Prediction and analysis of intrinsically disordered proteins

Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn

Intrinsically disordered proteins (IDPs) that include one or more intrinsically disordered regions (IDRs) are abundant across all domains of life and viruses and play numerous functional roles in various cellular processes. Due to a relatively low throughput and high cost of experimental techniques for identifying IDRs, there is a growing need for fast and accurate computational algorithms that accurately predict IDRs/IDPs from protein sequences. We describe one of the leading disorder predictors, flDPnn. Results from a recent community-organized Critical Assessment of Intrinsic Disorder (CAID) experiment show that flDPnn provides fast and state-of-the-art predictions of disorder, which are supplemented with the predictions of several major disorder functions. This chapter provides a practical guide to flDPnn, which includes a brief explanation of its predictive model, descriptions of its web server and standalone versions, and a case study that showcases how to read and understand flDPnn's predictions.

flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins

Prediction of the intrinsic disorder in protein sequences is an active research area, with well over 100 predictors that were released to date. These efforts are motivated by the functional importance and high levels of abundance of intrinsic disorder, combined with relatively low amounts of experimental annotations. The disorder predictors are periodically evaluated by independent assessors in the Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiments. The recently completed CAID2 experiment assessed close to 40 state-of-the-art methods demonstrating that some of them produce accurate results. In particular, flDPnn2 method, which is the successor of flDPnn that performed well in the CAID1 experiment, secured the overall most accurate results on the Disorder-NOX dataset in CAID2. flDPnn2 implements a number of improvements when compared to its predecessor including changes to the inputs, increased size of the deep network model that we retrained on a larger training set, and addition of an alignment module. Using results from CAID2, we show that flDPnn2 produces accurate predictions very quickly, modestly improving over the accuracy of flDPnn and reducing the runtime by half, to about 27 s per protein. flDPnn2 is freely available as a convenient web server at http://biomine.cs.vcu.edu/servers/flDPnn2/.

CoMemMoRFPred: Sequence-based Prediction of MemMoRFs by Combining Predictors of Intrinsic Disorder, MoRFs and Disordered Lipid-binding Regions

Molecular recognition features (MoRFs) are a commonly occurring type of intrinsically disordered regions (IDRs) that undergo disorder-to-order transition upon binding to partner molecules. We focus on recently characterized and functionally important membrane-binding MoRFs (MemMoRFs). Motivated by the lack of computational tools that predict MemMoRFs, we use a dataset of experimentally annotated MemMoRFs to conceptualize, design, evaluate and release an accurate sequence-based predictor. We rely on state-of-the-art tools that predict residues that possess key characteristics of MemMoRFs, such as intrinsic disorder, disorder-to-order transition and lipid-binding. We identify and combine results from three tools that include flDPnn for the disorder prediction, DisoLipPred for the prediction of disordered lipid-binding regions, and MoRFCHiBiLight for the prediction of disorder-to-order transitioning protein binding regions. Our empirical analysis demonstrates that combining results produced by these three methods generates accurate predictions of MemMoRFs. We also show that use of a smoothing operator produces predictions that closely mimic the number and sizes of the native MemMoRF regions. The resulting CoMemMoRFPred method is available as an easy-to-use webserver at http://biomine.cs.vcu.edu/servers/CoMemMoRFPred. This tool will aid future studies of MemMoRFs in the context of exploring their abundance, cellular functions, and roles in pathologic phenomena.

Establishment of iPSC lines and zebrafish with loss-of-function AHDC1 variants: models for Xia-Gibbs syndrome

Xia-Gibbs syndrome (XGS) is a syndromic form of intellectual disability caused by heterozygous AHDC1 variants, but the pathophysiological mechanisms underlying this syndrome are still unclear. In this manuscript, we describe the development of two different functional models: three induced pluripotent stem cell (iPSC) lines with different loss-of-function (LoF) AHDC1 variants, derived by reprogramming peripheral blood mononuclear cells from XGS patients, and a zebrafish strain with a LoF variant in the ortholog gene (ahdc1) obtained through CRISPR/Cas9-mediated editing. The three iPSC lines showed expression of pluripotency factors (SOX2, SSEA-4, OCT3/4, and NANOG). To verify the capacity of iPSC to differentiate into the three germ layers, we obtained embryoid bodies (EBs), induced their differentiation, and confirmed the mRNA expression of ectodermal, mesodermal, and endodermal markers using the TaqMan hPSC Scorecard. The iPSC lines were also approved for the following quality tests: chromosomal microarray analysis (CMA), mycoplasma testing, and short tandem repeat (STR) DNA profiling. The zebrafish model has an insertion of four base pairs in the ahdc1 gene, is fertile, and breeding between heterozygous and wild-type (WT) animals generated offspring in a genotypic proportion in agreement with Mendelian law. The established iPSC and zebrafish lines were deposited on the hpscreg.eu and zfin.org platforms, respectively. These biological models are the first for XGS and will be used in future studies that investigate the pathophysiology of this syndrome, unraveling its underlying molecular mechanisms.

