighbours and interacting partners. Most of the side-chain atoms and a few main-chain atoms of the high-resolution crystal structures deposited in the Protein Data Bank adopt alternate conformations. This kind of conformational behaviour prompted the authors to explore the relationship, if any, between the alternate conformations and the function of the protein molecule. Thus, a knowledge base of the alternate conformations of the main- and side-chain atoms of protein structures has been developed. It provides a detailed description of the alternate conformations of various residues for more than 60 000 high-resolution crystal structures. The proposed knowledge base is very user friendly and has various flexible options. The knowledge base will be updated periodically and can be accessed at http://iris.physics.iisc.ac.in/acms.

scholarly journals Learning structural motif representations for efficient protein structure search

2017 ◽  
Author(s):  
Yang Liu ◽  
Qing Ye ◽  
Liwei Wang ◽  
Jian Peng
Keyword(s):  
Protein Structure ◽  
Fundamental Problem ◽  
Sequence Similarity ◽  
Structural Alignment ◽  
Protein Structures ◽  
Computational Cost ◽  
Data Bank ◽  
Hierarchical Organization ◽  
Structural Motif ◽  
And Function

AbstractMotivationUnderstanding the relationship between protein structure and function is a fundamental problem in protein science. Given a protein of unknown function, fast identification of similar protein structures from the Protein Data Bank (PDB) is a critical step for inferring its biological function. Such structural neighbors can provide evolutionary insights into protein conformation, interfaces and binding sites that are not detectable from sequence similarity. However, the computational cost of performing pairwise structural alignment against all structures in PDB is prohibitively expensive. Alignment-free approaches have been introduced to enable fast but coarse comparisons by representing each protein as a vector of structure features or fingerprints and only computing similarity between vectors. As a notable example, FragBag represents each protein by a “bag of fragments”, which is a vector of frequencies of contiguous short backbone fragments from a predetermined library.ResultsHere we present a new approach to learning effective structural motif presentations using deep learning. We develop DeepFold, a deep convolutional neural network model to extract structural motif features of a protein structure. Similar to FragBag, DeepFold represents each protein structure or fold using a vector of learned structural motif features. We demonstrate that DeepFold substantially outperforms FragBag on protein structural search on a non-redundant protein structure database and a set of newly released structures. Remarkably, DeepFold not only extracts meaningful backbone segments but also finds important long-range interacting motifs for structural comparison. We expect that DeepFold will provide new insights into the evolution and hierarchical organization of protein structural motifs.Availabilityhttps://github.com/largelymfs/[email protected]

scholarly journals Check your metal - not every density blob is a water molecule

2014 ◽  
Vol 70 (a1) ◽  
pp. C1483-C1483
Author(s):  
Heping Zheng ◽  
Mahendra Chordia ◽  
David Cooper ◽  
Ivan Shabalin ◽  
Maksymilian Chruszcz ◽  
...  
Keyword(s):  
Coordination Chemistry ◽  
Metal Binding ◽  
Binding Sites ◽  
Metal Ion ◽  
Protein Structures ◽  
Data Bank ◽  
Careful Examination ◽  
Biological Macromolecules ◽  
Metal Binding Sites ◽  
Heterogeneous Chemical

Metals play vital roles in both the mechanism and architecture of biological macromolecules, and are the most frequently encountered ligands (i.e. non-solvent heterogeneous chemical atoms) in the determination of macromolecular crystal structures. However, metal coordinating environments in protein structures are not always easy to check in routine validation procedures, resulting in an abundance of misidentified and/or suboptimally modeled metal ions in the Protein Data Bank (PDB). We present a solution to identify these problems in three distinct yet related aspects: (1) coordination chemistry; (2) agreement of experimental B-factors and occupancy; and (3) the composition and motif of the metal binding environment. Due to additional strain introduced by macromolecular backbones, the patterns of coordination of metal binding sites in metal-containing macromolecules are more complex and diverse than those found in inorganic or organometallic chemistry. These complications make a comprehensive library of "permitted" coordination chemistry in protein structures less feasible, and the usage of global parameters such as the bond valence method more practical, in the determination and validation of metal binding environments. Although they are relatively infrequent, there are also cases where the experimental B-factor or occupancy of a metal ion suggests careful examination. We have developed a web-based tool called CheckMyMetal [1](http://csgid.org/csgid/metal_sites/) for the quick validation of metal binding sites. Moreover, the acquired knowledge of the composition and spatial arrangement (motif) of the coordinating atoms around the metal ion may also help in the modeling of metal binding sites in macromolecular structures. All of the studies described herein were performed using the NEIGHBORHOOD SQL database [2], which connects information about all modeled non-solvent heterogeneous chemical motifs in PDB structure by vectors describing all contacts to neighboring residues and atoms. NEIGHBORHOOD has broad applications for the validation and data mining of ligand binding environments in the PDB.

scholarly journals Assessment of Globularity of Protein Structures via Minimum Volume Ellipsoids and Voxel-Based Atom Representation

