Prediction of intrinsic disorder and its use in functional proteomics

Mieap forms membrane-less organelles involved in cardiolipin metabolism

Biomolecular condensates (BCs) are formed by proteins with intrinsically disordered regions (IDRs) via liquid-liquid phase separation. Mieap/Spata18, a p53-inducible protein, participates in suppression of colorectal tumors by promoting mitochondrial quality control. However, the regulatory mechanism involved remains unclear. Here, we report that Mieap is an IDR-containing protein that drives formation of BCs involved in cardiolipin metabolism. Mieap BCs specifically phase separate the mitochondrial phospholipid, cardiolipin. Mieap directly binds to cardiolipin in vitro. Lipidomic analysis of cardiolipin suggests that Mieap promotes enzymatic reactions in cardiolipin biosynthesis and remodeling. Accordingly, four cardiolipin biosynthetic enzymes, TAMM41, PGS1, PTPMT1, and CRLS1 and two remodeling enzymes, PLA2G6 and TAZ, are phase-separated by Mieap BCs. Mieap-deficient cells exhibit altered crista structure, leading to decreased respiration activity and ATP production in mitochondria. These results suggest that Mieap may form membrane-less organelles to compartmentalize and facilitate cardiolipin metabolism, thus potentially contributing to mitochondrial quality control.

Shell Disorder Models Detect That Omicron Has Harder Shells with Attenuation but Is Not a Descendant of the Wuhan-Hu-1 SARS-CoV-2

Before the SARS-CoV-2 Omicron variant emergence, shell disorder models (SDM) suggested that an attenuated precursor from pangolins may have entered humans in 2017 or earlier. This was based on a shell disorder analysis of SARS-CoV-1/2 and pangolin-Cov-2017. The SDM suggests that Omicron is attenuated with almost identical N (inner shell) disorder as pangolin-CoV-2017 (N-PID (percentage of intrinsic disorder): 44.8% vs. 44.9%-lower than other variants). The outer shell disorder (M-PID) of Omicron is lower than that of other variants and pangolin-CoV-2017 (5.4% vs. 5.9%). COVID-19-related CoVs have the lowest M-PIDs (hardest outer shell) among all CoVs. This is likely to be responsible for the higher contagiousness of SARS-CoV-2 and Omicron, since hard outer shell protects the virion from salivary/mucosal antimicrobial enzymes. Phylogenetic study using M reveals that Omicron branched off from an ancestor of the Wuhan-Hu-1 strain closely related to pangolin-CoVs. M, being evolutionarily conserved in COVID-19, is most ideal for COVID-19 phylogenetic study. Omicron may have been hiding among burrowing animals (e.g., pangolins) that provide optimal evolutionary environments for attenuation and increase shell hardness, which is essential for fecal-oral-respiratory transmission via buried feces. Incoming data support SDM e.g., the presence of fewer infectious particles in the lungs than in the bronchi upon infection.

Predicting Protein Conformational Disorder and Disordered Binding Sites

In the last two decades it has become increasingly evident that a large number of proteins adopt either a fully or a partially disordered conformation. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded by the amino acid sequence, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting protein disorder and identifying intrinsically disordered binding sites.

The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction

The secondary structure prediction (SSP) of proteins has long been an essential structural biology technique with various applications. Despite its vital role in many research and industrial fields, in recent years, as the accuracy of state-of-the-art secondary structure predictors approaches the theoretical upper limit, SSP has been considered no longer challenging or too challenging to make advances. With the belief that the substantial improvement of SSP will move forward many fields depending on it, we conducted this study, which focused on three issues that have not been noticed or thoroughly examined yet but may have affected the reliability of the evaluation of previous SSP algorithms. These issues are all about the sequence homology between or within the developmental and evaluation datasets. We thus designed many different homology layouts of datasets to train and evaluate SSP prediction models. Multiple repeats were performed in each experiment by random sampling. The conclusions obtained with small experimental datasets were verified with large-scale datasets using state-of-the-art SSP algorithms. Very different from the long-established assumption, we discover that the sequence homology between query datasets for training, testing, and independent tests exerts little influence on SSP accuracy. Besides, the sequence homology redundancy between or within most datasets would make the accuracy of an SSP algorithm overestimated, while the redundancy within the reference dataset for extracting predictive features would make the accuracy underestimated. Since the overestimating effects are more significant than the underestimating effect, the accuracy of some SSP methods might have been overestimated. Based on the discoveries, we propose a rigorous procedure for developing SSP algorithms and making reliable evaluations, hoping to bring substantial improvements to future SSP methods and benefit all research and application fields relying on accurate prediction of protein secondary structures.

Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins

With over 60 disorder predictors, users need help navigating the predictor selection task. We review 28 surveys of disorder predictors, showing that only 11 include assessment of predictive performance. We identify and address a few drawbacks of these past surveys. To this end, we release a novel benchmark dataset with reduced similarity to the training sets of the considered predictors. We use this dataset to perform a first-of-its-kind comparative analysis that targets two large functional families of disordered proteins that interact with proteins and with nucleic acids. We show that limiting sequence similarity between the benchmark and the training datasets has a substantial impact on predictive performance. We also demonstrate that predictive quality is sensitive to the use of the well-annotated order and inclusion of the fully structured proteins in the benchmark datasets, both of which should be considered in future assessments. We identify three predictors that provide favorable results using the new benchmark set. While we find that VSL2B offers the most accurate and robust results overall, ESpritz-DisProt and SPOT-Disorder perform particularly well for disordered proteins. Moreover, we find that predictions for the disordered protein-binding proteins suffer low predictive quality compared to generic disordered proteins and the disordered nucleic acids-binding proteins. This can be explained by the high disorder content of the disordered protein-binding proteins, which makes it difficult for the current methods to accurately identify ordered regions in these proteins. This finding motivates the development of a new generation of methods that would target these difficult-to-predict disordered proteins. We also discuss resources that support users in collecting and identifying high-quality disorder predictions.

Prediction of Intrinsic Disorder with Quality Assessment Using QUARTER

Intrinsically disordered regions (IDRs) are estimated to be highly abundant in nature. While only several thousand proteins are annotated with experimentally derived IDRs, computational methods can be used to predict IDRs for the millions of currently uncharacterized protein chains. Several dozen disorder predictors were developed over the last few decades. While some of these methods provide accurate predictions, unavoidably they also make some mistakes. Consequently, one of the challenges facing users of these methods is how to decide which predictions can be trusted and which are likely incorrect. This practical problem can be solved using quality assessment (QA) scores that predict correctness of the underlying (disorder) predictions at a residue level. We motivate and describe a first-of-its-kind toolbox of QA methods, QUARTER (QUality Assessment for pRotein inTrinsic disordEr pRedictions), which provides the scores for a diverse set of ten disorder predictors. QUARTER is available to the end users as a free and convenient webserver at http://biomine.cs.vcu.edu/servers/QUARTER/ . We briefly describe the predictive architecture of QUARTER and provide detailed instructions on how to use the webserver. We also explain how to interpret results produced by QUARTER with the help of a case study.

Modeling aspects of the language of life through transfer-learning protein sequences

BACKGROUND

Predicting protein function and structure from sequence is one important challenge for computational biology. For 26 years, most state-of-the-art approaches combined machine learning and evolutionary information. However, for some applications retrieving related proteins is becoming too time-consuming. Additionally, evolutionary information is less powerful for small families, e.g. for proteins from the Dark Proteome. Both these problems are addressed by the new methodology introduced here.

RESULTS

We introduced a novel way to represent protein sequences as continuous vectors (embeddings) by using the language model ELMo taken from natural language processing. By modeling protein sequences, ELMo effectively captured the biophysical properties of the language of life from unlabeled big data (UniRef50). We refer to these new embeddings as SeqVec (Sequence-to-Vector) and demonstrate their effectiveness by training simple neural networks for two different tasks. At the per-residue level, secondary structure (Q3 = 79% ± 1, Q8 = 68% ± 1) and regions with intrinsic disorder (MCC = 0.59 ± 0.03) were predicted significantly better than through one-hot encoding or through Word2vec-like approaches. At the per-protein level, subcellular localization was predicted in ten classes (Q10 = 68% ± 1) and membrane-bound were distinguished from water-soluble proteins (Q2 = 87% ± 1). Although SeqVec embeddings generated the best predictions from single sequences, no solution improved over the best existing method using evolutionary information. Nevertheless, our approach improved over some popular methods using evolutionary information and for some proteins even did beat the best. Thus, they prove to condense the underlying principles of protein sequences. Overall, the important novelty is speed: where the lightning-fast HHblits needed on average about two minutes to generate the evolutionary information for a target protein, SeqVec created embeddings on average in 0.03 s. As this speed-up is independent of the size of growing sequence databases, SeqVec provides a highly scalable approach for the analysis of big data in proteomics, i.e. microbiome or metaproteome analysis.

