Large-scale prediction of long disordered regions in proteins using random forests

Background  

Many proteins contain disordered regions that lack fixed three-dimensional (3D) structure under physiological conditions but have important biological functions. Prediction of disordered regions in protein sequences is important for understanding protein function and in high-throughput determination of protein structures. Machine learning techniques, including neural networks and support vector machines have been widely used in such predictions. Predictors designed for long disordered regions are usually less successful in predicting short disordered regions. Combining prediction of short and long disordered regions will dramatically increase the complexity of the prediction algorithm and make the predictor unsuitable for large-scale applications. Efficient batch prediction of long disordered regions alone is of greater interest in large-scale proteome studies.     

Resolving the ambiguity: Making sense of intrinsic disorder when PDB structures disagree

Missing regions in X‐ray crystal structures in the Protein Data Bank (PDB) have played a foundational role in the study of intrinsically disordered protein regions (IDPRs), especially in the development of in silico predictors of intrinsic disorder. However, a missing region is only a weak indication of intrinsic disorder, and this uncertainty is compounded by the presence of ambiguous regions, where more than one structure of the same protein sequence “disagrees” in terms of the presence or absence of missing residues. The question is this: are these ambiguous regions intrinsically disordered, or are they the result of static disorder that arises from experimental conditions, ensembles of structures, or domain wobbling? A novel way of looking at ambiguous regions in terms of the pattern between multiple PDB structures has been demonstrated. It was found that the propensity for intrinsic disorder increases as the level of ambiguity decreases. However, it is also shown that ambiguity is more likely to occur as the protein region is placed within different environmental conditions, and even the most ambiguous regions as a set display compositional bias that suggests flexibility. The results suggested that ambiguity is a natural result for many IDPRs crystallized under different conditions and that static disorder and wobbling domains are relatively rare. Instead, it is more likely that ambiguity arises because many of these regions were conditionally or partially disordered.     

Progress at protein structure prediction,as seen in CASP15

In Dec 2020, the results of AlphaFold version 2 were presented at CASP14, sparking a revolution in the field of protein structure predictions. For the first time, a purely computational method could challenge experimental accuracy for structure prediction of single protein domains. The code of AlphaFold v2 was released in the summer of 2021, and since then, it has been shown that it can be used to accurately predict the structure of most ordered proteins and many protein–protein interactions. It has also sparked an explosion of development in the field, improving AI-based methods to predict protein complexes, disordered regions, and protein design. Here I will review some of the inventions sparked by the release of AlphaFold.     

Deciphering the cause of evolutionary variance within intrinsically disordered regions in human proteins

Why the intrinsically disordered regions evolve within human proteome has became an interesting question for a decade. Till date, it remains an unsolved yet an intriguing issue to investigate why some of the disordered regions evolve rapidly while the rest are highly conserved across mammalian species. Identifying the key biological factors, responsible for the variation in the conservation rate of different disordered regions within the human proteome, may revisit the above issue. We emphasized that among the other biological features (multifunctionality, gene essentiality, protein connectivity, number of unique domains, gene expression level and expression breadth) considered in our study, the number of unique protein domains acts as a strong determinant that negatively influences the conservation of disordered regions. In this context, we justified that proteins having a fewer types of domains preferably need to conserve their disordered regions to enhance their structural flexibility which in turn will facilitate their molecular interactions. In contrast, the selection pressure acting on the stretches of disordered regions is not so strong in the case of multi-domains proteins. Therefore, we reasoned that the presence of conserved disordered stretches may compensate the functions of multiple domains within a single domain protein. Interestingly, we noticed that the influence of the unique domain number and expression level acts differently on the evolution of disordered regions from that of well-structured ones.     

Natively unfolded proteins

It is now clear that a significant fraction of eukaryotic genomes encode proteins with substantial regions of disordered structure. In spite of the lack of structure, these proteins nevertheless are functional; many are involved in critical steps of the cell cycle and regulatory processes. In general, intrinsically disordered proteins interact with a target ligand (often DNA) and undergo a structural transition to a folded form when bound. Several features of intrinsically disordered proteins make them well suited to interacting with multiple targets and to cell regulation. New algorithms have been developed to identify disordered regions of proteins and have demonstrated their presence in cancer-associated proteins and proteins regulated by phosphorylation.     

