Molecular simulations of proteins: From simplified physical interactions to complex biological phenomena

Citrullination at the N-terminal region of MDM2 by the PADI4 enzyme

PADI4 is one of the human isoforms of a family of enzymes involved in the conversion of arginine to citrulline. MDM2 is an E3 ubiquitin ligase that is critical for degradation of the tumor suppressor gene p53. We have previously shown that there is an interaction between MDM2 and PADI4 in cellulo, and that such interaction occurs through the N-terminal region of MDM2, N-MDM2, and in particular through residues Thr26, Val28, Phe91, and Lys98. Here, by using a "divide-and-conquer" approach, we have designed and synthesized peptides comprising these two polypeptide stretches (residues Ala21-Lys36, and Lys94-Val108), either in the wild-type species or in their citrullinated versions. Some of the citrullinated peptides were aggregation-prone, as suggested by DOSY-NMR experiments, but the wild-type versions of both fragments were monomeric in solution. We found out that wild-type and modified peptides were disordered in all cases, as also tested by far-UV circular dichroism (CD), and citrullination mainly affected the NMR chemical shifts of adjacent residues. Isothermal titration calorimetry (ITC) in the absence and presence of GSK484, an enzymatic PADI4 inhibitor, indicated that this compound blocked binding of the peptides to the enzyme. Binding to the active site of the N-MDM2 fragments was also confirmed by in silico experiments. The affinities of PADI4 for the wild-type peptides were more favorable than those of the corresponding citrullinated ones, but all measured values were within the micromolar range, indicating that there were no major variations in the thermodynamics of binding due to sequence effects. The kinetic dissociation rates, koff, measured by biolayer interferometry (BLI), were always one-order of magnitude faster for the citrullinated peptides than for the wild-type ones. Taken together, all these findings indicate that MDM2 is a substrate for PADI4 and is prone to citrullination in the identified (and specific) positions of its N-terminal region.

Comparison Between Molecular Dynamics Potentials for Simulation of Graphene-Based Nanomaterials for Biomedical Applications

OBJECTIVE

This article provides a substantial review of recent research and comparison on molecular dynamics potentials to determine which are most suitable for simulating the phenomena in graphene-based nanomaterials (GBNs).

SIGNIFICANCE

GBNs gain significant attention due to their remarkable properties and potential applications, notably in nanomedicine. However, the physical and chemical characteristics toward macromolecules that justify their nanomedical applications are not yet fully understood. The molecular interaction through molecular dynamic simulation offers the benefits for simulating inorganic molecules like GBNs, with necessary adjustments to account for physical and chemical interactions, or thermodynamic conditions.

METHOD

In this review, we explore various molecular dynamics potentials (force fields) used to simulate interactions and phenomena in graphene-based nanomaterials. Additionally, we offer a brief overview of the benefits and drawbacks of each force fields that available for analysis to assess which one is suitable to study the molecular interaction of graphene-based nanomaterials.

RESULT

We identify and compare various molecular dynamics potentials that available for analysing GBNs, providing insights into their suitability for simulating specific phenomena in graphene-based nanomaterials. The specification of each force fields and its purpose can be used for further application of molecular dynamics simulation on GBNs.

CONCLUSION

GBNs hold significant promise for applications like nanomedicine, but their physical and chemical properties must be thoroughly studied for safe clinical use. Molecular dynamics simulations, using either reactive or non-reactive MD potentials depending on the expected chemical changes, are essential for accurately modeling these properties, requiring careful selection based on the specific application.

Predicting the polyspecificity of aminoacyl-tRNA synthetase for non-canonical amino acids using molecular dynamics simulation and MM/PBSA

With the advancement of genetic code expansion, the field is progressing towards incorporating multiple non-canonical amino acids (ncAAs). The specificity of aminoacyl-tRNA synthetases (aaRSs) towards ncAAs is a critical factor, as engineered aaRSs frequently show polyspecificity, complicating the precise incorporation of multiple ncAAs. To address this challenge, predicting binding affinity can be beneficial. In this study, we expressed sfGFP using an orthogonal aaRS/tRNA pair with 4-Azido-L-phenylalanine (AzF) and another four different ncAAs. The experimental results showed specificity with O-Methyl-L-tyrosine as well as AzF, and these results were compared with computational predictions. We constructed a mutant aaRS structure specific for AzF through homology modelling and conducted docking studies with tyrosine and five ncAAs, followed by molecular dynamics simulations. The binding affinity was calculated using the molecular mechanics/Poisson-Boltzmann surface area, focusing on nonpolar solvation terms. While the analysis is based on the incorporation of limited number of ncAAs, the cavity and dispersion term method showed consistency with experimental data, highlighting its potential utility compared to the surface area term method. These findings enhance understanding of the ncAA specificity of aaRS in relation to computer simulations and energy calculations, which can be utilized to rationally design or predict the specificity of aaRS.

