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

Abstract Motivation Machine-learning scoring functions (SFs) have been found to outperform standard SFs for binding affinity prediction of protein–ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited. Results Herein, we introduce Extended Connectivity Interaction Features (ECIF) to describe protein–ligand complexes and build machine-learning SFs with improved predictions of binding affinity. ECIF are a set of protein−ligand atom-type pair counts that take into account each atom’s connectivity to describe it and thus define the pair types. ECIF were used to build different machine-learning models to predict protein–ligand affinities (pKd/pKi). The models were evaluated in terms of ‘scoring power’ on the Comparative Assessment of Scoring Functions 2016. The best models built on ECIF achieved Pearson correlation coefficients of 0.857 when used on its own, and 0.866 when used in combination with ligand descriptors, demonstrating ECIF descriptive power. Availability and implementation Data and code to reproduce all the results are freely available at https://github.com/DIFACQUIM/ECIF. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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Development of a Protein-Ligand Extended Connectivity (PLEC) Fingerprint and Its Application for Binding Affinity Predictions.

10.26434/chemrxiv.5928406.v1 ◽

2018 ◽

Author(s):

Maciej Wójcikowski ◽

Michał Kukiełka ◽

Marta Stepniewska-Dziubinska ◽

Pawel Siedlecki

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Linear Model ◽

Binding Affinity ◽

Small Molecule ◽

3D Structure ◽

Protein Ligand Interactions ◽

Core Set ◽

Ligand Interactions ◽

Extended Connectivity

<div>Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of fingerprints, 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. Here, we present a Protein-Ligand Extended Connectivity (PLEC) fingerprint that implicitly encodes protein-ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC fingerprints were used to construct different machine learning (ML) models tailored for predicting protein-ligand affinities (pK<sub>i/d</sub>). Even the simplest linear model built on the PLEC fingerprint achieved R<sub>p</sub>=0.83 on the PDBbind v2016 "core set”, demonstrating its descriptive power. The PLEC fingerprint has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt).</div>

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Development of a Protein-Ligand Extended Connectivity (PLEC) Fingerprint and Its Application for Binding Affinity Predictions.

10.26434/chemrxiv.5928406 ◽

2018 ◽

Author(s):

Maciej Wójcikowski ◽

Michał Kukiełka ◽

Marta Stepniewska-Dziubinska ◽

Pawel Siedlecki

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Linear Model ◽

Binding Affinity ◽

Small Molecule ◽

3D Structure ◽

Protein Ligand Interactions ◽

Core Set ◽

Ligand Interactions ◽

Extended Connectivity

<div>Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of fingerprints, 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. Here, we present a Protein-Ligand Extended Connectivity (PLEC) fingerprint that implicitly encodes protein-ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC fingerprints were used to construct different machine learning (ML) models tailored for predicting protein-ligand affinities (pK<sub>i/d</sub>). Even the simplest linear model built on the PLEC fingerprint achieved R<sub>p</sub>=0.83 on the PDBbind v2016 "core set”, demonstrating its descriptive power. The PLEC fingerprint has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt).</div>

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Antiproliferative and antiviral effects of mutated IFN-alpha with increased IFN receptor binding affinity

Zeitschrift für Gastroenterologie ◽

10.1055/s-0030-1269699 ◽

2011 ◽

Vol 49 (01) ◽

Author(s):

MF Sprinzl ◽

L Bührer ◽

D Strand ◽

G Schreiber ◽

PR Galle ◽

...

Keyword(s):

Binding Affinity ◽

Receptor Binding ◽

Receptor Binding Affinity ◽

Ifn Alpha ◽

Antiviral Effects

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Enhancement of Plasminogen Binding to U937 Cells and Fibrin by Complestatin

Thrombosis and Haemostasis ◽

10.1055/s-0038-1655921 ◽

1997 ◽

Vol 77 (01) ◽

pp. 137-142 ◽

Cited By ~ 20

Author(s):

Kiyoshi Tachikawa ◽

Keiji Hasurni ◽

Akira Endo

Keyword(s):

Binding Site ◽

Binding Affinity ◽

Blood Cells ◽

Aminocaproic Acid ◽

U937 Cells ◽

Biochemical Basis ◽

Equilibrium Binding ◽

Pharmacological Stimulation ◽

Endogenous Fibrinolysis ◽

Stimulation Of

SummaryPlasminogen binds to endothelial and blood cells as well as to fibrin, where the zymogen is efficiently activated and protected from inhibition by α2-antiplasmin. In the present study we have found that complestatin, a peptide-like metabolite of a streptomyces, enhances binding of plasminogen to cells and fibrin. Complestatin, at concentrations ranging from 1 to 5 μM, doubled 125I-plasminogen binding to U937 cells both in the absence and presence of lipoprotein(a), a putative physiological competitor of plasminogen. The binding of 125I-plasminogen in the presence of complestatin was abolished by e-aminocaproic acid, suggesting that the lysine binding site(s) of the plasminogen molecule are involved in the binding. Equilibrium binding analyses indicated that complestatin increased the maximum binding of 125I-plasminogen to U937 cells without affecting the binding affinity. Complestatin was also effective in increasing 125I-plasminogen binding to fibrin, causing 2-fold elevation of the binding at ~1 μM. Along with the potentiation of plasminogen binding, complestatin enhanced plasmin formation, and thereby increased fibrinolysis. These results would provide a biochemical basis for a pharmacological stimulation of endogenous fibrinolysis through a promotion of plasminogen binding to cells and fibrin.

