scholarly journals Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data

2018 ◽  
Vol 19 (11) ◽  
pp. 3406
Author(s):  
Didier Devaurs ◽  
Dinler Antunes ◽  
Lydia Kavraki
Keyword(s):  
Protein Structure ◽  
Computational Methods ◽  
Hydrogen Exchange ◽  
Protein Structures ◽  
Conformational Space ◽  
Conformational Sampling ◽  
Computational Framework ◽  
Large Proteins ◽  
Unknown Protein ◽  
Exchange Data

Both experimental and computational methods are available to gather information about a protein’s conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes.

scholarly journals Coarse-Grained Conformational Sampling of Protein Structure Improves the Fit to Experimental Hydrogen-Exchange Data

2017 ◽  
Vol 4 ◽  
Author(s):  
Didier Devaurs ◽  
Dinler A. Antunes ◽  
Malvina Papanastasiou ◽  
Mark Moll ◽  
Daniel Ricklin ◽  
...  
Keyword(s):  
Protein Structure ◽  
Hydrogen Exchange ◽  
Coarse Grained ◽  
Conformational Sampling ◽  
Exchange Data

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 Solving the first novel protein structure by 3D micro-crystal electron diffraction

2019 ◽  
Author(s):  
H. Xu ◽  
H. Lebrette ◽  
M.T.B. Clabbers ◽  
J. Zhao ◽  
J.J. Griese ◽  
...  
Keyword(s):  
Protein Structure ◽  
Electron Diffraction ◽  
Model Building ◽  
Protein Structures ◽  
X Ray ◽  
X Ray Crystallography ◽  
Unknown Protein ◽  
Potential Map ◽  
Crystal Electron ◽  
And Function

AbstractMicro-crystal electron diffraction (MicroED) has recently shown potential for structural biology. It enables studying biomolecules from micron-sized 3D crystals that are too small to be studied by conventional X-ray crystallography. However, to the best of our knowledge, MicroED has only been applied to re-determine protein structures that had already been solved previously by X-ray diffraction. Here we present the first unknown protein structure – an R2lox enzyme – solved using MicroED. The structure was phased by molecular replacement using a search model of 35% sequence identity. The resulting electrostatic scattering potential map at 3.0 Å resolution was of sufficient quality to allow accurate model building and refinement. Our results demonstrate that MicroED has the potential to become a widely applicable tool for revealing novel insights into protein structure and function, opening up new opportunities for structural biologists.

scholarly journals Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction

2021 ◽  
Vol 22 (11) ◽  
pp. 6032
Author(s):  
Donghyuk Suh ◽  
Jai Woo Lee ◽  
Sun Choi ◽  
Yoonji Lee
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Learning Techniques ◽  
Residue Contacts ◽  
Unknown Protein

The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins’ 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug–target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.

A Robotics Approach to Enhance Conformational Sampling of Proteins

2012 ◽  
Author(s):  
Juan Cortés ◽  
Ibrahim Al-Bluwi
Keyword(s):  
Monte Carlo ◽  
Computational Methods ◽  
Conformational Space ◽  
Conformational Sampling ◽  
Biological Macromolecules ◽  
Dynamics And Function ◽  
Living Organisms ◽  
Dynamics Simulations ◽  
And Function ◽  
The Relationship

Proteins are biological macromolecules that play essential roles in living organisms. Furthermore, the study of proteins and their function is of interest in other fields in addition to biology, such as pharmacology and biotechnology. Understanding the relationship between protein structure, dynamics and function is indispensable for advances in all these areas. This requires a combination of experimental and computational methods, whose development is the object of very active interdisciplinary research. In such a context, this paper presents a technique to enhance conformational sampling of proteins carried out with computational methods such as molecular dynamics simulations or Monte Carlo methods. Our approach is based on a mechanistic representation of proteins that enables the application of efficient methods originating from robotics. The paper explains the generalities of the approach, and gives details on its application to devise Monte Carlo move classes. Results show the good performance of the method for sampling the conformational space of different types of proteins.

Sequence Specific Dihedral Angle Distribution: Application in Protein Structure Prediction and Evaluation

1970 ◽  
Vol 19 (2) ◽  
pp. 217-226
Author(s):  
S. M. Minhaz Ud-Dean ◽  
Mahdi Muhammad Moosa
Keyword(s):  
Protein Structure ◽  
Dihedral Angle ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Angle Distribution ◽  
Ramachandran Plot ◽  
Specific Data ◽  
Specific Distribution ◽  
Structure Evaluation

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Structure Prediction ◽  
Protein Structures ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Model Refinement ◽  
Protein Model ◽  
Lower Accuracy ◽  
Simulation Based

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

Analysis of Oncogene Protein Structure Using Small World Network Concept

2020 ◽  
Vol 15 (7) ◽  
pp. 732-740
Author(s):  
Neetu Kumari ◽  
Anshul Verma
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Degree Distribution ◽  
Protein Structures ◽  
Small World ◽  
Extreme Condition ◽  
Centrality Measures ◽  
Small World Network ◽  
Network Concept ◽  
Oncogene Protein

Background: The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges. Objective: Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept. Methods: Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming. Results: Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted. Conclusion: Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

scholarly journals On the Use of the Discrete Constant pH Molecular Dynamics to Describe the Conformational Space of Peptides

Polymers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 99
Author(s):  
Cristian Privat ◽  
Sergio Madurga ◽  
Francesc Mas ◽  
Jaime Rubio-Martínez
Keyword(s):  
Molecular Dynamics ◽  
Amino Acids ◽  
Md Simulations ◽  
Point Of View ◽  
Conformational Space ◽  
Conformational Sampling ◽  
Protonation State ◽  
Constant Ph ◽  
Biochemical Systems ◽  
Constant Ph Molecular Dynamics

Solvent pH is an important property that defines the protonation state of the amino acids and, therefore, modulates the interactions and the conformational space of the biochemical systems. Generally, this thermodynamic variable is poorly considered in Molecular Dynamics (MD) simulations. Fortunately, this lack has been overcome by means of the Constant pH Molecular Dynamics (CPHMD) methods in the recent decades. Several studies have reported promising results from these approaches that include pH in simulations but focus on the prediction of the effective pKa of the amino acids. In this work, we want to shed some light on the CPHMD method and its implementation in the AMBER suitcase from a conformational point of view. To achieve this goal, we performed CPHMD and conventional MD (CMD) simulations of six protonatable amino acids in a blocked tripeptide structure to compare the conformational sampling and energy distributions of both methods. The results reveal strengths and weaknesses of the CPHMD method in the implementation of AMBER18 version. The change of the protonation state according to the chemical environment is presumably an improvement in the accuracy of the simulations. However, the simulations of the deprotonated forms are not consistent, which is related to an inaccurate assignment of the partial charges of the backbone atoms in the CPHMD residues. Therefore, we recommend the CPHMD methods of AMBER program but pointing out the need to compare structural properties with experimental data to bring reliability to the conformational sampling of the simulations.

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