A semi-automated workflow for DIA-based global discovery to pathway-driven PRM analysis.

Targeted proteomics, which includes parallel reaction monitoring (PRM), is typically utilized for more precise detection and quantitation of key proteins and/or pathways derived from complex discovery proteomics datasets. Initial discovery-based analysis using data independent acquisition (DIA) can obtain deep proteome coverage with low data missingness while targeted PRM assays can provide additional benefits in further eliminating missing data and optimizing measurement precision. However, PRM method development from bioinformatic predictions can be tedious and time-consuming because of the DIA output complexity. We address this limitation with a Python script that rapidly generates a PRM method for the TIMS-TOF platform using DIA data and a user-defined target list. To evaluate the script, DIA data obtained from HeLa cell lysate (200 ng, 45-min gradient method) as well as canonical pathway information from Ingenuity Pathway Analysis was utilized to generate a pathway-driven PRM method. Subsequent PRM analysis of targets within the example pathway, regulation of apoptosis, resulted in improved chromatographic data and enhanced quantitation precision (100% peptides below 10% CV with a median CV of 2.9%, n = 3 technical replicates). The script is freely available at https://github.com/StevensOmicsLab/PRM-script and provides a framework that can be adapted to multiple DDA/DIA data outputs and instrument-specific PRM method types.

Polarizable AMOEBA Model for Simulating Mg2+·Protein·Nucleotide Complexes.

Molecular mechanics (MM) simulations have the potential to provide detailed insights into the mechanisms of enzymes that utilize nucleotides as cofactors. In most cases, the activities of these enzymes also require the binding of divalent cations to catalytic sites. However, modeling divalent cations in MM simulations has been challenging. The inclusion of explicit polarization was considered promising, but despite improvements over nonpolarizable force fields and despite the inclusion of "Nonbonded-fix (NB-fix)" corrections, errors in interaction energies of divalent cations with proteins remain large. Importantly, the application of these models fails to reproduce the experimental structural data on Mg2+·Protein·ATP complexes. Focusing on these complexes, here we provide a systematic assessment of the polarizable AMOEBA model and recommend critical changes that substantially improve its predictive performance. Our key results are as follows. We first show that our recent revision of the AMOEBA protein model (AMOEBABIO18-HFC), which contains high field corrections (HFCs) to induced dipoles, dramatically improves Mg2+-protein interaction energies, reducing the mean absolute error (MAE) from 17 to 10 kcal/mol. This further supports the general applicability of AMOEBABIO18-HFC. The inclusion of many-body NB-fix corrections further reduces MAE to 6 kcal/mol, which amounts to less than 2% error. The errors are estimated with respect to vdW-inclusive density functional theory that we benchmark against CCSD(T) calculations and experiments. We also present a new model of ATP with revised polarization parameters to better capture its high field response, as well as new vdW and dihedral parameters. The ATP model accurately predicts experimental Mg2+-ATP binding free energy in the aqueous phase and provides new insights into how Mg2+ associates with ATP. Finally, we show that molecular dynamics (MD) simulations of Mg2+·Kinase·ATP complexes carried out with these improvements lead to a better agreement in global and local catalytic site structures between MD and X-ray crystallography.

High-Dimensional Parameter Search Method to Determine Force Field Mixing Terms in Molecular Simulations.

Molecular dynamics (MD) force fields for lipids and ions are typically developed independently of one another. In simulations consisting of both lipids and ions, lipid-ion interaction energies are estimated using a predefined set of mixing rules for Lennard-Jones (LJ) interactions. This, however, does not guarantee their reliability. In fact, compared to the quantum mechanical reference data, Lorentz-Berthelot mixing rules substantially underestimate the binding energies of Na+ ions with small-molecule analogues of lipid headgroups, yielding errors on the order of 80 and 130 kJ/mol, respectively, for methyl acetate and diethyl phosphate. Previously, errors associated with mixing force fields have been reduced using approaches such as "NB-fix" in which LJ interactions are computed using explicit cross terms rather than those from mixing rules. Building on this idea, we derive explicit lipid-ion cross terms that also may implicitly include many-body cooperativity effects. Additionally, to account for the interdependency between cross terms, we optimize all cross terms simultaneously by performing high-dimensional searches using our ParOpt software. The cross terms we obtain reduce the errors due to mixing rules to below 10 kJ/mol. MD simulation of the lipid bilayer conducted using these optimized cross terms resolves the structural discrepancies between our previous simulations and small-angle X-ray and neutron scattering experiments. These results demonstrate that simulations of lipid bilayers with ions that are accurate up to structural data from scattering experiments can be performed without explicit polarization terms. However, it is worth noting that such NB-fix cross terms are not based on any physical principle; a polarizable lipid model would be more realistic and is still desired. Our approach is generic and can be applied to improve the accuracies of simulations employing mixed force fields.