Development of an efficient NUPR1 inhibitor with anticancer activity

Pancreatic cancer is highly lethal and has limited treatment options available. Our team had previously developed ZZW-115, a promising drug candidate that targets the nuclear protein 1 (NUPR1), which is involved in pancreatic cancer development and progression. However, clinical translation of ZZW-115 was hindered due to potential cardiotoxicity caused by its interaction with the human Ether-à-go-go-Related Gene (hERG) potassium channel. To address this, we have performed a high-throughput screening of 10,000 compounds from the HitFinder Chemical Library, and identified AJO14 as a lead compound that binds to NUPR1, without having favorable affinity towards hERG. AJO14 induced cell death through apoptosis, necroptosis, and parthanatos (induced by the poly-ADP ribose polymerase (PARP) overactivation), driven by mitochondrial catastrophe and decreased ATP production. This process seemed to be mediated by the hyperPARylation (an excessive modification of proteins by PARP, leading to cellular dysfunction), as it could be reversed by Olaparib, a PARP inhibitor. In xenografted mice, AJO14 demonstrated a dose-dependent tumor reduction activity. Furthermore, we attempted to improve the anti-cancer properties of AJO14 by molecular modification of the lead compound. Among the 51 candidates obtained and tested, 8 compounds exhibited a significant increase in efficacy and have been retained for further studies, especially LZX-2-73. These AJO14-derived compounds offer potent NUPR1 inhibition for pancreatic cancer treatment, without cardiotoxicity concerns.

Molecular docking and molecular dynamics of hypoxia-inducible factor (HIF-1alpha): towards potential inhibitors

HIF-1α is a primary regulator in the adaptation of cancer cells to hypoxia. The aim was to find out new inhibitors of the HIF-1α. A molecular dynamic (MD) simulation performed on HIF-1α showed stable dynamic features. Virtual screening of 217 anticancer drugs was performed along with a positive control (2-Methoxyestradiolm, 2-ME2) on an optimized HIF-1α and dynamically simulated structure. Docking results produced two compounds namely pycnidione and nilotinib of high binding affinity -9.34 kcal/mol and -9.04 kcal/mol respectively, whereas 2-ME2 displayed a relatively lower affinity (-6.68 kcal/mol). For the three complexes, MD of 200 ns simulation was run. Data analysis showed that the three medications behaved similarly in the MD simulation. Nilotinib had a lower RMSD and higher SASA than the other complexes. In addition, the Nilotinib-HIF-1α combination had a lower RMSF value, a flatter Rg, and a number of hydrogen bonds similar to other complexes. MM-GBSA analysis revealed that nilotinib, pycnidione and 2-ME2 compounds had free binding energy of -23.77 ± 5.29, -21.85 ± 4.24 and -7.53 ± 6.62 kcal/mol respectively. Nilotinib and pycnidione bind competitively to HIF-1α, with nilotinib showing consistent molecular-dynamic properties. They relatively pass the blood-brain barrier, non-carcinogenic, and have IV-category acute oral toxicity. They have low CYP inhibitory characteristics. Further investigations are therefore warranted to elucidate their implications in hypoxia pathways, cell proliferation, apoptosis, survival, and metastatic potential.

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems

Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Nanotherapeutics, which merge nanotechnology with targeted drug delivery, exemplify this advancement by offering improved precision and reduced side effects in cancer therapy. The development of these nanotherapeutic agents depends critically on understanding nanoparticle (NP) properties and their biological interactions, often analyzed through molecular dynamics (MD) simulations. This study enhances these analyses by integrating ML with MD simulations, significantly improving both prediction accuracy and computational efficiency. We introduce a comprehensive three-stage methodology for predicting the solvent-accessible surface area (SASA) of NPs, which is crucial for their therapeutic efficacy. The process involves training an ML model to forecast the many-body tensor representation (MBTR) for future time steps, applying data augmentation to increase dataset realism, and refining the SASA predictor with both augmented and original data. Results demonstrate that our methodology can predict SASA values 299 time steps ahead with a 40-fold speed improvement and a 25% accuracy increase over existing methods. Importantly, it provides a 300-fold increase in computational speed compared to traditional simulation techniques, offering substantial cost and time savings for nanotherapeutic research and development.

