植物SET蛋白

SET蛋白是一类包含保守的SET结构域、与组蛋白甲基化密切相关的蛋白质。组蛋白修饰作为调控基因表达的重要因素,在植物体基因转录调控中发挥关键的作用。有关SET蛋白的研究为深入了解组蛋白修饰的机制提供了重要信息。植物SET蛋白具有保守的结构特征及进化机制,参与众多细胞核内的反应过程,如染色体的浓缩和分离,基因的转录,以及DNA的复制和修复等,调控植物基因的表达,影响植物体的发育。     

植物甲基结合蛋白（MBD）

DNA甲基化是生物体内普遍存在的一种基因修饰,甲基结合蛋白（MBD）是与甲基化DNA结合的反式作用因子,在植物生长发育过程中起调控作用。本文介绍了植物DNA甲基化和MBD蛋白在植物生长发育调控中的研究进展,并对其研究前景作了展望。     

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

Motivation

The 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.

Results

In 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.

Availability

The DomHR is available at http://cal.tongji.edu.cn/domain/.     

Classification of Beta-Lactamases and Penicillin Binding Proteins Using Ligand-Centric Network Models

β-lactamase mediated antibiotic resistance is an important health issue and the discovery of new β-lactam type antibiotics or β-lactamase inhibitors is an area of intense research. Today, there are about a thousand β-lactamases due to the evolutionary pressure exerted by these ligands. While β-lactamases hydrolyse the β-lactam ring of antibiotics, rendering them ineffective, Penicillin-Binding Proteins (PBPs), which share high structural similarity with β-lactamases, also confer antibiotic resistance to their host organism by acquiring mutations that allow them to continue their participation in cell wall biosynthesis. In this paper, we propose a novel approach to include ligand sharing information for classifying and clustering β-lactamases and PBPs in an effort to elucidate the ligand induced evolution of these β-lactam binding proteins. We first present a detailed summary of the β-lactamase and PBP families in the Protein Data Bank, as well as the compounds they bind to. Then, we build two different types of networks in which the proteins are represented as nodes, and two proteins are connected by an edge with a weight that depends on the number of shared identical or similar ligands. These models are analyzed under three different edge weight settings, namely unweighted, weighted, and normalized weighted. A detailed comparison of these six networks showed that the use of ligand sharing information to cluster proteins resulted in modules comprising proteins with not only sequence similarity but also functional similarity. Consideration of ligand similarity highlighted some interactions that were not detected in the identical ligand network. Analysing the β-lactamases and PBPs using ligand-centric network models enabled the identification of novel relationships, suggesting that these models can be used to examine other protein families to obtain information on their ligand induced evolutionary paths.     

CODA: Accurate Detection of Functional Associations between Proteins in Eukaryotic Genomes Using Domain Fusion

Background

In order to understand how biological systems function it is necessary to determine the interactions and associations between proteins. Gene fusion prediction is one approach to detection of such functional relationships. Its use is however known to be problematic in higher eukaryotic genomes due to the presence of large homologous domain families. Here we introduce CODA (Co-Occurrence of Domains Analysis), a method to predict functional associations based on the gene fusion idiom.

Methodology/Principal Findings

We apply a novel scoring scheme which takes account of the genome-specific size of homologous domain families involved in fusion to improve accuracy in predicting functional associations. We show that CODA is able to accurately predict functional similarities in human with comparison to state-of-the-art methods and show that different methods can be complementary. CODA is used to produce evidence that a currently uncharacterised human protein may be involved in pathways related to depression and that another is involved in DNA replication.

Conclusions/Significance

The relative performance of different gene fusion methodologies has not previously been explored. We find that they are largely complementary, with different methods being more or less appropriate in different genomes. Our method is the only one currently available for download and can be run on an arbitrary dataset by the user. The CODA software and datasets are freely available from ftp://ftp.biochem.ucl.ac.uk/pub/gene3d_data/v6.1.0/CODA/. Predictions are also available via web services from http://funcnet.eu/.     

AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features

It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computational methodology based on the notion of deep latent space encoding. A deep neural network classifier fused with an auto-encoder learns class labels in a pruned latent space. This strategy has eliminated the need to separately develop classifier and the feature selection model, allowing the standalone model to effectively harness discriminating feature space and perform improved predictions. A thorough analytical study has been presented alongwith the PCA/tSNE visualization and PCA-GCNR scores to show the discriminating power of the proposed method. The proposed method showed a high MCC value of 0.43 and a balanced accuracy of 76.2%, which is superior to the existing models. The model has been evaluated on an independent dataset during which it outperformed the contemporary methods by correctly identifying the novel proteins with an accuracy of 95%.     

