On the Dielectric "Constant" of Proteins: Smooth Dielectric Function for Macromolecular Modeling and Its Implementation in DelPhi

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

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

Integrated Experimental and Theoretical Investigation of Copper Active Site Properties of a Lytic Polysaccharide Monooxygenase from Serratia marcescens

In this paper, we employed a multidisciplinary approach, combining experimental techniques and density functional theory (DFT) calculations to elucidate key features of the copper coordination environment of the bacterial lytic polysaccharide monooxygenase (LPMO) from Serratia marcescens (SmAA10). The structure of the holo-enzyme was successfully obtained by X-ray crystallography. We then determined the copper(II) binding affinity using competing ligands and observed that the affinity of the histidine brace ligands for copper is significantly higher than previously described. UV-vis, advanced electron paramagnetic resonance (EPR), and X-ray absorption spectroscopy (XAS) techniques, including high-energy resolution fluorescence detected (HERFD) XAS, were further used to gain insight into the copper environment in both the Cu(II) and Cu(I) redox states. The experimental data were successfully rationalized by DFT models, offering valuable information on the electronic structure and coordination geometry of the copper center. Finally, the Cu(II)/Cu(I) redox potential was determined using two different methods at ca. 350 mV vs NHE and rationalized by DFT calculations. This integrated approach not only advances our knowledge of the active site properties of SmAA10 but also establishes a robust framework for future studies of similar enzymatic systems.

Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Scalable Analysis of Dipole Moment Fluctuations for Characterizing Intermolecular Interactions and Structural Stability

Accurately predicting protein-ligand interactions is essential in computational molecular biochemistry and in silico drug development. Monitoring changes in molecular dipole moments through molecular dynamics simulations provides valuable insights into dipole-dipole interactions, which are critical for understanding protein structure stability and predicting protein-ligand binding affinity. In this study, we propose a novel method to monitor changes in the interangle between dipole vectors of residue molecules within proteins and ligand molecules, aiming to evaluate the strength and consistency of interactions within the complex. Additionally, we extend the concept of positional root-mean-square fluctuation (RMSF), commonly used for protein structure stability analysis, to dipole moments, thus defining dipole moment RMSF. This enables us to analyze the stability of dipole moments for each residue within the protein and compare them across residues and between binding and non-binding complexes. Using the CRBP1-retinoic acid complex as our model system, we observed a significant difference in the interangle change of dipole moments for the key residue at the residue-level between the non-binding and binding complexes. Furthermore, we found that the dipole moment RMSF value of the non-binding complex was substantially larger than that of the binding complex, indicating greater dipole moment instability in the non-binding complex. Leveraging the concept of scalability inherent in the calculation of dipole moment vectors, we systematically expanded the residues within the protein's primary secondary structure. Our dipole moment analysis approach can provide valuable predictive insights into complex candidates, especially in situations where experimental comparisons are challenging.

Tofacitinib and peficitinib inhibitors of Janus kinase for autoimmune disease treatment: a quantum biochemistry approach

Autoimmune inflammatory diseases, such as rheumatoid arthritis (RA) and ulcerative colitis, are associated with an uncontrolled production of cytokines leading to the pronounced inflammatory response of these disorders. Their therapy is currently focused on the inhibition of cytokine receptors, such as the Janus kinase (JAK) protein family. Tofacitinib and peficitinib are JAK inhibitors that have been recently approved to treat rheumatoid arthritis. In this study, an in-depth analysis was carried out through quantum biochemistry to understand the interactions involved in the complexes formed by JAK1 and tofacitinib or peficitinib. Computational analyses provided new insights into the binding mechanisms between tofacitinib or peficitinib and JAK1. The essential amino acid residues that support the complex are also identified and reported. Additionally, we report new interactions, such as van der Waals; hydrogen bonds; and alkyl, pi-alkyl, and pi-sulfur forces, that stabilize the complexes. The computational results revealed that peficitinib presents a similar affinity to JAK1 compared to tofacitinib based on their interaction energies.

Coupling finite and boundary element methods to solve the Poisson-Boltzmann equation for electrostatics in molecular solvation

The Poisson-Boltzmann equation is widely used to model electrostatics in molecular systems. Available software packages solve it using finite difference, finite element, and boundary element methods, where the latter is attractive due to the accurate representation of the molecular surface and partial charges, and exact enforcement of the boundary conditions at infinity. However, the boundary element method is limited to linear equations and piecewise constant variations of the material properties. In this work, we present a scheme that couples finite and boundary elements for the linearised Poisson-Boltzmann equation, where the finite element method is applied in a confined solute region and the boundary element method in the external solvent region. As a proof-of-concept exercise, we use the simplest methods available: Johnson-Nédélec coupling with mass matrix and diagonal preconditioning, implemented using the Bempp-cl and FEniCSx libraries via their Python interfaces. We showcase our implementation by computing the polar component of the solvation free energy of a set of molecules using a constant and a Gaussian-varying permittivity. As validation, we compare against well-established finite difference solvers for an extensive binding energy data set, and with the finite difference code APBS (to 0.5%) for Gaussian permittivities. We also show scaling results from protein G B1 (955 atoms) up to immunoglobulin G (20,148 atoms). For small problems, the coupled method was efficient, outperforming a purely boundary integral approach. For Gaussian-varying permittivities, which are beyond the applicability of boundary elements alone, we were able to run medium to large-sized problems on a single workstation. The development of better preconditioning techniques and the use of distributed memory parallelism for larger systems remains an area for future work. We hope this work will serve as inspiration for future developments that consider space-varying field parameters, and mixed linear-nonlinear schemes for molecular electrostatics with implicit solvent models.

Carbonic Anhydrases: Different Active Sites, Same Metal Selectivity Rules

Carbonic anhydrases are mononuclear metalloenzymes catalyzing the reversible hydration of carbon dioxide in organisms belonging to all three domains of life. Although the mechanism of the catalytic reaction is similar, different families of carbonic anhydrases do not have a common ancestor nor do they exhibit significant resemblance in the amino acid sequence or the structure and composition of the metal-binding sites. Little is known about the physical principles determining the metal affinity and selectivity of the catalytic centers, and how well the native metal is protected from being dislodged by other metal species from the local environment. Here, we endeavor to shed light on these issues by studying (via a combination of density functional theory calculations and polarizable continuum model computations) the thermodynamic outcome of the competition between the native metal cation and its noncognate competitor in various metal-binding sites. Typical representatives of the competing cations from the cellular environments of the respective classes of carbonic anhydrases are considered. The calculations reveal how the Gibbs energy of the metal competition changes when varying the metal type, structure, composition, and solvent exposure of the active center. Physical principles governing metal competition in different carbonic anhydrase metal-binding sites are delineated.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Surface modification of polydimethylsiloxane by the cataractous eye protein isolate

Polydimethylsiloxane (PDMS), even though widely used in microfluidic applications, its hydrophobic nature restricts its utility in some cases. To address this, PDMS may be used in conjunction with a hydrophilic material. Herein, the PDMS surface is modified by plasma treatment followed by cross-linking with the cataractous eye protein isolate (CEPI). CEPI-PDMS composites are prepared at three pH and the effects of CEPI on the chemical, physical, and electrical properties of PDMS are extensively investigated. The cross-linking between PDMS and the protein are confirmed by FTIR, and the contact angle measurements indicate the improved hydrophilic nature of the composite films as compared to PDMS. Atomic Force Microscopy results demonstrate that the surface roughness is enhanced by the incorporation of the protein and is a function of the pH. The effective elastic modulus of the composites is improved by the incorporation of protein into the PDMS matrix. Measurements of the dielectric properties of these composites indicate that they behave as capacitors at lower frequency range while demonstrating resistive characteristics at higher frequency. These composites provide preliminary ideas in developing flexible devices for potential applications in diverse areas such as energy storage materials, and thermo-elective wireless switching devices.

