Molecular Dynamics Free Energy Simulations Research Articles

Extending the lore of curcumin as dipteran Butyrylcholine esterase (BChE) inhibitor: A holistic molecular interplay assessment.

Since its origin, the emergence of vector-borne infections has taken a toll on incalculable human lives. The use of chemical insecticides is one of the early known methods of vector control and although their use is still a prevalent way to combat insect population sadly the perils of insects related transmission still persists. Most commonly, the existing insecticides face the wrath of getting resisted repeatedly, paying way to develop resilient, efficient, and cost-effective natural insecticides. In this study, computational screening was performed using homology modelling, E-pharmacophore feature mapping, molecular docking, Density Function Theory (DFT) assessment, Molecular mechanics generalized Born surface area (MM-GBSA) based binding free energy calculations and Molecular Dynamics (MD) simulation to identify a potential lead phytochemical out of a manually curated library from published literature. The protein target used under this study is insect Butyrylcholine esterase (BChE). Additionally, in vitro insect (Aedes aegypti) BChE inhibition assay was also performed with the top phytochemical identified from in silico assessments. Our research highlights that curcumin leads to inhibition of enzyme BChE of Ae. aegypti. The identified mode of action of curcumin as an insect BChE inhibitor indicates the possibility of its use as an environment friendly and natural futuristic insecticide.

Rejuvenation effect of waste cooking oil on the adhesion characteristics of aged asphalt to aggregates

There are many studies on the rejuvenation of waste cooking oil (WCO) on reclaimed asphalt pavement (RAP), focusing mostly on mechanical properties of asphalt mixture while rarely on the adhesion of asphalt to aggregates. In this study, the surface free energy (SFE) method and molecular dynamics (MD) simulation were used to study the effect of WCO on the adhesion of asphalt and aggregate from the macroscopic and nanoscopic levels, respectively. Four types of aggregates (basalt, limestone, granite, and sandstone) and minerals (SiO2, MgO, CaCO3, and CaO) representing aggregates were selected. The SFE of asphalt and aggregate was obtained, then the adhesion work, debonding work, and the energy ratio (ER) were calculated to evaluate moisture susceptibility. Then the asphalt-mineral interface model was built and the adhesion work under dry and wet conditions and interaction energy were analyzed. The results show that from the SFE perspective, WCO can improve the SFE parameters of aged asphalt and its adhesion work with aggregates, whereas the rejuvenation effect decreases with the increase of WCO content. The aging process will increase the debonding work of the interface, resulting in aged asphalt easier stripping from the aggregate. The resistance to moisture damage ranks as basalt > limestone > granite > sandstone. From the SFE perspective, the van der Waals interaction contributes the main component of adhesion work of asphalt with SiO2, MgO, and CaO, while the electrostatic interaction is the main component with CaCO3. Under the wet condition, the adhesion work of the asphalt-SiO2 interface is affected to the smallest extent due to the minimal interaction between SiO2 and water molecules. Combining with the results of the SFE method and MD simulation, WCO plays a rejuvenation effect, improving the resistance to moisture damage. This study can guide the selection of aggregates to make WCO recycled asphalt mixture with sound moisture resistance.

Computational Indicator Approach for Assessment of Nanotoxicity of Two-Dimensional Nanomaterials.

The increasing growth in the development of various novel nanomaterials and their biomedical applications has drawn increasing attention to their biological safety and potential health impact. The most commonly used methods for nanomaterial toxicity assessment are based on laboratory experiments. In recent years, with the aid of computer modeling and data science, several in silico methods for the cytotoxicity prediction of nanomaterials have been developed. An affordable, cost-effective numerical modeling approach thus can reduce the need for in vitro and in vivo testing and predict the properties of designed or developed nanomaterials. We propose here a new in silico method for rapid cytotoxicity assessment of two-dimensional nanomaterials of arbitrary chemical composition by using free energy analysis and molecular dynamics simulations, which can be expressed by a computational indicator of nanotoxicity (CIN2D). We applied this approach to five well-known two-dimensional nanomaterials promising for biomedical applications: graphene, graphene oxide, layered double hydroxide, aloohene, and hexagonal boron nitride nanosheets. The results corroborate the available laboratory biosafety data for these nanomaterials, supporting the applicability of the developed method for predictive nanotoxicity assessment of two-dimensional nanomaterials.