CONCLUSION

Transfer-learning succeeded to extract information from unlabeled sequence databases relevant for various protein prediction tasks. SeqVec modeled the language of life, namely the principles underlying protein sequences better than any features suggested by textbooks and prediction methods. The exception is evolutionary information, however, that information is not available on the level of a single sequence.

Computational prediction of functions of intrinsically disordered regions

Intrinsically disorder regions (IDRs) are abundant in nature, particularly among Eukaryotes. While they facilitate a wide spectrum of cellular functions including signaling, molecular assembly and recognition, translation, transcription and regulation, only several hundred IDRs are annotated functionally. This annotation gap motivates the development of fast and accurate computational methods that predict IDR functions directly from protein sequences. We introduce and describe a comprehensive collection of 25 methods that provide accurate predictions of IDRs that interact with proteins and nucleic acids, that function as flexible linkers and that moonlight multiple functions. Virtually all of these predictors can be accessed online and many were developed in the last few years. They utilize a wide range of predictive architectures and take advantage of modern machine learning algorithms. Our empirical analysis shows that predictors that are available as webservers enjoy high rates of citations, attesting to their practical value and popularity. The most cited methods include DISOPRED3, ANCHOR, alpha-MoRFpred, MoRFpred, fMoRFpred and MoRFCHiBi. We present two case studies to demonstrate that predictions produced by these computational tools are relatively easy to interpret and that they deliver valuable functional clues. However, the current computational tools cover a relatively narrow range of disorder functions. Further development efforts that would cover a broader range of functions should be pursued. We demonstrate that a sufficient amount of functionally annotated IDRs that are associated with several other disorder functions is already available and can be used to design and validate novel predictors.

Intrinsically Disordered Human T Lymphotropic Virus Type 1 p30 Protein: Experimental and Computational Evidence

Human T lymphotropic virus type 1 (HTLV-1) causes adult T cell leukemia and lymphoma and other neuroinflammatory diseases. The pX region of HTLV-1 genome encodes an accessory protein p30 that is required for viral persistence and spread in the host. p30 regulates viral gene expression at the transcription level by competing with Tax for p300 binding and at posttranscriptional level by nuclear retention of tax/rex messenger RNA (mRNA). In addition, p30 modulates the host cellular environment by binding to various host proteins such as ATM, REGγ, and PRMT5. However, the low expression levels of p30 has been a major hurdle in studying its structure-function relationship in the context of HTLV-1 pathobiology, which is most likely due to its intrinsically disordered nature. To investigate the unstable nature of p30, flow cytometric analysis of p30-GFP fusion protein expressed in Escherichia coli was conducted and bioinformatics analysis of p30 was performed. The bacterial cells were green fluorescent protein (GFP) positive, indicating that p30-GFP was in the soluble fraction. Induction, particularly at higher temperature, reduced the expression of p30-GFP. Moreover, p30-GFP was detected exclusively in insoluble fraction upon cell lysis, suggesting its unstable and disordered nature. The bioinformatics analysis of p30 protein sequence and amino acid content revealed that p30 has highly disordered regions from amino acids 75-155 and 197-241. Furthermore, p30 has regions for macromolecular interactions that could stabilize it and these regions coincide with the unstable regions. Collectively, the study indicates that HTLV-1 p30 is an intrinsically disordered protein.

On the Need to Develop Guidelines for Characterizing and Reporting Intrinsic Disorder in Proteins

Since the early 2000s, numerous computational tools have been created and used to predict intrinsic disorder in proteins. At present, the output from these algorithms is difficult to interpret in the absence of standards or references for comparison. There are many reasons to establish a set of standard-based guidelines to evaluate computational protein disorder predictions. This viewpoint explores a handful of these reasons, including standardizing nomenclature to improve communication, rigor and reproducibility, and making it easier for newcomers to enter the field. An approach for reporting predicted disorder in single proteins with respect to whole proteomes is discussed. The suggestions are not intended to be formulaic; they should be viewed as a starting point to establish guidelines for interpreting and reporting computational protein disorder predictions.

