scholarly journals Blinded prediction of protein–ligand binding affinity using Amber thermodynamic integration for the 2018 D3R grand challenge 4

2019 ◽  
Vol 33 (12) ◽  
pp. 1021-1029 ◽  
Author(s):  
Junjie Zou ◽  
Chuan Tian ◽  
Carlos Simmerling
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Thermodynamic Integration ◽  
Grand Challenge

scholarly journals An Analysis of Proteochemometric and Conformal Prediction Machine Learning Protein-Ligand Binding Affinity Models

2020 ◽  
Author(s):  
conor parks ◽  
Zied Gaieb ◽  
Rommie Amaro
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Prediction Intervals ◽  
Grand Challenge ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Conformal Prediction ◽  
External Test

<div><div><div><p>Protein-ligand binding affinity is a key pharmacodynamic endpoint in drug discovery. Sole reliance on experimental design, make, and test cycles is costly and time consuming, providing an opportunity for computational methods to assist. Herein, we present results comparing random forest and feed-forward neural network proteochemometric models for their ability to predict pIC50 measurements for held out generic Bemis-Murcko scaffolds. In addition, we assess the ability of conformal prediction to provide calibrated prediction intervals in both a retrospective and semi-prospective test using the recently released Grand Challenge 4 data set as an external test set. In total, random forest and deep neural network proteochemometric models show quality retrospective performance but suffer in the semi-prospective setting. However, the conformal predictor prediction intervals prove to be well calibrated both retrospectively and semi-prospectively showing that they can be used to guide hit discovery and lead optimization campaigns.</p></div></div></div>

scholarly journals Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2

2017 ◽  
Vol 32 (1) ◽  
pp. 175-185 ◽  
Author(s):  
Zeynep Kurkcuoglu ◽  
Panagiotis I. Koukos ◽  
Nevia Citro ◽  
Mikael E. Trellet ◽  
J. P. G. L. M. Rodrigues ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Grand Challenge ◽  
Simple Contact

scholarly journals An Analysis of Proteochemometric and Conformal Prediction Machine Learning Protein-Ligand Binding Affinity Models

2020 ◽  
Author(s):  
conor parks ◽  
Zied Gaieb ◽  
Rommie Amaro
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Prediction Intervals ◽  
Grand Challenge ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Conformal Prediction ◽  
External Test

<div><div><div><p>Protein-ligand binding affinity is a key pharmacodynamic endpoint in drug discovery. Sole reliance on experimental design, make, and test cycles is costly and time consuming, providing an opportunity for computational methods to assist. Herein, we present results comparing random forest and feed-forward neural network proteochemometric models for their ability to predict pIC50 measurements for held out generic Bemis-Murcko scaffolds. In addition, we assess the ability of conformal prediction to provide calibrated prediction intervals in both a retrospective and semi-prospective test using the recently released Grand Challenge 4 data set as an external test set. In total, random forest and deep neural network proteochemometric models show quality retrospective performance but suffer in the semi-prospective setting. However, the conformal predictor prediction intervals prove to be well calibrated both retrospectively and semi-prospectively showing that they can be used to guide hit discovery and lead optimization campaigns.</p></div></div></div>

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

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>

Distinct Modulation of Wild-Type and Selective Gene Mutated Vitamin D Receptor by Essential Poly Unsaturated Fatty Acids

2021 ◽  
Vol 21 ◽  
Author(s):  
Hari Balaji ◽  
Selvaraj Ayyamperuma ◽  
Niladri Saha ◽  
Shyam Sundar Pottabathula ◽  
Jubie Selvaraj ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Vitamin D ◽  
Ligand Binding ◽  
Vitamin D Deficiency ◽  
Binding Affinity ◽  
Unsaturated Fatty Acids ◽  
Binding Domain ◽  
Ligand Binding Domain ◽  
Binding Energies ◽  
Wild Type

