Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications

Calcium-dependent lectin I from Pseudomonas aeruginosa (PA-IL) binds specifically to oligosaccharides presenting an alpha-galactose residue at their nonreducing end, such as the disaccharides alphaGal1-2betaGalOMe, alphaGal1-3betaGalOMe, and alphaGal1-4betaGalOMe. This provides a unique model for studying the effect of the glycosidic linkage of the ligands on structure and thermodynamics of the complexes by means of experimental and theoretical tools. The structural features of PA-IL in complex with the three disaccharides were established by docking and molecular dynamics simulations and compared with those observed in available crystal structures, including PA-IL.alphaGal1-2betaGalOMe complex, which was solved at 2.4 A resolution and reported herein. The role of a structural bridge water molecule in the binding site of PA-IL was also elucidated through molecular dynamics simulations and free energy calculations. This water molecule establishes three very stable hydrogen bonds with O6 of nonreducing galactose, oxygen from Pro-51 main chain, and nitrogen from Gln-53 main chain of the lectin binding site. Binding free energies for PA-IL in complex with the three disaccharides were investigated, and the results were compared with the experimental data determined by titration microcalorimetry. When the bridge water molecule was included in the free energy calculations, the simulations predicted the correct binding affinity trends with the 1-2-linked disaccharide presenting three times stronger affinity ligand than the other two. These results highlight the role of the water molecule in the binding site of PA-IL and indicate that it should be taken into account when designing glycoderivatives active against P. aeruginosa adhesion.

The term molecular mechanics (MM) refers to the use of simple potential-energy functions (e.g., harmonic oscillator or Coulombic potentials) to model molecular systems. Molecular mechanics approaches are widely applied in molecular structure refinement, molecular dynamics (MD) simulations, Monte Carlo (MC) simulations, and ligand-docking simulations. Typically, molecular mechanics models consist of spherical atoms connected by springs which represent bonds. Internal forces experienced in the model structure are described using simple mathematical functions. For example, Hooke’s law is commonly used to describe bonded interactions, and the nonbonded atoms might be treated as inelastic hard spheres or may interact according to a Lennard-Jones potential. Using these simple models, a molecular dynamics simulation numerically solves Newton’s equations of motion, thus allowing structural fluctuations to be observed with respect to time. Dynamic simulation methods are widely used to obtain information on the time evolution of conformations of proteins and other biological macromolecules1–4 and also kinetic and thermodynamic information. Simulations can provide fine detail concerning the motions of individual particles as a function of time. They can be utilized to quantify the properties of a system at a precision and on a time scale that is otherwise inaccessible, and simulation is, therefore, a valuable tool in extending our understanding of model systems. Theoretical consideration of a system additionally allows one to investigate the specific contributions to a property through “computational alchemy”,5 that is, modifying the simulation in a way that is nonphysical but nonetheless allows a model’s characteristics to be probed. One particular example is the artificial conversion of the energy function from that representing one system to that of another during a simulation. This is an important technique in free-energy calculations.6 Thus, molecular dynamics simulations, along with a range of complementary computational approaches, have become valuable tools for investigating the basis of protein structure and function. This review offers an outline of the origin of molecular dynamics simulation for protein systems and how it has developed into a robust and trusted tool. This review then covers more recent advances in theory and an illustrative selection of practical studies in which it played a central role. The range of studies in which MD has played a considerable or pivotal role is immense, and this review cannot do justice to them; MD simulations of biomedical importance were recently reviewed.4 Particular emphasis will be placed on the study of dynamic aspects of protein recognition, an area where molecular dynamics has scope to provide broad and far-ranging insights. This review concludes with a brief discussion of the future potential offered to advancement of the biological and biochemical sciences and the remaining issues that must be overcome to allow the full extent of this potential to be realized. 1.1. Historical Background MD methods were originally conceived within the theoretical physics community during the 1950s. In 1957, Alder and Wainwright7 performed the earliest MD simulation using the so-called hard-sphere model, in which the atoms interacted only through perfect collisions. Rahman8 subsequently applied a smooth, continuous potential to mimic real atomic interactions. During the 1970s, as computers became more widespread, MD simulations were developed for more complex systems, culminating in 1976 with the first simulation of a protein9,10 using an empirical energy function constructed using physics-based first-principles assumptions. MD simulations are now widely and routinely applied and especially popular in the fields of materials science11,12 and biophysics. As will be discussed later in this review, a variety of experimental conditions may be simulated with modern theories and algorithms. The initial simulations only considered single molecules in vacuo. Over time, more realistic or at least biologically relevant simulations could be performed. This trend is continuing today. The initial protein MD simulation, of the small bovine pancreatic trypsin inhibitor (BPTI), covered only 9.2 ps of simulation time. Modern simulations routinely have so-called equilibration periods much longer than that, and production simulations of tens of nanoseconds are routine, with the first microsecond MD simulation being reported in 1998.13 In addition, the original BPTI simulation included only about 500 atoms rather than the 104-106 atoms that are common today. While much of this advancement results from an immense increase in availability of computing power, major theoretical and methodological developments also contribute significantly. The number of publications regarding MD theory and application of MD to biological systems is growing at an extraordinary pace. A single review cannot do justice to the recent applications of MD. Using data from ISI Web of Science, the authors estimate that during 2005 at least 800 articles will be published that discuss molecular dynamics and proteins. The historical counts are shown in Figure 1. Open in a separate window Figure 1 Articles matching ISI Web of Science query “TS=(protein) AND TS=(molecular dynamics)”.

