“Space, the final frontier” - azevedolab.net · azevedolab.net ~1060 moléculas orgânicas...

“Space, the final frontier...”

Como explorar o espaço de funções escores para o desenho de fármacos.

Prof. Dr. Walter F. de Azevedo Jr.azevedolab.net

O que é o espaço químico?

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~1060 moléculas orgânicas

Bohacek RS, McMartin C, Guida WC. The art and practice of structure-based drug design: a molecular modeling perspective.

Med Res Rev. 1996; 16(1):3–50. PubMed

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https://www.ncbi.nlm.nih.gov/pubmed/8788213

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Vantagens...

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Abstração de sistemas...

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Definição de subespaços

de interesse



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Subespaço de fármacos



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Subespaço de produtos

naturais



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Subespaço de inibidores

de CDK



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O que é o espaço de proteínas?

Hou J, Jun SR, Zhang C, Kim SH. Global mapping of the protein structure space and application in structure-based inference of protein function.

Proc Natl Acad Sci U S A. 2005; 102(10):3651-6.

Representation of protein space. Fonte: http://www.pnas.org/content/102/10/3651/tab-figures-data

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http://www.pnas.org/content/102/10/3651/tab-figures-data

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Representation of protein space. Fonte: http://www.pnas.org/content/102/10/3651/tab-figures-data

O que é o espaço de proteínas?

Hou J, Jun SR, Zhang C, Kim SH. Global mapping of the protein structure space and application in structure-based inference of protein function.

Proc Natl Acad Sci U S A. 2005; 102(10):3651-6.

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http://www.pnas.org/content/102/10/3651/tab-figures-data

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Smith JM. Natural selection and the concept of a protein space. Nature. 1970; 225(5232): 563–564. PubMed

Espaço de proteínas

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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

O que é o espaço de funções

escores?

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https://www.researchgate.net/publication/317848912_Supervised_Machine_Learning_Methods_Applied_to_Predict_Ligand-Binding_Affinity

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Como pesquisar o espaço de

funções escores?

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Espaço químico

Ki, IC50, Kd ou G

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log(𝐾𝑖) =

𝑖=0

𝑁

𝛼𝑖𝑥𝑖 +

𝑗=0

𝑀

𝛽𝑗𝑦𝑖2

log(𝐾𝑖) =

𝑖=0

𝑁

𝛼𝑖𝑥𝑖 +

𝑗=0

𝑀

𝑘=0

𝑀

𝛾𝑗,𝑘 𝑧𝑗,𝑘 +

𝑗=0

𝑀

𝛽𝑗𝑦𝑖2

log(𝐾𝑖) =

𝑖=0

𝑁

𝛼𝑖𝑥𝑖 +

𝑗=0

𝑀

𝑘=0

𝑀

𝛾𝑗,𝑘 𝑧𝑗,𝑘 + 𝐴.𝐸𝑥𝑝(𝑤)

𝑗=0

𝑀

𝛽𝑗𝑦𝑖2

log(𝐾𝑖) =

𝑖=0

𝑁

𝛼𝑖𝑥𝑖 + 𝐴

𝑗=0

𝑁

𝛽𝑗𝑦𝑗 + 𝐵

𝑘=0

𝑁

𝛾𝑘𝑧𝑘 + 𝐶

𝑘=0

𝑁

𝜔𝑘𝑧𝑘2 + 𝐷

𝑘=0

𝑁

𝜔𝑘𝑧𝑘3 + 𝐸

𝑘=0

𝑁

𝜔𝑘𝑧𝑘4

log(𝐾𝑖) =

𝑖=0

𝑁

𝑗=0

𝑀

𝑘=0

𝑀

𝛾𝑖,𝑗,𝑘 𝑧𝑖,𝑗,𝑘 + 𝐵. 𝑆𝑖𝑛(𝑤)

𝑗=0

𝑀

𝛽𝑗𝑦𝑖2

log 𝐾𝑖 =

𝑖=0

𝑁

𝛼𝑖𝑥𝑖 + 𝐴.𝐸𝑥𝑝 𝜔

𝑗=0

𝑀

𝛽𝑗𝑦𝑖2 …


Espaço de funções escores

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azevedolab.net Ferramentas para explorar o espaço de funções escoresIn

