CASP Research Articles

Association between the expression of ve-cadherin and mesentery microcirculation blood flow of sepsis CASP rats and its clinical significance

Objective To monitor the association between the expression of ve-cadherin and mesentery microcirculation blood flow of sepsis CASP rats, Analysis the relationship of each other. Methods Based on the standard criteria, we established sepsis CASP rats, and the rats were divided into several groups according to the gauge of the vein detained needle in the ascending colon, every group has six rats.The groups consisted of Sepsis A group 22 G(0.9 mm×25 mm, 33 ml/min), sepis B group 20 G(1.1 mm×32 mm, 54 ml/min), sepsis C group 18 G(1.3 mm×32 mm, 80 ml/min), sepsis D group 14 G(2.0 mm×45 mm, 270 ml/min), in addition we established the control group. Real-time observation was performed by microscope on the 6 h′s blood velcocity of each sepsis CASP rats group. Then we applied the immunohistochemistry to quantitatively analyze the expression of ve-cadherin of each group. Results The blood velcocity of the mesentery microcirculation in control group was (583.21±52.39) μm/s, it was higher compared with the sepsis D group(213.30±52.39) μm/s (P<0.05), the blood velocity in sepsis A group was (482.71±58.62) μm/s, higher compared with the sepsis D group(P<0.05). The score of the immunohistochemistry quantitative analysis of mesentery ve-cadherin in control group was 11.17±0.34, higher compared with the sepsis D group(5.43±0.43)(P<0.01). The score of sepsis A group was 10.07±0.30, higher compared with the sepsis D group(P<0.05). Conclusions The expression of ve-cadherin of sepsis CASP rats had positive correlation with the blood velocity of the mesentery microcirculation of sepsis CASP rats, and it can indirectly reflect the degree of the sepsis. Key words: Sepsis; VE-cadherin; Mesenteric microcirculation

UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling.

Motivation: Recent experimental studies have suggested that proteins fold via stepwise assembly of structural units named ‘foldons’ through the process of sequential stabilization. Alongside, latest developments on computational side based on probabilistic modeling have shown promising direction to perform de novo protein conformational sampling from continuous space. However, existing computational approaches for de novo protein structure prediction often randomly sample protein conformational space as opposed to experimentally suggested stepwise sampling.Results: Here, we develop a novel generative, probabilistic model that simultaneously captures local structural preferences of backbone and side chain conformational space of polypeptide chains in a united-residue representation and performs experimentally motivated conditional conformational sampling via stepwise synthesis and assembly of foldon units that minimizes a composite physics and knowledge-based energy function for de novo protein structure prediction. The proposed method, UniCon3D, has been found to (i) sample lower energy conformations with higher accuracy than traditional random sampling in a small benchmark of 6 proteins; (ii) perform comparably with the top five automated methods on 30 difficult target domains from the 11th Critical Assessment of Protein Structure Prediction (CASP) experiment and on 15 difficult target domains from the 10th CASP experiment; and (iii) outperform two state-of-the-art approaches and a baseline counterpart of UniCon3D that performs traditional random sampling for protein modeling aided by predicted residue-residue contacts on 45 targets from the 10th edition of CASP.Availability and Implementation: Source code, executable versions, manuals and example data of UniCon3D for Linux and OSX are freely available to non-commercial users at http://sysbio.rnet.missouri.edu/UniCon3D/.Contact: chengji@missouri.eduSupplementary information: Supplementary data are available at Bioinformatics online.

The Maximum Entropy Fallacy Redux?

Protein structure can be predicted in silico given sufficiently good templates, as demonstrated in successive installments of the biannual Critical Assessment of protein Structure Prediction (CASP) competition [1]. Since the number of known protein sequences currently increases much faster than the number of known protein structures, and is likely to continue to do so in the foreseeable future, reliable ab initio protein structure prediction, without recourse to templates, would be highly desirable. It is currently not possible to achieve sufficient sampling in unrestrained folding to achieve predictions close to the native structure. Recently, very important progress has been made on the restricted problem of predicting spatial amino acid contacts in proteins from many homologous sequences [2]. While it is not yet clear if these techniques, collectively known as direct coupling analysis (DCA), can be leveraged to systematically predict full protein structures, preliminary results indicate that this may be the case [3,4]. DCA has also been used with success for several related problems, such as predicting structures of protein complexes [4] or alternative protein conformations [5]. The central ingredient in DCA is to learn generative probabilistic models from a set of homologous protein sequences. These models are chosen from an exponential family with linear and quadratic interactions, commonly referred to as Potts models (see Eq 1) [6]. In the literature, this procedure has been motivated by maximum entropy arguments [7–10]. In this Perspective, I will point out that these arguments are mistaken and that the successes of DCA can have nothing to do with maximum entropy. To the contrary, maximum entropy hides the real nature and questions raised by DCA and is thus an obstacle to progress. In addition, maximum entropy has a long and contested history in statistical physics, the field in which it was first introduced [11,12]. Definite and precise results derived in the last decade and a half have here conclusively falsified maximum entropy. Appeals to maximum entropy are therefore prejudicial to a more general acceptance and adoption of DCA.