The use of artificial intelligence (AI) and machine learning (ML) for exploring vast amino acid sequence spaces has recently gained traction in the discovery of antibiotics and the design of biomaterials with a wide array of algorithms. However, despite some advancements, designing peptide inhibitors to specifically modulate protein-protein interactions remains a significant challenge. In this contribution, we explore the use of a Long Short-Term Memory (LSTM) network - a type of recurrent neural network - to model peptide sequences, given its ability to process sequential data and capture long-term dependencies, critical for peptide design. Our research focuses on developing precision peptides that target the interaction between the Notch intracellular domain (NICD) and CBF1/RBPJ transcription factors, key regulators of the Notch signaling pathway implicated in breast and pancreatic cancer stemness. We inferred hydrophobic, hydrophilic, van der Waals, and salt bridge interactions from experimentally determined three-dimensional protein complex structures, which were used for feature engineering via one-hot encoding. Peptides, each 20 amino acids in length, were generated using temperature scaling in the LSTM model. These peptides were then structurally optimized and subjected to molecular dynamics (MD) simulations, followed by molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) analysis to assess their interactions and binding affinities with the Notch receptor. The MD simulations provide valuable molecular-level insights into the peptide-Notch interactions, helping to evaluate their binding strength. Further biological testing is underway to validate the efficacy of these lead peptide inhibitors and elucidate their molecular mechanisms in targeting cancer stem cells associated with breast cancer. Citation Format: Gurudeeban Selvaraj, Satyavani Kaliamurthi, Gilles H. Peslherbe. Design and deciphering of precision peptide inhibitors for cancer stemness using generative deep learning and molecular dynamics simulations. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A015