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Tool to Analyze the Binding Affinity

http://azevedolab.net/taba.php

Silva, A.D.; Bitencourt-Ferreira, G.; de Azevedo

Jr., W.F. Taba: A Tool to Analyze the Binding

Affinity. J. Comput. Chem. 2019, DOI:

10.1002/JCC.26048TA

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http://azevedolab.net/taba.php

azevedolab.net Sistema massa-mola para simulação das interações

proteína-ligante (visão estilizada)

Silva, A.D.; Bitencourt-Ferreira, G.; de Azevedo Jr., W.F. Taba: A Tool to Analyze the Binding Affinity. J.

Comput. Chem. 2019, DOI: 10.1002/JCC.26048

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𝑉 𝑥 = 𝑉 𝑑0 + 𝑉 𝑑0′ 𝑥 − 𝑑0 + ( Τ1 2!)𝑉 𝑑0

′′ 𝑥 − 𝑑02 + ( Τ1 3!)𝑉 𝑑0

′′′ 𝑥 − 𝑑03 +⋯ (1)

𝑉 𝑥 ≈ 𝑉 𝑑0 + ( Τ1 2!)𝑉 𝑑0′′ 𝑥 − 𝑑0

2 (2)

d0

d0 = equilíbrio

Sistema massa-mola em movimento harmônico simples

Sistema massa-mola para simulação das interações

proteína-ligante (física básica)



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𝑉 𝑥 ≈ 𝑉 𝑑0 + ( Τ1 2!)𝑉 𝑑0′′ 𝑥 − 𝑑0

2 (2)


proteína-ligante (física básica)



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Materiais homogêneos isotrópicos (Kot et al., 2015; Kot & Nagahashi, 2017)

Folha de grafeno (Kim et al., 2014)

Tunelamento de elétrons em transistors(Pasupathy et al., 2005),

Kim,M.H. et al. (2014) Vibrational characteristics of graphene sheets elucidated using an elastic network model. Phys. Chem. Chem. Phys., 16, 15263–15271.

Kot,M. et al. (2015) Elastic moduli of simple mass spring models. Vis. Comput., 31(10), 1339–1350.

Kot,M. and Nagahashi,H. (2017) Mass spring models with adjustable Poisson’s ratio. Vis. Comput., 33(3), 283–291.

Pasupathy,A.N. et al. (2005) Vibration-assisted electron tunneling in C140 transistors. Nano Lett., 5, 203-207.

Sistema massa-mola para simulação (exemplos)TA

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proteína-ligante (distância de equilíbrio)



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proteína-ligante (fluxograma)



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proteína-ligante (pares de átomos)



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𝑃𝐵𝐴 = 𝛼0 + σ𝑖σ𝑗 𝛼𝑖,𝑗(𝑑𝑖,𝑗 − 𝑑0,𝑖,𝑗)2 (3)


proteína-ligante (afinidade de ligação)



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𝑃𝐵𝐴 = 𝛼0 + σ𝑖σ𝑗 𝛼𝑖,𝑗(𝑑𝑖,𝑗 − 𝑑0,𝑖,𝑗)2 (3)

𝑅𝑆𝑆 = σ𝑖=1𝑀 (𝑦𝑖 − 𝑃𝐵𝐴𝑖)

2 + 𝜆1σ𝑗=1𝑁 𝜔𝑗 + 𝜆2σ𝑗=1

𝑁 𝜔𝑗2

(4)

[1] Legendre, AM. Nouvelle méthodes pour la déterminiation des orbites des comètes, Courcier, Paris, 1805.

[2] Tibshirani, R. Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Series B Stat. Methodol. 1996, 58, 267–288.

https://doi.org/10.1111/j.1467-9868.2011.00771.x.

[3] Tikhonov, AN. On the regularization of ill-posed problems, Dokl. Akad. Nauk SSSR. 1963, 153, 49–52.