Some of the most interesting CASP11 targets through the eyes of their authors.

ABSTRACTThe Critical Assessment of protein Structure Prediction (CASP) experiment would not have been possible without the prediction targets provided by the experimental structural biology community. In this article, selected crystallographers providing targets for the CASP11 experiment discuss the functional and biological significance of the target proteins, highlight their most interesting structural features, and assess whether these features were correctly reproduced in the predictions submitted to CASP11. Proteins 2016; 84(Suppl 1):34–50. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

General overview on structure prediction of twilight-zone proteins.

Protein structure prediction from amino acid sequence has been one of the most challenging aspects in computational structural biology despite significant progress in recent years showed by critical assessment of protein structure prediction (CASP) experiments. When experimentally determined structures are unavailable, the predictive structures may serve as starting points to study a protein. If the target protein consists of homologous region, high-resolution (typically <1.5 Å) model can be built via comparative modelling. However, when confronted with low sequence similarity of the target protein (also known as twilight-zone protein, sequence identity with available templates is less than 30 %), the protein structure prediction has to be initiated from scratch. Traditionally, twilight-zone proteins can be predicted via threading or ab initio method. Based on the current trend, combination of different methods brings an improved success in the prediction of twilight-zone proteins. In this mini review, the methods, progresses and challenges for the prediction of twilight-zone proteins were discussed.

Assessment of the utility of contact-based restraints in accelerating the prediction of protein structure using molecular dynamics simulations.

Molecular dynamics (MD) simulation is a well-established tool for the computational study of protein structure and dynamics, but its application to the important problem of protein structure prediction remains challenging, in part because extremely long timescales can be required to reach the native structure. Here, we examine the extent to which the use of low-resolution information in the form of residue-residue contacts, which can often be inferred from bioinformatics or experimental studies, can accelerate the determination of protein structure in simulation. We incorporated sets of 62, 31, or 15 contact-based restraints in MD simulations of ubiquitin, a benchmark system known to fold to the native state on the millisecond timescale in unrestrained simulations. One-third of the restrained simulations folded to the native state within a few tens of microseconds-a speedup of over an order of magnitude compared with unrestrained simulations and a demonstration of the potential for limited amounts of structural information to accelerate structure determination. Almost all of the remaining ubiquitin simulations reached near-native conformations within a few tens of microseconds, but remained trapped there, apparently due to the restraints. We discuss potential methodological improvements that would facilitate escape from these near-native traps and allow more simulations to quickly reach the native state. Finally, using a target from the Critical Assessment of protein Structure Prediction (CASP) experiment, we show that distance restraints can improve simulation accuracy: In our simulations, restraints stabilized the native state of the protein, enabling a reasonable structural model to be inferred.

Automated protein structure refinement using i3Drefine software and its assessment in CASP10

Automated protein structure refinement using i3Drefine software and its assessment in CASP10

DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach

The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS) obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.AvailabilityThe DisoMCS is available at http://cal.tongji.edu.cn/disorder/.

Community challenges in biomedical text mining over 10 years: success, failure and the future.

One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively. Next, we describe the general workflow of organizing a Biomedical NLP (BioNLP) challenge and involved stakeholders (task organizers, task data producers, task participants and end users). Finally, we summarize the impact and contributions by taking into account different BioNLP challenges as a whole, followed by a discussion of their limitations and difficulties. We conclude with future trends in BioNLP challenge evaluations.

IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences.

