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

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 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 .

scholarly journals Roles, Characteristics, and Analysis of Intrinsically Disordered Proteins: A Minireview

Life ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 320
Author(s):  
Frederik Lermyte
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Neurological Disorders ◽  
Intrinsically Disordered Proteins ◽  
Intrinsic Disorder ◽  
Disordered Proteins ◽  
Dynamic Nature ◽  
Intrinsically Disordered ◽  
Unfolded State ◽  
Underlying Causes

In recent years, there has been a growing understanding that a significant fraction of the eukaryotic proteome is intrinsically disordered, and that these conformationally dynamic proteins play a myriad of vital biological roles in both normal and pathological states. In this review, selected examples of intrinsically disordered proteins are highlighted, with particular attention for a few which are relevant in neurological disorders and in viral infection. Next, the underlying causes for intrinsic disorder are discussed, along with computational methods used to predict whether a given amino acid sequence is likely to adopt a folded or unfolded state in solution. Finally, biophysical methods for the analysis of intrinsically disordered proteins will be discussed, as well as the unique challenges they pose in this context due to their highly dynamic nature.

Exclusively Heteronuclear13C-Detected Amino-Acid-Selective NMR Experiments for the Study of Intrinsically Disordered Proteins (IDPs)

ChemBioChem ◽  
2012 ◽  
Vol 13 (16) ◽  
pp. 2425-2432 ◽  
Author(s):  
Wolfgang Bermel ◽  
Ivano Bertini ◽  
Jordan Chill ◽  
Isabella C. Felli ◽  
Noam Haba ◽  
...  
Keyword(s):  
Amino Acid ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Intrinsically Disordered

NOT THAT RIGID MIDGETS AND NOT SO FLEXIBLE GIANTS: ON THE ABUNDANCE AND ROLES OF INTRINSIC DISORDER IN SHORT AND LONG PROTEINS

2012 ◽  
Vol 20 (04) ◽  
pp. 471-511 ◽  
Author(s):  
MARK HOWELL ◽  
RYAN GREEN ◽  
ALEXIS KILLEEN ◽  
LAMAR WEDDERBURN ◽  
VINCENT PICASCIO ◽  
...  
Keyword(s):  
Amino Acid ◽  
Intrinsically Disordered Proteins ◽  
Biological Activities ◽  
Intrinsic Disorder ◽  
Amino Acid Sequences ◽  
Disordered Proteins ◽  
Biological Functions ◽  
Intrinsically Disordered ◽  
Eukaryotic Proteins ◽  
Disordered Regions

Intrinsically disordered proteins or proteins with disordered regions are very common in nature. These proteins have numerous biological functions which are complementary to the biological activities of traditional ordered proteins. A noticeable difference in the amino acid sequences encoding long and short disordered regions was found and this difference was used in the development of length-dependent predictors of intrinsic disorder. In this study, we analyze the scaling of intrinsic disorder in eukaryotic proteins and investigate the presence of length-dependent functions attributed to proteins containing long disordered regions.

scholarly journals NSP 11 of SARS-CoV-2 is an Intrinsically Disordered Protein

2020 ◽  
Author(s):  
Kundlik Gadhave ◽  
Prateek Kumar ◽  
Ankur Kumar ◽  
Taniya Bhardwaj ◽  
Neha Garg ◽  
...  
Keyword(s):  
Amino Acid ◽  
Intrinsically Disordered Proteins ◽  
Conformational Dynamics ◽  
Alpha Helix ◽  
Intrinsically Disordered Protein ◽  
Disordered Proteins ◽  
Helical Conformation ◽  
Structural Protein ◽  
Intrinsically Disordered ◽  
Protein Size

AbstractThe intrinsically disordered proteins/regions (IDPs/IDPRs) are known to be responsible for multiple cellular processes and are associated with many chronic diseases. In viruses, the existence of disordered proteome is also proven and are related with its conformational dynamics inside the host. The SARS-CoV-2 virus has a large proteome, in which, structure and functions of many proteins are not known as of yet. Previously, we have investigated the dark proteome of SARS-CoV-2. However, the disorder status of non-structural protein 11 (nsp11) was not possible because of very small in size, just 13 amino acid long, and for most of the IDP predictors, the protein size should be at least 30 amino acid long. Also, the structural dynamics and function status of nsp11 was not known. Hence, we have performed extensive experimentation on nsp11. Our results, based on the Circular dichroism spectroscopy gives characteristic disordered spectrum for IDPs. Further, we investigated the conformational behaviour of nsp11 in the presence of membrane mimetic environment, alpha helix inducer, and natural osmolyte. In the presence of negatively charged and neutral liposomes, nsp11 remains disordered. However, with SDS micelle, it adopted an α-helical conformation, suggesting the helical propensity of nsp11. At the end, we again confirmed the IDP behaviour of nsp11 using molecular dynamics simulations.

scholarly journals Examining the Ensembles of Amyloid-β Monomer Variants and their Propensities to Form Fibers Using an Energy Landscape Visualization Method

2021 ◽  
Author(s):  
Murilo N Sanches ◽  
Kaitlin Knapp ◽  
Antonio Bento Oliveira Junior ◽  
Peter G Wolynes ◽  
Jose N Onuchic ◽  
...  
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Energy Landscape ◽  
Single Point ◽  
Point Mutations ◽  
Amyloid Β ◽  
Coarse Grained ◽  
Disordered Proteins ◽  
Visualization Method ◽  
Intrinsically Disordered ◽  
Single Point Mutations

The amyloid-β (Aβ) monomer, an intrinsically disordered peptide, is produced by the cleavage of the amyloid precursor protein, leading to Aβ40 and Aβ42 as major products. These two isoforms generate pathological aggregates, whose accumulation correlates with Alzheimer's disease (AD). Experiments have shown that even though the natural abundance of Aβ42 is smaller than that for Aβ40, the Aβ42 is more aggregation-prone compared to Aβ40. Moreover, several single-point mutations are associated with early-onset forms of AD. This work analyzes coarse-grained AWSEM simulations of normal Aβ40 and Aβ42 monomers, along with six single-point mutations associated with early on set disease. We analyzed the simulations using the Energy Landscape Visualization Method (ELViM), a reaction coordinate-free approach suited to explore the frustrated energy landscapes of intrinsically disordered proteins. ELViM is shown to distinguish the monomer ensembles of variants that rapidly form fibers from those that do not form fibers as readily. It also delineates the amino-acid contacts characterizing each ensemble. The results shed light on the potential of ELViM to probe intrinsically disordered proteins.

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