A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in <i>Escherichia coli</i>

Idicula‐Thomas, Susan; Kulkarni, Abhijit J.; Kulkarni, Bhaskar D.; Jayaraman, Vaidyanathan; Balaji, Petety V.

doi:10.1093/bioinformatics/bti810

Cited by 100 publications

(87 citation statements)

References 59 publications

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“…of proteins in E. coli [8][9][10][11][12]22 are also employed for predicting aggregation propensities of proteins in vitro. [13][14][15][16][17][18][19][20] These results hint at the existence of a link between aggregation rates measured in vitro and concentrations detected in vivo.…”

Section: Aggregation and Chemical Properties Of Proteinsmentioning

confidence: 99%

“…[8][9][10][11][12][13][14][15][16][17][18][19][20] and the aggregation propensities themselves are correlated to the in vivo concentrations, 7 it should also be possible to make predictions about a 4 The toxicity of mutational variants of the Ab peptide can be better predicted using the propensities to form protofibrillar assemblies rather than the propensities to form fibrillar aggregates. 21 (a) Correlation between aggregation propensity and neurotoxicity in Drosophila of Ab mutational variants.…”

Section: Aggregation and Chemical Properties Of Proteinsmentioning

confidence: 99%

“…2) to develop strategies to make quantitative predictions in cell biology through the analysis of the chemical properties of proteins. We first describe the advances that have been recently made for predicting the solubility of proteins [8][9][10][11][12] and their propensity for aggregation. [13][14][15][16][17][18][19][20] We then explain how the relationship between protein aggregation rates and mRNA expression levels ( Fig.…”

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confidence: 99%

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Towards quantitative predictions in cell biology using chemical properties of proteins

Vendruscolo

Tartaglia

2008

Mol. BioSyst.

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It has recently been suggested that the concentrations of proteins in the cell are tuned towards their critical values, and that the alteration of this balance often results in misfolding diseases. This concept is intriguing because the in vivo concentrations of proteins are closely regulated by complex cellular processes, while their critical concentrations are primarily determined by the chemical characters of their amino acid sequences. We discuss here how the presence of a link between the upper levels of in vivo concentrations and critical concentrations offers an opportunity to make quantitative predictions in cell biology based on the chemical properties of proteins.

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Section: Aggregation and Chemical Properties Of Proteinsmentioning

confidence: 99%

Section: Aggregation and Chemical Properties Of Proteinsmentioning

confidence: 99%

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confidence: 99%

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Towards quantitative predictions in cell biology using chemical properties of proteins

Vendruscolo

Tartaglia

2008

Mol. BioSyst.

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“…AI, such as SVM, is very commonly used in disease prediction and pattern recognition in microarray data analysis, especially for cancer prediction. SVM algorithms have been successfully used in bacterial proteins (17,18,30,39), metabolites (11), and pattern recognition and yielded Ͼ90% accuracy. In the present study the results from two independent forms of AI, SVM and neural network analyses, were compared to the true status of each sample to calculate sensitivity, specificity, and overall accuracy of the output.…”

Section: Discussionmentioning

confidence: 99%

Genetic Classification of Severe Early Childhood Caries by Use of Subtracted DNA Fragments from Streptococcus mutans

Saxena¹,

Caufield

Li³

et al. 2008

J Clin Microbiol

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Streptococcus mutans is one of several members of the oral indigenous biota linked with severe early childhood caries (S-ECC). Because most humans harbor S. mutans, but not all manifest disease, it has been proposed that the strains of S. mutans associated with S-ECC are genetically distinct from those found in caries-free (CF) children. The objective of this study was to identify common DNA fragments from S. mutans present in S-ECC but not in CF children. Using suppressive subtractive hybridization, we found a number of DNA fragments (biomarkers) present in 88 to 95% of the S-ECC S. mutans strains but not in CF S. mutans strains. We then applied machine learning techniques including support vector machines and neural networks to identify the biomarkers with the most predictive power for disease status, achieving a 92% accurate classification of the strains as either S-ECC or CF associated. The presence of these gene fragments in 90 to 100% of the 26 S-ECC isolates tested suggested their possible functional role in the pathogenesis of S. mutans associated with dental caries.The mutans streptococci (MS) are strongly associated with dental caries by virtue of their metabolic, ecological, and epidemiological attributes (23,46). Among the MS, Streptococcus mutans appears to be a predominant bacterial species in the microbiota of preschool children with severe early childhood caries (S-ECC) (4-6, 49). Although the association between S. mutans and S-ECC seems convincing, most children colonized by S. mutans do not manifest the disease (8), suggesting that among other possibilities, S. mutans vary in their ability to initiate caries.In our previous study, we demonstrated that strains of S. mutans strains associated with S-ECC differ in their genomic composition compared to caries-free (CF) controls (42). Using the power of suppressive subtractive DNA hybridization (SSH), several unique gene segments were identified from a strain of S. mutans (AF199) that was isolated from a child with S-ECC. The presence of unique genetic loci among S. mutans strains is consistent with the recent work by Waterhouse and Russell (51), as they described the presence of "dispensable genes" distributed among strains of S. mutans. These segments include mobile genetic elements that are widely distributed in S. mutans (2) and have been shown to modulate sucrose (31) and melibiose metabolism (40). S. mutans strains also vary in content in terms of the presence of plasmids (10, 32), mutacin I, II, III, and IV operons (3,19,[35][36][37], serotypic antigens (43); competence (34), the comBCD genes (28), and gtfBC (14, 48, 52), among other genetic loci. Based on the wide diversity of genotypes and genetic loci within S. mutans, different strains of S. mutans apparently comprise both common and unique genetic loci, and it seems that these differences are unequally distributed among strains (42, 51). Identifying the unique DNA fragments that are common to most of the strains isolated from S-ECC but not CF children will be important even if their fun...

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“…Dataset was comprised of four groups while the accuracy reported was 72% [10]. Moreover, an extension of work was also reported in 2006, on the basis of SVM, KNN and linear logistic regression and the accuracy observed was 76% [34].…”

Section: Prediction Of Solubility and Performance Evaluationmentioning

confidence: 99%

Prediction of Protein Solubility using Primary Structure Compositional Features: A Machine Learning Perspective

Rasool¹,

Hussain²,

Mahmood³

2017

J Proteomics Bioinform

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A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli

Cited by 100 publications

References 59 publications

Towards quantitative predictions in cell biology using chemical properties of proteins

Towards quantitative predictions in cell biology using chemical properties of proteins

Genetic Classification of Severe Early Childhood Caries by Use of Subtracted DNA Fragments from Streptococcus mutans

Prediction of Protein Solubility using Primary Structure Compositional Features: A Machine Learning Perspective

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