Evolutionary rate heterogeneity in proteins with long disordered regions

The dominant view in protein science is that a three-dimensional (3-D) structure is a prerequisite for protein function. In contrast to this dominant view, there are many counterexample proteins that fail to fold into a 3-D structure, or that have local regions that fail to fold, and yet carry out function. Protein without fixed 3-D structure is called intrinsically disordered. Motivated by anecdotal accounts of higher rates of sequence evolution in disordered protein than in ordered protein we are exploring the molecular evolution of disordered proteins. To test whether disordered protein evolves more rapidly than ordered protein, pairwise genetic distances were compared between the ordered and the disordered regions of 26 protein families having at least one member with a structurally characterized region of disorder of 30 or more consecutive residues. For five families, there were no significant differences in pairwise genetic distances between ordered and disordered sequences. The disordered region evolved significantly more rapidly than the ordered region for 19 of the 26 families. The functions of these disordered regions are diverse, including binding sites for protein, DNA, or RNA and also including flexible linkers. The functions of some of these regions are unknown. The disordered regions evolved significantly more slowly than the ordered regions for the two remaining families. The functions of these more slowly evolving disordered regions include sites for DNA binding. More work is needed to understand the underlying causes of the variability in the evolutionary rates of intrinsically ordered and disordered protein.     

Prediction of amyloidogenic and disordered regions in protein chains

The determination of factors that influence protein conformational changes is very important for the identification of potentially amyloidogenic and disordered regions in polypeptide chains. In our work we introduce a new parameter, mean packing density, to detect both amyloidogenic and disordered regions in a protein sequence. It has been shown that regions with strong expected packing density are responsible for amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for eight of 12 amyloid-forming proteins and peptides in which the positions of amyloidogenic regions have been revealed experimentally. Our findings support the concept that the mechanism of amyloid fibril formation is similar for different peptides and proteins. Moreover, we have demonstrated that regions with weak expected packing density are responsible for the appearance of disordered regions. Our method has been tested on datasets of globular proteins and long disordered protein segments, and it shows improved performance over other widely used methods. Thus, we demonstrate that the expected packing density is a useful value with which one can predict both intrinsically disordered and amyloidogenic regions of a protein based on sequence alone. Our results are important for understanding the structural characteristics of protein folding and misfolding.     

Understanding protein non-folding

This review describes the family of intrinsically disordered proteins, members of which fail to form rigid 3-D structures under physiological conditions, either along their entire lengths or only in localized regions. Instead, these intriguing proteins/regions exist as dynamic ensembles within which atom positions and backbone Ramachandran angles exhibit extreme temporal fluctuations without specific equilibrium values. Many of these intrinsically disordered proteins are known to carry out important biological functions which, in fact, depend on the absence of a specific 3-D structure. The existence of such proteins does not fit the prevailing structure–function paradigm, which states that a unique 3-D structure is a prerequisite to function. Thus, the protein structure–function paradigm has to be expanded to include intrinsically disordered proteins and alternative relationships among protein sequence, structure, and function. This shift in the paradigm represents a major breakthrough for biochemistry, biophysics and molecular biology, as it opens new levels of understanding with regard to the complex life of proteins. This review will try to answer the following questions: how were intrinsically disordered proteins discovered? Why don't these proteins fold? What is so special about intrinsic disorder? What are the functional advantages of disordered proteins/regions? What is the functional repertoire of these proteins? What are the relationships between intrinsically disordered proteins and human diseases?     

A comprehensive overview of computational protein disorder prediction methods

Over the past decade there has been a growing acknowledgement that a large proportion of proteins within most proteomes contain disordered regions. Disordered regions are segments of the protein chain which do not adopt a stable structure. Recognition of disordered regions in a protein is of great importance for protein structure prediction, protein structure determination and function annotation as these regions have a close relationship with protein expression and functionality. As a result, a great many protein disorder prediction methods have been developed so far. Here, we present an overview of current protein disorder prediction methods including an analysis of their advantages and shortcomings. In order to help users to select alternative tools under different circumstances, we also evaluate 23 disorder predictors on the benchmark data of the most recent round of the Critical Assessment of protein Structure Prediction (CASP) and assess their accuracy using several complementary measures.     