A Computer-Aided Approach for the Discovery of D-Peptides as Inhibitors of SARS-CoV-2 Main Protease.

The SARS-CoV-2 main protease, also known as 3-chymotrypsin-like protease (3CLpro), is a cysteine protease responsible for the cleavage of viral polyproteins pp1a and pp1ab, at least, at eleven conserved sites, which leads to the formation of mature nonstructural proteins essential for the replication of the virus. Due to its essential role, numerous studies have been conducted so far, which have confirmed 3CLpro as an attractive drug target to combat Covid-19 and have reported a vast number of inhibitors and their co-crystal structures. Despite all the ongoing efforts, D-peptides, which possess key advantages over L-peptides as therapeutic agents, have not been explored as potential drug candidates against 3CLpro. The current work fills this gap by reporting an in silico approach for the discovery of D-peptides capable of inhibiting 3CLpro that involves structure-based virtual screening (SBVS) of an in-house library of D-tripeptides and D-tetrapeptides into the protease active site and subsequent rescoring steps, including Molecular Mechanics Generalized-Born Surface Area (MM-GBSA) free energy calculations and molecular dynamics (MD) simulations. In vitro enzymatic assays conducted for the four top-scoring D-tetrapeptides at 20 μM showed that all of them caused 55–85% inhibition of 3CLpro activity, thus highlighting the suitability of the devised approach. Overall, our results present a promising computational strategy to identify D-peptides capable of inhibiting 3CLpro, with broader application in problems involving protein inhibition.

Structure-based thermodynamics of ion selectivity (Mg2+versus Ca2+ and K+versus Na+) in the active site of the eukaryotic lariat group II intron from algae Pylaiella littoralis.

Group II introns are metalloenzymes that can catalyze self-splicing. Recently, the crystal structures of the eukaryotic group IIB lariat intron from the brown algae Pylaiella littoralis have been reported for two intermediate states (pre-hydrolytic (2s) and post-hydrolytic) along the self-splicing pathway. Three characteristic metal-ion binding sites (M1 and M2 sites for catalytic Mg2+ ions, and K1 site for K+) in the catalytic pocket of the lariat intron have been identified and proposed to be crucial for self-splicing. Using the X-ray structures as a template, we quantitatively estimated the energetics of divalent (Mg2+versus Ca2+) and monovalent (K+versus Na+) ion selectivity and established a direct link between the energetics and structures of this lariat intron (bound to cognate and near-cognate metal ions). Molecular dynamics (MD) free energy simulations showed that the lariat intron was strongly selective between divalent metal ions. The strength of divalent metal-ion selectivity was noticeably high in the post-hydrolytic state (ΔΔG ≈ 20 kcal mol-1) relative to its pre-hydrolytic (2s) state (ΔΔG ≈ 13 kcal mol-1). Quantum chemical calculations ensured that the sign of the estimated divalent metal-ion selectivity was correct. The M1-binding pocket was less solvent-exposed in the case of the post-hydrolytic state relative to the pre-hydrolytic (2s) state, which boosted the metal-ion selectivity of the former. Surprisingly, in contrast to the bacterial linear group II intron, the lariat intron was found to be non-selective between monovalent ions (K+versus Na+). The interaction network in the first coordination shell of Ca2+ in the M1-binding pocket was different relative to Mg2+. Mg2+ substitution by Ca2+ resulted in the substitution of a single M1-RNA interaction by the M1-water interaction. In the pre-hydrolytic (2s) state, Ca2+ substitution completely disrupted the M1⋯5'-exon interaction; thus, the nature of the divalent metal ion is critical for catalysis. The interaction network in the M2 site was independent of the nature of the divalent metal ions (Mg2+ or Ca2+). The monovalent ion was loosely bound in the wet binding pocket (K1 site) of the lariat intron; thus, the substitution of K+ by Na+ could not significantly alter the free energy of the complex. The metal ion selectivity was dependent on the solvent accessibility of the metal-ion-binding-pocket, dry pocket enhanced the selectivity.

In silico identification of natural product inhibitors against Octamer-binding transcription factor 4 (Oct4) to impede the mechanism of glioma stem cells.