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The Role of the Initiator System in the Synthesis of Acidic Multifunctional Nanoparticles Designed for Molecular Imprinting of Proteins

Periodica Polytechnica Chemical Engineering ◽

10.3311/ppch.15414 ◽

2020 ◽

Vol 65 (1) ◽

pp. 28-41

Author(s):

Marwa Aly Ahmed ◽

Júlia Erdőssy ◽

Viola Horváth

Keyword(s):

Acrylic Acid ◽

Binding Affinity ◽

Molecular Imprinting ◽

Low Temperatures ◽

Ammonium Persulfate ◽

Sodium Bisulfite ◽

Multifunctional Nanoparticles ◽

High Affinity ◽

Initiator System

Multifunctional nanoparticles have been shown earlier to bind certain proteins with high affinity and the binding affinity could be enhanced by molecular imprinting of the target protein. In this work different initiator systems were used and compared during the synthesis of poly (N-isopropylacrylamide-co-acrylic acid-co-N-tert-butylacrylamide) nanoparticles with respect to their future applicability in molecular imprinting of lysozyme. The decomposition of ammonium persulfate initiator was initiated either thermally at 60 °C or by using redox activators, namely tetramethylethylenediamine or sodium bisulfite at low temperatures. Morphology differences in the resulting nanoparticles have been revealed using scanning electron microscopy and dynamic light scattering. During polymerization the conversion of each monomer was followed in time. Striking differences were demonstrated in the incorporation rate of acrylic acid between the tetramethylethylenediamine catalyzed initiation and the other systems. This led to a completely different nanoparticle microstructure the consequence of which was the distinctly lower lysozyme binding affinity. On the contrary, the use of sodium bisulfite activation resulted in similar nanoparticle structural homogeneity and protein binding affinity as the thermal initiation.

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Binding Affinity of Triphenyl Acrylonitriles to Estrogen Receptors: Quantitative Structure-Activity Relationships

Folia Medica ◽

10.2478/v10153-010-0005-2 ◽

2010 ◽

Vol 52 (3) ◽

Cited By ~ 1

Author(s):

Sorana Bolboacă ◽

Monica Marta ◽

Lorentz Jäntschi

Keyword(s):

Estrogen Receptors ◽

Binding Affinity ◽

Quantitative Structure ◽

Structure Activity Relationships ◽

Structure Activity ◽

Quantitative Structure Activity Relationships

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Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

10.26434/chemrxiv.12781310.v1 ◽

2020 ◽

Author(s):

E. Prabhu Raman ◽

Thomas J. Paul ◽

Ryan L. Hayes ◽

Charles L. Brooks III

Keyword(s):

Free Energy ◽

Ligand Binding ◽

Binding Affinity ◽

Binding Free Energy ◽

Computational Cost ◽

Combinatorial Libraries ◽

Free Energy Calculations ◽

Lead Optimization ◽

Efficient Estimation ◽

Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

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Crosslinker Chemistry Determines the Uptake Potential of Perfluorinated Alkyl Substances by β-Cyclodextrin Polymers

10.26434/chemrxiv.7492631.v2 ◽

2018 ◽

Author(s):

Leilei Xiao ◽

Casey Ching ◽

Yuhan Ling ◽

Mohammadreza Nasiri ◽

Max Justin Klemes ◽

...

Keyword(s):

Binding Affinity ◽

Polymer Networks ◽

Cyclodextrin Polymer ◽

Perfluorinated Alkyl Substances ◽

Cyclodextrin Polymers

This work describes several crosslinked β-cyclodextrin polymer networks and correlates the crosslinker chemistry with binding affinity for per- and polyfluorinated alkyl substances (PFASs), including PFOA and PFOS.

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GRAM: A True Null Model for Relative Binding Affinity Predictions

10.26434/chemrxiv.9956474 ◽

2019 ◽

Author(s):

Guanglei Cui ◽

Alan P. Graves ◽

Eric S. Manas

Keyword(s):

Binding Affinity ◽

Performance Metrics ◽

Null Model ◽

Performance Measure ◽

Interval Estimate ◽

Empirical Observation ◽

Binding Affinity Prediction ◽

Affinity Prediction ◽

Relative Binding Affinity ◽

Relative Binding

Relative binding affinity prediction is a critical component in computer aided drug design. Significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of random in an objective manner. Although many performance metrics, such as correlation coefficient (r2), mean unsigned error (MUE), and room mean square error (RMSE), are frequently used in the literature, a true and non-trivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are 1) it provides the uncertainty range in the predicted activities, which is important in prospective applications; 2) a true null model with well-defined PI can be established. We provide one such example termed Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, s). Having an analytically defined PI that only depends on the variation in the activities, GRAM should in principle allow us to compare the performance of relative binding affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.<br>

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