Electro-osmotic flow in nanoconfinement: Solid-state and protein nanopores

Electro-osmotic flow (EOF) is a phenomenon where fluid motion occurs in porous materials or micro/nano-channels when an external electric field is applied. In the particular example of single-molecule electrophoresis using single nanopores, the role of EOF on the translocation velocity of the analyte molecule through the nanopore is not fully understood. The complexity arises from a combination of effects from hydrodynamics in restricted environments, electrostatics emanating from charge decorations and geometry of the pores. We address this fundamental issue using the Poisson-Nernst-Planck and Navier-Stokes (PNP-NS) equations for cylindrical solid-state nanopores and three representative protein nanopores (α-hemolysin, MspA, and CsgG). We present the velocity profiles inside the nanopores as a function of charge decoration and geometry of the pore and applied electric field. We report several unexpected results: (a) The apparent charges of the protein nanopores are different from their net charge and the surface charge of the whole protein geometry, and the net charge of inner surface is consistent with the apparent charge. (b) The fluid velocity depends non-monotonically on voltage. The three protein nanopores exhibit unique EOF and velocity-voltage relations, which cannot be simply deduced from their net charge. Furthermore, effective point mutations can significantly change both the direction and the magnitude of EOF. The present computational analysis offers an opportunity to further understand the origins of the speed of transport of charged macromolecules in restricted space and to design desirable nanopores for tuning the speed of macromolecules through nanopores.

Effect of counterion size on polyelectrolyte conformations and thermodynamics

We present a theoretical model to study the effect of counterion size on the effective charge, size, and thermodynamic behavior of a single, isolated, and flexible polyelectrolyte (PE) chain. We analyze how altering counterion size modifies the energy and entropy contributions to the system, including the ion-pair free energy, excluded volume interactions, entropy of free and condensed ions, and dipolar attraction among monomer-counterion pairs, which result in competing effects challenging intuitive predictions. The PE self-energy is calculated using the Edwards-Muthukumar Hamiltonian, considering a Gaussian monomer distribution for the PE. The condensed ions are assumed to be confined within a cylindrical volume around the PE backbone. The dipolar and excluded volume interactions are described by the second and third virial coefficients. The assumption of freely rotating dipoles results in a first-order coil-globule transition of the PE chain. A more realistic, weaker dipolar attraction, parameterized in our theory, shifts it to a second-order continuous transition. We calculate the size scaling-exponent of the PE and find exponents according to the relative dominance of the electrostatic, excluded volume, or dipolar effects. We further identify the entropy- and energy-driven regimes of the effective charge and conformation of the PE, highlighting the interplay of free ion entropy and ion-pair energy with varying electrostatic strengths. The crossover strength, dependent on the counterion size, indicates that diminishing sizes favor counterion condensation at the expense of free ion entropy. The predictions of the model are consistent with trends in simulations and generalize the findings of the point-like counterion theories.

Revealing the Key Packing Features Determining the Stability of Peptide Bilayer Membrane

Short surfactant-like amphiphilic peptide, A3K, resembling a surfactant with a hydrophobic tail (A3) and a polar headgroup (K), is experimentally determined to form a membrane. Although the peptides are known to exist as β-strands, the exact packing architecture stabilizing the membrane is unknown. Earlier simulation studies have reported successful packing configurations through trial and error. In this work, we present a systematic protocol to identify the best peptide configurations for different packing patterns. The influence of stacking peptides in square and hexagonal packing geometry with the neighboring peptides in parallel and antiparallel orientations was explored. The best peptide configurations were determined from the free energy of bringing 2-4 peptides together as a bundle that can be stacked into a membrane. The stability of the assembled bilayer membrane was further investigated through molecular dynamics simulation. The role of peptide tilting, interpeptide distance, the nature and the extent of interactions, and the conformational degrees of freedom on the stability of the membrane is discussed. The consistency with the experimental findings suggests hexagonal antiparallel as the most relevant molecular architecture.

Development of a Fluctuating Charge Model for Zinc-Containing Metalloproteins

Metalloproteins widely exist in biology and play important roles in various processes. To accurately simulate metalloprotein systems, modeling polarization and charge transfer effects is vital. The fluctuating charge (FQ) model can efficiently generate atomic charges and simulate the charge transfer effect; it has been developed for a wide range of applications, but few models have been specifically tailored for metalloproteins. In this study, we present a fluctuating charge model specifically for zinc-containing metalloproteins based on the extended charge equilibration (EQeq) scheme. Our model was parametrized to reproduce CM5 charges instead of RESP/CHELPG charges because the former is less dependent on the conformation or basis set, does not suffer from unphysical charges for buried atoms, and is still being able to well reproduce the molecular dipoles. During our study, we found that adding the Pauling-bond-order-like term (referred to as the "+C term" in a previous study) between the zinc ion and ligating atoms significantly improves the model's performance. Although our model was trained for four-coordinated zinc sites only, our results indicated it can well describe the atomic charges in diverse zinc sites. Morever, our model was used to generate partial charges for the metal sites in three different zinc-containing metalloproteins (with four-, five-, and six-coordinated metal sites, respectively). These charges exhibited performance comparable to that of the RESP charges in molecular dynamics (MD) simulations. Additional tests indicated our model could also well reproduce the CM5 charges when geometric changes were involved. Those results indicate that our model can efficiently calculate the atomic charges for metal sites and well simulate the charge transfer effect, which marks an important step toward developing versatile polarizable force fields for metalloproteins.

How do Antimicrobial Peptides Interact with the Outer Membrane of Gram-Negative Bacteria? Role of Lipopolysaccharides in Peptide Binding, Anchoring, and Penetration

Gram-negative bacteria possess a complex structural cell envelope that constitutes a barrier for antimicrobial peptides that neutralize the microbes by disrupting their cell membranes. Computational and experimental approaches were used to study a model outer membrane interaction with an antimicrobial peptide, melittin. The investigated membrane included di[3-deoxy-d-manno-octulosonyl]-lipid A (KLA) in the outer leaflet and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE) in the inner leaflet. Molecular dynamics simulations revealed that the positively charged helical C-terminus of melittin anchors rapidly into the hydrophilic headgroup region of KLA, while the flexible N-terminus makes contacts with the phosphate groups of KLA, supporting melittin penetration into the boundary between the hydrophilic and hydrophobic regions of the lipids. Electrochemical techniques confirmed the binding of melittin to the model membrane. To probe the peptide conformation and orientation during interaction with the membrane, polarization modulation infrared reflection absorption spectroscopy was used. The measurements revealed conformational changes in the peptide, accompanied by reorientation and translocation of the peptide at the membrane surface. The study suggests that melittin insertion into the outer membrane affects its permeability and capacitance but does not disturb the membrane's bilayer structure, indicating a distinct mechanism of the peptide action on the outer membrane of Gram-negative bacteria.