Voltage gated potassium ion (Kv) channels regulate action potentials of the nervous system by responding to changes in transmembrane voltage, enabling K+ transport across the membrane to restore cells to their resting potential. Comprised of four identical subunits, Kv channels contain four voltage sensing domains arranged on the periphery of a central pore domain. Each voltage sensor is comprised of four transmembrane helices, numbered S1 through S4. The S4 helix, containing four to six highly-conserved, positively-charged arginine or lysine residues, is responsible for voltage sensitivity in Kv channels. The pore domain consists of two transmembrane helices, S5 and S6. The S5 helix constitutes the periphery of the pore domain and is believed to be relatively immobile. The S6 helices, lining the interior of the channel, gate the protein and regulate K+ permeation. Because each subunit of Kv channels contains six transmembrane helices, they are often referred to as 6TM Kv channels. The depolarization of an action potential is initiated as sodium ions enter the cell. At the cellular resting potential of -70 mV, potassium ion channels are closed, and the S4 helix is in its “down” state. As the electrochemical gradient changes, the S4 helices of Kv channels begin to reorient within the membrane. At the peak of the action potential (roughly +20 mV), the S4 helices exist in their “up” state. This conformational transition of the S4 helix is coupled to the pore domain via the S4-S5 linker, a short, amphipathic helix along the intracellular membrane-water interface. By bridging the C-terminus of the voltage sensor to the N-terminus of the pore domain, the S4-S5 linker couples the voltage sensitivity of the voltage sensor to K+ conduction in the pore domain. Because they begin opening at voltages less than 0 mV, all crystal structures of Kv channels contain an open pore domain. With no structure in the closed conformation, the mechanism of gating in Kv channels remains unclear. Nevertheless, significant biophysical studies have revealed insights into both the closed conformation and the gating transition itself. In this dissertation, I will explore questions relevant to the gating mechanism in voltage gated potassium ion channels through fully-atomistic molecular dynamics (MD) simulations. First, in Chapter 2, I will address the potential role of the 310 helical conformation found in the C-terminal end of S4 in the crystal structures of Kv channels. Spanning eight or more residues, these 310 helices are both uncharacteristically long and conserved in K+ channel crystal structures. By simulating the Kv1.2/2.1 chimera channel’s voltage sensor embedded in a lipid bilayer, I find that an alpha to 310 helical interconversion of the S4 helix reproduces many experimental measurements of the open and closed states of Kv channels. In Chapter 3, I perform molecular dynamics simulations of the entire Kv1.2/2.1 chimera channel. First, I examine the impact of an alpha to 310 helical interconversion of the S4 helix on the pore domain of the channel. Though the results are consistent with the results in Chapter 2 (and the corresponding experimental measurements), I find that this secondary structural modification is insufficient to influence the pore domain of the channel on the timescale of my simulations. In the second half of Chapter 3, I use molecular dynamics simulations to generate a closed state model of the Kv1.2/2.1 chimera from luminescence resonance energy transfer (LRET) measurements of the closed conformation of KvAP. The resulting structure is indeed closed, and also recapitulates a number of experimentally determined measurements of the closed channel. In Chapter 4, I focus on the pore domain. First, using targeted molecular dynamics simulations, I generate a transition between a closed model of the KvAP linker and pore domain to the open conformation. Then, using an umbrella sampling method, I quantify the energetics of the gating transition in KvAP and assess the physiological implications. In agreement with experimental studies of Kv channel energetics, I find that the open pore is roughly 2.7 kcal/mol lower in free energy than the closed conformation. The targeted molecular dynamics and umbrella sampling simulations reveal additional insights into the gating mechanism of KvAP. Lastly, in Chapter 5, I use MD simulations to gain insights into the binding mechanism of VSTx1, a Kv channel inhibitor. By using the experimentally determined neutron scattering density profile of the VSTx1 toxin bound to a lipid bilayer as a restraint for molecular dynamics simulations, I recreate the experimental scattering density profile, and also offer insight into the binding of VSTx1 to a lipid membrane.