Are structural proteins in insect cuticles dominated by intrinsically disordered regions?

Fifty years ago it was concluded that the highly elastic cuticular protein, resilin, is devoid of secondary structure and that the peptide chains are randomly coiled and easily and reversibly deformed. These properties indicate that resilin is an intrinsically disordered protein and suggest that also other cuticular proteins may contain disordered regions. Amino acid sequences are now available for cuticular proteins from many insect species, and several programs have been developed to predict the probability for a given protein to contain disordered regions.The present paper describes the results obtained when the predictors are applied to various types of cuticular proteins from several insects. The results suggest that most cuticular proteins contain shorter or longer disordered regions, and the possible functions for such regions are briefly discussed.     

FoldUnfold: web server for the prediction of disordered regions in protein chain

Identification of disordered regions in polypeptide chains is very important because such regions are essential for protein function. A new parameter, namely mean packing density of residues has been introduced to detect disordered regions in a protein sequence. We have demonstrated that regions with weak expected packing density would be responsible for the appearance of disordered regions. Our method (FoldUnfold) has been tested on datasets of globular proteins (559 proteins) and long disordered protein segments (129 proteins) and showed improved performance over some other widely used methods, such as DISOPRED, PONDR VL3H, IUPred and GlobPlot. AVAILABILITY: The FoldUnfold server is available for users at http://skuld.protres.ru/~mlobanov/ogu/ogu.cgi. There is a link to our server through the web site of DisProt (http://www.disprot.org/predictors.php).     

Library of disordered patterns in 3D protein structures

Intrinsically disordered regions serve as molecular recognition elements, which play an important role in the control of many cellular processes and signaling pathways. It is useful to be able to predict positions of disordered regions in protein chains. The statistical analysis of disordered residues was done considering 34,464 unique protein chains taken from the PDB database. In this database, 4.95% of residues are disordered (i.e. invisible in X-ray structures). The statistics were obtained separately for the N- and C-termini as well as for the central part of the protein chain. It has been shown that frequencies of occurrence of disordered residues of 20 types at the termini of protein chains differ from the ones in the middle part of the protein chain. Our systematic analysis of disordered regions in PDB revealed 109 disordered patterns of different lengths. Each of them has disordered occurrences in at least five protein chains with identity less than 20%. The vast majority of all occurrences of each disordered pattern are disordered. This allows one to use the library of disordered patterns for predicting the status of a residue of a given protein to be ordered or disordered. We analyzed the occurrence of the selected patterns in three eukaryotic and three bacterial proteomes.     

Evolution of structurally disordered proteins promotes neostructuralization

Protein structure is generally more conserved than sequence, but for regions that can adopt different structures in different environments, does this hold true? Understanding how structurally disordered regions evolve altered secondary structure element propensities as well as conformational flexibility among paralogs are fundamental questions for our understanding of protein structural evolution. We have investigated the evolutionary dynamics of structural disorder in protein families containing both orthologs and paralogs using phylogenetic tree reconstruction, protein structure disorder prediction, and secondary structure prediction in order to shed light upon these questions. Our results indicate that the extent and location of structurally disordered regions are not universally conserved. As structurally disordered regions often have high conformational flexibility, this is likely to have an effect on how protein structure evolves as spatially altered conformational flexibility can also change the secondary structure propensities for homologous regions in a protein family.     

Influence of Sequence Changes and Environment on Intrinsically Disordered Proteins

Many large-scale studies on intrinsically disordered proteins are implicitly based on the structural models deposited in the Protein Data Bank. Yet, the static nature of deposited models supplies little insight into variation of protein structure and function under diverse cellular and environmental conditions. While the computational predictability of disordered regions provides practical evidence that disorder is an intrinsic property of proteins, the robustness of disordered regions to changes in sequence or environmental conditions has not been systematically studied. We analyzed intrinsically disordered regions in the same or similar proteins crystallized independently and studied their sensitivity to changes in protein sequence and parameters of crystallographic experiments. The observed changes in the existence, position, and length of disordered regions indicate that their appearance in X-ray structures dramatically depends on changes in amino acid sequence and peculiarities of the crystallographic experiment. Our study also raises general questions regarding protein evolution and the regulation of protein structure, dynamics, and function via variations in cellular and environmental conditions.     