How to Predict Disorder in a Protein of Interest

Currently available computational tools, which are many, provide a researcher with the multitude of options for prediction of intrinsic disorder in a protein of interest and for finding at least some of its disorder-based functions. This chapter provides a highly subjective guideline on how not to be lost in the "dark forest" of available tools for the analysis of intrinsic disorder. By no means it gives a unique pathway through this forest, but simply presents some of the tools the author uses in his everyday research.

How disordered is my protein and what is its disorder for? A guide through the "dark side" of the protein universe

In the last 2 decades it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins lack a stable 3D structure, are ubiquitous and fulfill essential biological functions. Their conformational heterogeneity is encoded in their amino acid sequences, thereby allowing intrinsically disordered proteins or regions to be recognized based on properties of these sequences. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to structural determination with X-ray crystallization. This article discusses a comprehensive selection of databases and methods currently employed to disseminate experimental and putative annotations of disorder, predict disorder and identify regions involved in induced folding. It also provides a set of detailed instructions that should be followed to perform computational analysis of disorder.

Predicting Conformational Disorder

In the last two decades, it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded at the amino acid sequence level, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting disorder and identifying regions involved in induced folding.

Structural insights into activation of the retinal L-type Ca²⁺ channel (Cav1.4) by Ca²⁺-binding protein 4 (CaBP4)

CaBP4 modulates Ca(2+)-dependent activity of L-type voltage-gated Ca(2+) channels (Cav1.4) in retinal photoreceptor cells. Mg(2+) binds to the first and third EF-hands (EF1 and EF3), and Ca(2+) binds to EF1, EF3, and EF4 of CaBP4. Here we present NMR structures of CaBP4 in both Mg(2+)-bound and Ca(2+)-bound states and model the CaBP4 structural interaction with Cav1.4. CaBP4 contains an unstructured N-terminal region (residues 1-99) and four EF-hands in two separate lobes. The N-lobe consists of EF1 and EF2 in a closed conformation with either Mg(2+) or Ca(2+) bound at EF1. The C-lobe binds Ca(2+) at EF3 and EF4 and exhibits a Ca(2+)-induced closed-to-open transition like that of calmodulin. Exposed residues in Ca(2+)-bound CaBP4 (Phe(137), Glu(168), Leu(207), Phe(214), Met(251), Phe(264), and Leu(268)) make contacts with the IQ motif in Cav1.4, and the Cav1.4 mutant Y1595E strongly impairs binding to CaBP4. We conclude that CaBP4 forms a collapsed structure around the IQ motif in Cav1.4 that we suggest may promote channel activation by disrupting an interaction between IQ and the inhibitor of Ca(2+)-dependent inactivation domain.

Dynamic New World: Refining Our View of Protein Structure, Function and Evolution

Proteins are crucial to the functioning of all lifeforms. Traditional understanding posits that a single protein occupies a single structure ("fold"), which performs a single function. This view is radically challenged with the recognition that high structural dynamism-the capacity to be extra "floppy"-is more prevalent in functional proteins than previously assumed. As reviewed here, this dynamic take on proteins affects our understanding of protein "structure", function, and evolution, and even gives us a glimpse into protein origination. Specifically, this review will discuss historical developments concerning protein structure, and important new relationships between dynamism and aspects of protein sequence, structure, binding modes, binding promiscuity, evolvability, and origination. Along the way, suggestions will be provided for how key parts of textbook definitions-that so far have excluded membership to intrinsically disordered proteins (IDPs)-could be modified to accommodate our more dynamic understanding of proteins.

Analysis and consensus of currently available intrinsic protein disorder annotation sources in the MobiDB database

BACKGROUND

Intrinsic protein disorder is becoming an increasingly important topic in protein science. During the last few years, intrinsically disordered proteins (IDPs) have been shown to play a role in many important biological processes, e.g. protein signalling and regulation. This has sparked a need to better understand and characterize different types of IDPs, their functions and roles. Our recently published database, MobiDB, provides a centralized resource for accessing and analysing intrinsic protein disorder annotations.

