Conformational heterogeneity in tails of DNA-binding proteins is augmented by proline containing repeats

2017 ◽  
Vol 13 (12) ◽  
pp. 2531-2544
Author(s):  
Harshavardhan Khare ◽  
Debayan Dey ◽  
Chilakapati Madhu ◽  
Dillip Senapati ◽  
Srinivasarao Raghothama ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Intrinsic Disorder ◽  
Dna Binding Proteins ◽  
Conformational Heterogeneity ◽  
Intrinsically Disordered ◽  
Disordered Peptides

We model intrinsically disordered peptides mimicking the tails of DNA-binding proteins and propose parameters for the design of intrinsic disorder.

Functional roles of intrinsic disorder in CRISPR-associated protein Cas9

2017 ◽  
Vol 13 (9) ◽  
pp. 1770-1780 ◽  
Author(s):  
Zhihua Du ◽  
Vladimir N. Uversky
Keyword(s):  
Dna Binding ◽  
Intrinsic Disorder ◽  
Dna Binding Proteins ◽  
Functional Roles ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
P Protein ◽  
Protein Intrinsic Disorder ◽  
Rna And Dna ◽  
Different Origin

Protein intrinsic disorder is an important characteristic commonly detected in multifunctional or RNA- and DNA-binding proteins. We show here that the CRISPR-associated Cas9 proteins of different origin contain functionally important intrinsically disordered regions.

scholarly journals Evidence for DNA-mediated nuclear compartmentalization distinct from phase separation

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
David Trombley McSwiggen ◽  
Anders S Hansen ◽  
Sheila S Teves ◽  
Hervé Marie-Nelly ◽  
Yvonne Hao ◽  
...  
Keyword(s):  
Phase Separation ◽  
Dna Binding ◽  
Rna Polymerase Ii ◽  
Protein Interactions ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Pol Ii ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Simplex Virus

RNA Polymerase II (Pol II) and transcription factors form concentrated hubs in cells via multivalent protein-protein interactions, often mediated by proteins with intrinsically disordered regions. During Herpes Simplex Virus infection, viral replication compartments (RCs) efficiently enrich host Pol II into membraneless domains, reminiscent of liquid-liquid phase separation. Despite sharing several properties with phase-separated condensates, we show that RCs operate via a distinct mechanism wherein unrestricted nonspecific protein-DNA interactions efficiently outcompete host chromatin, profoundly influencing the way DNA-binding proteins explore RCs. We find that the viral genome remains largely nucleosome-free, and this increase in accessibility allows Pol II and other DNA-binding proteins to repeatedly visit nearby DNA binding sites. This anisotropic behavior creates local accumulations of protein factors despite their unrestricted diffusion across RC boundaries. Our results reveal underappreciated consequences of nonspecific DNA binding in shaping gene activity, and suggest additional roles for chromatin in modulating nuclear function and organization.

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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