Protein disorder prediction by condensed PSSM considering propensity for order or disorder

Background  

More and more disordered regions have been discovered in protein sequences, and many of them are found to be functionally significant. Previous studies reveal that disordered regions of a protein can be predicted by its primary structure, the amino acid sequence. One observation that has been widely accepted is that ordered regions usually have compositional bias toward hydrophobic amino acids, and disordered regions are toward charged amino acids. Recent studies further show that employing evolutionary information such as position specific scoring matrices (PSSMs) improves the prediction accuracy of protein disorder. As more and more machine learning techniques have been introduced to protein disorder detection, extracting more useful features with biological insights attracts more attention.     

Limited proteolysis of natively unfolded protein 4E‐BP1 in the presence of trifluoroethanol

Natively unfolded (intrinsically disordered (ID) proteins) have been attracting an increasing attention due to their involvement in many regulatory processes. Natively unfolded proteins can fold upon binding to their metabolic partners. Coupled folding and binding events usually involve only relatively short motifs (binding motifs). These binding motifs which are able to fold should have an increased propensity to form a secondary structure. The aim of the present work was to probe the conformation of the intrinsically disordered protein 4E‐BP1 in the native and partly folded states by limited proteolysis and to reveal regions with a high propensity to form an ordered structure. Trifuoroethanol (TFE) in low concentrations (up to 15 vol%) was applied to increase the helical population of protein regions with a high intrinsic propensity to fold. When forming helical structures, these regions lose mobility and become more protected from proteases than random/unfolded protein regions. Limited proteolysis followed by mass spectrometry analysis allows identification of the regions with decreased mobility in TFE solutions. Trypsin and V8 proteases were used to perform limited proteolysis of the 4E‐BP1 protein in buffer and in solutions with low TFE concentrations at 37°C and at elevated temperatures (42 and 50°C). Comparison of the results obtained with the previously established 4E‐BP1 structure and the binding motif illustrates the ability of limited proteolysis in the presence of a folding assistant (TFE) to map the regions with high and low propensities to form a secondary structure revealing potential binding motifs inside the intrinsically disordered protein. © 2013 Wiley Periodicals, Inc. Biopolymers 101: 591–602, 2014.     

CARd: Carbon distribution analysis program for protein sequences

Carbon distribution is responsible for stability and structure of proteins. Arrangement of carbon along the protein sequence is depends on how the amino acids are organized and is guided by mRNAs. An atomic level revision is important for understanding these codes. This will ultimately help in identification of disorders and suggest mutations. For this purpose a carbon distribution analysis program has been developed. This program captures the hydrophobic / hydrophilic / disordered regions in a protein. The program gives accurate results. The calculations are precise and sensitive to single amino acid resolution. This program is to help in mutational studies leading to protein stabilisation.     

GlobPlot: Exploring protein sequences for globularity and disorder

A major challenge in the proteomics and structural genomics era is to predict protein structure and function, including identification of those proteins that are partially or wholly unstructured. Non-globular sequence segments often contain short linear peptide motifs (e.g. SH3-binding sites) which are important for protein function. We present here a new tool for discovery of such unstructured, or disordered regions within proteins. GlobPlot (http://globplot.embl.de) is a web service that allows the user to plot the tendency within the query protein for order/globularity and disorder. We show examples with known proteins where it successfully identifies inter-domain segments containing linear motifs, and also apparently ordered regions that do not contain any recognised domain. GlobPlot may be useful in domain hunting efforts. The plots indicate that instances of known domains may often contain additional N- or C-terminal segments that appear ordered. Thus GlobPlot may be of use in the design of constructs corresponding to globular proteins, as needed for many biochemical studies, particularly structural biology. GlobPlot has a pipeline interface--GlobPipe--for the advanced user to do whole proteome analysis. GlobPlot can also be used as a generic infrastructure package for graphical displaying of any possible propensity.