scholarly journals Denatured proteins and early folding intermediates simulated in a reduced conformational space.

2005 ◽  
Vol 53 (1) ◽  
pp. 131-143 ◽  
Author(s):  
Sebastian Kmiecik ◽  
Mateusz Kurcinski ◽  
Aleksandra Rutkowska ◽  
Dominik Gront ◽  
Andrzej Kolinski
Keyword(s):  
Short Range ◽  
Hydrophobic Interactions ◽  
Molten Globule ◽  
Conformational Space ◽  
Denatured State ◽  
Statistical Potentials ◽  
Knowledge Based ◽  
Monte Carlo Dynamics ◽  
Folded Proteins ◽  
Folded Structures

Conformations of globular proteins in the denatured state were studied using a high-resolution lattice model of proteins and Monte Carlo dynamics. The model assumes a united-atom and high-coordination lattice representation of the polypeptide conformational space. The force field of the model mimics the short-range protein-like conformational stiffness, hydrophobic interactions of the side chains and the main-chain hydrogen bonds. Two types of approximations for the short-range interactions were compared: simple statistical potentials and knowledge-based protein-specific potentials derived from the sequence-structure compatibility of short fragments of protein chains. Model proteins in the denatured state are relatively compact, although the majority of the sampled conformations are globally different from the native fold. At the same time short protein fragments are mostly native-like. Thus, the denatured state of the model proteins has several features of the molten globule state observed experimentally. Statistical potentials induce native-like conformational propensities in the denatured state, especially for the fragments located in the core of folded proteins. Knowledge-based protein-specific potentials increase only slightly the level of similarity to the native conformations, in spite of their qualitatively higher specificity in the native structures. For a few cases, where fairly accurate experimental data exist, the simulation results are in semiquantitative agreement with the physical picture revealed by the experiments. This shows that the model studied in this work could be used efficiently in computational studies of protein dynamics in the denatured state, and consequently for studies of protein folding pathways, i.e. not only for the modeling of folded structures, as it was shown in previous studies. The results of the present studies also provide a new insight into the explanation of the Levinthal's paradox.

scholarly journals Efficient conformational space exploration in ab initio protein folding simulation

2015 ◽  
Vol 2 (8) ◽  
pp. 150238 ◽  
Author(s):  
Ahammed Ullah ◽  
Nasif Ahmed ◽  
Subrata Dey Pappu ◽  
Swakkhar Shatabda ◽  
A. Z. M. Dayem Ullah ◽  
...  
Keyword(s):  
Protein Folding ◽  
Ab Initio ◽  
Energy Function ◽  
Hydrophobic Interactions ◽  
Protein Structures ◽  
Search Algorithms ◽  
Conformational Space ◽  
Energy Functions ◽  
Knowledge Based ◽  
Folding Simulation

Ab initio protein folding simulation largely depends on knowledge-based energy functions that are derived from known protein structures using statistical methods. These knowledge-based energy functions provide us with a good approximation of real protein energetics. However, these energy functions are not very informative for search algorithms and fail to distinguish the types of amino acid interactions that contribute largely to the energy function from those that do not. As a result, search algorithms frequently get trapped into the local minima. On the other hand, the hydrophobic–polar (HP) model considers hydrophobic interactions only. The simplified nature of HP energy function makes it limited only to a low-resolution model. In this paper, we present a strategy to derive a non-uniform scaled version of the real 20×20 pairwise energy function. The non-uniform scaling helps tackle the difficulty faced by a real energy function, whereas the integration of 20×20 pairwise information overcomes the limitations faced by the HP energy function. Here, we have applied a derived energy function with a genetic algorithm on discrete lattices. On a standard set of benchmark protein sequences, our approach significantly outperforms the state-of-the-art methods for similar models. Our approach has been able to explore regions of the conformational space which all the previous methods have failed to explore. Effectiveness of the derived energy function is presented by showing qualitative differences and similarities of the sampled structures to the native structures. Number of objective function evaluation in a single run of the algorithm is used as a comparison metric to demonstrate efficiency.