Predicting the Geometry of Core-Shell Structures: How a Shape Changes with Constant Added Thickness

The core-shell assembly motif is ubiquitous in chemistry. While the most obvious examples are core/shell-type nanoparticles, many other examples exist. The shape of the core/shell constructs is poorly understood, making it impossible to separate chemical effects from geometric effects. Here, we create a model for the core/shell construct and develop proof for how the eccentricity is expected to change as a function of the shell. We find that the addition of a constant thickness shell always creates a relatively more spherical shape for all shapes covered by our model unless the shape is already spherical or has some underlying radial symmetry. We apply this work to simulated AOT reverse micelles and demonstrate that it is remarkably successful at explaining the observed shapes of the chemical systems. We identify the three specific cases where the model breaks down and how this impacts eccentricity.

S-Se oxidative addition to auranofin derivatives: a DFT study

Oxidative addition of the S-Se bond to Au(I) complexes is discussed for a series of 26 auranofin (AF) derivatives. AF and its analogues are Au(I) complexes with recognized anticancer activity that act by binding and inhibiting the thioredoxin reductase (TrxR) enzyme. Generally, the oxidative addition to Au(I) is a sluggish reaction under mild conditions (i.e., a high activation barrier - ΔH‡), which is also verified here for AF, ΔH‡ = 33.0 kcal mol-1. However, we predicted that subtle changes in the AF ligands can make the process feasible under standard conditions. For instance, the exchange of -PEt3 by -P(Et2)(OEt), which is a weaker electron σ-donor, reduced the activation barrier to 17.1 kcal mol-1. Furthermore, substitution of the -SAtg ligand by -Cl- leads to a ΔH‡ value of 22.5 kcal mol-1. Overall, the reaction is driven by the nucleophilic attack of the S-Se bond on the Au(I) center, attributed mainly to the charge transfer (4p)Se → (6p)Au, which characterizes the addition step. At the transition state (TS) point, the (5d)Au → σ*(S-Se) charge transfer becomes relevant, facilitating the S-Se bond breakage and the oxidation step. In addition to the electron transfers, the strain energy to deform the linear Au(I) geometry to the tetracoordinated Au(III) arrangement in the TS structure plays a primary role in explaining the trends in the activation barriers. Finally, the activation barrier (ΔH‡) and reaction energy (ΔH°) were correlated for most of the complexes studied, which suggests that the reaction passes through a late or product-like TS and, therefore, the steric and electronic factors affecting ΔH‡ also act on ΔH°. Overall, the results presented here might open up a new field of investigation for interactions between AF derivatives and TrxR, which contributes to a full understanding of the biological mechanism of action of these species.

Next-Generation Tags for Fluorine Nuclear Magnetic Resonance: Designing Amplification of Chemical Shift Sensitivity

Fluorine NMR is a highly sensitive technique for delineating the conformational states of biomolecules and has shown great utility in drug screening and in understanding protein function. Current fluorinated protein tags leverage the intrinsic chemical shift sensitivity of the 19F nucleus to detect subtle changes in protein conformation and topology. This chemical shift sensitivity can be amplified by embedding the fluorine or trifluoromethyl reporter within a pyridone. Due to their polarizability and rapid tautomerization, pyridones exhibit a greater range of electron delocalization and correspondingly greater 19F NMR chemical shift dispersion. To assess the chemical shift sensitivity of these tautomeric probes to the local environment, 19F NMR spectra of all possible monofluorinated and trifluoromethyl-tagged versions of 2-pyridone were recorded in methanol/water mixtures ranging from 100% methanol to 100% water. 4-Fluoro-2-pyridone and 6-(trifluoromethyl)-2-pyridone (6-TFP) displayed the greatest sensitivity of the monofluorinated and trifluoromethylated pyridones, exceeding that of known conventional CF3 reporters. To evaluate the utility of tautomeric pyridone tags for 19F NMR of biomolecules, the alpha subunit of the stimulatory G protein (Gsα) and human serum albumin (HSA) were each labeled with a thiol-reactive derivative of 6-TFP and the spectra were recorded as a function of various adjuvants and drugs. The tautomeric tag outperformed the conventional tag, 2-bromo-N-(4-(trifluoromethyl)phenyl)acetamide through the improved resolution of several functional states.

Sequence - dynamics - function relationships in protein tyrosine phosphatases

Protein tyrosine phosphatases (PTPs) are crucial regulators of cellular signaling. Their activity is regulated by the motion of a conserved loop, the WPD-loop, from a catalytically inactive open to a catalytically active closed conformation. WPD-loop motion optimally positions a catalytically critical residue into the active site, and is directly linked to the turnover number of these enzymes. Crystal structures of chimeric PTPs constructed by grafting parts of the WPD-loop sequence of PTP1B onto the scaffold of YopH showed WPD-loops in a wide-open conformation never previously observed in either parent enzyme. This wide-open conformation has, however, been observed upon binding of small molecule inhibitors to other PTPs, suggesting the potential of targeting it for drug discovery efforts. Here, we have performed simulations of both enzymes and show that there are negligible energetic differences in the chemical step of catalysis, but significant differences in the dynamical properties of the WPD-loop. Detailed interaction network analysis provides insight into the molecular basis for this population shift to a wide-open conformation. Taken together, our study provides insight into the links between loop dynamics and chemistry in these YopH variants specifically, and how WPD-loop dynamic can be engineered through modification of the internal protein interaction network.

Quantum biochemical analysis of the binding interactions between a potential inhibitory drug and the Ebola viral glycoprotein

Ebola virus disease (EVD) causes outbreaks and epidemics in West Africa that persist until today. The envelope glycoprotein of Ebola virus (GP) consists of two subunits, GP1 and GP2, and plays a key role in anchoring or fusing the virus to the host cell in its active form on the virion surface. Toremifene (TOR) is a ligand that mainly acts as an estrogen receptor antagonist; however, a recent study showed a strong and efficient interaction with GP. In this context, we aimed to evaluate the energetic affinity features involved in the interaction between GP and toremifene by computer simulation techniques using the Molecular Fractionation Method with Conjugate Caps (MFCC) scheme and quantum-mechanical (QM) calculations, as well as missense mutations to assess protein stability. We identified ASP522, GLU100, TYR517, THR519, LEU186, LEU515 as the most attractive residues in the EBOV glycoprotein structure that form the binding pocket. We divided toremifene into three regions and evaluated that region i was more important than region iii and region ii for the formation of the TOR-GP1/GP2 complex, which might control the molecular remodeling process of TOR. The mutations that caused more destabilization were ARG134, LEU515, TYR517 and ARG559, while those that caused stabilization were GLU523 and ASP522. TYR517 is a critical residue for the binding of TOR, and is highly conserved among EBOV species. Our results may help to elucidate the mechanism of drug action on the GP protein of the Ebola virus and subsequently develop new pharmacological approaches against EVD.Communicated by Ramaswamy H. Sarma.

Calculation of electrostatic free energy for the nonlinear Poisson-Boltzmann model based on the dimensionless potential

The Poisson-Boltzmann (PB) equation governing the electrostatic potential with a unit is often transformed to a normalized form for a dimensionless potential in numerical studies. To calculate the electrostatic free energy (EFE) of biological interests, a unit conversion has to be conducted, because the existing PB energy functionals are all described in terms of the original potential. To bypass this conversion, this paper proposes energy functionals in terms of the dimensionless potential for the first time in the literature, so that the normalized PB equation can be directly derived by using the Euler-Lagrange variational analysis. Moreover, alternative energy forms have been rigorously derived to avoid approximating the gradient of singular functions in the electrostatic stress term. A systematic study has been carried out to examine the surface integrals involved in alternative energy forms and their dependence on finite domain size and mesh step size, which leads to a recommendation on the EFE forms for efficient computation of protein systems. The calculation of the EFE in the regularization formulation, which is an analytical approach for treating singular charge sources of the PB equation, has also been studied. The proposed energy forms have been validated by considering smooth dielectric settings, such as diffuse interface and super-Gaussian, for which the EFE of the nonlinear PB model is found to be significantly different from that of the linearized PB model. All proposed energy functionals and EFE forms are designed such that the dimensionless potential can be simply plugged in to compute the EFE in the unit of kcal/mol, and they can also be applied in the classical sharp interface PB model.

Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment

The accuracy of computational models of water is key to atomistic simulations of biomolecules. We propose a computationally efficient way to improve the accuracy of the prediction of hydration-free energies (HFEs) of small molecules: the remaining errors of the physics-based models relative to the experiment are predicted and mitigated by machine learning (ML) as a postprocessing step. Specifically, the trained graph convolutional neural network attempts to identify the "blind spots" in the physics-based model predictions, where the complex physics of aqueous solvation is poorly accounted for, and partially corrects for them. The strategy is explored for five classical solvent models representing various accuracy/speed trade-offs, from the fast analytical generalized Born (GB) to the popular TIP3P explicit solvent model; experimental HFEs of small neutral molecules from the FreeSolv set are used for the training and testing. For all of the models, the ML correction reduces the resulting root-mean-square error relative to the experiment for HFEs of small molecules, without significant overfitting and with negligible computational overhead. For example, on the test set, the relative accuracy improvement is 47% for the fast analytical GB, making it, after the ML correction, almost as accurate as uncorrected TIP3P. For the TIP3P model, the accuracy improvement is about 39%, bringing the ML-corrected model's accuracy below the 1 kcal/mol threshold. In general, the relative benefit of the ML corrections is smaller for more accurate physics-based models, reaching the lower limit of about 20% relative accuracy gain compared with that of the physics-based treatment alone. The proposed strategy of using ML to learn the remaining error of physics-based models offers a distinct advantage over training ML alone directly on reference HFEs: it preserves the correct overall trend, even well outside of the training set.

Computational Prediction of Coiled-Coil Protein Gelation Dynamics and Structure

Protein hydrogels represent an important and growing biomaterial for a multitude of applications, including diagnostics and drug delivery. We have previously explored the ability to engineer the thermoresponsive supramolecular assembly of coiled-coil proteins into hydrogels with varying gelation properties, where we have defined important parameters in the coiled-coil hydrogel design. Using Rosetta energy scores and Poisson-Boltzmann electrostatic energies, we iterate a computational design strategy to predict the gelation of coiled-coil proteins while simultaneously exploring five new coiled-coil protein hydrogel sequences. Provided this library, we explore the impact of in silico energies on structure and gelation kinetics, where we also reveal a range of blue autofluorescence that enables hydrogel disassembly and recovery. As a result of this library, we identify the new coiled-coil hydrogel sequence, Q5, capable of gelation within 24 h at 4 °C, a more than 2-fold increase over that of our previous iteration Q2. The fast gelation time of Q5 enables the assessment of structural transition in real time using small-angle X-ray scattering (SAXS) that is correlated to coarse-grained and atomistic molecular dynamics simulations revealing the supramolecular assembling behavior of coiled-coils toward nanofiber assembly and gelation. This work represents the first system of hydrogels with predictable self-assembly, autofluorescent capability, and a molecular model of coiled-coil fiber formation.

Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.

Bound ion effects: Using machine learning method to study the kinesin Ncd's binding with microtubule

Drosophila Ncd proteins are motor proteins that play important roles in spindle organization. Ncd and the tubulin dimer are highly charged. Thus, it is crucial to investigate Ncd-tubulin dimer interactions in the presence of ions, especially ions that are bound or restricted at the Ncd-tubulin dimer binding interfaces. To consider the ion effects, widely used implicit solvent models treat ions implicitly in the continuous solvent environment without focusing on the individual ions' effects. But highly charged biomolecules such as the Ncd and tubulin dimer may capture some ions at highly charged regions as bound ions. Such bound ions are restricted to their binding sites; thus, they can be treated as part of the biomolecules. By applying multiscale computational methods, including the machine-learning-based Hybridizing Ions Treatment-2 program, molecular dynamics simulations, DelPhi, and DelPhiForce, we studied the interaction between the Ncd motor domain and the tubulin dimer using a hybrid solvent model, which considers the bound ions explicitly and the other ions implicitly in the solvent environment. To identify the importance of treating bound ions explicitly, we also performed calculations using the implicit solvent model without considering the individual bound ions. We found that the calculations of the electrostatic features differ significantly between those of the hybrid solvent model and the pure implicit solvent model. The analyses show that treating bound ions at highly charged regions explicitly is crucial for electrostatic calculations. This work proposes a machine-learning-based approach to handle the bound ions using the hybrid solvent model. Such an approach is not only capable of handling kinesin-tubulin complexes but is also appropriate for other highly charged biomolecules, such as DNA/RNA, viral capsid proteins, etc.

Label-Free, Impedance-Based Biosensor for Kidney Disease Biomarker Uromodulin

We demonstrate the development of a label-free, impedance-based biosensor by using a passivation layer of 50-nm tantalum pentoxide (Ta2O5) on interdigitated electrodes (IDE). This layer was fabricated by atomic layer deposition (ALD) and has a high dielectric constant (high-κ), which improves the capacitive property of the IDE. We validate the biosensor's performance by measuring uromodulin, a urine biomarker for kidney tubular damage, from artificial urine samples. The passivation layer is functionalized with uromodulin antibodies for selective binding. The passivated IDE enables the non-faradaic impedance measurement of uromodulin concentrations with a measurement range from 0.5 ng/mL to 8 ng/mL and with a relative change in impedance of 15 % per ng/mL at a frequency of 150 Hz (log scale). This work presents a concept for point-of-care biosensing applications for disease biomarkers.

Near-Temperature-Independent Electron Transport Well beyond Expected Quantum Tunneling Range via Bacteriorhodopsin Multilayers

A key conundrum of biomolecular electronics is efficient electron transport (ETp) through solid-state junctions up to 10 nm, often without temperature activation. Such behavior challenges known charge transport mechanisms, especially via nonconjugated molecules such as proteins. Single-step, coherent quantum-mechanical tunneling proposed for ETp across small protein, 2-3 nm wide junctions, but it is problematic for larger proteins. Here we exploit the ability of bacteriorhodopsin (bR), a well-studied, 4-5 nm long membrane protein, to assemble into well-defined single and multiple bilayers, from ∼9 to 60 nm thick, to investigate ETp limits as a function of junction width. To ensure sufficient signal/noise, we use large area (∼10-3 cm2) Au-protein-Si junctions. Photoemission spectra indicate a wide energy separation between electrode Fermi and the nearest protein-energy levels, as expected for a polymer of mostly saturated components. Junction currents decreased exponentially with increasing junction width, with uniquely low length-decay constants (0.05-0.5 nm-1). Remarkably, even for the widest junctions, currents are nearly temperature-independent, completely so below 160 K. While, among other things, the lack of temperature-dependence excludes, hopping as a plausible mechanism, coherent quantum-mechanical tunneling over 60 nm is physically implausible. The results may be understood if ETp is limited by injection into one of the contacts, followed by more efficient charge propagation across the protein. Still, the electrostatics of the protein films further limit the number of charge carriers injected into the protein film. How electron transport across dozens of nanometers of protein layers is more efficient than injection defines a riddle, requiring further study.