HighlightsAccording to the design of the experiment (DoE), multivariate analysis models were used to optimize the critical process parameters combined with multi-objective optimization.Based on the optimized operating conditions, the MILT-HD method not only enhances the extraction efficiency from Amomi fructus but also reduces energy demands and CO2 emissions.Based on the density functional theoretical (DFT) and molecular dynamics (MD) simulations, the mechanisms for ionic liquids (ILs) to improve the extraction efficiency of essential oil was comprehensively revealed.In this paper, Amomi fructus (Latin) was used to explore the mechanism of ionic liquids (ILs) in improving the extraction efficiency of essential oils. Microwave assisted ionic liquid treatment followed by a hydro-distillation (MILT-HD) process for isolating Amomi fructus essential oil was optimized by multi-objective optimization. Under optimum operating conditions, the IL-assisted extraction method not only enhances extraction efficiency but also reduces energy demands and CO2 emissions. Since the hydrogen bond structure network of cellulose in the cell wall is an important reason for hindering diffusion of essential oils, the mechanism of ILs was explored by density functional theoretical (DFT) and molecular dynamics (MD) simulations. According to DFT calculations, ILs can facilitate the cleavage of cellulose chains and have strong non-covalent interactions with cellulose. Based on the MD simulations, the degree of destruction of the cellulose hydrogen bond structure was explored. According to the DFT and MD simulations, the ILs can significantly destroy cellulose structure, thereby promoting essential oil release from the plant. These results were confirmed by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). This work is conducive to better understand the MILT-HD process for isolating essential oil and comprehensively understand the mechanism of ILs in the extraction process.

Introduction: Milk kefir, a fermenting milk made with kefir grains, has shown potential in promoting apoptosis, regulating the cell cycle, and reducing tumor growth in breast cancer cells. This study aimed to investigate the stability and potential of milk kefir metabolites as inhibitors of breast cancer growth by interacting with the estrogen receptor alpha (ER-α), a key protein involved in breast cancer cell proliferation. We used computational methods, specifically molecular docking simulations with AutoDock and molecular dynamics (MD) simulations with Gromacs, to analyze how these metabolites bind to ER-α. Methods: A combination of molecular docking and MD simulations was used to explore how metabolites derived from milk kefir interact with ER-α, a crucial target in breast cancer therapy. The methodology included multiple stages: preparation of target proteins, preparation and screening of the metabolites, geometry optimization, molecular docking, and MD simulations. Results: The molecular docking simulations of 43 metabolites revealed three promising candidates: 2-Methyl (S35), benzeneethanol (S42), and 2,6-dimethyl-4-heptanone (S54), with binding affinities (ΔG) of -5.08, -5.06, and -4.90 kcal/mol, respectively. MD simulations further showed that the selected metabolites stabilized the ER-α-metabolite complex, with the 2,6-dimethyl-4-heptanone (S54) metabolite demonstrating the most negative total MM-GBSA energy value (ΔG = -22.98 kcal/mol), indicating a strong and stable binding interaction. Conclusion: 2,6-Dimethyl-4-heptanone, a metabolite from milk kefir, showed promising potential as a candidate for further development as a breast cancer treatment, offering a novel alternative to conventional therapies.

Selectivity and transport in aquaporins from molecular simulation studies.

To investigate the antioxidant mechanisms of phenolic antioxidants in base oils, this study employed the reactive force field (ReaxFF) molecular dynamics (MD) simulations to reveal the reaction pathways and product distributions of trihydroxymethylpropyl trioleate (TMPTO, base oil) and antioxidants AO-80 and XH-245 under high-temperature conditions. A multi-output regression prediction model (MORP) was developed to achieve accurate prediction of carbon atom types and chemical bond types in the reaction systems. Furthermore, a chemical reaction prediction model (CRPM) based on graph neural networks (GNN) was developed to predict the product types and distributions at different times during the antioxidant process of lubricating oils. The results show that the oxidative decomposition of TMPTO is characterised by the cleavage of C–C bonds and C–O bonds. The main small-molecule oxidation products include CO2, C2H4, H2O, CO, formaldehyde, formic acid, and other C2 and C3 compounds. The ArOH groups in phenolic antioxidant molecules can delay the oxidative cleavage process of base oil through hydrogen transfer steps. The system with the two types of antioxidants exhibits better antioxidant effects than the single antioxidant system under the same conditions. The MORP and CRPM models provide good prediction results for carbon atom types, chemical bond types, and product distributions.

