Prediction Of Intrinsic Disorder Research Articles

Local disorder of multi-site of Eu dopant and its effect on spectroscopy properties in NaCaPO4 host

Optical spectroscopy and defect disorder of multi-site of Eu dopant in NaCaPO4 structure was studied by computational and experimental methods. The prediction of intrinsic disorder and the symmetry and detailed geometry of multi-site of Eu dopant was made by atomistic modeling. The crystal field parameters are then calculated and used to obtain the 7F1 sublevels of the Eu3+ ion, which we used to discuss the local symmetry of the multi-site of Eu3+. The calculations suggest that the most energetically feasible configurations involve the substitution of Eu3+ and Eu2+ ions at the Ca2 site. For Eu3+ substitution, charge charge compensation by NaCa2′ defect is the most provable. The solution energies for the other configuration, for the two Eu3+ dopant ions, are very similar. This means that the Eu3+ dopant can be found in all Ca sites in the NaCaPO4. The theoretical spectroscopic study showed that the symmetry of the Eu3+ ions, based on the amount of non-null crystal field parameters, is a C4 symmetry for all multi-site of Eu3+. A set of phenomenological charge factors and the 7F1 manifold split were satisfactorily reproduced in agreement with the experimental data.

Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses

Intrinsic disorder predictors were evaluated in several studies including the two large CAID experiments. However, these studies are biased towards eukaryotic proteins and focus primarily on the residue-level predictions. We provide first-of-its-kind assessment that comprehensively covers the taxonomy and evaluates predictions at the residue and disordered region levels. We curate a benchmark dataset that uniformly covers eukaryotic, archaeal, bacterial, and viral proteins. We find that predictive performance differs substantially across taxonomy, where viruses are predicted most accurately, followed by protists and higher eukaryotes, while bacterial and archaeal proteins suffer lower levels of accuracy. These trends are consistent across predictors. We also find that current tools, except for flDPnn, struggle with reproducing native distributions of the numbers and sizes of the disordered regions. Moreover, analysis of two variants of disorder predictions derived from the AlphaFold2 predicted structures reveals that they produce accurate residue-level propensities for archaea, bacteria and protists. However, they underperform for higher eukaryotes and generally struggle to accurately identify disordered regions. Our results motivate development of new predictors that target bacteria and archaea and which produce accurate results at both residue and region levels. We also stress the need to include the region-level assessments in future assessments.

Investigating the Interactions of the Cucumber Mosaic Virus 2b Protein with the Viral 1a Replicase Component and the Cellular RNA Silencing Factor Argonaute 1.

The cucumber mosaic virus (CMV) 2b protein is a suppressor of plant defenses and a pathogenicity determinant. Amongst the 2b protein's host targets is the RNA silencing factor Argonaute 1 (AGO1), which it binds to and inhibits. In Arabidopsis thaliana, if 2b-induced inhibition of AGO1 is too efficient, it induces reinforcement of antiviral silencing by AGO2 and triggers increased resistance against aphids, CMV's insect vectors. These effects would be deleterious to CMV replication and transmission, respectively, but are moderated by the CMV 1a protein, which sequesters sufficient 2b protein molecules into P-bodies to prevent excessive inhibition of AGO1. Mutant 2b protein variants were generated, and red and green fluorescent protein fusions were used to investigate subcellular colocalization with AGO1 and the 1a protein. The effects of mutations on complex formation with the 1a protein and AGO1 were investigated using bimolecular fluorescence complementation and co-immunoprecipitation assays. Although we found that residues 56-60 influenced the 2b protein's interactions with the 1a protein and AGO1, it appears unlikely that any single residue or sequence domain is solely responsible. In silico predictions of intrinsic disorder within the 2b protein secondary structure were supported by circular dichroism (CD) but not by nuclear magnetic resonance (NMR) spectroscopy. Intrinsic disorder provides a plausible model to explain the 2b protein's ability to interact with AGO1, the 1a protein, and other factors. However, the reasons for the conflicting conclusions provided by CD and NMR must first be resolved.

Assessment of Disordered Linker Predictions in the CAID2 Experiment.

