Evaluation of features for catalytic residue prediction in novel folds

Youn, Eunseog; Peters, Brandon; Radivojac, Predrag; Mooney, Sean D.

doi:10.1110/ps.062523907

Cited by 66 publications

(107 citation statements)

References 34 publications

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“…For the benchmark data set, an MCC of 0.23 was reported (compared with an MCC of 0.31 for THEMATICS-SVM). Youn et al (2007) used SVM with sequence alignments, 3D structure properties, and structural environment conservation; these authors also reported a 57% residue recall rate and an 18.5% precision with the ASTRAL 40 database (compared with a 61% recall and 20% precision for THEMATICS-SVM). THEMATICS-SVM performance, achieved with the 3D structure of the query protein alone, is therefore of similar quality to that of methods that take advantage of similarities in sequence and structure, if quality is measured by performance on sets of well-characterized enzymes that tend to have many sequence homologues.…”

Section: Some Specific Examplesmentioning

confidence: 99%

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Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines

et al. 2008

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Theoretical microscopic titration curves (THEMATICS) is a computational method for the identification of active sites in proteins through deviations in computed titration behavior of ionizable residues. While the sensitivity to catalytic sites is high, the previously reported sensitivity to catalytic residues was not as high, about 50%. Here THEMATICS is combined with support vector machines (SVM) to improve sensitivity for catalytic residue prediction from protein 3D structure alone. For a test set of 64 proteins taken from the Catalytic Site Atlas (CSA), the average recall rate for annotated catalytic residues is 61%; good precision is maintained selecting only 4% of all residues. The average false positive rate, using the CSA annotations is only 3.2%, far lower than other 3D-structure-based methods. THEMATICS-SVM returns higher precision, lower false positive rate, and better overall performance, compared with other 3D-structure-based methods. Comparison is also made with the latest machine learning methods that are based on both sequence alignments and 3D structures. For annotated sets of well-characterized enzymes, THEMATICS-SVM performance compares very favorably with methods that utilize sequence homology. However, since THEMATICS depends only on the 3D structure of the query protein, no decline in performance is expected when applied to novel folds, proteins with few sequence homologues, or even orphan sequences. An extension of the method to predict non-ionizable catalytic residues is also presented. THEMATICS-SVM predicts a local network of ionizable residues with strong interactions between protonation events; this appears to be a special feature of enzyme active sites.

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Section: Some Specific Examplesmentioning

confidence: 99%

“…Other methods use structural relationships in conjunction with sequence analysis (de Rinaldis et al 1998;Aloy et al 2001;Carter et al 2001;Landgraf et al 2001;Gutteridge et al 2003;Ota et al 2003;Innis et al 2004;Meng et al 2004;Petrova and Wu 2006;Youn et al 2007). …”

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confidence: 99%

Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines

et al. 2008

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“…Our method significantly outperforms existing methods for functional site prediction both in terms of precision and recall (Table 1). In comparison with the existing ML methods for functional site prediction, we obtained 18.78 percentage point improvement over relational classifier and CRF used by Sankararaman et al [30], 39 percentage point over neural network based predictor [11] and 31 percentage point improvement over SVM based predictor [40] at 18% precision. The recall obtained by our method is 70 percentage point more than the methods using sequence conservation features alone [31,3,21].…”

Section: Performance Of Our Methodsmentioning

confidence: 79%

“…These methods classify residues into functional and non-functional classes based on their structural, physicochemical, evolutionary and electrostatic features. The following are some of the examples of ML techniques used for addressing the problem: Wei et al [37] used naïve Bayes classifier, Gutteridge et al [11] used neural network, Youn et al [40] used support vector machines (SVMs) and Sankararaman et al [30] used L1 logistic regression classifier. Among all the existing methods, the L1 logistic regression classifier achieves the best performance on standard benchmark datasets.…”

Section: Introductionmentioning

confidence: 99%

Functional site prediction by exploiting correlations between labels of interacting residues

