Unsupervised integration of multiple protein disorder predictors

BackgroundIntrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task.MethodsIn this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others).ResultsTo evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors.ConclusionsUsing HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. The resource is based around diagnostic models (profile hidden Markov models, profiles, position-specific scoring matrices and regular expressions) provided by 14 different member databases, against which protein sequences are searched to determine their potential functions. Two recently added member databases, CDD and SLFD, also provide specific residue-level annotation. Their addition has enabled identification of important amino acids, such as those responsible for ligand-binding, protein-protein interactions, or comprising active sites, providing an additional tier to the annotations already provided by InterPro. In a further expansion, InterPro has also added prediction of intrinsically disordered regions (IDRs) – polypeptide segments that have little or no three-dimensional structure. IDRs have a wide range of potential functions, from acting as flexible linkers between domains, to interacting with other proteins and modifying their activity. Due to their bias composition and differing pattern of amino acid conservation, IDRs are hard to model using profile approaches, hence annotation of these regions has been limited to date. To address this, InterPro has integrated the MobiDB Lite tool, which combines eight different prediction methods to generate consensus IDR annotations, focusing on long disordered regions (>20 amino acids). This approach has yielded IDR annotations for ~ 24% of sequences in UniProtKB, covering ~ 5.5% of amino acid residues. Here we describe how the new per-residue and IDR developments in InterPro enable more richly-detailed and informative annotation of protein sequences, and how they can be used to support more specific functional inferences.

Meeting review: the Second Meeting on the Critical Assessment of Techniques for Protein Structure Prediction (CASP2), Asilomar, California, December 13–16, 1996

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTPathological Unfoldomics of Uncontrolled Chaos: Intrinsically Disordered Proteins and Human DiseasesVladimir N. Uversky*†‡§, Vrushank Davé∥⊥, Lilia M. Iakoucheva#, Prerna Malaney∥, Steven J. Metallo▽, Ravi Ramesh Pathak∥, and Andreas C. Joerger*▲View Author Information∥ †Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute and ∥Department of Pathology and Cell Biology, Morsani College of Medicine, University of South Florida, Tampa, Florida 33612, United States‡ Institute for Biological Instrumentation, Russian Academy of Sciences, 142290 Pushchino, Moscow Region, Russia§ Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah 22254, Saudi Arabia⊥ Department of Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, United States# Department of Psychiatry, University of California San Diego, La Jolla, California 92093, United States▽ Department of Chemistry, Georgetown University, Washington, District of Columbia 20057, United States▲ Medical Research Council Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom*Phone 1-813-974-5816; fax 1-813-974-7357; e-mail [email protected]*Phone +44-1223-267290; e-mail [email protected]Cite this: Chem. Rev. 2014, 114, 13, 6844–6879Publication Date (Web):May 15, 2014Publication History Received15 December 2013Published online15 May 2014Published inissue 9 July 2014https://pubs.acs.org/doi/10.1021/cr400713rhttps://doi.org/10.1021/cr400713rreview-articleACS PublicationsCopyright © 2014 American Chemical SocietyRequest reuse permissionsArticle Views3882Altmetric-Citations215LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Cancer,Diseases and disorders,Genetics,Peptides and proteins,Post-translational modification Get e-Alerts

Protein sequence information is believed to embed the hint of their structures. To uncover the nature between protein sequence and their structures, this study motivates to inspect the dynamic interactions between various protein sequence features, and identify the sequential differences between the different protein structures. Protein sequence data from all structural classes in CATH and SCOP, and the structural disordered proteins from DisProt, as well as the structural motifs in PROSITE, are analyzed in this study. Betweenness and closeness centrality measures are employed to capture the topology of the networks constructed from amino acid feature interactions, while statistical tests are further implemented to compare the feature series distributions. Key findings suggest that in all structural classes, the features for Ala and α-helix and bend preference property, Ala and side-chain size, Ala and Gly, as well as Met and Leu attain significant interactions between each other, and the feature for Leu, Val, and Asn are acted as the critical sources of feature interactions, whereas Cys, His, Trp, and Met exhibit weak intra-type interactions with other features. These implicate that these feature interactions may have little impact in coding the structural differences. For the α structures, Glu, Pro and side-chain size, hydrophobicity properties exhibit high importance in feature interactions, whereas Gly, Thr and physical properties such as α-helix and bend preference, extended structural preference, pK-C value and surrounding hydrophobicity for β structures, show special high importance in β structures. Both α and β types of structures show Ser as the common sources of feature interactions, while the mixed α and β structures not only show common characters with the α and β types of structures, but also preferred interactions between Met, Lys and double-bend preference property, and between the sequence arrangements of Cys, His, Met, Tyr and amino acid composition features. The intrinsically disordered proteins (IDPs) present high frequency for the repetition patterns of certain amino acids, while the different structural motifs also show special characters. More sequential differences between the structures can also be identified from K-mers statistics and feature series distributions. The new discoveries reveal the nature of amino acid feature interaction mechanics, and show great importance of these interactions in coding the different types of protein structures. The results can not only contribute to future molecular design for protein-based vaccine or drug, but also enlighten the development for new protein structural classifiers.

