CHARMM: the biomolecular simulation program

Multisite λ-Dynamics for Protein-DNA Binding Affinity Prediction

Transcription factors (TFs) regulate gene expression by binding to specific DNA sequences, playing critical roles in cellular processes and disease pathways. Computational methods, particularly λ-Dynamics, offer a promising approach for predicting TF relative binding affinities. This study evaluates the effectiveness of different λ-Dynamics perturbation schemes in determining binding free energy changes (ΔΔGb) of the WRKY transcription factor upon mutating its W-box binding site (GGTCAA) to a nonspecific sequence (GATAAA). Among the schemes tested, the single λ per base pair protocol demonstrated the fastest convergence and highest precision. Extending this protocol to additional mutants (GGTCCG and GGACAA) yielded ΔΔGb values that successfully ranked binding affinities, showcasing its strong potential for high-throughput screening of DNA binding sites.

Load-based divergence in the dynamic allostery of two TCRs recognizing the same pMHC

Increasing evidence suggests that mechanical load on the αβ T-cell receptor (TCR) is crucial for recognizing the antigenic peptide-bound major histocompatibility complex (pMHC) molecule. Our recent all-atom molecular dynamics (MD) simulations revealed that the inter-domain motion of the TCR is responsible for the load-induced catch bond behavior of the TCR-pMHC complex and peptide discrimination (Chang-Gonzalez et al., 2024). To further examine the generality of the mechanism, we perform all-atom MD simulations of the B7 TCR under different conditions for comparison with our previous simulations of the A6 TCR. The two TCRs recognize the same pMHC and have similar interfaces with pMHC in crystal structures. We find that the B7 TCR-pMHC interface stabilizes under ∼15 pN load using a conserved dynamic allostery mechanism that involves the asymmetric motion of the TCR chassis. However, despite forming comparable contacts with pMHC as A6 in the crystal structure, B7 has fewer high-occupancy contacts with pMHC and exhibits higher mechanical compliance during the simulation. These results indicate that the dynamic allostery common to the TCRαβ chassis can amplify slight differences in interfacial contacts into distinctive mechanical responses and nuanced biological outcomes.

Adsorption of Per- and Polyfluoroalkyl Substances by Edible Nutraceutical-Amended Montmorillonite Clays: In Vitro, In Vivo and In Silico Enterosorption Strategies

Exposure of animals and humans to PFAS through contaminated water and foods pose significant threats to public health. To tackle this challenge, this study aimed to develop edible clays that might enhance the binding, detoxification, and elimination of PFAS in the gastrointestinal tract. Montmorillonite clays (CM) were amended with caffeine (CMCAF), curcumin (CMCUR), and riboflavin (CMRIB), and the binding efficacy for a mixture of four PFAS (PFOS, GenX, PFOA and PFBS) was determined. In vitro studies were used to explore adsorption isotherms while computational simulations investigate PFAS mixture, delineate the contribution of each PFAS molecule to clays and determine if amended clays can contribute to enhanced binding of different PFAS in the mixture. In vivo models (Lemna minor and Hydra vulgaris) were used to validate in vitro and in silico studies and establish the safety and effectiveness of these amended clays. The resulting Qmax and Kd values along with the curved shape of the Langmuir plot indicated saturable binding of GenX, PFOA and PFOS to active surfaces of CM and the amended clays. All three clays demonstrated a slightly higher binding capacity for GenX than the parent clay. Furthermore, the simulations elucidated the binding contribution of each PFAS molecule to parent and amended clays as well as predicting how amended clays can contribute to mechanisms of binding of different PFAS in the mixture. The proof-of-concept for the efficacy of the clays was established in Caenorhabditis elegans, Lemna minor and Hydra vulgaris, where the clays (at 1% w/v inclusion) protected against toxicities of the four PFAS controls. This protection could be attributed to PFAS binding to the amended clays and the biological activities of these nutraceuticals (caffeine, riboflavin, and curcumin) including antioxidative, anti-inflammatory and modulatory activities which mitigate the oxidative stress and inflammatory effects of PFAS. These edible toxin binders may be delivered in mixtures as additives in flavored drinking water and food to decrease PFAS exposure.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11270-025-07930-2.

Multiscale Simulations and Profiling of Human Thymidine Phosphorylase Mutations: Insights into Structural, Dynamics, and Functional Impacts in Mitochondrial Neurogastrointestinal Encephalopathy

Mitochondrial neurogastrointestinal encephalopathy (MNGIE) is a rare metabolic disorder caused by missense mutations in the TYMP gene, leading to the loss of human thymidine phosphorylase (HTP) activity and subsequent mitochondrial dysfunction. Despite its well-characterized biochemical basis, the molecular mechanisms by which MNGIE-associated mutations alter HTP's structural stability, dynamics, and substrate (thymidine) binding remain unclear. In this study, we employ a multiscale computational approach, integrating AlphaFold2-based structural modeling, all-atom and coarse-grained molecular dynamics (MD) simulations, protein-ligand (HTP-thymidine) docking, HTP-thymidine complex simulations, binding free-energy landscape analysis, and systematic mutational profiling to investigate the impact of key MNGIE-associated mutations (R44Q, G145R, G153S, K222S, and E289A) on HTP function. Analyses of our long-duration multiscale simulations (comprising 9 μs coarse-grained, 1.2 μs all-atom apo HTP, and 1.2 μs HTP-thymidine complex MD simulations) and physicochemical properties reveal that while wild-type HTP maintains structural integrity and strong thymidine-binding affinity, MNGIE-associated mutations induce substantial destabilization, increased flexibility, and reduced enzymatic efficiency. Free-energy landscape analysis highlights a shift toward less stable conformational states in mutant HTPs, further supporting their functional impairment. Additionally, the G145R mutation introduces steric hindrance at the active site, preventing thymidine binding and causing off-site interactions. These findings not only provide fundamental insights into the physicochemical and dynamic alterations underlying HTP dysfunction in MNGIE but also establish a computational framework for guiding future experimental studies and the rational design of therapeutic interventions aimed at restoring HTP function.

Screening and Design of Aqueous Zinc Battery Electrolytes Based on the Multimodal Optimization of Molecular Simulation

Aqueous batteries, such as aqueous zinc-ion batteries (AZIB), have garnered significant attention because of their advantages in intrinsic safety, low cost, and eco-friendliness. However, aqueous electrolytes tend to freeze at low temperatures, which limits their potential industrial applications. Thus, one of the core challenges in aqueous electrolyte design is optimizing the formula to prevent freezing while maintaining good ion conductivity. However, the experimental trial-and-error approach is inefficient for this purpose, and existing simulation tools are either inaccurate or too expensive for high-throughput phase transition predictions. In this work, we employ a small amount of experimental data and differentiable simulation techniques to develop a multimodal optimization workflow. With minimal human intervention, this workflow significantly enhances the prediction power of classical force fields for electrical conductivity. Most importantly, the simulated electrical conductivity can serve as an effective predictor of electrolyte freezing at low temperatures. Generally, the workflow developed in this work introduces a new paradigm for electrolyte design. This paradigm leverages both easily measurable experimental data and fast simulation techniques to predict properties that are challenging to access by using either approach alone.

QUANTIFYING ACYL CHAIN INTERDIGITATION IN SIMULATED BILAYERS VIA DIRECT TRANSBILAYER INTERACTIONS

In a lipid bilayer, the interactions between lipid hydrocarbon chains from opposing leaflets can influence membrane properties. These interactions include the phenomenon of interdigitation, in which an acyl chain of one leaflet extends past the bilayer midplane and into the opposing leaflet. While static interdigitation is well understood in gel phase bilayers from X-ray diffraction measurements, much less is known about dynamic interdigitation in fluid phases. In this regard, atomistic molecular dynamics simulations can provide mechanistic information on interleaflet interactions that can be used to generate experimentally testable hypotheses. To address limitations of existing computational methodologies which provide results that are either indirect or averaged over time and space, here we introduce three novel ways of quantifying the extent of chain interdigitation. Our protocols include the analysis of instantaneous interactions at the level of individual carbon atoms, thus providing temporal and spatial resolution for a more nuanced picture of dynamic interdigitation. We compare the methods on bilayers composed of lipids with equal total number of carbon atoms but different mismatches between the sn-1 and sn-2 chain lengths. We find that these metrics, which are based on freely available software packages and are easy to implement, provide complementary details that help characterize various features of lipid-lipid contacts at the bilayer midplane. The new frameworks thus allow for a deeper look at fundamental molecular mechanisms underlying bilayer structure and dynamics, and present a valuable expansion of the membrane biophysics toolkit.

