fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization

Background: Development of predictors of propensity of protein sequences for successful crystallization has been actively pursued for over a decade. A few novel methods that expanded the scope of these predictions to address additional steps of protein production and structure determination pipelines were released in recent years. The predictive performance of the current methods is modest. This is because the only input that they use is the protein sequence and since the experimental annotations of these data might be inconsistent given that they were collected across many laboratories and centers. However, even these modest levels of predictive quality are still practical compared to the reported low success rates of crystallization, which are below 10%. We focus on another important aspect related to a high computational cost of running the predictors that offer the expanded scope. Results: We introduce a novel fDETECT webserver that provides very fast and modestly accurate predictions of the success of protein production, purification, crystallization, and structure determination. Empirical tests on two datasets demonstrate that fDETECT is more accurate than the only other similarly fast method, and similarly accurate and three orders of magnitude faster than the currently most accurate predictors. Our method predicts a single protein in about 120 milliseconds and needs less than an hour to generate the four predictions for an entire human proteome. Moreover, we empirically show that fDETECT secures similar levels of predictive performance when compared with four representative methods that only predict success of crystallization, while it also provides the other three predictions. A webserver that implements fDETECT is available at http://biomine.cs.vcu.edu/servers/ fDETECT/. Conclusions: fDETECT is a computational tool that supports target selection for protein production and X-ray crystallography-based structure determination. It offers predictive quality that matches or exceeds other state-ofthe-art tools and is especially suitable for the analysis of large protein sets

Critical assessment of protein intrinsic disorder prediction

Abstract: Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

Computational Prediction of Intrinsic Disorder in Proteins

Computational prediction of intrinsically disordered proteins (IDPs) is a mature research field. These methods predict disordered residues and regions in an input protein chain. More than 60 predictors of IDPs have been developed. This unit defines computational prediction of intrinsic disorder, summarizes major types of predictors of disorder, and provides details about three accurate and recently released methods. We demonstrate the use of these methods to predict intrinsic disorder for several illustrative proteins, provide insights into how predictions should be interpreted, and quantify and discuss predictive performance. Predictions can be freely and conveniently obtained using webservers. We point to the availability of databases that provide access to annotations of intrinsic disorder determined by structural studies and putative intrinsic disorder pre-computed by computational methods. Lastly, we also summarize experimental methods that can be used to validate computational predictions. © 2017 by John Wiley & Sons, Inc

fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization

Abstract Background Development of predictors of propensity of protein sequences for successful crystallization has been actively pursued for over a decade. A few novel methods that expanded the scope of these predictions to address additional steps of protein production and structure determination pipelines were released in recent years. The predictive performance of the current methods is modest. This is because the only input that they use is the protein sequence and since the experimental annotations of these data might be inconsistent given that they were collected across many laboratories and centers. However, even these modest levels of predictive quality are still practical compared to the reported low success rates of crystallization, which are below 10%. We focus on another important aspect related to a high computational cost of running the predictors that offer the expanded scope. Results We introduce a novel fDETECT webserver that provides very fast and modestly accurate predictions of the success of protein production, purification, crystallization, and structure determination. Empirical tests on two datasets demonstrate that fDETECT is more accurate than the only other similarly fast method, and similarly accurate and three orders of magnitude faster than the currently most accurate predictors. Our method predicts a single protein in about 120 milliseconds and needs less than an hour to generate the four predictions for an entire human proteome. Moreover, we empirically show that fDETECT secures similar levels of predictive performance when compared with four representative methods that only predict success of crystallization, while it also provides the other three predictions. A webserver that implements fDETECT is available at http://biomine.cs.vcu.edu/servers/fDETECT/ . Conclusions fDETECT is a computational tool that supports target selection for protein production and X-ray crystallography-based structure determination. It offers predictive quality that matches or exceeds other state-of-the-art tools and is especially suitable for the analysis of large protein sets

Comprehensive Review of Methods for Prediction of Intrinsic Disorder and Its Molecular Functions

Computational prediction of intrinsic disorder in protein sequences dates back to late 1970 and has flourished in the last two decades. We provide a brief historical overview, and we review over 30 recent predictors of disorder. We are the first to also cover predictors of molecular functions of disorder, including 13 methods that focus on disordered linkers and disordered protein–protein, protein–RNA, and protein–DNA binding regions. We overview their predictive models, usability, and predictive performance. We highlight newest methods and predictors that offer strong predictive performance measured based on recent comparative assessments. We conclude that the modern predictors are relatively accurate, enjoy widespread use, and many of them are fast. Their predictions are conveniently accessible to the end users, via web servers and databases that store pre-computed predictions for millions of proteins. However, research into methods that predict many not yet addressed functions of intrinsic disorder remains an outstanding challenge

Compartmentalization and Functionality of Nuclear Disorder: Intrinsic Disorder and Protein-Protein Interactions in Intra-Nuclear Compartments

The cell nucleus contains a number of membrane-less organelles or intra-nuclear compartments. These compartments are dynamic structures representing liquid-droplet phases which are only slightly denser than the bulk intra-nuclear fluid. They possess different functions, have diverse morphologies, and are typically composed of RNA (or, in some cases, DNA) and proteins. We analyzed 3005 mouse proteins localized in specific intra-nuclear organelles, such as nucleolus, chromatin, Cajal bodies, nuclear speckles, promyelocytic leukemia (PML) nuclear bodies, nuclear lamina, nuclear pores, and perinuclear compartment and compared them with ~29,863 non-nuclear proteins from mouse proteome. Our analysis revealed that intrinsic disorder is enriched in the majority of intra-nuclear compartments, except for the nuclear pore and lamina. These compartments are depleted in proteins that lack disordered domains and enriched in proteins that have multiple disordered domains. Moonlighting proteins found in multiple intra-nuclear compartments are more likely to have multiple disordered domains. Protein-protein interaction networks in the intra-nuclear compartments are denser and include more hubs compared to the non-nuclear proteins. Hubs in the intra-nuclear compartments (except for the nuclear pore) are enriched in disorder compared with non-nuclear hubs and non-nuclear proteins. Therefore, our work provides support to the idea of the functional importance of intrinsic disorder in the cell nucleus and shows that many proteins associated with sub-nuclear organelles in nuclei of mouse cells are enriched in disorder. This high level of disorder in the mouse nuclear proteins defines their ability to serve as very promiscuous binders, possessing both large quantities of potential disorder-based interaction sites and the ability of a single such site to be involved in a large number of interactions

