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"Biologia de Sistemas Computacional Aplicada ao Desenvolvimento de Fármacos. Um Estudo de
Inibidores da Quinase Dependente de Ciclina (CDK)"
Prof. Dr. Walter F. de Azevedo Jr. Laboratório de Biologia de Sistemas Computacional
Escola de Ciências-Pontifícia Universidade Católica do Rio Grande do Sul-PUCRS
print(cdk)
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• Apresentação
• Biologia de Sistemas Computacional
• Docking Molecular
• CDK
• Projeto SAnDReS
• Espaço de Funções Escores
• Outros Projetos em Desenvolvimento
• Usos Recentes do SAnDReS
• Considerações Finais
• Agradecimentos
Sumário
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Prof. Harold Morowitz.
Santa Fé Institute. Disponível em:
http://www.santafe.edu/about/people/profile/Harold%20Moro
witz . Acesso em: 23 de março de 2018.
Biologia de Sistemas Computacional
“Computers are to biology what mathematics is to physics.”
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Biologia Física
Biologia de
Sistemas
Química
Computação
Matemática
Biologia de Sistemas Computacional
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Biologia de Sistemas Computacional
Genes mRNAs Proteínas Metabólitos
Genômica Transcriptômica Proteômica Metabolômica
Verificação
experimental
Análise computacional dos dados
Modelo
Computacional Previsão
Sistema Biológico
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P
L
Docking Molecular
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Algoritmos Evolucionários
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Indivíduo
População
Algoritmos Evolucionários
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População da primeira geração
População da segunda geração
Fitness
x
y
Algoritmos Evolucionários
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101101011010 000010000100 000001111111 100111100111 111110011000 000001011010 000010111100 110100111100
Strings binárias
(populatção incial)
Operador seleção
Operador crossover
Operador mutação
110100111010 101101011100 100110011000
1111111100111 101101011010 100111100111 111110011000 110100111100
Strings binárias
(Nova população)
Algoritmos Evolucionários
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110100111010 101101011100
100110011000
1111111100111
110100111100 101101011010
100111100111 111110011000
Locus
Locus
Pais Descendentes
Crossover
Algoritmos Evolucionários
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110100111010 101101011100
100110011000
1111111100111
110100111100 101101011010
100111100111 111110011000
Locus
Locus
Pais Descendentes
Crossover
Gera um número aleatório
(Rn)
Rn<=Pc?
Escolha novo par de pais
Sim
Não
Algoritmos Evolucionários
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Initialização
A B
C D
Algoritmos Evolucionários
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Simulação de Docking Molecular
Ligante (chave)
Sítio ativo (fechadura)
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N
zzyyxx
RMSD
N
j
jposejcristaljposejcristaljposejcristal
1
2
,,
2
,,
2
,,
Simulação de Docking Molecular
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Simulação de Docking Molecular
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Pai 1 Pai 2
crossover
Filha
Simulação de Docking Molecular
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Filha
Filha mutada
Mutação
Simulação de Docking Molecular
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Simulação de Docking Molecular azevedolab.net
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Algoritmos Evolucionários (Evolução Diferencial)
Operador seleção
Operador crossover
Operador mutação
Critério de parada
satisfeito?
Mostra resultados
Sim
Não
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cromossomo(n) = cromossomo(m) + peso.[cromossomo(k) - cromossomo(l)]
o peso varia entre 0 e 2.
Algoritmos Evolucionários (Evolução Diferencial)
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Algoritmos Evolucionários (Evolução Diferencial)
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Cromossomo k Cromossomo l
Cromossomo m
cromossomo(n) = cromossomo(m) + peso.[cromossomo(k) - cromossomo(l)]
Algoritmos Evolucionários (Evolução Diferencial)
Cromossomo n
Cromossomo j
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Cromossomo n
Cromossomo n mutado
Mutação
Algoritmos Evolucionários (Evolução Diferencial)
Cromossomo j é deletado e
substituído pelo n
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CDK azevedolab.net
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CDK
Referências: De Azevedo WF, Leclerc S, Meijer L, Havlicek L, Strnad M, Kim SH. Inhibition of cyclin-dependent kinases by purine analogues: crystal structure of human cdk2 complexed with roscovitine. Eur J Biochem. 1997; 243(1-2): 518-26. PubMed De Azevedo WF Jr, Mueller-Dieckmann HJ, Schulze-Gahmen U, Worland PJ, Sausville E, Kim SH. Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase. Proc Natl Acad Sci U S A. 1996; 93(7): 2735-40. PubMed Kim SH, Schulze-Gahmen U, Brandsen J, de Azevedo Júnior WF. Structural basis for chemical inhibition of CDK2. Prog Cell Cycle Res; 2: 137-45. PubMed
DNA damage
p53 p21
CDK2+cyclin A
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Thomsen R, Christensen MH. MolDock: a new technique for high-accuracy molecular docking. J Med Chem. 2006;49:3315– 21.