Disordered linkers (DLs) are intrinsically disordered regions that facilitate movement between adjacent functional regions/domains, contributing to many key cellular functions. The recently completed second Critical Assessments of protein Intrinsic Disorder prediction (CAID2) experiment evaluated DL predictions by considering a rather narrow scenario when predicting 40 proteins that are already known to have DLs. We expand this evaluation by using a much larger set of nearly 350 test proteins from CAID2 and by investigating three distinct scenarios: (1) prediction residues in DLs vs. in non-DL regions (typical use of DL predictors); (2) prediction of residues in DLs vs. other disordered residues (to evaluate whether predictors can differentiate residues in DLs from other types of intrinsically disordered residues); and (3) prediction of proteins harboring DLs. We find that several methods provide relatively accurate predictions of DLs in the first scenario. However, only one method, APOD, accurately identifies DLs among other types of disordered residues (scenario 2) and predicts proteins harboring DLs (scenario 3). We also find that APOD's predictive performance is modest, motivating further research into the development of new and more accurate DL predictors. We note that these efforts will benefit from a growing amount of training data and the availability of sophisticated deep network models and emphasize that future methods should provide accurate results across the three scenarios.

DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model

Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.

IDPpred: a new sequence-based predictor for identification of intrinsically disordered protein with enhanced accuracy

Discovery of intrinsically disordered proteins (IDPs) and protein hybrids that contain both intrinsically disordered protein regions (IDPRs) along with ordered regions has changed the sequence–structure–function paradigm of protein. These proteins with lack of persistently fixed structure are often found in all organisms and play vital roles in various biological processes. Some of them are considered as potential drug targets due to their overrepresentation in pathophysiological processes. The major bottlenecks for characterizing such proteins are their occasional overexpression, difficulty in getting purified homogeneous form and the challenge of investigating them experimentally. Sequence-based prediction of intrinsic disorder remains a useful strategy especially for many large-scale proteomic investigations. However, worst accuracy still occurs for short disordered regions with less than ten residues, for the residues close to order–disorder boundaries, for regions that undergo coupled folding and binding in presence of partner, and for prediction of fully disordered proteins. Annotation of fully disordered proteins mostly relies on the far-UV circular dichroism experiment which gives overall secondary structure composition without residue-level resolution. Current methods including that using secondary structure information failed to predict half of target IDPs correctly in the recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment. This study utilized profiles of random sequential appearance of physicochemical properties of amino acids and random sequential appearance of order and disorder promoting amino acids in protein together with the existing CIDER feature for the prediction of IDP from sequence input. Our method was found to significantly outperform the existing predictors across different datasets.Communicated by Ramaswamy H. Sarma

IDP-LM: Prediction of protein intrinsic disorder and disorder functions based on language models.

Intrinsically disordered proteins (IDPs) and regions (IDRs) are a class of functionally important proteins and regions that lack stable three-dimensional structures under the native physiologic conditions. They participate in critical biological processes and thus are associated with the pathogenesis of many severe human diseases. Identifying the IDPs/IDRs and their functions will be helpful for a comprehensive understanding of protein structures and functions, and inform studies of rational drug design. Over the past decades, the exponential growth in the number of proteins with sequence information has deepened the gap between uncharacterized and annotated disordered sequences. Protein language models have recently demonstrated their powerful abilities to capture complex structural and functional information from the enormous quantity of unlabelled protein sequences, providing opportunities to apply protein language models to uncover the intrinsic disorders and their biological properties from the amino acid sequences. In this study, we proposed a computational predictor called IDP-LM for predicting intrinsic disorder and disorder functions by leveraging the pre-trained protein language models. IDP-LM takes the embeddings extracted from three pre-trained protein language models as the exclusive inputs, including ProtBERT, ProtT5 and a disorder specific language model (IDP-BERT). The ablation analysis shown that the IDP-BERT provided fine-grained feature representations of disorder, and the combination of three language models is the key to the performance improvement of IDP-LM. The evaluation results on independent test datasets demonstrated that the IDP-LM provided high-quality prediction results for intrinsic disorder and four common disordered functions.

OpenProt 2.0 builds a path to the functional characterization of alternative proteins.