Autophagy-related Intrinsically Disordered Proteins in Intra-nuclear Compartments

Recent analyses indicated that autophagy can be regulated via some nuclear transcriptional networks and many important players in the autophagy and other forms of programmed cell death are known to be intrinsically disordered. To this end, we analyzed similarities and differences in the intrinsic disorder distribution of nuclear and non-nuclear proteins related to autophagy. We also looked at the peculiarities of the distribution of the intrinsically disordered autophagy-related proteins in various intra-nuclear organelles, such as the nucleolus, chromatin, Cajal bodies, nuclear speckles, promyelocytic leukemia (PML) nuclear bodies, nuclear lamina, nuclear pores, and perinucleolar compartment. This analysis revealed that the autophagy-related proteins constitute about 2.5% of the non-nuclear proteins and 3.3% of the nuclear proteins, which corresponds to a substantial enrichment by about 32% in the nucleus. Curiously, although, in general, the autophagy-related proteins share similar characteristics of disorder with a generic set of all non-nuclear proteins, chromatin and nuclear speckles are enriched in the intrinsically disordered autophagy proteins (29 and 37% of these proteins are disordered, respectively) and have high disorder content at 0.24 and 0.27, respectively. Therefore, our data suggest that some of the nuclear disordered proteins may play important roles in autophagy

Betaine supplementation alleviates corticosterone-induced hepatic cholesterol accumulation through epigenetic modulation of HMGCR and CYP7A1 genes in laying hens

ABSTRACT: Excessive corticosterone (CORT) exposure could cause hepatic cholesterol accumulation in chickens and maternal betaine supplementation could decrease hepatic cholesterol deposition through epigenetic modifications in offspring chickens. Nevertheless, it remains uncertain whether providing betaine to laying hens could protect CORT-induced hepatic cholesterol accumulation via epigenetic mechanisms. This study aimed to examine the effects of dietary betaine on plasma and hepatic cholesterol contents, expression of cholesterol metabolic genes, as well as DNA methylation on their promoters in the liver of laying hens exposed to CORT. A total of 72 laying hens at 130 d of age were randomly divided into 3 groups: control (CON), CORT, and CORT+betaine (CORT+BET) groups. The experiment lasted for 35 d. Chickens in CON and CORT groups were fed a basal diet, whereas the CORT+BET group chickens were fed the basal diet supplemented with 0.1% betaine for 35 d. On d 28 of the experiment, chickens in CORT and CORT+BET groups received daily subcutaneous injections of CORT (4.0 mg/kg body weight), whereas the CON group chickens were injected with an equal volume of solvent for 7 d. The results showed that CORT administration led to a significant increase (P < 0.05) in the contents of cholesterol in plasma and liver, associated with activation (P < 0.05) of sterol regulatory element binding transcription factor 2 (SREBP2), 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), lecithin-cholesterol acyltransferase (LCAT) and low-density lipoprotein receptor (LDLR) genes expression, and inhibition of cholesterol-7-alpha hydroxylase (CYP7A1) and sterol 27-hydroxylase (CYP27A1) genes expression in the liver compared to the CON. In contrast, CORT-induced up-regulation of HMGCR mRNA and protein abundances and downregulation of CYP7A1 mRNA and protein abundances were completely normalized (P < 0.05) by betaine supplementation. Besides, CORT injection led to significant hypomethylation (P < 0.05) on HMGCR promoter and hypermethylation (P < 0.05) on CYP7A1 promoter. Moreover, dietary betaine rescued (P < 0.05) CORT-induced changes in methylation status of HMGCR and CYP7A1 genes promoters. These results indicate that dietary betaine addition protects laying hens from CORT-induced hepatic cholesterol accumulation via epigenetic modulation of HMGCR and CYP7A1 genes

Unstructural Biology of the Dengue Virus Proteins

In this study, we used a wide spectrum of bioinformatics techniques to evaluate the extent of intrinsic disorder in the complete proteomes of genotypes of four human dengue virus (DENV), to analyze the peculiarities of disorder distribution within individual DENV proteins, and to establish potential roles for the structural disorder with respect to their functions. We show that several proteins (ER, E, 1, 2A and 4A) are predicted to be mostly ordered, whereas four proteins (C, 2k, NS3 and NS5) are expected to have high disorder levels. The profiles of disorder propensities are similar across the four genotypes, except for the NS5 protein. Cleavage sites are depleted in polymorphic sites, and have a high propensity for disorder, especially relative to neighboring residues. Disordered regions are highly polymorphic in type 1 DENV but have a relatively low number of polymorphic sites in the type 4 virus. There is a high density of polymorphisms in proteins 2A and 4A, which are depleted in disorder. Thus, a high density of polymorphism is not unique to disordered regions. Analysis of disorder/function association showed that the predominant function of the disordered regions in the DENV proteins is protein–protein interaction and binding of nucleic acids, metals and other small molecules. These regions are also associated with phosphorylation, which may regulate their function