Sequence Versus Composition: What Prescribes IDP Biophysical Properties?

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.

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Amino acid substitution scoring matrices specific to intrinsically disordered regions in proteins

Scientific Reports ◽

10.1038/s41598-019-52532-8 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Rakesh Trivedi ◽

Hampapathalu Adimurthy Nagarajaram

Keyword(s):

Amino Acid ◽

Amino Acid Substitution ◽

Intrinsically Disordered Proteins ◽

Disordered Proteins ◽

Compositional Bias ◽

Amino Acid Residues ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Scoring Matrices ◽

Disordered Regions

Abstract An amino acid substitution scoring matrix encapsulates the rates at which various amino acid residues in proteins are substituted by other amino acid residues, over time. Database search methods make use of substitution scoring matrices to identify sequences with homologous relationships. However, widely used substitution scoring matrices, such as BLOSUM series, have been developed using aligned blocks that are mostly devoid of disordered regions in proteins. Hence, these substitution-scoring matrices are mostly inappropriate for homology searches involving proteins enriched with disordered regions as the disordered regions have distinct amino acid compositional bias, and therefore expected to have undergone amino acid substitutions that are distinct from those in the ordered regions. We, therefore, developed a novel series of substitution scoring matrices referred to as EDSSMat by exclusively considering the substitution frequencies of amino acids in the disordered regions of the eukaryotic proteins. The newly developed matrices were tested for their ability to detect homologs of proteins enriched with disordered regions by means of SSEARCH tool. The results unequivocally demonstrate that EDSSMat matrices detect more number of homologs than the widely used BLOSUM, PAM and other standard matrices, indicating their utility value for homology searches of intrinsically disordered proteins.

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Sequence Specificity Despite Intrinsic Disorder: How a Disease-Associated Val/Met Polymorphism Shifts Tertiary Interactions in a Long Disordered Protein

10.26434/chemrxiv.8135777.v1 ◽

2019 ◽

Author(s):

Ruchi Lohia ◽

Reza Salari ◽

Grace Brannigan

Keyword(s):

Secondary Structure ◽

Electrostatic Interactions ◽

Intrinsically Disordered Proteins ◽

Disordered Proteins ◽

Sequence Specificity ◽

Intrinsically Disordered ◽

Specific Effects ◽

Heterogeneous Polymer ◽

Non Local ◽

Dynamics Simulations

<div>The role of electrostatic interactions and mutations that change charge states in intrinsically disordered proteins (IDPs) is well-established, but many disease-associated mutations in IDPs are charge-neutral. The Val66Met single nucleotide polymorphism (SNP) encodes a hydrophobic-to-hydrophobic mutation at the midpoint of the prodomain of precursor brain-derived neurotrophic factor (BDNF), one of the earliest SNPs to be associated with neuropsychiatric disorders, for which the underlying molecular mechanism is unknown. Here we report on over 250 μs of fully-atomistic, explicit solvent, temperature replica exchange molecular dynamics simulations of the 91 residue BDNF prodomain, for both the V66 and M66 sequence.</div><div>The simulations were able to correctly reproduce the location of both local and non-local secondary changes due to the Val66Met mutation when compared with NMR spectroscopy. We find that the local structure change is mediated via entropic and sequence specific effects. We show that the highly disordered prodomain can be meaningfully divided into domains based on sequence alone. Monte Carlo simulations of a self-excluding heterogeneous polymer, with monomers representing each domain, suggest the sequence would be effectively segmented by the long, highly disordered polyampholyte near the sequence midpoint. This is qualitatively consistent with observed interdomain contacts within the BDNF prodomain, although contacts between the two segments are enriched relative to the self-excluding polymer. The Val66Met mutation increases interactions across the boundary between the two segments, due in part to a specific Met-Met interaction with a Methionine in the other segment. This effect propagates to cause the non-local change in secondary structure around the second methionine, previously observed in NMR. The effect is not mediated simply via changes in inter-domain contacts but is also dependent on secondary structure formation around residue 66, indicating a mechanism for secondary structure coupling in disordered proteins. </div>

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An Evolutionary Algorithm for the Design of Different Degrees of Secondary Structure in Intrinsically Disordered Proteins (IDPs)

Biophysical Journal ◽

10.1016/j.bpj.2014.11.1259 ◽

2015 ◽

Vol 108 (2) ◽

pp. 228a ◽

Cited By ~ 1

Author(s):

Tyler S. Harmon ◽

Rohit V. Pappu

Keyword(s):

Secondary Structure ◽

Evolutionary Algorithm ◽

Intrinsically Disordered Proteins ◽

Disordered Proteins ◽

Intrinsically Disordered

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Random coil chemical shifts for serine, threonine and tyrosine phosphorylation over a broad pH range

Journal of Biomolecular NMR ◽

10.1007/s10858-019-00283-z ◽

2019 ◽

Vol 73 (12) ◽

pp. 713-725 ◽

Cited By ~ 4

Author(s):

Ruth Hendus-Altenburger ◽

Catarina B. Fernandes ◽

Katrine Bugge ◽

Micha B. A. Kunze ◽

Wouter Boomsma ◽

...

Keyword(s):

Chemical Shift ◽

Secondary Structure ◽

Intrinsically Disordered Proteins ◽

Chemical Shifts ◽

Random Coil ◽

Disordered Proteins ◽

Phosphoryl Group ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Secondary Chemical

Abstract Phosphorylation is one of the main regulators of cellular signaling typically occurring in flexible parts of folded proteins and in intrinsically disordered regions. It can have distinct effects on the chemical environment as well as on the structural properties near the modification site. Secondary chemical shift analysis is the main NMR method for detection of transiently formed secondary structure in intrinsically disordered proteins (IDPs) and the reliability of the analysis depends on an appropriate choice of random coil model. Random coil chemical shifts and sequence correction factors were previously determined for an Ac-QQXQQ-NH2-peptide series with X being any of the 20 common amino acids. However, a matching dataset on the phosphorylated states has so far only been incompletely determined or determined only at a single pH value. Here we extend the database by the addition of the random coil chemical shifts of the phosphorylated states of serine, threonine and tyrosine measured over a range of pH values covering the pKas of the phosphates and at several temperatures (www.bio.ku.dk/sbinlab/randomcoil). The combined results allow for accurate random coil chemical shift determination of phosphorylated regions at any pH and temperature, minimizing systematic biases of the secondary chemical shifts. Comparison of chemical shifts using random coil sets with and without inclusion of the phosphoryl group, revealed under/over estimations of helicity of up to 33%. The expanded set of random coil values will improve the reliability in detection and quantification of transient secondary structure in phosphorylation-modified IDPs.

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Analysis of Heterodimeric “Mutual Synergistic Folding”-Complexes

International Journal of Molecular Sciences ◽

10.3390/ijms20205136 ◽

2019 ◽

Vol 20 (20) ◽

pp. 5136 ◽

Cited By ~ 3

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.

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

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720019500045 ◽

2019 ◽

Vol 17 (01) ◽

pp. 1950004 ◽

Cited By ~ 2

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

Life ◽

10.3390/life10120320 ◽

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.

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