Kar¹,

Vijayakeerthi

Tendulkar

et al. 2012

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

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Functional site prediction is an important problem in the structural genomics era where we have a large number of experimentally determined protein structures with unknown function. The functional sites provide useful insights into protein function. In this paper, we propose a method for prediction of functional residues in a given protein from its three-dimensional (3D) structure. Our method exploits correlation between labels of interacting residues to obtain significant performance improvements over the existing methods on the benchmark dataset. We represent each protein as a weighted undirected residue interaction network, where spatially proximal residues in terms of their Van der Waal radii are connected by an edge. The edge weight captures correlation between the labels of interacting residues. The correlation is estimated based on the features of interacting residues. We then obtain a label assignment by minimizing combined cost of residue-wise label misclassification and violation of label correlation constraints. We solve this problem in two stages, where the first stage minimizes residue-wise label misclassification cost followed by an iterative collective inference scheme that adjusts the labels predicted in the first stage so as to minimize the correlation constraint violations. Our approach significantly outperforms state of the art methods on standard benchmark dataset. It achieves 23.06% precision at 69% recall and 87.78% recall at 18% precision, which translates to an improvement of 5.06 percentage points in the precision at 69% recall and 18.78 percentage point improvement in recall at 18% precision.

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“…In the prediction PTM sites, this speci ic problem is particularly prominent due to the sequence diversity. For instance, some motifs are very weak and some are not available without the sequence evolutionary information [30][31][32][33][34][35]. To address this Figure 1: A brief fl owchart template for computational prediction PTM sites.…”

Section: Feature Representationmentioning

confidence: 99%

Opinion Prediction of protein Post-Translational Modification sites: An overview

Hasan¹,

Khatun²

2017

Ann Proteom Bioinform

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BackgroundPost-translational modi ication (PTM) refers to the covalent and enzymatic modi ication of proteins during or after protein biosynthesis. In the protein biosynthesis process, the ribosomal mRNA is translated into polypeptide chains, which may further undergo PTM to form the product of mature protein [1]. PTM is a common biological mechanism of both eukaryotic and prokaryotic organisms, which regulates the protein functions, the proteolytic cleavage of regulatory subunits or the degradation of entire proteins and affects all aspects of cellular life. The PTM of a protein can also determine the cell signaling state, turnover, localization, and interactions with other proteins [2]. Therefore, the analysis of proteins and their PTMs are particularly important for the study of heart disease, cancer, neurodegenerative diseases and diabetes [3,4]. Although the characterization of PTMs gets invaluable insight into the cellular functions in etiological processes, there are still challenges. Technically, the major challenges in studying PTMs are the development of speci ic detection and puri ication methods.The PTMs of proteins have been detected by a variety of experimental techniques including the mass spectrometry (MS) [5,6], liquid chromatography [7], radioactive chemical method [8], chromatin immune precipitation (ChIP) [9], western blotting [10], and eastern blotting [7]. The MS technique is one of the mainstay routes in detecting PTMs in a high-throughput manner. The new MS and capillary liquid chromatography instrumentation have made revolutionary advance in enrichment strategies in our growing knowledge of various PTMs [11]. The last decade of the actual description of many PTMs complexity has emerged through the diverse technologies and thousands of precise modi ication sites can now be identi ied with high con idence [12][13][14][15][16][17][18][19][20]. A similar strategy of fragmentation for PTM identi ication is the beam-type collisioninduced dissociation, also called higher energy collisional dissociation [21]. These types of fragmentation are characterized by the higher activation energy. Most of the fragmentation methods of precursor ions are based on the radical anions or thermal electrons [22]. These methods are advantageous over collisionally activated dissociation methods for detecting the unstable PTMs (e.g. O-GlcNAc and phosphorylation), due to the peptide support fragmentation method is effectively independent of the amino acid sequence [23][24][25]. To date, more than 350 types of PTMs have been experimentally discovered in vivo [26]. The common PTMs are phosphorylation, ubiquitination, succinylation, acetylation, pupylation, sumoylation, glycosylation, and so on. In addition, pupylation referring to the modi ication of lysine residues with a prokaryotic, ubiquitin-like protein (i.e. Pup) is another PTM in bacteria.In general, the experimental analysis of PTMs often requires labor-intensive sample preparations and hazardous or expensive chemical reagents. For instance, in the radioactive...

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Evaluation of features for catalytic residue prediction in novel folds

Cited by 66 publications

References 34 publications

Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines

Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines

Functional site prediction by exploiting correlations between labels of interacting residues

Opinion Prediction of protein Post-Translational Modification sites: An overview

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