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

Bacteriorhodopsin proton-pumping mechanism: Successes and challenges in computational approaches

Bacteriorhodopsin (bR) is perhaps the best-studied proton pump. Over about four decades, research on this fascinating photocyclic light-driven protein inspired the development of key experimental and computational methodologies that are now widely used in membrane protein studies. We review here failures and successes in computational approaches that have been applied to study the bR proton-transfer steps. Conflict between experimental results pertaining to the proton transfer mechanisms in the early photocycle intermediates was resolved by detailed quantum mechanical/molecular mechanical computation, the results of which were confirmed more than a decade later. Key to this approach was the realization that, to understand how the pump works and achieves directional transfer of protons, the individual reaction steps-proton transfer and reorganization of the internal hydrogen-bond network-needed to be considered within the context of the energy landscape of the complete reaction cycle.

Design of CoQ10 crops based on evolutionary history

Coenzyme Q (CoQ) is essential for energy production by mitochondrial respiration, and it is a supplement most often used to promote cardiovascular health. Humans make CoQ10, but cereals and some vegetable/fruit crops synthesize CoQ9 with a side chain of nine isoprene units. Engineering CoQ10 production in crops would benefit human health, but this is hindered by the fact that the specific residues of the enzyme Coq1 that control chain length are unknown. Based on an extensive investigation of the distribution of CoQ9 and CoQ10 in land plants and the associated Coq1 sequence variation, we identified key amino acid changes at the base of the Coq1 catalytic pocket that occurred independently in multiple angiosperm lineages and repeatedly drove CoQ9 formation. Guided by this knowledge, we used gene editing to modify the native Coq1 genes of rice and wheat to produce CoQ10, paving the way for developing additional dietary sources of CoQ10.

Redox regulation and dynamic control of brain-selective kinases BRSK1/2 in the AMPK family through cysteine-based mechanisms

In eukaryotes, protein kinase signaling is regulated by a diverse array of post-translational modifications, including phosphorylation of Ser/Thr residues and oxidation of cysteine (Cys) residues. While regulation by activation segment phosphorylation of Ser/Thr residues is well understood, relatively little is known about how oxidation of cysteine residues modulate catalysis. In this study, we investigate redox regulation of the AMPK-related brain-selective kinases (BRSK) 1 and 2, and detail how broad catalytic activity is directly regulated through reversible oxidation and reduction of evolutionarily conserved Cys residues within the catalytic domain. We show that redox-dependent control of BRSKs is a dynamic and multilayered process involving oxidative modifications of several Cys residues, including the formation of intramolecular disulfide bonds involving a pair of Cys residues near the catalytic HRD motif and a highly conserved T-loop Cys with a BRSK-specific Cys within an unusual CPE motif at the end of the activation segment. Consistently, mutation of the CPE-Cys increases catalytic activity in vitro and drives phosphorylation of the BRSK substrate Tau in cells. Molecular modeling and molecular dynamics simulations indicate that oxidation of the CPE-Cys destabilizes a conserved salt bridge network critical for allosteric activation. The occurrence of spatially proximal Cys amino acids in diverse Ser/Thr protein kinase families suggests that disulfide-mediated control of catalytic activity may be a prevalent mechanism for regulation within the broader AMPK family.

Assessing the interaction between the N-terminal region of the membrane protein magnesium transporter A and a lipid bilayer

This study investigates the interaction of KEIF, the intrinsically disordered N-terminal region of the magnesium transporter MgtA, with lipid bilayers mimicking cell membranes. Combining experimental techniques such as neutron reflectometry (NR), quartz-crystal microbalance with dissipation monitoring (QCM-D), synchrotron radiation circular dichroism (SRCD), and oriented circular dichroism (OCD), with molecular dynamics (MD) simulations, we characterized KEIF's adsorption behavior.

HYPOTHESIS

KEIF undergoes conformational changes upon interacting with lipid bilayers, potentially influencing MgtA's function within the plasma membrane.

EXPERIMENTS

The study assessed KEIF's structural transitions and position within lipid bilayers under various conditions, including zwitterionic versus anionic bilayers and different salt concentrations. The techniques analyzed adsorption-induced structural shifts and peptide localization within the bilayer.

FINDINGS

KEIF transitions from a disordered to a more structured state, notably increasing α-helical content upon adsorption to lipid bilayers. The peptide resides primarily in the hydrophobic tail region of the bilayer, where it may displace lipids. Electrostatic interactions, modulated by bilayer charge and ionic strength, play a critical role. These results suggest that KEIF's conformational changes and bilayer interactions can be integral to its potential modulatory role in MgtA function within the plasma membrane. This research highlights the importance of surface-induced structural transitions in intrinsically disordered proteins and their implications for membrane protein modulation.

Anti-breast cancer potential of thieno-pyrimidine derivatives as VEGFR-2 inhibitors

BACKGROUND

Thieno-pyrimidine derivatives have emerged as promising candidates for VEGFR-2 inhibition. This study aimed to design, synthesize, and evaluate novel thieno-pyrimidine derivatives for their anti-cancer potential.

METHODS

A series of thieno-pyrimidine compounds were synthesized and screened for in vitro cytotoxicity against MDA-231 and MCF-7 cell lines. The most active compound, 6b, was further analyzed for VEGFR-2 kinase inhibition, wound healing, apoptosis induction, and cell cycle arrest. Molecular docking, 200 ns molecular dynamics simulations, MM-GBSA, ProLIF PCAT, and FEL analyses were conducted to assess binding stability. DFT calculations evaluated electronic properties, while in silico ADMET profiling predicted pharmacokinetics and toxicity.

RESULTS

Compound 6b exhibited potent cytotoxicity with IC50 values of 5.91 µM (MDA-231) and 7.16 µM (MCF-7). It demonstrated VEGFR-2 inhibition is comparable to sorafenib (IC50: 53.63 ± 3.14 nM). Wound healing assays showed significant inhibition of MDA-231 migration. Flow cytometry confirmed apoptosis induction (57.20% early apoptosis) and G1 phase arrest. Gene expression analysis revealed upregulation of pro-apoptotic markers and downregulation of Bcl-2. Computational studies confirmed stable VEGFR-2 binding, and ADMET predictions indicated a favorable safety profile.

CONCLUSION

Compound 6b exhibits strong VEGFR-2 inhibition, potent anti-cancer effects, and a favorable toxicity profile, highlighting its potential for further therapeutic development.

Saber F, K. Arafa R, Schiano Moriello A, A. Rasle R, Soria‐Lopez A, G. Abd EL‐Gawwad S, Rocchetti G, Otero P, Kulinowski Ł, Skalicka‐Woźniak K, Lucini L, Simal‐Gandara J. The Phenolic Signature of Psidium cattleianum Fruits and Leaves Modulates TRPV1 and TRPA1 Transient Receptor Potential Channels: A Metabolomics, In Vitro, and In Silico Study

Although Psidium cattleianum (strawberry guava, Myrtaceae) is known for its anti-inflammatory, antioxidant, antimicrobial, and antidiabetic properties, its phytochemical profile and associated bioactivities remain largely underexplored. This study employed UHPLC-QTOF-HRMS for untargeted phenolic profiling of leaf and fruit extracts from P. cattleianum, followed by semi-quantification of phenolic subclasses and multivariate data analysis. Four hundred sixty-nine metabolites, including various phenolic subclasses-predominantly flavonoids and phenolic acids were- identified and annotated. Using HEK-293 cells stably transfected with TRPA1 or TRPV1 cation channels, it was found that both leaf and fruit extracts activate and rapidly desensitize TRPA1 in a concentration-dependent manner (EC50 18 and 30 μg/mL; IC50 60 and 47 μg/mL, respectively). Additionally, molecular docking analysis provided deeper insights into the interactions between P. cattleianum phytochemicals and the TRPA1 cation channel, identifying theaflavin 3,3'-O-digallate as the phenolic compound with the highest affinity (S score of -9.27 Kcal/mol). Interestingly, except for theaflavin 3,3'-O-digallate, compounds enriched in the leaf extract exhibited weaker binding interactions and lower S scores (approximately -7 Kcal/mol) compared to those enriched in the fruit extract. Also, a 100 ns molecular dynamics study of theaflavin 3,3'-O-digallate with TRAP1 demonstrated high binding stability of the complex. Overall, this study offers valuable insights into the phytochemical characteristics of P. cattleianum extracts and reveals their mechanism of action through affinity for the TRPA1 cation channel-receptors.

