Assessment of ligand-binding residue predictions in CASP9

We developed a computational method to identify NAD- and FAD-binding sites in proteins. First, we extracted from the Protein Data Bank structures of proteins that bind to at least one of these ligands. NAD-/FAD-binding residue templates were then constructed by identifying binding residues through the ligand-binding database BioLiP. The fragment transformation method was used to identify structures within query proteins that resembled the ligand-binding templates. By comparing residue types and their relative spatial positions, potential binding sites were identified and a ligand-binding potential for each residue was calculated. Setting the false positive rate at 5%, our method predicted NAD- and FAD-binding sites at true positive rates of 67.1% and 68.4%, respectively. Our method provides excellent results for identifying FAD- and NAD-binding sites in proteins, and the most important is that the requirement of conservation of residue types and local structures in the FAD- and NAD-binding sites can be verified.

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Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

Frontiers in Genetics ◽

10.3389/fgene.2021.793800 ◽

2022 ◽

Vol 12 ◽

Author(s):

Shuang Xu ◽

Xiuzhen Hu ◽

Zhenxing Feng ◽

Jing Pang ◽

Kai Sun ◽

...

Keyword(s):

Ligand Binding ◽

Metal Ion ◽

Biological Function ◽

Structural Information ◽

Basic Feature ◽

Improved Method ◽

Binding Residue ◽

Protein Functions ◽

Binding Residues ◽

Correlation Information

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+, Mg2+, Na+, and K+). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the Sn and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

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Development of a Machine Learning Method to Predict Membrane Protein-Ligand Binding Residues Using Basic Sequence Information

Advances in Bioinformatics ◽

10.1155/2015/843030 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 7

Author(s):

M. Xavier Suresh ◽

M. Michael Gromiha ◽

Makiko Suwa

Keyword(s):

Membrane Protein ◽

Ligand Binding ◽

Binding Sites ◽

Transmembrane Domain ◽

Three Dimensional ◽

Sequence Information ◽

Bayes Classifier ◽

Binding Residue ◽

Ligand Binding Sites ◽

Binding Residues

Locating ligand binding sites and finding the functionally important residues from protein sequences as well as structures became one of the challenges in understanding their function. Hence a Naïve Bayes classifier has been trained to predict whether a given amino acid residue in membrane protein sequence is a ligand binding residue or not using only sequence based information. The input to the classifier consists of the features of the target residue and two sequence neighbors on each side of the target residue. The classifier is trained and evaluated on a nonredundant set of 42 sequences (chains with at least one transmembrane domain) from 31 alpha-helical membrane proteins. The classifier achieves an overall accuracy of 70.7% with 72.5% specificity and 61.1% sensitivity in identifying ligand binding residues from sequence. The classifier performs better when the sequence is encoded by psi-blast generated PSSM profiles. Assessment of the predictions in the context of three-dimensional structures of proteins reveals the effectiveness of this method in identifying ligand binding sites from sequence information. In 83.3% (35 out of 42) of the proteins, the classifier identifies the ligand binding sites by correctly recognizing more than half of the binding residues. This will be useful to protein engineers in exploiting potential residues for functional assessment.

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Protein–ligand binding residue prediction enhancement through hybrid deep heterogeneous learning of sequence and structure data

Bioinformatics ◽

10.1093/bioinformatics/btaa110 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3018-3027 ◽

Cited By ~ 2

Author(s):

Chun-Qiu Xia ◽

Xiaoyong Pan ◽

Hong-Bin Shen

Keyword(s):

Ligand Binding ◽

Large Scale ◽

Protein Structures ◽

Imbalanced Data ◽

Machine Learning Algorithms ◽

Feature Representation ◽

Structure Alignment ◽

Supplementary Information ◽

Binding Residue ◽

Binding Residue Prediction

Abstract Motivation Knowledge of protein–ligand binding residues is important for understanding the functions of proteins and their interaction mechanisms. From experimentally solved protein structures, how to accurately identify its potential binding sites of a specific ligand on the protein is still a challenging problem. Compared with structure-alignment-based methods, machine learning algorithms provide an alternative flexible solution which is less dependent on annotated homogeneous protein structures. Several factors are important for an efficient protein–ligand prediction model, e.g. discriminative feature representation and effective learning architecture to deal with both the large-scale and severely imbalanced data. Results In this study, we propose a novel deep-learning-based method called DELIA for protein–ligand binding residue prediction. In DELIA, a hybrid deep neural network is designed to integrate 1D sequence-based features with 2D structure-based amino acid distance matrices. To overcome the problem of severe data imbalance between the binding and nonbinding residues, strategies of oversampling in mini-batch, random undersampling and stacking ensemble are designed to enhance the model. Experimental results on five benchmark datasets demonstrate the effectiveness of proposed DELIA pipeline. Availability and implementation The web server of DELIA is available at www.csbio.sjtu.edu.cn/bioinf/delia/. Supplementary information Supplementary data are available at Bioinformatics online.

