SPA-LN: a scoring function of ligand–nucleic acid interactions via optimizing both specificity and affinity

Yan, Zhiqiang; Wang, Jin

doi:10.1093/nar/gkx255

Cited by 37 publications

(79 citation statements)

References 45 publications

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“…Also, benchmarking is required to develop highly accurate docking methods that can overcome the limitation of the existing methods. There are a number of benchmarking studies on protein-protein [49,50], protein-ligand [51,52] and nucleic acid-ligand [53] docking interactions. Comparatively, limited attempts had been made to benchmark docking method on protein-peptide complexes.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of different molecular docking methods for protein-peptide docking

et al. 2019

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Background: Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD. Result: Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively. Conclusion: The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of redocking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench (http://webs.iiitd.edu.in/ raghava/ppdbench/).

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Section: Introductionmentioning

confidence: 99%

Benchmarking of different molecular docking methods for protein-peptide docking

et al. 2019

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“…Regrettably, we were unable to run the DrugScore RNA scoring function, despite contacting the authors and obtaining the source code [32]. Also, the authors of the recently published SPA-LN scoring function [33] were unable to provide us with the working copy of their program nor give access to the dedicated web server. Results expressed as RMSD of the best-selected pose to the reference pose (S(1)), S(3), and S (5) are summarized in the Fig 2, Supplementary Fig S1, and Supplementary Table S8 in S1 Text.…”

Section: Comparison Of Annapurna With Other Scoring Methodsmentioning

confidence: 99%

“…It likewise defines a special atom-typing scheme for nucleic acids, and it was parameterized on ligand complexes with proteins (2422 complexes), DNA (300 complexes), and RNA (97 complexes). Yan and Wang described a distant-dependent scoring function termed SPA-LN, where Tripos atom types are used for RNA and ligand representation [33]. During the optimization of parameters, they focused not only on recapitulating the near-native pose of the ligand but also on the affinity estimation.…”

Section: Introductionmentioning

confidence: 99%

AnnapuRNA: a scoring function for predicting RNA-small molecule interactions

Stefaniak

Bujnicki

2020

Preprint

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RNA is considered as an attractive target for new small molecule drugs. Designing active compounds can be facilitated by computational modeling. Most of the available tools developed for these prediction purposes, such as molecular docking or scoring functions, are parametrized for protein targets. The performance of these methods, when applied to RNA-ligand systems, is insufficient. To overcome these problems, we developed AnnapuRNA, a new knowledge-based scoring function designed to evaluate RNA-ligand complex structures, generated by any computational docking method. We also evaluated three main factors that may influence the structure prediction, i.e., starting conformer of a ligand, the docking program, and the scoring function used. We applied the AnnapuRNA method for a post-hoc study of the recently published structures of the FMN riboswitch. Software is available at https://github.com/filipspl/AnnapuRNAAuthor SummaryDrug development is a lengthy and complicated process, which requires costly experiments on a very large number of chemical compounds. The identification of chemical molecules with desired properties can be facilitated by computational methods. A number of methods were developed for computer-aided design of drugs that target protein molecules. However, recently the ribonucleic acid (RNA) emerged as an attractive target for the development of new drugs. Unfortunately, the portfolio of the computer methods that can be applied to study RNA and its interactions with small chemical molecules is very limited. This situation motivated us to develop a new computational method, with which to predict RNA-small molecule interactions. To this end, we collected the information on the statistics of interactions in experimentally determined structures of complexes formed by RNA with small molecules. We then used the statistical data to train machine learning methods aiming to distinguish between RNA-ligand interactions observed experimentally and other interactions that can be observed in theoretical analyses, but are not observed in nature. The resulting method called AnnapuRNA is superior to other similar tools and can be used to predict preferred ligands of RNA molecules and how RNA and small molecules interact with each other.

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“…Indeed, combining predicted higher scores and high docking frequencies would generate better results, closer to the experimental geometries. To this purpose, a novel scoring function SPA-LN was developed for Autodock improving both prediction of binding affinity and specificity with accuracy values around 76% (a less stringent 3Å RMSD cut-off was considered) [80]. Recently, the introduction of a vibrational entropy term in the Autodock scoring function further The average value of protein-docking benchmarks (80%), which has been retrieved from comparative assessments of published benchmarks [75][76][77][78][79], is used as indicator for comparison.…”

Section: Benchmark Of Docking Programs Applied To Nucleic Acidsmentioning

confidence: 99%

How ‘Protein-Docking’ Translates into the New Emerging Field of Docking Small Molecules to Nucleic Acids?

Tessaro

Scapozza

2020

Molecules

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In this review, we retraced the ‘40-year evolution’ of molecular docking algorithms. Over the course of the years, their development allowed to progress from the so-called ‘rigid-docking’ searching methods to the more sophisticated ‘semi-flexible’ and ‘flexible docking’ algorithms. Together with the advancement of computing architecture and power, molecular docking’s applications also exponentially increased, from a single-ligand binding calculation to large screening and polypharmacology profiles. Recently targeting nucleic acids with small molecules has emerged as a valuable therapeutic strategy especially for cancer treatment, along with bacterial and viral infections. For example, therapeutic intervention at the mRNA level allows to overcome the problematic of undruggable proteins without modifying the genome. Despite the promising therapeutic potential of nucleic acids, molecular docking programs have been optimized mostly for proteins. Here, we have analyzed literature data on nucleic acid to benchmark some of the widely used docking programs. Finally, the comparison between proteins and nucleic acid targets docking highlighted similarity and differences, which are intrinsically related to their chemical and structural nature.

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SPA-LN: a scoring function of ligand–nucleic acid interactions via optimizing both specificity and affinity

Cited by 37 publications

References 45 publications

Benchmarking of different molecular docking methods for protein-peptide docking

Benchmarking of different molecular docking methods for protein-peptide docking

AnnapuRNA: a scoring function for predicting RNA-small molecule interactions

How ‘Protein-Docking’ Translates into the New Emerging Field of Docking Small Molecules to Nucleic Acids?

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