IntFOLD is an independent web server that integrates our leading methods for structure and function prediction. The server provides a simple unified interface that aims to make complex protein modelling data more accessible to life scientists. The server web interface is designed to be intuitive and integrates a complex set of quantitative data, so that 3D modelling results can be viewed on a single page and interpreted by non-expert modellers at a glance. The only required input to the server is an amino acid sequence for the target protein. Here we describe major performance and user interface updates to the server, which comprises an integrated pipeline of methods for: tertiary structure prediction, global and local 3D model quality assessment, disorder prediction, structural domain prediction, function prediction and modelling of protein-ligand interactions. The server has been independently validated during numerous CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiments, as well as being continuously evaluated by the CAMEO (Continuous Automated Model Evaluation) project. The IntFOLD server is available at: http://www.reading.ac.uk/bioinf/IntFOLD/

Dysregulation of IL12 Signaling As a Novel Cause of an Autoimmune Lymphoproliferative like Syndrome

IntroductionAutoimmune lymphoproliferative syndrome (ALPS) is characterized by abnormal lymphocyte homeostasis caused by defective apoptosis. Mutations in genes involved in the Fas death receptor pathway (FAS, FASLG or CASP10 genes) are the cause for the pathogenesis of ALPS. However, in 20-30% of all ALPS cases, collectively classified as ALPS-U (undetermined), the genetic defect is still unknown. The objective of this study was to employ whole-exome sequencing to search for novel gene candidates underlying ALPS-U or ALPS-like disease. Resulting candidates should be validated and their impact on the Fas death receptor pathway studied.MethodsPeripheral blood samples were collected from 26 patients diagnosed with ALPS (based on clinical phenotype and accumulation of DNT cells), relatives and healthy controls. PCR and Sanger sequencing were performed to check for germline and somatically acquired FAS, FASLG and CASP10 mutations. Whole-exome sequencing was carried out for all patients with unknown mutation and their parents. Candidate genes were identified by a KEGG-based protein interaction analysis interface of our in-house developed proprietary MySQL database driven workbench, termed SNuPy (Single Nucleotide Polymorphism Database). STRING 9.1 was used to identify high confidence (≥0.900) interaction partners of the Fas pathway. Single nucleotide variations (SNVs) were verified by PCR and Sanger sequencing. The impact of detected mutations on the candidate genes´ expression and functionality was analyzed by immunoblot. The candidate genes´ impact on Fas death receptor pathway and Fas/FasL expression was examined by flow cytometry, ELISA and qRT-PCR.ResultsOut of 26 analyzed ALPS cases 4 unrelated patients harbored heterozygous germline mutations in the death domain of the Fas receptor (p.Q282K, p.R249G, p.NVQ265-267KQT) or the extracellular domain (p.R191C), respectively. Two siblings with homozygous FASLGtruncating mutation (A69fs*138) and complete loss of FasL surface expression as a consequence were identified. Whole-exome sequencing also identified a homozygous R212* mutation in the IL12/IL23 receptor-component IL12RB1 (Interleukin 12 receptor, beta 1 subunit) in a patient who presented with classical ALPS symptoms (chronic non-malignant, noninfectious lymphadenopathy, splenomegaly, hepatomegaly, elevation of DNT cells, autoimmune cytopenias with polyclonal hypergammaglobulinemia, persistently increased vitamin B12 and IL10 levels). The p.R212* mutation in IL12RB1 leads to premature protein truncation by a stop codon gain, resulting in a complete loss of cell surface expression of IL12RB1 in the patient. IL12 is a factor known to regulate expression of both Fas and FasL. IL12 signaling was abrogated as demonstrated by deficient downstream STAT4 phosphorylation and IFNγ production. Low FasL expression on T-cells and low soluble FasL plasma levels were probably due to lack of IFNγ mediated transcriptional activation of FasL. In contrast to healthy controls, prolonged IL12 stimulation did not trigger upregulation of FasL nor apoptosis in the IL12RB1 deficient and FasL deficient (A69fs*138) patients, indicating that IL12 mediated apoptosis is FasL dependent. Heterozygous carriers of the IL12RB1 or the FASLG mutation showed an intermediate response but were asymptomatic and sub-clinically affected (showing e.g. moderate elevation of DNT cells).ConclusionOur data show that IL12 employs Fas signaling to achieve T-cell apoptosis and reveal IL12 signaling deficiency as a new cause of ALPS like disease via its impact on FasL expression. DisclosuresNo relevant conflicts of interest to declare.