Crystals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1539
Author(s):  
Mateusz Banach
Keyword(s):  
Tertiary Structure ◽  
Protein Structures ◽  
Kernel Density ◽  
Data Bank ◽  
Molecular Surface ◽  
Uniform Grid ◽  
Minimum Volume ◽  
Minimum Volume Ellipsoid ◽  
Preparation Step ◽  
Structure Of Proteins

A computer algorithm for assessment of globularity of protein structures is presented. By enclosing the input protein in a minimum volume ellipsoid (MVEE) and calculating a profile measuring how voxelized space within this shape (cubes on a uniform grid) is occupied by atoms, it is possible to estimate how well the molecule resembles a globule. For any protein to satisfy the proposed globularity criterion, its ellipsoid profile (EP) should first confirm that atoms adequately fill the ellipsoid’s center. This property should then propagate towards the surface of the ellipsoid, although with diminishing importance. It is not required to compute the molecular surface. Globular status (full or partial) is assigned to proteins with values of their ellipsoid profiles, called here the ellipsoid indexes (EI), above certain levels. Due to structural outliers which may considerably distort the measurements, a companion method for their detection and reduction of their influence is also introduced. It is based on kernel density estimation and is shown to work well as an optional input preparation step for MVEE. Finally, the complete workflow is applied to over two thousand representatives of SCOP 2.08 domain superfamilies, surveying the landscape of tertiary structure of proteins from the Protein Data Bank.

Parameterization of Divalent Cations for Coarse-Grained Simulations

2020 ◽  
Author(s):  
Florencia Klein ◽  
Daniela Cáceres-Rojas ◽  
Monica Carrasco ◽  
Juan Carlos Tapia ◽  
Julio Caballero ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Metal Ions ◽  
Molecular Dynamics Simulations ◽  
Divalent Cations ◽  
Computational Cost ◽  
Data Bank ◽  
Coarse Grained ◽  
Interaction Parameters ◽  
Dynamics Simulations ◽  
Dynamical Description

<p>Although molecular dynamics simulations allow for the study of interactions among virtually all biomolecular entities, metal ions still pose significant challenges to achieve an accurate structural and dynamical description of many biological assemblies. This is particularly the case for coarse-grained (CG) models. Although the reduced computational cost of CG methods often makes them the technique of choice for the study of large biomolecular systems, the parameterization of metal ions is still very crude or simply not available for the vast majority of CG- force fields. Here, we show that incorporating statistical data retrieved from the Protein Data Bank (PDB) to set specific Lennard-Jones interactions can produce structurally accurate CG molecular dynamics simulations. Using this simple approach, we provide a set of interaction parameters for Calcium, Magnesium, and Zinc ions, which cover more than 80% of the metal-bound structures reported on the PDB. Simulations performed using the SIRAH force field on several proteins and DNA systems show that using the present approach it is possible to obtain non-bonded interaction parameters that obviate the use of topological constraints. </p>

Perspectives in biological physics: The nDDB project for a neutron Dynamics Data Bank for biological macromolecules

2013 ◽  
Vol 36 (7) ◽  
Author(s):  
Leonid Rusevich ◽  
Victoria García Sakai ◽  
Bruno Franzetti ◽  
Mark Johnson ◽  
Francesca Natali ◽  
...  
Keyword(s):  
Data Bank ◽  
Biological Macromolecules ◽  
Biological Physics

scholarly journals Macromolecular Structure Databases: Past Progress and Future Challenges

1998 ◽  
Vol 54 (6) ◽  
pp. 1085-1094 ◽  
Author(s):  
Helge Weissig ◽  
Ilya N. Shindyalov ◽  
Philip E. Bourne
Keyword(s):  
Management Practices ◽  
Temporal Trends ◽  
Protein Structures ◽  
Data Bank ◽  
Macromolecular Structure ◽  
Paper Briefly ◽  
Future Challenges ◽  
Structure Databases ◽  
Full Discussion ◽  
Self Consistency

Databases containing macromolecular structure data provide a crystallographer with important tools for use in solving, refining and understanding the functional significance of their protein structures. Given this importance, this paper briefly summarizes past progress by outlining the features of the significant number of relevant databases developed to date. One recent database, PDB+, containing all current and obsolete structures deposited with the Protein Data Bank (PDB) is discussed in more detail. PDB+ has been used to analyze the self-consistency of the current (1 January 1998) corpus of over 7000 structures. A summary of those findings is presented (a full discussion will appear elsewhere) in the form of global and temporal trends within the data. These trends indicate that challenges exist if crystallographers are to provide the community with complete and consistent structural results in the future. It is argued that better information management practices are required to meet these challenges.

scholarly journals Protein dynamics revealed by NMR relaxation methods

2018 ◽  
Vol 2 (1) ◽  
pp. 93-105 ◽  
Author(s):  
Fa-An Chao ◽  
R. Andrew Byrd
Keyword(s):  
Protein Dynamics ◽  
Structural Biology ◽  
Dynamic Models ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Data Bank ◽  
Biological Macromolecules ◽  
Relaxation Methods ◽  
Genomic Databases ◽  
Wide Range

