Biology subjects

Basu, S.

Publications and source records attributed to Basu, S..

198 records · Page 11Linked to original sources

bioRxiv2020

Integrating Lung Tissue and Lavage Proteomes Reveals Unique Pathways in Allergen-Challenged Mice

To capture interplay between biological pathways we analyzed the proteome from matched lung tissue and bronchoalveolar lavage fluid (BALF) of individual allergen-naive and house dust mite (HDM)-challenged BALB/c mice, a model of allergic asthma. Unbiased label-free LC-MS/MS analysis quantified 2,675 proteins from tissue and BALF of allergen-naive and HDM-exposed mice. In comparing the four datasets we found significantly greater diversity in proteins between lung tissue and BALF than in the changes induced by HDM challenge. The biological pathways enriched after allergen exposure were compartment-dependent. Lung tissue featured innate immune responses and oxidative stress, while BALF most strongly revealed changes in metabolism. We combined lung tissue and BALF proteomes, which principally highlighted oxidation reduction (redox) pathways, a finding influenced chiefly by the lung tissue dataset. Integrating lung and BALF proteomes also uncovered new proteins and biological pathways that may mediate lung tissue and BALF interactions after allergen challenge, for example, B Cell Receptor signaling. We demonstrate that enhanced insight is fostered when different biological compartments from the lung are investigated in parallel. Integration of proteomes from lung tissue and BALF compartments reveals new information about protein networks in the response to environmental challenge and interaction between intracellular and extracellular process.

systems biology↗

bioRxiv2019

3dSpAn: an interactive software for 3D segmentation and analysis of dendritic spines

Three dimensional segmentation and analysis of dendritic spines involve two major challenges: 1) how to segment individual spines from the dendrites and 2) how to quantitatively assess the morphology of individual spines. We developed a software named 3dSpAn to address these two issues by implementing our previously published 3D multiscale opening algorithm in shared intensity space and using effective morphological features for individual dendritic spine plasticity analysis. 3dSpAn consists of four modules: Preprocessing and ROI selection, Intensity thresholding and seed selection, Multiscale segmentation and Quantitative morphological feature extraction. We show the results of segmentation and morphological analysis for different observation methods, including in vitro and ex vivo imaging with confocal microscopy, and in vivo samples, using high-resolution two-photon microscopy. The software is freely available, the source code, windows installer, the software manual and video tutorial can be obtained from: https://sites.google.com/view/3dSpAn/.

neuroscience↗

bioRxiv2019

A sex difference in the composition of the rodent postsynaptic density

SynGAP is a postsynaptic density (PSD) protein that binds to PDZ domains of the scaffold protein PSD-95. We previously reported that heterozygous deletion of synGAP in mice is correlated with increased steady-state levels of other key PSD proteins that bind PSD-95, although the level of PSD-95 remains constant (Walkup et al., 2016). For example, the ratio to PSD-95 of Transmembrane AMPA-Receptor-associated Proteins (TARPs), which mediate binding of AMPA-type glutamate receptors to PSD-95, was increased in young synGAP+/- mice. Here we show that a highly significant increase in TARP in the PSDs of young synGAP+/- rodents is present only in females and not in males. The data reveal a sex difference in the adaptation of the PSD scaffold to synGAP heterozygosity.

neuroscience↗

bioRxiv2019

Functions for Cdc42p BEM Adaptors in Regulating a Differentiation-Type MAP Kinase Pathway