Dynamic and Energetic Aspects of Carotenoids In-and-Around Model Lipid Membranes Revealed in Molecular Modelling

In contrast to plants, humans are unable to synthesise carotenoids and have to obtain them from diet. Carotenoids fulfil several crucial biological functions in the organism; however, due to poor solubility in water, their bioavailability from plant-based food is low. The processes of carotenoid absorption and availability in the human body have been intensively studied. The recent experimental findings concerning these processes are briefly presented in the introductory part of this review, together with a summary of such topics as carotenoid carriers, body transport and tissue delivery, to finally report on molecular-level studies of carotenoid binding by membrane receptors. The main message of the review is contained in the section describing computational investigations of carotenoid intercalation and dynamic behaviour in lipid bilayers. The relevance of these computational studies lies in showing the direct link between the microscopic behaviour of molecules and the characteristics of their macroscopic ensembles. Furthermore, studying the interactions between carotenoids and lipid bilayers, and certainly proteins, on the molecular- and atomic-level using computational methods facilitates the interpretation and explanation of their macroscopic properties and, hopefully, helps to better understand the biological functions of carotenoids.

Concentration Fluctuation/Microheterogeneity Duality Illustrated with Aqueous 1,4-Dioxane Mixtures

The structural properties of aqueous 1-4 dioxane mixtures are studied by computer simulations of different water and dioxane force field models, from the perspective of illustrating the link between structural properties at the molecular level and measurable properties such as radiation scattering intensities and Kirkwood-Buff integrals (KBIs). A strategy to consistently correct the KBI obtained from simulations is proposed, which allows us to obtain the genuine KBI corresponding to a given pair of molecular species, in the entire concentration range, and without necessitating excessively large system sizes. The application of this method to the aqueous dioxane mixtures, with an all-atom CHARMM dioxane model and 2 water models, namely, SPC/E and TIP3P, allows one to understand the differences in the structure of the corresponding mixtures at the molecular level, particularly concerning the role of the water aggregates and its model dependence. This study allows us to characterize the dual role played by the concentration fluctuations and the domain segregation, particularly in what concerns the calculated X-ray spectra.

In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review

BACKGROUND

This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement.

OBJECTIVES

This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research.

METHODS

Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed.

RESULT

The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them.

CONCLUSION

In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.

Osmotic Force Balance Evaluation of Aqueous Electrolyte Osmotic Pressures and Chemical Potentials

Concentrated aqueous salt solutions are ubiquitous in problems of biological and environmental relevance. The development of accurate force fields that capture the interactions between dissolved species in solution is crucial to simulating these systems to gain molecular insights into the underlying processes under saline conditions. The osmotic pressure is a relatively simple thermodynamic property connecting the experimental and simulation measurements of the associative properties of the ions in solution. Milner [C. Gillespie and S. T. Milner, Soft Matter, 16, 9816 (2020)] proposed a simulation approach to evaluate the osmotic pressures of salts in solution by applying a restraint potential to the ions alone in solution and determining the resulting pressure required to balance that potential, referred to here as the osmotic force balance. Here, we expand Milner's approach, demonstrating that the chemical potentials of the salts in solution as a function of concentration can be fitted to the concentration profiles determined from simulation, additionally providing an analytical expression for the osmotic pressure. This approach is used to determine the osmotic pressures of 15 alkali halide salts in water from simulations. The cross interactions between cations and anions in solution are subsequently optimized to capture their experimental osmotic pressures.

Conformational Stability of the N-Terminal Region of MDM2

MDM2 is an E3 ubiquitin ligase which is crucial for the degradation and inhibition of the key tumor-suppressor protein p53. In this work, we explored the stability and the conformational features of the N-terminal region of MDM2 (N-MDM2), through which it binds to the p53 protein as well as other protein partners. The isolated domain possessed a native-like conformational stability in a narrow pH range (7.0 to 10.0), as shown by intrinsic and 8-anilinonapthalene-1-sulfonic acid (ANS) fluorescence, far-UV circular dichroism (CD), and size exclusion chromatography (SEC). Guanidinium chloride (GdmCl) denaturation followed by intrinsic and ANS fluorescence, far-UV CD and SEC at physiological pH, and differential scanning calorimetry (DSC) and thermo-fluorescence experiments showed that (i) the conformational stability of isolated N-MDM2 was very low; and (ii) unfolding occurred through the presence of several intermediates. The presence of a hierarchy in the unfolding intermediates was also evidenced through DSC and by simulating the unfolding process with the help of computational techniques based on constraint network analysis (CNA). We propose that the low stability of this protein is related to its inherent flexibility and its ability to interact with several molecular partners through different routes.

Engineering and Expression Strategies for Optimization of L-Asparaginase Development and Production

Genetic engineering for heterologous expression has advanced in recent years. Model systems such as Escherichia coli, Bacillus subtilis and Pichia pastoris are often used as host microorganisms for the enzymatic production of L-asparaginase, an enzyme widely used in the clinic for the treatment of leukemia and in bakeries for the reduction of acrylamide. Newly developed recombinant L-asparaginase (L-ASNase) may have a low affinity for asparagine, reduced catalytic activity, low stability, and increased glutaminase activity or immunogenicity. Some successful commercial preparations of L-ASNase are now available. Therefore, obtaining novel L-ASNases with improved properties suitable for food or clinical applications remains a challenge. The combination of rational design and/or directed evolution and heterologous expression has been used to create enzymes with desired characteristics. Computer design, combined with other methods, could make it possible to generate mutant libraries of novel L-ASNases without costly and time-consuming efforts. In this review, we summarize the strategies and approaches for obtaining and developing L-ASNase with improved properties.