RESULTS

Here, we present a thorough description and analysis of the data made available by MobiDB, providing descriptive statistics on the various available annotation sources. Version 1.2.1 of the database contains annotations for ca. 4,500,000 UniProt sequences, covering all eukaryotic proteomes. In addition, we describe a novel consensus annotation calculation and its related weighting scheme. The comparison between disorder information sources highlights how the MobiDB consensus captures the main features of intrinsic disorder and correlates well with manually curated datasets. Finally, we demonstrate the annotation of 13 eukaryotic model organisms through MobiDB's datasets, and of an example protein through the interactive user interface.

CONCLUSIONS

MobiDB is a central resource for intrinsic disorder research, containing both experimental data and predictions. In the future it will be expanded to include additional information for all known proteins.

Independent structural domains in paramyxovirus polymerase protein

All enzymatic activities required for genomic replication and transcription of nonsegmented negative strand RNA viruses (or Mononegavirales) are believed to be concentrated in the viral polymerase (L) protein. However, our insight into the organization of these different enzymatic activities into a bioactive tertiary structure remains rudimentary. Fragments of Mononegavirales polymerases analyzed to date cannot restore bioactivity through trans-complementation, unlike the related L proteins of segmented NSVs. We investigated the domain organization of phylogenetically diverse Paramyxovirus L proteins derived from measles virus (MeV), Nipah virus (NiV), and respiratory syncytial virus (RSV). Through a comprehensive in silico and experimental analysis of domain intersections, we defined MeV L position 615 as an interdomain candidate in addition to the previously reported residue 1708. Only position 1708 of MeV and the homologous positions in NiV and RSV L also tolerated the insertion of epitope tags. Splitting of MeV L at residue 1708 created fragments that were unable to physically interact and trans-complement, but strikingly, these activities were reconstituted by the addition of dimerization tags to the fragments. Equivalently split fragments of NiV, RSV, and MeV L oligomerized with comparable efficiency in all homo- and heterotypic combinations, but only the homotypic pairs were able to trans-complement. These results demonstrate that synthesis as a single polypeptide is not required for the Mononegavirales polymerases to adopt a proper tertiary conformation. Paramyxovirus polymerases are composed of at least two truly independent folding domains that lack a traditional interface but require molecular compatibility for bioactivity. The functional probing of the L domain architecture through trans-complementation is anticipated to be applicable to all Mononegavirales polymerases.

An FPGA implementation to detect selective cationic antibacterial peptides

Exhaustive prediction of physicochemical properties of peptide sequences is used in different areas of biological research. One example is the identification of selective cationic antibacterial peptides (SCAPs), which may be used in the treatment of different diseases. Due to the discrete nature of peptide sequences, the physicochemical properties calculation is considered a high-performance computing problem. A competitive solution for this class of problems is to embed algorithms into dedicated hardware. In the present work we present the adaptation, design and implementation of an algorithm for SCAPs prediction into a Field Programmable Gate Array (FPGA) platform. Four physicochemical properties codes useful in the identification of peptide sequences with potential selective antibacterial activity were implemented into an FPGA board. The speed-up gained in a single-copy implementation was up to 108 times compared with a single Intel processor cycle for cycle. The inherent scalability of our design allows for replication of this code into multiple FPGA cards and consequently improvements in speed are possible. Our results show the first embedded SCAPs prediction solution described and constitutes the grounds to efficiently perform the exhaustive analysis of the sequence-physicochemical properties relationship of peptides.

NMR-based detection of acetylation sites in peptides

Acetylation of histone tails as well as non-histone proteins was found to be a major component of the 'chromatin code' that regulates transcription through the recruitment of transcription factors, co-regulators and DNA-binding proteins. Acetylation can have several effects modifying protein-protein interactions, protein activity, localization and stability. Using NMR spectroscopy, we provide a simple way to detect acetyl moieties at the epsilon-amino function of lysine residues based on peptides derived from Histone H4 and TDG amino-terminal domains. Significant changes of acetyl-lysine resonances as compared to non-acetylated residues allow a direct identification of specific acetylated lysine. We also show that, in unfolded peptides, acetylation of lysine side chains leads to characteristic NMR signals that vary only weakly depending on the primary sequence or the total number of acetylated sites, indicating that the acetamide group does not establish any interactions with other residues. Furthermore, resonance changes upon acetylation are restricted to residues nearby the acetylation site, indicating that acetylation does not modify the overall peptide conformation.