A Multi-Template Combination Algorithm for Protein Model Refinement

As the gap grows tremendously between the numbers of protein sequence and structure, in-silico protein structure prediction plays more and more critical roles in life science. Biennial experiments of Critical Assessment of protein Structure Prediction (CASP), the most authoritative in the field of protein structure prediction, shows that most prediction methods of today are successful in certain aspects, such as comparative modeling. However, incomplete models unexpectedly appear and require further refinement works. Therefore, the present study designed an automated multi-template combination algorithm to perform such refinement works. A total of 59 proteins released during CASP9 prediction season (human group) were selected as experimental targets. Four prediction methods HHpred, Pcons, Modeller, and SAM were used to generate protein models, among which 318 models were incomplete. Automated multitemplate combination algorithm was used in this study to work on each incomplete model, find the missing structures from other models, combine them with the original model, and finally obtain a recombined new model. Our results indicated that the quality of 95.56% of these 318 models was improved after the combination, and the improvement was statistically significant. Therefore, this study provided an effective method to improve the protein model quality.

CASP8 (Caspase 8, Apoptosis-Related Cysteine Peptidase)

Review on CASP8, with data on DNA/RNA, on the protein encoded and where the gene is implicated.

Assessment of ligand binding site predictions in CASP10

The identification of amino acid residues in proteins involved in binding small molecule ligands is an important step for their functional characterization, as the function of a protein often depends on specific interactions with other molecules. The accuracy of computational methods aiming to predict such binding residues was evaluated within the "function prediction (prediction of binding sites, FN)" category of the critical assessment of protein structure prediction (CASP) experiment. In the last edition of the experiment (CASP10), 17 research groups participated in this category, and their predictions were evaluated on 13 prediction targets containing biologically relevant ligands. The results of this experiment indicate that several methods achieved an overall good performance, showing the usefulness of such methods in predicting ligand binding residues. As in previous years, methods based on a homology transfer approach were dominating. In comparison to CASP9, a larger fraction of the top predictors are automated servers. However, due to the small number of targets and the characteristics of the prediction format, the differences observed among the first ten methods were not statistically significant and it was also not possible to analyze differences in accuracy for different ligand types or overall structure, difficulty. To overcome these limitations and to allow for a more detailed evaluation, in future editions of CASP, methods in the FN category will no longer be evaluated on the "normal" CASP targets, but assessed continuously by CAMEO (continuous automated model evaluation) based on weekly prereleased sequences from the PDB.

Assessment of template‐based protein structure predictions in CASP10

Template-based modeling (TBM) is a major component of the critical assessment of protein structure prediction (CASP). In CASP10, some 41,740 predicted models submitted by 150 predictor groups were assessed as TBM predictions. The accuracy of protein structure prediction was assessed by geometric comparison with experimental X-ray crystal and NMR structures using a composite score that included both global alignment metrics and distance-matrix-based metrics. These included GDT-HA and GDC-all global alignment scores, and the superimposition-independent LDDT distance-matrix-based score. In addition, a superimposition-independent RPF metric, similar to that described previously for comparing protein models against experimental NMR data, was used for comparing predicted protein structure models against experimental protein structures. To score well on all four of these metrics, models must feature accurate predictions of both backbone and side-chain conformations. Performance rankings were determined independently for server and the combined server plus human-curated predictor groups. Final rankings were made using paired head-to-head Student's t-test analysis of raw metric scores among the top 25 performing groups in each category.

Computing the Relative Stabilities and the Per-Residue Components in Protein Conformational Changes

Protein molecules often undergo conformational changes. In order to gain insights into the forces that drive such changes, it would be useful to have a method that computes the per-residue contributions to the conversion free energy. Here, we describe the "confine-convert-release" (CCR) method, which is applicable to large conformational changes. We show that CCR correctly predicts the stable states of several "chameleon" sequences that have previously been challenging for molecular simulations. CCR can often discriminate better from worse predictions of native protein models in critical assessment of protein structure prediction (CASP). We show how the total conversion free energies can be parsed into per-residue free-energy components. Such parsing gives insights into which amino acids are most responsible for given transformations. For example, here we are able to "reverse-engineer" the known design principles of the chameleon proteins. This opens up the possibility for systematic improvements in structure-prediction scoring functions, in the design of protein conformational switches, and in interpreting protein mechanisms at the amino-acid level.

