G Protein-Coupled Receptor Interaction Prediction Based on Deep Transfer Learning

G protein-coupled receptors (GPCRs) account for about 40% to 50% of drug targets. Many human diseases are related to G protein coupled receptors. Accurate prediction of GPCR interaction is not only essential to understand its structural role, but also helps design more effective drugs. At present, the prediction of GPCR interaction mainly uses machine learning methods. Machine learning methods generally require a large number of independent and identically distributed samples to achieve good results. However, the number of available GPCR samples that have been marked is scarce. Transfer learning has a strong advantage in dealing with such small sample problems. Therefore, this paper proposes a transfer learning method based on sample similarity, using XGBoost as a weak classifier and using the TrAdaBoost algorithm based on JS divergence for data weight initialization to transfer samples to construct a data set. After that, the deep neural network based on the attention mechanism is used for model training. The existing GPCR is used for prediction. In short-distance contact prediction, the accuracy of our method is 0.26 higher than similar methods.

[1]  Ying Fu,et al.  Identification of novel inhibitors of p-hydroxyphenylpyruvate dioxygenase using receptor-based virtual screening , 2019, Journal of the Taiwan Institute of Chemical Engineers.

[2]  Li Shang,et al.  Flower Species Recognition System Combining Object Detection and Attention Mechanism , 2019, ICIC.

[3]  Li Shang,et al.  Hierarchical Attention Network for Predicting DNA-Protein Binding Sites , 2019, ICIC.

[4]  Li Shang,et al.  Motif Discovery via Convolutional Networks with K-mer Embedding , 2019, ICIC.

[5]  Li Shang,et al.  Leaf Recognition Based on Capsule Network , 2019, ICIC.

[6]  Li Shang,et al.  Plant Leaf Recognition Based on Conditional Generative Adversarial Nets , 2019, ICIC.

[7]  Yuanyuan Hou,et al.  Comprehensive TCM molecular networking based on MS/MS in silico spectra with integration of virtual screening and affinity MS screening for discovering functional ligands from natural herbs , 2019, Analytical and Bioanalytical Chemistry.

[8]  P. Saw,et al.  Phage display screening of therapeutic peptide for cancer targeting and therapy , 2019, Protein & Cell.

[9]  Jianrong Xu,et al.  Development of Small-Molecules Targeting Receptor Activator of Nuclear Factor-κB Ligand (RANKL)-Receptor Activator of Nuclear Factor-κB (RANK) Protein-Protein Interaction by Structure-Based Virtual Screening and Hit Optimization. , 2019, Journal of medicinal chemistry.

[10]  L. Lai,et al.  Efficient ligand discovery from natural herbs by integrating virtual screening, affinity mass spectrometry and targeted metabolomics. , 2019, The Analyst.

[11]  Tilman Flock,et al.  An online resource for GPCR structure determination and analysis , 2019, Nature Methods.

[12]  Priya Singh,et al.  Exploring the role of water molecules in the ligand binding domain of PDE4B and PDE4D:Virtual screening based molecular docking of some active scaffolds. , 2019, Current computer-aided drug design.

[13]  Md Taufiq Nasseef,et al.  Expression map of 78 brain-expressed mouse orphan GPCRs provides a translational resource for neuropsychiatric research , 2018, Communications Biology.

[14]  Daniel Wacker,et al.  How the ubiquitous GPCR receptor family selectively activates signalling pathways , 2018, Nature.

[15]  M. von Zastrow,et al.  Subcellular Organization of GPCR Signaling. , 2018, Trends in pharmacological sciences.

[16]  B. Kobilka,et al.  Structure and dynamics of GPCR signaling complexes , 2018, Nature Structural & Molecular Biology.

[17]  Nicolas J. Wheeler,et al.  Tissue-specific transcriptome analyses provide new insights into GPCR signalling in adult Schistosoma mansoni , 2018, PLoS pathogens.

[18]  J. Åqvist,et al.  Characterization of Ligand Binding to GPCRs Through Computational Methods. , 2018, Methods in molecular biology.

[19]  Song Li,et al.  Identification of regulatory modules in genome scale transcription regulatory networks , 2017, BMC Systems Biology.

[20]  M. Raj,et al.  Abstract LB-171: Throwing the 'book' at the cancer cell speciifically: A new paradigm and a new strategy for cancer prevention and cure , 2017 .

[21]  Arthur Christopoulos,et al.  Structural features embedded in G protein-coupled receptor co-crystal structures are key to their success in virtual screening , 2017, PloS one.

[22]  Zhu-Hong You,et al.  Identifying Spurious Interactions in the Protein-Protein Interaction Networks Using Local Similarity Preserving Embedding , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[23]  C. Ronco,et al.  Sudden cardiac death and chronic kidney disease: From pathophysiology to treatment strategies. , 2016, International journal of cardiology.

[24]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[25]  David T. Jones,et al.  MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins , 2014, Bioinform..

[26]  Lei Zhang,et al.  Prediction of protein-protein interactions based on protein-protein correlation using least squares regression. , 2014, Current protein & peptide science.

[27]  Changjun Jiang,et al.  A New Strategy for Protein Interface Identification Using Manifold Learning Method , 2014, IEEE Transactions on NanoBioscience.

[28]  Zhu-Hong You,et al.  t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks , 2013, PloS one.

[29]  Massimiliano Pontil,et al.  PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments , 2012, Bioinform..

[30]  M. Mezei,et al.  Molecular docking: a powerful approach for structure-based drug discovery. , 2011, Current computer-aided drug design.

[31]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[32]  Zhu-Hong You,et al.  Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data , 2010, Bioinform..

[33]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[34]  Xingming Zhao,et al.  Predicting protein–protein interactions from protein sequences using meta predictor , 2010, Amino Acids.

[35]  Kyungsook Han,et al.  Sequence-based prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor. , 2010, Protein and peptide letters.

[36]  Kyungsook Han,et al.  Predicting key long-range interaction sites by B-factors. , 2008, Protein and peptide letters.

[37]  Xing-Ming Zhao,et al.  Classifying protein sequences using hydropathy blocks , 2006, Pattern Recognit..

[38]  B. Neel,et al.  Neuronal PTP1B regulates body weight, adiposity and leptin action , 2006, Nature Medicine.

[39]  De-Shuang Huang,et al.  Identifying protein-protein interfacial residues in heterocomplexes using residue conservation scores. , 2006, International journal of biological macromolecules.

[40]  R. Friesner,et al.  Novel procedure for modeling ligand/receptor induced fit effects. , 2006, Journal of medicinal chemistry.

[41]  Peng Chen,et al.  Predicting protein interaction sites from residue spatial sequence profile and evolution rate , 2006, FEBS Letters.

[42]  Xing-Ming Zhao,et al.  A novel Markov pairwise protein sequence alignment method for sequence comparison. , 2005, Protein and peptide letters.

[43]  C Ghez,et al.  Learning of Visuomotor Transformations for Vectorial Planning of Reaching Trajectories , 2000, The Journal of Neuroscience.

[44]  W L Jorgensen,et al.  Rusting of the lock and key model for protein-ligand binding. , 1991, Science.

[45]  L. Argote,et al.  The persistence and transfer of learning in industrial settings , 1990 .

[46]  J. Pearson.  Drug screening by enzyme immunoassay with the American Monitor KDA. , 1978, Clinical chemistry.

[47]  D. L. Ross,et al.  Drug screening by enzymatic immunoassay with the centrifugal analyzer. , 1975, Clinical chemistry.