SuCOS is Better than RMSD for Evaluating Fragment Elaboration and Docking Poses

Leung, Susan W.S.; Bodkin, Michael J.; Delft, Frank von; Brennan, Paul; Morris, Garrett M.

doi:10.26434/chemrxiv.8100203

Cited by 4 publications

(7 citation statements)

References 40 publications

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“…To circumvent the above mentioned drawbacks of RMSD, several alternatives have been proposed, however, they were designed and tested, to the best of our knowledge, exclusively for protein complexes. The list includes methods such as RSR (Real Space R-factor, which measures how well a group of ligand atoms fits the experimental electron density, [ 82 ]), GARD (Generally Applicable Replacement for rmsD, which takes into account relative importance to binding of atoms, [ 81 ]), TFD (Torsion Fingerprint Deviation, which compares conformations of molecules [ 83 ]), or SuCOS (for assessing shape complementarity and overlapping of chemical features [ 84 ]). Also, several metrics utilizing a comparison of contacts between proteins and ligands have been proposed.…”

Section: Resultsmentioning

confidence: 99%

fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions

et al. 2022

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Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.

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Section: Resultsmentioning

confidence: 99%

fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions

et al. 2022

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show abstract

“…To circumvent the above mentioned drawbacks of RMSD, several alternatives have been proposed, however, they were designed and tested, to the best of our knowledge, exclusively for protein complexes. The list includes methods such as RSR (Real Space R-factor, which measures how well a group of ligand atoms fits the experimental electron density, (63)), GRAD (Generally Applicable Replacement for rmsD, which takes into account relative importance to binding of atoms, (64)), TFD (Torsion Fingerprint Deviation, which compares conformations of molecules (65)), or SuCOS (for assessing shape complementarity and overlapping of chemical features (66)). Also, several metrics utilizing a comparison of contacts between proteins and ligands have been proposed.…”

Section: Resultsmentioning

confidence: 99%

“…Leung et al . benchmarked the PLIF similarity (Protein-Ligand Interaction Fingerprint) as a metric for evaluating docking of the ligands to proteins (66). They concluded that this metric, contrary to ligand-centric ones (such as the RMSD and SuCOS), was able to capture information about interactions across multiple crystal structures of ligands bound to the same protein, making it a handy feature for experiments where multiple protein conformations are used.…”

Section: Resultsmentioning

confidence: 99%

“…Currently, the fingeRNAt package offers eight methods for calculating the similarity or distance of bit vectors. Here, we used a widely used metric for the comparison of interaction fingerprints - the Tversky similarity (66,72). It focuses on the recapitulation of the true interactions (formed in the reference complex) in the predicted model of a complex and ranges from 0 (investigated model has no interactions which are present in the reference complex) to 1 (model has all interactions the reference has).…”

Section: Resultsmentioning

confidence: 99%

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fingeRNAt - a novel tool for high-throughput analysis of nucleic acid-ligand interactions

Szulc

Mackiewicz

Bujnicki

et al. 2021

Preprint

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Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt - a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt) - a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction-based similarity based on fingerprints as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of molecular fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties.

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“…The performance of docking programs is often assessed by their ability to reproduce the crystallographic pose of the bound ligand. A common metric to evaluate the difference between the predicted binding pose and the crystallographic pose is the heavy-atoms root mean square displacement (RMSD) [ 1 ], although other metrics have been suggested [ 2 ]. RMSD calculations are also used in other contexts, for example for the evaluation of diversity in generated conformers [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

spyrmsd: symmetry-corrected RMSD calculations in Python

Meli

Biggin

2020

J Cheminform

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Root mean square displacement (RMSD) calculations play a fundamental role in the comparison of different conformers of the same ligand. This is particularly important in the evaluation of protein-ligand docking, where different ligand poses are generated by docking software and their quality is usually assessed by RMSD calculations. Unfortunately, many RMSD calculation tools do not take into account the symmetry of the molecule, remain difficult to integrate flawlessly in cheminformatics and machine learning pipelines-which are often written in Python-or are shipped within large code bases. Here we present a new open-source RMSD calculation tool written in Python, designed to be extremely lightweight and easy to integrate into existing software.

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SuCOS is Better than RMSD for Evaluating Fragment Elaboration and Docking Poses

Cited by 4 publications

References 40 publications

fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions

fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions

fingeRNAt - a novel tool for high-throughput analysis of nucleic acid-ligand interactions

spyrmsd: symmetry-corrected RMSD calculations in Python

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