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作者： Ahmed Abdelkarim Eldud Omer RHODES UNIVERSITY

学位级别：硕士

The secondary structure of proteins is predicted using various binary classifiers. The data are adopted from the RS126 database. The original data consists of protein primary and secondary structure sequences. The original data is encoded using alphabetic letters. These data are encoded into unary vectors comprising ones and zeros only. Different binary classifiers, namely the naive Bayes, logistic regression and classification trees using hold-out and 5-fold cross validation are trained using the encoded data. For each of the classifiers three classification tasks are considered, namely helix against not helix (H/∼H), sheet against not sheet (S/∼S) and coil against not coil (C/∼C). The performance of these binary classifiers are compared using the overall accuracy in predicting the protein secondary structure for various window sizes. Our result indicate that hold-out cross validation achieved higher accuracy than 5-fold cross validation. The Naive Bayes classifier, using 5-fold cross validation achieved, the lowest accu- racy for predicting helix against not helix. The classification tree classifiers, using 5-fold cross validation, achieved the lowest accuracies for both coil against not coil and sheet against not sheet classifications. The logistic regression classier accuracy is dependent on the window size; there is a positive relationship between the accuracy and window size. The logistic regression classier approach achieved the highest accuracy when compared to the classification tree and Naive Bayes classifiers for each classification task; predicting helix against not helix with accu- racy 77.74%, for sheet against not sheet with accuracy 81.22% and for coil against not coil with accuracy 73.39%. It is noted that it is easier to compare classifiers if the classification process could be completely facilitated in R. Alternatively, it would be easier to assess these logistic regression classifiers if SPSS had a function to determine the accuracy of the logis

关键词： Classification tree Naive Bayes logistic regression hold-out 5-fold cross validation protein secondary structure prediction

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Association classification algorithm based on structure sequence in protein secondary structure prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第9期37卷 6381-6389页

作者： Zhou, Zhun Yang, Bingru Hou, Wei Tsinghua Univ Dept Environm Sci & Engn Beijing 100084 Peoples R China Univ Sci & Technol Beijing Dept Informat Engn Beijing 100083 Peoples R China

Objective: To propose a novel associate classification algorithm SAC (structural association classification) and develop a compound pyramid model for accurate and precise protein secondary structure prediction. Method: Based on the slide window theory, the protein sequence was treated as a window with length of 13, in which the target amino acid resided in the center, while the remaining area was targeted as secondary amino acid structures. To the head and tail of the sequence, the mirror method was employed to fill the space with an opposite- position structure in relation to the central position. In the mining process, the KDD center dot model not only focuses on the high support and confidence rules, but also pay attention to high confidence and low support rules, which is called 'knowledge in shortage'. Towards the end of the mining process, sets H, E and C, consisted of rule sets whose consequents are alpha-helix, beta-sheet and C-coil, were created respectively to meet the basic requirements for the protein secondary structure prediction. The knowledge base of protein secondary structure was then established with these three newly-acquired rule sets. Through the CMAR (Classification based on Multiple Association rules) algorithm, a novel multi-classifier was developed to determine the best likelihood of a given window to the secondary structure through the adjacent information on amino acid sequential window and screening of three different rule sets. Result: The protein knowledge base consisted of 8049 rules corresponding to sets H, E and C with 2642, 1895 and 3512 rules, respectively, was obtained. Experiment shows, theoretically, accuracy ratio exceeded 85% when confidence threshold value was 70% and 90%. Through the classification process using the multi-classifier SAC developed in four experiments, the significantly high accuracy and recall ratios up to 83.06% (According to Q(3) criterion, followed by abbreviation) in RS126 (Chen & Chaudhari, 2007;Guo et

关键词： Association classification protein secondary structure prediction KDD center dot (Knowledge Discovery(center dot)) Compound pyramid model

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SOV_refine: A further refined definition of segment overlap score and its significance for protein structure similarity

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SOURCE CODE FOR BIOLOGY AND MEDICINE 2018年第1期13卷 1-1页