Conformational diseases: looking into the eyes

Conformational diseases, a general term comprising more than 40 disorders are caused by the accumulation of unfolded or misfolded proteins. Improper protein folding (misfolding) as well as accrual of unfolded proteins can lead to the formation of disordered (amorphous) or ordered (amyloid fibril) aggregates. The gradual accumulation of protein aggregates and the acceleration of their formation by stress explain the characteristic late or episodic onset of the diseases. The best studied in this group are neurodegenerative diseases and amyloidosis accompanied by the deposition of a specific aggregation-prone proteins or protein fragments and formation of insoluble fibrils. Amyloidogenic protein accumulation often occurs in the brain tissues, e.g. in Alzheimer's disease with the deposition of amyloid-beta and Tau, in scrapie and bovine spongiform encephalopathy with the accumulation of prion protein, in Parkinson's disease with the deposition of alpha-synuclein. Other examples of amyloid proteins are transthyretin, immunoglobulin light chain, gelsolin, etc. In addition to the brain, the accumulation of unfolded or misfolded proteins leading to pathology takes place in a wide variety of organs and tissues, including different parts of the eye. The best studied ocular conformational diseases are cataract in the lens and retinitis pigmentosa in the retina, but accumulation of misfolded proteins also occurs in other parts of the eye causing various disorders. Furthermore, ocular manifestation of systemic amyloidosis often causes the deposition of amyloidogenic proteins in different ocular tissues. Here we present the data regarding naturally unfolded and misfolded proteins in eye tissues, their structure-function relationships, and molecular mechanisms underlying their involvement in diseases. We also summarize the etiology of ocular conformational diseases and discuss approaches to their treatment.

Computational resources for the prediction and analysis of native disorder in proteins

Proteomics attempts to characterise the gene products expressed in a cell or tissue via a range of biophysical techniques including crystallography and NMR and, more relevantly to this volume, chromatography and mass spectrometry. It is becoming increasingly clear that the native states of segments of many of the cellular proteins are not stable, folded structures, and much of the proteome is in an unfolded, disordered state. These proteins and their disordered segments have functionally interesting properties and provide novel challenges for the biophysical techniques that are used to study them. This chapter focuses on computational approaches to predicting such regions and analyzing the functions linked to them, and has implications for protein scientists who wish to study such properties as molecular recognition and post-translational modifications. We also discuss resources where the results of predictions have been collated, making them publicly available to the wider biological community.

Conformational disorder

In recent years it was shown that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins are ubiquitary proteins that fulfill essential biological functions while lacking a stable 3D structure. Despite the large abundance of disorder, disordered regions are still poorly detected. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental in delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting disorder and identifying regions involved in induced folding.

Overlapping genes produce proteins with unusual sequence properties and offer insight into de novo protein creation

It is widely assumed that new proteins are created by duplication, fusion, or fission of existing coding sequences. Another mechanism of protein birth is provided by overlapping genes. They are created de novo by mutations within a coding sequence that lead to the expression of a novel protein in another reading frame, a process called "overprinting." To investigate this mechanism, we have analyzed the sequences of the protein products of manually curated overlapping genes from 43 genera of unspliced RNA viruses infecting eukaryotes. Overlapping proteins have a sequence composition globally biased toward disorder-promoting amino acids and are predicted to contain significantly more structural disorder than nonoverlapping proteins. By analyzing the phylogenetic distribution of overlapping proteins, we were able to confirm that 17 of these had been created de novo and to study them individually. Most proteins created de novo are orphans (i.e., restricted to one species or genus). Almost all are accessory proteins that play a role in viral pathogenicity or spread, rather than proteins central to viral replication or structure. Most proteins created de novo are predicted to be fully disordered and have a highly unusual sequence composition. This suggests that some viral overlapping reading frames encoding hypothetical proteins with highly biased composition, often discarded as noncoding, might in fact encode proteins. Some proteins created de novo are predicted to be ordered, however, and whenever a three-dimensional structure of such a protein has been solved, it corresponds to a fold previously unobserved, suggesting that the study of these proteins could enhance our knowledge of protein space.