The formation mechanisms of hydrogen peroxide due to the interaction of oxygen atom from the cold atmospheric plasmas in contact with water are not fully understood. Previous work on molecular dynamics (MD) simulations of interactions of O atoms in bulk water based on reactive force field and density-functional tight-binding method did not observe the formation of . In this work we applied density functional theory in MD simulations of 192 trajectories considering system to explore the reaction mechanisms for atomic oxygen radical in water. Our calculations revealed that triplet (ground) state oxygen was not reactive. Oxywater-similar structure was a transient product. Perhydroxyl anion and its counterpart hydronium were formed. In most of simulated cases, hydrogen peroxide was observed as a final product. The formation pathways of hydrogen peroxide exhibited large complexities for the simple hydrogen bonded system. According to the sources and pathways of the hydrogen atom being bonded in hydrogen peroxide molecule, mechanisms can be classified into (1) hydrogen-abstraction, (2) hydrogen-transfer n (n = 3, 4, 5, 6, 7, 8), (3) proton-delivery n = 2, 3, (4) proton-transfer. It was confirmed that for correct prediction of reaction mechanisms is better to use quantum molecular dynamic simulations.

Physics-Informed Neural Network for modeling and predicting temperature fluctuations in proton exchange membrane electrolysis

Background: Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a disorder with human-to-human rapid transmission. With several vaccines introduced, we need to find out the effectiveness of such medications in a short-period therapeutic procedure. The NTPase/helicase plays a key role in the replication of the viral RNA. Materials and Methods: We estimated the binding affinity of several natural polyphenolics, commonly found in fruits and vegetables, with the catalytic site of SARS-CoV-2 helicase by molecular docking analysis using the AutoDock tool. The stability of connections between top-ranked components inside the catalytic site of the helicase was evaluated by molecular dynamics (MD) simulations. The most active residues within the catalytic site of the helicase were ranked based on their degree in a phenolic-residue interaction (PRI) network. Results: Amentoflavone, theaflavin 3'-gallate, and procyanidin were estimated to be the most potential effective SARS-CoV-2 helicase inhibitors with the salient inhibition constant value (Ki) at the picomolar scale. The docked pose of these compounds was also found to be stable after MD simulations. The binding energy of these compounds with the helicase catalytic site was estimated between −13.90 and −12.77 kcal/mol. Asp534 and Leu412 demonstrated more degrees in the PRI network compared to the other residues. Conclusion: The present study predicts that amentoflavone, theaflavin 3'-gallate, and procyanidin might be helpful for the treatment of COVID-19.

Prediction of nonlocal elasticity parameters using high-throughput molecular dynamics simulations and machine learning

Corticotropin-releasing factor receptor 1 (CRF1R), a member of class B G-protein-coupled receptors (GPCRs), plays an important role in the treatment of osteoporosis, diabetes, depression, migraine and anxiety. To explore the escape pathway of the antagonist CP-376395 in the binding pocket of CRF1R, molecular dynamics (MD) simulations, dynamical network analysis, random acceleration molecular dynamics (RAMD) simulations and adaptive biasing force (ABF) calculations were performed on the crystal structure of CRF1R in complex with CP-376395. The results of dynamical network analysis show that TM7 of CRF1R has the strongest edges during MD simulation. The bent part of TM7 forms a V-shape pocket with Gly356(7.50). Asn283(5.50) has high hydrogen bond occupancy during 100 ns MD simulations and is the key interaction residue with the antagonist in the binding pocket of CRF1R. RAMD simulation has identified three possible pathways (PW1, PW2 and PW3) for CP-376395 to escape from the binding pocket of CRF1R. The PW3 pathway was proved to be the most likely escape pathway for CP-376395. The free energy along the PW3 pathway was calculated by using ABF simulations. Two energy barriers were found along the reaction coordinates. Residues Leu323(6.49), Asn283(5.50) and Met206(3.47) contribute to the steric hindrance for the first energy barrier. Residues His199(3.40) and Gln355(7.49) contribute to the second energy barrier through the hydrogen bonding interaction between CP-376395 and CRF1R. The results of our study can not only provide useful information to understand the interaction mechanism between CP-376395 and CRF1R, but also provide the details about the possible escape pathway and the free energy profile of CP-376395 in the pocket of CRF1R.