Inclusion of High-Field Target Data in AMOEBA's Calibration Improves Predictions of Protein-Ion Interactions.

The reliability of molecular mechanics simulations to predict effects of ion binding to proteins depends on their ability to simultaneously describe ion-protein, ion-water, and protein-water interactions. Force fields (FFs) to describe protein-water and ion-water interactions have been constructed carefully and have also been refined routinely to improve accuracy. Descriptions for ion-protein interactions have also been refined, although in an a posteriori manner through the use of "nonbonded-fix (NB-fix)" approaches in which parameters from default Lennard-Jones mixing rules are replaced with those optimized against some reference data. However, even after NB-fix corrections, there remains a significant need for improvement. This is also true for polarizable FFs that include self-consistent inducible moments. Our recent studies on the polarizable AMOEBA FF suggested that the problem associated with modeling ion-protein interactions could be alleviated by recalibrating polarization models of cation-coordinating functional groups so that they respond better to the high electric fields present near ions. Here, we present such a recalibration of carbonyls, carboxylates, and hydroxyls in the AMOEBA protein FF and report that it does improve predictions substantially─mean absolute errors in Na+-protein and K+-protein interaction energies decrease from 8.7 to 5.3 and 9.6 to 6.3 kcal/mol, respectively. Errors are computed with respect to estimates from van der Waals-inclusive density functional theory benchmarked against high-level quantum mechanical calculations and experiments. While recalibration does improve ion-protein interaction energies, they still remain underestimated, suggesting that further improvements can be made in a systematic manner through modifications in classical formalism. Nevertheless, we show that by applying our many-body NB-fix correction to Lennard-Jones components, these errors are further reduced to 2.7 and 2.6 kcal/mol, respectively, for Na+ and K+ ions. Finally, we show that the recalibrated AMOEBA protein FF retains its intrinsic reliability in predicting protein structure and dynamics in the condensed phase.

Molecular basis for higher affinity of SARS-CoV-2 spike RBD for human ACE2 receptor.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused substantially more infections, deaths, and economic disruptions than the 2002-2003 SARS-CoV. The key to understanding SARS-CoV-2's higher infectivity lies partly in its host receptor recognition mechanism. Experiments show that the human angiotensin converting enzyme 2 (ACE2) protein, which serves as the primary receptor for both CoVs, binds to the receptor binding domain (RBD) of CoV-2's spike protein stronger than SARS-CoV's spike RBD. The molecular basis for this difference in binding affinity, however, remains unexplained from X-ray structures. To go beyond insights gained from X-ray structures and investigate the role of thermal fluctuations in structure, we employ all-atom molecular dynamics simulations. Microseconds-long simulations reveal that while CoV and CoV-2 spike-ACE2 interfaces have similar conformational binding modes, CoV-2 spike interacts with ACE2 via a larger combinatorics of polar contacts, and on average, makes 45% more polar contacts. Correlation analysis and thermodynamic calculations indicate that these differences in the density and dynamics of polar contacts arise from differences in spatial arrangements of interfacial residues, and dynamical coupling between interfacial and non-interfacial residues. These results recommend that ongoing efforts to design spike-ACE2 peptide blockers will benefit from incorporating dynamical information as well as allosteric coupling effects.

Methyl-Induced Polarization Destabilizes the Noncovalent Interactions of N-Methylated Lysines.