Unraveling the electronic origin of a special feature in the triplet-minus-singlet spectra of carotenoids in natural photosystems

The influence of carotenoid triplet states on the Qy electronic transitions of chlorophylls has been observed in experiments on light-harvesting complexes over the past three decades, but the interpretation of the resulting spectral feature in the triplet minus singlet (T-S) absorption spectra of photosystems is still debated, as the physical-chemical explanation of this feature has been elusive. Here, we resolve this debate, by explaining the T-S spectra of pigment complexes over the Qy-band spectral region through a comparative study of chlorophyll-carotenoid model dyads and larger pigment complexes from the main light harvesting complex of higher plants (LHCII). This goal is achieved by combining state-of-the-art time-dependent density functional theory with analysis of the relationship between electronic properties and nuclear structure, and by comparison to the experiment. We find that the special signature in the T-S spectra of both model and natural photosystems is determined by singlet-like triplet excitations that can be described as effective singlet excitations on chlorophylls influenced by a stable electronic triplet on the carotenoid. The comparison with earlier experiments on different light-harvesting complexes confirms our theoretical interpretation of the T-S spectra in the Qy spectral region. Our results indicate an important role for the chlorophyll-carotenoid electronic coupling, which is also responsible for the fast triplet-triplet energy transfer, suggesting a fast trapping of the triplet into the relaxed carotenoid structure. The gained understanding of the interplay between the electronic and nuclear structures is potentially informative for future studies of the mechanism of photoprotection by carotenoids.

Grid-Robust Efficient Neural Interface Model for Universal Molecule Surface Construction from Point Clouds

Molecular surfaces play a pivotal role in elucidating the properties and functions of biological complexes. While various surfaces have been proposed for specific scenarios, their widespread adoption faces challenges due to limited efficiency stemming from hand-crafted modeling designs. In this work, we proposed a general framework that incorporates both the point cloud concept and neural networks. The use of matrix multiplication in this framework enables efficient implementation across diverse platforms and libraries. We applied this framework to develop the GENIUSES (Grid-robust Efficient Neural Interface for Universal Solvent-Excluded Surface) model for constructing SES. GENIUSES demonstrates high accuracy and efficiency across data sets with varying conformations and complexities. Compared to the classical implementation of SES in the AMBER software package, our framework achieved a 26-fold speedup while retaining ∼95% accuracy when ported to the GPU platform using CUDA. Greater speedups can be obtained in large-scale systems. Importantly, our model exhibits robustness against variations in the grid spacing. We have integrated this infrastructure into AMBER to enhance accessibility for research in drug screening and related fields, where efficiency is of paramount importance.

OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining

The "bottom-up" approach to coarse-graining, for building accurate and efficient computational models to simulate large-scale and complex phenomena and processes, is an important approach in computational chemistry, biophysics, and materials science. As one example, the Multiscale Coarse-Graining (MS-CG) approach to developing CG models can be rigorously derived using statistical mechanics applied to fine-grained, i.e., all-atom simulation data for a given system. Under a number of circumstances, a systematic procedure, such as MS-CG modeling, is particularly valuable. Here, we present the development of the OpenMSCG software, a modularized open-source software that provides a collection of successful and widely applied bottom-up CG methods, including Boltzmann Inversion (BI), Force-Matching (FM), Ultra-Coarse-Graining (UCG), Relative Entropy Minimization (REM), Essential Dynamics Coarse-Graining (EDCG), and Heterogeneous Elastic Network Modeling (HeteroENM). OpenMSCG is a high-performance and comprehensive toolset that can be used to derive CG models from large-scale fine-grained simulation data in file formats from common molecular dynamics (MD) software packages, such as GROMACS, LAMMPS, and NAMD. OpenMSCG is modularized in the Python programming framework, which allows users to create and customize modeling "recipes" for reproducible results, thus greatly improving the reliability, reproducibility, and sharing of bottom-up CG models and their applications.

Deconstructing Electrostatics of Functionalized Metal Nanoparticles from Molecular Dynamics Simulations

Gold nanoparticles (NPs) with different surface functionalizations can selectively interact with specific proteins, allowing a wide range of possible applications in biotechnology and biomedicine. To prevent their tendency to aggregate and to modulate their interaction with charged biomolecules or substrates (e.g., for biosensing applications), they can be functionalized with charged groups, introducing a mutual interaction which can be modulated by changing the ionic strength of the solvent. In silico modeling of these systems is often addressed with low-resolution models, which must account for these effects in the, often implicit, solvent representation. Here, we present a systematic conformational dynamic characterization of ligand-coated gold nanoparticles with different sizes, charges, and functionalizations by means of atomistic molecular dynamics simulations. Based on these, we deconstruct their electrostatic properties and propose a general representation of their average-long-range interactions extendable to different sizes, charges, and ionic strengths. This study clarifies in detail the role of the different features of the NP (charge, size, structure) and of the ionic strength in determining the details of the interparticle interaction and represents the first step toward a general strategy for the parametrization of NP coarse-grained models able to account for varying ionic strengths.

In silico drug repurposing and lipid bilayer molecular dynamics puzzled out potential breast cancer resistance protein (BCRP/ABCG2) inhibitors

Multidrug resistance (MDR) is a fundamental reason for the fiasco of carcinoma chemotherapy. A wide variety of anticarcinoma drugs are expelled from neoplasm cells through the ATP-binding cassette (ABC) transporter superfamily, rendering the neoplasm cells resistant to treatment. The ATP-binding cassette transporter G2 (ABCG2, gene symbol BCRP) is an ABC efflux transporter that plays a key function in MDR to antineoplastic therapies. For these reasons, the identification of medicaments as BCRP inhibitors could assist in discovering better curative approaches for breast cancer therapy. Because of the deficiency of prospective BCRP inhibitors, the SuperDRUG2 database was virtually screened for inhibitor activity towards the BCRP transporter using molecular docking computations. The most potent drug candidates were then characterized utilizing molecular dynamics (MD) simulations. Furthermore, molecular mechanics-generalized Born surface area (MM-GBSA) binding affinities of the most potent drug candidates were estimated. Based on the MM-GBSA binding affinities throughout 150 ns MD simulations, three drugs-namely zotarolimus (SD002595), temsirolimus (SD003393), and glecaprevir (SD006009)-revealed greater binding affinities towards BCRP transporter compared to the co-crystallized BWQ ligand with ΔGbinding values of -86.6 ± 5.6, -79.5 ± 8.0, -75.8 ± 4.6 and -59.5 ± 4.1 kcal/mol, respectively. The steadiness of these promising drugs bound with BCRP transporter was examined utilizing their structural and energetical analyses throughout a 150 ns MD simulation. To imitate the physiological environment, 150 ns MD simulations for the identified drugs bound with BCRP transporter were conducted in the 1-palmitoyl-2-oleoyl-phosphatidylcholine lipid bilayer. These findings identify zotarolimus, temsirolimus and glecaprevir as auspicious anti-MDR drug leads that warrant further experimental assays.Communicated by Ramaswamy H. Sarma.