SAnDReS azevedolab.net
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SAnDReS
+25 mil linhas de código Código aberto Testado em diversos sistemas biológicos
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SAnDReS
www.sandres.net
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SAnDReS
GUI window
Text window
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SAnDReS
SAnDReS GUI
SAnDReS main
program
Input file (.in)
log files (.log)
csv files (.csv)
Plot files
Machine-learning
techniques
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SAnDReS (Docking Molecular)
Binding Affinity Total Number of
Available Structures
Total Number of
Structures Determined
by X-ray
Crystallography
Total Number of
Structures
Determined by
NMR
Total Number of
Structures
Determined by
Neutron
Crystallography
Total Number of
Structures
Determined by
hybrid methods
Total Number of
Structures
Determined by
electronic
micrography
Percentage of
Structures Determined
by X-ray
Crystallography
Ki 5503 5467 34 1 1 0 99.35
Kd 6392 6044 344 1 1 2 94.56
Ka 110 110 0 0 0 0 100,0
IC50 6207 6176 29 1 1 0 99.50
DeltaG 139 136 2 0 1 0 97.84
DeltaH 59 59 0 0 0 0 100.0
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Selection of
Biomolecular
System
Download
Structures and
Experimental
Binding
Information
Pre-Docking
Analysis
Re-Docking
Statistical
Analysis of Re-
docking Results
Ensemble-
Docking
Statistical
Analysis of
Ensemble-
Docking Results
Scoring Function
Calculation
Statistical
Analysis of
Scoring
Functions
Results
Virtual Screening
with a Dataset
with Decoys +
Actives
Statistical
Analysis of
Virtual Screening
with Decoys +
Actives
Virtual Screening
Simulation
Statistical
Analysis of
Virtual Screening
Simulation
Results
Best Hits
SAnDReS (Docking Molecular)
Referência:
Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL, Azevedo WF Jr. SAnDReS a Computational Tool for Statistical
Analysis of Docking Results and Development of Scoring Functions. Comb Chem High Throughput Screen. 2016; 19(10): 801-812.
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Selection of
Biomolecular
System
Download
Structures and
Experimental
Binding
Information
Pre-Docking
Analysis
Re-Docking
Statistical Analysis
of Re-Docking
Results
Ensemble-Docking
Statistical Analysis
of Ensemble-
Docking Results
and Structural
Parameters
Scoring Function
Calculation
Building New
Scoring Functions
Results
Statistical Analysis
of Scoring
Functions Results
Virtual Screening
with a Dataset with
Decoys + Actives
Statistical Analysis
of Virtual
Screening with
Decoys + Actives
Virtual Screening
Simulation
Best Hits
Statistical Analysis
of Virtual
Screening
Simulation Results
SAnDReS (Docking Molecular)
Referência:
Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL, Azevedo WF Jr. SAnDReS a Computational Tool for Statistical
Analysis of Docking Results and Development of Scoring Functions. Comb Chem High Throughput Screen. 2016; 19(10): 801-812.