Rho GTPases regulate cell polarity and signal transduction pathways to control morphogenetic responses in different settings. In yeast, the Rho GTPase Cdc42p regulates cell polarity, and through the p21-activated kinase Ste20p, Cdc42p also regulates mitogen-activated protein kinase (MAPK) pathways (mating, filamentous growth or fMAPK, and HOG). Although much is known about how Cdc42p regulates cell polarity and the mating pathway, how Cdc42p regulates the fMAPK pathway is not clear. To address this question, Cdc42p-dependent MAPK pathways were compared in the filamentous ({sum}1278b) strain background. Each MAPK pathway showed a unique activation profile, with the fMAPK pathway exhibiting slow activation kinetics compared to the mating and HOG pathways. A previously characterized version of Cdc42p, Cdc42pE100A, that is specifically defective for fMAPK pathway signaling, was defective for interaction with Bem4p, the pathway-specific adaptor for the fMAPK pathway. Corresponding residues in Bem4p were identified that were required for interaction with Cdc42p and fMAPK pathway signaling. The polarity adaptor Bem1p also regulated the fMAPK pathway. In the fMAPK pathway, Bem1p recruited Ste20p to the plasma membrane, cycled between an open and closed conformation, and interacted with the GEF for Cdc42, Cdc24p. Bem1p also regulated effector pathways in different ways, behaving as a multi-functional adaptor in some pathways and an inert scaffold in others. Genetic suppression tests showed that Bem4p and Bem1p regulate the fMAPK pathway in an ordered sequence. Collectively, the study demonstrates unique and sequential functions for Rho GTPase adaptors in regulating MAPK pathways.\n\nHIGHLIGHTSO_LIComparing Cdc42p-dependent MAPK pathways showed that the fMAPK pathway had slow activation kinetics compared to the mating and HOG pathways.\nC_LIO_LIA collection of cdc42 alleles was tested for MAPK pathway functions.[§] Cdc42pE100A, previously characterized as being specifically defective for fMAPK signaling, showed reduced interaction with the fMAPK pathway adaptor Bem4p.\n[§]Corresponding residues in Bem4p were identified that were required for interaction with Cdc42p and fMAPK signaling.\n\nC_LIO_LIThe polarity adaptor Bem1p regulated the fMAPK pathway.[§] Bem1p regulated the fMAPK pathway by recruiting Ste20p to the plasma membrane, cycling between an open and closed conformation, and interacting with the Cdc42p GEF, Cdc24p.\n\nC_LIO_LIDifferent domains of Bem1p had different roles in regulating effector pathways.[§] Bem1p may function as a multi-functional adaptor in some pathways and an inert scaffold in others.\n\nC_LIO_LIBem4p and Bem1p regulated the fMAPK pathway in an ordered sequence.[§] The data support a model where Bem4p recruits Cdc24p to GDP-Cdc42p, and Bem1p directs GTP-Cdc42p to Ste20p at the plasma membrane.\n[§]The bud-site GTPase Rsr1p regulates Cdc24p in the fMAPK pathway but does not initiate signaling.\n\nC_LI

genetics↗

bioRxiv2019

A temporal map of gene expression pattern during zebrafish liver regeneration

Background & AimsZebrafish is increasingly being used to study liver injury and regeneration. However, very little is known about molecular players that respond to injury and participate in liver regeneration. Here we aim to generate a temporal map of gene expression changes at injury and during regeneration of the adult zebrafish liver.\n\nMethodsWe use a metronidazole-nitroreductase (MTZ-nfsb) based system to selectively ablate hepatocytes in adult zebrafish to create a model for liver injury and regeneration. Through RNA sequencing of liver samples at multiple time points we generate a comprehensive temporal map of gene expression changes during injury and regeneration.\n\nResultsGene expression reveals that soon after injury the immediate early transcription factor MYC induces a battery of genes that respond to the metronidazole-induced ROS by activating oxido-reductase pathways and apoptosis machinery. Upon injury, liver cells down regulate genes encoding complement proteins, bile acid and lipid biosynthesis pathway in a concerted manner. Midway through regeneration, we discover a spike of cholesterol biosynthesis and protein folding machinery genes suggesting an important role for these pathways in liver regeneration.\n\nConclusionsThe temporal transcriptomic map of liver regeneration would serve as a framework for further studies in understanding, and for screening for compounds that augment liver regeneration.\n\nGeneral significanceUsing a hepatocyte specific ablation of zebrafish liver, we create a model of adult liver regeneration. This model was used to generate a comprehensive transcriptomic map of gene expression trends during liver regeneration. This temporal map lays the groundwork to study important events in liver regeneration.\n\nHighlightsO_LIZebrafish is a valuable model for developing therapeutic strategies to augment liver regeneration\nC_LIO_LILiver regeneration in zebrafish is not well studied and pathways poorly understood\nC_LIO_LIWe develop a hepatocyte ablation model of liver injury and regeneration in adult zebrafish\nC_LIO_LIWe generate a comprehensive transcriptomic map of various stages of liver injury and regeneration\nC_LIO_LIWe discover a novel regulation of cholesterol biosynthesis pathways during liver regeneration\nC_LI\n\nGraphical abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC=\"FIGDIR/small/677781v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (16K):\norg.highwire.dtl.DTLVardef@13a322corg.highwire.dtl.DTLVardef@1b40200org.highwire.dtl.DTLVardef@ef8f80org.highwire.dtl.DTLVardef@1b61e01_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2019