KORP: knowledge-based 6D potential for fast protein and loop modeling

2019 ◽  
Vol 35 (17) ◽  
pp. 3013-3019 ◽  
Author(s):  
José Ramón López-Blanco ◽  
Pablo Chacón
Keyword(s):  
Structure Prediction ◽  
Protein Structures ◽  
Joint Probability ◽  
Protein Modeling ◽  
Supplementary Information ◽  
Joint Probability Distribution ◽  
Loop Modeling ◽  
Statistical Potentials ◽  
Knowledge Based ◽  
Backbone Atoms

Abstract Motivation Knowledge-based statistical potentials constitute a simpler and easier alternative to physics-based potentials in many applications, including folding, docking and protein modeling. Here, to improve the effectiveness of the current approximations, we attempt to capture the six-dimensional nature of residue–residue interactions from known protein structures using a simple backbone-based representation. Results We have developed KORP, a knowledge-based pairwise potential for proteins that depends on the relative position and orientation between residues. Using a minimalist representation of only three backbone atoms per residue, KORP utilizes a six-dimensional joint probability distribution to outperform state-of-the-art statistical potentials for native structure recognition and best model selection in recent critical assessment of protein structure prediction and loop-modeling benchmarks. Compared with the existing methods, our side-chain independent potential has a lower complexity and better efficiency. The superior accuracy and robustness of KORP represent a promising advance for protein modeling and refinement applications that require a fast but highly discriminative energy function. Availability and implementation http://chaconlab.org/modeling/korp. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals All-Atom Four-Body Knowledge-Based Statistical Potentials to Distinguish Native Protein Structures from Nonnative Folds

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Majid Masso
Keyword(s):  
Structure Prediction ◽  
Protein Structures ◽  
Binding Energies ◽  
Coarse Grained ◽  
Amber Force Field ◽  
Statistical Potentials ◽  
Knowledge Based ◽  
Native Proteins ◽  
Interacting Atoms ◽  
Atomic Coordinates

Recent advances in understanding protein folding have benefitted from coarse-grained representations of protein structures. Empirical energy functions derived from these techniques occasionally succeed in distinguishing native structures from their corresponding ensembles of nonnative folds or decoys which display varying degrees of structural dissimilarity to the native proteins. Here we utilized atomic coordinates of single protein chains, comprising a large diverse training set, to develop and evaluate twelve all-atom four-body statistical potentials obtained by exploring alternative values for a pair of inherent parameters. Delaunay tessellation was performed on the atomic coordinates of each protein to objectively identify all quadruplets of interacting atoms, and atomic potentials were generated via statistical analysis of the data and implementation of the inverted Boltzmann principle. Our potentials were evaluated using benchmarking datasets from Decoys-‘R’-Us, and comparisons were made with twelve other physics- and knowledge-based potentials. Ranking 3rd, our best potential tied CHARMM19 and surpassed AMBER force field potentials. We illustrate how a generalized version of our potential can be used to empirically calculate binding energies for target-ligand complexes, using HIV-1 protease-inhibitor complexes for a practical application. The combined results suggest an accurate and efficient atomic four-body statistical potential for protein structure prediction and assessment.

scholarly journals On the emergence of orientational order in folded proteins with implications for allostery

2021 ◽  
Author(s):  
Debayan Chakraborty ◽  
Mauro Lorenzo Mugnai ◽  
D. Thirumalai
Keyword(s):  
Orientational Order ◽  
Protein Structures ◽  
Structural Elements ◽  
Nematic Order ◽  
Allosteric Proteins ◽  
External Stresses ◽  
Zero Values ◽  
Action At A Distance ◽  
Folded Proteins ◽  
Folded Structures

AbstractThe beautiful structures of single and multi-domain proteins are clearly ordered in some fashion but cannot be readily classified using group theory methods that are successfully used to describe periodic crystals. For this reason, protein structures are considered to be aperiodic, and may have evolved this way for functional purposes, especially in instances that require a combination of softness and rigidity within the same molecule. By analyzing the solved protein structures, we show that orientational symmetry is broken in the aperiodic arrangement of the secondary structural elements (SSEs), which we deduce by calculating the nematic order parameter, P2. We find that the folded structures are nematic droplets with a broad distribution of P2. We argue that non-zero values of P2, leads to an arrangement of the SSEs that can resist external stresses forces, which is a requirement for allosteric proteins. Such proteins, which resist mechanical forces in some regions while being flexible in others, transmit signals from one region of the protein to another (action at a distance) in response to binding of ligands (oxygen, ATP or other small molecules).