[4] H. Zou, T. Hastie, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Series B Stat. Methodol. 2005, 67, 301–220.

https://doi.org/10.1111/j.1467-9868.2005.00503.x.

Método 1 2 Referências

Regressão Linear Ordinária 0 0 [1]

Least absolute shrinkage and selection operator (Lasso) >0 0 [2]

Ridge 0 > 0 [3]

Elastic Net > 0 > 0 [4]


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https://doi.org/10.1111/j.1467-9868.2011.00771.x

https://doi.org/10.1111/j.1467-9868.2005.00503.x


proteína-ligante (sistema biológico)



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M

G2

S

G1

CDK2/

Cyclin E

CDK1/

Cyclin A

CDK2/

Cyclin A

CDK1/

Cyclin B

CDK4/

Cyclin D CDK6/

Cyclin D


proteína-ligante (sistema biológico)



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Espaço de funções escores

Espaço de proteínasEspaço químico

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

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PDB access code Ligand Identification

Ligand Chain

Ligand Number

Ki (nM) Test Set

1E1V CMG A 401 12000 11E1X NW1 A 401 1300 01H1S 4SP A 1298 6 01JSV U55 A 400 2000 11OGU ST8 A 1298 2400 01PXM CK5 A 500 60 11PXN CK6 A 500 195 01PXO CK7 A 500 2 11PXP CK8 A 500 220 01PYE PM1 A 700 386 11V1K 3FP A 299 35000 12CLX F18 A 1299 13300 02EXM ZIP A 400 78000 02FVD LIA A 299 3 02XMY CDK A 500 0.11 12XNB Y8L A 1299 149 13BLR CPB A 940 3 03DDQ RRC A 299 250 03LFN A27 A 299 3160 03LFS A07 A 299 2500 13MY5 RFZ A 300 65000 04ACM 7YG A 1302 210 04BCK T3E A 1298 4 04BCM T7Z A 1297 123 04BCN T9N A 1299 12 04BCO T6Q A 1299 131 04BCP T3C A 1299 568 04BCQ TJF A 1296 147 04EOP 1RO A 301 890 04NJ3 2KD A 301 140 05D1J 56H A 4000 38 0


proteína-ligante (conjunto de dados)

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Regression are the following:

α0 = -6.581356;

αC,N = -0.111232;

αC,O = -0.406456;

αN,F = -0.353717.

The equilibrium distances are the following:

d0,C,N =3.99463;

d0,C,O =3.88190;

d0,N,F = 4.21672 Å.

Taba obtained these results using the elastic net with

cross-validation (CV) as a regression method.

𝑃𝐵𝐴 = 𝛼0 + σ𝑖σ𝑗 𝛼𝑖,𝑗(𝑑𝑖,𝑗 − 𝑑0,𝑖,𝑗)2


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Scoring Functions p-value1 R p-value2Free Energya -0.133 0.7324 0.204 0.2227Final Intermolecular Energya 0.133 0.7324 0.204 0.2228vdW+Hbond+desolv Energya 0.133 0.7324 0.204 0.2228Electrostatic Energya 0.533 0.1392 0.376 0.0789Final Total Internal Energya -0.133 0.7324 0.089 0.4365Torsional Free Energya 0.068 0.8630 0.000 0.9792Plants Scoreb 0.183 0.6368 0.001 0.9348MolDock Scoreb 0.217 0.5755 0.010 0.7950Rerank Scoreb 0.333 0.3807 0.007 0.8336Interaction Scoreb 0.367 0.3317 0.013 0.7698Protein Scoreb 0.367 0.3317 0.025 0.6839Water Scoreb -0.569 0.1098 0.395 0.0699Internal Scoreb 0.033 0.9322 0.001 0.9369Electrostatic Scoreb 0.548 0.1269 0.204 0.2218Electrostatic Long Scoreb -0.548 0.1269 0.204 0.2218H-Bond Scoreb 0.650 0.0581 0.512 0.0301Ligand Efficiency 1 Scoreb 0.150 0.7001 0.024 0.6935Ligand Efficiency 3 Scoreb 0.283 0.4600 0.023 0.6968Affinity Scorec -0.067 0.8647 0.117 0.3669Gauss1 Scorec -0.367 0.3317 0.120 0.3603Gauss2 Scorec -0.283 0.4600 0.018 0.7297Repulsion Scorec -0.700 0.0358 0.240 0.1804Hydrophobic Scorec 0.100 0.7980 0.002 0.9157Hydrogen Scorec -0.583 0.0992 0.340 0.0993Taba (3 variables, d 4.5 Å) 0.783 0.01252 0.794 0.0107

