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.

Energy landscapes and solved protein–folding problems

2004 ◽  
Vol 363 (1827) ◽  
pp. 453-467 ◽  
Author(s):  
Peter G. Wolynes
Keyword(s):  
Protein Folding ◽  
Protein Design ◽  
Structure Prediction ◽  
Energy Landscape ◽  
Protein Structures ◽  
Energy Landscapes ◽  
Folding Kinetics ◽  
Energy Functions ◽  
Energy Landscape Theory ◽  
Novel Proteins

Energy–landscape theory has led to much progress in protein folding kinetics, protein structure prediction and protein design. Funnel landscapes describe protein folding and binding and explain how protein topology determines kinetics. Landscape–optimized energy functions based on bioinformatic input have been used to correctly predict low–resolution protein structures and also to design novel proteins automatically.

Different rates of formation of secondary and tertiary structure during renaturation of urea-denatured human serum albumin

1989 ◽  
Vol 54 (9) ◽  
pp. 2542-2549 ◽  
Author(s):  
Josef Chmelík
Keyword(s):  
Protein Folding ◽  
Human Serum Albumin ◽  
Serum Albumin ◽  
Human Serum ◽  
Disulfide Bonds ◽  
Tertiary Structure ◽  
Hydrophobic Interactions ◽  
Protein Structures ◽  
Three Dimensional ◽  
Comparison Of The Results

A comparison of the results of our polarimetric measurements with the polarographic experiments reported earlier shows that the restoration of the secondary structure during the renaturation of human serum albumin is a process which is faster than the formation of the tertiary structure. These results, which are in agreement with the data on the kinetic control of protein folding, are discussed from the viewpoint of the importance of the individual types of interactions which take place during the formation and stabilization of three-dimensional protein structures. We have been able to demonstrate the great importance of electrostatic and hydrophobic interactions which together with the disulfide bonds are essential for the reversibility of the denaturation phenomena. The discussion also shows the essential role which evolution processes play in the selection of the mode of protein folding.

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 Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields

2018 ◽  
Author(s):  
Maciej Pawel Ciemny ◽  
Aleksandra Elzbieta Badaczewska-Dawid ◽  
Monika Pikuzinska ◽  
Andrzej Kolinski ◽  
Sebastian Kmiecik
Keyword(s):  
Experimental Data ◽  
Monte Carlo ◽  
Protein Folding ◽  
Case Studies ◽  
Protein Structures ◽  
Coarse Grained ◽  
Computational Time ◽  
Modeling Tools ◽  
Disordered Protein ◽  
Knowledge Based

The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling disordered protein interactions and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein-peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein-peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution and studies of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution.

scholarly journals Combining statistical and neural network approaches to derive energy functions for completely flexible protein backbone design

2019 ◽  
Author(s):  
Bin Huang ◽  
Yang Xu ◽  
Haiyan Liu
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Conformational Space ◽  
Sequence Information ◽  
Protein Backbone ◽  
Energy Functions ◽  
Non Local ◽  
Energy Surfaces

AbstractA designable protein backbone is one for which amino acid sequences that stably fold into it exist. To design such backbones, a general method is much needed for continuous sampling and optimization in the backbone conformational space without specific amino acid sequence information. The energy functions driving such sampling and optimization must faithfully recapitulate the characteristically coupled distributions of multiplexes of local and non-local conformational variables in designable backbones. It is also desired that the energy surfaces are continuous and smooth, with easily computable gradients. We combine statistical and neural network (NN) approaches to derive a model named SCUBA, standing for Side-Chain-Unspecialized-Backbone-Arrangement. In this approach, high-dimensional statistical energy surfaces learned from known protein structures are analytically represented as NNs. SCUBA is composed as a sum of NN terms describing local and non-local conformational energies, each NN term derived by first estimating the statistical energies in the corresponding multi-variable space via neighbor-counting (NC) with adaptive cutoffs, and then training the NN with the NC-estimated energies. To determine the relative weights of different energy terms, SCUBA-driven stochastic dynamics (SD) simulations of natural proteins are considered. As initial computational tests of SCUBA, we apply SD simulated annealing to automatically optimize artificially constructed polypeptide backbones of different fold classes. For a majority of the resulting backbones, structurally matching native backbones can be found with Dali Z-scores above 6 and less than 2 Å displacements of main chain atoms in aligned secondary structures. The results suggest that SCUBA-driven sampling and optimization can be a general tool for protein backbone design with complete conformational flexibility. In addition, the NC-NN approach can be generally applied to develop continuous, noise-filtered multi-variable statistical models from structural data.Linux executables to setup and run SCUBA SD simulations are publicly available (http://biocomp.ustc.edu.cn/servers/download_scuba.php). Interested readers may contact the authors for source code availability.

scholarly journals Limited conformational space for early-stage protein folding simulation

2004 ◽  
Vol 20 (2) ◽  
pp. 199-205 ◽  
Author(s):  
M. Brylinski ◽  
W. Jurkowski ◽  
L. Konieczny ◽  
I. Roterman
Keyword(s):  
Protein Folding ◽  
Early Stage ◽  
Conformational Space ◽  
Folding Simulation

scholarly journals Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields

2019 ◽  
Vol 20 (3) ◽  
pp. 606 ◽  
Author(s):  
Maciej Ciemny ◽  
Aleksandra Badaczewska-Dawid ◽  
Monika Pikuzinska ◽  
Andrzej Kolinski ◽  
Sebastian Kmiecik
Keyword(s):  
Experimental Data ◽  
Monte Carlo ◽  
Protein Folding ◽  
Case Studies ◽  
Protein Structures ◽  
Coarse Grained ◽  
Computational Time ◽  
Modeling Tools ◽  
Disordered Protein ◽  
Knowledge Based

The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling protein interactions, which involve disordered peptide partners or intrinsically disordered protein regions, and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein–peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein–peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution, and simulation of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution.

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