Biobased Electronics: Tunable Dielectric and Piezoelectric Cellulose Nanocrystal-Protein Films

Cellulose has been a go-to material for its dielectric properties from the onset of capacitor development. The demand for an energy storage solution continues to grow, but the supply remains limited and relies too often on fossil and mined materials. This work proposes a fully sustainable and green method with which to produce dielectric thin films made of renewable and degradable materials. Cellulose nanocrystals (CNC) made an excellent matrix for the dispersion of proteins and the fabrication of robust transparent thin films with enhanced dielectric permittivity. A range of proteins sources, additives and concentrations allowed for us to control the dielectric permittivity from εr = 4 to 50. The proteins screened came from animal and plant sources. The films were formed from drying a water suspension of the CNC and proteins through evaporation-induced self-assembly. This yielded nano-layered structures with very high specific surface areas, ideal for energy storage devices. The resulting films were characterized with respect to the electrical, mechanical, piezoelectric, and optical properties to be compared. Electrically conductive (σ = 1.53 × 103 S/m) CNC films were prepared with carbon nanotubes (CNT). The fabricated films were used to make flexible, sustainable, and degradable capacitors by layering protein-based films between CNC-CNT composite films.

Tryptophan Stabilization of a Biochemical Carbocation Evaluated by Analysis of π Complexes of 3-Ethylindole with the t-Butyl Cation

Understanding how the highly unstable carbocation intermediates in terpenoid biosynthesis are stabilized and protected during their transient existence in enzyme active sites is an intriguing challenge which has to be addressed computationally. Our efforts have focused on evaluating the stabilization afforded via carbocation-π complexation between a biochemical carbocation and an aromatic amino acid residue. This has involved making measurements on an X-ray structure of an enzyme active site that shows a π donor proximate to a putative carbocation site and using these to build models which are analyzed computationally to provide an estimated stabilization energy (SE). Previously, we reported estimated SEs for several such carbocation-π complexes involving phenylalanine. Herein, we report the first such estimate involving tryptophan as the π donor. Because there was almost no published information about indole as a π-complexation donor, we first located computationally equilibrium π and σ complexes of 3-ethylindole with the t-butyl cation as relevant background information. Then, measurements on the X-ray structure of the enzyme CotB2 complexed with geranylgeranyl thiodiphosphate (GGSPP), specifically on the geometric relationship of the putative carbocation at C15 of GGSPP to W186, were used to build a model that afforded a computed SE of -15.3 kcal/mol.

Preferred microenvironments of halogen bonds and hydrogen bonds revealed using statistics and QM/MM calculation studies

Hydrogen bonds (HBs) and halogen bonds (XBs) are two essential non-covalent interactions for molecular recognition and drug design. As proteins are heterogeneous in structure, the microenvironments of protein structures should have effects on the formation of HBs and XBs with ligands. However, there are no systematic studies reported on this effect to date. For quantitatively describing protein microenvironments, we defined the local hydrophobicities (LHs) and local dielectric constants (LDCs) in this study. With the defined parameters, we conducted an elaborate database survey on the basis of 22 011 ligand-protein structures to explore the microenvironmental preference of HBs (91 966 in total) and XBs (1436 in total). The statistics show that XBs prefer hydrophobic microenvironments compared to HBs. The polar residues like ASP are more likely to form HBs with ligands, while nonpolar residues such as PHE and MET prefer XBs. Both the LHs and LDCs (10.69 ± 4.36 for HBs; 8.86 ± 4.00 for XBs) demonstrate that XBs are prone to hydrophobic microenvironments compared with HBs with significant differences (p < 0.001), indicating that evaluating their strengths in the corresponding environments should be necessary. Quantum Mechanics-Molecular Mechanics (QM/MM) calculations reveal that in comparison with vacuum environments, the interaction energies of HBs and XBs are decreased to varying degrees given different microenvironments. In addition, the strengths of HBs are impaired more than those of XBs when the local dielectric constant's difference between the XB microenvironments and the HB microenvironments is large.

Probing the Conformational Preference to β-Strand during Peptide Self-Assembly

Alanine-rich tetrapeptides like A₃K dominantly exist as polyproline II helices in dilute aqueous solutions. However, during self-assembly, based on the free energy calculation in implicit solvent for various peptide conformations, only the peptides in the β-strand conformation can be packed closely. This necessitates the conformational transition to the β-strand commonly observed during peptide self-assembly such as in amyloid fibril formation. In fact, the closest interpeptide distance of 4.8 Å is consistent with the interstrand distance determined from the X-ray diffraction pattern of many amyloid fibrils. The position of free energy minimum obtained from implicit solvent calculation matches exactly with the explicit solvent simulation through umbrella sampling when the peptide conformations are restrained, demonstrating the applicability of the former for rapid screening of peptide configurations favorable for self-assembly. The barrier in the free energy profile in the presence of water arises out of the entropic restriction on the interstitial water molecules while satisfying the hydrogen bonding of both the peptides by forming water mediated hydrogen bond bridge. Further, the high energy barrier observed for the β-strand suggests that peptides initially tend to self-assemble in the polyproline II structure to mitigate the desolvation energy cost; the transition to the β-strand would happen only in the later stage after crossing the barrier. The umbrella sampling simulations with peptides allowed to change conformations, relative to each other, confirm the dynamic conformational transition during the course of the self-assembly supporting the "dock and lock" mechanism suggested for amyloid fibrillar growth.

Light-Driven Hydrogen Evolution Reaction Catalyzed by a Molybdenum-Copper Artificial Hydrogenase

Orange protein (Orp) is a small bacterial metalloprotein of unknown function that harbors a unique molybdenum/copper (Mo/Cu) heterometallic cluster, [S₂MoS₂CuS₂MoS₂]^3-. In this paper, the performance of Orp as a catalyst for the photocatalytic reduction of protons into H₂ has been investigated under visible light irradiation. We report the complete biochemical and spectroscopic characterization of holo-Orp containing the [S₂MoS₂CuS₂MoS₂]^3- cluster, with docking and molecular dynamics simulations suggesting a positively charged Arg, Lys-containing pocket as the binding site. Holo-Orp exhibits excellent photocatalytic activity, in the presence of ascorbate as the sacrificial electron donor and [Ru(bpy)₃]Cl₂ as the photosensitizer, for hydrogen evolution with a maximum turnover number of 890 after 4 h irradiation. Density functional theory (DFT) calculations were used to propose a consistent reaction mechanism in which the terminal sulfur atoms are playing a key role in promoting H₂ formation. A series of dinuclear [S₂MS₂M'S₂MS₂]^(4n)- clusters, with M = Mo^VI, W^VI and M'⁽ⁿ⁺⁾ = Cu^I, Fe^I, Ni^I, Co^I, Zn^II, Cd^II were assembled in Orp, leading to different M/M'-Orp versions which are shown to display catalytic activity, with the Mo/Fe-Orp catalyst giving a remarkable turnover number (TON) of 1150 after 2.5 h reaction and an initial turnover frequency (TOF°) of 800 h^-1 establishing a record among previously reported artificial hydrogenases.

Stacked binding of a PET ligand to Alzheimer's tau paired helical filaments

Accumulation of filamentous aggregates of tau protein in the brain is a pathological hallmark of Alzheimer's disease (AD) and many other neurodegenerative tauopathies. The filaments adopt disease-specific cross-β amyloid conformations that self-propagate and are implicated in neuronal loss. Development of molecular diagnostics and therapeutics is of critical importance. However, mechanisms of small molecule binding to the amyloid core is poorly understood. We used cryo-electron microscopy to determine a 2.7 Å structure of AD patient-derived tau paired-helical filaments bound to the PET ligand GTP-1. The compound is bound stoichiometrically at a single site along an exposed cleft of each protofilament in a stacked arrangement matching the fibril symmetry. Multiscale modeling reveals pi-pi aromatic interactions that pair favorably with the small molecule-protein contacts, supporting high specificity and affinity for the AD tau conformation. This binding mode offers critical insight into designing compounds to target different amyloid folds found across neurodegenerative diseases.

