Multidimensional Global Optimization and Robustness Analysis in the Context of Protein-Ligand Binding

Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains

The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.

Comprehending interaction mechanism of natural actives of Colchicum autumnale L. for rheumatoid arthritis using integrative chemoinformatic approaches

This research delves into the realm of therapeutic potential within natural compounds derived from Colchicum autumnale L., emphasizing a holistic perspective on medications used in human therapy. Rather than confining the study to their primary actions, the research endeavors to unveil molecular targets for these natural compounds, with a specific focus on their potential applicability in the treatment of rheumatoid arthritis (RA). The study focuses on understanding interactions between specific natural actives that target RA. Fifteen RA target proteins were identified from OMIM, GeneScan and PharmaGKB. Their structures were downloaded from RCSB PDB. Two active components of C. autumnale L. were chosen for mass spectrometry investigation. Ligand characteristics were determined using the ADMETlab and SwissADME software tools. Molecular docking was performed, and the top three complexes were simulated for 200 ns, along with identification of free binding energies. The compounds β-sitosterol-IL-10 (-6.50 kcal/mol), colchicine-IL-10 (-6.01 kcal/mol), linoleic acid-IL-10 (-7.22 kcal/mol) and linoleic acid-IL-10 (-7.22 kcal/mol) exhibited best binding energies. β-Sitosterol and colchicine showed the highest stability in simulations, confirmed by molecular mechanics free energy binding calculations. This work provides insights into the molecular interaction of natural compounds against RA targets, offering potential therapeutic anti-RA medications.Communicated by Ramaswamy H. Sarma.

AmberTools

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

Physics-Guided Deep Generative Model for New Ligand Discovery

Structure-based drug discovery aims to identify small molecules that can attach to a specific target protein and change its functionality. Recently, deep learning has shown great promise in generating drug-like molecules with specific biochemical features and conditioned with structural features. However, they usually fail to incorporate an essential factor: the underlying physics which guides molecular formation and binding in real-world scenarios. In this work, we describe a physics-guided deep generative model for new ligand discovery, conditioned not only on the binding site but also on physics-based features that describe the binding mechanism between a receptor and a ligand. The proposed hybrid model has been tested on large protein-ligand complexes and small host-guest systems. Using the top-N methodology, on average more than 75% of the generated structures by our hybrid model were stronger binders than the original reference ligand. All of them had higher ΔGbind (affinity) values than the ones generated by the previous state-of-the-art method by an average margin of 1.88 kcal/mol. The visualization of the top-5 ligands generated by the proposed physics-guided model and the reference deep learning model demonstrate more feasible conformations and orientations by the former. The future directions include training and testing the hybrid model on larger datasets, adding more relevant physics-based features, and interpreting the deep learning outcomes from biophysical perspectives.

A Physics-Guided Neural Network for Predicting Protein–Ligand Binding Free Energy: From Host–Guest Systems to the PDBbind Database

Calculation of protein–ligand binding affinity is a cornerstone of drug discovery. Classic implicit solvent models, which have been widely used to accomplish this task, lack accuracy compared to experimental references. Emerging data-driven models, on the other hand, are often accurate yet not fully interpretable and also likely to be overfitted. In this research, we explore the application of Theory-Guided Data Science in studying protein–ligand binding. A hybrid model is introduced by integrating Graph Convolutional Network (data-driven model) with the GBNSR6 implicit solvent (physics-based model). The proposed physics-data model is tested on a dataset of 368 complexes from the PDBbind refined set and 72 host–guest systems. Results demonstrate that the proposed Physics-Guided Neural Network can successfully improve the “accuracy” of the pure data-driven model. In addition, the “interpretability” and “transferability” of our model have boosted compared to the purely data-driven model. Further analyses include evaluating model robustness and understanding relationships between the physical features.

Molecular dynamics simulations, docking and MMGBSA studies of newly designed peptide-conjugated glucosyloxy stilbene derivatives with tumor cell receptors