Herbal plants are often used as alternative medicine because they contain active compounds for the treatment of diseases and disorders with minimal side effects, and are easily obtained from the surrounding environment. Some of them have antiviral activity. This study aimed to analyze the potential of phytochemical compounds in the leaf of “pangi” (Pangium edule Reinw) as antiSARS-CoV-2 using molecular docking study. The drug- and lead-likeness properties of the selected compounds were obtained from the Swiss ADME and admetSAR online server tools. Molecular dynamics (MD) simulation of the selected ligand was carried out to validate the stability of the interaction. The results suggested that pangi leaves contain three compounds with remarkable binding affinities with Mpro (main protease) and RBD (receptor binding domain) were (5.beta.) pregnane-3,20.beta.-diol, 14.alpha.,18.alpha.-[4-methyl-3-oxo-(1-oxa-4-azabutane-1,4-diyl)]-, diacetate (PD), ethyl cholate (EC), and bis(3,5,5-trimethylhexyl) phthalate. Because EC will be metabolized in the body into cholic acid (Cho), this compound was then docked and validated using MD simulation. The compound has the best free binding energy (ΔG) with SARS-CoV-2 (–7.1 kcal/ mol with Mpro and –6.0 kcal/mol with RBD). Moreover, the compound is bound strongly to the active cavity of Mpro on Thr24, Thr26, His41, and Cys145 residues. The MM-GBSA calculation showed that the interaction of Cho with Mpro was higher than with RBD. According to the RMSD (root mean square deviation), RMSF (root mean square fluctuation), the radius of gyration (Rg), and intermolecular hydrogen bond (H-bond) analysis obtained from 50 ns MD simulations, Cho formed stable interactions with Mpro and RBD. The finding of this study indicated that Cho showed good anti-SARS-CoV-2 activity. The potential of the compound to inhibit the virus can serve as a starting point in the process of developing COVID-19 therapeutic natural medicine.

The opportunistic fungal pathogen Candida albicans (C. albicans) employs quorum sensing (QS) to regulate virulence factors such as biofilm formation and morphological switching. Targeting QS offers a promising approach to attenuate pathogenicity without promoting resistance. The objective of the present study was to computationally screen a library of 106 propolis-derived compounds to identify natural quorum-sensing inhibitors targeting the CYC and RAS1 receptors. Advanced techniques, including molecular docking, molecular dynamics (MD) simulations, and ADMET predictions, were employed to establish a workflow for structure-based virtual screening. Farnesol, used as a reference compound, showed a good binding affinity (-7.0 kcal/mol) toward CYC and RAS1 receptors. Two propolis flavonoids, kaempferol-3-O-4-O-p-coumaryl-glucoside (KCG) and isorhamnetin-3-glucoside-7-rhamnoside (IGR), exhibited stronger binding affinities with CYC and RAS1 receptors (between − 8.4 and − 10.3 kcal/mol). Based on Prime MMGBSA binding free energy calculations, the RAS1-KCG complex demonstrated the most favorable interaction, driven by significant coulombic and Van der Waals contributions. The CYC-KCG complex also showed relatively strong binding. These results highlight the potential of KCG as a versatile inhibitor capable of interacting with both targets. MD simulations further confirmed the excellent conformational stability of both KCG-receptor complexes, as indicated by low RMSD values, suggesting favorable dynamic behavior. These findings provide a strong foundation for subsequent validation.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-18001-1.