Lysine methylation can modify noncovalent interactions by altering lysine's hydrophobicity as well as its electronic structure. Although the ramifications of the former are documented, the effects of the latter remain largely unknown. Understanding the electronic structure is important for determining how biological methylation modulates protein-protein binding, and the impact of artificial methylation experiments in which methylated lysines are used as spectroscopic probes and protein crystallization facilitators. The benchmarked first-principles calculations undertaken here reveal that methyl-induced polarization weakens the electrostatic attraction of amines with protein functional groups - salt bridges, hydrogen bonds and cation-π interactions weaken by as much as 10.3, 7.9 and 3.5 kT, respectively. Multipole analysis shows that weakened electrostatics is due to the altered inductive effects, which overcome increased attraction from methyl-enhanced polarizability and dispersion. Due to their fundamental nature, these effects are expected to be present in many cases. A survey of methylated lysines in protein structures reveals several cases in which methyl-induced polarization is the primary driver of altered noncovalent interactions; in these cases, destabilizations are found to be in the 0.6-4.7 kT range. The clearest case of where methyl-induced polarization plays a dominant role in regulating biological function is that of the PHD1-PHD2 domain, which recognizes lysine-methylated states on histones. These results broaden our understanding of how methylation modulates noncovalent interactions.

Contrasting Local and Macroscopic Effects of Collagen Hydroxylation.

Collagen is heavily hydroxylated. Experiments show that proline hydroxylation is important to triple helix (monomer) stability, fibril assembly, and interaction of fibrils with other molecules. Nevertheless, experiments also show that even without hydroxylation, type I collagen does assemble into its native D-banded fibrillar structure. This raises two questions. Firstly, even though hydroxylation removal marginally affects macroscopic structure, how does such an extensive chemical change, which is expected to substantially reduce hydrogen bonding capacity, affect local structure? Secondly, how does such a chemical perturbation, which is expected to substantially decrease electrostatic attraction between monomers, affect collagen's mechanical properties? To address these issues, we conduct a benchmarked molecular dynamics study of rat type I fibrils in the presence and absence of hydroxylation. Our simulations reproduce the experimental observation that hydroxylation removal has a minimal effect on collagen's D-band length. We also find that the gap-overlap ratio, monomer width and monomer length are minimally affected. Surprisingly, we find that de-hydroxylation also has a minor effect on the fibril's Young's modulus, and elastic stress build up is also accompanied by tightening of triple-helix windings. In terms of local structure, de-hydroxylation does result in a substantial drop (23%) in inter-monomer hydrogen bonding. However, at the same time, the local structures and inter-monomer hydrogen bonding networks of non-hydroxylated amino acids are also affected. It seems that it is this intrinsic plasticity in inter-monomer interactions that preclude fibrils from undergoing any large changes in macroscopic properties. Nevertheless, changes in local structure can be expected to directly impact collagen's interaction with extra-cellular matrix proteins. In general, this study highlights a key challenge in tissue engineering and medicine related to mapping collagen chemistry to macroscopic properties but suggests a path forward to address it using molecular dynamics simulations.

Improved description of ligand polarization enhances transferability of ion-ligand interactions.

The reliability of molecular mechanics (MM) simulations in describing biomolecular ion-driven processes depends on their ability to accurately model interactions of ions simultaneously with water and other biochemical groups. In these models, ion descriptors are calibrated against reference data on ion-water interactions, and it is then assumed that these descriptors will also satisfactorily describe interactions of ions with other biochemical ligands. The comparison against the experiment and high-level quantum mechanical data show that this transferability assumption can break down severely. One approach to improve transferability is to assign cross terms or separate sets of non-bonded descriptors for every distinct pair of ion type and its coordinating ligand. Here, we propose an alternative solution that targets an error-source directly and corrects misrepresented physics. In standard model development, ligand descriptors are never calibrated or benchmarked in the high electric fields present near ions. We demonstrate for a representative MM model that when the polarization descriptors of its ligands are improved to respond to both low and high fields, ligand interactions with ions also improve, and transferability errors reduce substantially. In our case, the overall transferability error reduces from 3.3 kcal/mol to 1.8 kcal/mol. These improvements are observed without compromising on the accuracy of low-field interactions of ligands in gas and condensed phases. Reference data for calibration and performance evaluation are taken from the experiment and also obtained systematically from "gold-standard" CCSD(T) in the complete basis set limit, followed by benchmarked vdW-inclusive density functional theory.

Transferable interactions of Li+ and Mg2+ ions in polarizable models.