Predictive performance of scoring functions (test set).aAutoDock 4, bMolegro Virtual Docker (MVD), cAutoDock Vina.

p-value1 and p-value2 are related to ρ and R, respectively.


proteína-ligante (análise estatística)

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𝑃𝐵𝐴 = 𝛼0 + σ𝑖σ𝑗 𝛼𝑖,𝑗(𝑑𝑖,𝑗 − 𝑑0,𝑖,𝑗)2

Hernandes MZ, Cavalcanti SM, Moreira DR, de Azevedo Junior WF, Leite AC. Halogen atoms in the

modern medicinal chemistry: hints for the drug design. Curr Drug Targets. 2010; 11(3):303–314. PubMed

Regression weights are the following:

α0 = -6.581356;

αC,N = -0.111232;

αC,O = -0.406456;

αN,F = -0.353717.

The equilibrium distances are the following:

d0,C,N =3.99463;

d0,C,O =3.88190;

d0,N,F = 4.21672 Å.


proteína-ligante (modelo)

45 combinations

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http://www.ncbi.nlm.nih.gov/pubmed/20210755

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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. PubMed PDF GitHub

SAnDReS

Statistical Analysis of Docking Results

and Scoring Functions

http://azevedolab.net/sandres.php

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https://www.researchgate.net/publication/308948438_SAnDReS_a_Computational_Tool_for_Statistical_Analysis_of_Docking_Results_and_Development_of_Scoring_Functions

https://github.com/azevedolab/sandres

http://azevedolab.net/sandres.php

azevedolab.net

𝑃𝐵𝐴 = 𝛼0 + 𝛼1𝑥1 + 𝛼2𝑥2 + 𝛼3𝑥3 +𝛼4𝑥1𝑥2 + 𝛼5𝑥1𝑥3 + 𝛼6𝑥2𝑥3+

𝛼7𝑥12 + 𝛼8𝑥2

2 + 𝛼9𝑥32

Onde x1, x2 e x3 são termos de energias tiradas de programas como o AutoDock 4, AutoDock Vinae Molegro Virtual Docker



Chem High Throughput Screen. 2016; 19(10): 801–812. Link PubMed Go To SAnDReS PDF GitHub

Função escore polinomialS

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http://benthamscience.com/journals/combinatorial-chemistry-and-high-throughput-screening/article/145824/


http://sandres.net/



azevedolab.netScoring Functions and Energy Terms DescriptionMolDock Score Protein-ligand Scoring Function. This scoring function is the sum of internal ligand

energies, protein interaction energy and soft penalties

PLANTS Score Protein ligand Scoring FunctionRe-rank Score Protein ligand Scoring FunctionEnergy Term 1 Interaction energy between the ligand and the target molecule(s) (Interaction)

Energy Term 2 Interaction energy between the ligand and the co-factor (Cofactor)

Energy Term 3 Interaction energy between the ligand and the protein (Protein)

Energy Term 4 Interaction energy between the ligand and the water molecules (Water)

Energy Term 5 Internal energy of the ligand (Internal)

Energy Term 6 Short-range electrostatic protein-ligand interactions (r<4.5Å) (Electro)

Energy Term 7 Long-range electrostatic protein-ligand interactions (r>4.5A) (ElectroLong)

Energy Term 8 Hydrogen bonding energy (HBond)LE1 Score Ligand Efficiency 1: MolDock Score divided by Heavy Atoms count

LE3 Score Ligand Efficiency 3: Rerank Score divided by Heavy Atoms count

Docking Score Score evaluated before post-processing (either PLANTS or MolDock). Only used for re-

docking.Displaced Water Score Energy contributions from non-displaced and displaced water interactions.

