Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes

Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Ruifu; Song, Qing

doi:10.1186/s12859-016-1087-5

Cited by 38 publications

(32 citation statements)

References 73 publications

(83 reference statements)

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“…1,2 In particular, one of the most extensive PTMs is phosphorylation, which regulates various cellular processes, such as differentiation, growth, metabolism, apoptosis, cellular transport, and signal transduction. 3,4 In prokaryotic and eukaryotic proteins, phosphorylation can occur on serine, threonine, tyrosine, histidine 5 , arginine or lysine 6 . The addition of a phosphate (PO 4 ) to a polar group of an amino acid in phosphorylation can turn a portion of a protein more hydrophilic, and hence change local conformation/dynamics and induce protein’s activity.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Phosphorylation Mechanism by Using Quantum Chemical Calculations and Molecular Dynamics Simulations

et al. 2016

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Phosphorylation is one of the most frequent post-translational modifications on proteins. It regulates many cellular processes by modulation of phosphorylation on protein structure and dynamics. However, the mechanism of phosphorylation-induced conformational changes of proteins is still poorly understood. Here, we report a computational study of three representative groups of tyrosine in ADP-ribosylhydrolase 1, serine in BTG2, and serine in Sp100C by using six molecular dynamics (MD) simulations and quantum chemical calculations. Added phosphorylation was found to disrupt hydrogen bond (H-bond), and increase new weak interactions (H-bond and hydrophobic interaction) during MD simulations, leading to conformational changes. Quantum chemical calculations further indicate that the phosphorylation on tyrosine, threonine and serine could decrease the optical band gap energy (Egap) energy, which can trigger electronic transitions to form or disrupt interactions easily. Our results provide an atomic and electronic description of how phosphorylation facilitates conformational and dynamic changes in proteins, which may be useful for studying protein function and protein design.

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Section: Introductionmentioning

confidence: 99%

Understanding the Phosphorylation Mechanism by Using Quantum Chemical Calculations and Molecular Dynamics Simulations

et al. 2016

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“…The Free Energy Landscape (FEL), whose representation is achieved by projecting the trajectories on their first two principal components of motion 41 – 44 are shown in Fig. 6(a) and (b) .…”

Section: Resultsmentioning

confidence: 99%

“…The average values of F max and F sum based on ten SMD trajectories are presented in Table 6 . The F sum value is calculated based on the saved data points, for avoiding deviation of its real energy, an arbitrary unit is performed in the average values calculation 44 . An arbitrary unit is given to avoid deviation of its real energy.…”

Section: Resultsmentioning

confidence: 99%

Understanding the differences of the ligand binding/unbinding pathways between phosphorylated and non-phosphorylated ARH1 using molecular dynamics simulations

Zhu

Han

et al. 2017

Sci Rep

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ADP-ribosylhydrolases (ARH1, ARH2 and ARH3) are a family of enzymes to catalyze ADP-ribosylation, a reversible and covalent post-translational modification (PTM). There are four phosphorylated sites (Tyr-4, Tyr-19, Tyr-20, and Tyr-205) in ARH1. To explore the structural changes and functional impact induced by phosphorylation, molecular dynamics (MD) simulations and steered molecular dynamics (SMD) simulations were performed for the phosphorylated and non-phosphorylated ARH1 with the ligands. MD simulations results indicate that: (1) Glu-25 is more frequently in the α helix group in the phosphorylated state with the adenosine-5-diphosphate-ribosylarginine (ADP-RA) complex (51.56%) than that of the non-phosphorylated state(2.12%); (2) Ser-124 and Ser-264 become less flexible in the phosphorylated state with ADP-RA complex, which helps two residues form hydrogen bonds with ADP-RA; and (3) Tyr-211 is also less flexible in the phosphorylated state with ADP-RA complex, which helps stabilize the cation-π interaction of Y211-R119. All these changes facilitate ADP-RA to bind ARH1. In addition, according to the crystal structure of adenosine-5-diphosphate-ribose (ADP-ribose) in complex with non-phosphorylated and phosphorylated ARH1, the possible unbinding pathways of ADP-ribose from non-phosphorylated and phosphorylated ARH1 were explored respectively using SMD simulations. Our results show that phosphorylated ARH1 has more ordered structures than the non-phosphorylated type.

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“…Converting an input sample sequence into a set of numerical features is a crucial problem in designing a predictor. Previous studies [7,8] had shown that pseudo-amino acids and pseudo-nucleotides were effective features for predicting protein-aptamer interaction pairs. The specific binding between aptamers and proteins is closely related to their respective physicochemical properties, which are crucial factors for their secondary structures [21].…”

Section: Feature Extractionmentioning

confidence: 99%

PPAI: a web server for predicting protein-aptamer interactions

et al. 2020

Preprint

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Background: The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies. Results: In this study, a novel web server named PPAI is developed to predict aptamers and protein-aptamer interactions with key sequence features of proteins/aptamers and a machine learning framework integrated adaboost and random forest. A new method for extracting several key sequence features of both proteins and aptamers is presented, where the features for proteins are extracted from amino acid composition, pseudo-amino acid composition, grouped amino acid composition, C/T/D composition and sequence-order-coupling number, while the features for aptamers are extracted from nucleotide composition, pseudo-nucleotide composition (PseKNC) and Moreau-Broto autocorrelation coefficient. On the basis of these feature sets and balanced the samples with SMOTE algorithm, we validate the performance of PPAI by the independent test set. The results demonstrate that the Area Under Curve (AUC) is 0.907 for prediction of aptamer, while the AUC reaches 0.871 for prediction of protein-aptamer interactions. Conclusion: These results indicate that PPAI can query aptamers, predict aptamers and predict protein-aptamer interactions in batch mode precisely and efficiently, which would be a novel bioinformatics tool for the research of protein-aptamer interactions. PPAI web-server is freely available at http://39.96.85.9/PPAI.

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Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes

Cited by 38 publications

References 73 publications

Understanding the Phosphorylation Mechanism by Using Quantum Chemical Calculations and Molecular Dynamics Simulations

Understanding the Phosphorylation Mechanism by Using Quantum Chemical Calculations and Molecular Dynamics Simulations

Understanding the differences of the ligand binding/unbinding pathways between phosphorylated and non-phosphorylated ARH1 using molecular dynamics simulations

PPAI: a web server for predicting protein-aptamer interactions

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