Biopolymers are versatile molecules that perform myriad cellular functions such as gene regulation and high-energy molecule synthesis. Alterations in biomolecular structure such as those arising from mutations can disrupt important cellular processes and lead to disease. Therefore, accurate biopolymer structure characterization plays an outsized role in improving human health. X-ray crystallography, cryogenic electron microscopy (Cryo-EM), and nuclear magnetic resonance (NMR) spectroscopy are the most powerful techniques for obtaining high-resolution, 3D structures.Valuable structural insight is also obtained using optical rotation, small angle X-ray scattering (SAXS), circular dichroism (CD) spectroscopy, and Forster resonance energy transfer (FRET) spectroscopy. Over the past few years, remarkable advances in computational power and algorithm development have revolutionized the field of biopolymer structure prediction. For example, leveraging machine learning (ML) and artificial intelligence (AI), AlphaFold and Open fold have demonstrated a remarkable ability to predict biopolymer structure from sequence. Although powerful, these techniques are limited in their ability to provide information about challenging systems where the structure is either unknown or highly flexible under physiological conditions. In this work, studies are presented in which structural information is gleaned from challenging biopolymer systems using novel computational and experimental methods. In Chapter 2, a new structure characterization methodology based on hydrogen-deuterium exchange-mass spectrometry (HDX-MS) in combination with molecular dynamics (MD) simulations is introduced. The approach is used to describe the structural motifs of the unstructured peptide, Nt17, a conserved sequence in Huntingtin protein and associated with Huntington’s Disease progression. The model is used to show that, although highly flexible, Nt17 is intrinsically primed for facile conversion to α-helical conformation upon binding with molecular partners. Having shown that HDX reactivity is predictive of the degree of structural flexibility and bias (propensity to form 2° structural elements such as α-helix, β-sheet, and β-turn) for intrinsically disordered regions (IDRs), the work in Chapter 3 explores an extension of structure characterization to protein-ligand systems. In this study human FK506 binding protein (FKBP12) structure is examined in the absence and presence of two, well-recognized ligand molecules (i.e., FK506 and rapamycin). The work provides a better understanding of binding/unbinding events, competitive binding, and folded/unfolded conformer heterogeneity. Native MS and HDX-MS were utilized in conjunction with MD simulations to understand this select intrinsically disordered region (IDR) and IDR-drug conformational landscape. Chapter 4 explores the functional property prediction of Proteorhodopsin, by integrating experimental studies with MD simulations validation. Here, a novel linear regression model accurately predicts the spectral

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs—and intrinsically disordered regions (IDRs) interspersed between folded domains—are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.

Bioinformatic Analysis of the Role of Intrinsic Disorder in Multiple Specificity

An estimator for the Ricean $K$ factor is designed by approximating the maximum a posterior (MAP) probability. The performance of the approximate MAP estimator is examined. Numerical results show that the approximate MAP estimator outperforms the approximate maximum likelihood estimator. It also outperforms the best moment-based estimator at small sample sizes.

Machine learning techniques have gained much interest in reservoir characterization. However, the model prediction often lacks interpretability and reliability due to the 'black-box' nature of, for example, neural networks. In this paper, we present a method to identify core lithofacies and measure the uncertainty by U-net based Bayesian Neural Networks. This approach employs variational dropout and maximum a posterior (MAP) estimate, where we can quantify the uncertainties from both the data and model. We applied the method to predict the lithofacies of cores from the Wilcox Group, Gulf of Mexico. A prediction accuracy of 85% was obtained, and the associated uncertainties were evaluated. Although our current database is not complex and large enough to mimic the realworld scenario, it demonstrates the importance of understanding the uncertainties from data and model. Such uncertainties improve the data predictions and enhance the model reliability. They can be employed to quality control the training data and to improve the performance of the trained model.

The expectation-maximization (EM) algorithm can compute the maximum-likelihood (ML) or maximum a posterior (MAP) point estimate of the mixture models or latent variable models such as latent Dirichlet allocation (LDA), which has been one of the most popular probabilistic topic modeling methods in the past decade. However, batch EM has high time and space complexities to learn big LDA models from big data streams. In this paper, we present a fast online EM (FOEM) algorithm that infers the topic distribution from the previously unseen documents incrementally with constant memory requirements. Within the stochastic approximation framework, we show that FOEM can converge to the local stationary point of the LDA's likelihood function. By dynamic scheduling for the fast speed and parameter streaming for the low memory usage, FOEM is more efficient for some lifelong topic modeling tasks than the state-of-the-art online LDA algorithms to handle both big data and big models (aka, big topic modeling) on just a PC.