The OpenProt proteogenomic resource (https://www.openprot.org/) provides users with a complete and freely accessible set of non-canonical or alternative open reading frames (AltORFs) within the transcriptome of various species, as well as functional annotations of the corresponding protein sequences not found in standard databases. Enhancements in this update are largely the result of user feedback and include the prediction of structure, subcellular localization, and intrinsic disorder, using cutting-edge algorithms based on machine learning techniques. The mass spectrometry pipeline now integrates a machine learning-based peptide rescoring method to improve peptide identification. We continue to help users explore this cryptic proteome by providing OpenCustomDB, a tool that enables users to build their own customized protein databases, and OpenVar, a genomic annotator including genetic variants within AltORFs and protein sequences. A new interface improves the visualization of all functional annotations, including a spectral viewer and the prediction of multicoding genes. All data on OpenProt are freely available and downloadable. Overall, OpenProt continues to establish itself as an important resource for the exploration and study of new proteins.

Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning.

Intrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention in the past few decades. Predicting IDPs from the primary structures of proteins offers a rapid and facile means of protein analysis without necessitating crystal structures. In particular, machine learning methods have demonstrated their potential in this field. Recently, protein language models (PLMs) are emerging as a promising approach to extracting essential information from protein sequences and have been employed in protein modeling to utilize their advantages of precision and efficiency. In this article, we developed a novel IDP prediction method named IDP-ELM to predict the intrinsically disordered regions (IDRs) as well as their functions including disordered flexible linkers and disordered protein binding. This method utilizes high-dimensional representations extracted from several state-of-the-art PLMs and predicts IDRs by ensemble learning based on bidirectional recurrent neural networks. The performance of the method was evaluated on two independent test data sets from CAID (critical assessment of protein intrinsic disorder prediction) and CAID2, indicating notable improvements in terms of area under the receiver operating characteristic (AUC), Matthew's correlation coefficient (MCC), and F1 score. Moreover, IDP-ELM requires solely protein sequences as inputs and does not entail a time-consuming process of protein profile generation, which is a prerequisite for most existing state-of-the-art methods, enabling an accurate, fast, and convenient tool for proteome-level analysis. The corresponding reproducible source code and model weights are available at https://github.com/xu-shi-jie/idp-elm.

Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins.

Intrinsic disorder is instrumental for a wide range of protein functions, and its analysis, using computational predictions from primary structures, complements secondary and tertiary structure-based approaches. In this Tutorial, we provide an overview and comparison of 23 publicly available computational tools with complementary parameters useful for intrinsic disorder prediction, partly relying on results from the Critical Assessment of protein Intrinsic Disorder prediction experiment. We consider factors such as accuracy, runtime, availability and the need for functional insights. The selected tools are available as web servers and downloadable programs, offer state-of-the-art predictions and can be used in a high-throughput manner. We provide examples and instructions for the selected tools to illustrate practical aspects related to the submission, collection and interpretation of predictions, as well as the timing and their limitations. We highlight two predictors for intrinsically disordered proteins, flDPnn as accurate and fast and IUPred as very fast and moderately accurate, while suggesting ANCHOR2 and MoRFchibi as two of the best-performing predictors for intrinsically disordered region binding. We link these tools to additional resources, including databases of predictions and web servers that integrate multiple predictive methods. Altogether, this Tutorial provides a hands-on guide to comparatively evaluating multiple predictors, submitting and collecting their own predictions, and reading and interpreting results. It is suitable for experimentalists and computational biologists interested in accurately and conveniently identifying intrinsic disorder, facilitating the functional characterization of the rapidly growing collections of protein sequences.

Critical assessment of protein intrinsic disorder prediction (CAID) - Results of round 2.

Protein intrinsic disorder (ID) is a complex and context-dependent phenomenon that covers a continuum between fully disordered states and folded states with long dynamic regions. The lack of a ground truth that fits all ID flavors and the potential for order-to-disorder transitions depending on specific conditions makes ID prediction challenging. The CAID2 challenge aimed to evaluate the performance of different prediction methods across different benchmarks, leveraging the annotation provided by the DisProt database, which stores the coordinates of ID regions when there is experimental evidence in the literature. The CAID2 challenge demonstrated varying performance of different prediction methods across different benchmarks, highlighting the need for continued development of more versatile and efficient prediction software. Depending on the application, researchers may need to balance performance with execution time when selecting a predictor. Methods based on AlphaFold2 seem to be good ID predictors but they are better at detecting absence of order rather than ID regions as defined in DisProt. The CAID2 predictors can be freely used through the CAID Prediction Portal, and CAID has been integrated into OpenEBench, which will become the official platform for running future CAID challenges.