作者： Liu, Tong Wang, Zheng Univ Miami Dept Comp Sci 1365 Mem Dr Coral Gables FL 33124 USA

Background: The segment overlap score (SOV) has been used to evaluate the predicted protein secondary structures, a sequence composed of helix (H), strand (E), and coil (C), by comparing it with the native or reference secondary structures, another sequence of H, E, and C. SOV's advantage is that it can consider the size of continuous overlapping segments and assign extra allowance to longer continuous overlapping segments instead of only judging from the percentage of overlapping individual positions as Q3 score does. However, we have found a drawback from its previous definition, that is, it cannot ensure increasing allowance assignment when more residues in a segment are further predicted accurately. Results: A new way of assigning allowance has been designed, which keeps all the advantages of the previous SOV score definitions and ensures that the amount of allowance assigned is incremental when more elements in a segment are predicted accurately. Furthermore, our improved SOV has achieved a higher correlation with the quality of protein models measured by GDT-TS score and TM-score, indicating its better abilities to evaluate tertiary structure quality at the secondary structure level. We analyzed the statistical significance of SOV scores and found the threshold values for distinguishing two protein structures (SOV_refine > 0.19) and indicating whether two proteins are under the same CATH fold (SOV_refine > 0.94 and > 0.90 for three-and eight-state secondary structures respectively). We provided another two example applications, which are when used as a machine learning feature for protein model quality assessment and comparing different definitions of topologically associating domains. We proved that our newly defined SOV score resulted in better performance. Conclusions: The SOV score can be widely used in bioinformatics research and other fields that need to compare two sequences of letters in which continuous segments have important meanings. We also genera

关键词： Segment overlap score SOV score protein secondary structure prediction Assessment of protein secondary structure predictions protein structure similarity Similarity of segmented biological sequences Comparing different definitions of topologically associating domains

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protein secondary structure prediction

Protein Secondary Structure Prediction

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作者： Walter Pirovano Jaap Heringa

While the prediction of a native protein structure from sequence continues to remain a challenging problem, over the past decades computational methods have become quite successful in exploiting the mechanisms behind secondary structure formation. The great effort expended in this area has resulted in the development of a vast number of secondary structure prediction methods. Especially the combination of well-optimized/sensitive machine-learning algorithms and inclusion of homologous sequence information has led to increased prediction accuracies of up to 80%. In this chapter, we will first introduce some basic notions and provide a brief history of secondary structure prediction advances. Then a comprehensive overview of state-of-the-art prediction methods will be given. Finally, we will discuss open questions and challenges in this field and provide some practical recommendations for the user. less

关键词： Multiple Sequence Alignment protein secondary structure prediction structure prediction protein structure Analysis secondary structure

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prediction of HPV Risk Types from protein secondary structure

Prediction of HPV Risk Types from Protein Secondary Structur...

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International Conference on Computational Intelligence and Communication Networks

作者： Nishant Choudhary Bhawani Sankar Biswal Anjali Mohapatra Dept. of CSE IIIT Bhubaneswar Bhubaneswar India

ISBN: (纸本)9781509000777

Identification of high risk among viruses is crucial for interpreting its oncogenic mechanisms. Also it is gainful in developing ultra modern clinical tools for its diagnosis, prevention and treatment. prediction of the structure of a particular virus helps to classify the risk type of that virus. prediction methods based on text based classification do not provide satisfactory results as compared to sequence-based techniques. In particular, text-mining approaches only relay on the documentation procured from the studies. Hence insufficiency of the evidences for an unknown virus gives rise to classification issues. Therefore risk prediction of viruses from protein sequence and its structure is an effective alternative to text based classification. In this work, Machine learning techniques are used for risk type classification of Papillomaviruses which is having high potentials to cause cancer.