AlphaFold 2 (AF2) has placed Molecular Biology in a new era where we can visualize, analyze and interpret the structures and functions of all proteins solely from their primary sequences. We performed AF2 structure predictions for various protein systems, including globular proteins, a multi-domain protein, an intrinsically disordered protein (IDP), a randomized protein, two larger proteins (> 1000 AA), a heterodimer and a homodimer protein complex. Our results show that along with the three dimensional (3D) structures, AF2 also decodes protein sequences into residue flexibilities via both the predicted local distance difference test (pLDDT) scores of the models, and the predicted aligned error (PAE) maps. We show that PAE maps from AF2 are correlated with the distance variation (DV) matrices from molecular dynamics (MD) simulations, which reveals that the PAE maps can predict the dynamical nature of protein residues. Here, we introduce the AF2-scores, which are simply derived from pLDDT scores and are in the range of [0, 1]. We found that for most protein models, including large proteins and protein complexes, the AF2-scores are highly correlated with the root mean square fluctuations (RMSF) calculated from MD simulations. However, for an IDP and a randomized protein, the AF2-scores do not correlate with the RMSF from MD, especially for the IDP. Our results indicate that the protein structures predicted by AF2 also convey information of the residue flexibility, i.e., protein dynamics.

Intrinsically disordered regions (IDRs) play a critical role in phase separation and are essential for the formation of membraneless organelles (MLOs). Mutations within IDRs can disrupt their multivalent interaction networks, altering phase behavior and contributing to various diseases. Therefore, examining the evolutionary constraints of IDRs provides valuable insights into the relationship between protein sequences and phase separation. In this study, we utilized the ESM2 protein language model to map the residue-level mutational tolerance landscapes landscape of IDRs. Our findings reveal that IDRs, particularly those actively participating in phase separation, contain conserved amino acids. This conservation is evident through mutational constraints predicted by ESM2 and supported by direct analyses of multiple sequence alignments. These conserved, disordered amino acids include residues traditionally identified as “stickers” as well as “spacers” and frequently form continuous sequence motifs. The strong conservation, combined with their potential role in phase separation, suggests that these motifs may act as functional units under evolutionary selection to support stable MLO formation. Our findings underscore the insights into phase separation’s molecular grammar made possible through evolutionary analysis enabled by protein language models.

Intrinsically disordered regions (IDRs) in protein sequences are emerging as functionally important elements for interaction and regulation. While being generally flexible, we previously showed, by observation of experimentally obtained structures, that they contain regions of reduced sequence complexity that have an increased propensity to form structure. Here we expand the universe of cases taking advantage of structural predictions by AlphaFold. Our studies focus on low complexity regions (LCRs) found within IDRs, where these LCRs have only one or two residue types (polyX and polyXY, respectively). In addition to confirming previous observations that polyE and polyEK have a tendency towards helical structure, we find a similar tendency for other LCRs such as polyQ and polyER, most of them including charged residues. We analyzed the position of polyXY containing IDRs within proteins, which allowed us to show that polyAG and polyAK accumulate at the N-terminal, with the latter showing increased helical propensity at that location. Functional enrichment analysis of polyXY with helical propensity indicated functions requiring interaction with RNA and DNA. Our work adds evidence of the function of LCRs in interaction-dependent structuring of disordered regions, encouraging the development of tools for the prediction of their dynamic structural properties.

Surfactants, block-copolymers, and other types of micellar systems are used in a wide variety of biomedical and industrial processes. However, most commonly used surfactants are synthetically derived and pose environmental and toxicological concerns throughout their product life cycle. Because of this, bio-derived and bio-degradable surfactants are promising alternatives. For bio-surfactants to be implemented industrially, they need to be produced on a large scale and also have tailorable properties that match those afforded by the polymerization of synthetic surfactants. In this paper, a scalable and versatile production method for bio-surfactants based on a hydrophilic intrinsically disordered protein (IDP) sequence with a genetically engineered hydrophobic domain is used to study variables that impact their physicochemical and self-assembling properties. These amphiphilic sequences were found to self-assemble into micelles over a broad range of temperatures, pH values, and ionic strengths. To investigate the role of the IDP hydrophilic domain on self-assembly, variants with increased overall charges and systematically decreased IDP domain lengths were produced and examined for their sizes, morphologies, and critical micelle concentrations (CMCs). The results of these studies indicate that decreasing the length of the IDP domain and, consequently, the molecular weight and hydrophilic fraction, leads to smaller micelles. Additionally, significantly increasing the amount of charged residues in the hydrophilic IDP domain results in micelles of similar sizes, but with higher CMC values. This represents an initial step in developing a quantitative model for the future engineering of bio-surfactants based on this IDP sequence.