Computational Study of Base-Catalyzed Thiohemiacetal Decomposition in Pseudomonas mevalonii HMG-CoA Reductase

Thiohemiacetals are key intermediates in the active sites of many enzymes catalyzing a variety of reactions. In the case of Pseudomonas mevalonii 3-hydroxy-3-methylglutaryl coenzyme A reductase (PmHMGR), this intermediate connects the two hydride transfer steps where a thiohemiacetal is the product of the first hydride transfer and its breakdown forms the substrate of the second one, serving as the intermediate during cofactor exchange. Despite the many examples of thiohemiacetals in a variety of enzymatic reactions, there are few studies that detail their reactivity. Here, we present computational studies on the decomposition of the thiohemiacetal intermediate in PmHMGR using both QM-cluster and QM/MM models. This reaction mechanism involves a proton transfer from the substrate hydroxyl to an anionic Glu83 followed by a C-S bond elongation stabilized by a cationic His381. The reaction provides insight into the varying roles of the residues in the active site that favor this multistep mechanism.

Interaction of lectin Sambucus nigra with sialylated trisaccharides in the presence of osmolytes. Chronopotentiometric sensing

Trisaccharides bind to their interaction partners-lectins relatively weakly, which makes detection of their complexes challenging. In this work, we show that an osmolyte presence improves the distinguishing complexes of lectin Sambucus nigra with trisialyllactoses with various binding affinities. The addition of osmolyte, non-binding sugar mannose significantly improved the precision of binding experiments performed using chronopotentiometric stripping at the electrode surface and fluorescence analysis in solution. Osmolytes minimized nonspecific interactions between binding sugar and lectin. Obtained findings can be utilized in any in vitro methods studying interactions of carbohydrates, respectively their conjugates with proteins. The study of carbohydrate interactions appears important since they play essential roles in a variety of biological processes including carcinogenesis.

Fragment-Based Calculations of Enzymatic Thermochemistry Require Dielectric Boundary Conditions

Electronic structure calculations on enzymes require hundreds of atoms to obtain converged results, but fragment-based approximations offer a cost-effective solution. We present calculations on enzyme models containing 500-600 atoms using the many-body expansion, comparing to benchmarks in which the entire enzyme-substrate complex is described at the same level of density functional theory. When the amino acid fragments contain ionic side chains, the many-body expansion oscillates under vacuum boundary conditions but rapid convergence is restored using low-dielectric boundary conditions. This implies that full-system calculations in the gas phase are inappropriate benchmarks for assessing errors in fragment-based approximations. A three-body protocol retains sub-kilocalorie per mole fidelity with respect to a supersystem calculation, as does a two-body calculation combined with a full-system correction at a low-cost level of theory. These protocols pave the way for application of high-level quantum chemistry to large systems via rigorous, ab initio treatment of many-body polarization.

Adenine Fine-Tunes DNA Photolyase's Repair Mechanism

The comparative study of DNA repair by mesophilic and extremophilic photolyases helps us understand the evolution of these enzymes and their role in preserving life on our changing planet. The mechanism of repair of cyclobutane pyrimidine dimer lesions in DNA by electron transfer from the flavin adenine dinucleotide cofactor is the subject of intense interest. The role of adenine in mediating this process remains unresolved. Using microsecond molecular dynamics simulations, we find that adenine mediates the electron transfer in both mesophile and extremophile DNA photolyases through a similar mechanism. In fact, in all photolyases studied, the molecular conformations with the largest electronic couplings between the enzyme cofactor and DNA show the presence of adenine in 10-20% of the strongest-coupling tunneling pathways between the atoms of the electron donor and acceptor. Our theoretical analysis finds that adenine serves the critical role of fine-tuning rather than maximizing the donor-acceptor coupling within the range appropriate for the repair function.

Tautomerisation Mechanisms in the Adenine-Thymine Nucleobase Pair during DNA Strand Separation

The adenine-thymine tautomer (A*-T*) has previously been discounted as a spontaneous mutagenesis mechanism due to the energetic instability of the tautomeric configuration. We study the stability of A*-T* while the nucleobases undergo DNA strand separation. Our calculations indicate an increase in the stability of A*-T* as the DNA strands unzip and the hydrogen bonds between the bases stretch. Molecular Dynamics simulations reveal the time scales and dynamics of DNA strand separation and the statistical ensemble of opening angles present in a biological environment. Our results demonstrate that the unwinding of DNA, an inherently out-of-equilibrium process facilitated by helicase, will change the energy landscape of the adenine-thymine tautomerization reaction. We propose that DNA strand separation allows the stable tautomerization of adenine-thymine, providing a feasible pathway for genetic point mutations via proton transfer between the A-T bases.

Inorganic Drugs as a Tool for Protein Structure Solving and Studies on Conformational Changes

Inorganic drugs are capable of tight interactions with proteins through coordination towards aminoacidic residues, and this feature is recognized as a key aspect for their pharmacological action. However, the "protein metalation process" is exploitable for solving the phase problem and structural resolution. In fact, the use of inorganic drugs bearing specific metal centers and ligands capable to drive the binding towards the desired portions of the protein target could represent a very intriguing and fruitful strategy. In this context, a theoretical approach may further contribute to solve protein structures and their refinement. Here, we delineate the main features of a reliable experimental-theoretical integrated approach, based on the use of metallodrugs, for protein structure solving.

Potential drug candidates as P-glycoprotein inhibitors to reverse multidrug resistance in cancer: an in silico drug discovery study

The failure of chemotherapy in the treatment of carcinoma is mainly due to the development of multidrug resistance (MDR), which is largely caused by the overexpression of P-glycoprotein (P-gp/ABCB1/MDR1). Until recently, the 3D structure of the P-gp transporter has not been experimentally resolved, which restricted the discovery of prospective P-gp inhibitors utilizing in silico techniques. In this study, the binding energies of 512 drug candidates in clinical or investigational stages were assessed as potential P-gp inhibitors employing in silico methods. On the basis of the available experimental data, the performance of the AutoDock4.2.6 software to predict the drug-P-gp binding mode was initially validated. Molecular docking and molecular dynamics (MD) simulations combined with molecular mechanics-generalized Born surface area (MM-GBSA) binding energy computations were subsequently conducted to screen the investigated drug candidates. Based on the current results, five promising drug candidates, namely valspodar, dactinomycin, elbasvir, temsirolimus, and sirolimus, showed promising binding energies against P-gp transporter with ΔGbinding values of -126.7, -112.1, -111.9, -102.9, and -101.4 kcal/mol, respectively. The post-MD analyses revealed the energetical and structural stabilities of the identified drug candidates in complex with the P-gp transporter. Furthermore, in order to mimic the physiological conditions, the potent drugs complexed with the P-gp were subjected to 100 ns MD simulations in an explicit membrane-water environment. The pharmacokinetic properties of the identified drugs were predicted and demonstrated good ADMET characteristics. Overall, these results indicated that valspodar, dactinomycin, elbasvir, temsirolimus, and sirolimus hold promise as prospective P-gp inhibitors and warrant further invitro/invivo investigations.