A comprehensive computational study of amino acid interactions in membrane proteins

Transmembrane proteins play a fundamental role in a wide series of biological processes but, despite their importance, they are less studied than globular proteins, essentially because their embedding in lipid membranes hampers their experimental characterization. In this paper, we improved our understanding of their structural stability through the development of new knowledge-based energy functions describing amino acid pair interactions that prevail in the transmembrane and extramembrane regions of membrane proteins. The comparison of these potentials and those derived from globular proteins yields an objective view of the relative strength of amino acid interactions in the different protein environments, and their role in protein stabilization. Separate potentials were also derived from -helical and {beta}-barrel transmembrane regions to investigate possible dissimilarities. We found that, in extramembrane regions, hydrophobic residues are less frequent but interactions between aromatic and aliphatic amino acids as well as aromatic-sulfur interactions contribute more to stability. In transmembrane regions, polar residues are less abundant but interactions between residues of equal or opposite charges or non-charged polar residues as well as anion-{pi} interactions appear stronger. This shows indirectly the preference of the water and lipid molecules to interact with polar and hydrophobic residues, respectively. We applied these new energy functions to predict whether a residue is located in the trans- or extramembrane region, and obtained an AUC score of 83% in cross validation, which demonstrates their accuracy. As their application is, moreover, extremely fast, they are optimal instruments for membrane protein design and large-scale investigations of membrane protein stability.

bioinformatics↗

bioRxiv2019

Extended insight into the preponderance of LRRs - a docking & simulation study with members of the TLR1 subfamily

The widespread structural motif of Leucine-rich repeats (LRR) constitute the extracellular part of the Toll-like receptor (TLR) family preceded by an intracellular Toll/interleukin-1 receptor (TIR) domain at the C-terminus. The benefit of using LRRs in these pattern recognition receptors (PRR) that are responsible for early detection of pathogens to elicit inflammatory/innate immune response still remains elusive. Phylogenetic analyses (Maximum Likelihood and Bayesian Inference) of nine TLR (TLR 1-9) genes from 36 mammals reconfirmed the existence of two distinct clades, one (TLR1/2/6) for recognizing bacterial cell wall derivatives and another (TLR7/8/9) for various nucleic acids. TLR3, TLR4 and TLR5 showed independent line of evolution. The distinction of the TLR1 subfamily to form heterodimers within its members and the existence of the paralogs TLR1 and TLR6 therein, was appealing enough to carry out further studies with the extracellular recognition domain. Dimerizing and ligand binding residues from the crystal structures of TLR1 and TLR6 were interchanged to generate chimeric proteins. The dimer forming ability of these variants with their common partner, TLR2, were checked before running MD simulations. The chimeras were compared with wild type dimers to find no significant alterations in the overall structure. Finally, interchanged ligands were docked to the variants to ratify reversal of the binding function. Intriguingly, sequence change in substantial numbers, 16 in TLR1 and 18 in TLR6, preserves the native scaffold offered by LRRs. This exercise thus depicts how the LRR motif has been advantageous to be selected as an evolutionarily conserved motif for essential cellular processes.

bioinformatics↗

bioRxiv2018

Refining interaction search through signed iterative Random Forests

Advances in supervised learning have enabled accurate prediction in biological systems governed by complex interactions among biomolecules. However, state-of-the-art predictive algorithms are typically \"black-boxes,\" learning statistical interactions that are difficult to translate into testable hypotheses. The iterative Random Forest (iRF) algorithm took a step towards bridging this gap by providing a computationally tractable procedure to identify the stable, high-order feature interactions that drive the predictive accuracy of Random Forests (RF). Here we refine the interactions identified by iRF to explicitly map responses as a function of interacting features. Our method, signed iRF (s-iRF), describes \"subsets\" of rules that frequently occur on RF decision paths. We refer to these \"rule subsets\" as signed interactions. Signed interactions share not only the same set of interacting features but also exhibit similar thresholding behavior, and thus describe a consistent functional relationship between interacting features and responses. We describe stable and predictive importance metrics (SPIMs) to rank signed interactions in terms of their stability, predictive accuracy, and strength of interaction. For each SPIM, we define null importance metrics that characterize its expected behavior under known structure. We evaluate our proposed approach in biologically inspired simulations and two case studies: predicting enhancer activity and spatial gene expression patterns. In the case of enhancer activity, s-iRF recovers one of the few experimentally validated high-order interactions and suggests novel enhancer elements where this interaction may be active. In the case of spatial gene expression patterns, s-iRF recovers all 11 reported links in the gap gene network. By refining the process of interaction recovery, our approach has the potential to guide mechanistic inquiry into systems whose scale and complexity is beyond human comprehension.