The secondary metabolites of the alga-derived fungus Aspergillus niveoglaucus КММ 4176 and their antimicrobial and antibiofilm activities

Marine alga-derived fungal strain КММ 4176 was identified as Aspergillus niveoglaucus based on ITS region BenA, CaM and RPB2 gene sequence analysis. The anthraquinone derivatives emodin anthrone (1) and 4-hydroxyemodin anthrone (2), chromone derivative aloesone (3), and indole diketopiperazine alkaloid neoechinulin B (4) were isolated from the ethyl acetate extract of this fungus. In addition, UPLC MS data analysis of the KMM 4176 extract showed the presence of 17 echinulin-family alkaloids, as well as their biogenetic precursor cyclo(L-alanyl-L-tryptophyl) and a number of polyketide compounds. Emodin anthrone and 4-hydroxyemodin anthrone were found as inhibitors of biofilm formation by Staphylococcus aureus with half-maximal inhibitory concentrations (IC50) of 5.5 µM and 23.7 µM, respectively. Moreover, emodin anthrone (1) and 4-hydroxyemodin anthrone (2) inhibited staphylococcal sortase A activity with IC50 of 9.2 µM and 37.6 µM, respectively. Aloesone (3) also inhibited S. aureus biofilm formation but was less active. The first data on neoechinulin B (4) antibiofilm activity and sortase A inhibition were obtained. The positive effects of the isolated compounds on the growth of HaCaT keratinocytes infected with S. aureus were also observed.

Coarse-Grained Simulations of Phosphorylation Regulation of p53 Autoinhibition

Intrinsically disordered proteins (IDPs) are key components of cellular signaling and regulatory networks. They frequently remain dynamic even in complexes and thus rely on potentially subtle shifts in the disordered conformational ensemble for function. Understanding the molecular basis of these fascinating mechanisms of IDP function and regulation requires a detailed characterization of dynamic ensembles in various biologically relevant states. Here, we study the phosphorylation dependence of the dynamic interaction between the N-terminal transactivation domain (NTAD) and DNA-binding domain (DBD) of tumor suppressor p53, which plays a key role in the autoinhibition and regulation of p53 activation or termination during various stages of stress response. By extending the hybrid-resolution (HyRes) coarse-grained (CG) protein force field to model phosphorylated side chains, we show that HyRes simulations accurately recapitulate the effects of phosphorylation on the p53 NTAD/DBD interactions. The simulated ensembles show that phosphorylation of Thr55 as well as Ser46 enhances dynamic NTAD/DBD interactions and further induces conformational shifts that promote trans interactions between two p53 dimers to drive dissociation from DNA. These CG simulations thus provide a strong molecular basis in support of previous experimental studies suggesting the central role of dynamic interactions of disordered domains and phosphorylation in the function of p53. The success of this study also suggests that HyRes provides an efficient and viable tool for studying dynamic interactions and post-translational modifications in IDP function and regulation.

The Six Ds of Exponentials and drug discovery: A path toward reversing Eroom's law

Many technological sectors underwent recent exponential growth because of digital disruption, a phenomenon Peter Diamantis characterized as the 'Six Ds of Exponentials': digitization, deception, disruption, demonetization, dematerialization, and democratization. In contrast, drug discovery has been marked by rising costs and modest growth, if any, of annual drug approvals. We argue that the exponential growth of drug discovery can be also achieved through digital disruption brought by data expansion, mature artificial intelligence (AI), automation of experiments, public-private partnerships, and open science. We detected the emergence of all 'Six Ds of Exponentials' within modern drug discovery and discuss how each of the 'Six Ds' can further empower the field and forcefully address the societal demand for novel, potent, affordable, and accessible medicines.

Synthesis of New Cationic Dicephalic Surfactants and Their Nonequivalent Adsorption at the Air/Solution Interface

The interfacial behavior of aqueous solutions of newly synthesized 2-alkyl-N,N,N,N',N',N'-hexamethylpropan-1,3-ammonium dibromides with decyl, dodecyl, and tetradecyl alkyl chains was investigated both experimentally and theoretically. The results of the surface tension measurements were described using the modified surface quasi-two-dimensional electrolyte (mSTDE) model of ionic surfactant adsorption, which was supported by molecular dynamics simulations. Our contribution encompasses the design, synthesis, and characterization of a novel class of dicephalic-type cationic surfactants, branched on a methine motif, possessing two symmetric trimethylammonium groups, which constitute a double-head extension of the standard alkyltrimethylammonium salts of the single-head, single-tail structure. The convenient synthetic route and final purification steps allowed for the high-yield, high-purity production of the surfactants. Dicephalic-type surfactants demonstrated lower surface activity and higher critical micelle concentration values when compared with their single head-single tail counterparts. That can be attributed primarily to the presence of strong electrostatic repulsive forces within the bulky, double-charge headgroups and significant counterion condensation. Furthermore, molecular dynamics simulations demonstrated a propensity for the desorption of surfactants from the interface, even in diluted solutions, which constrained the attainable surface concentration and resulted in a lower reduction in surface tension. The mSTDE model of adsorption provided an excellent description of the experimental surface isotherms with a concise set of parameters. The model's predictive power was demonstrated by the studies of the effect of inorganic salts on the surface activity of investigated surfactants. Our unique approach enabled us to gain a theoretical explanation of the newly devised surfactants' behavior at the water/air interface.

An In Silico Approach to Uncover Selective JAK1 Inhibitors for Breast Cancer from Life Chemicals Database

JAK1, a key regulator of multiple oncogenic pathways, is a sought-out target, and its expression in immune cells and tumour-infiltrating lymphocytes (TILs) is associated with a favorable prognosis in breast cancer. JAK1 activates IL-6 via ERBB2 receptor tyrosine kinase signalling and promotes metastatic cancer and STAT3 activation in breast cancer cells. Hence, targeting JAK1 in breast cancer is being explored as a potential therapeutic strategy. A comprehensive in silico approach was utilised in this study to identify selective JAK1 inhibitors from the Life chemicals database. First, we utilised an anticancer focussed library and performed molecular docking to screen against JAK1 protein. The top 10 compounds from docking were taken for cross-docking, to assess the selectivity towards JAK1 target. Lipinski's RO5 was checked for eliminating the compounds that violate rules. Toxicity, biological activity and reactivity for the identified best compounds were predicted by Protox-II server, PASS server and cDFT analysis respectively. MD simulations were carried out to examine the stability and dynamic behaviour of the top leads, including the long-term stability of the ligand-receptor complex and any conformational changes. Lastly, the MM/PBSA method was used to determine the binding free energy of the protein-ligand complex. Our in silico approach has yielded a promising set of compounds F2638-0133, F3408-0020 and F5833-7435 with the potential to selectively target JAK1, a critical player in breast cancer progression. The docking, simulation and MM/PBSA results were compared with standard drug abrocitinib. Identified compounds exhibit favorable binding interactions, electronic properties and robust stability profiles compared to standard drug, making them promising leads for further experimental validation.

Using Short Molecular Dynamics Simulations to Determine the Important Features of Interactions in Antibody-Protein Complexes

The last few years have seen the rapid proliferation of machine learning methods to design binding proteins. Although these methods have shown large increases in experimental success rates compared to prior approaches, the majority of their predictions fail when they are experimentally tested. It is evident that computational methods still struggle to distinguish the features of real protein binding interfaces from false predictions. Short molecular dynamics simulations of 20 antibody-protein complexes were conducted to identify features of interactions that should occur in binding interfaces. Intermolecular salt bridges, hydrogen bonds, and hydrophobic interactions were evaluated for their persistences, energies, and stabilities during the simulations. It was found that only the hydrogen bonds where both residues are stabilized in the bound complex are expected to persist and meaningfully contribute to binding between the proteins. In contrast, stabilization was not a requirement for salt bridges and hydrophobic interactions to persist. Still, interactions where both residues are stabilized in the bound complex persist significantly longer and have significantly stronger energies than other interactions. Two hundred and twenty real antibody-protein complexes and 8194 decoy complexes were used to train and test a random forest classifier using the features of expected persistent interactions identified in this study and the macromolecular features of interaction energy (IE), buried surface area (BSA), IE/BSA, and shape complementarity. It was compared to a classifier trained only on the expected persistent interaction features and another trained only on the macromolecular features. Inclusion of the expected persistent interaction features reduced the false positive rate of the classifier by two- to five-fold across a range of true positive classification rates.