Therapeutic implications of Li+, in many cases, stem from its ability to inhibit certain Mg2+-dependent enzymes, where it interacts with or substitutes for Mg2+. The underlying details of its action are, however, unknown. Molecular simulations can provide insights, but their reliability depends on how well they describe relative interactions of Li+ and Mg2+ with water and other biochemical groups. Here, we explore, benchmark, and recommend improvements to two simulation approaches: the one that employs an all-atom polarizable molecular mechanics (MM) model and the other that uses a hybrid quantum and MM implementation of the quasi-chemical theory (QCT). The strength of the former is that it describes thermal motions explicitly and that of the latter is that it derives local contributions from electron densities. Reference data are taken from the experiment, and also obtained systematically from CCSD(T) theory, followed by a benchmarked vdW-inclusive density functional theory. We find that the QCT model predicts relative hydration energies and structures in agreement with the experiment and without the need for additional parameterization. This implies that accurate descriptions of local interactions are essential. Consistent with this observation, recalibration of local interactions in the MM model, which reduces errors from 10.0 kcal/mol to 1.4 kcal/mol, also fixes aqueous phase properties. Finally, we show that ion-ligand transferability errors in the MM model can be reduced significantly from 10.3 kcal/mol to 1.2 kcal/mol by correcting the ligand's polarization term and by introducing Lennard-Jones cross-terms. In general, this work sets up systematic approaches to evaluate and improve molecular models of ions binding to proteins.

Interactions of Monovalent and Divalent Cations at Palmitoyl-Oleoyl-Phosphatidylcholine Interface.

Li+ is a biologically active and medically important cation. Experiments show that Li+ modulates some phospholipid bilayer properties in a manner similar to divalent cations, rather than other monovalent cations. We previously performed a comparative simulation study of the interaction of several monovalent cations with palmitoyl-oleoyl-phosphatidylcholine bilayers and reported that Li+ exhibited the highest association with lipids and formed a unique tetrahedral coordinated structure with lipid head groups. Here we extend these studies to two biologically important divalent cations, Mg2+ and Ca2+, and observe that, just like monovalent cations, Mg2+ and Ca2+ reduce bilayer areas and increase chain order. Bilayer area changes induced by cations are strongly correlated with the amount of charge inside the headgroup region; however, Mg2+ and Li+ are clear outliers. At the same time though, Mg2+ adsorption in the bilayer is the smallest among all cations, which is in contrast to Li+ that binds strongly to lipids. In fact, in contrast to all other cations, Mg2+ remains fully hydrated in the lipid headgroup region. However, Li+ and Mg2+ share high overlap between their inner-shell coordination topologies. This suggests that Li+ can structurally replace Mg2+, which is bound to other biomolecules with up to fourfold coordination, provided such replacement is energetically feasible. We compute structural topologies and compare them quantitatively using a new weighted-graphs-based method. Finally, we find that the specificity of cation interaction with lipid head groups exhibit consistent trend with the solvation shell energetics of ions in lipid headgroup and bulk water regions.

Machine learning approaches to evaluate correlation patterns in allosteric signaling: A case study of the PDZ2 domain.

Many proteins are regulated by dynamic allostery wherein regulator-induced changes in structure are comparable with thermal fluctuations. Consequently, understanding their mechanisms requires assessment of relationships between and within conformational ensembles of different states. Here we show how machine learning based approaches can be used to simplify this high-dimensional data mining task and also obtain mechanistic insight. In particular, we use these approaches to investigate two fundamental questions in dynamic allostery. First, how do regulators modify inter-site correlations in conformational fluctuations (Cij)? Second, how are regulator-induced shifts in conformational ensembles at two different sites in a protein related to each other? We address these questions in the context of the human protein tyrosine phosphatase 1E's PDZ2 domain, which is a model protein for studying dynamic allostery. We use molecular dynamics to generate conformational ensembles of the PDZ2 domain in both the regulator-bound and regulator-free states. The employed protocol reproduces methyl deuterium order parameters from NMR. Results from unsupervised clustering of Cij combined with flow analyses of weighted graphs of Cij show that regulator binding significantly alters the global signaling network in the protein; however, not by altering the spatial arrangement of strongly interacting amino acid clusters but by modifying the connectivity between clusters. Additionally, we find that regulator-induced shifts in conformational ensembles, which we evaluate by repartitioning ensembles using supervised learning, are, in fact, correlated. This correlation Δij is less extensive compared to Cij, but in contrast to Cij, Δij depends inversely on the distance from the regulator binding site. Assuming that Δij is an indicator of the transduction of the regulatory signal leads to the conclusion that the regulatory signal weakens with distance from the regulatory site. Overall, this work provides new approaches to analyze high-dimensional molecular simulation data and also presents applications that yield new insight into dynamic allostery.

