scholarly journals A distance geometry-based description and validation of protein main-chain conformation

IUCrJ ◽  
2017 ◽  
Vol 4 (5) ◽  
pp. 657-670 ◽  
Author(s):  
Joana Pereira ◽  
Victor S. Lamzin
Keyword(s):  
Main Chain ◽  
Dimensional Space ◽  
Distance Geometry ◽  
Protein Structures ◽  
Three Dimensional ◽  
Principal Component ◽  
Data Bank ◽  
Conformational Space ◽  
Protein Backbone ◽  
Space Forms

Understanding the protein main-chain conformational space forms the basis for the modelling of protein structures and for the validation of models derived from structural biology techniques. Presented here is a novel idea for a three-dimensional distance geometry-based metric to account for the fine details of protein backbone conformations. The metrics are computed for dipeptide units, defined as blocks of Cαi−1—Oi−1—Cαi—Oi—Cαi+1atoms, by obtaining the eigenvalues of their Euclidean distance matrices. These were computed for ∼1.3 million dipeptide units collected from nonredundant good-quality structures in the Protein Data Bank and subjected to principal component analysis. The resulting new Euclidean orthogonal three-dimensional space (DipSpace) allows a probabilistic description of protein backbone geometry. The three axes of the DipSpace describe the local extension of the dipeptide unit structure, its twist and its bend. By using a higher-dimensional metric, the method is efficient for the identification of Cαatoms in an unlikely or unusual geometrical environment, and its use for both local and overall validation of protein models is demonstrated. It is also shown, for the example of trypsin proteases, that the detection of unusual conformations that are conserved among the structures of this protein family may indicate geometrically strained residues of potentially functional importance.

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 A Constraint Solver for Flexible Protein Model

2013 ◽  
Vol 48 ◽  
pp. 953-1000 ◽  
Author(s):  
F. Campeotto ◽  
A. Dal Palù ◽  
A. Dovier ◽  
F. Fioretto ◽  
E. Pontelli
Keyword(s):  
Structure Prediction ◽  
Dimensional Space ◽  
Protein Structures ◽  
Empirical Evaluation ◽  
Geometric Constraints ◽  
Conformational Space ◽  
Loop Modeling ◽  
Geometric Properties ◽  
Constraint Solver ◽  
Multi Body

This paper proposes the formalization and implementation of a novel class of constraints aimed at modeling problems related to placement of multi-body systems in the 3-dimensional space. Each multi-body is a system composed of body elements, connected by joint relationships and constrained by geometric properties. The emphasis of this investigation is the use of multi-body systems to model native conformations of protein structures---where each body represents an entity of the protein (e.g., an amino acid, a small peptide) and the geometric constraints are related to the spatial properties of the composing atoms. The paper explores the use of the proposed class of constraints to support a variety of different structural analysis of proteins, such as loop modeling and structure prediction. The declarative nature of a constraint-based encoding provides elaboration tolerance and the ability to make use of any additional knowledge in the analysis studies. The filtering capabilities of the proposed constraints also allow to control the number of representative solutions that are withdrawn from the conformational space of the protein, by means of criteria driven by uniform distribution sampling principles. In this scenario it is possible to select the desired degree of precision and/or number of solutions. The filtering component automatically excludes configurations that violate the spatial and geometric properties of the composing multi-body system. The paper illustrates the implementation of a constraint solver based on the multi-body perspective and its empirical evaluation on protein structure analysis problems.