The intrinsically disordered, epigenetic factor RYBP binds to the citrullinating enzyme PADI4 in cancer cells

RYBP (Ring1 and YY 1 binding protein) is a multifunctional, intrinsically disordered protein (IDP), best described as a transcriptional regulator. It exhibits a ubiquitin-binding functionality, binds to other transcription factors, and has a key role during embryonic development. RYBP, which folds upon binding to DNA, has a Zn-finger domain at its N-terminal region. By contrast, PADI4 is a well-folded protein and it is one the human isoforms of a family of enzymes implicated in the conversion of arginine to citrulline. As both proteins intervene in signaling pathways related to cancer development and are found in the same localizations within the cell, we hypothesized they may interact. We observed their association in the nucleus and cytosol in several cancer cell lines, by using immunofluorescence (IF) and proximity ligation assays (PLAs). Binding also occurred in vitro, as measured by isothermal titration calorimetry (ITC) and fluorescence, with a low micromolar affinity (~1 μM). AlphaFold2-multimer (AF2) results indicate that PADI4's catalytic domain interacts with the Arg53 of RYBP docking into its active site. As RYBP sensitizes cells to PARP (Poly (ADP-ribose) polymerase) inhibitors, we applied them in combination with an enzymatic inhibitor of PADI4 observing a change in cell proliferation, and the hampering of the interaction of both proteins. This study unveils for the first time the possible citrullination of an IDP, and suggests that this new interaction, whether it involves or not citrullination of RYBP, might have implications in cancer development and progression.

Construction of an MLR-QSAR Model Based on Dietary Flavonoids and Screening of Natural α-Glucosidase Inhibitors

Postprandial hyperglycemia can be reduced by inhibiting α-glucosidase activity. Common α-glucosidase inhibitors such as acarbose may have various side effects. Therefore, it is important to find natural products that are non-toxic and have high α-glucosidase-inhibitory activity. In the present study, a comprehensive computational analysis of 27 dietary flavonoid compounds with α-glucosidase-inhibitory activity was performed. These included flavonoids, flavanones, isoflavonoids, dihydrochalcone, flavan-3-ols, and anthocyanins. Firstly, molecular fingerprint similarity clustering analysis was performed on the target molecules. Secondly, multiple linear regression quantitative structure-activity relationship (MLR-QSAR) models of dietary flavonoids (2D descriptors and 3D descriptors optimized), with R² of 0.927 and 0.934, respectively, were constructed using genetic algorithms. Finally, the MolNatSim tool based on the COCONUT database was used to match the similarity of each flavonoid in this study, and to sequentially perform molecular enrichment, similarity screening, and QSAR prediction. After screening, five kinds of natural product molecule (2-(3,5-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one, norartocarpetin, 2-(2,5-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one, 2-(3,4-dihydroxyphenyl)-5-hydroxy-4H-chromen-4-one, and morelosin) were finally obtained. Their IC_50pre values were 8.977, 31.949, 78.566, 87.87, and 94.136 µM, respectively. Pharmacokinetic predictions evaluated the properties of the new natural products, such as bioavailability and toxicity. Molecular docking analysis revealed the interaction of candidate novel natural flavonoid compounds with the amino acid residues of α-glucosidase. Molecular dynamics (MD) simulations and molecular mechanics/generalized Born surface area (MMGBSA) further validated the stability of these novel natural compounds bound to α-glucosidase. The present findings may provide new directions in the search for novel natural α-glucosidase inhibitors.

COGRIMEN: Coarse-Grained Method for Modeling of Membrane Proteins in Implicit Environments

The presented methodology is based on coarse-grained representation of biomolecules in implicit environments and is designed for the molecular dynamics simulations of membrane proteins and their complexes. The membrane proteins are not only found in the cell membrane but also in all membranous compartments of the cell: Golgi apparatus, mitochondria, endosomes and lysosomes, and they usually form large complexes. To investigate such systems the methodology is proposed based on two independent approaches combining the coarse-grained MARTINI model for proteins and the effective energy function to mimic the water/membrane environments. The latter is based on the implicit environment developed for all-atom simulations in the IMM1 method. The force field solvation parameters for COGRIMEN were initially calculated from IMM1 all-atom parameters and then optimized using Genetic Algorithms. The new methodology was tested on membrane proteins, their complexes and oligomers. COGRIMEN method is implemented as a patch for NAMD program and can be useful for fast and brief studies of large membrane protein complexes.