: Vitamin-D deficiency is a global concern. Gene mutations in the vitamin D receptor’s (VDR) ligand binding domain (LBD) variously alter the ligand binding affinity, heterodimerization with retinoid X receptor (RXR) and inhibit coactivator interactions. These LBD mutations may result in partial or total hormone unresponsiveness. A plethora of evidence report that selective long chain polyunsaturated fatty acids (PUFAs) including eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and arachidonic acid (AA) bind to the ligand-binding domain of VDR and lead to transcriptional activation. We therefore hypothesize that selective PUFAs would modulate the dynamics and kinetics of VDRs, irrespective bioactive of vitamin-D binding. The spatial arrangements of the selected PUFAs in VDR active site were examined by in-silico docking studies. The docking results revealed that PUFAs have fatty acid structure-specific binding affinity towards VDR. The calculated EPA, DHA & AA binding energies (Cdocker energy) were lesser compared to vitamin-D in wild type of VDR (PDB id: 2ZLC). Of note, the DHA has higher binding interactions to the mutated VDR (PDB id: 3VT7) when compared to the standard Vitamin-D. Molecular dynamic simulation was utilized to confirm the stability of potential compound binding of DHA with mutated VDR complex. These findings suggest the unique roles of PUFAs in VDR activation and may offer alternate strategy to circumvent vitamin-D deficiency.

scholarly journals SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Surendra Kumar ◽  
Mi-hyun Kim
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Ligand Interaction ◽  
Fingerprint Pattern ◽  
Comparable Performance ◽  
Direct Interpretation ◽  
Benchmark Datasets ◽  
Complex Features

AbstractIn drug discovery, rapid and accurate prediction of protein–ligand binding affinities is a pivotal task for lead optimization with acceptable on-target potency as well as pharmacological efficacy. Furthermore, researchers hope for a high correlation between docking score and pose with key interactive residues, although scoring functions as free energy surrogates of protein–ligand complexes have failed to provide collinearity. Recently, various machine learning or deep learning methods have been proposed to overcome the drawbacks of scoring functions. Despite being highly accurate, their featurization process is complex and the meaning of the embedded features cannot directly be interpreted by human recognition without an additional feature analysis. Here, we propose SMPLIP-Score (Substructural Molecular and Protein–Ligand Interaction Pattern Score), a direct interpretable predictor of absolute binding affinity. Our simple featurization embeds the interaction fingerprint pattern on the ligand-binding site environment and molecular fragments of ligands into an input vectorized matrix for learning layers (random forest or deep neural network). Despite their less complex features than other state-of-the-art models, SMPLIP-Score achieved comparable performance, a Pearson’s correlation coefficient up to 0.80, and a root mean square error up to 1.18 in pK units with several benchmark datasets (PDBbind v.2015, Astex Diverse Set, CSAR NRC HiQ, FEP, PDBbind NMR, and CASF-2016). For this model, generality, predictive power, ranking power, and robustness were examined using direct interpretation of feature matrices for specific targets.

scholarly journals EGF induces increased ligand binding affinity and dimerization of soluble epidermal growth factor (EGF) receptor extracellular domain.

1991 ◽  
Vol 266 (32) ◽  
pp. 22035-22043
Author(s):  
D.R. Hurwitz ◽  
S.L. Emanuel ◽  
M.H. Nathan ◽  
N. Sarver ◽  
A. Ullrich ◽  
...  
Keyword(s):  
Growth Factor ◽  
Epidermal Growth Factor ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Egf Receptor ◽  
Extracellular Domain ◽  
Epidermal Growth

A fragment-based approach for ligand binding affinity and selectivity for the liver X receptor beta

2012 ◽  
Vol 32 ◽  
pp. 19-31 ◽  
Author(s):  
Lívia Barros Salum ◽  
Adriano Defini Andricopulo ◽  
Káthia Maria Honório
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Liver X Receptor ◽  
Receptor Beta

scholarly journals DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity

2021 ◽  
Vol 15 ◽  
pp. 117793222110303
Author(s):  
Asad Ahmed ◽  
Bhavika Mam ◽  
Ramanathan Sowdhamini
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Chemical Space ◽  
Biological Significance ◽  
Protein Crystal ◽  
Ligand Docking ◽  
Complex Data ◽  
Binding Prediction ◽  
Data Set

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

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