genomics↗

bioRxiv2018

A multifaceted approach for analyzing complex phenotypic data in rodent models of autism

AutDB features a modular framework that aims at collating multifactorial risk factors associated with autism spectrum disorder (ASD). The animal model (AM) module of AutDB was first developed for mouse models of genes and CNVs associated with ASD (Kumar et al., 2011). Subsequently, environmentally induced rodent models were introduced to capture the full spectrum of risk-factors associated with ASD, along with idiopathic models represented by inbred strains. Using the data systematically annotated in AutDB, we depict the intricate trends in the research findings based on rodent models of ASD. We identify the top 30 most frequently studied phenotypes extracted from 911 genetic, 269 induced and 17 inbred rodent models of ASD extracted from 787 publications. As expected, many of these include animal model equivalents of the core phenotypes associated with ASD, as well as several comorbid features of ASD including anxiety, seizures and motor-control deficits. Uniquely, AutDB curates rescue models where various treatment strategies were used in rodent ASD models to alleviate ASDrelevant phenotypes. We further examine ASD models based on 52 genes and 2 CNV loci to identify 24 pharmaceutical agents that were used in 2 or more paradigms for testing their efficacy. As a case study, we analyze various Shank3 mouse models providing a highresolution view of the in vivo role of this high-confidence ASD gene. Together, this resource provides a snapshot of genetic and induced models of ASD within a shared annotation platform to examine the complex meshing of diverse ASD-associated risk-factors.

bioinformatics↗

bioRxiv2018

Structure of a human intramembrane ceramidase explains enzymatic dysfunction found in leukodystrophy

Alkaline ceramidases (ACERs) are a class of poorly understood transmembrane enzymes controlling the homeostasis of ceramides. They are implicated in human pathophysiology, including progressive leukodystrophy, colon cancer as well as acute myeloid leukemia. We report here the crystal structure of the human ACER type 3 (ACER3). Together with computational studies, the structure reveals that ACER3 is an intramembrane enzyme with a seven transmembrane domain architecture and a catalytic Zn2+ binding site in its core, similar to adiponectin receptors. Interestingly, we uncover a Ca2+ binding site physically and functionally connected to the Zn2+ providing a structural explanation for the known regulatory role of Ca2+ on ACER3 enzymatic activity and for the loss of function in E33G-ACER3 mutant found in leukodystrophic patients.

biochemistry↗

bioRxiv2018

Insights into the evolution of extracellular leucine rich repeats in metazoans with special reference to Toll-like receptor 4

The importance of the widely spread leucine-rich repeat (LRR) motif has been studied considering TLRs, the LRR-containing protein involved in animal immune response. The protein connects intracellular signalling with a chain of molecular interaction through the presence of LRRs in the ectodomain and TIR in the endodomain. Domain analyses with human TLR1-9 reported ectodomain with tandem repeats, transmembrane domain and TIR domain. The repeat number varied across members of TLRs and remains characteristic to a particular member. Analysis of gene structure revealed absence of codon interruption with TLR3 and TLR4 as exceptions. Extensive study with TLR4 from metazoans confirmed the presence of 23 LRRs in tandem. Distinct clade formation using coding and amino acid sequence of individual repeats illustrated independent evolution. Although ectodomain and endodomain exhibited differential selection pressure, however, within the ectodomain, the individual repeats displayed positive, negative and neutral selection pressure depending on their structural and functional significance.

bioinformatics↗

bioRxiv2017

iterative Random Forests to discover predictive and stable high order interactions

Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on Random Forests (RF), Random Intersection Trees (RITs), and through extensive, biologically inspired simulations, we developed the iterative Random Forest algorithm (iRF). iRF trains a feature-weighted ensemble of decision trees to detect stable, high-order interactions with same order of computational cost as RF. We demonstrate the utility of iRF for high-order interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and alternative splicing of primary transcripts in human derived cell lines. In Drosophila, among the 20 pairwise transcription factor interactions iRF identifies as stable (returned in more than half of bootstrap replicates), 80% have been previously reported as physical interactions. Moreover, novel third-order interactions, e.g. between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order relationships that are candidates for follow-up experiments. In human-derived cells, iRF re-discovered a central role of H3K36me3 in chromatin-mediated splicing regulation, and identified novel 5th and 6th order interactions, indicative of multi-valent nucleosomes with specific roles in splicing regulation. By decoupling the order of interactions from the computational cost of identification, iRF opens new avenues of inquiry into the molecular mechanisms underlying genome biology.

