QSE: A new 3‐D solvent exposure measure for the analysis of protein structure

Li, Peipei; Pok, Gouchol; Jung, Kwang Su; Shon, Ho Sun; Ryu, Keun Ho

doi:10.1002/pmic.201100189

Cited by 11 publications

(5 citation statements)

References 54 publications

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“…Bioinformatics studies over the past few decades have shed light on topics such as sequence analysis, structural analysis, and network and systems biology [ 1 , 2 ]. In recent years, rapid improvements in technology and decreased sequencing costs have made next-generation sequencing possible, facilitating millions of short sequence reads that have broad genomic research applications [ 3 – 5 ].…”

Section: Introductionmentioning

confidence: 99%

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data

et al. 2015

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BackgroundRecently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments.ResultsIn this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results.ConclusionSpearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0778-7) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data

et al. 2015

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“…The imprecision and uncertainty in measurement of the SEA of amino acid residues accordingly inspire the employment of fuzzy logic to characterize amino acids in PSSP. As numerous studies have found the significance of the amino acid SEA in understanding protein structures and functions [26][27][28], utilizing the SEA therefore would enhance the predictability of the protein secondary structure. In this paper, triples representing SEA of amino acids are employed as part of the amino acid representation.…”

Section: ) Solvent Exposed Area (Sea) Of Amino Acids (å 2 )mentioning

confidence: 99%

Structural classification of proteins through amino acid sequence using interval type-2 fuzzy logic system

Nguyen

Khosravi

Creighton

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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This paper introduces a new multi-output interval type-2 fuzzy logic system (MOIT2FLS) that is automatically constructed from unsupervised data clustering method and trained using heuristic genetic algorithm for a protein secondary structure classification. Three structure classes are distinguished including helix, strand (sheet) and coil which correspond to three outputs of the MOIT2FLS. Quantitative properties of amino acids are used to characterize the twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Amino acid sequences are parsed into learnable patterns using a local moving window strategy. Three clustering tasks are performed using the adaptive vector quantization method to derive an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS with the purpose of maximizing the Q3 measure. Comprehensive experimental results demonstrate the strong superiority of the proposed approach over the traditional methods including Chou-Fasman method, GarnierOsguthorpe-Robson method, and artificial neural network models.

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“…Raw data in the form of 3d chain antigen structures containing information on residues exposed at the atomic level and labeled epitope and non-epitope was taken from Gao et al [7]. These residues are enriched with features of structural analysis with PSAIA, which include ASA, RSA, and Protrusion Index [26], Contact Number [27], QSE [28], HSE [29], AAIndex [30], B Factor [31] and Log Odd Ratio [32]. The dimension of the vector properties is 601.…”

Section: Dataset Collectionmentioning

confidence: 99%

A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

Solihah

Musdholifah

Azhari

2023

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Naturally, the epitope dataset can be presented as a graph. Dataset preparation in the previous methods is part of model development. There are many graph-based classification and regression methods. Still, it is difficult to identify their performance on the conformational epitope prediction model because datasets in a suitable format are unavailable. This research aims to build a dataset in a suitable format to evaluate kernel graph and graph convolution network. This dataset, which results from graph clustering on graph antigens, can be used to identify the performance of many graph neural network-based algorithms for conformational epitope prediction. The Ag-Ab complexes that meet the criteria for forming a conformational epitope prediction dataset from previous studies were downloaded from the Protein Data Bank. Raw datasets in the form of specific exposed antigen chain residues are labeled as epitope or non-epitope based on their proximity to the paratope. The engineering features in the raw dataset are derived from the structure of the antigen-antibody complex and the propensity score. Aggregating atomic-level interactions into residual levels create an initial graph of the antigen chain. The MCL, MLR-MCL, and PS-MCL are graph clustering algorithms to obtain labeled sub-clusters from the initial graph. A balance factor parameter is set to several values to identify the optimal dataset formation based on minimal fragmentation. The output of the MCL algorithm is used as a baseline. As a result of the fragmentation analysis that occurs, the MLR-MCL algorithm gives the best model performance at a balance factor equal to 2. PS-MCL gives the best performance at a value of 0.9. Based on the minimum fragmentation, the MLR-MCL algorithm provides the best model performance compared to MCL and PS-MCL. The dataset in a format according to benchmarking dataset can be used to identify the characteristics of antigen subgraphs formed from the graph clustering process and to explore the performance of graph-based learning conformational epitope prediction models such as graph convolution networks.

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QSE: A new 3‐D solvent exposure measure for the analysis of protein structure

Cited by 11 publications

References 54 publications

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data

Structural classification of proteins through amino acid sequence using interval type-2 fuzzy logic system

A Novel Epitope Dataset: Performance of the MCL-Based Algorithms to Generate Dataset for Graph Learning Model

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