How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation

XDock: A General Docking Method for Modeling Protein-Ligand and Nucleic Acid-Ligand Interactions

Molecular docking is an essential computational tool in structure-based drug discovery and the investigation of the molecular mechanisms underlying biological processes. Despite the development of many molecular docking programs for various systems, a universal tool that can accurately dock ligands across multiple system types remains elusive. Meeting the need, we developed XDock, a versatile docking framework built for both protein-ligand and nucleic acid-ligand interactions. XDock efficiently accounts for ligand flexibility by docking multiple conformations of a ligand and flexibly refining the final binding poses. It utilizes a distance geometric method for ligand sampling and leverages our knowledge-based scoring functions for assessing protein-ligand and nucleic acid-ligand interactions. XDock has undergone extensive validations on diverse benchmarks of protein-ligand and nucleic acid-ligand complexes and was compared with six other docking methods, including DOCK 6, AutoDock Vina, PLANTS, LeDock, rDock, and RLDock. In addition, XDock is also computationally efficient and on average can dock a ligand within 1 min.

Essential Considerations for Free Energy Calculations of RNA-Small Molecule Complexes: Lessons from the Theophylline-Binding RNA Aptamer

Alchemical free energy calculations are widely used to predict the binding affinity of small molecule ligands to protein targets; however, the application of these methods to RNA targets has not been deeply explored. We systematically investigated how modeling decisions affect the performance of absolute binding free energy calculations for a relatively simple RNA model system: theophylline-binding RNA aptamer with theophylline and five analogs. The goal of this investigation was 2-fold: (1) understanding the performance levels we can expect from absolute free energy calculations for a simple RNA complex and (2) learning about practical modeling considerations that impact the success of RNA-binding predictions, which may be different from the best practices established for protein targets. We learned that magnesium ion (Mg2+) placement is a critical decision that impacts affinity predictions. When information regarding Mg2+ positions is lacking, implementing RNA backbone restraints is an alternative way of stabilizing the RNA structure that recapitulates prediction accuracy. Since mistakes in Mg2+ placement can be detrimental, omitting magnesium ions entirely and using RNA backbone restraints are attractive as a risk-mitigating approach. We found that predictions are sensitive to modeling experimental buffer conditions correctly, including salt type and ionic strength. We explored the effects of sampling in the alchemical protocol, choice of the ligand force field (GAFF2/OpenFF Sage), and water model (TIP3P/OPC) on predictions, which allowed us to give practical advice for the application of free energy methods to RNA targets. By capturing experimental buffer conditions and implementing RNA backbone restraints, we were able to compute binding affinities accurately (mean absolute error (MAE) = 2.2 kcal/mol, Pearson's correlation coefficient = 0.9, Kendall's τ = 0.7). We believe there is much to learn about how to apply free energy calculations for RNA targets and how to enhance their performance in prospective predictions. This study is an important first step for learning best practices and special considerations for RNA-ligand free energy calculations. Future studies will consider increasingly complicated ligands and diverse RNA systems and help the development of general protocols for therapeutically relevant RNA targets.

SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening

The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.

Can Current Molecular Docking Methods Accurately Predict RNA Inhibitors?

Ribonucleic acids (RNAs), particularly the noncoding RNAs, play key roles in cancer, making them attractive drug targets. While conventional methods such as high throughput screening are resource-intensive, computational methods such as RNA-ligand docking can be used as an alternative. However, currently available docking methods are fine-tuned to perform protein-ligand and protein-protein docking. In this work, we evaluated three commonly used docking methods─AutoDock Vina, HADDOCK, and HDOCK─alongside RLDOCK, which is specifically designed for RNA-ligand docking. Our evaluation was based on several criteria including cognate docking, blind docking, scoring potential, and ranking potential. In cognate docking, only RLDOCK showed a success rate of 70% for the top-scoring docked pose. Despite this, all four docking methods did not achieve an overall success rate exceeding 50% amidst our attempt to refine the top-scoring docked poses using molecular dynamics simulations. Meanwhile, all four docking methods showed poor performance in scoring potential evaluation. Although AutoDock Vina achieved an area under the receiver operating characteristic curve of 0.70, it showed poor performance in terms of Matthews' correlation coefficient, precision, enrichment factors, and normalized enrichment factors at 1, 2, and 5%. These results highlight the growing need for further optimization of docking methods to assess RNA-ligand interactions.

