Residual dipolar couplings measured in unfolded proteins are sensitive to amino-acid-specific geometries as well as local conformational sampling

2012 ◽  
Vol 40 (5) ◽  
pp. 989-994 ◽  
Author(s):  
Jie-rong Huang ◽  
Martin Gentner ◽  
Navratna Vajpai ◽  
Stephan Grzesiek ◽  
Martin Blackledge
Keyword(s):  
Amino Acid ◽  
Residual Dipolar Couplings ◽  
Intrinsically Disordered Proteins ◽  
Dipolar Couplings ◽  
Disordered Proteins ◽  
Conformational Sampling ◽  
Intrinsically Disordered ◽  
Potential Energy Landscape ◽  
Order Of Magnitude ◽  
Folded Structures

Many functional proteins do not have well defined folded structures. In recent years, both experimental and computational approaches have been developed to study the conformational behaviour of this type of protein. It has been shown previously that experimental RDCs (residual dipolar couplings) can be used to study the backbone sampling of disordered proteins in some detail. In these studies, the backbone structure was modelled using a common geometry for all amino acids. In the present paper, we demonstrate that experimental RDCs are also sensitive to the specific geometry of each amino acid as defined by energy-minimized internal co-ordinates. We have modified the FM (flexible-Meccano) algorithm that constructs conformational ensembles on the basis of a statistical coil model, to account for these differences. The modified algorithm inherits the advantages of the FM algorithm to efficiently sample the potential energy landscape for coil conformations. The specific geometries incorporated in the new algorithm result in a better reproduction of experimental RDCs and are generally applicable for further studies to characterize the conformational properties of intrinsically disordered proteins. In addition, the internal-co-ordinate-based algorithm is an order of magnitude more efficient, and facilitates side-chain construction, surface osmolyte simulation, spin-label distribution sampling and proline cis/trans isomer simulation.

scholarly journals Application of the maximum entropy principle to determine ensembles of intrinsically disordered proteins from residual dipolar couplings

2014 ◽  
Vol 16 (47) ◽  
pp. 26030-26039 ◽  
Author(s):  
M. Sanchez-Martinez ◽  
R. Crehuet
Keyword(s):  
Maximum Entropy ◽  
Residual Dipolar Couplings ◽  
Intrinsically Disordered Proteins ◽  
Protein Structures ◽  
Maximum Entropy Principle ◽  
Dipolar Couplings ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Entropy Principle

We present a method based on the maximum entropy principle that can re-weight an ensemble of protein structures based on data from residual dipolar couplings (RDCs).

scholarly journals Mapping the Potential Energy Landscape of Intrinsically Disordered Proteins at Amino Acid Resolution

2012 ◽  
Vol 134 (36) ◽  
pp. 15138-15148 ◽  
Author(s):  
Valéry Ozenne ◽  
Robert Schneider ◽  
Mingxi Yao ◽  
Jie-rong Huang ◽  
Loïc Salmon ◽  
...  
Keyword(s):  
Amino Acid ◽  
Potential Energy ◽  
Intrinsically Disordered Proteins ◽  
Energy Landscape ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Potential Energy Landscape

scholarly journals Quantitative Determination of the Conformational Properties of Partially Folded and Intrinsically Disordered Proteins Using NMR Dipolar Couplings

Structure ◽  
2009 ◽  
Vol 17 (9) ◽  
pp. 1169-1185 ◽  
Author(s):  
Malene Ringkjøbing Jensen ◽  
Phineus R.L. Markwick ◽  
Sebastian Meier ◽  
Christian Griesinger ◽  
Markus Zweckstetter ◽  
...  
Keyword(s):  
Quantitative Determination ◽  
Intrinsically Disordered Proteins ◽  
Dipolar Couplings ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Conformational Properties

scholarly journals Enhancing Binding Affinity of an Intrinsically Disordered Protein by α-Methylation of Key Amino Acid Residues

2019 ◽  
Author(s):  
Valentin Bauer ◽  
Boris Schmidtgall ◽  
Gergő Gógl ◽  
Jozica Dolenc ◽  
Judit Osz ◽  
...  
Keyword(s):  
Amino Acids ◽  
Protein Interactions ◽  
Intrinsically Disordered Proteins ◽  
Intrinsically Disordered Protein ◽  
Selective Inhibition ◽  
Disordered Proteins ◽  
Creb Binding Protein ◽  
Alpha Helices ◽  
Intrinsically Disordered ◽  
Order Of Magnitude

Intrinsically disordered proteins (IDPs), which undergo folding upon binding to their targets, are critical players in protein interaction networks. Here we demonstrate that incorporation of non-canonical alpha-methylated amino acids into the unstructured activation domain of the transcriptional coactivator ACTR can stabilize helical conformations and strengthen binding interactions with the nuclear coactivator binding domain (NCBD) of CREB-binding protein (CBP). A combinatorial alpha-methylation scan of the ACTR sequence converged on two substitutions at positions 1055 and 1076 that increase affinity for both NCBD and the full length 270 kDa CBP by one order of magnitude. The first X-ray structure of the modified ACTR domain bound to NCBD revealed that the key alpha-methylated amino acids were localized within alpha-helices. Biophysical studies showed that the observed changes in binding energy are the result of long-range interactions and redistribution of enthalpy and entropy. This proof-of-concept study establishes a potential strategy for selective inhibition of protein-protein interactions involving IDPs in cells.<br>

scholarly journals Sequence Versus Composition: What Prescribes IDP Biophysical Properties?