Synthesis, molecular docking and molecular dynamics simulations, drug-likeness studies, ADMET prediction and biological evaluation of novel pyrazole-carboxamides bearing sulfonamide moiety as potent carbonic anhydrase inhibitors

Pyrazoles are unique bioactive molecules with a versatile biological profile and they have gained an important place on pharmaceutical chemistry. Pyrazole compounds containing sulfonamide nuclei also attract attention as carbonic anhydrase (CA) inhibitors. In this study, a library of pyrazole-carboxamides were synthesized and the structures of the synthesized molecules were characterized using FT-IR, 1H-NMR, 13C-NMR and HRMS. Then the inhibition effects of newly synthesized molecules on human erythrocyte hCA I and hCA II isoenzymes were investigated. Ki values of the compounds were in the range of 0.063-3.368 µM for hCA I and 0.007-4.235 µM for hCA II. Molecular docking studies were performed between the most active compounds 6a, 6b and the reference inhibitor, acetazolamide (AAZ) and the hCA I and hCA II receptors to investigate the binding mechanisms between the compounds and the receptors. These compounds showed better interactions than the AAZ. ADMET analyzes were performed for the compounds and it was seen that the compounds did not show AMES toxicity. The stability of the molecular docking results over time was analysed by 50 ns molecular dynamics simulations. Molecular dynamics simulations revealed that 6a and 6b exhibited good stability after docking to the binding sites of hCA I and hCA II receptors, with minor conformational changes and fluctuations.

In silico identification and characterization of small molecule binding to the CD1d immunoreceptor

CD1 immunoreceptors are a non-classical major histocompatibility complex (MHC) that present antigens to T cells to elucidate immune responses against disease. The antigen repertoire of CD1 has been composed primarily of lipids until recently when CD1d-restricted T cells were shown to be activated by non-lipidic small molecules, such as phenyl pentamethyl dihydrobenzofuran sulfonate (PPBF) and related benzofuran sulfonates. To date structural insights into PPBF/CD1d interactions are lacking, so it is unknown whether small molecule and lipid antigens are presented and recognized through similar mechanisms. Furthermore, it is unknown whether CD1d can bind to and present a broader range of small molecule metabolites to T cells, acting out functions analogous to the MHC class I related protein MR1. Here, we perform in silico docking and molecular dynamics simulations to structurally characterize small molecule interactions with CD1d. PPBF was supported to be presented to T cell receptors through the CD1d F' pocket. Virtual screening of CD1d against more than 17,000 small molecules with diverse geometry and chemistry identified several novel scaffolds, including phytosterols, cholesterols, triterpenes, and carbazole alkaloids, that serve as candidate CD1d antigens. Protein-ligand interaction profiling revealed conserved residues in the CD1d F' pocket that similarly anchor small molecules and lipids. Our results suggest that CD1d could have the intrinsic ability to bind and present a broad range of small molecule metabolites to T cells to carry out its function beyond lipid antigen presentation.

Electric charge and salting in/out effects on glucagon's dipole moments and polarizabilities using the GruPol database

This work demonstrates the use of the GruPol database to predict the functional group dipole moments and polarizabilities of glucagon in the presence of NaCl, simulating an electric charge distribution on the protein's backbone. A new feature of the database allows for the inclusion of ions on the protein backbone, effectively simulating a protein salt and predicting the impact on electrical properties. Glucagon was selected as a proof-of-concept molecule due to its relatively small chain, which enabled benchmarking against quantum mechanical calculations. Firstly, we simulated 70 different ionic configurations, varying the number of Na+ and Cl- ions from zero to four NaCl moieties. Additionally, we investigated the effects of solvation under two distinct conditions: one involving just the peptide and water, and the other also including NaCl at a concentration of approximately 4.2 mol L-1. Regarding the ab initio results, GruPol showed good accuracy, with an angular direction error of around 10° and a 15% difference in the magnitude of the dipole moments. However, the error in polarizability values was higher, most likely due to the lack of an augmented basis set in the ab initio quantum calculations (M06-HF/cc-pVDZ). The database entries were generated using the same functional along with the aug-cc-pVDZ basis set. In solution, a high ionic concentration lowered the overall dipole moment, while the main components of polarizability increased.

Distinct Allosteric Networks in CDK4 and CDK6 in the Cell Cycle and in Drug Resistance

Cyclin-dependent kinases 4 and 6 (CDK4 and CDK6) are key regulators of the G1-S phase transition in the cell cycle. In cancer cells, CDK6 overexpression often outcompetes CDK4 in driving cell cycle progression, contributing to resistance against CDK4/6 inhibitors (CDK4/6i). This suggests distinct functional and conformational differences between these two kinases, despite their striking structural and sequence similarities. Understanding the mechanisms that differentiate CDK4 and CDK6 is crucial, as resistance to CDK4/6i-frequently linked to CDK6 overexpression-remains a significant therapeutic challenge. Notably, CDK6 is often upregulated in CDK4/6i-resistant cancers and rapidly proliferating hematopoietic stem cells, underscoring its unique regulatory roles. We hypothesize that their distinct conformational dynamics explain their differences in phosphorylation of retinoblastoma protein, Rb, inhibitor efficacy, and cell cycle control. This leads us to question how their dissimilar conformational dynamics encode their distinct actions. To elucidate their differential activities, molecular mechanisms, and inhibitor binding, we combine biochemical assays and molecular dynamics (MD) simulations. We discover that CDK4 and CDK6 have distinct allosteric networks connecting the β3-αC loop and the G-loop. CDK6 exhibits stronger coupling and shorter path lengths between these regions, resulting in higher kinase activity upon cyclin binding and impacting inhibitor specificity. We also discover an unrecognized role of the unstructured CDK6 C-terminus, which allosterically connects and stabilizes the R-spine, facilitating slightly higher activity. Our findings bridge the gap between the structural similarity and functional divergence of CDK4 and CDK6, advancing the understanding of kinase regulation in cancer biology.

Synthesis, Antitumor Activities, and Apoptosis-Inducing Activities of Schiff's Bases Incorporating Imidazolidine-2,4-dione Scaffold: Molecular Docking Studies and Enzymatic Inhibition Activities

Background/Objective: Cancer is the leading cause of death worldwide despite the diversity of antitumor therapies, which highlights the necessity to explore new anticancer agents. Methods: We synthesized 5,5-diphenylhydantoin derivatives including Schiff's bases 7-27 and evaluated their cytotoxicity via the MTT assay. Enzymatic inhibition assays, cell cycle and apoptosis analyses, and molecular docking studies were also conducted. Results: Derivative 24 demonstrated the highest cytotoxic activity, with IC50 values of 12.83 ± 0.9 μM, 9.07 ± 0.8 μM, and 4.92 ± 0.3 μM against the cell lines HCT-116, HePG-2, and MCF-7, respectively. Compounds 10, 13, and 21 showed potent antitumor activities versus the examined cell lines (average IC50 = 13.2, 14.5, and 13.1 μM), respectively; moreover, these compounds also demonstrated promising EGFR and HER2 inhibitory activities, with IC50 values in the range 0.28-1.61 µM. Derivative 24 displayed the highest EGFR and HER2 inhibitory activity values (IC50 = 0.07 and 0.04 µM), respectively, which were close to those of the reference drugs erlotinib and lapatinib. Therefore, compound 24 was selected for further examinations and exhibited an inducing effect on apoptosis via diminishing the anti-apoptotic protein levels of BCL-2 (8.598 ± 0.29 ng/mL) and MCL-1 (261.20 ± 8.97 pg/mL) and promoting cell cycle arrest at the G2/M phase (33.46%). The binding relationships between compound 24 and the active sites of EGFR and HER2, which are similar to the co-crystallized inhibitors, were investigated using a molecular docking approach. Conclusions: These findings provide insights into the potential anticancer activities of the synthesized derivatives for further optimization to achieve therapeutic use.

Discovery of Novel Antimicrobial-Active Compounds and Their Analogues by In Silico Small Chemical Screening Targeting Staphylococcus aureus MurB

Methicillin-resistant Staphylococcus aureus is a serious problem in healthcare due to its lethal severe infections and resistance to most antimicrobial agents. The number of new approved antimicrobial agents is declining, and combined with the spread of drug-resistant bacteria, it is predicted that effective antimicrobial agents against multidrug-resistant bacteria will be exhausted. We conducted in silico and in vitro discovery of novel antimicrobial small molecules targeting the SaMurB enzyme involved in cell wall synthesis in Staphylococcus aureus (S. aureus). We performed hierarchical structure-based drug screenings to identify compounds and their analogues using a library of approximately 1.3 million compound structures. In vitro experiments with Staphylococcus epidermidis (S. epidermidis) identified three compounds (SH5, SHa6, and SHa13) that exhibit antibacterial activity. These three compounds do not have toxicity against human-derived cells. SHa13 exhibited remarkable activity (IC50 value =1.64 ± 0.01 µM). The active compound was predicted to bind to the active site of SaMurB by forming a hydrogen bond with Arg188 in both R and S bodies. These data provide a starting point for the development of novel cell wall synthesis inhibitors as antimicrobial agents targeting SaMurB.