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SAnDReS (CDK) (Re-docking) azevedolab.net
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Referência: Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8. https://doi.org/10.1016/j.bpc.2018.01.004 Link PubMed PDF
SAnDReS (CDK) (Re-docking)
Using MVD (Protocol 13)
Scoring Function/Energy Term RMSDa b p-value1c R2 d p-value2e
MolDock Score 1.383 0.880 3.741.10-17 0.853 1.368.10-21
Re-rank Score 0.846 0.359 1.048.10-2 0.033 2.037.10-1
Interaction Score 0.809 0.941 2.771.10-24 0.764 1.128.10-16
Protein Score 0.809 0.941 2.771.10-24 0.764 1.128.10-16
Internal Score 8.758 -0.261 6.667.10-2 0.026 2.642.10-1
H-Bond Score 1.670 0.576 1.181.10-5 0.460 6.254.10-8
LE1 Score 1.383 0.880 3.741.10-17 0.853 1.367.10-21
LE3 Score 0.846 0.359 1.048.10-2 0.033 2.037.10-1
Docking Score 0.600 0.899 7.047.10-19 0.871 5.248.10-23
Displaced Water Score 6.288 0.352 1.213.10-2 0.360 4.117.10-6
Using AD4
Scoring Function/Energy Term RMSDa b p-value1c R2 d p-value2e
Free Energy 1.230 0.652 4.087.10-2 0.549 1.419.10-2
Final Intermolecular Energy 1.230 0.675 3.231.10-2 0.573 1.128.10-2
vdW+Hbond+desolv Energy 0.740 0.766 9.787.10-3 0.767 9.018.10-4
Electrostatic Energy 2.210 -0.612 6.023.10-2 0.793 5.532.10-4
Final Total Internal Energy 1.330 -0.317 3.720.10-1 0.028 6.437.10-1
Using Vina
Scoring Function/Energy Term RMSDa b p-value1c R2 d p-value2e
Affinity 1.359 0.232 3.259.10-1 0.202 4.669.10-2
Gauss1 6.374 -0.179 4.503.10-1 0.114 1.454.10-1
Gauss2 5.770 0.090 7.052.10-1 0.059 3.001.10-1
Repulsion 6.928 -0.060 8.011.10-1 0.005 7.775.10-1
Hydrophobic 6.819 -0.495 2.658.10-2 0.043 3.821.10-1
Hydrogen 6.597 0.106 6.576.10-1 0.000 9.461.10-1
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Type of CDK PDB Access Codes
CDK2 1DI8,1DM2,1E9H,1FVT,1FVV,1GII,1H00,1H07,1H0W,1H1Q,1H1R,1JVP,
1KE5,1KE6,1KE7,1KE8,1KE9,1OGU,1OI9,1OIQ,1OIR,1OIT,1OIU,1OIY,1P2A,1PXI,1PXL,1PYE,1R78,1URW,1V1K,1VYW,1VYZ,1W0X,
1WCC,1Y8Y,1Y91,1YKR,2A0C,2B52,2B53,2B54,2BHE,2BKZ,2BPM,2BTS,2C4G,2C5N,2C5O,2C5Y,2C68,2C69,2C6I,2C6K,2C6L,
2C6M,2DS1,2DUV,2G9X,2I40,2IW6,2IW9,2R3F,2R3G,2R3H,2R3I,2R3J,2R3K,2R3L,2R3M,2R3N,2R3O,2R3P,2R64,2UUE,2UZB,2UZD,2U
ZE,2UZL,2UZN,2UZO,2VTA,2VTH,2VTI,2VTJ,2VTL,2VTM,2VTN,2VTO,2VTP,2VTQ,
2VTR,2VTS,2VTT,2VU3,2VV9,2W05,2W06,2W17,2W1H,2WEV,2WIH,2WXV,3BHT,3BHU,3BHV,3DDP,3DDQ,3DOG,3EZR,3EZV,3FZ1
,3IG7,3IGG,3LE6,3LFN,3LFS,3NS9,3PJ8,3PXZ,3PY0,3QQK,3QTR,3QTS,3QTU,3QTW,3QTX,3QTZ,3QU0,3R8U,3R8V,3R8Z,3R9D,
3R9H,3R9N,3R9O,3RAH,3RAL,3RJC,3RK5,3RK7,3RK9,3RKB,3RMF,3RNI,
3RPR,3RPV,3RPY,3RZB,3S00,3S0O,3S1H,3S2P,3TI1,3TIY,3TIZ,3ULI,3UNJ,
3UNK,3WBL,4BGH,4CFN,4CFW,4ERW,4EZ3,4GCJ,4LYN
CDK5 1UNG,1UNH,3O0G,4AU8
CDK6 4AUA
CDK8 3RGF
CDK9 3BLR,3LQ5,3TN8
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SAnDReS (CDK) (Ensemble Docking)
Scoring Function/Energy Term RMSDa b p-value1c R2 d p-value2e
MolDock Scoref 0.440 0.155 3.944.10-2 0.038 9.913.10-3
Re-rank Scoref 0.538 0.262 4.451.10-4 0.015 1.078.10-1
Interaction Scoref 0.592 0.094 2.123.10-1 0.013 1.276.10-1
Protein Scoref 0.592 0.100 1.880.10-1 0.013 1.319.10-1
Internal Scoref 0.311 -0.164 2.943.10-2 0.003 4.436.10-1
H-Bond Scoref 0.311 0.309 3.014.10-5 0.010 1.952.10-1