关键词： Human papillomaviruses (HPVs) HMMs SVM Kernel method protein secondary structure prediction

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Flour Quality and Related Molecular Characterization of High Molecular Weight Glutenin Subunit Genes from Wild Emmer Wheat Accession TD-256

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JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2016年第24期64卷 5128-5136页

作者： Zhang, Da-Le He, Ting-Ting Liang, Hui-Hui Huang, Lu-Yu Su, Ya-Zhong Li, Yu-Ge Li, Suo-Ping Henan Univ Sch Life Sci Kaifeng 475001 Henan Peoples R China

To clarify the effect of high molecular weight glutenin subunit (HMW-GS) from wild emmer wheat on flour quality, which has the same mobility as that from common wheat, the composition and molecular characterization of HMW-GS from wild emmer wheat accession TD-256, as well as its flour quality, were intensively analyzed. It is found that the mobilities of Glu-A1 and Glu-B1 subunits from TD-256 are consistent with those of bread wheat cv. 'XiaoYan 6'. Nevertheless, dough theological properties of TD-256 reveal its poor flour quality. In the aspect of molecular structure from HMW-GS, only two conserved cysteine residues can be observed in the deduced protein sequence of 1Bx14* from TD-256, while most Glu-1Bx contain four conserved cysteine residues. In addition, as can be predicted from secondary structure, the quantity both of alpha-helixes and their amino acid residues of the subunits from TD-256 is fewer than those of common wheat. Though low molecular weight glutenin subunit (LMW-GS) and gliadin can also greatly influence flour quality, the protein structure of the HMW-GS revealed in this work can partly explain the poor flour quality of wild emmer accession TD-256.

关键词： Triticum dicoccoides high molecular weight glutenin subunit (HMW-GS) dough rheological properties gene cloning protein secondary structure prediction

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prediction of protein secondary structure Using Feature Selection and Analysis Approach

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ACTA BIOTHEORETICA 2014年第1期62卷 1-14页

作者： Feng, Yonge Lin, Hao Luo, Liaofu Inner Mongolia Agr Univ Coll Sci Hohhot 010018 Peoples R China Univ Elect Sci & Technol China Key Lab NeuroInformat Minist Educ Ctr BioinformatSch Life Sci & Technol Chengdu 610054 Peoples R China Inner Mongolia Univ Sch Phys Sci & Technol Hohhot 010021 Peoples R China

The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. However, the accuracy of ab initio secondary structure prediction from sequence is about 80 % currently, which is still far from satisfactory. In this study, we proposed a novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state secondary structure. A benchmark dataset including 2,640 proteins with sequence identity of less than 25 % was used to train and test the proposed method. The results indicate that overall accuracy of 87.8 % was achieved in secondary structure prediction by using ten-fold cross-validation. Moreover, the accuracy of predicted secondary structures ranges from 84 to 89 % at the level of residue. These results suggest that the feature selection technique can detect the optimized tetrapeptide structural words which affect the accuracy of predicted secondary structures.

关键词： protein secondary structure prediction Confidence level Tetrapeptide structural words Increment of diversity Quadratic discriminant analysis Error allowed scope

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Special HMW-GSs and their genes of Triticum turgidum subsp dicoccoides accession D141 and the potential utilization in common wheat

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GENETIC RESOURCES AND CROP EVOLUTION 2016年第5期63卷 833-844页

作者： Guo, Xiao-Hui Hu, Ji-Liang Wu, Bi-Hua Wang, Zhen-Zhen Wang, Dong Liu, Deng-Cai Zheng, You-Liang Sichuan Agr Univ Triticeae Res Inst Chengdu 611130 Peoples R China Xuchang Univ Coll Food & Biol Engn Xuchang 461000 Peoples R China