Binding site hotspot map of PI3Kα and mTOR in the presence of selective and dual ATP-competitive inhibitors

The PI3K/Akt/mTOR signaling pathway plays a pivotal role in cellular metabolism, growth and survival. PI3Kα hyperactivation impairs downstream signaling, including mTOR regulation, and are linked to poor prognosis and refractory cancer treatment. To support multi-target drug discovery, we took advantage from existing PI3Kα and mTOR crystallographic structures to map similarities and differences in their ATP-binding pockets in the presence of selective or dual inhibitors. Molecular dynamics and MM/PBSA calculations were employed to study the binding profile and identify the relative contribution of binding site residues. Our analysis showed that while varying parameters of solute and solvent dielectric constant interfered in the absolute binding free energy, it had no effect in the relative per residue contribution. In all complexes, the most important interactions were observed within 3-3.5 Å from inhibitors, responding for ∼75-100% of the total calculated interaction energy. While closest residues are essential for the strength of the binding of all ligands, more distant residues seem to have a larger impact on the binding of the dual inhibitor, as observed for PI3Kα residues Phe934, Lys802 and Asp805 and, mTOR residues Leu2192, Phe2358, Leu2354, Lys2187 and Tyr2225. A detailed description of individual residue contribution in the presence of selective or dual inhibitors is provided as an effort to improve the understanding of molecular mechanisms controlling multi-target inhibition. This work provides key information to support further studies seeking the rational design of potent PI3K/mTOR dual inhibitors for cancer treatment.Communicated by Ramaswamy H. Sarma.

Structural Determinants of Redox Conduction Favor Robustness over Tunability in Microbial Cytochrome Nanowires

Helical homopolymers of multiheme cytochromes catalyze biogeochemically significant electron transfers with a reported 10 3 -fold variation in conductivity. Herein, classical molecular dynamics and hybrid quantum/classical molecular mechanics are used to elucidate the structural determinants of the redox potentials and conductivities of the tetra-, hexa-, and octaheme outer-membrane cytochromes E, S, and Z, respectively, from Geobacter sulfurreducens . Second-sphere electrostatic interactions acting on minimally polarized heme centers are found to regulate redox potentials over a computed 0.5-V range. However, the energetics of redox conduction are largely robust to the structural diversity: Single-step electronic couplings (⟨H mn ⟩), reaction free energies , and reorganization energies (λ mn ) are always respectively <|0.026|, <|0.26|, and between 0.5 - 1.0 eV. With these conserved parameter ranges, redox conductivity differed by less than a factor of 10 among the 'nanowires' and is sufficient to meet the demands of cellular respiration if 10 2 - 10 3 'nanowires' are expressed. The 'nanowires' are proposed to be differentiated by the protein packaging to interface with a great variety of environments, and not by conductivity, because the rate-limiting electron transfers are elsewhere in the respiratory process. Conducting-probe atomic force microscopy measurements that find conductivities 10 3 -10 6 -fold more than cellular demands are suggested to report on functionality that is either not used or not accessible under physiological conditions. The experimentally measured difference in conductivity between Omc- S and Z is suggested to not be an intrinsic feature of the CryoEM-resolved structures.

Computing the Differences between Asn-X and Gln-X Deamidation and Their Impact on Pharmaceutical and Physiological Proteins: A Theoretical Investigation Using Model Dipeptides

Protein deamidation is a degradation mechanism that significantly impacts both pharmaceutical and physiological proteins. Deamidation impacts two amino acids, Asn and Gln, where the net neutral residues are converted into their acidic forms. While there are multiple similarities between the reaction mechanisms of the two residues, the impact of Gln deamidation has been noted to be most significant on physiological proteins while Asn deamidation has been linked to both pharmaceutical and physiological proteins. For this purpose, we sought to analyze the thermochemical and kinetic properties of the different reactions of Gln deamidation relative to Asn deamidation. In this study, we mapped the deamidation of Gln-X dipeptides into Glu-X dipeptides using density functional theory (DFT). Full network mapping facilitated the prediction of reaction selectivity between the two primary pathways, as well as between the two products of Gln-X deamidation as a function of solvent dielectric. To achieve this analysis, we studied a total of 77 dipeptide reactions per solvent dielectric (308 total reactions). Modeled at a neutral pH and using quantum chemical and statistical thermodynamic methods, we computed the following values: enthalpy of reaction (ΔH_RXN), entropy (ΔS_RXN), Gibbs free energy of reaction (ΔG_RXN), activation energy (E_A), and the Arrhenius preexponential factor (log(A)) for each dipeptide. Additionally, using chemical reaction principles, we generated a database of computed rate coefficients for all possible N-terminus Gln-X deamidation reactions at a neutral pH, predicted the most likely deamidation reaction mechanism for each dipeptide reaction, analyzed our results against our prior study on Asn-X deamidation, and matched our results against qualitative trends previously noted by experimental literature.

A Comprehensive Study on the Electrostatic Properties of Tubulin-Tubulin Complexes in Microtubules

Microtubules are key players in several stages of the cell cycle and are also involved in the transportation of cellular organelles. Microtubules are polymerized by α/β tubulin dimers with a highly dynamic feature, especially at the plus ends of the microtubules. Therefore, understanding the interactions among tubulins is crucial for characterizing microtubule dynamics. Studying microtubule dynamics can help researchers make advances in the treatment of neurodegenerative diseases and cancer. In this study, we utilize a series of computational approaches to study the electrostatic interactions at the binding interfaces of tubulin monomers. Our study revealed that among all the four types of tubulin-tubulin binding modes, the electrostatic attractive interactions in the α/β tubulin binding are the strongest while the interactions of α/α tubulin binding in the longitudinal direction are the weakest. Our calculations explained that due to the electrostatic interactions, the tubulins always preferred to form α/β tubulin dimers. The interactions between two protofilaments are the weakest. Thus, the protofilaments are easily separated from each other. Furthermore, the important residues involved in the salt bridges at the binding interfaces of the tubulins are identified, which illustrates the details of the interactions in the microtubule. This study elucidates some mechanistic details of microtubule dynamics and also identifies important residues at the binding interfaces as potential drug targets for the inhibition of cancer cells.

The significance of bioelectricity on all levels of organization of an organism. Part 1: From the subcellular level to cells

Bioelectricity plays an essential role in the structural and functional organization of biological organisms. In this first article of our three-part series, we summarize the importance of bioelectricity for the basic structural level of biological organization, i.e. from the subcellular level (charges, ion channels, molecules and cell organelles) to cells.

Interaction preferences between protein side chains and key epigenetic modifications 5-methylcytosine, 5-hydroxymethycytosine and N6-methyladenine

Covalent modifications of standard DNA/RNA nucleobases affect epigenetic regulation of gene expression by modulating interactions between nucleic acids and protein readers. We derive here the absolute binding free energies and analyze the binding modalities between key modified nucleobases 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC) and N⁶-methyladenine (m⁶A) and all non-prolyl/non-glycyl protein side chains using molecular dynamics simulations and umbrella sampling in both water and methanol, the latter mimicking the low dielectric environment at the dehydrated nucleic-acid/protein interfaces. We verify the derived affinities by comparing against a comprehensive set of high-resolution structures of nucleic-protein complexes involving 5mC. Our analysis identifies protein side chains that are highly tuned for detecting cytosine methylation as a function of the environment and can thus serve as microscopic readers of epigenetic marks. Conversely, we show that the relative ordering of sidechain affinities for 5hmC and m⁶A does not differ significantly from those for their precursor bases, cytosine and adenine, respectively, especially in the low dielectric environment. For those two modified bases, the effect is more nuanced and manifests itself primarily at the level of absolute changes in the binding free energy. Our results contribute towards establishing a quantitative foundation for understanding, predicting and modulating the interactions between modified nucleic acids and proteins at the atomistic level.