Liquid-liquid phase separation in tumor biology

Liquid-liquid phase separation (LLPS) is a novel principle for explaining the precise spatial and temporal regulation in living cells. LLPS compartmentalizes proteins and nucleic acids into micron-scale, liquid-like, membraneless bodies with specific functions, which were recently termed biomolecular condensates. Biomolecular condensates are executors underlying the intracellular spatiotemporal coordination of various biological activities, including chromatin organization, genomic stability, DNA damage response and repair, transcription, and signal transduction. Dysregulation of these cellular processes is a key event in the initiation and/or evolution of cancer, and emerging evidence has linked the formation and regulation of LLPS to malignant transformations in tumor biology. In this review, we comprehensively summarize the detailed mechanisms of biomolecular condensate formation and biophysical function and review the recent major advances toward elucidating the multiple mechanisms involved in cancer cell pathology driven by aberrant LLPS. In addition, we discuss the therapeutic perspectives of LLPS in cancer research and the most recently developed drug candidates targeting LLPS modulation that can be used to combat tumorigenesis.

Structural disorder of plasmid-encoded proteins in Bacteria and Archaea

Background

In the last decade and a half it has been firmly established that a large number of proteins do not adopt a well-defined (ordered) structure under physiological conditions. Such intrinsically disordered proteins (IDPs) and intrinsically disordered (protein) regions (IDRs) are involved in essential cell processes through two basic mechanisms: the entropic chain mechanism which is responsible for rapid fluctuations among many alternative conformations, and molecular recognition via short recognition elements that bind to other molecules. IDPs possess a high adaptive potential and there is special interest in investigating their involvement in organism evolution.

Results

We analyzed 2554 Bacterial and 139 Archaeal proteomes, with a total of 8,455,194 proteins for disorder content and its implications for adaptation of organisms, using three disorder predictors and three measures. Along with other findings, we revealed that for all three predictors and all three measures (1) Bacteria exhibit significantly more disorder than Archaea; (2) plasmid-encoded proteins contain considerably more IDRs than proteins encoded on chromosomes (or whole genomes) in both prokaryote superkingdoms; (3) plasmid proteins are significantly more disordered than chromosomal proteins only in the group of proteins with no COG category assigned; (4) antitoxin proteins in comparison to other proteins, are the most disordered (almost double) in both Bacterial and Archaeal proteomes; (5) plasmidal proteins are more disordered than chromosomal proteins in Bacterial antitoxins and toxin-unclassified proteins, but have almost the same disorder content in toxin proteins.

Conclusion

Our results suggest that while disorder content depends on genome and proteome characteristics, it is more influenced by functional engagements than by gene location (on chromosome or plasmid).

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2158-6) contains supplementary material, which is available to authorized users.

Prediction of protein disorder based on IUPred

Many proteins contain intrinsically disordered regions (IDRs), functional polypeptide segments that in isolation adopt a highly flexible conformational ensemble instead of a single, well-defined structure. Disorder prediction methods, which can discriminate ordered and disordered regions from the amino acid sequence, have contributed significantly to our current understanding of the distinct properties of intrinsically disordered proteins by enabling the characterization of individual examples as well as large-scale analyses of these protein regions. One popular method, IUPred provides a robust prediction of protein disorder based on an energy estimation approach that captures the fundamental difference between the biophysical properties of ordered and disordered regions. This paper reviews the energy estimation method underlying IUPred and the basic properties of the web server. Through an example, it also illustrates how the prediction output can be interpreted in a more complex case by taking into account the heterogeneous nature of IDRs. Various applications that benefited from IUPred to provide improved disorder predictions, complementing domain annotations and aiding the identification of functional short linear motifs are also described here. IUPred is freely available for noncommercial users through the web server (http://iupred.enzim.hu and http://iupred.elte.hu) . The program can also be downloaded and installed locally for large-scale analyses.