Structural biology often focuses primarily on three-dimensional structures of biological macromolecules, deposited in the Protein Data Bank (PDB). This resource is a remarkable entity for the worldwide scientific and medical communities, as well as the general public, as it is a growing translation into three-dimensional space of the vast information in genomic databases, e.g. GENBANK. There is, however, significantly more to understanding biological function than the three-dimensional co-ordinate space for ground-state structures of biomolecules. The vast array of biomolecules experiences natural dynamics, interconversion between multiple conformational states, and molecular recognition and allosteric events that play out on timescales ranging from picoseconds to seconds. This wide range of timescales demands ingenious and sophisticated experimental tools to sample and interpret these motions, thus enabling clearer insights into functional annotation of the PDB. NMR spectroscopy is unique in its ability to sample this range of timescales at atomic resolution and in physiologically relevant conditions using spin relaxation methods. The field is constantly expanding to provide new creative experiments, to yield more detailed coverage of timescales, and to broaden the power of interpretation and analysis methods. This review highlights the current state of the methodology and examines the extension of analysis tools for more complex experiments and dynamic models. The future for understanding protein dynamics is bright, and these extended tools bring greater compatibility with developments in computational molecular dynamics, all of which will further our understanding of biological molecular functions. These facets place NMR as a key component in integrated structural biology.

scholarly journals Enriched Conformational Sampling of DNA and Proteins with a Hybrid Hamiltonian Derived from the Protein Data Bank

2018 ◽  
Vol 19 (11) ◽  
pp. 3405 ◽  
Author(s):  
Emanuel Peter ◽  
Jiří Černý
Keyword(s):  
Partition Function ◽  
Protein Data Bank ◽  
Protein Structures ◽  
Data Bank ◽  
Weighting Factor ◽  
Potential Of Mean Force ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Conformational Sampling ◽  
Speed Increase

In this article, we present a method for the enhanced molecular dynamics simulation of protein and DNA systems called potential of mean force (PMF)-enriched sampling. The method uses partitions derived from the potentials of mean force, which we determined from DNA and protein structures in the Protein Data Bank (PDB). We define a partition function from a set of PDB-derived PMFs, which efficiently compensates for the error introduced by the assumption of a homogeneous partition function from the PDB datasets. The bias based on the PDB-derived partitions is added in the form of a hybrid Hamiltonian using a renormalization method, which adds the PMF-enriched gradient to the system depending on a linear weighting factor and the underlying force field. We validated the method using simulations of dialanine, the folding of TrpCage, and the conformational sampling of the Dickerson–Drew DNA dodecamer. Our results show the potential for the PMF-enriched simulation technique to enrich the conformational space of biomolecules along their order parameters, while we also observe a considerable speed increase in the sampling by factors ranging from 13.1 to 82. The novel method can effectively be combined with enhanced sampling or coarse-graining methods to enrich conformational sampling with a partition derived from the PDB.

scholarly journals Expanding our knowledge of the protein universe: Modelling of protein structures

2014 ◽  
Vol 70 (a1) ◽  
pp. C491-C491
Author(s):  
Jürgen Haas ◽  
Alessandro Barbato ◽  
Tobias Schmidt ◽  
Steven Roth ◽  
Andrew Waterhouse ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Structure Prediction ◽  
Structural Information ◽  
Protein Structures ◽  
Model Organism ◽  
Data Bank ◽  
Continuous Model ◽  
Structure Modeling ◽  
Structure Comparison ◽  
Modeling And Prediction

Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing goal in structural biology. Over the last two decades, a paradigm shift has occurred: starting from a large "knowledge gap" between the huge number of protein sequences compared to a small number of experimentally known structures, today, some form of structural information – either experimental or computational – is available for the majority of amino acids encoded by common model organism genomes. Methods for structure modeling and prediction have made substantial progress of the last decades, and template based homology modeling techniques have matured to a point where they are now routinely used to complement experimental techniques. However, computational modeling and prediction techniques often fall short in accuracy compared to high-resolution experimental structures, and it is often difficult to convey the expected accuracy and structural variability of a specific model. Retrospectively assessing the quality of blind structure prediction in comparison to experimental reference structures allows benchmarking the state-of-the-art in structure prediction and identifying areas which need further development. The Critical Assessment of Structure Prediction (CASP) experiment has for the last 20 years assessed the progress in the field of protein structure modeling based on predictions for ca. 100 blind prediction targets per experiment which are carefully evaluated by human experts. The "Continuous Model EvaluatiOn" (CAMEO) project aims to provide a fully automated blind assessment for prediction servers based on weekly pre-released sequences of the Protein Data Bank PDB. CAMEO has been made possible by the development of novel scoring methods such as lDDT, which are robust against domain movements to allow for automated continuous structure comparison without human intervention.

Sign in / Sign up

Export Citation Format

Share Document