In this work, for the first time, we designed derivatives of beta-D-glucosyloxy-3-hydroxy-trans-stiblene-2-carboxylic acid (GHS), by conjugating GHS with tumor targeting peptides RPARPAR and GGKRPAR to target over-expressed receptors in tumor cells. The sequences RPARPAR and GGKRPAR are known to target the neuropilin1 (NRP1) receptor due to the C-terminal Arg domain; however, their effectiveness has never been examined with other commonly over-expressed receptors in tumor cells, particularly of chronic lymphocytic leukemia that include integrin α1β1 and CD22. By conjugating these peptides with GHS, which is known for its inherent anti-cancer properties, the goal is to further enhance tumor cell targeting by developing compounds that can target multiple receptors. The physicochemical properties of the conjugates and individual peptides were analyzed using Turbomole and COSMOthermX20 in order to determine their hydrogen bond accepting and donating capabilities. The web server POCASA was used in order to determine the surface cavities and binding pockets of the three receptors. To explore the binding affinities, we conducted molecular docking studies with the peptides and the conjugates with each of the receptors. After molecular docking, the complexes were analyzed using Protein-Ligand Interaction Profiler to determine the types of interactions involved. Molecular dynamics simulation studies were conducted to explore the stability of the receptor-ligand complexes. Our results indicated that in most cases the conjugates showed higher binding and stability with the receptors. Additionally, highly stable complexes of conjugates were obtained with CD22, NRP1 and in most cases with the integrin α1β1 receptor as well. The binding energies were calculated for each of the receptor ligand complexes through trajectory analysis using MMGBSA studies. SwissADME studies revealed that the compounds showed low GI absorption and were not found to be CYP inhibitors and had bioavailability score that would allow them to be considered as potential drug candidates. Overall, our results for the first time show that the designed conjugates can target multiple over-expressed receptors in tumor cells and may be potentially developed as future therapeutics for targeting tumor cells.

How much can physics do for protein design?

Physics and physical chemistry are an important thread in computational protein design, complementary to knowledge-based tools. They provide molecular mechanics scoring functions that need little or no ad hoc parameter readjustment, methods to thoroughly sample equilibrium ensembles, and different levels of approximation for conformational flexibility. They led recently to the successful redesign of a small protein using a physics-based folded state energy. Adaptive Monte Carlo or molecular dynamics schemes were discovered where protein variants are populated as per their ligand-binding free energy or catalytic efficiency. Molecular dynamics have been used for backbone flexibility. Implicit solvent models have been refined, polarizable force fields applied, and many physical insights obtained.

An Effective MM/GBSA Protocol for Absolute Binding Free Energy Calculations: A Case Study on SARS-CoV-2 Spike Protein and the Human ACE2 Receptor

The binding free energy calculation of protein-ligand complexes is necessary for research into virus-host interactions and the relevant applications in drug discovery. However, many current computational methods of such calculations are either inefficient or inaccurate in practice. Utilizing implicit solvent models in the molecular mechanics generalized Born surface area (MM/GBSA) framework allows for efficient calculations without significant loss of accuracy. Here, GBNSR6, a new flavor of the generalized Born model, is employed in the MM/GBSA framework for measuring the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor. A computational protocol is developed based on the widely studied Ras-Raf complex, which has similar binding free energy to SARS-CoV-2/ACE2. Two options for representing the dielectric boundary of the complexes are evaluated: one based on the standard Bondi radii and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein-ligand binding. Predictions based on the two radii sets provide upper and lower bounds on the experimental references: -14.7(ΔGbindBondi)<-10.6(ΔGbindExp.)<-4.1(ΔGbindOPT1) kcal/mol. The consensus estimates of the two bounds show quantitative agreement with the experiment values. This work also presents a novel truncation method and computational strategies for efficient entropy calculations with normal mode analysis. Interestingly, it is observed that a significant decrease in the number of snapshots does not affect the accuracy of entropy calculation, while it does lower computation time appreciably. The proposed MM/GBSA protocol can be used to study the binding mechanism of new variants of SARS-CoV-2, as well as other relevant structures.

MMGB/SA Consensus Estimate of the Binding Free Energy Between the Novel Coronavirus Spike Protein to the Human ACE2 Receptor

The ability to estimate protein-protein binding free energy in a computationally efficient via a physics-based approach is beneficial to research focused on the mechanism of viruses binding to their target proteins. Implicit solvation methodology may be particularly useful in the early stages of such research, as it can offer valuable insights into the binding process, quickly. Here we evaluate the potential of the related molecular mechanics generalized Born surface area (MMGB/SA) approach to estimate the binding free energy ΔGbind between the SARS-CoV-2 spike receptor-binding domain and the human ACE2 receptor. The calculations are based on a recent flavor of the generalized Born model, GBNSR6. Two estimates of ΔGbind are performed: one based on standard bondi radii, and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein-ligand binding. We take the average of the resulting two ΔGbind values as the consensus estimate. For the well-studied Ras-Raf protein-protein complex, which has similar binding free energy to that of the SARS-CoV-2/ACE2 complex, the consensus ΔGbind = -11.8 ± 1 kcal/mol, vs. experimental -9.7 ± 0.2 kcal/mol. The consensus estimates for the SARS-CoV-2/ACE2 complex is ΔGbind = -9.4 ± 1.5 kcal/mol, which is in near quantitative agreement with experiment (-10.6 kcal/mol). The availability of a conceptually simple MMGB/SA-based protocol for analysis of the SARS-CoV-2 /ACE2 binding may be beneficial in light of the need to move forward fast.