Octamer-binding transcription factor 4 (Oct4) is a core regulator in the retention of stemness, invasive, and self-renewal properties in glioma initiating cells (GSCs) and its overexpression inhibits the differentiation of glioma cells promoting tumor cell proliferation. The Pit-Oct-Unc (POU) domain comprising POU-specific domain (POUS) and POU-type homeodomain (POUHD) subdomains is the most critical part of the Oct4 for the generation of induced pluripotent stem cells from somatic cells that lead to tumor initiation, invasion, posttreatment relapse, and therapeutic resistance. Therefore, the present investigation hunts for natural product inhibitors (NPIs) against the POUHD domain of Oct4 by employing receptor-based virtual screening (RBVS) followed by binding free energy calculation and molecular dynamics simulation (MDS). RBVS provided 13 compounds with acceptable ranges of pharmacokinetic properties and good docking scores having key interactions with the POUHD domain. More Specifically, conformational and interaction stability analysis of 13 compounds through MDS unveiled two compounds ZINC02145000 and ZINC32124203 which stabilized the backbone of protein even in the presence of linker and POUS domain. Additionally, ZINC02145000 and ZINC32124203 exhibited stable and strong interactions with key residues W277, R242, and R234 of the POUHD domain even in dynamic conditions. Interestingly, ZINC02145000 and ZINC32124203 established communication not only with the POUHD domain but also with the POUS domain indicating their incredible potency toward thwarting the function of Oct4. ZINC02145000 and ZINC32124203 also reduced the flexibility and escalated the correlations between the amino acid residues of Oct4 evidenced by PCA and DCCM analysis. Finally, our examination proposed two NPIs that can impede the Oct4 function and may help to improve overall survival, diminish tumor relapse, and achieve a cure not only in deadly disease GBM but also in other cancers with minimal side effects.

Structure-based virtual screening and molecular dynamics simulation for the identification of sphingosine kinase-2 inhibitors as potential analgesics

Neuropathic pain is due to an injury or disease of the somatosensory nervous system, which accounts for a significant economical and health burden to society. Due to poor understanding of their underlying mechanisms, the available treatments merely provide symptomatic relief and precipitates a variety of adverse effects. This suggests that there is an unmet medical need that must be addressed with effective strategies for the development of novel therapeutics. Sphingosine kinase 2 (SphK2) is an oncogenic lipid kinase that has emerged as a promising target for chronic pain and other diseases. In the present study, we have explored the structure-based virtual high-throughput screening of the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE) to identify potent natural products as inhibitors of SphK2. A molecular docking study was performed to calculate binding affinities and specificity to identify potential leads against SphK2. Initially, hits were selected by the implementation of absorption, distribution, metabolism, excretion and toxicity properties, Lipinski rule, and PAINS filters. The top-scoring hits also exhibiting an optimal ADMET profile were subjected to MM/GBSA free binding free energy calculation and molecular dynamics simulation. The results from molecular dynamics simulation revealed a stable ligand -SphK2 complex with protein and ligand RMSD within reasonable limits. Overall, we identified compounds, NuBBE_972 and NuBBE_1107 as potential inhibitors of SphK2 with optimal pharmacokinetic properties which have the potential to be developed as novel therapeutics for the management of chronic pain. Communicated by Ramaswamy H. Sarma

Exploring the Binding Mechanism of PF-07321332 SARS-CoV-2 Protease Inhibitor through Molecular Dynamics and Binding Free Energy Simulations.

The novel coronavirus disease, caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), rapidly spreading around the world, poses a major threat to the global public health. Herein, we demonstrated the binding mechanism of PF-07321332, α-ketoamide, lopinavir, and ritonavir to the coronavirus 3-chymotrypsin-like-protease (3CLpro) by means of docking and molecular dynamic (MD) simulations. The analysis of MD trajectories of 3CLpro with PF-07321332, α-ketoamide, lopinavir, and ritonavir revealed that 3CLpro–PF-07321332 and 3CLpro–α-ketoamide complexes remained stable compared with 3CLpro–ritonavir and 3CLpro–lopinavir. Investigating the dynamic behavior of ligand–protein interaction, ligands PF-07321332 and α-ketoamide showed stronger bonding via making interactions with catalytic dyad residues His41–Cys145 of 3CLpro. Lopinavir and ritonavir were unable to disrupt the catalytic dyad, as illustrated by increased bond length during the MD simulation. To decipher the ligand binding mode and affinity, ligand interactions with SARS-CoV-2 proteases and binding energy were calculated. The binding energy of the bespoke antiviral PF-07321332 clinical candidate was two times higher than that of α-ketoamide and three times than that of lopinavir and ritonavir. Our study elucidated in detail the binding mechanism of the potent PF-07321332 to 3CLpro along with the low potency of lopinavir and ritonavir due to weak binding affinity demonstrated by the binding energy data. This study will be helpful for the development and optimization of more specific compounds to combat coronavirus disease.