genomics↗

bioRxiv2017

Salt-bridge Dynamics in Intrinsically Disordered Proteins: A trade-off between electrostatic interactions and structural flexibility

Intrinsically Disordered Proteins (IDPs) are enriched in charged and polar residues; and, therefore, electrostatic interactions play a predominant role in their dynamics. In order to remain multi-functional and exhibit their characteristic binding promiscuity, they need to retain considerable dynamic flexibility. At the same time, they also need to accommodate a large number of oppositely charged residues, which eventually lead to the formation of salt-bridges, imparting local rigidity. The formation of salt-bridges therefore oppose the desired dynamic flexibility. Hence, there appears to be a meticulous trade-off between the two mechanisms which the current study attempts to unravel. With this objective, we identify and analyze salt-bridges, both as isolated as well as composite ionic bond motifs, in the molecular dynamic trajectories of a set of appropriately chosen IDPs. Time evolved structural properties of these salt-bridges like persistence, associated secondary structural 'order-disorder' transitions, correlated atomic movements, contribution in the overall electrostatic balance of the proteins have been studied in necessary detail. The results suggest that the key to maintain such a trade-off over time is the continuous formation and dissolution of salt-bridges with a wide range of persistence. Also, the continuous dynamic interchange of charged-atom-pairs (coming from a variety of oppositely charged side-chains) in the transient ionic bonds supports a model of dynamic flexibility concomitant with the well characterized stochastic conformational switching in these proteins. The results and conclusions should facilitate the future design of salt-bridges as a mean to further explore the disordered-globular interface in proteins.

bioinformatics↗

bioRxiv2017

Endogenous α-SYN protein analysis on human brain tissues using single-molecule pull-down assay

Alpha-synuclein (-SYN) is a central molecule in Parkinsons disease pathogenesis. Despite several studies, the molecular nature of endogenous -SYN especially in human brain samples is still not well understood due to the lack of reliable methods and the limited amount of bio-specimens. Here, we introduce -SYN single-molecule pull-down (-SYN SiMPull) assay combined with in vivo protein crosslinking to count individual -SYN protein and assess its native oligomerization states from biological samples including human postmortem brains. A SiMPull assay enables us to count the number of immuno-precipitated proteins, and reveal the stoichiometry of protein complexes by single-molecule fluorescence imaging. This powerful assay can be highly useful in diagnostic applications using various specimens for neurodegenerative diseases including Alzheimers disease and Parkinsons disease.

biophysics↗

bioRxiv2017

CPdock: The Complementarity Plot for Docking of Proteins: Implementing Multi-dielectric Continuum Electrostatics

The Complementarity plot (CP) is an established validation tool for protein structures, applicable to both, globular proteins (folding) as well as protein-protein complexes (binding). It computes the shape and electrostatic complementarities (Sm, Em) for amino acid side-chains buried within the protein interior or interface and plots them in a two-dimensional plot having knowledge-based probabilistic quality estimates for the residues as well as for the whole structure. The current report essentially presents an upgraded version of the plot with the implementation of the advanced multi-dielectric functionality (as in Delphi version 6.2 or higher) in the computation of electrostatic complementarity to make the validation tool physico-chemically more realistic. The two methods (single- and multi-dielectric) agrees decently in their resultant Em values and hence, provisions for both methods have been kept in the software suite. So to speak, the global electrostatic balance within a well-folded protein and / or a well-packed interface seems only marginally perturbed by the choice of different internal dielectric values. However, both from theoretical as well as practical grounds, the more advanced multi-dielectric version of the plot is certainly recommended for potentially producing more reliable results. The report also presents a new methodology and a variant plot, namely, CPdock, based on the same principles of complementarity, specifically designed to be used in the docking of proteins. The efficacy of the method to discriminate between good and bad docked protein complexes have been tested on a recent state-of-the-art docking benchmark. The results unambiguously indicate that CPdock can indeed be effective in the initial screening phase of a docking scoring pipeline before going into more sophisticated and computationally expensive scoring functions. CPdock has been made available at https://github.com/nemo8130/CPdock