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Ligand-Binding Residue Prediction

Introduction to Protein Structure Prediction ◽

10.1002/9780470882207.ch16 ◽

2010 ◽

pp. 343-368 ◽

Cited By ~ 2

Author(s):

Chris Kauffman ◽

George Karypis

Keyword(s):

Ligand Binding ◽

Binding Residue ◽

Binding Residue Prediction

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Influence of phenylalanine-481 substitutions on the catalytic activity of cytochrome P450 2D6

Biochemical Journal ◽

10.1042/bj3550373 ◽

2001 ◽

Vol 355 (2) ◽

pp. 373-379 ◽

Cited By ~ 18

Author(s):

Graham P. HAYHURST ◽

Jacky HARLOW ◽

Joey CHOWDRY ◽

Esme GROSS ◽

Emma HILTON ◽

...

Keyword(s):

Cytochrome P450 ◽

Ligand Binding ◽

Active Site ◽

Dominant Role ◽

Yeast Cells ◽

Primary Role ◽

Cytochrome P450 2D6 ◽

Wild Type ◽

Binding Residue ◽

P450 2D6

Homology models of the active site of cytochrome P450 2D6 (CYP2D6) have identified phenylalanine 481 (Phe481) as a putative ligand-binding residue, its aromatic side chain being potentially capable of participating in π-π interactions with the benzene ring of ligands. We have tested this hypothesis by replacing Phe481 with tyrosine (Phe481 → Tyr), a conservative substitution, and with leucine (Phe481 → Leu) or glycine (Phe481 → Gly), two non-aromatic residues, and have compared the properties of the wild-type and mutant enzymes in microsomes prepared from yeast cells expressing the appropriate cDNA-derived protein. The Phe481 → Tyr substitution did not alter the kinetics [Km (µM) and Vmax (pmol/min per pmol) respectively] of oxidation of S-metoprolol (27; 4.60), debrisoquine (46; 2.46) or dextromethorphan (2; 8.43) relative to the respective wild-type values [S-metoprolol (26; 3.48), debrisoquine (51; 3.20) and dextromethorphan (2; 8.16)]. The binding capacities [Ks (µM)] of a range of CYP2D6 ligands to the Phe481 → Tyr enzyme (S-metoprolol, 22.8; debrisoquine, 12.5; dextromethorphan, 2.3; quinidine, 0.13) were also similar to those for the wild-type enzyme (S-metoprolol, 10.9; debrisoquine, 8.9; dextromethorphan, 3.1; quinidine, 0.10). In contrast, the Phe481 → Leu and Phe481 → Gly substitutions increased significantly (3-16-fold) the Km values of oxidation of the three substrates [S-metoprolol (120-124µM), debrisoquine (152-184µM) and dextromethorphan (20-31µM)]. Similarly, the Ks values of the ligands to Phe481 → Leu and Phe481 → Gly mutants were also increased 3 to 10-fold (S-metoprolol, 33.2-41.9µM; debrisoquine, 85-90µM; dextromethorphan, 15.7-18.8µM; quinidine 0.35-0.53µM). However, contrary to a recent proposal that Phe481 has the dominant role in the binding of substrates that undergo CYP2D6-mediated N-dealkylation routes of metabolism, the Phe481 → Gly substitution did not substantially decrease the capacity of the enzyme to N-deisopropylate metoprolol (wild-type, 1.12pmol/min per pmol of P450; Phe481 → Gly, 0.71), whereas an Asp301 → Gly substitution decreased the N-dealkylation reaction by 95% of the wild-type rate. Overall, our results are consistent with the proposal that Phe481 is a ligand-binding residue in the active site of CYP2D6 and that the residue interacts with ligands via a π-π interaction between its phenyl ring and the aromatic moiety of the ligand. However, the relative importance of Phe481 in binding is ligand-dependent; furthermore, its importance is secondary to that of Asp301. Finally, contrary to predictions of a recent homology model, Phe481 does not seem to have a primary role in CYP2D6-mediated N-dealkylation.

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