Controlling Protein Crystallization by Free Energy Guided Design of Interactions at Crystal Contacts

Protein crystallization can function as an effective method for protein purification or formulation. Such an application requires a comprehensive understanding of the intermolecular protein–protein interactions that drive and stabilize protein crystal formation to ensure a reproducible process. Using alcohol dehydrogenase from Lactobacillus brevis (LbADH) as a model system, we probed in our combined experimental and computational study the effect of residue substitutions at the protein crystal contacts on the crystallizability and the contact stability. Increased or decreased contact stability was calculated using molecular dynamics (MD) free energy simulations and showed excellent qualitative correlation with experimentally determined increased or decreased crystallizability. The MD simulations allowed us to trace back the changes to their physical origins at the atomic level. Engineered charge–charge interactions as well as engineered hydrophobic effects could be characterized and were found to improve crystallizability. For example, the simulations revealed a redesigning of a water mediated electrostatic interaction (“wet contact”) into a water depleted hydrophobic effect (“dry contact”) and the optimization of a weak hydrogen bonding contact towards a strong one. These findings explained the experimentally found improved crystallizability. Our study emphasizes that it is difficult to derive simple rules for engineering crystallizability but that free energy simulations could be a very useful tool for understanding the contribution of crystal contacts for stability and furthermore could help guide protein engineering strategies to enhance crystallization for technical purposes.

Molecular Dynamics Free Energy Simulations Reveal the Mechanism for the Antiviral Resistance of the M66I HIV-1 Capsid Mutation.

While drug resistance mutations can often be attributed to the loss of direct or solvent-mediated protein−ligand interactions in the drug-mutant complex, in this study we show that a resistance mutation for the picomolar HIV-1 capsid (CA)-targeting antiviral (GS-6207) is mainly due to the free energy cost of the drug-induced protein side chain reorganization in the mutant protein. Among several mutations, M66I causes the most suppression of the GS-6207 antiviral activity (up to ~84,000-fold), and only 83- and 68-fold reductions for PF74 and ZW-1261, respectively. To understand the molecular basis of this drug resistance, we conducted molecular dynamics free energy simulations to study the structures, energetics, and conformational free energy landscapes involved in the inhibitors binding at the interface of two CA monomers. To minimize the protein−ligand steric clash, the I66 side chain in the M66I−GS-6207 complex switches to a higher free energy conformation from the one adopted in the apo M66I. In contrast, the binding of GS-6207 to the wild-type CA does not lead to any significant M66 conformational change. Based on an analysis that decomposes the absolute binding free energy into contributions from two receptor conformational states, it appears that it is the free energy cost of side chain reorganization rather than the reduced protein−ligand interaction that is largely responsible for the drug resistance against GS-6207.

Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics.

We demonstrate that physics-based calculations of intrinsic aqueous solubility can rival cheminformatics-based machine learning predictions. A proof-of-concept was developed for a physics-based approach via a sublimation thermodynamic cycle, building upon previous work that relied upon several thermodynamic approximations, notably the 2RT approximation, and limited conformational sampling. Here, we apply improvements to our sublimation free-energy model with the use of crystal phonon mode calculations to capture the contributions of the vibrational modes of the crystal. Including these improvements with lattice energies computed using the model-potential-based Ψmol method leads to accurate estimates of sublimation free energy. Combining these with hydration free energies obtained from either molecular dynamics free-energy perturbation simulations or density functional theory calculations, solubilities comparable to both experiment and informatics predictions are obtained. The application to coronene, succinic acid, and the pharmaceutical desloratadine shows how the methods must be adapted for the adoption of different conformations in different phases. The approach has the flexibility to extend to applications that cannot be covered by informatics methods.