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 654 ◽  
Author(s):  
Jiří Vymětal ◽  
Jiří Vondrášek ◽  
Klára Hlouchová
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Intrinsically Disordered Proteins ◽  
Random Sequence ◽  
Bioinformatic Analysis ◽  
Globular Proteins ◽  
Disordered Proteins ◽  
Compositional Bias ◽  
Intrinsically Disordered ◽  
Sequence Versus

Intrinsically disordered proteins (IDPs) represent a distinct class of proteins and are distinguished from globular proteins by conformational plasticity, high evolvability and a broad functional repertoire. Some of their properties are reminiscent of early proteins, but their abundance in eukaryotes, functional properties and compositional bias suggest that IDPs appeared at later evolutionary stages. The spectrum of IDP properties and their determinants are still not well defined. This study compares rudimentary physicochemical properties of IDPs and globular proteins using bioinformatic analysis on the level of their native sequences and random sequence permutations, addressing the contributions of composition versus sequence as determinants of the properties. IDPs have, on average, lower predicted secondary structure contents and aggregation propensities and biased amino acid compositions. However, our study shows that IDPs exhibit a broad range of these properties. Induced fold IDPs exhibit very similar compositions and secondary structure/aggregation propensities to globular proteins, and can be distinguished from unfoldable IDPs based on analysis of these sequence properties. While amino acid composition seems to be a major determinant of aggregation and secondary structure propensities, sequence randomization does not result in dramatic changes to these properties, but for both IDPs and globular proteins seems to fine-tune the tradeoff between folding and aggregation.

scholarly journals Analysis of Heterodimeric “Mutual Synergistic Folding”-Complexes

2019 ◽  
Vol 20 (20) ◽  
pp. 5136 ◽  
Author(s):  
Mentes ◽  
Magyar ◽  
Fichó ◽  
Simon
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Intrinsically Disordered Proteins ◽  
Driving Forces ◽  
Disordered Proteins ◽  
Subunit Interactions ◽  
Amino Acid Compositions ◽  
Intrinsically Disordered ◽  
Β Sheet

Several intrinsically disordered proteins (IDPs) are capable to adopt stable structures without interacting with a folded partner. When the folding of all interacting partners happens at the same time, coupled with the interaction in a synergistic manner, the process is called Mutual Synergistic Folding (MSF). These complexes represent a discrete subset of IDPs. Recently, we collected information on their complexes and created the MFIB (Mutual Folding Induced by Binding) database. In a previous study, we compared homodimeric MSF complexes with homodimeric and monomeric globular proteins with similar amino acid sequence lengths. We concluded that MSF homodimers, compared to globular homodimeric proteins, have a greater solvent accessible main-chain surface area on the contact surface of the subunits, which becomes buried during dimerization. The main driving force of the folding is the mutual shielding of the water-accessible backbones, but the formation of further intermolecular interactions can also be relevant. In this paper, we will report analyses of heterodimeric MSF complexes. Our results indicate that the amino acid composition of the heterodimeric MSF monomer subunits slightly diverges from globular monomer proteins, while after dimerization, the amino acid composition of the overall MSF complexes becomes more similar to overall amino acid compositions of globular complexes. We found that inter-subunit interactions are strengthened, and additionally to the shielding of the solvent accessible backbone, other factors might play an important role in the stabilization of the heterodimeric structures, likewise energy gain resulting from the interaction of the two subunits with different amino acid compositions. We suggest that the shielding of the β-sheet backbones and the formation of a buried structural core along with the general strengthening of inter-subunit interactions together could be the driving forces of MSF protein structural ordering upon dimerization.

Identifying short disorder-to-order binding regions in disordered proteins with a deep convolutional neural network method

2019 ◽  
Vol 17 (01) ◽  
pp. 1950004 ◽  
Author(s):  
Chun Fang ◽  
Yoshitaka Moriwaki ◽  
Aikui Tian ◽  
Caihong Li ◽  
Kentaro Shimizu
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Convolutional Neural Network ◽  
Intrinsically Disordered Proteins ◽  
Protein Sequences ◽  
Interaction Network ◽  
Deep Convolutional Neural Network ◽  
Disordered Proteins ◽  
Related Factors ◽  
Intrinsically Disordered

Molecular recognition features (MoRFs) are key functional regions of intrinsically disordered proteins (IDPs), which play important roles in the molecular interaction network of cells and are implicated in many serious human diseases. Identifying MoRFs is essential for both functional studies of IDPs and drug design. This study adopts the cutting-edge machine learning method of artificial intelligence to develop a powerful model for improving MoRFs prediction. We proposed a method, named as en_DCNNMoRF (ensemble deep convolutional neural network-based MoRF predictor). It combines the outcomes of two independent deep convolutional neural network (DCNN) classifiers that take advantage of different features. The first, DCNNMoRF1, employs position-specific scoring matrix (PSSM) and 22 types of amino acid-related factors to describe protein sequences. The second, DCNNMoRF2, employs PSSM and 13 types of amino acid indexes to describe protein sequences. For both single classifiers, DCNN with a novel two-dimensional attention mechanism was adopted, and an average strategy was added to further process the output probabilities of each DCNN model. Finally, en_DCNNMoRF combined the two models by averaging their final scores. When compared with other well-known tools applied to the same datasets, the accuracy of the novel proposed method was comparable with that of state-of-the-art methods. The related web server can be accessed freely via http://vivace.bi.a.u-tokyo.ac.jp:8008/fang/en_MoRFs.php .

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