Development of CDK-targeted scoring functions for prediction of binding affinity

Levin, Nayara Maria Bernhardt; Pintro, Val Oliveira; Bitencourt-Ferreira, Gabriela; Mattos, Bruna Boldrini de; Silvério, Ariadne de Castro; Azevedo, Walter Filgueira de

doi:10.1016/j.bpc.2018.01.004

Cited by 41 publications

(14 citation statements)

References 36 publications

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“…Table 1 summarizes recently published protein systems related to the development of machine learning models to predict binding affinity for a specific protein system. Table 2 shows the predictive performance of SAnDReS polynomial scoring functions and classical scoring functions [29,30,[35][36][37]. All these studies bring predictive performance comparisons of classical scoring functions against the targeted-scoring functions generated with SAnDReS for systems involving specific protein families and based on crystallographic structural data and experimental binding affinity information.…”

Section: Resultsmentioning

confidence: 99%

“…We also see from the protein systems for which SAnDReS was tested so far, that its performance is not restricted to a specific enzymatic class or type of binding affinity. We have models for CDK [36], HIV-1 protease [35], 3dehydroquinate dehydratase [37] and coagulation factor Xa [29]. SAnDReS analyzed protein systems with experimental data such as K i [29,35,37], IC 50 [36] and ΔG [15].…”

Section: Resultsmentioning

confidence: 99%

“…We have models for CDK [36], HIV-1 protease [35], 3dehydroquinate dehydratase [37] and coagulation factor Xa [29]. SAnDReS analyzed protein systems with experimental data such as K i [29,35,37], IC 50 [36] and ΔG [15]. SAnDReS can handle any binding affinity data or thermodynamic parameters in the development of machine learning models.…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

Bitencourt-Ferreira

Rizzotto

Azevedo

2021

CMC

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Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. Method: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding, and thermodynamic data to create targeted scoring functions. Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases, and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker, and AutoDock Vina.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

Bitencourt-Ferreira

Rizzotto

Azevedo

2021

CMC

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“…These approaches are also adequate to assess the structural features responsible for the molecular recognition process. This type of integration of structural data and machine learning techniques has been successfully applied to a wide range of protein targets, such as cyclin-dependent kinases (EC 2.7.11.22) [33,34], proteases [35][36][37][38], and more recently to SARS-CoV-2 drug targets [39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

Veit-Acosta

Azevedo

2022

CMC

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Background: CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. Objective: This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. Method: We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. Results: Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. Conclusion: All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

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“…Such approaches recognize the residues responsible for the binding affinity and reveal the most promising chemical moieties involved in inhibiting the protein targets. Also, several authors have built computational models to predict binding based on the atomic coordinates of protein-ligand complexes [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. These models rely heavily on computational methods and structural and protein-ligand binding affinity data to develop targeted scoring functions with superior predictive performance compared with classical scoring functions.…”

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confidence: 99%

Application of Machine Learning Techniques for Drug Discovery

Azevedo

2021

CMC

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Development of CDK-targeted scoring functions for prediction of binding affinity

Cited by 41 publications

References 36 publications

Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

Application of Machine Learning Techniques for Drug Discovery

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