AutoDock4 Scoring Function This scoring function makes use of five energetic terms: the torsional term, the

hydrogen bonding interactions, the electrostatic potential, the desolvation energy, and

the van der Waals interactions

AutoDock Vina Scoring Function Vina makes use of the following energy terms: Gauss1, Gauss2, repulsion, hydrophobic,

hydrogen bond, and torsion. They are defined elsewhere

List of all scoring functions used in this study.




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Datasets PDB Access Codes

HRIC50

We used a dataset composed of an ensemble of high-resolutioncrystallographic structures solved to resolution better than 1.5 Å,and for which there is experimental data for half-maximal inhibitory concentration (IC50) for the active ligands

2GG3,2GG7,2GG9,2HU6,2I5F,2IKG,2NMZ,2NNG,2OW6,2PDG,2PIY,2PZN,2QCF,2R3I,2W14,2W3B,2W9H,2WUU

,2WZX,2X5O,2XPC,2XU3,2XU4,2Y1O,2Y68,2YC3,2YEX,2YJ2,2YJ8,2YJ9,2YJC,2YK9,2YKE,2YKJ,3B28,3B7E,3B8Z,

3BCJ,3BLB,3CBP,3DCR,3DD0,3DN5,3EJS,3EJT,3EJU,3ESS,3EWZ,3EX3,3F66,3FCI,3FS6,3GHV,3GHW,3H5B,3HHA,

3HJ0,3HNB,3HS4,3HYG,3I06,3I33,3I6C,3I6O,3IOG,3IU7,3KFA,3KIG,3KKU,3KL6,3KWZ,3L14,3M0I,3M4H,3NKK,

3NTZ,3NU0,3NU3,3NWB,3NXO,3NXX,3NZB,3OND,3OT3,3OVX,3OZS,3OZT,3PA3,3PKA,3PKB,3PX8,3R6T,3RL4,

3S1Y,3S71,3SPK,3TEM,3U2C,3UHM,3VF3,3VHV,3VW9,3WFG,3ZSJ,3ZXH,4A6V,4A6W,4BW1,4DHR,4DRI,4DRN,

4DRO,4DRQ,4E4A,4F3I,4FH2,4FLK,4FYO,4GCJ,4GQR,4GV1,4HCT,4HCU,4HCV,4HWW,4HXQ,4HXS,4HY4,4HYI,

4IGH,4IKU,4JHT,4KEB,4L7G,4M5R

CDK2IC50 1GII, 1OIR, 2B53, 2B54, 2R3H, 3IGG, 3LE6, 3PXZ, 3PY0, 3RZB, 4RJ3

List of PDB access codes used for both datasets.

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

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https://www.researchgate.net/publication/320354236_Supervised_machine_learning_techniques_to_predict_binding_affinity_A_study_for_cyclin-dependent_kinase_2

azevedolab.net

)0.000040(x .z)0.001090(y-.z)0.000185(x+.y)0.000069(x+5.763674 2=)log( 50IC

where x is Interaction energy between the pose and the protein, y is

Internal energy of the ligand (Internal), and z is Hydrogen bonding

energy (HBond).



310. PubMed PDF

Scoring Functions

and Energy Terms

(training

set)

p-value

(training

set)

(test set)p-value (test

set)

PLANTS Score 0.266 3.797.10-03 0.167 2.185.10-01

MolDock Score 0.284 1.939.10-03 0.224 9.678.10-02

Rerank Score 0.227 1.371.10-02 0.109 4.219.10-01

Term 1 0.334 2.305.10-04 0.215 1.109.10-01

Term 2 0.130 1.623.10-01 0.211 1.192.10-01

Term 3 0.340 1.795.10-04 0.147 2.810.10-01

Term 4 0.214 2.032.10-02 0.083 5.455.10-01

Term 5 -0.077 4.104.10-01 0.155 2.541.10-01

Term 6 -0.107 2.514.10-01 -0.179 1.871.10-01

Term 7 0.134 1.511.10-01 0.101 4.568.10-01

Term 8 -0.067 4.746.10-01 -0.237 7.889.10-02

Polscore0000060 0.401 7.243.10-06 0.328 1.363.10-02

Results for training and test sets for HRIC50 dataset.

