NEEMP: software for validation, accurate calculation and fast parameterization of EEM charges

Raček, Tomáš; Pazúriková, Jana; Vařeková, Radka Svobodová; Geidl, Stanislav; Křenek, Aleš; Falginella, Francesco Luca; Horský, Vladimír; Hejret, Václav; Koča, Jaroslav

doi:10.1186/s13321-016-0171-1

Cited by 6 publications

(5 citation statements)

References 54 publications

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“…The charges for the protein pockets of PDB IDs 4JDA, 5X5G, 6B8Y, and 4Y16 were calculated using the Charge Equilibration (QEq) method with the parameters presented by Rappé and Goddard . The atomic charges for the protein pocket of PDB ID 5U2F were calculated using the Electronegativity Equalization Method with Raček 2016 (ccd2016_npa) parameters . It is important to note that ACC2 will rarely supply charge estimates which are unreasonable.…”

Section: Methodsmentioning

confidence: 99%

“…81 The atomic charges for the protein pocket of PDB ID 5U2F were calculated using the Electronegativity Equalization Method 82 with Racěk 2016 (ccd2016_npa) parameters. 83 It is important to note that ACC2 will rarely supply charge estimates which are unreasonable. To account for this, we removed the complexes containing at least one atom with a partial charge assignment of magnitude greater than 2.0 (34 of 19,438 complexes; 0.17%).…”

Section: ■ Summarymentioning

confidence: 99%

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HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein–Ligand Binding Affinity Prediction

Kyro

Brent

Batista

2023

J. Chem. Inf. Model.

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Applying deep learning concepts from image detection and graph theory has greatly advanced protein−ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advances by designing a novel deep learning architecture consisting of a 3-dimensional convolutional neural network utilizing channel-wise attention and two graph convolutional networks utilizing attention-based aggregation of node features. HAC-Net (Hybrid Attention-Based Convolutional Neural Network) obtains state-of-the-art results on the PDBbind v.2016 core set, the most widely recognized benchmark in the field. We extensively assess the generalizability of our model using multiple train-test splits, each of which maximizes differences between either protein structures, protein sequences, or ligand extendedconnectivity fingerprints of complexes in the training and test sets. Furthermore, we perform 10-fold cross-validation with a similarity cutoff between SMILES strings of ligands in the training and test sets and also evaluate the performance of HAC-Net on lowerquality data. We envision that this model can be extended to a broad range of supervised learning problems related to structurebased biomolecular property prediction. All of our software is available as an open-source repository at https://github.com/gregorykyro/HAC-Net/, and the HACNet Python package is available through PyPI.

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Section: Methodsmentioning

confidence: 99%

Section: ■ Summarymentioning

confidence: 99%

HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein–Ligand Binding Affinity Prediction

Kyro

Brent

Batista

2023

J. Chem. Inf. Model.

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“…Although it is also based on QM calculations, it is an empirical approach, where a model is fit to a set of reference electron densities. Compared to the schemes mentioned before, EEM is computationally less demanding, but on the other hand careful parametrization is required …”

Section: Introductionmentioning

confidence: 99%

Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations

Bleiziffer

Schaller

Riniker

2018

J. Chem. Inf. Model.

150

190

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Parametrization of small organic molecules for classical molecular dynamics simulations is not trivial. The vastness of the chemical space makes approaches using building blocks challenging. The most common approach is therefore an individual parametrization of each compound by deriving partial charges from semiempirical or ab initio calculations and inheriting the bonded and van der Waals (Lennard-Jones) parameters from a (bio)molecular force field. The quality of the partial charges generated in this fashion depends on the level of the quantum-chemical calculation as well as on the extraction procedure used. Here, we present a machine learning (ML) based approach for predicting partial charges extracted from density functional theory (DFT) electron densities. The training set was chosen with the goal to provide a broad coverage of the known chemical space of druglike molecules. In addition to the speed of the approach, the partial charges predicted by ML are not dependent on the three-dimensional conformation in contrast to the ones obtained by fitting to the electrostatic potential (ESP). To assess the quality and compatibility with standard force fields, we performed benchmark calculations for the free energy of hydration and liquid properties such as density and heat of vaporization.

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“…In this paper, we utilized three datasets of molecules, described in Table 1 . The first two datasets are composed of organic molecules and were also used for the comparison and parameterization of empirical charge calculation methods in previous publications [ 33 , 34 ]. DTP_small is a simple set (a low number of small-sized molecules with low variability) while CCD_gen is more complex.…”

Section: Resultsmentioning

confidence: 99%

“…Multiple schemes for assigning types can be employed, from the simplest, in which an atom’s element represents the type, to more complex ones. One of the widely used approaches is to differentiate the atoms based on the element and the highest bond order of the bond they are part of [ 32 – 34 ]. In this text, we use the acronym HBO (highest bond order) to denote such classification (e.g., a carbon with a double bond would be C/2, an oxygen with only single bonds is O/1).…”

Section: Methodsmentioning

confidence: 99%

Optimized SQE atomic charges for peptides accessible via a web application

et al. 2021

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Background Partial atomic charges find many applications in computational chemistry, chemoinformatics, bioinformatics, and nanoscience. Currently, frequently used methods for charge calculation are the Electronegativity Equalization Method (EEM), Charge Equilibration method (QEq), and Extended QEq (EQeq). They all are fast, even for large molecules, but require empirical parameters. However, even these advanced methods have limitations—e.g., their application for peptides, proteins, and other macromolecules is problematic. An empirical charge calculation method that is promising for peptides and other macromolecular systems is the Split-charge Equilibration method (SQE) and its extension SQE+q0. Unfortunately, only one parameter set is available for these methods, and their implementation is not easily accessible. Results In this article, we present for the first time an optimized guided minimization method (optGM) for the fast parameterization of empirical charge calculation methods and compare it with the currently available guided minimization (GDMIN) method. Then, we introduce a further extension to SQE, SQE+qp, adapted for peptide datasets, and compare it with the common approaches EEM, QEq EQeq, SQE, and SQE+q0. Finally, we integrate SQE and SQE+qp into the web application Atomic Charge Calculator II (ACC II), including several parameter sets. Conclusion The main contribution of the article is that it makes SQE methods with their parameters accessible to the users via the ACC II web application (https://acc2.ncbr.muni.cz) and also via a command-line application. Furthermore, our improvement, SQE+qp, provides an excellent solution for peptide datasets. Additionally, optGM provides comparable parameters to GDMIN in a markedly shorter time. Therefore, optGM allows us to perform parameterizations for charge calculation methods with more parameters (e.g., SQE and its extensions) using large datasets. Graphic Abstract

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NEEMP: software for validation, accurate calculation and fast parameterization of EEM charges

Cited by 6 publications

References 54 publications

HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein–Ligand Binding Affinity Prediction

HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein–Ligand Binding Affinity Prediction

Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations

Optimized SQE atomic charges for peptides accessible via a web application

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