Analyzing Motion Properties of Proteins Affected by Localized Structures From a Robot Kinematics Perspective

2015 ◽  
Author(s):  
Keisuke Arikawa
Keyword(s):  
Protein Data Bank ◽  
Complex Shape ◽  
Structural Data ◽  
Data Bank ◽  
Robot Kinematics ◽  
Motion Prediction ◽  
Serial Manipulators ◽  
Localized Structures ◽  
Motion Modes ◽  
Structural Compliance

On the basis of robot kinematics, we have thus far developed a method for predicting the motion of proteins from their 3D structural data given in the Protein Data Bank (PDB data). In this method, proteins are modeled as serial manipulators constrained by springs and the structural compliance properties of the models are evaluated. We focus on localized instead of whole structures of proteins. Employing the same model used in our method of motion prediction, the motion properties of the localized structures and the relation between the motion properties of localized and whole structures are analyzed. First, we present a method for graphically expressing the deformation of objects with a complex shape, such as proteins, by approximating the shape as a rectangular prism with a mesh on its surface. We then formulate a method for comparing the motion properties of localized structures cleaved from the whole structure and those remaining in it by expressing the motion of the latter using the decomposed motion modes of the former according to the structural compliance. Finally, we show a method for evaluating the effect of a localized structure on the motion properties of proteins by applying forces to localized structures. In the formulations, we demonstrate applications as illustrative examples using the PDB data of a real protein.

A Computational Framework for Predicting the Motions of a Protein System From a Robot Kinematics Viewpoint

2013 ◽  
Author(s):  
Keisuke Arikawa
Keyword(s):  
Robot Manipulator ◽  
Structural Data ◽  
Protein Molecule ◽  
Data Bank ◽  
Computational Time ◽  
Robot Kinematics ◽  
Efficient Computation ◽  
Elastic Network Model ◽  
Computational Framework ◽  
Structural Compliance

There is an analogy between the kinematic structures of proteins and robotic mechanisms. On the basis of this analogy, we have so far developed some methods for predicting the internal motions of proteins from their three-dimensional structural data in protein data bank (PDB). However, these methods are basically applicable to a single protein molecule. In this study, we extended these methods to apply them to systems that consist of multiple molecules including proteins (protein systems), and developed a computational framework for predicting the motions of the molecules. The model used in this method is a type of elastic network model. In particular, proteins are modeled as a robot manipulator constrained by the springs (the dihedral angles on the main chains correspond to the joint angles). The interactions between molecules are also modeled as springs. The basic concept for predicting the motions is based on the analysis of structural compliance. By applying statically balanced forces to the model in various directions, we extracted those motions with larger structural compliance. To reduce the computational time, we formulated the method with the prospect of efficient computation including parallel computation. In addition, we developed a preparatory computer program implementing the proposed algorithms, and analyzed some protein systems. The results showed that the proposed computational framework can efficiently analyze large protein systems.

Extension of the Kinematics-Based Method for Predicting the Motion of Proteins

2014 ◽  
Author(s):  
Keisuke Arikawa
Keyword(s):  
Computational Cost ◽  
Three Dimensional ◽  
Structural Data ◽  
Data Bank ◽  
Serial Manipulators ◽  
Protein Model ◽  
The Matrix ◽  
Basic Equations ◽  
Definition Of ◽  
Structural Compliance

On the basis of an analogy between the kinematic structures of proteins and robotic mechanisms, we have so far developed methods for predicting the internal motion of proteins from three-dimensional structural data in the protein data bank (PDB). With these methods, we model proteins as serial manipulators constrained by springs, and calculate the structural compliance of the protein model. In this study, toward more practical purposes, we reformulate and extend the existing methods by broadening the definition of structural compliance and reducing the number of variables for expressing the conformation of the model. The broadening is performed by separating the parts whose deformations are evaluated from those where forces are applied. This separation allows the calculation of the effective forces causing deformation in other specified parts. We also reduce the number of conformation variables from the consideration based on the algebraic structure of the basic equations. The size of the matrix whose inverse must be calculated is thus minimized, and the computational cost is reduced. We verify the effectiveness of these extensions by analyzing the PDB data of some proteins.

scholarly journals Protein Data Bank Japan (PDBj): maintaining a structural data archive and resource description framework format

2011 ◽  
Vol 40 (D1) ◽  
pp. D453-D460 ◽  
Author(s):  
A. R. Kinjo ◽  
H. Suzuki ◽  
R. Yamashita ◽  
Y. Ikegawa ◽  
T. Kudou ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Resource Description Framework ◽  
Structural Data ◽  
Data Bank ◽  
Data Archive ◽  
Description Framework ◽  
Resource Description