An effective treatment approach of DPP-IV inhibitor encapsulated polymeric nanoparticles conjugated with anti-CD-4 mAb for type 1 diabetes.

Nanotechnology based biomedical approaches and surface modification techniques made it easier for targeting specific site and improving the treatment efficacy. The present study reports on targeted polymeric nanoparticles conjugated with antibody as a site-specific carrier system for effective treatment of type 1 diabetes. Sitagliptin (SP)-loaded Poly (lactic-co-glycolic acid) (PLGA) nanoparticles (NP) were prepared by nanoprecipitation cum solvent evaporation method and were characterized in terms of morphology, size, surface charge, and entrapment efficiency. Optimized batch demonstrated a particle size of 105.24 nm, with significant entrapment efficacy. In vitro release studies exhibited a controlled release pattern of 67.76 ± 1.30% in 24 h, and a maximum of 96.59 ± 1.26% at the end of 48 h. Thiol groups were introduced on the surface of SP-NPs whose concentration on SP-NPs was 27 ± 2.6 mmol/mol PLGA-NPs, anti-CD4 antibody clone Q4120 was conjugated to the thiolated SP-NPs via a sulfo-MBS cross-linker, ∼70% conjugation was observed. The in vitro cytotoxicity studies performed on RIN-5 F cells for mAb-SP-NPs presented an IC50 of 76 µg/mL, and the insulin release assay had revealed an increased release at 5.15 ± 0.16 IU/mL. The results indicate that mAb-SP-NPs allowed a controlled release of SP and thereby produced insulin levels comparable with control. Therefore, mAb-SP-NPs system appears to be effective in the treatment of auto immune diabetes, subject to further analysis.

Nanomechanics of Type I Collagen.

Type I collagen is the predominant collagen in mature tendons and ligaments, where it gives them their load-bearing mechanical properties. Fibrils of type I collagen are formed by the packing of polypeptide triple helices. Higher-order structures like fibril bundles and fibers are assembled from fibrils in the presence of other collagenous molecules and noncollagenous molecules. Curiously, however, experiments show that fibrils/fibril bundles are less resistant to axial stress compared to their constituent triple helices-the Young's moduli of fibrils/fibril bundles are an order-of-magnitude smaller than the Young's moduli of triple helices. Given the sensitivity of the Young's moduli of triple helices to solvation environment, a plausible explanation is that the packing of triple helices into fibrils perhaps reduces the Young's modulus of an individual triple helix, which results in fibrils having smaller Young's moduli. We find, however, from molecular dynamics and accelerated conformational sampling simulations that the Young's modulus of the buried core of the fibril is of the same order as that of a triple helix in aqueous phase. These simulations, therefore, suggest that the lower Young's moduli of fibrils/fibril bundles cannot be attributed to the specific packing of triple helices in the fibril core. It is not the fibril core that yields initially to axial stress. Rather, it must be the portion of the fibril exposed to the solvent and/or the fibril-fibril interface that bears the initial strain. Overall, this work provides estimates of Young's moduli and persistence lengths at two levels of collagen's structural assembly, which are necessary to quantitatively investigate the response of various biological factors on collagen mechanics, including congenital mutations, posttranslational modifications and ligand binding, and also engineer new collagen-based materials.

Stimulation of Nipah Fusion: Small Intradomain Changes Trigger Extensive Interdomain Rearrangements.