LE1 Scoref 0.311 0.323 1.223.10-5 0.011 1.590.10-1
LE3 Scoref 0.311 0.352 1.719.10-6 0.012 1.565.10-1
Docking Scoref 0.440 0.133 7.858.10-2 0.026 3.186.10-2
Displaced Water Scoref 0.145 0.400 3.795.10-8 0.014 1.123.10-1
Free Energyg 0.900 0.137 7.016.10-2 0.177 5.991.10-9
Final Intermolecular Energyg 2.840 0.003 9.661.10-1 0.104 1.302.10-5
vdW+Hbond+desolv Energyg 2.840 0.036 6.354.10-1 0.268 1.807.10-13
Electrostatic Energyg 1.820 -0.054 4.778.10-1 0.553 2.888.10-32
Final Total Internal Energyg 2.840 -0.235 1.703.10-3 0.001 7.306.10-1
Affinityh 0.271 0.372 3.591.10-7 0.282 3.599.10-14
Gauss1h 1.213 -0.251 7.732.10-4 0.061 9.409.10-4
Gauss2h 4.1383 -0.134 7.601.10-2 0.073 2.935.10-4
Repulsionh 4.1383 -0.321 1.444.10-5 0.002 5.559.10-1
Hydrophobich 7.260 -0.093 2.207.10-1 0.041 7.068.10-3
Hydrogenh 5.593 -0.251 7.641.10-4 0.042 6.626.10-3
Docking results for all structures in the CDK data set aRMSD is the RMSD for the lowest scoring function value, and its unit is Å.
b is Spearman rank-order correlation coefficient. cp-value1 is related to . dR2 is the squared Pearson correlation coefficient. ep-value2 is related to R2 . fUsing MVD (protocol 13). gUsing AD4. hUsing Vina.
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SAnDReS (CDK) (Binding Affinity) azevedolab.net
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Referência: Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8. https://doi.org/10.1016/j.bpc.2018.01.004 Link PubMed PDF
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SAnDReS (CDK) (Binding Affinity)
Scoring Function/Energy Term a p-value1b R2 c p-value2d
MolDock Scoree -0.092 2.259.10-1 0.097 2.697.10-5
Re-rank Scoree -0.207 5.798.10-3 0.079 1.643.10-4
Interaction Scoree 0.038 6.170.10-1 0.067 5.315.10-4
Cofactor Scoree 0.115 1.277.10-1 0.022 5.147.10-2
Protein Scoree 0.069 3.643.10-1 0.056 1.496.10-3
Water Scoree -0.094 2.127.10-1 0.013 1.345.10-1
Internal Scoree 0.201 7.347.10-3 0.062 8.585.10-4
Electro Scoree 0.105 1.642.10-1 0.000 9.698.10-1
Electro Long Scoree -0.186 1.369.10-2 0.017 8.408.10-2
H-Bond Scoree 0.104 1.675.10-1 0.018 7.992.10-2
LE1 Scoree -0.041 5.861.10-1 0.002 5.627.10-1
LE3 Score -0.078 3.049.10-1 0.001 7.117.10-1
Free Energyf 0.167 2.660.10-2 0.000 8.983.10-1
Final Intermolecular Energyf 0.142 5.983.10-2 0.000 8.977.10-1
vdW+Hbond+desolv Energyf 0.213 4.510.10-3 0.000 8.941.10-1
Electrostatic Energyf 0.026 7.317.10-1 0.000 8.940.10-1
Final Total Internal Energyf 0.312 2.433.10-5 0.000 9.203.10-1
Torsional Free Energyf -0.128 9.082.10-2 0.017 8.689.10-2
Affinityg 0.275 2.188.10-4 0.005 3.339.10-1
Gauss1g -0.220 3.320.10-3 0.023 4.641.10-2
Gauss2g -0.287 1.122.10-4 0.022 4.798.10-2
Repulsiong -0.199 8.012.10-3 0.054 1.923.10-3
Hydrophobicg -0.178 1.789.10-2 0.055 1.765.10-3
Hydrogeng -0.196 9.114.10-3 0.044 5.324.10-3
Correlation between scoring function/energy term and experimental binding affinity (log(IC50)) for all structures in the CDK data set. a is Spearman rank-order correlation coefficient. bp-value1 is related to . cR2 is the squared Pearson correlation coefficient. dp-value2 is related to R2 . eUsing MVD. fUsing AD4. gUsing Vina.
