IChem: A Versatile Toolkit for Detecting, Comparing, and Predicting Protein–Ligand Interactions

Silva, Franck Da; Desaphy, Jérémy; Rognan, Didier

doi:10.1002/cmdc.201700505

Cited by 75 publications

(99 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used two types of features, which represent the ligand's activebinding site environment; one is based on interaction pattern observed between a ligand and protein's binding site amino acid residues and the second is based on ligand fragments based on atoms and neighboring atoms. The IFP between each protein-ligand complex was calculated 페이지 8 / 34 using the IChem tool, which is based on OEChem TK 35…”

Section: Features Constructionmentioning

confidence: 99%

“…The fingerprint pattern can help to annotate the protein families into their bound ligands. Recently, versatile tools, which captures the protein-ligand binding interaction information as fingerprint pattern, with a binary string of 1 (if an interaction is present) or 0 (if an interaction is absent), have been developed such as PLIP (Protein-Ligand Interaction Profiler) 34 , IFP (Interaction Fingerprint Pattern) 35 , SIFt (Structural Interaction Fingerprints) 36 and APIF (Atom-pair based interaction fingerprint) 37 . Among these tools, IFP has gained considerable popularity and suitability in drug discovery experiments such as i) post-processing the docking result 38 , ii) prioritizing the scaffold pose 39 , iii) predicting the ligand pose 40 , iv) selecting the virtual hits 41 , v) binding site comparison 42 and v) to design target-oriented libraries 43 .…”

Section: Introductionmentioning

confidence: 99%

“…Among these tools, IFP has gained considerable popularity and suitability in drug discovery experiments such as i) post-processing the docking result 38 , ii) prioritizing the scaffold pose 39 , iii) predicting the ligand pose 40 , iv) selecting the virtual hits 41 , v) binding site comparison 42 and v) to design target-oriented libraries 43 . The notable merit of IFP is the on-the-fly calculation of the interactions based on a certain set of rules (atom-types) and geometric relationship (distances, angles) between the interacting atoms from proteins and ligands 35 . Based on the IFP, Chupakhin et al built a neural network model to predict the ligand-binding mode for chosen three targets (CDK2, p38-α, HSP90-α).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SMPLIP-Score: Predicting the Ligand Binding Affinity from Simple and Interpretable On-The-Fly Interaction Fingerprint Pattern Descriptors

Kumar

Kim

2020

Preprint

View full text Add to dashboard Cite

In 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 high correlation between a docking score and a pose with key interactive residues, though scoring functions as a free energy surrogate of a protein-ligand complex have failed to provide the collinearity. Recently, various machine learning or deep learning methods have been proposed to overcome the drawback of scoring functions. Despite their high accuracy, their featurization process is complex and requires high cost for its interpretation (less compatible for human recognition). Here, we propose SMPLIP-Score (Substructural Molecular and Protein-Ligand Interaction Pattern Score), a simple interpretable predictor of the absolute binding affinity. Our simple featurization embedded 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 lower complexity than state-of-the-art models, SMPLIP-Score achieved comparable performance, a Pearson’s correlation coefficient up to 0.80 and a RMSE up to 1.18 in pK units on 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 also were examined with direct interpretation of feature matrices for specific targets.

show abstract

Section: Features Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SMPLIP-Score: Predicting the Ligand Binding Affinity from Simple and Interpretable On-The-Fly Interaction Fingerprint Pattern Descriptors

Kumar

Kim

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Interaction fingerprints between the FK228 and the HDAC1&6 were calculated via Ichem (Da Silva et al, 2018;Southan, 2018), and the calculation system mainly consisted of the ligand and the binding site (residues within 6 Å of the FK228's mass center). Firstly, conformation optimization and energy optimization were carried out for the docking poses of FK228 in HDAC1&6, and then interaction fingerprints was applied to carry out for the optimized conformations to calculate the interaction between FK228 and receptors in the initial conformation.…”

Section: Protein-ligand Interaction Fingerprints Analysismentioning

confidence: 99%

Selective Inhibition of HDAC1 by Macrocyclic Polypeptide for the Treatment of Glioblastoma: A Binding Mechanistic Analysis Based on Molecular Dynamics

Wang

Ren

et al. 2020

Front. Mol. Biosci.