Nipah is an emerging paramyxovirus that is of serious concern to human health. It invades host cells using two of its membrane proteins-G and F. G binds to host ephrins and this stimulates G to activate F. Upon activation, F mediates virus-host membrane fusion. Here we focus on mechanisms that underlie the stimulation of G by ephrins. Experiments show that G interacts with ephrin and F through separate sites located on two different domains, the receptor binding domain (RBD) and the F activation domain (FAD). No models explain this allosteric coupling. In fact, the analogous mechanisms in other paramyxoviruses also remain undetermined. The structural organization of G is such that allosteric coupling must involve at least one of the two interfaces-the RBD-FAD interface and/or the RBD-RBD interface. Here we examine using molecular dynamics the effect of ephrin binding on the RBD-RBD interface. We find that despite inducing small changes in individual RBDs, ephrin reorients the RBD-RBD interface extensively, and in a manner that will enhance solvent exposure of the FAD. While this finding supports a proposed model of G stimulation, we also find from additional simulations that ephrin induces a similar RBD-RBD reorientation in a stimulation-deficient G mutant, V209 VG → AAA. Together, our simulations suggest that while inter-RBD reorientation may be important, it is not, by itself, a sufficient condition for G stimulation. Additionally, we find that the mutation affects the conformational ensemble of RBD globally, including the RBD-FAD interface, suggesting the latter's role in G stimulation. Because ephrin induces small changes in individual RBDs, a proper analysis of conformational ensembles required that they are compared directly-we employ a method we developed recently, which we now release at SimTK, and show that it also performs excellently for non-Gaussian distributions.

The disruption of prenylation leads to pleiotropic rearrangements in cellular behavior in Staphylococcus aureus.

Prenylation is the addition of prenyl groups to peptide chains or metabolites via the condensation of geranyl- or isopentenyl-diphosphate moieties by geranyltranstransferases. Although this process is extensively studied in eukaryotes, little is known about the influence of prenylation in prokaryotic species. To explore the role of this modification in bacteria, we generated a mutation in the geranyltranstransferase (IspA) of Staphylococcus aureus. Quite strikingly, the ispA mutant completely lacked pigment and exhibited a previously undescribed small colony variant-like phenotype. Further pleiotropic defects in cellular behavior were noted, including impaired growth, decreased ATP production, increased sensitivity to oxidative stress, increased resistance to aminoglycosides and cationic antimicrobial peptides, and decreased resistance to cell wall-targeting antibiotics. These latter effects appear to result from differences in envelope composition as ispA mutants have highly diffuse cell walls (particularly at the septum), marked alterations in fatty acid composition and increased membrane fluidity. Taken together, these data present an important characterization of prokaryotic prenylation and demonstrate that this process is central to a wealth of pathways involved in mediating cellular homeostasis in S. aureus.

POPC Bilayers Supported on Nanoporous Substrates: Specific Effects of Silica-Type Surface Hydroxylation and Charge Density.

Recent advances in nanotechnology bring to the forefront a new class of extrinsic constraints for remodeling lipid bilayers. In this next-generation technology, membranes are supported over nanoporous substrates. The nanometer-sized pores in the substrate are too small for bilayers to follow the substrate topology; consequently, the bilayers hang over the pores. Experiments demonstrate that nanoporous substrates remodel lipid bilayers differently from continuous substrates. The underlying molecular mechanisms, however, remain largely undetermined. Here we use molecular dynamics (MD) simulations to probe the effects of silica-type hydroxylation and charge densities on adsorbed palmitoyl-oleoylphosphatidylcholine (POPC) bilayers. We find that a 50% porous substrate decorated with a surface density of 4.6 hydroxyls/nm(2) adsorbs a POPC bilayer at a distance of 4.5 Å, a result consistent with neutron reflectivity experiments conducted on topologically similar silica constructs under highly acidic conditions. Although such an adsorption distance suggests that the interaction between the bilayer and the substrate will be buffered by water molecules, we find that the substrate does interact directly with the bilayer. The substrate modifies several properties of the bilayer-it dampens transverse lipid fluctuations, reduces lipid diffusion rates, and modifies transverse charge densities significantly. Additionally, it affects lipid properties differently in the two leaflets. Compared to substrates functionalized with sparser surface hydroxylation densities, this substrate adheres to bilayers at smaller distances and also remodels POPC more extensively, suggesting a direct correspondence between substrate hydrophilicity and membrane properties. A partial deprotonation of surface hydroxyls, as expected of a silica substrate under mildly acidic conditions, however, produces an inverse effect: it increases the substrate-bilayer distance, which we attribute to the formation of an electric double layer over the negatively charged substrate, and restores, at least partially, leaflet asymmetry and headgroup orientations. Overall, this study highlights the intrinsic complexity of lipid-substrate interactions and suggests the prospect of making two surface attributes-dipole densities and charge densities-work antagonistically toward remodeling lipid bilayer properties.

