Computational Systems Biology

We have been working on the development of computational models for unraveling the molecular mechanisms underlying enzyme inhibition and protein-ligand interactions (Bitencourt-Ferreira and de Azevedo, 2019a; 2019b; 2019c; da Silva et al., 2020). We can use these computational models to predict the binding affinity of a potential inhibitor for an enzyme; such knowledge has the potential to speed up drug discovery and decrease the cost of development of new drugs (de Ávila et al., 2017; Pintro and Azevedo, 2017). Furthermore, the availability of computational models to predict binding affinity based on the atomic coordinates of protein-ligand complexes adds flexibility to the process of drug discovery (Xavier et al., 2016; Heck et al., 2017). It allows us to computationally test different scenarios where a potential new drug may interact with a protein target (Bitencourt-Ferreira and de Azevedo, 2021). We developed the programs SAnDReS (Xavier et al., 2016; Bitencourt-Ferreira et al., 2021) and Taba (da Silva et al., 2020, Bitencourt-Ferreira et al., 2021) to create machine-learning models targeted to the biological system of interest. We have successfully employed SAnDReS to study coagulation factor Xa (EC 3.4.21.6) (Xavier et al., 2016), cyclin-dependent kinases (EC 2.7.11.22) (de Ávila et al., 2017; Levin et al., 2018), HIV-1 protease (EC 3.4.23.16) (Pintro and de Azevedo, 2017), estrogen receptor (Amaral et al., 2018), cannabinoid receptor 1 (Russo and de Azevedo, 2019; Russo and de Azevedo, 2020), and 3-dehydroquinate dehydratase (EC 4.2.1.10) (de Ávila and de Azevedo, 2018). Also, we used SAnDReS (Xavier et al., 2016) to develop a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes (Bitencourt-Ferreira and de Azevedo Jr., 2018).  

References  

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; 36(5): 782–796.   PubMed    PDF  

Bitencourt-Ferreira G, de Azevedo Jr. WF. Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys Chem. 2018; 240: 63–69.   PubMed  

Bitencourt-Ferreira G, de Azevedo WF Jr. Machine Learning to Predict Binding Affinity. Methods Mol Biol. 2019a; 2053: 251–273.   PubMed   

Bitencourt-Ferreira G, de Azevedo WF Jr. Exploring the Scoring Function Space. Methods Mol Biol. 2019b; 2053: 275–281.   PubMed   

Bitencourt-Ferreira G, de Azevedo WF Jr. How Docking Programs Work. Methods Mol Biol. 2019c; 2053: 35–50.   PubMed   

Bitencourt-Ferreira G, da Silva AD, de Azevedo WF Jr. Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets. A Study of Cyclin-Dependent Kinase 2. Curr Med Chem. 2021; 28(2): 253–265.   PubMed   

Bitencourt-Ferreira G, Rizzotto C, de Azevedo Junior WF. Machine Learning-Based Scoring Functions. Development and Applications With SAnDReS. Curr Med Chem. 2021; 28(9): 1746–1746.   PubMed   

Bitencourt-Ferreira G, de Azevedo Junior WF. Electrostatic Potential Energy in Protein-Drug Complexes. Curr Med Chem. doi: 10.2174/0929867328666210201150842.   PubMed   

Da Silva AD, Bitencourt-Ferreira G, de Azevedo WF Jr. Taba: A Tool to Analyze the Binding Affinity. J Comput Chem. 2020; 41(1): 69–73.   PubMed 

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   

De Ávila MB, de Azevedo WF Jr. Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem Biol Drug Des. 2018; 92: 1468–1474.   PubMed   PDF       

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 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.  PubMed   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   

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.   PubMed   PDF      

Russo S, de Azevedo WF Jr. Computational Analysis of Dipyrone Metabolite 4-Aminoantipyrine as a Cannabinoid Receptor 1 Agonist. Curr Med Chem. 2020; 27(28): 4741–4749.   PubMed  

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   Go To SAnDReS   PDF