bioinformatics↗

bioRxiv2017

A Zebrafish Model For Ocular Tuberculosis

Ocular tuberculosis (TB) commonly causes severe inflammation and vision loss in TB-endemic countries. The mechanism by which tuberculous infection becomes established in the eye is poorly understood. We used Mycobacterium marinum-infected zebrafish larvae to study the early pathogenesis of ocular TB and found hematogenous bacterial seeding of the eye despite a functional blood retinal barrier. Prototypical early granulomas formed that involved the retinal vasculature and retinal pigment epithelium-choroid complex; characteristic locations for human ocular TB. Peripheral blood monocytes were recruited to the growing granuloma suggesting that the immune privileged nature of the eye is breached by this inflammatory focus.\n\nConflict of interestnone disclosed\n\nFundingThis work was supported in part by a Short-term fellowship to SB by Department of Health Research, Government of India.

microbiology↗

bioRxiv2017

Nitric oxide sensing by chlorophyll a

Nitric oxide (NO) acts as a signalling molecule that has direct and indirect regulatory roles in various functional processes in biology, though in plant kingdom its role is relatively unexplored. One reason for this is the fact that sensing of NO is always challenging. There are very few probes that can classify the different NO species. The present paper proposes a simple but straightforward way for sensing different NO species using chlorophyll, the source of inspiration being hemoglobin that serves as a NO sink in most mamalian system. The proposed method is able to classify NO from DETA-NONOate or (Z)-1-[N-(2-aminoethyl)-N-(2-ammonioethyl) amino] diazen-1-ium-1,2-diolate, nitrite, nitrate and S-nitrosothiol or SNO. This discrimination is carried out by chlorophyll a (chl a) at nano molar (nM) order of sensitivity and at 293K to 310K. Molecular docking reveals the differential binding behaviour of NO and SNO with chlorophyll, the predicted binding affinity matching with the experimental observation. Additional expreiments with diverse range of cyanobacteria reveals that apart from spectroscopic approach the proposed sensing module can be used in microscopic inspection of NO speices. Binding of NO is sensitive to tempertaure and static magnetic field. This provides additional support to the involvement of the porphyrin ring structure to the NO sensing process. This also broadens the scope of the sensing methods as hinted in the text.

bioengineering↗

bioRxiv2016

Proteus: A Random Forest Classifier to Predict disorder-to-order Transitioning Binding Regions in Intrinsically Disordered Proteins.

The focus of the computational structural biology community has taken a dramatic shift over the past one-and-a-half decades from the classical protein structure prediction problem to the possible understanding of intrinsically disordered proteins (IDP) or proteins containing regions of disorder (IDPR). The current interest lies in the unraveling of a disorder-to-order transitioning code embedded in the amino acid sequences of IDPs / IDPRs. Disordered proteins are characterized by an enormous amount of structural plasticity which makes them promiscuous in binding to different partners, multi-functional in cellular activity and atypical in folding energy landscapes resembling partially folded molten globules. Also, their involvement in several deadly human diseases (e.g. cancer, cardiovascular and neurodegenerative diseases) makes them attractive drug targets, and important for a biochemical understanding of the disease(s). The study of the structural ensemble of IDPs is rather difficult, in particular for transient interactions. When bound to a structured partner, an IDPR adapts an ordered conformation in the complex. The residues that undergo this disorder-to-order transition are called protean residues, generally found in short contiguous stretches and the first step in understanding the modus operandi of an IDP / IDPR would be to predict these residues. There are a few available methods which predict these protean segments from their amino acid sequences; however, their performance reported in the literature leaves clear room for improvement. With this background, the current study presents 'Proteus', a random forest classifier that predicts the likelihood of a residue undergoing a disorder-to-order transition upon binding to a potential partner protein. The prediction is based on features that can be calculated using the amino acid sequence alone. Proteus compares favorably with existing methods predicting twice as many true positives as the second best method (55% vs. 27%) with a much higher precision on an independent data set. The current study also sheds some light on a possible 'disorder-to-order' transitioning consensus, untangled, yet embedded in the amino acid sequence of IDPs. Some guidelines have also been suggested for proceeding with a real-life structural modeling involving an IDPR using Proteus.\n\nSoftware Availabilityhttps://github.com/bjornwallner/proteus

bioinformatics↗