Molecular crowding effect on dynamics of DNA-binding proteins search for their targets

2014 ◽  
Vol 141 (22) ◽  
pp. 225102 ◽  
Author(s):  
Lin Liu ◽  
Kaifu Luo
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Molecular Crowding ◽  
Crowding Effect

Disparity in anomalous diffusion of proteins searching for their target DNA sites in a crowded medium is controlled by the size, shape and mobility of macromolecular crowders

Soft Matter ◽  
2019 ◽  
Vol 15 (9) ◽  
pp. 1960-1969 ◽  
Author(s):  
Pinki Dey ◽  
Arnab Bhattacherjee
Keyword(s):  
Dna Binding ◽  
Physical Properties ◽  
Computer Simulations ◽  
Anomalous Diffusion ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Molecular Crowding ◽  
Target Search ◽  
Target Dna

Using extensive computer simulations, we analyzed the role of physical properties of molecular crowding agents such as size, shape and mobility in the target search dynamics of DNA binding proteins.

scholarly journals Structural Basis of Enhanced Facilitated Diffusion of DNA Binding Proteins in Crowded Cellular Milieu

2019 ◽  
Author(s):  
P. Dey ◽  
A. Bhattacherjee
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Structural Basis ◽  
Facilitated Diffusion ◽  
Molecular Crowding ◽  
Cellular Environment ◽  
Target Dna ◽  
Nonspecific Interactions ◽  
Search Modes

ABSTRACTDNA binding proteins (DBPs) rapidly recognize and specifically associate with their target DNA sites inside cell nucleus that contains up to 400 g/L macromolecules, most of which are proteins. While the fast association between DBPs and DNA is explained by a facilitated diffusion mechanism, where DBPs adopt a weighted combination of 3D diffusion and 1D sliding and hopping modes of transportation, the role of cellular environment that contains many nonspecifically interacting proteins and other biomolecules is mostly overlooked. By performing large scale computational simulations with an appropriately tuned model of protein and DNA in the presence of nonspecifically interacting bulk and DNA bound crowders (genomic crowders), we demonstrate the structural basis of the enhanced facilitated diffusion of DBPs inside a crowded cellular milieu through novel 1D scanning mechanisms. In the presence of bulk crowders, we identify the protein to float along the DNA under the influence of protein-crowder nonspecific interactions. The search mode is distinctly different compared to usual 1D sliding and hopping dynamics where protein diffusion is regulated by the DNA electrostatics. In contrast, the presence of genomic crowders expedite the target search process by transporting the protein over DNA segments through the formation of a transient protein-crowder bridged complex. By analyzing the ruggedness of the associated potential energy landscape, we underpin the molecular origin of the kinetic advantages of these search modes and show that they successfully explain the experimentally observed acceleration of facilitated diffusion of DBPs by molecular crowding agents and crowder concentration dependent enzymatic activity of transcription factors. Our findings provide crucial insights into gene regulation kinetics inside the crowded cellular milieu.SIGNIFICANCE10-40% of the intracellular volume is occupied by proteins, and other biomolecules, collectively known as macromolecular crowders. Their presence has been found to promote faster translocation of DNA binding proteins (DBPs) during the search of their target DNA sites for crucial cellular processes. Using molecular simulations, we probe the underlying structural basis and underscore the existence of novel DNA scanning mechanisms actuated by protein-crowder nonspecific interactions. We show that the observed search modes are kinetically beneficial and can successfully explain the acceleration of facilitated diffusion of DBPs by molecular crowding agents and crowderconcentration dependent enzymatic activity of transcription factors.Our study sheds new light on the long-standing facilitated diffusion problem of DBPs in the crowded cellular environment for regulating gene expression.

DBP-PSSM: Combination of evolutionary profiles with the XGBoost algorithm to improve the identification of DNA-binding proteins

2020 ◽  
Vol 23 ◽  
Author(s):  
Yanping Zhang ◽  
Pengcheng Chen ◽  
Ya Gao ◽  
Jianwei Ni ◽  
Xiaosheng Wang
Keyword(s):  
Logistic Regression ◽  
Protein Structure ◽  
Dna Binding ◽  
Molecular Biology ◽  
Binding Proteins ◽  
Protein Sequences ◽  
Low Complexity ◽  
Dna Binding Proteins ◽  
Position Information ◽  
Position Representation

Aim and Objective:: Given the rapidly increasing number of molecular biology data available, computational methods of low complexity are necessary to infer protein structure, function, and evolution. Method:: In the work, we proposed a novel mthod, FermatS, which based on the global position information and local position representation from the curve and normalized moments of inertia, respectively, to extract features information of protein sequences. Furthermore, we use the generated features by FermatS method to analyze the similarity/dissimilarity of nine ND5 proteins and establish the prediction model of DNA-binding proteins based on logistic regression with 5-fold crossvalidation. Results:: In the similarity/dissimilarity analysis of nine ND5 proteins, the results are consistent with evolutionary theory. Moreover, this method can effectively predict the DNA-binding proteins in realistic situations. Conclusion:: The findings demonstrate that the proposed method is effective for comparing, recognizing and predicting protein sequences. The main code and datasets can download from https://github.com/GaoYa1122/FermatS.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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