Akash Deep Biswas

Although protein–RNA interactions are crucial for many biological processes, predicting their binding free energies (Δ G ) is a challenging task due to limited available experimental data and the complexity of these interactions. To address this issue, we developed a machine learning–based model designed to predict energy-based scores for protein–RNA complexes, called PANTHER Score. By applying a local-to-global approach, we proposed a methodology further subdivided into five steps: (1) We derived 87,117 pairwise local interaction energies from 331,744 MD-derived interactions across 46 curated protein–RNA complexes; (2) we trained ML models on pairwise interaction features to predict local interaction energies without performing MD simulations; (3) we integrated predicted local interaction energies using a local-to-global methodology, to compute model-specific PANTHER Score; (4) we evaluate model-specific PANTHER Score on an independent test set of seven complexes; and (5) we validated and selected the optimal model using an external stress set of 110 complexes with experimental Δ G values for implementation in the PANTHER Scoring pipeline. Among the regression models developed, Random Forest Regression exhibited the highest predictive performance as a model-specific PANTHER Score, achieveing a Pearson correlation ( r ) of 0.80 and MAE of 1.79 kcal/mol on the test set. It maintained strong predictive capabilities on the stress set ( r = 0.64, MAE = 1.63 kcal/mol). Benchmarking against existing tools on the stress test set, the PANTHER Score demonstrated superior accuracy and reliability. This study highlights the effectiveness of MD and machine learning in addressing data limitations through innovative strategies, positioning the PANTHER Score as a robust tool for predicting protein–RNA binding affinities in biomolecular research, drug discovery and mainly in RNA-therapeutics.

The COVID-19 pandemic demonstrates that a top priority for society, now and in the future, is to be able to respond quickly to diseases with effective treatments. Among the new tools that pharmaceutical industries and researchers have in their hands nowadays, there are the extensive computer simulations capable of evaluating in-silico the interaction between possible drugs and the target proteins. The central goal of the LIGATE project is to create and validate a leading application solution for drug discovery in High-Performance Computing (HPC) systems up to the exascale level. The overall project purpose is the automation of the drug design process, which is currently performed with substantial human effort throughout the different phases of the process: preparation of input parameters, management of data sets with billions of molecules, interaction with HPC queue management systems to handle jobs, and optimization of scoring function parameters and thresholds.

Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.

Here we present a computational approach based on molecular dynamics (MD) simulation to study the dependence of the protein hydration-shell density on the size of the protein molecule. The hydration-shell density of eighteen different proteins, differing in size, shape and function (eight of them are antifreeze proteins), is calculated. The results obtained show that an increase in the hydration-shell density, relative to that of the bulk, is observed (in the range of 4–14%) for all studied proteins and that this increment strongly correlates with the protein size. In particular, a decrease in the density increment is observed for decreasing protein size. A simple model is proposed in which the basic idea is to approximate the protein molecule as an effective ellipsoid and to partition the relevant parameters, i.e. the solvent-accessible volume and the corresponding solvent density, into two regions: inside and outside the effective protein ellipsoid. It is found that, within the model developed here, almost all of the hydration-density increase is located inside the protein ellipsoid, basically corresponding to pockets within, or at the surface of the protein molecule. The observed decrease in the density increment is caused by the protein size only and no difference is found between antifreeze and non-antifreeze proteins.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Antifreeze proteins (AFPs) are able to inhibit ice growth by binding to ice nuclei. Their ice-binding mechanism is still unclear, yet the hydration layer is thought to play a fundamental role. Here, we use molecular dynamics simulations to characterize the hydration shell of two AFPs and two non-AFPs. The calculated shell thickness and density of the AFPs do not feature any relevant difference with respect to the non-AFPs. Moreover, the hydration shell density is always higher than the bulk density and, thus, no low-density, ice-like layer is detected at the ice-binding surface (IBS) of AFPs. Instead, we observe local water-density differences in AFPs between the IBS (lower density) and the non-IBS (higher density). The lower solvent density at the ice-binding site can pave the way to the protein binding to ice nuclei, while the higher solvent density at the non ice-binding surfaces might provide protection against ice growth.