Designing self-assembling kinetics with differentiable statistical physics models

The inverse problem of designing component interactions to target emergent structure is fundamental to numerous applications in biotechnology, materials science, and statistical physics. Equally important is the inverse problem of designing emergent kinetics, but this has received considerably less attention. Using recent advances in automatic differentiation, we show how kinetic pathways can be precisely designed by directly differentiating through statistical physics models, namely free energy calculations and molecular dynamics simulations. We consider two systems that are crucial to our understanding of structural self-assembly: bulk crystallization and small nanoclusters. In each case, we are able to assemble precise dynamical features. Using gradient information, we manipulate interactions among constituent particles to tune the rate at which these systems yield specific structures of interest. Moreover, we use this approach to learn nontrivial features about the high-dimensional design space, allowing us to accurately predict when multiple kinetic features can be simultaneously and independently controlled. These results provide a concrete and generalizable foundation for studying nonstructural self-assembly, including kinetic properties as well as other complex emergent properties, in a vast array of systems.

Aminoglycosides as potential inhibitors of SARS-CoV-2 main protease: an in silico drug repurposing study on FDA-approved antiviral and anti-infection agents

BackgroundThe emergence and spread of SARS-CoV-2 throughout the world has created an enormous socioeconomic impact. Although there are several promising drug candidates in clinical trials, none is available clinically. Thus, the drug repurposing approach may help to overcome the current pandemic. MethodsThe main protease (Mpro) of SARS-CoV-2 is crucial for cleaving nascent polypeptide chains. Here, FDA-approved antiviral and anti-infection drugs were screened by high-throughput virtual screening (HTVS) followed by re-docking with standard-precision (SP) and extra-precision (XP) molecular docking. The most potent drug's binding was further validated by free energy calculations (Prime/MM-GBSA) and molecular dynamics (MD) simulation. ResultsOut of 1397 potential drugs, 157 showed considerable affinity toward Mpro. After HTVS, SP, and XP molecular docking, four high-affinity lead drugs (Iodixanol, Amikacin, Troxerutin, and Rutin) with docking energies −10.629 to −11.776kcal/mol range were identified. Among them, Amikacin exhibited the lowest Prime/MM-GBSA energy (−73.800kcal/mol). It led us to evaluate other aminoglycosides (Neomycin, Paramomycin, Gentamycin, Streptomycin, and Tobramycin) against Mpro. All aminoglycosides were bound to the substrate-binding site of Mpro and interacted with crucial residues. Altogether, Amikacin was found to be the most potent inhibitor of Mpro. MD simulations of the Amikacin-Mpro complex suggested the formation of a complex stabilized by hydrogen bonds, salt bridges, and van der Waals interactions. ConclusionAminoglycosides may serve as a scaffold to design potent drug molecules against COVID-19. However, further validation by in vitro and in vivo studies is required before using aminoglycosides as an anti-COVID-19 agent.

Atomic Scale Interactions between RNA and DNA Aptamers with the TNF-α Protein.

Interest in the design and manufacture of RNA and DNA aptamers as apta-biosensors for the early diagnosis of blood infections and other inflammatory conditions has increased considerably in recent years. The practical utility of these aptamers depends on the detailed knowledge about the putative interactions with their target proteins. Therefore, understanding the aptamer-protein interactions at the atomic scale can offer significant insights into the optimal apta-biosensor design. In this study, we consider one RNA and one DNA aptamer that were previously used as apta-biosensors for detecting the infection biomarker protein TNF-α, as an example of a novel computational workflow for selecting the aptamer candidate with the highest binding strength to a target. We combine information from the binding free energy calculations, molecular docking, and molecular dynamics simulations to investigate the interactions of both aptamers with TNF-α. The results reveal that the RNA aptamer has a more stable structure relative to the DNA aptamer. Interaction of aptamers with TNF-α does not have any negative effect on its structure. The results of molecular docking and molecular dynamics simulations suggest that the RNA aptamer has a stronger interaction with the protein. Also, these findings illustrate that basic residues of TNF-α establish more atomic contacts with the aptamers compared to acidic or pH-neutral ones. Furthermore, binding energy calculations show that the interaction of the RNA aptamer with TNF-α is thermodynamically more favorable. In total, the findings of this study indicate that the RNA aptamer is a more suitable candidate for using as an apta-biosensor of TNF-α and, therefore, of greater potential use for the diagnosis of blood infections. Also, this study provides more information about aptamer-protein interactions and increases our understanding of this phenomenon.