Structural insights into lipid chain-length selectivity and allosteric regulation of FFA2

The free fatty acid receptor 2 (FFA2) is a G protein-coupled receptor (GPCR) that selectively recognizes short-chain fatty acids to regulate metabolic and immune functions. As a promising therapeutic target, FFA2 has been the focus of intensive development of synthetic ligands. However, the mechanisms by which endogenous and synthetic ligands modulate FFA2 activity remain unclear. Here, we present the structures of the human FFA2-Gi complex activated by the synthetic orthosteric agonist TUG-1375 and the positive allosteric modulator/allosteric agonist 4-CMTB, along with the structure of the inactive FFA2 bound to the antagonist GLPG0974. Structural comparisons with FFA1 and mutational studies reveal how FFA2 selects specific fatty acid chain lengths. Moreover, our structures reveal that GLPG0974 functions as an allosteric antagonist by binding adjacent to the orthosteric pocket to block agonist binding, whereas 4-CMTB binds the outer surface of transmembrane helices 6 and 7 to directly activate the receptor. Supported by computational and functional studies, these insights illuminate diverse mechanisms of ligand action, paving the way for precise GPCR-targeted drug design.

Probing nanopores: molecular dynamics insights into the mechanisms of DNA and protein translocation through solid-state and biological nanopores

Nanopore sequencing technology has revolutionized single-molecule analysis through its unique capability to detect and characterize individual biomolecules with unprecedented precision. This perspective provides a comprehensive analysis of molecular dynamics (MD) simulations in nanopore research, with particular emphasis on comparing molecular transport mechanisms between biological and solid-state platforms. We first examine how MD simulations at atomic resolution reveal distinct characteristics: biological nanopores exhibit sophisticated molecular recognition through specific amino acid interactions, while solid-state nanopores demonstrate advantages in structural stability and geometric control. Through detailed analysis of simulation methodologies and their applications, we show how computational approaches have advanced our understanding of critical phenomena such as ion selectivity, conformational dynamics, and surface effects in both nanopore types. Despite computational challenges including limited simulation timescales and force field accuracy constraints, recent advances in high-performance computing and artificial intelligence integration have significantly improved simulation capabilities. By synthesizing perspectives from physics, chemistry, biology, and computational science, this perspective provides both theoretical insights and practical guidelines for developing next-generation nanopore platforms. The integration of computational and experimental approaches discussed here offers promising directions for advancing nanopore technology in applications ranging from DNA/RNA sequencing and protein post-translational modification analysis to disease diagnosis and drug screening.

Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set

Small molecule empirical force fields (FFs), including the CHARMM General Force Field (CGenFF), are designed to have wide coverage of organic molecules and to rapidly assign parameters to molecules not explicitly included in the FF. Assignment of parameters to new molecules in CGenFF is based on a trained bond-angle-dihedral charge increment linear interpolation scheme for the partial atomic charges along with bonded parameters assigned based on analogy using a rules-based penalty score scheme associated with atom types and chemical connectivity. Accordingly, the accuracy of CGenFF is related to the extent of the training set of available parameters. In the present study that training set is extended by 1390 molecules selected to represent connectivities new to CGenFF training compounds. Quantum mechanical (QM) data for optimized geometries, bond, valence angle, and dihedral angle potential energy scans, interactions with water, molecular dipole moments, and electrostatic potentials were used as target data. The resultant bonded parameters and partial atomic charges were used to train a new version of the CGenFF program, v5.0, which was used to generate parameters for a validation set of molecules, including drug-like molecules approved by the FDA, which were then benchmarked against both experimental and QM data. CGenFF v5.0 shows overall improvements with respect to QM intramolecular geometries, vibrations, dihedral potential energy scans, dipole moments and interactions with water. Tests of pure solvent properties of 216 molecules show small improvements versus the previous release of CGenFF v2.5.1 reflecting the high quality of the Lennard-Jones parameters that were explicitly optimized during the initial optimization of both the CGenFF and the CHARMM36 force field. CGenFF v5.0 represents an improvement that is anticipated to more accurately model intramolecular geometries and strain energies as well as noncovalent interactions of drug-like and other organic molecules.

Modeling the different conformations of the human mitochondrial ADP/ATP carrier using AlphaFold and molecular dynamics simulations of the protein-ligand complexes

The ADP/ATP Carrier (AAC), a member of the mitochondrial Solute Carrier Family 25 (SLC25), facilitates the exchange of cytosolic ADP for mitochondrial ATP across the inner mitochondrial membrane (IMM). It serves as a master regulator of the cellular ADP/ATP ratio and is involved in various pathologies, including cancer. Its transport mechanism involves a conformational transition that alternates the accessibility of the binding site between the cytoplasmic (c-state) and mitochondrial (m-state) sides of the IMM. In this study, the human AAC was used as a case study to evaluate the performance of AlphaFold2 (AF2) and AlphaFold3 (AF3) for structural modeling of members of the SLC25 family. The study also compared the AF3 approach for predicting protein-ligand complexes with the standard methodology of modeling followed by molecular docking. Both AF2 and AF3 display a bias toward the c-state conformation. On the other hand, ColabFold implementation of AF2 successfully generated the first ab initio structural model of the human AAC in the m-state conformation. Modeling of the complexes coupled to molecular dynamics (MD) simulations allowed to obtain structural insight into AAC's substrate binding and stabilization mechanisms, and the possible effects of pathogenic mutations on its conformational dynamics and functionality. These analyses provided a deeper understanding of AAC's alternating access mechanism and highlighted the potential of AF3 in modeling protein-ligand interactions, though only in the c-state. This work demonstrates the reliability of AlphaFold models when aligned with experimental data and provides further confirmation of their utility for investigating solute carriers and membrane proteins.

Narrowed pore conformations of aquaglyceroporins AQP3 and GlpF

Aquaglyceroporins such as aquaporin-3 (AQP3) and its bacterial homologue GlpF facilitate water and glycerol permeation across lipid bilayers. X-ray crystal structures of GlpF showed open pore conformations, and AQP3 has also been predicted to adopt this conformation. Here we present cryo-electron microscopy structures of rat AQP3 and GlpF in different narrowed pore conformations. In n-dodecyl-β-D-maltopyranoside detergent micelles, aromatic/arginine constriction filter residues of AQP3 containing Tyr212 form a 2.8-Å diameter pore, whereas in 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) nanodiscs, Tyr212 inserts into the pore. Molecular dynamics simulation shows the Tyr212-in conformation is stable and largely suppresses water permeability. AQP3 reconstituted in POPC liposomes exhibits water and glycerol permeability, suggesting that the Tyr212-in conformation may be altered during permeation. AQP3 Y212F and Y212T mutant structures suggest that the aromatic residue drives the pore-inserted conformation. The aromatic residue is conserved in AQP7 and GlpF, but neither structure exhibits the AQP3-like conformation in POPC nanodiscs. Unexpectedly, the GlpF pore is covered by an intracellular loop, but the loop is flexible and not primarily related to the GlpF permeability. Our findings illuminate the unique AQP3 conformation and structural diversity of aquaglyceroporins.

Controlled Enzyme Cargo Loading in Engineered Bacterial Microcompartment Shells

Bacterial microcompartments (BMCs) are nanometer-scale organelles with a protein-based shell that serve to colocalize and encapsulate metabolic enzymes. They may provide a range of benefits to improve pathway catalysis, including substrate channeling and selective permeability. Several groups are working toward using BMC shells as a platform for enhancing engineered metabolic pathways. The microcompartment shell of Haliangium ochraceum (HO) has emerged as a versatile and modular shell system that can be expressed and assembled outside its native host and with non-native cargo. Further, the HO shell has been modified to use the engineered protein conjugation system SpyCatcher-SpyTag for non-native cargo loading. Here, we used a model enzyme, triose phosphate isomerase (Tpi), to study non-native cargo loading into four HO shell variants and begin to understand maximal shell loading levels. We also measured activity of Tpi encapsulated in the HO shell variants and found that activity was determined by the amount of cargo loaded and was not strongly impacted by the predicted permeability of the shell variant to large molecules. All shell variants tested could be used to generate active, Tpi-loaded versions, but the simplest variants assembled most robustly. We propose that the simple variant is the most promising for continued development as a metabolic engineering platform.

