Feature-incorporated alignment based ligand-binding residue prediction for carbohydrate-binding modules

Chou, Wei-Yao; Chou, Wei-Yuan; Pai, Tun-Wen; Li, Chia-Jung; Jiang, Ting-Ying; Tang, Chuan-Yi; Chang, Margaret Dah‐Tsyr

doi:10.1093/bioinformatics/btq084

Cited by 6 publications

(12 citation statements)

References 47 publications

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“…The used alignment programs are FIA [21], MUSCLE [32], ClustalW2 [33], ProbCons [36], T-COFFEE [35], DIALIGN-TX [34], and MAFFT [37]. The surface potential z scores are evaluated by PROSA2003 [38].…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, polar residues adjacent to aromatic residues can enlarge the surface area of neighboring regions thereby increasing the contact area(s) between the binding residues and ligands. In our previous study, the number of preferred occurrence time that polar residues flanked an aromatic residue was determined for starch-binding CBMs [21], and the aromatic residues that were flanked on both sides by polar residues were defined as HARs. For this study, the occurrence time for two upstream and downstream amino acids that flank 97 known ligand-binding aromatic residues was summarized in Table 3 (the structure template profiles including known ligand-binding aromatic residues are shown in Supporting Information Table S2).…”

Section: Methodsmentioning

confidence: 99%

“…To evaluate the target-template sequence matching, a target sequence t was aligned one by one with the templates in P as described by Equation 1, where FIA represents the feature-incorporated alignment [21], and A is the set of preliminary alignments used in the template filter step. …”

Section: Methodsmentioning

confidence: 99%

“…The prototype of this idea was successfully applied to predict an in silico structure for Rhizopus oryzae glucoamylase (CBM21) in low sequence identity condition and the predicted ligand-binding residues were experimentally verified [20]. When the positions of the conserved HARs and the secondary structure elements of CBMs were integrated into a feature-incorporated alignment (FIA) algorithm [21], we found an ∼5% improvement in the average sequence similarity and identity compared with target-template alignments obtained using six leading alignment algorithms. The improved alignments were used to identify conserved ligand-binding aromatic residues in CBM domains for which 3D structures were unavailable.…”

Section: Introductionmentioning

confidence: 99%

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Hydrophilic Aromatic Residue and in silico Structure for Carbohydrate Binding Module

et al. 2011

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Carbohydrate binding modules (CBMs) are found in polysaccharide-targeting enzymes and increase catalytic efficiency. Because only a relatively small number of CBM structures have been solved, computational modeling represents an alternative approach in conjunction with experimental assessment of CBM functionality and ligand-binding properties. An accurate target-template sequence alignment is the crucial step during homology modeling. However, low sequence identities between target/template sequences can be a major bottleneck. We therefore incorporated the predicted hydrophilic aromatic residues (HARs) and secondary structure elements into our feature-incorporated alignment (FIA) algorithm to increase CBM alignment accuracy. An alignment performance comparison for FIA and six others was made, and the greatest average sequence identities and similarities were achieved by FIA. In addition, structure models were built for 817 representative CBMs. Our models possessed the smallest average surface-potential z scores. Besides, a large true positive value for liagnd-binding aromatic residue prediction was obtained by HAR identification. Finally, the pre-simulated CBM structures have been deposited in the Database of Simulated CBM structures (DS-CBMs). The web service is publicly available at http://dscbm.life.nthu.edu.tw/ and http://dscbm.cs.ntou.edu.tw/.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hydrophilic Aromatic Residue and in silico Structure for Carbohydrate Binding Module

et al. 2011

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“…Several other QA methods also integrate various protein features including QMEAN and QMEANclust [30], [31], ProQ [32] and a more recent method by Kalman and Ben-Tal [33]. Several methods also incorporate protein feature analysis for the prediction of ligand binding site residues, these methods include DISCERN [5], a meta-functional signature method by Wang et al [34] and a carbohydrate-binding module, binding site residue prediction method [35].…”

Section: Introductionmentioning

confidence: 99%

FunFOLDQA: A Quality Assessment Tool for Protein-Ligand Binding Site Residue Predictions

2012

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The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

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Plant α‐glucan phosphatases SEX4 and LSF2 display different affinity for amylopectin and amylose

et al. 2016

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These authors contributed equally to the work. The plant glucan phosphatases Starch EXcess 4 (SEX4) and Like Sex Four2 (LSF2) apply different starch binding mechanisms. SEX4 contains a carbohydrate binding module, and LSF2 has two surface binding sites (SBSs). We determined K Dapp for amylopectin and amylose, and K D for b-cyclodextrin and validated binding site mutants deploying affinity gel electrophoresis (AGE) and surface plasmon resonance. SEX4 has a higher affinity for amylopectin; LSF2 prefers amylose and b-cyclodextrin. SEX4 has 50-fold lower K Dapp for amylopectin compared to LSF2. Molecular dynamics simulations and AGE data both support long-distance mutual effects of binding at SBSs and the active site in LSF2.

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Feature-incorporated alignment based ligand-binding residue prediction for carbohydrate-binding modules

Cited by 6 publications

References 47 publications

Hydrophilic Aromatic Residue and in silico Structure for Carbohydrate Binding Module

Hydrophilic Aromatic Residue and in silico Structure for Carbohydrate Binding Module

FunFOLDQA: A Quality Assessment Tool for Protein-Ligand Binding Site Residue Predictions

Plant α‐glucan phosphatases SEX4 and LSF2 display different affinity for amylopectin and amylose

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