Targeting Telomeres: Molecular Dynamics and Free Energy Simulation of Gold-Carbene Binding to DNA

DNA sequences in regulatory regions and in telomers at the ends of chromosomes frequently contain tandem repeats of guanine nucleotides that can form stacked structures stabilized by Hoogsten pairing and centrally bound monovalent cations. The replication and elongation of telomeres requires the disruption of these G-quadruplex structures. Hence, drug molecules such as gold (Au)-carbene that stabilize G-quadruplexes may also interfere with the elongation of telomeres and, in turn, could be used to control cell replication and growth. To better understand the molecular mechanism of Au-carbene binding to G-quadruplexes, we employed molecular dynamics simulations and free energy simulations. Whereas very restricted mobility of two Au-carbene ligands was found upon binding as a doublet to one side of the G-quadruplex, much larger translational and orientational mobility was observed for a single Au-carbene binding at the second G-quadruplex surface. Comparative simulations on duplex DNA in the presence of Au-carbene ligands indicates a preference for the minor groove and weaker unspecific and more salt-dependent binding than to the G-quadruplex surface. Analysis of energetic contributions reveals a dominance of nonpolar and van der Waals interactions to drive binding. The simulations can also be helpful for proposing possible modifications that could improve Au-carbene affinity and specificity for G-quadruplex binding.

Abstract A98: Molecular dynamics study of pH-dependent interactions between immune checkpoint receptor PD-1 and PD-L1

Abstract Understanding of how small variations in checkpoint protein chemistry (e.g., mutations) and tumor immune microenvironment (e.g., pH) influence receptor-ligand binding and define both the mechanisms and magnitude of immune response is of great importance, but the molecular details are still missing. Here, we employ enhanced free-energy sampling molecular dynamics simulations to unveil pH-dependent binding/unbinding mechanisms and kinetics of PD-1/PD-L1 interactions. By identifying the binding pocket in the PD-1/PD-L1 complex, we show a key role of the His68 protonation state in PD-1 in the complex stabilization at low pH. Specifically, we estimate the unbinding free-energy barrier to be around 15 kJ/mol higher at pH 5.5 than at pH 7.4. The estimated ratio of binding (kon) and unbinding (koff) rate constants is found to agree well with recent experimental data on KD values at different pH. Also, simulations show an important role of solvation state of the binding pocket in mediating polar interactions at the PD-1/PD-L1 binding interface. These initial molecular insights are important for further engineering of binding/unbinding kinetics to formulate more efficient immune checkpoint blockade strategies. Citation Format: Vitaly Alexandrov, Konstantin Klyukin. Molecular dynamics study of pH-dependent interactions between immune checkpoint receptor PD-1 and PD-L1 [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr A98.

Molecular dynamics free energy simulations of ATP:Mg2+ and ADP:Mg2+ using the polarisable force field AMOEBA

ABSTRACT ATPases and GTPases are two important classes of protein that play critical roles in energy transduction, cellular signalling, gene regulation and catalysis. These proteins use cofactors such as nucleoside di and tri-phosphates (NTP, NDP) and can detect the difference between NDP and NTP which then induce different protein conformations. Mechanisms that drive proteins into the NTP or NDP conformation may depend on factors such as ligand structure and how Mg2+ coordinates with the ligand, amino acids in the pocket and water molecules. Here, we have used the advanced electrostatic and polarisable force field AMOEBA and molecular dynamics free energy simulations (MDFE) to examine the various binding mechanisms of ATP:Mg2+ and ADP:Mg2+. We compared the ATP:Mg2+ binding with previous studies using non-polarisable force fields and experimental data on the binding affinity. It was found that the total free energy of binding for ATP:Mg2+ (−7.00 2.13 kcal/mol) is in good agreement with experimental values (−8.6 .2 kcal/mol) [1]. In addition, parameters for relevant protonation states of ATP, ADP, GTP and GDP have been derived. These parameters will allow for researchers to investigate biochemical phenomena involving NTP’s and NDP’s with greater accuracy than previous studies involving non-polarisable force fields.

