scholarly journals A Suggestion of Converting Protein Intrinsic Disorder to Structural Entropy Using Shannon’s Information Theory

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 591
Author(s):  
Hao-Bo Guo ◽  
Yue Ma ◽  
Gerald Tuskan ◽  
Hong Qin ◽  
Xiaohan Yang ◽  
...  
Keyword(s):  
Information Theory ◽  
Intrinsically Disordered Proteins ◽  
Structural Information ◽  
Intrinsic Disorder ◽  
High Energy ◽  
Disordered Proteins ◽  
Sequence Length ◽  
Intrinsically Disordered ◽  
Structural Entropy ◽  
Protein Intrinsic Disorder

We propose a framework to convert the protein intrinsic disorder content to structural entropy (H) using Shannon’s information theory (IT). The structural capacity (C), which is the sum of H and structural information (I), is equal to the amino acid sequence length of the protein. The structural entropy of the residues expands a continuous spectrum, ranging from 0 (fully ordered) to 1 (fully disordered), consistent with Shannon’s IT, which scores the fully-determined state 0 and the fully-uncertain state 1. The intrinsically disordered proteins (IDPs) in a living cell may participate in maintaining the high-energy-low-entropy state. In addition, under this framework, the biological functions performed by proteins and associated with the order or disorder of their 3D structures could be explained in terms of information-gains or entropy-losses, or the reverse processes.

scholarly journals Quantitative proteome-based guidelines for intrinsic disorder characterization

2015 ◽  
Author(s):  
Michael Vincent ◽  
Mark Whidden ◽  
Santiago Schnell
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Objective Assessment ◽  
Intrinsic Disorder ◽  
Disordered Proteins ◽  
Dimensional Structure ◽  
Sequence Length ◽  
Valuable Insight ◽  
Disorder Prediction ◽  
Intrinsically Disordered ◽  
Prediction Algorithms

AbstractIntrinsically disordered proteins fail to adopt a stable three-dimensional structure under physiological conditions. It is now understood that many disordered proteins are not dysfunctional, but instead engage in numerous cellular processes, including signaling and regulation. Disorder characterization from amino acid sequence relies on computational disorder prediction algorithms. While numerous large-scale investigations of disorder have been performed using these algorithms, and have offered valuable insight regarding the prevalence of protein disorder in many organisms, critical proteome-based descriptive statistical guidelines that would enable the objective assessment of intrinsic disorder in a protein of interest remain to be established. Here we present a quantitative characterization of numerous disorder features using a rigorous non-parametric statistical approach, providing expected values and percentile cutoffs for each feature in ten eukaryotic proteomes. Our estimates utilize multiple ab initio disorder prediction algorithms grounded on physicochemical principles. Furthermore, we present novel threshold values, specific to both the prediction algorithms and the proteomes, defining the longest primary sequence length in which the significance of a continuous disordered region can be evaluated on the basis of length alone. The guidelines presented here are intended to improve the interpretation of disorder content and continuous disorder predictions from the proteomic point of view.

scholarly journals Critical Assessment of Protein Intrinsic Disorder Prediction

2020 ◽  
Author(s):  
Marco Necci ◽  
Damiano Piovesan ◽  
Silvio C.E. Tosatto ◽  
◽  
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Intrinsic Disorder ◽  
Critical Assessment ◽  
Disordered Proteins ◽  
Blind Test ◽  
Disorder Prediction ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Binding Regions ◽  
Protein Intrinsic Disorder

AbstractIntrinsically disordered proteins defying the traditional protein structure-function paradigm represent a challenge to study experimentally. As a large part of our knowledge rests on computational predictions, it is crucial for their accuracy to be high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in predicting intrinsically disordered regions in proteins and the subset of disordered residues involved in binding other molecules. A total of 43 methods, 32 for disorder and 11 for binding regions, were evaluated on a dataset of 646 novel manually curated proteins from DisProt. The best methods use deep learning techniques and significantly outperform widely used earlier physicochemical methods across different types of targets. Disordered binding regions remain hard to predict correctly. Depending on the definition used, the top disorder predictor has an FMax of 0.483 (DisProt) or 0.792 (DisProt-PDB). As the top binding predictor only attains an FMax of 0.231, this suggests significant potential for improvement. Intriguingly, computing times among the top performing methods vary by up to four orders of magnitude.

