Template-Based Structure Prediction and Classification of Transcription Factors in \u3ci\u3eArabidopsis thaliana\u3c/i\u3e

Transcription factors (TFs) play important roles in plants. However, there is no systematic study of their structures and functions of most TFs in plants. Here, we performed template-based structure prediction for all TFs in Arabidopsis thaliana, with their full-length sequences as well as C-terminal and N-terminal regions. A total of 2,918 model structures were obtained with a high confidence score. We find that TF families employ only a smaller number of templates for DNA-binding domains (DBD) but a diverse number of templates for transcription regulatory domains (TRD). Although TF families are classified according to DBD, their sizes have a significant correlation with the number of unique non-DNA-binding templates employed in the family (Pearson correlation coefficient of 0.74). That is, the size of TF family is related to its functional diversity. Network analysis reveals new connections between TF families based on shared TRD or DBD templates; 81% TF families share DBD and 67% share TRD templates. Two large fully connected family clusters in this network are observed along with 69 island families. In addition, 25 genes with unknown functions are found to be DNA-binding and/or TF factors according to predicted structures. This work provides a global view of the classification of TFs based on their DBD or TRD templates, and hence, a deeper understanding of DNA-binding and regulatory functions from structural perspective. All structural models of TFs are deposited in the online database for public usage at http://sysbio.unl.edu/AthTF

Template-Based Structure Prediction and Classification of Transcription Factors in \u3ci\u3eArabidopsis thaliana\u3c/i\u3e

Transcription factors (TFs) play important roles in plants. However, there is no systematic study of their structures and functions of most TFs in plants. Here, we performed template-based structure prediction for all TFs in Arabidopsis thaliana, with their full-length sequences as well as C-terminal and N-terminal regions. A total of 2,918 model structures were obtained with a high confidence score. We find that TF families employ only a smaller number of templates for DNA-binding domains (DBD) but a diverse number of templates for transcription regulatory domains (TRD). Although TF families are classified according to DBD, their sizes have a significant correlation with the number of unique non-DNA-binding templates employed in the family (Pearson correlation coefficient of 0.74). That is, the size of TF family is related to its functional diversity. Network analysis reveals new connections between TF families based on shared TRD or DBD templates; 81% TF families share DBD and 67% share TRD templates. Two large fully connected family clusters in this network are observed along with 69 island families. In addition, 25 genes with unknown functions are found to be DNA-binding and/or TF factors according to predicted structures. This work provides a global view of the classification of TFs based on their DBD or TRD templates, and hence, a deeper understanding of DNA-binding and regulatory functions from structural perspective. All structural models of TFs are deposited in the online database for public usage at http://sysbio.unl.edu/AthTF

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward

Automatic recognition of epileptic EEG patterns via Extreme Learning Machine and multiresolution feature extraction

Epilepsy is one of the most common neurological disorders- approximately one in every 100 people worldwide are suffering from it. In this paper, a novel pattern recognition model is presented for automatic epilepsy diagnosis. Wavelet transform is investigated to decompose EEG into five EEG frequency bands which approximate to delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) bands. Complexity based features such as permutation entropy (PE), sample entropy (SampEn), and the Hurst exponent (HE) are extracted from both the original EEG signals and each of the frequency bands. The wavelet-based methodology separates the alterations in PE, SampEn, and HE in specific frequency bands of the EEG. The effectiveness of these complexity based measures in discriminating between normal brain state and brain state during the absence of seizures is evaluated using the Extreme Learning Machine (ELM). It is discovered that although there exists no significant differences in the feature values extracted from the original EEG signals, differences can be recognized when the features are examined within specific EEG frequency bands. A genetic algorithm (GA) is developed to choose feature subsets that are effective for enhancing the recognition performance. The GA is also examined for weight alteration for both sensitivity and specificity. The results show that the abnormal EEG diagnosis rate of the model without the involvement of the genetic algorithm is 85.9%. However, the diagnosis rate of the model increases to 94.2% when the genetic algorithm is integrated as a feature selector

Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine

Background Epilepsy is one of the most common neurological disorders approximately one in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. The objective of this research is to develop and present a novel classification framework that is utilised to discriminate interictal and preictal brain activities via quantitative analysis of electroencephalogram (EEG) recordings. New method Sample entropy-based features were extracted in parallel from 6 intracranial EEG channels, and then these features were fed to the extreme learning machine model for classification. Performance was evaluated on the basis of sensitivity and specificity and validation was performed using stratified cross-validation approach. Results The proposed method can correctly distinguish interictal and preictal EEGs with a sensitivity of 86.75% and a specificity of 83.80%, on average, across 21 patients and 6 epileptic seizure origins

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Epilepsy is one of the most common neurological disorders – approximately one in every 100 people worldwide are suffering from it. The electroencephalogram (EEG) is the most common source of information used to monitor, diagnose and manage neurological disorders related to epilepsy. Large amounts of data are produced by EEG monitoring devices, and analysis by visual inspection of long recordings of EEG in order to find traces of epilepsy is not routinely possible. Therefore, automated detection of epilepsy has been a goal of many researchers for a long time. This paper presents a novel method for automatic epileptic seizure detection. An optimized sample entropy (O-SampEn) algorithm is proposed and combined with extreme learning machine (ELM) to identify the EEG signals regarding the existence of seizure or not. To the knowledge of the authors, there exists no similar work in the literature. A public dataset was utilized for evaluating the proposed method. Results show that the proposed epilepsy detection approach achieves not only high detection accuracy but also a very fast computation speed, which demonstrates its huge potential for the real-time detection of epileptic seizures

Redar: A Remote Desktop Architecture for the Distributed Virtual Personal Computing”, National Research Center for Intelligent Computing Systems

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