High molecular weight glutenin subunits (HMW-GSs) are the critical components of wheat seed storage proteins. They play a crucial role in determining dough viscoelastic properties and end-use quality of wheat flour. To investigate the potential utilization of the special HMW-GSs from wild emmer wheat (2n = 4x = 28, AABB) accession D141 in common wheat (2n = 6x = 42, AABBDD), continued to our previous report of the 1Ay and its coding sequence, the other three HMW-GSs, 1Ax, 1Bx13.1 and 1By16.1, and their coding genes were isolated, characterized and transferred from D141. The 1Ax gene was silent because of containing a premature stop codon TGA at position 835-837. The ORFs of 1Bx13.1 and 1By16.1 were 2373 and 2157 bp in length, and encoded 789 and 717 amino acid residues, respectively. Compared with the other 20 Glu-1Bx alleles in GenBank, seven SNPs and one 18-bp deletion were detected in 1Bx13.1. Compared with the other 10 Glu-1By alleles in GenBank, nine SNPs were observed in 1By16.1. The mobility of 1Bx13.1 and 1By16.1 on the SDS-PAGE gel was most similar to that of 1Bx13 and 1By16 from common wheat (T. aestivum, 2n = 6x = 42, AABBDD) cv. 'Jimai 20', respectively. protein secondary structure prediction showed that the combination of 1Bx13.1 + 1By16.1 possessed more alpha-helixes with more amino acid residues than that of 1Bx13 + 1By16. It suggested that 1Bx13.1 + 1By16.1 from wild emmer accession D141 might have more positive effects on bread-making quality than the superior subunit combination 1Bx13 + 1By16. The HMW-GS composition analysis of F-1 and F-2 between common wheat for female and wild emmer accession D141 for male, indicated that the special HMW-GSs from wild emmer accession D141 could be easily introduced into common wheat (2n = 42) by general inter-species hybridization.

关键词： Gene cloning High molecular weight glutenin subunit Interspecies hybridization protein secondary structure prediction Triticum aestivum Wild emmer

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LOV2-Controlled Photoactivation of protein

LOV2-Controlled Photoactivation of Protein

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作者： Anam Qudrat Abdullah Mosabbir Kevin Truong

protein trans-splicing is a posttranslational modification that joins two protein fragments together via a peptide a bond in a process that does not require exogenous cofactors. Towards achieving cellular control, synthetically engineered systems have used a variety of stimuli such as small molecules and light. Recently, split inteins have been engineered to be photoactive by the LOV2 domain (named LOVInC). Herein, we discuss (1) designing of LOV2-activated target proteins (e.g., inteins), (2) selecting feasible splice sites for the extein, and (3) imaging cells that express LOVInC-based target exteins. less

关键词： PCR SDS-PAGE Molecular Dynamics Simulation protein secondary structure prediction DNA Purification Cell And Tissue Culture Transfection Trypsination MetaMorph Escherichia coli Photoactivation Fluorescence Inteins protein trans-splicing Light-oxygen-voltage domain

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Flour Quality and Related Molecular Characterization of High Molecular Weight Glutenin Subunit Genes from Wild Emmer Wheat Accession TD-256

Flour Quality and Related Molecular Characterization of High...

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第八届全国小麦基因组学及分子育种大会

作者： Dale Zhang Tingting He Huihui Liang Luyu Huang Yazhong Su Yuge Li Suoping Li School of Life Science Henan University

To clarify the effect of high molecular weight glutenin subunit（HMW-GS）from wild emmer wheat on flour quality,which has the same mobility as that from common wheat,the composition and molecular characterization of HMW-GS from wild emmer wheat accession TD-256,as well as its flour quality,were intensively *** is found that the mobilities of Glu-A1 and Glu-B1 subunits from TD-256 are consistent with those of bread wheat cv.'XiaoYan 6'.Nevertheless,dough rheo logical properties of TD-256 reveal its poor flour *** the aspect of molecular structure from HMW-GS,only two conserved cysteine residues can be observed in the deduced protein sequence of1 Bx14 from TD-256,while most Glu-1 Bx contain four conserved cysteine *** addition,as can be predicted from secondary structure,the quantity both of a-helixes and their amino acid residues of the subunits from TD-256 is fewer than those of common *** low molecular weight glutenin subunit（LMW-GS） and gliadin can also greatly influence flour quality,the protein structure of the HMW-GS revealed in this work can partly explain the poor flour quality of wild emmer accession TD-256.

关键词： Triticum dicoccoides high molecular weight glutenin sub unit(HMW-GS) dough rheological properties gene cloning protein secondary structure prediction

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