Transport mechanism and drug discovery of human monocarboxylate transporter 1

Human monocarboxylate transporters (MCTs) are crucial for tumour cell glycolysis. Inhibiting MCT-mediated lactate transport can suppress the proliferation of solid tumours and enhance the efficacy of the immune system against tumours. Despite the importance of this transporter, the molecular mechanism of lactate transport by MCT1 remains elusive, hindering the development of targeted therapies. Here, we used principal component analysis to elucidate the allosteric mechanisms of the MCT family. Enhanced sampling revealed that specific residue pairs (E46-K289 and E376-R143) are essential for maintaining the inwards and outwards conformations of MCT1. Quantum chemical calculations and umbrella sampling demonstrated that lactate molecules and protons are co-transported sequentially, with K38 and R313 playing key roles in lactate translocation. On the basis of these data, we conducted a drug screening campaign targeting the core pocket of MCT1 and identified silybin as a selective MCT1 inhibitor. Silybin had significant inhibitory effects on tumour cells with high MCT1 expression. These findings provide a comprehensive understanding of the lactate transport mechanism of MCT1 and lay the groundwork for the rational design of antitumour drugs targeting MCT1.

Integrative Protein Assembly With LZerD and Deep Learning in CAPRI 47-55

We report the performance of the protein complex prediction approaches of our group and their results in CAPRI Rounds 47-55, excluding the joint CASP Rounds 50 and 54, as well as the special COVID-19 Round 51. Our approaches integrated classical pipelines developed in our group as well as more recently developed deep learning pipelines. In the cases of human group prediction, we surveyed the literature to find information to integrate into the modeling, such as assayed interface residues. In addition to any literature information, generated complex models were selected by a rank aggregation of statistical scoring functions, by generative model confidence, or by expert inspection. In these CAPRI rounds, our human group successfully modeled eight interfaces and achieved the top quality level among the submissions for all of them, including two where no other group did. We note that components of our modeling pipelines have become increasingly unified within deep learning approaches. Finally, we discuss several case studies that illustrate successful and unsuccessful modeling using our approaches.

Exploiting the Achilles' heel of cancer through a structure-based drug-repurposing approach and experimental validation of top drugs using the TRAP assay

Telomerase, a reverse transcriptase implicated in replicative immortality of cancers, remains a challenging target for therapeutic intervention due to its structural complexity and the absence of clinically approved small-molecule inhibitors. In this study, we explored drug repurposing as a pragmatic approach to address this gap, leveraging FDA-approved drugs to accelerate the identification of potential telomerase inhibitors. Using a structure-based drug discovery framework, we screened the DrugBank database through a previously validated pharmacophore model for the FVYL pocket in the hTERT thumb domain, the established binding site of BIBR1532. This was followed by molecular docking, pharmacokinetic filtering, and molecular dynamics (MD) simulations to evaluate the stability of protein-ligand complexes. Binding free energy calculations (MM-PBSA and MM-GBSA) were employed for cross-validation, identifying five promising candidates. Experimental validation using the Telomerase Repeat Amplification Protocol (TRAP) assay confirmed the inhibitory potential of Raltitrexed, showing significant inhibition with IC50 8.899 µM in comparison to control. Decomposition analysis and Structure-Activity Relationship (SAR) studies further offered insights into the binding mechanism, reinforcing the utility of the FVYL pocket as a druggable site. Raltitrexed's dual mechanism of action, targeting both telomerase and thymidylate synthase, underscores its potential as a versatile anticancer agent, suitable for combination therapies or standalone treatment. As the top lead, Raltitrexed demonstrates the potential of repurposed drugs in telomerase-targeted therapies, offering a time and cost-effective strategy for advancing its clinical development. The study also provides a robust framework for future drug development, addressing challenges in targeting telomerase for anticancer therapy.

Discovery of mammalian collagens I and III within ancient poriferan biopolymer spongin

Spongin is a fundamental biopolymer that has played a crucial role in the skeletogenesis of keratosan sponges for over 800 million years. This biomaterial had so far remained chemically unidentified and believed to be an enigmatic type of halogenated collagen-keratin-based bioelastomer. Here we show collagen I and III as the main structural components of spongin. Proteomics, 13C solid state NMR and Raman spectroscopy confirm the identity of collagenous domains in spongin with collagen from mammals. Using an HPLC-MS analysis, we found halogenated di- and tri-tyrosines as crosslinking agents in spongin. Using molecular dynamics modeling, we solvated the crystal structures of collagen mimetic peptides for type I and type III collagens in four different systems, including selected brominated crosslinks. The results underscore the complex interplay between the collagen structures and crosslinks, raising intriguing questions about the molecular mechanisms underlying collagen chemistry within spongin as an ancient biocomposite.

Conformational ensembles for protein structure prediction

Acquisition of conformational ensembles for a protein is a challenging task, which is actually involving to the solution for protein folding problem and the study of intrinsically disordered protein. Despite AlphaFold with artificial intelligence acquired unprecedented accuracy to predict structures, its result is limited to a single state of conformation and it cannot provide multiple conformations to display protein intrinsic disorder. To overcome the barrier, a FiveFold approach was developed with a single sequence method. It applied the protein folding shape code (PFSC) uniformly to expose local folds of five amino acid residues, formed the protein folding variation matrix (PFVM) to reveal local folding variations along sequence, obtained a massive number of folding conformations in PFSC strings, and then an ensemble of multiple conformational protein structures is constructed. The P53_HUMAN as a well-known protein and LEF1_HUMAN and Q8GT36_SPIOL as typical disordered proteins are token as the benchmark to evaluate the predicted outcomes. The results demonstrated an effective algorithm and biological meaningful process well to predict protein multiple conformation structures.

TinkerModeller: An Efficient Tool for Building Biological Systems in Tinker Simulations

Polarizable force fields advance our understanding of electrostatic interactions in molecular systems; however, their widespread application is limited by the complexity of required molecular modeling. We here present TinkerModeller (TKM), a versatile software package designed to streamline the construction of biological systems in the Tinker molecular simulation software. The core functionality of TKM lies in its capacity to generate input files for complex molecular systems and facilitate the conversion from classical to polarizable force fields. With a user-friendly, standalone script, TKM provides an intuitive interface that supports users from molecular modeling through to postanalysis, creating a comprehensive platform for molecular dynamics simulations within Tinker. Furthermore, TKM includes an electric field (EF) postanalysis module, introducing a novel approach that employs charge methods and point charge approximations for efficient internal EF estimation. This module offers a computationally low-demand solution for high-throughput EF estimation. Our work paves the way for broader, more accessible use of polarizable force fields within Tinker and introduces a new method for EF estimation, advancing our capacity to explore electrostatic effects in biological and materials science applications.

Computational Tool for Determining Local Dielectric Constants in Heterogeneous Nanoscale Systems from Molecular Dynamics Trajectories

In this work, we describe a computational tool designed to determine the local dielectric constants (ε) of charge-neutral heterogeneous systems by analyzing dipole moment fluctuations from molecular dynamics (MD) trajectories. Unlike conventional methods, our tool can calculate dielectric constants for dynamically evolving selections of molecules within a defined region of space, rather than for fixed sets of molecules. We validated our approach by computing the dielectric constants of TIP3P water nanospheres, achieving results consistent with literature values for bulk water. We then applied our tool to more complex systems, the water slabs around solvated phospholipid bilayers, where we observed a lower dielectric constant of water near the bilayer headgroups (ε = 20-50) compared to nanospheres of bulk water (ε = 58-62) with the same number of molecules. Our tool also enabled us to compute the dielectric constants of water in more heterogeneous systems, where water surrounding asymmetrically distributed phospholipids on single-walled carbon nanotubes also exhibited lower dielectric constants than in bulk water nanospheres. Addition of positively charged peptides that bind to phospholipid-nanotube conjugates further lowered the dielectric constants of water in the immediate vicinity of these conjugates. Moreover, we estimated dielectric constants for lipids in symmetric bilayers, where values are well-documented, and for asymmetric phospholipid-wrapped nanotube systems, which were previously unexplored, and found that dielectric constants of phospholipids depend on their arrangement in the assembled aggregate. The results align with the literature for bilayers and provide new insights for phospholipid-nanotube systems. The ability of our tool to provide local dielectric constants for both well-studied and novel systems advances our understanding of molecular environments and interactions.