Electron transfer and spin dynamics of the radical-pair in the cryptochrome from Chlamydomonas reinhardtii by computational analysis

In an effort to elucidate the origin of avian magnetoreception, it was postulated that a radical-pair formed in a cryptochrome upon light activation provided the basis for the mechanism that enables an inclination compass sensitive to the geomagnetic field. Photoreduction in this case involves formation of a flavin adenine dinucleotide (FAD)-tryptophan (TRP) radical-pair, following electron transfer within a conserved TRP triad in the cryptochrome. Recently, an animal-like cryptochrome from Chlamydomonas reinhardtii (CraCRY) was analyzed, demonstrating the role of a fourth aromatic residue, which serves as a terminal electron donor in the photoreduction pathway, resulting in the creation of a more distal radical-pair and exhibiting fast electron transfer. In this work, we investigated the electron transfer in CraCRY with a combination of free energy molecular dynamics (MD) simulations, frozen density functional theory, and QM/MM MD simulations, supporting the suggestion of a proton coupled electron transfer mechanism. Spin dynamics simulations discerned details on the dependence of the singlet yield on the direction of the external magnetic field for the [FAD•- TYRH•+] and [FAD•- TYR•] radical-pairs in CraCRY, in comparison with the previously modeled [FAD•- TRPH•+] radical-pair.

Determinants of ion selectivity in ASIC1a- and ASIC2a-containing acid-sensing ion channels

Trimeric acid-sensing ion channels (ASICs) contribute to neuronal signaling by converting extracellular acidification into excitatory sodium currents. Previous work with homomeric ASIC1a implicates conserved leucine (L7') and consecutive glycine-alanine-serine (GAS belt) residues near the middle, and conserved negatively charged (E18') residues at the bottom of the pore in ion permeation and/or selectivity. However, a conserved mechanism of ion selectivity throughout the ASIC family has not been established. We therefore explored the molecular determinants of ion selectivity in heteromeric ASIC1a/ASIC2a and homomeric ASIC2a channels using site-directed mutagenesis, electrophysiology, and molecular dynamics free energy simulations. Similar to ASIC1a, E18' residues create an energetic preference for sodium ions at the lower end of the pore in ASIC2a-containing channels. However, and in contrast to ASIC1a homomers, ion permeation through ASIC2a-containing channels is not determined by L7' side chains in the upper part of the channel. This may be, in part, due to ASIC2a-specific negatively charged residues (E59 and E62) that lower the energy of ions in the upper pore, thus making the GAS belt more important for selectivity. This is confirmed by experiments showing that the L7'A mutation has no effect in ASIC2a, in contrast to ASIC1a, where it eliminated selectivity. ASIC2a triple mutants eliminating both L7' and upper charges did not lead to large changes in selectivity, suggesting a different role for L7' in ASIC2a compared with ASIC1a channels. In contrast, we observed measurable changes in ion selectivity in ASIC2a-containing channels with GAS belt mutations. Our results suggest that ion conduction and selectivity in the upper part of the ASIC pore may differ between subtypes, whereas the essential role of E18' in ion selectivity is conserved. Furthermore, we demonstrate that heteromeric channels containing mutations in only one of two ASIC subtypes provide a means of functionally testing mutations that render homomeric channels nonfunctional.

Principles of tRNAAla Selection by Alanyl-tRNA Synthetase Based on the Critical G3·U70 Base Pair.

Throughout evolution, the presence of a single G3·U70 mismatch in the acceptor stem of tRNAAla is the major determinant for aminoacylation with alanine by alanyl–tRNA synthetase (AlaRS). Recently reported crystal structures of the complexes AlaRS–tRNAAla/G3·U70 and AlaRS–tRNAAla/A3·U70 suggest two very different conformations, representing a reactive and a nonreactive state, respectively. On the basis of these structures, it has been proposed that the G3·U70 base pair guides the −CCA end of the tRNA acceptor stem into the active site of AlaRS, thereby enabling aminoacylation. The crystal structures open up the possibility of directly computing the energetics of tRNA specificity by AlaRS. We have carried out molecular dynamics free-energy simulations to quantitatively estimate tRNA discrimination by AlaRS, focusing on the mutations of the single critical base pair G3·U70 to uncover the energetics underlying the accuracy of tRNA selection. The calculations show that the reactive complex is highly selective in favor of the cognate tRNAAla/G3·U70 over its noncognate analogues (A3·U70/G3·C70/A3·C70). In contrast, the nonreactive complex is predicted to be unselective between tRNAAla/G3·U70 and tRNAAla/A3·U70. Utilizing our calculated relative binding free energies, we show how a simple three-step kinetic scheme for aminoacylation, involving both an initial nonspecific binding step and a subsequent transition to a selective reactive complex, accounts for the observed kinetics of the process.