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins.

There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.

Proteome-Wide Analysis of Protein Disorder in Durum Wheat

Introduction: Intrinsically Disordered Proteins (IDPs) are natively unstructured proteins. Interestingly, IDPs are ubiquitous and play key roles in cellular and proteins functions. While IDPs are studied in some proteomes, many remain to be uncovered Methods: The data were retrieved from MobiDB database version 4. Intrinsic disorder predictions are made with various prediction tools. We focus on IUpred-L predictions. Results: Here, we have explored the first large-scale study of IDPs in T. turgidum. Additionally, a comparative analysis of T. turgidum and T. aestivum IDPs was performed for highlighting the dis-order use in each species. The data indicated that the T.turgidum proteome is significantly more dis-ordered than the T. aestivum proteome. Gene ontology analysis revealed that IDPs in T. turgidum are mainly catalytic and binding proteins involved in regulation of cellular and metabolic processes. Conclusion: These findings may constitute a starting point for deeper understanding of IDP roles in stress tolerance and the mechanisms underlying the adaptation capacities differences between T. turgidium and related species.

Fast and accurate protein intrinsic disorder prediction by using a pretrained language model.

Determining intrinsically disordered regions of proteins is essential for elucidating protein biological functions and the mechanisms of their associated diseases. As the gap between the number of experimentally determined protein structures and the number of protein sequences continues to grow exponentially, there is a need for developing an accurate and computationally efficient disorder predictor. However, current single-sequence-based methods are of low accuracy, while evolutionary profile-based methods are computationally intensive. Here, we proposed a fast and accurate protein disorder predictor LMDisorder that employed embedding generated by unsupervised pretrained language models as features. We showed that LMDisorder performs best in all single-sequence-based methods and is comparable or better than another language-model-based technique in four independent test sets, respectively. Furthermore, LMDisorder showed equivalent or even better performance than the state-of-the-art profile-based technique SPOT-Disorder2. In addition, the high computation efficiency of LMDisorder enabled proteome-scale analysis of human, showing that proteins with high predicted disorder content were associated with specific biological functions. The datasets, the source codes, and the trained model are available at https://github.com/biomed-AI/LMDisorder.

ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers.

Intrinsically disordered proteins (IDPs) are important for a broad range of biological functions and are involved in many diseases. An understanding of intrinsic disorder is key to develop compounds that target IDPs. Experimental characterization of IDPs is hindered by the very fact that they are highly dynamic. Computational methods that predict disorder from the amino acid sequence have been proposed. Here, we present ADOPT (Attention DisOrder PredicTor), a new predictor of protein disorder. ADOPT is composed of a self-supervised encoder and a supervised disorder predictor. The former is based on a deep bidirectional transformer, which extracts dense residue-level representations from Facebook's Evolutionary Scale Modeling library. The latter uses a database of nuclear magnetic resonance chemical shifts, constructed to ensure balanced amounts of disordered and ordered residues, as a training and a test dataset for protein disorder. ADOPT predicts whether a protein or a specific region is disordered with better performance than the best existing predictors and faster than most other proposed methods (a few seconds per sequence). We identify the features that are relevant for the prediction performance and show that good performance can already be gained with <100 features. ADOPT is available as a stand-alone package at https://github.com/PeptoneLtd/ADOPT and as a web server at https://adopt.peptone.io/.

Rapid prediction and analysis of protein intrinsic disorder.

Protein intrinsic disorder is found in all kingdoms of life and is known to underpin numerous physiological and pathological processes. Computational methods play an important role in characterizing and identifying intrinsically disordered proteins and protein regions. Herein, we present a new high-efficiency web-based disorder predictor named Rapid Intrinsic Disorder Analysis Online (RIDAO) that is designed to facilitate the application of protein intrinsic disorder analysis in genome-scale structural bioinformatics and comparative genomics/proteomics. To demonstrate the potential applications, we construct a test set containing more than one million sequences from one hundred organisms comprising over 420 million residues. Using this test set, we compare RIDAO to five well-known and popular disorder predictors, namely: AUCpreD, IUPred3, metapredict V2, flDPnn, and SPOT-Disorder2. We show that RIDAO yields per-residue predictions at a rate two to six orders of magnitude greater than the other predictors and completely processes the test set in under an hour. RIDAO can be accessed free of charge at https://ridao.app.

Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins

We expand studies of AlphaFold2 (AF2) in the context of intrinsic disorder prediction by comparing it against a broad selection of 20 accurate, popular and recently released disorder predictors. We use 25% larger benchmark dataset with 646 proteins and cover protein-level predictions of disorder content and fully disordered proteins. AF2-based disorder predictions secure a relatively high Area Under receiver operating characteristic Curve (AUC) of 0.77 and are statistically outperformed by several modern disorder predictors that secure AUCs around 0.8 with median runtime of about 20 s compared to 1200 s for AF2. Moreover, AF2 provides modestly accurate predictions of fully disordered proteins (F1 = 0.59 vs. 0.91 for the best disorder predictor) and disorder content (mean absolute error of 0.21 vs. 0.15). AF2 also generates statistically more accurate disorder predictions for about 20% of proteins that have relatively short sequences and a few disordered regions that tend to be located at the sequence termini, and which are absent of disordered protein-binding regions. Interestingly, AF2 and the most accurate disorder predictors rely on deep neural networks, suggesting that these models are useful for protein structure and disorder predictions.

Rapid prediction and analysis of protein intrinsic disorder.

Protein intrinsic disorder is found in all kingdoms of life and is known to underpin numerous physiological and pathological processes. Computational methods play an important role in characterizing and identifying intrinsically disordered proteins and protein regions. Herein, we present a new high-efficiency web-based disorder predictor named Rapid Intrinsic Disorder Analysis Online (RIDAO) that is designed to facilitate the application of protein intrinsic disorder analysis in genome-scale structural bioinformatics and comparative genomics/proteomics. RIDAO integrates six established disorder predictors into a single, unified platform that reproduces the results of individual predictors with near-perfect fidelity. To demonstrate the potential applications, we construct a test set containing more than one million sequences from one hundred organisms comprising over 420 million residues. Using this test set, we compare the efficiency and accessibility (i.e., ease of use) of RIDAO to five well-known and popular disorder predictors, namely: AUCpreD, IUPred3, metapredict V2, flDPnn, and SPOT-Disorder2. We show that RIDAO yields per-residue predictions at a rate two to six orders of magnitude greater than the other predictors and completely processes the test set in under an hour. RIDAO can be accessed free of charge at https://ridao.app.

Resources for computational prediction of intrinsic disorder in proteins.

With over 40years of research, researchers in the intrinsic disorder prediction field developed over 100 computational predictors. This review offers a holistic perspective of this field by highlighting accurate and popular disorder predictors and introducing a wide range of practical resources that support collection, interpretation and application of disorder predictions. These resources include meta webservers that expedite collection of multiple disorder predictions, large databases of pre-computed disorder predictions that ease collection of predictions particularly for large datasets of proteins, and modern quality assessment tools. The latter methods facilitate identification of accurate predictions in a specific protein sequence, reducing uncertainty associated to the use of the putative disorder. Altogether, we review eleven predictors, four meta webservers, three databases and two quality assessment tools, all of which are conveniently available online. We also offer a perspective on future developments of the disorder prediction and the quality assessment tools. The availability of this comprehensive toolbox of useful resources should stimulate further growth in the application of the disorder predictions across many areas including rational drug design, systems medicine, structural bioinformatics and structural genomics.

Idpr: A package for profiling and analyzing Intrinsically Disordered Proteins in R.

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are proteins or protein-domains that do not have a single native structure, rather, they are a class of flexible peptides that can rapidly adopt multiple conformations. IDPs are quite abundant, and their dynamic characteristics provide unique advantages for various biological processes. The field of "unstructured biology" has emerged, in part, because of numerous computational studies that had identified the unique characteristics of IDPs and IDRs. The package 'idpr', short for Intrinsically Disordered Proteins in R, implements several R functions that match the established characteristics of IDPs to protein sequences of interest. This includes calculations of residue composition, charge-hydropathy relationships, and predictions of intrinsic disorder. Additionally, idpr integrates several amino acid substitution matrices and calculators to supplement IDP-based workflows. Overall, idpr aims to integrate tools for the computational analysis of IDPs within R, facilitating the analysis of these important, yet under-characterized, proteins. The idpr package can be downloaded from Bioconductor (https://bioconductor.org/packages/idpr/).