UCHL1 promotes the proliferation of porcine granulosa cells by stabilizing CCNB1

BACKGROUND

The proliferation of porcine ovarian granulosa cells (GCs) is essential to follicular development and the ubiquitin-proteasome system is necessary for maintaining cell cycle homeostasis. Previous studies found that the deubiquitinase ubiquitin carboxyl-terminal hydrolase 1 (UCHL1) regulates female reproduction, especially in ovarian development. However, the mechanism by which UCHL1 regulates porcine GC proliferation remains unclear.

RESULTS

UCHL1 overexpression promoted GC proliferation, and knockdown had the opposite effect. UCHL1 is directly bound to cyclin B1 (CCNB1), prolonging the half-life of CCNB1 and inhibiting its degradation, thereby promoting GC proliferation. What's more, a flavonoid compound-isovitexin improved the enzyme activity of UCHL1 and promoted the proliferation of porcine GCs.

CONCLUSIONS

UCHL1 promoted the proliferation of porcine GCs by stabilizing CCNB1, and isovitexin enhanced the enzyme activity of UCHL1. These findings reveal the role of UCHL1 and the potential of isovitexin in regulating proliferation and provide insights into identifying molecular markers and nutrients that affect follicle development.

Insights into the transcriptomic mechanism and characterization of endoglucanases from Aspergillus terreus in cellulose degradation

Filamentous fungi are the main industrial source of cellulases which are important in the process of converting cellulose to fermentable sugars. In this study, transcriptome analysis was conducted on Aspergillus terreus NEAU-7 cultivated using corn stover and glucose as carbon sources. Four putative endoglucanases (EG5A, EG7A, EG12A, and EG12C) from A. terreus NEAU-7 were efficiently expressed in Pichia pastoris. Among them, EG7A exhibited the highest enzyme activity (75.17 U/mg) with an optimal temperature of 40 °C and pH 5.0. EG5A and EG12A displayed specific activities of 19.92 U/mg and 14.62 U/mg, respectively, at 50 °C. EG12C showed acidophilic characteristics with an optimal pH of 3.0 and a specific activity of 12.21 U/mg at 40 °C. With CMC-Na as the substrate, the Km value of EG5A, EG7A, EG12A or, EG12C was, 11.08 ± 0.87 mg/mL, 6.82 ± 0.74 mg/mL, 7.26 ± 0.64 mg/mL, and 9.88 ± 0.86 mg/mL, with Vmax values of 1258.23 ± 51.62 μmol∙min-1∙mg-1, 842.65 ± 41.53 μmol∙min-1∙mg-1, 499.38 ± 20.42 μmol∙min-1∙mg-1, and 681.41 ± 30.08 μmol∙min-1∙mg-1, respectively. The co-treatment of EG7A with the commercial cellulase increased the yield of reducing sugar by 155.77 % (filter paper) and 130.49 % (corn stover). Molecular docking assay showed the interaction energy of EG7A with cellotetraose at -10.50 kcal/mol, surpassing EG12A (-10.43 kcal/mol), EG12C (-10.28 kcal/mol), and EG5A (-9.00 kcal/mol). Root Mean Square Deviation (RMSD) and Solvent Accessible Surface Area (SASA) values revealed that the presence of cellotetraose stabilized the molecular dynamics simulation of the cellotetraose-protein complex over a 100 ns time scale. This study provides valuable insights for developing recombinant enzymes and biomass degradation technologies.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Geometric deep learning for the prediction of magnesium-binding sites in RNA structures

Magnesium ions (Mg2+) are essential for the folding, functional expression, and structural stability of RNA molecules. However, predicting Mg2+-binding sites in RNA molecules based solely on RNA structures is still challenging. The molecular surface, characterized by a continuous shape with geometric and chemical properties, is important for RNA modelling and carries essential information for understanding the interactions between RNAs and Mg2+ ions. Here, we propose an approach named RNA-magnesium ion surface interaction fingerprinting (RMSIF), a geometric deep learning-based conceptual framework to predict magnesium ion binding sites in RNA structures. To evaluate the performance of RMSIF, we systematically enumerated decoy Mg2+ ions across a full-space grid within the range of 2 to 10 Å from the RNA molecule and made predictions accordingly. Visualization techniques were used to validate the prediction results and calculate success rates. Comparative assessments against state-of-the-art methods like MetalionRNA, MgNet, and Metal3DRNA revealed that RMSIF achieved superior success rates and accuracy in predicting Mg2+-binding sites. Additionally, in terms of the spatial distribution of Mg2+ ions within the RNA structures, a majority were situated in the deep grooves, while a minority occupied the shallow grooves. Collectively, the conceptual framework developed in this study holds promise for advancing insights into drug design, RNA co-transcriptional folding, and structure prediction.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.