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Referências: De Azevedo WF Jr, Dias R. Evaluation of ligand-binding affinity using polynomial empirical scoring functions. Bioorg Med Chem. 2008; 16(20):9378-82. Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8. https://doi.org/10.1016/j.bpc.2018.01.004 Link PubMed PDF
Função Escore Polinomial
SAnDReS (CDK) (Machine Learning)
2
39
2
28
2
17326
31521433
22110score
xxxxx
xxxxx
xx
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SAnDReS (CDK) (Machine Learning) azevedolab.net
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Referência: Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8. https://doi.org/10.1016/j.bpc.2018.01.004 Link PubMed PDF
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Scoring Function/Energy Term Training Set Test Set
p-value1 p-value1
MolDock Scorea 0.059 5.179.10-1 -0.291 3.265.10-2
Re-rank Scorea -0.162 7.515.10-2 -0.132 3.405.10-1
Interaction Scorea 0.154 8.952.10-2 -0.195 1.568.10-1
Co-factor Scorea -0.010 9.170.10-1 0.265 5.243.10-2
Protein Scorea 0.211 1.943.10-2 -0.298 2.874.10-2
Water Scorea -0.130 1.550.10-1 0.024 8.614.10-1
Internal Scorea 0.170 6.150.10-2 0.252 6.632.10-2
Electro Scorea 0.041 6.549.10-1 -0.123 3.762.10-1
Electro Long Scorea -0.173 5.712.10-2 -0.060 6.667.10-1
H-Bond Scorea 0.187 3.932.10-2 0.027 8.483.10-1
LE1 Scorea -0.016 8.603.10-1 -0.026 8.500.10-1
LE3 Scorea -0.077 3.984.10-1 -0.084 5.463.10-1
Score482 a 0.390 9.065.10-6 0.346 1.044.10-2
Free Energyb 0.190 3.890.10-2 0.213 1.082.10-1
Final Intermolecular Energyb 0.200 2.961.10-2 0.172 1.975.10-1
vdW+Hbond+desolv Energyb 0.222 1.576.10-2 0.203 1.270.10-1
Electrostatic Energyb 0.047 6.106.10-1 -0.062 6.445.10-1
Final Total Internal Energyb 0.331 2.469.10-4 0.147 2.697.10-1
Torsional Free Energyb -0.176 5.659.10-2 0.043 7.494.10-1
Score281b 0.457 1.963.10-7 0.221 9.607.10-2
Affinityc 0.339 1.495.104 0.207 1.267.10-1
Gauss1c -0.297 1.006.10-3 -0.114 4.023.10-1
Gauss2c -0.347 1.025.10-4 -0.218 1.058.10-1
Repulsionc -0.173 5.819.10-2 -0.146 2.836.10-1
Hydrophobicc -0.148 1.061.10-1 -0.027 8.436.10-1
Hydrogenc -0.167 6.901.10-2 -0.235 8.080.10-2
Results for training set and test set for CDK data set. aUsing MVD. bUsing AD4. cUsing Vina.
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SAnDReS (CDK) (Machine Learning)
x.y.0000030 +0.001136z-0.001529y +0.001829x - 7.074331- 482
score
where Re-rank (x), Internal (y), and Electro Long (z) Scores were used as explanatory variables. This polynomial equation
shows = 0.389 (p-value < 0.001) for the training set (122 structures) and = 0.345 (p-value = 0.0105) for a test set with 54
structures.
= 0.345 (p-value = 0.0105)
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Referência: Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8. https://doi.org/10.1016/j.bpc.2018.01.004 Link PubMed PDF
SAnDReS (CDK) (Machine Learning)
b.c.b+.a.b.a+.-.-score 200001600000840000016000001603477846281
where the explanatory variables were determined using AD4 taking a as the Free Energy, b as the Final Internal Energy, and c
as the Electrostatic Energy. This model shows = 0.457 (p-value < 0.001) for the training set (122 structures) and = 0.221 (p-
value = 0.096) for a test set with 54 structures.