Order, Disorder, and Everything in Between

In addition to the “traditional” proteins characterized by the unique crystal-like structures needed for unique functions, it is increasingly recognized that many proteins or protein regions (collectively known as intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs)), being biologically active, do not have a specific 3D-structure in their unbound states under physiological conditions. There are also subtler categories of disorder, such as conditional (or dormant) disorder and partial disorder. Both the ability of a protein/region to fold into a well-ordered functional unit or to stay intrinsically disordered but functional are encoded in the amino acid sequence. Structurally, IDPs/IDPRs are characterized by high spatiotemporal heterogeneity and exist as dynamic structural ensembles. It is important to remember, however, that although structure and disorder are often treated as binary states, they actually sit on a structural continuum.

Functional Interaction between the Cytoplasmic ABC Protein LptB and the Inner Membrane LptC Protein, Components of the Lipopolysaccharide Transport Machinery in Escherichia coli

The assembly of lipopolysaccharide (LPS) in the outer leaflet of the outer membrane (OM) requires the transenvelope Lpt (lipopolysaccharide transport) complex, made in Escherichia coli of seven essential proteins located in the inner membrane (IM) (LptBCFG), periplasm (LptA), and OM (LptDE). At the IM, LptBFG constitute an unusual ATP binding cassette (ABC) transporter, composed by the transmembrane LptFG proteins and the cytoplasmic LptB ATPase, which is thought to extract LPS from the IM and to provide the energy for its export across the periplasm to the cell surface. LptC is a small IM bitopic protein that binds to LptBFG and recruits LptA via its N- and C-terminal regions, and its role in LPS export is not completely understood. Here, we show that the expression level of lptB is a critical factor for suppressing lethality of deletions in the C-terminal region of LptC and the functioning of a hybrid Lpt machinery that carries Pa-LptC, the highly divergent LptC orthologue from Pseudomonas aeruginosa We found that LptB overexpression stabilizes C-terminally truncated LptC mutant proteins, thereby allowing the formation of a sufficient amount of stable IM complexes to support growth. Moreover, the LptB level seems also critical for the assembly of IM complexes carrying Pa-LptC which is otherwise defective in interactions with the E. coli LptFG components. Overall, our data suggest that LptB and LptC functionally interact and support a model whereby LptB plays a key role in the assembly of the Lpt machinery.

IMPORTANCE

The asymmetric outer membrane (OM) of Gram-negative bacteria contains in its outer leaflet an unusual glycolipid, the lipopolysaccharide (LPS). LPS largely contributes to the peculiar permeability barrier properties of the OM that prevent the entry of many antibiotics, thus making Gram-negative pathogens difficult to treat. In Escherichia coli the LPS transporter (the Lpt machine) is made of seven essential proteins (LptABCDEFG) that form a transenvelope complex. Here, we show that increased expression of the membrane-associated ABC protein LptB can suppress defects of LptC, which participates in the formation of the periplasmic bridge. This reveals functional interactions between these two components and supports a role of LptB in the assembly of the Lpt machine.

Identifying Similar Patterns of Structural Flexibility in Proteins by Disorder Prediction and Dynamic Programming

Computational methods are prevailing in identifying protein intrinsic disorder. The results from predictors are often given as per-residue disorder scores. The scores describe the disorder propensity of amino acids of a protein and can be further represented as a disorder curve. Many proteins share similar patterns in their disorder curves. The similar patterns are often associated with similar functions and evolutionary origins. Therefore, finding and characterizing specific patterns of disorder curves provides a unique and attractive perspective of studying the function of intrinsically disordered proteins. In this study, we developed a new computational tool named IDalign using dynamic programming. This tool is able to identify similar patterns among disorder curves, as well as to present the distribution of intrinsic disorder in query proteins. The disorder-based information generated by IDalign is significantly different from the information retrieved from classical sequence alignments. This tool can also be used to infer functions of disordered regions and disordered proteins. The web server of IDalign is available at (http://labs.cas.usf.edu/bioinfo/service.html).