Green-Engineered Montmorillonite Clays for the Adsorption, Detoxification, and Mitigation of Aflatoxin B1 Toxicity

Dietary and environmental exposure to aflatoxins via contaminated food items can pose major health challenges to both humans and animals. Studies have reported the coexistence of aflatoxins and other environmental toxins. This emphasizes the urgent need for efficient and effective mitigation strategies for aflatoxins. Previous reports from our laboratory have demonstrated the potency of the green-engineered clays (GECs) on ochratoxin and other toxic chemicals. Therefore, this study sought to investigate the binding and detoxification potential of chlorophyll (CMCH and SMCH) and chlorophyllin (CMCHin and SMCHin)-amended montmorillonite clays for aflatoxin B1 (AFB1). In addition to analyzing binding metrics including affinity, capacity, free energy, and enthalpy, the sorption mechanisms of AFB1 onto the surfaces of engineered clays were also investigated. Computational and experimental studies were performed to validate the efficacy and safety of the clays. CMCH showed the highest binding capacity (Qmax) of 0.43 mol/kg compared to the parent clays CM (0.34 mol/kg) and SM (0.32 mol/kg). Interestingly, there were no significant changes in the binding capacity of the clays at pH2 and pH6, suggesting that the clays can bind to AFB1 throughout the gastrointestinal track. In silico investigations employing molecular dynamics simulations also demonstrated that CMCH enhanced AFB1 binding as compared to parent clay and predicted hydrophobic interactions as the main mode of interaction between the AFB1 and CMCH. This was corroborated by the kinetic results which indicated that the interaction was best defined by chemosorption with favorable thermodynamics and Gibbs free energy (∆G) being negative. In vitro experiments in Hep G2 cells showed that clay treatment mitigated AFB1-induced cytotoxicity, with the exception of 0.5% (w/v) SMCH. Finally, the in vivo results validated the protection of all the clays against AFB1-induced toxicities in Hydra vulgaris. This study showed that these clays significantly detoxified AFB1 (86% to 100%) and provided complete protection at levels as low as 0.1%, suggesting that they may be used as AFB1 binders in feed and food.

In Silico Functional Annotation and Structural Characterization of Hypothetical Proteins in Bacillus paralicheniformis and Bacillus subtilis Isolated from Honey

Bacillus species are ubiquitous and survive in competitive microbial communities under adverse environmental conditions. Bacillus paralicheniformis and Bacillus subtilis obtained from honey revealed a significant proportion of proteins within their genomes as uncharacterized hypothetical proteins (HPs). A total of 1007 HP sequences were evaluated, resulting in the successful annotation of 56 HPs by assigning specific functions to them. A systematic in silico approach, integrating a range of bioinformatics tools and databases to annotate functions, characterize physicochemical properties, determine subcellular localization, and study protein-protein interactions, was used. Homology and de novo models were generated for the HPs, coupled with iterative remodeling and molecular dynamics (MD) simulations. HPs having significant roles in sporulation, biofilm formation, motility, ion transportation, regulation of metabolic processes, DNA repair, replication, and transcription were identified. Classical MD simulations of globular and transducer membrane proteins, along with postprocessing analyses, refined our structural predictions and provided deeper insights into the stability and functional dynamics of the protein structures under physiological conditions. Moreover, we observed a correlation between the percentage of α helix, β sheet, and coil structures in globular proteins and transducer membrane proteins. The integration of iterative loop modeling, MD simulations, and Dictionary of Secondary Structure in Proteins analysis further validated our predicted models and facilitated the identification of regions critical for protein function, thereby enhancing the overall reliability and robustness of our functional annotations. Furthermore, annotation of these hypothetical proteins aids in identifying novel proteins within bacterial cells, ultimately contributing to a deeper understanding of bacterial cell biology and their use for biotechnological purposes.

GUK1 activation is a metabolic liability in lung cancer

Little is known about metabolic vulnerabilities in oncogene-driven lung cancer. Here, we perform a phosphoproteomic screen in anaplastic lymphoma kinase (ALK)-rearranged ("ALK+") patient-derived cell lines and identify guanylate kinase 1 (GUK1), a guanosine diphosphate (GDP)-synthesizing enzyme, as a target of ALK signaling in lung cancer. We demonstrate that ALK binds to and phosphorylates GUK1 at tyrosine 74 (Y74), resulting in increased GDP biosynthesis. Spatial imaging of ALK+ patient tumor specimens shows enhanced phosphorylation of GUK1 that significantly correlates with guanine nucleotides in situ. Abrogation of GUK1 phosphorylation reduces intracellular GDP and guanosine triphosphate (GTP) pools and decreases mitogen-activated protein kinase (MAPK) signaling and Ras-GTP loading. A GUK1 variant that cannot be phosphorylated (Y74F) decreases tumor proliferation in vitro and in vivo. Beyond ALK, other oncogenic fusion proteins in lung cancer also regulate GUK1 phosphorylation. These studies may pave the way for the development of new therapeutic approaches by exploiting metabolic dependencies in oncogene-driven lung cancers.

Distinct allosteric networks in CDK4 and CDK6 in the cell cycle and in drug resistance

Cyclin-dependent kinases 4 and 6 (CDK4 and CDK6) are key regulators of the G1-S phase transition in the cell cycle. In cancer cells, CDK6 overexpression often outcompetes CDK4 in driving cell cycle progression, contributing to resistance against CDK4/6 inhibitors (CDK4/6i). This suggests distinct functional and conformational differences between these two kinases, despite their striking structural and sequence similarities. Understanding the mechanisms that differentiate CDK4 and CDK6 is crucial, as resistance to CDK4/6i-frequently linked to CDK6 overexpression-remains a significant therapeutic challenge. Notably, CDK6 is often upregulated in CDK4/6i-resistant cancers and rapidly proliferating hematopoietic stem cells, underscoring its unique regulatory roles. We hypothesize that their distinct conformational dynamics explain their differences in phosphorylation of retinoblastoma protein, Rb, inhibitor efficacy, and cell cycle control. This leads us to question how their dissimilar conformational dynamics encode their distinct actions. To elucidate their differential activities, molecular mechanisms, and inhibitor binding, we combine biochemical assays and molecular dynamics (MD) simulations. We discover that CDK4 and CDK6 have distinct allosteric networks connecting the β3-αC loop and the G-loop. CDK6 exhibits stronger coupling and shorter path lengths between these regions, resulting in higher kinase activity upon cyclin binding and impacting inhibitor specificity. We also discover an unrecognized role of the unstructured CDK6 C-terminus, which allosterically connects and stabilizes the R-spine, facilitating slightly higher activity. Our findings bridge the gap between the structural similarity and functional divergence of CDK4 and CDK6, advancing the understanding of kinase regulation in cancer biology.

Membrane-dependent dynamics and dual translocation mechanisms of ABCB4: Insights from molecular dynamics simulations

ABCB4 is an ATP-binding cassette transporter expressed at the canalicular membrane of hepatocytes and responsible for translocating phosphatidylcholine into bile. Despite the recent cryo-EM structures of ABCB4, knowledge about the molecular mechanism of phosphatidylcholine transport remains fragmented. In this study, we used all-atom molecular dynamics simulations to investigate ABCB4 dynamics during its transport cycle, leveraging both symmetric and asymmetric membrane models. Our results demonstrate that membrane composition influences the local conformational dynamics of ABCB4, revealing distinct lipid-binding patterns across different conformers, particularly for cholesterol. We explored the two potential mechanisms for phosphatidylcholine translocation: the canonical ATP-driven alternating access model and the "credit-card swipe" model. Critical residues were identified for phosphatidylcholine binding and transport pathway modulation, supporting the canonical mechanism while also indicating a possible additional pathway. The conformer-specific roles of kinking in transmembrane helices (TMH4 and TMH10) were highlighted as key events in substrate translocation. Overall, ABCB4 may utilize a cooperative transport mechanism, integrating elements of both models to facilitate efficient phosphatidylcholine motion across the membrane. This study provides new insights into the relationship between membrane environment and ABCB4 function, contributing to our understanding of its role in bile physiology and susceptibility to genetic and xenobiotic influences.

