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2D-classification and 3D-QSAR research of benzimidazole-based respiratory syncytial
virus fusion inhibitorsDingjue Ji, Yaojin Sun
Department of Bioinformatics and Biostatistics, School of Life Science, Shanghai Jiaotong University
800 Dongchuan RD, Shanghai, China
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Outline
•Introduction
•Methods
•Results
•Q&A
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Introduction“A virus is a small infectious agent that replicates only inside the living cells of other organisms.”
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•Human respiratory syncytial virus
•Isolated from chimpanzee
•respiratory health threat to newborn infants and young children.
•elderly and high-risk adults with underlying chronic pulmonary disease and the severely immunocompromised
What is RSV?
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Impact of RSV
•132,000 to 172,000 hospitalizations among children ,5 years of age
•100,000 to 126,000 hospitalizations among children ,1 year of age
•1.5 million outpatient visits among children ,5 years of age
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Therapy
•Palivizumab
•Medi-534
•Ribavirin
•CathelicidinMore!
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How we start
•CoMFA
•CoMSIA
•Molecular dynamics simulationCombination!
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Methods“Chemical biology is a scientific discipline spanning the fields of chemistry, biology, and physics. It involves the application of chemical techniques, tools, and analyses, and often compounds produced through synthetic chemistry, to the study and manipulation of biological systems.”
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Data sets
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Molecular Modeling and Alignment
•3D structure built by SYBYL modeling program
•Emphasis on the common structure
•BMS-433771 examined in vivo
•benzimidazole, benzimidazolinone, or both
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Two-dimensional representation
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Molecular Docking
•1G2C
•Well-defined binding site near Y198
•Autodock4.0/Vina package
•For QSAR model and initial structure of MD
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CoMFA Models
•Comparative Molecular Field Analysis
•Molecules aligned within a lattice which is rife with arranged probe
•Coulomb potential and Lennard-Jones potential
•Partial least squares
•3:1
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CoMSIA Models
•Comparative Molecular Similarity Indices Analysis
•Five force field
•Gaussian-type potential
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Results“Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data.”
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QSAR models
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QSAR models
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QSAR models
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QSAR models
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QSAR models
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QSAR models
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Preprocessing
Categorical x
[-1,1] with abundance over 80% x
Correlation over 80% x
Internal coding of perl
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Classification
potent(PEC50>7) and weak(PEC50<7)BP neural network
SVM
Random Forest
Coding of R
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Preview of BP results
pre Potent Weak Potent 147 33 Weak 7 104
pre2 Potent Weak Potent 25 23 Weak 9 22
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Preview of RF resultspre Potent Weak Potent 154 0 Weak 0 137
pre2 Potent Weak Potent 26 15 Weak 8 30
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Preview of SVM results
pre Potent Weak Potent 138 26 Weak 16 111
pre2 Potent Weak Potent 26 16 Weak 8 29
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Further Works
Basic statistics of evaluation
Analyze relation between QSAR model and receptor
Try to reveal binding mechanism and give advice on modification
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Q&A“Plato is dear to me, but dearer still is truth.”