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Scoring Functions

and Energy Terms

(training

set)

p-value

(training

set)

(test set)p-value (test

set)

PLANTS Score 0.266 3.797.10-03 0.167 2.185.10-01

MolDock Score 0.284 1.939.10-03 0.224 9.678.10-02

Rerank Score 0.227 1.371.10-02 0.109 4.219.10-01

Term 1 0.334 2.305.10-04 0.215 1.109.10-01

Term 2 0.130 1.623.10-01 0.211 1.192.10-01

Term 3 0.340 1.795.10-04 0.147 2.810.10-01

Term 4 0.214 2.032.10-02 0.083 5.455.10-01

Term 5 -0.077 4.104.10-01 0.155 2.541.10-01

Term 6 -0.107 2.514.10-01 -0.179 1.871.10-01

Term 7 0.134 1.511.10-01 0.101 4.568.10-01

Term 8 -0.067 4.746.10-01 -0.237 7.889.10-02

Polscore0000060 0.401 7.243.10-06 0.328 1.363.10-02

Results for training and test sets for HRIC50 dataset.



310. PubMed PDF

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PDB Access

Code

Active

Ligand

Code

Resolution (Å) IC50(nM) log(IC50) Predicted

log(IC50)

1GII 1PU 2.00 260 -6.585 -6.4631OIR HDY 1.91 32 -7.495 -7.4952B53 D23 2.00 600 -6.222 -6.2212B54 D05 1.85 20 -7.699 -7.6992R3H SCE 1.50 20000 -4.699 -3.8393IGG EFQ 1.80 80.75 -7.093 -6.1963LE6 2BZ 2.00 35 -7.456 -6.2773PXZ JWS 1.70 5900 -5.229 -5.2773PY0 SU9 1.75 79.25 -7.101 -6.6783RZB 02Z 1.90 100000 -4.000 -5.7794RJ3 3QS 1.63 93 -7.032 -6.544

Experimental and predicted log(IC50) for all structures in the CDK2IC50 dataset.



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Scoring Functions and

Energy Termsa

ρ p-value R2 p-value

Affinityb 0.418 2.006.10-01 0.237 1.289.10-01

Gauss1b -0.773 5.299.10-03 0.393 3.889.10-02

Gauss2b -0.645 3.196.10-02 0.386 4.125.10-02

Repulsionb -0.618 4.265.10-02 0.276 9.715.10-02

Hydrophobicb -0.391 2.345.10-01 0.223 1.424.10-01

Hydrogenb -0.730 1.069.10-02 0.280 9.386.10-02

Free Energyc 0.445 1.697.10-01 0.082 3.923.10-01

Final Intermolecular Energyc 0.400 2.229.10-01 0.082 3.923.10-01

vdW+Hbond+desolv Energyc 0.409 2.115.10-01 0.082 3.923.10-01

Electrostatic Energyc -0.209 5.372.10-01 0.082 3.922.10-01

Final Total Internal Energyc 0.588 5.725.10-02 0.345 5.730.10-02

Torsional Free Energyc -0.304 3.637.10-01 0.106 3.298.10-01

MolDock Scored 0.391 2.345.10-01 0.173 2.028.10-01

PLANTS Scored 0.682 2.084.10-02 0.507 1.401.10-02

Rerank Scored -0.591 5.558.10-02 0.768 4.044.10-04

Ligand Efficiency 1d -0.391 2.345.10-01 0.183 1.888.10-01

Ligand Efficiency 3d -0.345 2.981.10-01 0.294 8.516.10-02

Polscore 60e 0.845 1.045.10-03 0.608 4.650.10-03

Statistical analysis of predictive power for all structures in the CDK2IC50 dataset.

aEnergy term and scoring function values were calculated using the

crystallographic position for the ligands.bScoring function and energy terms calculated using AutoDock Vina.cScoring function and energy terms calculated using AutoDock 4. dScoring function and energy terms calculated using MVD.eMachine learning model generated using SAnDReS.