GeoMine: interactive pattern mining of protein–ligand interfaces in the Protein Data Bank

2020 ◽  
Author(s):  
Konrad Diedrich ◽  
Joel Graef ◽  
Katrin Schöning-Stierand ◽  
Matthias Rarey
Keyword(s):  
Protein Data Bank ◽  
Web Application ◽  
Pattern Mining ◽  
Structural Data ◽  
Data Bank ◽  
Supplementary Information ◽  
User Friendliness ◽  
Iterative Search ◽  
Potential Applications ◽  
Query Generation

Abstract Summary The searching of user-defined 3D queries in molecular interfaces is a computationally challenging problem that is not satisfactorily solved so far. Most of the few existing tools focused on that purpose are desktop based and not openly available. Besides that, they show a lack of query versatility, search efficiency and user-friendliness. We address this issue with GeoMine, a publicly available web application that provides textual, numerical and geometrical search functionality for protein–ligand binding sites derived from structural data contained in the Protein Data Bank (PDB). The query generation is supported by a 3D representation of a start structure that provides interactively selectable elements like atoms, bonds and interactions. GeoMine gives full control over geometric variability in the query while performing a deterministic, precise search. Reasonably selective queries are processed on the entire set of protein–ligand complexes in the PDB within a few minutes. GeoMine offers an interactive and iterative search process of successive result analyses and query adaptations. From the numerous potential applications, we picked two from the field of side-effect analyze showcasing the usefulness of GeoMine. Availability and implementation GeoMine is part of the ProteinsPlus web application suite and freely available at https://proteins.plus. Supplementary information Supplementary data are available at Bioinformatics online.

Structural Compliance Analysis and Internal Motion Properties of Proteins From a Robot Kinematics Perspective: Formulation of Basic Equations

2016 ◽  
Vol 8 (2) ◽  
Author(s):  
Keisuke Arikawa
Keyword(s):  
Kinematic Model ◽  
Structural Data ◽  
Forced Response ◽  
Internal Motion ◽  
Robot Kinematics ◽  
Dihedral Angles ◽  
Protein Model ◽  
Applied Forces ◽  
Basic Equations ◽  
Structural Compliance

From a perspective of robot kinematics, we develop a method for predicting internal motion properties and understanding the functions of proteins from their three-dimensional (3D) structural data (protein data bank (PDB) data). The key ideas are based on the structural compliance analysis of proteins. In this paper, we mainly discuss the basic equations for the analysis. First, a kinematic model of a protein is introduced. Proteins are simply modeled as serial manipulators constrained by linear springs, where the dihedral angles on the main chains correspond to the joint angles of manipulators. Then, the kinematic equations of the protein model are derived. In particular, the forced response or the deformation caused by the forces in static equilibrium forms the basis for the structural compliance analysis. In the formulations, the protein models are regarded as manipulators that control the positions in the model or the distances between them, by the dihedral angles on the main chains. Next, the structural compliance of the protein model is defined, and a method for extracting the information about the internal motion properties from the structural compliance is shown. In general, the structural compliance refers to the relationship between the applied forces and the deformation of the parts surrounded by the application points. We define it in a more general form by separating the parts whose deformations are evaluated from those where forces are applied. When decomposing motion according to the magnitude of the structural compliance, we can infer that the lower compliance motion will easily occur. Finally, we show two application examples using PDB data of lactoferrin and hemoglobin. Despite using an approximate protein model, the predicted internal motion properties agree with the measured ones.

scholarly journals PDBeCIF: an open-source mmCIF/CIF parsing and processing package

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Glen van Ginkel ◽  
Lukáš Pravda ◽  
José M. Dana ◽  
Mihaly Varadi ◽  
Peter Keller ◽  
...  
Keyword(s):  
Open Source ◽  
Protein Data Bank ◽  
Hybrid Methods ◽  
Structural Data ◽  
Data Bank ◽  
File Format ◽  
Macromolecular Complexes ◽  
Link Type ◽  
Information File ◽  
Multiscale Structures