View full text Add to dashboard Cite

Glioblastoma (GBM) is the most common and aggressive intracranial malignant brain tumor, and the abnormal expression of HDAC1 is closely correlated to the progression, recurrence and metastasis of GBM cells, making selective inhibition of HDAC1 a promising strategy for GBM treatments. Among all available selective HDAC1 inhibitors, the macrocyclic peptides have gained great attention due to their remarkable inhibitory selectivity on HDAC1. However, the binding mechanism underlying this selectivity is still elusive, which increases the difficulty of designing and synthesizing the macrocyclic peptide-based anti-GBM drug. Herein, multiple computational approaches were employed to explore the binding behaviors of a typical macrocyclic peptide FK228 in both HDAC1 and HDAC6. Starting from the docking conformations of FK228 in the binding pockets of HDAC1&6, relatively long MD simulation (500 ns) shown that the hydrophobic interaction and hydrogen bonding of E91 and D92 in the Loop2 of HDAC1 with the Cap had a certain traction effect on FK228, and the sub-pocket formed by Loop1 and Loop2 in HDAC1 could better accommodate the Cap group, which had a positive effect on maintaining the active conformation of FK228. While the weakening of the interactions between FK228 and the residues in the Loop2 of HDAC6 during the MD simulation led to the large deflection of FK228 in the binding site, which also resulted in the decrease in the interactions between the Linker region of FK228 and the previously identified key amino acids (H134, F143, H174, and F203). Therefore, the residues located in Loop1 and Loop2 contributed in maintaining the active conformation of FK228, which would provide valuable hints for the discovery and design of novel macrocyclic polypeptide HDAC inhibitors.

show abstract

“…Therefore, the use of chiral graphs and reduced chiral graphs in combination with the SR/GSR models provide a generalized structural framework to analyze the stereoselectivity in protein–ligand interactions for any chiral ligand because these graphs retain full topological information of all stereocenters and their protein interactions. They can also be combined with tools like IChem and PLIP that are used for analyzing protein–ligand interactions.…”

Section: Figurementioning

confidence: 99%

Chiral Graphs: Reduced Representations of Ligand Scaffolds for Stereoselective Biomolecular Recognition, Drug Design, and Enhanced Exploration of Chemical Structure Space

Mikhael

Abrol

2019

ChemMedChem

View full text Add to dashboard Cite

Rational structure‐based drug design relies on a detailed, atomic‐level understanding of protein–ligand interactions. The chiral nature of drug binding sites in proteins has led to the discovery of predominantly chiral drugs. A mechanistic understanding of stereoselectivity (which governs how one stereoisomer of a drug might bind stronger than the others to a protein) depends on the topology of stereocenters in the chiral molecule. Chiral graphs and reduced chiral graphs, introduced here, are new topological representations of chiral ligands using graph theory, to facilitate a detailed understanding of chiral recognition of ligands/drugs by proteins. These representations are demonstrated by application to all ≈14 000+ chiral ligands in the Protein Data Bank (PDB), which will facilitate an understanding of protein–ligand stereoselectivity mechanisms. Ligand modifications during drug development can be easily incorporated into these chiral graphs. In addition, these chiral graphs present an efficient tool for a deep dive into the enormous chemical structure space to enable sampling of unexplored structural scaffolds.

show abstract

IChem: A Versatile Toolkit for Detecting, Comparing, and Predicting Protein–Ligand Interactions

Cited by 75 publications

References 19 publications

SMPLIP-Score: Predicting the Ligand Binding Affinity from Simple and Interpretable On-The-Fly Interaction Fingerprint Pattern Descriptors

SMPLIP-Score: Predicting the Ligand Binding Affinity from Simple and Interpretable On-The-Fly Interaction Fingerprint Pattern Descriptors

Selective Inhibition of HDAC1 by Macrocyclic Polypeptide for the Treatment of Glioblastoma: A Binding Mechanistic Analysis Based on Molecular Dynamics

Chiral Graphs: Reduced Representations of Ligand Scaffolds for Stereoselective Biomolecular Recognition, Drug Design, and Enhanced Exploration of Chemical Structure Space

Contact Info

Product

Resources

About