= 0.457 (p-value < 0.001)
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Referência: Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8. https://doi.org/10.1016/j.bpc.2018.01.004 Link PubMed PDF
Lys 33
Asp 145
Glu 12
Glu 81
Asn 132
Asp 86 Gln 85
His 84
Lys 33 Glu 81
Asp 86
Asn 132
Asp 145
Residue Number
SAnDReS (CDK) (Machine Learning) azevedolab.net
azevedolab.net
Referência: Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8. https://doi.org/10.1016/j.bpc.2018.01.004 Link PubMed PDF
SAnDReS (CDK)
x.y.0000030 +0.001136z-0.001529y
+0.001829x - 7.074331- 482
score IC50
Referência: Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8. https://doi.org/10.1016/j.bpc.2018.01.004 Link PubMed PDF
azevedolab.net
azevedolab.net
Exploring the Scoring Function
Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF. Supervised Machine Learning Methods Applied to Predict Ligand-Binding Affinity. Curr Med Chem. 2017; 24(23): 2459-2470.
azevedolab.net
azevedolab.net
Improvement of the predictive power of a scoring function generated with the
program SAnDReS. Figure created by Ms. Gabriela Bitencourt-Ferreira.
Exploring the Scoring Function azevedolab.net
azevedolab.net
Tool to Analyze the Binding Affinity Desenvolvido por: Amauri Duarte & Prof. Dr. Walter Filgueira de Azevedo Jr http://taba.bio.br/
Exploring the Scoring Function azevedolab.net
azevedolab.net
Amaral MEA,, Nery LR, Leite CE, de Azevedo Junior WF, Campos MM. Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Invest New Drugs. 2018. doi: 10.1007/s10637-018-0568-y. PubMed PDF Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8. https://doi.org/10.1016/j.bpc.2018.01.004 Link PDF Pintro VO, Azevedo WF. Optimized Virtual Screening Workflow. Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease. Comb Chem High Throughput Screen. 2017; 20(9): 820-827. PubMed PDF de Ávila MB, Xavier MM, Pintro VO, de Azevedo WF. Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. Biochem Biophys Res Commun. 2017; 494: 305-310. PubMed PDF
Usos Recentes do SAnDReS azevedolab.net
azevedolab.net
Freitas PG, Elias TC, Pinto IA, Costa LT, de Carvalho PVSD, Omote DQ, Camps I, Ishikawa T, Arcuri HA, Vinga S, Oliveira AL, Junior WFA, da Silveira NJF. Computational Approach to the Discovery of Phytochemical Molecules with Therapeutic Potential Targets to the PKCZ protein. Letters in Drug Design & Discovery 2017 DOI: 10.2174/1570180814666170810120150 Link to the Paper Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF. Supervised Machine Learning Methods Applied to Predict Ligand-Binding Affinity. Curr Med Chem. 2017; 24(23): 2459-2470. PubMed PDF Levin NM, Pintro VO, de Ávila MB, de Mattos BB, De Azevedo WF Jr. Understanding the Structural Basis for Inhibition of Cyclin-Dependent Kinases. New Pieces in the Molecular Puzzle. Curr Drug Targets. 2017; 18(9): 1104-
1111. PubMed PDF ...
Usos Recentes do SAnDReS azevedolab.net
azevedolab.net
Considerações Finais
SAnDReS é um ambiente integrado para análise de resultados de docking
molecular e desenvolvimento de modelos de aprendizado de máquina;
SAnDReS é resultado de 3 anos de pesquisa e desenvolvimento;
SAnDReS apresenta mais de 25 mil linhas de código;
SAnDReS consumiu mais 12 mil horas/homem de trabalho;
SAnDReS é capaz de gerar modelos de aprendizado de máquina direcionados ao
sistema biológico de interesse;
Estudo da CDKcom informação de IC50 foi capaz de propor novas funções escores
com melhor poder de previsão;
Aplicável para análise de resultados gerados por qualquer programa de docking
molecular;
Disponível em: sandres.net
azevedolab.net
azevedolab.net
azevedolab.net
azevedolab.net
Nayara, Gabriela Heck, Carminha, Walter, Maurício e Val
Gabriela Bitencourt e Bruna
azevedolab.net
Prof. Dr. Ivan Cunha Bustamante Filho
Univates
CNPq ( Processo número: 308883/2014-4)
azevedolab.net
Dr. Walter Filgueira de Azevedo Jr.
Doutor em Ciências – Física Aplicada – Universidade de São Paulo –USP
Pesquisador Visitante na Universidade da Califórnia em Berkeley-EUA
Livre-Docente em Física – Universidade Estatual Paulista – UNESP
Editor Regional da Revista Current Drug Targets
Editor de Seção (Bioinformatics Applied to Drug Design) da Current
Mediicinal Chemistry
Membro do Corpo Editorial da Revista Current Bioinformatics
Pesquisador nível 1B do CNPq
E-mail: [email protected]
Facebook: https://facebook.com/Prof.Walter
Site: http://azevedolab.net