scholarly journals Critical assessment of protein intrinsic disorder prediction

2021 ◽  
Author(s):  
Marco Necci ◽  
◽  
Damiano Piovesan ◽  
Silvio C. E. Tosatto ◽  
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Intrinsic Disorder ◽  
Critical Assessment ◽  
Disordered Proteins ◽  
Blind Test ◽  
Disorder Prediction ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Full Dataset ◽  
Protein Intrinsic Disorder

AbstractIntrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude.

scholarly journals Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1788
Author(s):  
Vy T. Duong ◽  
Elizabeth M. Diessner ◽  
Gianmarc Grazioli ◽  
Rachel W. Martin ◽  
Carter T. Butts
Keyword(s):  
Protein Structure ◽  
Intrinsically Disordered Proteins ◽  
Dynamic Models ◽  
Structural Information ◽  
Coarse Graining ◽  
Molecular Structures ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Scalable Network ◽  
Atomic Coordinates

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

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.

scholarly journals Intrinsic disorder is an essential characteristic of components in the conserved circadian circuit

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Jacqueline F. Pelham ◽  
Jay C. Dunlap ◽  
Jennifer M. Hurley
Keyword(s):  
Circadian Clock ◽  
Neurospora Crassa ◽  
Mus Musculus ◽  
Intrinsically Disordered Proteins ◽  
Intrinsic Disorder ◽  
Cellular Systems ◽  
Disordered Proteins ◽  
Circadian Timing ◽  
Intrinsically Disordered ◽  
The Core

Abstract Introduction The circadian circuit, a roughly 24 h molecular feedback loop, or clock, is conserved from bacteria to animals and allows for enhanced organismal survival by facilitating the anticipation of the day/night cycle. With circadian regulation reportedly impacting as high as 80% of protein coding genes in higher eukaryotes, the protein-based circadian clock broadly regulates physiology and behavior. Due to the extensive interconnection between the clock and other cellular systems, chronic disruption of these molecular rhythms leads to a decrease in organismal fitness as well as an increase of disease rates in humans. Importantly, recent research has demonstrated that proteins comprising the circadian clock network display a significant amount of intrinsic disorder. Main body In this work, we focus on the extent of intrinsic disorder in the circadian clock and its potential mechanistic role in circadian timing. We highlight the conservation of disorder by quantifying the extent of computationally-predicted protein disorder in the core clock of the key eukaryotic circadian model organisms Drosophila melanogaster, Neurospora crassa, and Mus musculus. We further examine previously published work, as well as feature novel experimental evidence, demonstrating that the core negative arm circadian period drivers FREQUENCY (Neurospora crassa) and PERIOD-2 (PER2) (Mus musculus), possess biochemical characteristics of intrinsically disordered proteins. Finally, we discuss the potential contributions of the inherent biophysical principals of intrinsically disordered proteins that may explain the vital mechanistic roles they play in the clock to drive their broad evolutionary conservation in circadian timekeeping. Conclusion The pervasive conservation of disorder amongst the clock in the crown eukaryotes suggests that disorder is essential for optimal circadian timing from fungi to animals, providing vital homeostatic cellular maintenance and coordinating organismal physiology across phylogenetic kingdoms. Graphical abstract

scholarly journals Assemblages: Functional units formed by cellular phase separation

2014 ◽  
Vol 206 (5) ◽  
pp. 579-588 ◽  
Author(s):  
Jeffrey A. Toretsky ◽  
Peter E. Wright
Keyword(s):  
Phase Separation ◽  
Intrinsically Disordered Proteins ◽  
Intrinsic Disorder ◽  
Low Complexity ◽  
Disordered Proteins ◽  
Intracellular Space ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Disease States ◽  
Membrane Bound

The partitioning of intracellular space beyond membrane-bound organelles can be achieved with collections of proteins that are multivalent or contain low-complexity, intrinsically disordered regions. These proteins can undergo a physical phase change to form functional granules or other entities within the cytoplasm or nucleoplasm that collectively we term “assemblage.” Intrinsically disordered proteins (IDPs) play an important role in forming a subset of cellular assemblages by promoting phase separation. Recent work points to an involvement of assemblages in disease states, indicating that intrinsic disorder and phase transitions should be considered in the development of therapeutics.

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.

Sign in / Sign up

Export Citation Format

Share Document