Pockets of Short-Range Transient Order and Restricted Topological Heterogeneity in the Guanidine-Denatured State Ensemble of GED of Dynamin†

Biochemistry ◽  
2007 ◽  
Vol 46 (42) ◽  
pp. 11819-11832 ◽  
Author(s):  
Jeetender Chugh ◽  
Shilpy Sharma ◽  
Ramakrishna V. Hosur
Keyword(s):  
Short Range ◽  
Denatured State ◽  
State Ensemble

scholarly journals Redrawing the Ramachandran plot after inclusion of hydrogen-bonding constraints

2010 ◽  
Vol 108 (1) ◽  
pp. 109-113 ◽  
Author(s):  
Lauren L. Porter ◽  
George D. Rose
Keyword(s):  
Hydrogen Bonding ◽  
Hard Spheres ◽  
Two Dimensions ◽  
Conformational Space ◽  
Ramachandran Plot ◽  
Subsequent Work ◽  
Peptide Unit ◽  
Folded Proteins ◽  
Forbidden Region ◽  
Intramolecular Hydrogen

A protein backbone has two degrees of conformational freedom per residue, described by its φ,ψ-angles. Accordingly, the energy landscape of a blocked peptide unit can be mapped in two dimensions, as shown by Ramachandran, Sasisekharan, and Ramakrishnan almost half a century ago. With atoms approximated as hard spheres, the eponymous Ramachandran plot demonstrated that steric clashes alone eliminate ¾ of φ,ψ-space, a result that has guided all subsequent work. Here, we show that adding hydrogen-bonding constraints to these steric criteria eliminates another substantial region of φ,ψ-space for a blocked peptide; for conformers within this region, an amide hydrogen is solvent-inaccessible, depriving it of a hydrogen-bonding partner. Yet, this “forbidden” region is well populated in folded proteins, which can provide longer-range intramolecular hydrogen-bond partners for these otherwise unsatisfied polar groups. Consequently, conformational space expands under folding conditions, a paradigm-shifting realization that prompts an experimentally verifiable conjecture about likely folding pathways.

scholarly journals Behavior of Protein-Inspired Synthetic Random Heteropolymers

2020 ◽  
Author(s):  
Shayna Hilburg ◽  
Zhiyuan Ruan ◽  
Ting Xu ◽  
Alfredo Alexander-Katz
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Molten Globule ◽  
Structural Features ◽  
Conformational Space ◽  
Disordered Proteins ◽  
Protein Surfaces ◽  
Substantial Impact ◽  
Intrinsically Disordered ◽  
Dynamics Simulations ◽  
Complex Scenario

Random heteropolymers (RHPs) are an interesting class of materials useful in many theories and applications. While previous studies typically focused on simplified RHP systems, here we explore a more complex scenario inspired by highly heterogeneous molecules like proteins. Our system consists of four monomers mimicking different classes of amino acids. Using Molecular Dynamics simulations and Small-Angle X-Ray Scattering, we explore dynamical and structural features of these RHPs in solution. Our results show the RHPs assemble with heterogeneous interfaces reminiscent of protein surfaces. The polymer backbones appear frozen at room temperature on the nano- to micro-second timescale with molten globule morphology, albeit their conformational space has multiple metastable conformations for a given sequence, drawing comparison to Intrinsically Disordered Proteins. Local connectivity and chemistry are also shown to have substantial impact on polymer solvation. The work presented here indicates that RHPs share similarities with proteins to be leveraged in bio-mimetic and bio-inspired applications.

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