Protein Arginine Methyltransferase 5 (PRMT5) Inhibition Induces Lymphoma Cell Death through Reactivation of the Retinoblastoma Tumor Suppressor Pathway and Polycomb Repressor Complex 2 (PRC2) Silencing

Epigenetic regulation mediated by lysine- and arginine-specific enzymes plays an essential role in tumorigenesis, and enhanced expression of the type II protein arginine methyltransferase PRMT5 as well as the polycomb repressor complex PRC2 has been associated with increased cell proliferation and survival. Here, we show that PRMT5 is overexpressed in three different types of non-Hodgkin lymphoma cell lines and clinical samples as well as in mouse primary lymphoma cells and that it up-regulates PRC2 expression through inactivation of the retinoblastoma proteins RB1 and RBL2. Although PRMT5 epigenetically controls RBL2 expression, it indirectly promotes RB1 phosphorylation through enhanced cyclin D1 expression. Furthermore, we demonstrate that PRMT5 knockdown in non-Hodgkin lymphoma cell lines and mouse primary lymphoma cells leads to RBL2 derepression and RB1 reactivation, which in turn inhibit PRC2 expression and trigger derepression of its CASP10, DAP1, HOXA5, and HRK pro-apoptotic target genes. We also show that reduced PRMT5 expression leads to cyclin D1 transcriptional repression via loss of TP53K372 methylation, which results in decreased BCL3 expression and enhanced recruitment of NF-κB p52-HDAC1 repressor complexes to the cyclin D1 promoter. These findings indicate that PRMT5 is a master epigenetic regulator that governs expression of its own target genes and those regulated by PRC2 and that its inhibition could offer a promising therapeutic strategy for lymphoma patients.

A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks

BackgroundIn recent years, the use and importance of predicted protein residue-residue contacts has grown considerably with demonstrated applications such as drug design, protein tertiary structure prediction and model quality assessment. Nevertheless, reported accuracies in the range of 25-35% stubbornly remain the norm for sequence based, long range contact predictions on hard targets. This is in spite of a prolonged effort on behalf of the community to improve the performance of residue-residue contact prediction. A thorough study of the quality of current residue-residue contact predictions and the evaluation metrics used as well as an analysis of current methods is needed to stimulate further advancement in contact prediction and its application. Such a study will better explain the quality and nature of residue-residue contact predictions generated by current methods and as a result lead to better use of this contact information.ResultsWe evaluated several sequence based residue-residue contact predictors that participated in the tenth Critical Assessment of protein Structure Prediction (CASP) experiment. The evaluation was performed using standard assessment techniques such as those used by the official CASP assessors as well as two novel evaluation metrics (i.e., cluster accuracy and cluster count). An in-depth analysis revealed that while most residue-residue contact predictions generated are not accurate at the residue level, there is quite a strong contact signal present when allowing for less than residue level precision. Our residue-residue contact predictor, DNcon, performed particularly well achieving an accuracy of 66% for the top L/10 long range contacts when evaluated in a neighbourhood of size 2. The coverage of residue-residue contact areas was also greater with DNcon when compared to other methods. We also provide an analysis of DNcon with respect to its underlying architecture and features used for classification.ConclusionsOur novel evaluation metrics demonstrate that current residue-residue contact predictions do contain a strong contact signal and are of better quality than standard evaluation metrics indicate. Our method, DNcon, is a robust, state-of-the-art residue-residue sequence based contact predictor and excelled under a number of evaluation schemes. It is available as a web service at http://iris.rnet.missouri.edu/dncon/.

Segment assembly, structure alignment and iterative simulation in protein structure prediction

Segment assembly, structure alignment and iterative simulation in protein structure prediction

DomHR: Accurately Identifying Domain Boundaries in Proteins Using a Hinge Region Strategy

MotivationThe precise prediction of protein domains, which are the structural, functional and evolutionary units of proteins, has been a research focus in recent years. Although many methods have been presented for predicting protein domains and boundaries, the accuracy of predictions could be improved.ResultsIn this study we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy. A hinge region was defined as a segment of amino acids that covers part of a domain region and a boundary region. We developed a strategy to construct profiles of domain-hinge-boundary (DHB) features generated by sequence-domain/hinge/boundary alignment against a database of known domain structures. The DHB features had three elements: normalized domain, hinge, and boundary probabilities. The DHB features were used as input to identify domain boundaries in a sequence. DomHR used a nonredundant dataset as the training set, the DHB and predicted shape string as features, and a conditional random field as the classification algorithm. In predicted hinge regions, a residue was determined to be a domain or a boundary according to a decision threshold. After decision thresholds were optimized, DomHR was evaluated by cross-validation, large-scale prediction, independent test and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DomHR outperformed other well-established, publicly available domain boundary predictors for prediction accuracy.AvailabilityThe DomHR is available at http://cal.tongji.edu.cn/domain/.