Dataset Information

Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions.

ABSTRACT:

Motivation

Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein-ligand interactions.

Results

Here, we present a Protein-Ligand Extended Connectivity (PLEC) FP that implicitly encodes protein-ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC FPs were used to construct different machine learning models tailored for predicting protein-ligand affinities (pKi∕d). Even the simplest linear model built on the PLEC FP achieved Rp = 0.817 on the Protein Databank (PDB) bind v2016 'core set', demonstrating its descriptive power.

Availability and implementation

The PLEC FP has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt).

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Wojcikowski M

PROVIDER: S-EPMC6477977 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions.

Wójcikowski Maciej M Kukiełka Michał M Stepniewska-Dziubinska Marta M MM Siedlecki Pawel P

Bioinformatics (Oxford, England) 20190401 8

<h4>Motivation</h4>Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein-ligand interactions.<h4>Results</h4>Here, we present a Protein-Ligand Extended Connectivity (PLEC) FP that implicitly encodes protein-l ...[more]

PMID: 30202917

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Project description:BackgroundModels that are capable of reliably predicting binding affinities for protein-ligand complexes play an important role the field of structure-guided drug design.MethodsHere, we begin by applying the computational geometry technique of Delaunay tessellation to each set of atomic coordinates for over 1400 diverse macromolecular structures, for the purpose of deriving a four-body statistical potential that serves as a topological scoring function. Next, we identify a second, independent set of three hundred protein-ligand complexes, having both high-resolution structures and known dissociation constants. Two-thirds of these complexes are randomly selected to train a predictive model of binding affinity as follows: two tessellations are generated in each case, one for the entire complex and another strictly for the isolated protein without its bound ligand, and a topological score is computed for each tessellation with the four-body potential. Predicted protein-ligand binding affinity is then based on an empirically derived linear function of the difference between both topological scores, one that appropriately scales the value of this difference.ResultsA comparison between experimental and calculated binding affinity values over the two hundred complexes reveals a Pearson's correlation coefficient of r = 0.79 with a standard error of SE = 1.98 kcal/mol. To validate the method, we similarly generated two tessellations for each of the remaining protein-ligand complexes, computed their topological scores and the difference between the two scores for each complex, and applied the previously derived linear transformation of this topological score difference to predict binding affinities. For these one hundred complexes, we again observe a correlation of r = 0.79 (SE = 1.93 kcal/mol) between known and calculated binding affinities. Applying our model to an independent test set of high-resolution structures for three hundred diverse enzyme-inhibitor complexes, each with an experimentally known inhibition constant, also yields a correlation of r = 0.79 (SE = 2.39 kcal/mol) between experimental and calculated binding energies.ConclusionsLastly, we generate predictions with our model on a diverse test set of one hundred protein-ligand complexes previously used to benchmark 15 related methods, and our correlation of r = 0.66 between the calculated and experimental binding energies for this dataset exceeds those of the other approaches. Compared with these related prediction methods, our approach stands out based on salient features that include the reliability of our model, combined with the rapidity of the generated predictions, which are less than one second for an average sized complex.

Dataset Information

Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions.

Similar Datasets

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