An Overview of the De Novo Prediction of Enzyme Catalytic Residues (Supplementry file)

Zhang, Ziding; Tang, Yurong; Sheng, Zhi-Ya; Zhao, Dongyuan

doi:10.2174/157489309789071110

Cited by 5 publications

(8 citation statements)

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“…Generally, the 3D structural information can be used to obtain in-depth information on the binding site. However, the presence of a good structural match is not necessarily indicative of a similar function, an idea supported based on other functional sites [ 24 , 25 ]. Rather than directly relying on structural similarity to identify DNA-binding sites, the descriptors (e.g., structural motifs and electrostatics) exploited from structural information have been employed in the machine learning process to predict these sites [ 20 , 21 , 22 , 23 ].…”

Section: Methods Development Of Dna-binding Site Predictionmentioning

confidence: 99%

“…Generally, three-dimensional (3D) structural information can be used to obtain in-depth insights into the binding site. However, a good structural match is not necessarily indicative of a similar function, which has been supported based on other functional sites [ 24 , 25 ]. Sequence similarity and structure similarity-based strategies are shown in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

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An Overview of the Prediction of Protein DNA-Binding Sites

Zhao

2015

IJMS

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Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications.

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Section: Methods Development Of Dna-binding Site Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Overview of the Prediction of Protein DNA-Binding Sites

Zhao

2015

IJMS

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“…Identification of active cavity or site of protein is a key step for the identification of functional role of protein or enzymes [15]. Active Cavity prediction was achieved by submitting the model in to the online Active Cavity prediction supercomputing facility serverhttp://www.modelling.leeds.ac.uk/pocketfinder/help.html.…”

Section: Active Cavity Predictionmentioning

confidence: 99%

Comparative Modeling and Prediction of Carbohydrate Binding Pockets in 3D Structure of Wild Pulse Lablab Purpureus Arcelin

Nagarajan¹,

Janarthanan²,

Sakthivelkumar³

et al. 2011

IJCA

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Arcelin, a seed protein originally discovered in wild bean accession of Lablab purpureus was purified, characterized, and compared to phaseolin, the major seed protein of common bean Phaseolus vulgaris. There are several reports available for common bean arcelin from P. vulgaris and its defense mechanism against the stored product insect pests, but L. purpureus arcelin function is not yet studied well. To understand the molecular function of arcelin in L. purpureus the structural knowledge is essential. This work is an attempt to explore the molecular defense mechanism of L. purpureus arcelin based on homology modelling and binding pocket analysis to emphasize the structural and functional relationship. The structural template from P. vulgaris arcelin [1AVB] is selected for homology modelling of L. purpureus arcelin. The 3D structure of L. purpureus arcelin was generated using Modeller software. The best model is selected based on Ramachandran Plot, Errat and Energy minimization analysis (Steepest Descent). The overall quality of computed model showed 87.2 % amino acid residues under favored region with 93.5 % overall quality. The putative refined model of L. purpureus arcelin was deposited into Protein Model Data Base with ID: PM0076542. Deposited model is used for further active cavity analysis against carbohydrate (sugar) binding sites. These results will help further development of transgenic crops with arcelin for future integrated insect pest management (IPM) programme.

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“…By means of their structure and chemical properties, these residues directly take part in the catalysis process, determining to a certain extent, the chemical properties of the enzyme. Thus, gaining knowledge of the catalytic residues can not only help unravel enzyme functions, but in the long run, boost enzyme engineering and drug design applications [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…The computational approaches because of their time and resource utilization advantages have gained impetus through the years. These approaches fall broadly under similarity transfer based and ab initio or de novo methods [ 2 ]. The similarity transfer based methods identify putative catalytic residues in uncharacterized sequences based on their homology with sequences whose catalytic residues are known.…”

Section: Introductionmentioning

confidence: 99%

PINGU: PredIction of eNzyme catalytic residues usinG seqUence information

2015

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Identification of catalytic residues can help unveil interesting attributes of enzyme function for various therapeutic and industrial applications. Based on their biochemical roles, the number of catalytic residues and sequence lengths of enzymes vary. This article describes a prediction approach (PINGU) for such a scenario. It uses models trained using physicochemical properties and evolutionary information of 650 non-redundant enzymes (2136 catalytic residues) in a support vector machines architecture. Independent testing on 200 non-redundant enzymes (683 catalytic residues) in predefined prediction settings, i.e., with non-catalytic per catalytic residue ranging from 1 to 30, suggested that the prediction approach was highly sensitive and specific, i.e., 80% or above, over the incremental challenges. To learn more about the discriminatory power of PINGU in real scenarios, where the prediction challenge is variable and susceptible to high false positives, the best model from independent testing was used on 60 diverse enzymes. Results suggested that PINGU was able to identify most catalytic residues and non-catalytic residues properly with 80% or above accuracy, sensitivity and specificity. The effect of false positives on precision was addressed in this study by application of predicted ligand-binding residue information as a post-processing filter. An overall improvement of 20% in F-measure and 0.138 in Correlation Coefficient with 16% enhanced precision could be achieved. On account of its encouraging performance, PINGU is hoped to have eventual applications in boosting enzyme engineering and novel drug discovery.

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An Overview of the De Novo Prediction of Enzyme Catalytic Residues (Supplementry file)

Cited by 5 publications

References 0 publications

An Overview of the Prediction of Protein DNA-Binding Sites

An Overview of the Prediction of Protein DNA-Binding Sites

Comparative Modeling and Prediction of Carbohydrate Binding Pockets in 3D Structure of Wild Pulse Lablab Purpureus Arcelin

PINGU: PredIction of eNzyme catalytic residues usinG seqUence information

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