Abstract Background Biomacromolecular structural data outgrew the legacy Protein Data Bank (PDB) format which the scientific community relied on for decades, yet the use of its successor PDBx/Macromolecular Crystallographic Information File format (PDBx/mmCIF) is still not widespread. Perhaps one of the reasons is the availability of easy to use tools that only support the legacy format, but also the inherent difficulties of processing mmCIF files correctly, given the number of edge cases that make efficient parsing problematic. Nevertheless, to fully exploit macromolecular structure data and their associated annotations such as multiscale structures from integrative/hybrid methods or large macromolecular complexes determined using traditional methods, it is necessary to fully adopt the new format as soon as possible. Results To this end, we developed PDBeCIF, an open-source Python project for manipulating mmCIF and CIF files. It is part of the official list of mmCIF parsers recorded by the wwPDB and is heavily employed in the processes of the Protein Data Bank in Europe. The package is freely available both from the PyPI repository (http://pypi.org/project/pdbecif) and from GitHub (https://github.com/pdbeurope/pdbecif) along with rich documentation and many ready-to-use examples. Conclusions PDBeCIF is an efficient and lightweight Python 2.6+/3+ package with no external dependencies. It can be readily integrated with 3rd party libraries as well as adopted for broad scientific analyses.

scholarly journals Implementing an X-ray validation pipeline for the Protein Data Bank

2012 ◽  
Vol 68 (4) ◽  
pp. 478-483 ◽  
Author(s):  
Swanand Gore ◽  
Sameer Velankar ◽  
Gerard J. Kleywegt
Keyword(s):  
Protein Data Bank ◽  
Structural Data ◽  
Data Bank ◽  
Software Pipeline ◽  
X Ray ◽  
Validation Methods ◽  
Task Forces ◽  
Related Information ◽  
Ongoing Work

There is an increasing realisation that the quality of the biomacromolecular structures deposited in the Protein Data Bank (PDB) archive needs to be assessed critically using established and powerful validation methods. The Worldwide Protein Data Bank (wwPDB) organization has convened several Validation Task Forces (VTFs) to advise on the methods and standards that should be used to validate all of the entries already in the PDB as well as all structures that will be deposited in the future. The recommendations of the X-ray VTF are currently being implemented in a software pipeline. Here, ongoing work on this pipeline is briefly described as well as ways in which validation-related information could be presented to users of structural data.

scholarly journals Rapid response to emerging biomedical challenges and threats

IUCrJ ◽  
2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Marek Grabowski ◽  
Joanna M. Macnar ◽  
Marcin Cymborowski ◽  
David R. Cooper ◽  
Ivan G. Shabalin ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Biomedical Research ◽  
Large Scale ◽  
Structural Data ◽  
Data Bank ◽  
Rapid Response ◽  
Multiple Resources

As part of the global mobilization to combat the present pandemic, almost 100 000 COVID-19-related papers have been published and nearly a thousand models of macromolecules encoded by SARS-CoV-2 have been deposited in the Protein Data Bank within less than a year. The avalanche of new structural data has given rise to multiple resources dedicated to assessing the correctness and quality of structural data and models. Here, an approach to evaluate the massive amounts of such data using the resource https://covid19.bioreproducibility.org is described, which offers a template that could be used in large-scale initiatives undertaken in response to future biomedical crises. Broader use of the described methodology could considerably curtail information noise and significantly improve the reproducibility of biomedical research.

scholarly journals Protein engineering in the big data era: harnessing near-redundant structural data

2021 ◽  
Author(s):  
Samuel Coulbourn Flores ◽  
Athanasios Alexiou ◽  
Anastasios Glaros
Keyword(s):  
Positive Predictive Value ◽  
Protein Data Bank ◽  
Protein Interactions ◽  
Predictive Value ◽  
Structural Data ◽  
Data Bank ◽  
Affinity Maturation ◽  
Implicit Solvent ◽  
Protein Protein Interactions ◽  
Atomic Simulation

Abstract Motivation: Predicting the effect of mutations on protein-protein interactions is important for relating structure to function, as well as for in silico affinity maturation. The effect of mutations on protein-protein binding energy (ΔΔG) can be predicted by a variety of atomic simulation methods involving full or limited flexibility, and explicit or implicit solvent. Methods which consider only limited flexibility are naturally more economical, and many of them are quite accurate, however results are dependent on the atomic coordinate set used. In this work we perform a sequence and structure based search of the Protein Data Bank to find additional coordinate sets and repeat the calculation on each.Results: . We improve increase precision and Positive Predictive Value, and decrease Root Mean Square Error and higher Positive Predictive Value, compared to using single structures. Given the ongoing growth of near-redundant structures in the Protein Data Bank, our method will only increase in applicability and accuracy.Availability: Public web server at biodesign.scilifelab.se

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