MRPC (Missing Regions in Polypeptide Chains): a knowledgebase

2019 ◽  
Vol 52 (6) ◽  
pp. 1422-1426
Author(s):  
Rajendran Santhosh ◽  
Namrata Bankoti ◽  
Adgonda Malgonnavar Padmashri ◽  
Daliah Michael ◽  
Jeyaraman Jeyakanthan ◽  
...  
Keyword(s):  
Protein Structures ◽  
Three Dimensional ◽  
Protein Molecule ◽  
Data Bank ◽  
Protein Crystal ◽  
Dimensional Structure ◽  
Protein Structure Analysis ◽  
Three Dimensional Structure ◽  
X Ray Crystallography ◽  
Polypeptide Chains

Missing regions in protein crystal structures are those regions that cannot be resolved, mainly owing to poor electron density (if the three-dimensional structure was solved using X-ray crystallography). These missing regions are known to have high B factors and could represent loops with a possibility of being part of an active site of the protein molecule. Thus, they are likely to provide valuable information and play a crucial role in the design of inhibitors and drugs and in protein structure analysis. In view of this, an online database, Missing Regions in Polypeptide Chains (MRPC), has been developed which provides information about the missing regions in protein structures available in the Protein Data Bank. In addition, the new database has an option for users to obtain the above data for non-homologous protein structures (25 and 90%). A user-friendly graphical interface with various options has been incorporated, with a provision to view the three-dimensional structure of the protein along with the missing regions using JSmol. The MRPC database is updated regularly (currently once every three months) and can be accessed freely at the URL http://cluster.physics.iisc.ac.in/mrpc.

Statistical Approach to Shape from Shading: Reconstruction of Three-Dimensional Face Surfaces from Single Two-Dimensional Images

1996 ◽  
Vol 8 (6) ◽  
pp. 1321-1340 ◽  
Author(s):  
Joseph J. Atick ◽  
Paul A. Griffin ◽  
A. Norman Redlich
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Practical Interest ◽  
Principal Component ◽  
Shape From Shading ◽  
Shape Space ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Low Dimensional ◽  
Dimensional Object

The human visual system is proficient in perceiving three-dimensional shape from the shading patterns in a two-dimensional image. How it does this is not well understood and continues to be a question of fundamental and practical interest. In this paper we present a new quantitative approach to shape-from-shading that may provide some answers. We suggest that the brain, through evolution or prior experience, has discovered that objects can be classified into lower-dimensional object-classes as to their shape. Extraction of shape from shading is then equivalent to the much simpler problem of parameter estimation in a low-dimensional space. We carry out this proposal for an important class of three-dimensional (3D) objects: human heads. From an ensemble of several hundred laser-scanned 3D heads, we use principal component analysis to derive a low-dimensional parameterization of head shape space. An algorithm for solving shape-from-shading using this representation is presented. It works well even on real images where it is able to recover the 3D surface for a given person, maintaining facial detail and identity, from a single 2D image of his face. This algorithm has applications in face recognition and animation.

TOP: a new method for protein structure comparisons and similarity searches

2000 ◽  
Vol 33 (1) ◽  
pp. 176-183 ◽  
Author(s):  
Guoguang Lu
Keyword(s):  
User Interface ◽  
Protein Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Data Bank ◽  
Structure Alignment ◽  
Dimensional Structure ◽  
Protein Structure Alignment ◽  
Protein Structure Analysis ◽  
Structure Comparison

In order to facilitate the three-dimensional structure comparison of proteins, software for making comparisons and searching for similarities to protein structures in databases has been developed. The program identifies the residues that share similar positions of both main-chain and side-chain atoms between two proteins. The unique functions of the software also include database processingviaInternet- and Web-based servers for different types of users. The developed method and its friendly user interface copes with many of the problems that frequently occur in protein structure comparisons, such as detecting structurally equivalent residues, misalignment caused by coincident match of Cαatoms, circular sequence permutations, tedious repetition of access, maintenance of the most recent database, and inconvenience of user interface. The program is also designed to cooperate with other tools in structural bioinformatics, such as the 3DB Browser software [Prilusky (1998).Protein Data Bank Q. Newslett.84, 3–4] and the SCOP database [Murzin, Brenner, Hubbard & Chothia (1995).J. Mol. Biol.247, 536–540], for convenient molecular modelling and protein structure analysis. A similarity ranking score of `structure diversity' is proposed in order to estimate the evolutionary distance between proteins based on the comparisons of their three-dimensional structures. The function of the program has been utilized as a part of an automated program for multiple protein structure alignment. In this paper, the algorithm of the program and results of systematic tests are presented and discussed.

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