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Taba e SAnDReS são capazes de gerar modelos para previsão de

afinidade com desempenho superior às funções clássicas (Molegro

Virtual Docker, AutoDock 4, AutoDock Vina)

O conceito do espaço de funções escores é uma forma elegante de

desenvolvermos modelos computacionais direcionados para sistemas

biológicos de interesse

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SAnDReS 2.0

Taba com potencial eletrostático

SFSXplorer

Aplicação a sistemas biológicos de interesse

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https://github.com/azevedolab/

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Período: Janeiro-Julho/2019

de Ávila MB, Bitencourt-Ferreira G, de Azevedo Jr. WF. Structural Basis for Inhibition of Enoyl-[Acyl Carrier

Protein] Reductase (InhA) from Mycobacterium tuberculosis. Curr Med Chem. doi:

10.2174/0929867326666181203125229 Impact factor: 3.894

Volkart PA, Bitencourt-Ferreira G, Souto AA, de Azevedo WF. Cyclin-Dependent Kinase 2 in Cellular

Senescence and Cancer. A Structural and Functional Review. Curr Drug Targets. 2019; 20(7): 716–726.

Impact factor: 3.112

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Russo S, De Azevedo WF. Advances in the Understanding of the Cannabinoid Receptor 1 - Focusing on the

Inverse Agonists Interactions. Curr Med Chem. 2019; 26(10): 1908–1919. Impact factor: 3.894

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Comput. Chem. 2019, DOI: 10.1002/JCC.26048. Impact factor: 3.194

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de Azevedo WF, Jr. Docking Screens for Drug Discovery. Methods in Molecular Biology. Vol 2053. New York:

Springer Nature 2019.

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3 How Docking Programs Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Gabriela Bitencourt-Ferreira and Walter Filgueira de Azevedo Jr. 4 SAnDReS: A Computational Tool for Docking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Gabriela Bitencourt-Ferreira and Walter Filgueira de Azevedo Jr. 5 Electrostatic Energy in Protein–Ligand Complexes. . . . . . . . . . . . . . . . . . . . . . . . . . 67 Gabriela Bitencourt-Ferreira, Martina Veit-Acosta, and Walter Filgueira de Azevedo Jr. 6 Van der Waals Potential in Protein Complexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Gabriela Bitencourt-Ferreira, Martina Veit-Acosta, and Walter Filgueira de Azevedo Jr. 7 Hydrogen Bonds in Protein-Ligand Complexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Gabriela Bitencourt-Ferreira, Martina Veit-Acosta, and Walter Filgueira de Azevedo Jr. 8 Molecular Dynamics Simulations with NAMD2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Gabriela Bitencourt-Ferreira and Walter Filgueira de Azevedo Jr.

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9 Docking with AutoDock4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Gabriela Bitencourt-Ferreira, Val Oliveira Pintro, and Walter Filgueira de Azevedo Jr. 10 Molegro Virtual Docker for Docking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Gabriela Bitencourt-Ferreira and Walter Filgueira de Azevedo Jr. 11 Docking with GemDock. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Gabriela Bitencourt-Ferreira and Walter Filgueira de Azevedo Jr. 12 Docking with SwissDock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Gabriela Bitencourt-Ferreira and Walter Filgueira de Azevedo Jr. 13 Molecular Docking Simulations with ArgusLab. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Gabriela Bitencourt-Ferreira and Walter Filgueira de Azevedo Jr. 15 Homology Modeling of Protein Targets with MODELLER . . . . . . . . . . . . . . . . . 233 Gabriela Bitencourt-Ferreira and Walter Filgueira de Azevedo Jr. 16 Machine Learning to Predict Binding Affinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Gabriela Bitencourt-Ferreira and Walter Filgueira de Azevedo Jr. 17 Exploring the Scoring Function Space. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Gabriela Bitencourt-Ferreira and Walter Filgueira de Azevedo Jr.

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