Unraveling Hydration Shell Dynamics and Viscosity Effects Around Cyanamide Probes via 2D IR Spectroscopy

Hydration dynamics and solvent viscosity play critical roles in the structure and function of biomolecules. An overwhelming body of evidence suggests that protein and membrane fluctuations are closely linked to solvent fluctuations. While extensive research exists on the use of vibrational probes to detect local interactions and solvent dynamics, fewer studies have explored how the behavior of these reporters changes in response to bulk viscosity. To address this gap, two-dimensional infrared spectroscopy (2D IR) was employed in this study to investigate the ultrafast hydration dynamics around a cyanamide (NCN) probe attached to a nucleoside, deoxycytidine, in aqueous solutions with varying glycerol content. The use of a small vibrational probe on a targeted nucleic acid offers the potential to capture more localized hydration dynamics than alternative methods. The time scales for the frequency correlation decays were found to increase linearly with bulk viscosity, ranging from 0.9 to 11.4 ps over viscosities of 0.96-49.1 cP. Additionally, molecular dynamics (MD) simulations were performed to model the local hydration dynamics around the NCN probe. Interestingly, increasing the glycerol content did not significantly alter the hydration of the deoxycytidine. The MD simulations further suggested that the NCN probe's frequency fluctuations were primarily influenced by the dynamics of water in the second solvation shell. Cage correlation functions, which measure the movement of water molecules in and out of the second solvation shell, exhibited decays that closely matched those of the frequency-fluctuation correlation function (FFCF). These findings offer new insights into hydration dynamics and the impact of viscosity on biological systems.

Intracellular protein-lipid interactions drive presynaptic assembly prior to neurexin recruitment

Neurexin cell-adhesion molecules regulate synapse development and function by recruiting synaptic components. Here, we uncover a mechanism for presynaptic assembly that precedes neurexin recruitment, mediated by interactions between cytosolic proteins and membrane phospholipids. Developmental imaging in C. elegans reveals that the intracellular active zone protein SYD-1 accumulates at nascent presynapses prior to its binding partner neurexin. Combining molecular dynamics simulations to model intrinsic interactions between SYD-1 and lipid bilayers with biochemical and in vivo validation of these predictions, we find that PIP2-interacting residues in the SYD-1 C2 domain are required for active zone assembly. Genetic perturbation of a PIP2-generating enzyme disrupts synaptic SYD-1 accumulation, while the PIP2-interacting domain of mammalian RIM1 can compensate for the SYD-1 C2 domain, suggesting functional homology between these proteins. Finally, we propose that the evolutionarily conserved γ-neurexin isoform represents a minimal neurexin sequence that stabilizes nascent presynaptic assemblies, potentially a core function of this isoform.

THz Waves Improve Spatial Working Memory by Increasing the Activity of Glutamatergic Neurons in Mice

Terahertz (THz) waves, a novel type of radiation with quantum and electronic properties, have attracted increasing attention for their effects on the nervous system. Spatial working memory, a critical component of higher cognitive function, is coordinated by brain regions such as the infralimbic cortex (IL) region of the medial prefrontal cortex and the ventral cornu ammonis 1 (vCA1) of hippocampus. However, the regulatory effects of THz waves on spatial working memory and the underlying mechanisms remain unclear. In this study, the effects of 0.152 THz waves on glutamatergic neuronal activity and spatial working memory and the related mechanisms were investigated in cell, brain slice, and mouse models. Cellular experiments revealed that THz waves exposure for 60 min significantly increased the intrinsic excitability of primary hippocampal neurons, enhanced glutamatergic neuron activity, and upregulated the expression of molecules involved in glutamate metabolism. In brain slice experiments, THz waves markedly elevated neuronal activity, promoted synaptic plasticity, and increased glutamatergic synaptic transmission within the IL and vCA1 regions. Molecular dynamics simulations found that THz waves could inhibit the ion transport function of glutamate receptors. Moreover, Y-maze tests demonstrated that mice exposed to THz waves exhibited significantly improved spatial working memory. Multiomics analyses indicated that THz waves could induce changes in chromatin accessibility and increase the proportion of excitatory neurons. These findings suggested that exposure to 0.152 THz waves increased glutamatergic neuronal activity, promoted synaptic plasticity, and improved spatial working memory, potentially through modifications in chromatin accessibility and excitatory neuron proportions.

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble in water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design of LNCs, a detailed understanding of the composition-structure-function relationships is missing. This review presents currently available computational methods for LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, with the focus on molecular dynamics simulations in all-atom and coarse-grained resolution. Their strengths and weaknesses are discussed, highlighting the aspects necessary for obtaining reliable results in the simulations. Furthermore, a machine learning, i.e., data-based learning, approach to the design of lipid-mediated API delivery is introduced. The data produced by the experimental and theoretical approaches provide valuable insights. Processing these data can help optimize the design of LNCs for better performance. In the final section of this Review, state-of-the-art of computer simulations of LNCs are reviewed, specifically addressing the compatibility of experimental and computational insights.

Load-based divergence in the dynamic allostery of two TCRs recognizing the same pMHC

Increasing evidence suggests that mechanical load on the αβ T cell receptor (TCR) is crucial for recognizing the antigenic peptide-loaded major histocompatibility complex (pMHC) molecule. Our recent all-atom molecular dynamics (MD) simulations revealed that the inter-domain motion of the TCR is responsible for the load-induced catch bond behavior of the TCR-pMHC complex and peptide discrimination. To further examine the generality of the mechanism, we perform all-atom MD simulations of the B7 TCR under different conditions for comparison with our previous simulations of the A6 TCR. The two TCRs recognize the same pMHC and have similar interfaces with pMHC in crystal structures. We find that the B7 TCR-pMHC interface stabilizes under ~15-pN load using a conserved dynamic allostery mechanism that involves the asymmetric motion of the TCR chassis. However, despite forming comparable contacts with pMHC as A6 in the crystal structure, B7 has fewer high-occupancy contacts with pMHC and exhibits higher mechanical compliance during the simulation. These results indicate that the dynamic allostery common to the TCRαβ chassis can amplify slight differences in interfacial contacts into distinctive mechanical responses and nuanced biological outcomes.

2Danalysis: A toolbox for analysis of lipid membranes and biopolymers in two-dimensional space

Molecular simulations expand our ability to learn about the interplay of biomolecules. Biological membranes, composed of diverse lipids with varying physicochemical properties, are highly dynamic environments involved in cellular functions. Proteins, nucleic acids, glycans and bio-compatible polymers are the machinery of cellular processes both in the cytosol and at the lipid membrane interface. Lipid species directly modulate membrane properties, and affect the interaction and function of other biomolecules. Natural molecular diffusion results in changes of local lipid distribution, affecting the membrane properties. Projecting biophysical and structural membrane and biopolymer properties to a two-dimensional plane can be beneficial to quantify molecular signatures in a reduced dimensional space to identify relevant interactions at the interface of interest, i.e. the membrane surface or biopolymer-surface interface. Here, we present a toolbox designed to project membrane and biopolymer properties to a two-dimensional plane to characterize patterns of interaction and spatial correlations between lipid-lipid and lipid-biopolymer interfaces. The toolbox contains two hubs implemented using MDAKits architecture, one for membranes and one for biopolymers, that can be used independently or together. Three case studies demonstrate the versatility of the toolbox with detailed tutorials in GitHub. The toolbox and tutorials will be periodically updated with other functionalities and resolutions to expand our understanding of the structure-function relationship of biomolecules in two-dimensions.

Self-association of cyclodextrin inclusion complexes in a deep eutectic solvent enhances guest solubility

Cyclodextrins are widely used pharmaceutical excipients known to increase the solubility of drug compounds through formation of inclusion complexes. A prominent limitation of common cyclodextrins is their own scarce solubility in water, which renders them unsuitable for many drug formulations. Cyclodextrin solubility can be enhanced in appropriate media such as Deep Eutectic Solvents (DESs). However, DESs can also reduce the equilibrium constant for host-guest complexation, making it challenging to optimize drug solubility using cyclodextrin. To determine the impact and mechanism of cyclodextrin complexation in DES, we tracked changes in the solubility of methyl orange (MO), serving as a hardly soluble model compound, in the presence of β-cyclodextrin (CD) in hydrated urea-choline chloride DES. The highest achievable MO solubility is obtained in concentrated CD-in-DES mixtures at low hydration, resulting from the higher solubility of CD⊃MO complexes in DES compared to water as a solvent. Combining our results with molecular dynamics simulations, we provide evidence that CD⊃MO complexes self-associate into dimers and larger oligomers. This self-association of complexes greatly enhances MO solubilization by CD beyond that expected from the canonical 1:1 binding stoichiometry. This newly unraveled solubilization mechanism via cyclodextrins and its facilitation by DES should aid the design of future drug formulations.