蛋白质PX结构域的结构和功能

本文重点介绍了近年不断发现的许多蛋白质结构中含有一个特异的结构域（hox homolog)－PX结构域。蛋白质通过PX结构域与膜肌醇磷脂结合靶蛋白质结合到细胞膜上,然后发挥蛋白质的各自功能。现已鉴定含PX结构域的蛋白质约有100多种,这些蛋白质参与蛋白质转运和信号转导。     

Redox-Linked Domain Movements in the Catalytic Cycle of Cytochrome P450 Reductase

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Functional Classification of Immune Regulatory Proteins

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Highlights? An intermediate sequence based clustering algorithm, Brotherhood, is introduced ? The Brotherhood method is used to assign functional families within the human IgSF ? Newly annotated nectin-like family member, CRTAM, structure is solved ? Homophilic transinteraction is observed for CRTAM     

Domain Expansion and Functional Diversification in Vertebrate Reproductive Proteins

The rapid evolution of fertilization proteins has generated remarkable diversity in molecular structure and function. Glycoproteins of vertebrate egg coats contain multiple zona pellucida (ZP)-N domains (1–6 copies) that facilitate multiple reproductive functions, including species-specific sperm recognition. In this report, we integrate phylogenetics and machine learning to investigate how ZP-N domains diversify in structure and function. The most C-terminal ZP-N domain of each paralog is associated with another domain type (ZP-C), which together form a “ZP module.” All modular ZP-N domains are phylogenetically distinct from nonmodular or free ZP-N domains. Machine learning–based classification identifies eight residues that form a stabilizing network in modular ZP-N domains that is absent in free domains. Positive selection is identified in some free ZP-N domains. Our findings support that strong purifying selection has conserved an essential structural core in modular ZP-N domains, with the relaxation of this structural constraint allowing free N-terminal domains to functionally diversify.     

EEG Classification of Different Imaginary Movements within the Same Limb

The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.     

Studying the Unfolding Kinetics of Proteins under Pressure Using Long Molecular Dynamic Simulation Runs

The usefulness of computational methods such as molecular dynamics simulation has been extensively established for studying systems in equilibrium. Nevertheless, its application to complex non-equilibrium biological processes such as protein unfolding has been generally regarded as producing results which cannot be interpreted straightforwardly. In the present study, we present results for the kinetics of unfolding of apomyoglobin, based on the analysis of long simulation runs of this protein in solution at 3 kbar (1 atm = 1.01325, bar = 101 325 Pa). We hereby demonstrate that the analysis of the data collected within a simulated time span of 0.18 μs suffices for producing results, which coincide remarkably with the available unfolding kinetics experimental data. This not only validates molecular dynamics simulation as a valuable alternative for studying non-equilibrium processes, but also enables a detailed analysis of the actual structural mechanism which underlies the unfolding process of proteins under elusive denaturing conditions such as high pressure.     

Classification of Rhabdovirus Proteins: a Proposal

A simple alphabetical classification is offered as a means to codify the proteins of rhabdoviruses, using vesicular stomatitis virus as the prototype.     

细胞核内蛋白质动态变化研究进展

漂白后荧光恢复和漂白荧光丢失技术是蛋白质动态变化研究中常用的两项技术.近年来,利用这两项技术对细胞核内蛋白质动态变化的研究表明：一些蛋白质在细胞核内是运动的,能和各自所在的区域快速结合和解离;并且这种运动主要以被动扩散的方式进行,不消耗代谢的能量;另外蛋白质的共价修饰可对某些蛋白质的运动产生影响.细胞核内蛋白质的动态变化对细胞核的结构组成和基因表达的调控都具有重要的意义,但详细的机制还有待于进一步的研究.     

Construction of Artificial TNF-Binding Proteins Based on the 10th Human Fibronectin Type III Domain Using Bacterial Display

Construction of antibody mimetics on the base of alternative scaffold proteins is a promising strategy for obtaining new products for medicine and biotechnology. The aim of our work was to optimize the cell display system for the 10th human fibronectin type III domain (¹⁰Fn3) scaffold protein based on the AT877 autotransporter from Psychrobacter cryohalolentis K5^T and to construct new artificial TNF-binding proteins. We obtained a ¹⁰Fn3 gene combinatorial library and screened it using the bacterial display method. After expression of the selected ¹⁰Fn3 variants in Escherichia coli cells and analysis of their TNF-binding activity, we identified proteins that display high affinity for TNF and characterized their properties.     

Structural Differentiation of Graphs Using Hosoya-Based Indices

In this paper, we introduce the Hosoya-Spectral indices and the Hosoya information content of a graph. The first measure combines structural information captured by partial Hosoya polynomials and graph spectra. The latter is a graph entropy measure which is based on blocks consisting of vertices with the same partial Hosoya polynomial. We evaluate the discrimination power of these quantities by interpreting numerical results.     

Computational Design of Binding Proteins to EGFR Domain II

We developed a process to produce novel interactions between two previously unrelated proteins. This process selects protein scaffolds and designs protein interfaces that bind to a surface patch of interest on a target protein. Scaffolds with shapes complementary to the target surface patch were screened using an exhaustive computational search of the human proteome and optimized by directed evolution using phage display. This method was applied to successfully design scaffolds that bind to epidermal growth factor receptor (EGFR) domain II, the interface of EGFR dimerization, with high reactivity toward the target surface patch of EGFR domain II. One potential application of these tailor-made protein interactions is the development of therapeutic agents against specific protein targets.