Protein intrinsic disorder and influenza virulence: the 1918 H1N1 and H5N1 viruses

BACKGROUND

The 1918 H1N1 virus was a highly virulent strain that killed 20-50 million people. The cause of its virulence remains poorly understood.

METHODS

Intrinsic disorder predictor PONDR VLXT was used to compare various influenza subtypes and strains. Three-dimensional models using data from X-ray crystallographic studies annotated with disorder prediction were used to characterize the proteins.

RESULTS

The protein of interest is hemagglutin (HA), which is a surface glycoprotein that plays a vital role in viral entry. Distinct differences between HA proteins of the virulent and non-virulent strains are seen, especially in the region near residues 68-79 of the HA2. This region represents the tip of the stalk that is in contact with the receptor chain, HA1, and therefore likely to provide the greatest effect on the motions of the exposed portion of HA. Comparison of this region between virulent strains (1918 H1N1 and H5N1) and less virulent ones (H3N2 and 1930 H1N1) reveals that predicted disorder can be seen at this region among the more virulent strains and subtypes but is remarkably absent among the distinctly less virulent ones.

CONCLUSION

The motions created by disorder at crucial regions are likely to impair recognition by immunological molecules and increase the virulence of both the H5N1 and the 1918 H1N1 viruses. The results help explain many puzzling features of the H5N1 and the 1918 H1N1 viruses. Summarizing, HA (and especially its intrinsically disordered regions) can serve as a predictor of the influenza A virulence, even though there may be other proteins that contribute to or exacerbate the virulence.

At low concentrations, 3,4-dihydroxyphenylacetic acid (DOPAC) binds non-covalently to alpha-synuclein and prevents its fibrillation

Several studies have shown that catecholamines can inhibit the fibrillation of alpha-synuclein (alpha-Syn), a small presynaptic protein whose aggregation is believed to be a critical step in the etiology of Parkinson's disease and several other neurodegenerative disorders. However, the mechanism of this inhibition is uncertain. We show here that substoichiometric concentrations of 3,4-dihydroxyphenylacetic acid (DOPAC), a normal product of the metabolism of dopamine, can inhibit the fibrillation of alpha-Syn, due to non-covalent binding of DOPAC to alpha-Syn monomer. Intriguingly, the presence of alpha-Syn accelerates the spontaneous oxidation of DOPAC, and the oxidized form of DOPAC (the quinone) is responsible for the fibrillation inhibition. In addition, the presence of DOPAC leads to the oxidation of the methionine residues of alpha-Syn, probably due to the H(2)O(2) production as a by-product of DOPAC oxidation. The lack of fibrillation results from the formation of stable oligomers, which are very similar to those observed transiently at early stages of the alpha-Syn fibrillation. A possible explanation for this phenomenon is that DOPAC stabilizes the normally transient oligomers and prevents them from subsequent fibril formation. The analysis of the alpha-Syn Y39W variant suggests that DOPAC binds non-covalently to the same N-terminal region of alpha-Syn as lipid vesicles, probably in the vicinity of residue 39. In contrast to the compounds with 1,2-dihydroxyphenyl groups (DOPAC and catechol), their 1,4-dihydroxyphenyl isomers (hydroquinone and homogentisic acid) are able to modify alpha-Syn covalently, probably due to the less steric hindrance in the Michael addition.

MeDor: a metaserver for predicting protein disorder

Background

We have previously shown that using multiple prediction methods improves the accuracy of disorder predictions. It is, however, a time-consuming procedure, since individual outputs of multiple predictions have to be retrieved, compared to each other and a comprehensive view of the results can only be obtained through a manual, fastidious, non-automated procedure. We herein describe a new web metaserver, MeDor, which allows fast, simultaneous analysis of a query sequence by multiple predictors and provides a graphical interface with a unified view of the outputs.

Results

MeDor was developed in Java and is freely available and downloadable at: . Presently, MeDor provides a HCA plot and runs a secondary structure prediction, a prediction of signal peptides and transmembrane regions and a set of disorder predictions. MeDor also enables the user to customize the output and to retrieve the sequence of specific regions of interest.

Conclusion

As MeDor outputs can be printed, saved, commented and modified further on, this offers a dynamic support for the analysis of protein sequences that is instrumental for delineating domains amenable to structural and functional studies.