Role of methyl-induced polarization in ion binding.

The chemical property of methyl groups that renders them indispensable to biomolecules is their hydrophobicity. Quantum mechanical studies undertaken here to understand the effect of point substitutions on potassium (K-) channels illustrate quantitatively how methyl-induced polarization also contributes to biomolecular function. K- channels regulate transmembrane salt concentration gradients by transporting K(+) ions selectively. One of the K(+) binding sites in the channel's selectivity filter, the S4 site, also binds Ba(2+) ions, which blocks K(+) transport. This inhibitory property of Ba(2+) ions has been vital in understanding K-channel mechanism. In most K-channels, the S4 site is composed of four threonine amino acids. The K channels that carry serine instead of threonine are significantly less susceptible to Ba(2+) block and have reduced stabilities. We find that these differences can be explained by the lower polarizability of serine compared with threonine, because serine carries one less branched methyl group than threonine. A T→S substitution in the S4 site reduces its polarizability, which, in turn, reduces ion binding by several kilocalories per mole. Although the loss in binding affinity is high for Ba(2+), the loss in K(+) binding affinity is also significant thermodynamically, which reduces channel stability. These results highlight, in general, how biomolecular function can rely on the polarization induced by methyl groups, especially those that are proximal to charged moieties, including ions, titratable amino acids, sulfates, phosphates, and nucleotides.

Effect of intrinsic and extrinsic factors on the simulated D-band length of type I collagen.

A signature feature of collagen is its axial periodicity visible in TEM as alternating dark and light bands. In mature, type I collagen, this repeating unit, D, is 67 nm long. This periodicity reflects an underlying packing of constituent triple-helix polypeptide monomers wherein the dark bands represent gaps between axially adjacent monomers. This organization is visible distinctly in the microfibrillar model of collagen obtained from fiber diffraction. However, to date, no atomistic simulations of this diffraction model under zero-stress conditions have reported a preservation of this structural feature. Such a demonstration is important as it provides the baseline to infer response functions of physiological stimuli. In contrast, simulations predict a considerable shrinkage of the D-band (11-19%). Here we evaluate systemically the effect of several factors on D-band shrinkage. Using force fields employed in previous studies we find that irrespective of the temperature/pressure coupling algorithms, assumed salt concentration or hydration level, and whether or not the monomers are cross-linked, the D-band shrinks considerably. This shrinkage is associated with the bending and widening of individual monomers, but employing a force field whose backbone dihedral energy landscape matches more closely with our computed CCSD(T) values produces a small D-band shrinkage of < 3%. Since this force field also performs better against other experimental data, it appears that the large shrinkage observed in earlier simulations is a force-field artifact. The residual shrinkage could be due to the absence of certain atomic-level details, such as glycosylation sites, for which we do not yet have suitable data.

Discerning intersecting fusion-activation pathways in the Nipah virus using machine learning.

The fusion of Nipah with host cells is facilitated by two of their glycoproteins, the G and the F proteins. The binding of cellular ephrins to the G head domain causes the G stalk domain to interact differently with F, which activates F to mediate virus-host fusion. To gain insight into how the ephrin-binding signal transduces from the head to the stalk domain of G, we examine quantitatively the differences between the conformational ensembles of the G head domain in its ephrin-bound and unbound states. We consider the human ephrins B2 and B3, and a double mutant of B2, all of which trigger fusion. The ensembles are generated using molecular dynamics, and the differences between them are quantified using a new machine learning method. We find that the portion of the G head domain whose conformational density is altered equivalently by the three ephrins is large, and comprises ∼25% of the residues in the G head domain. This subspace also includes the residues that are known to be important to F activation, which suggests that it contains at least one common signaling pathway. The spatial distribution of the residues constituting this subspace supports the model of signal transduction in which the signal transduces via the G head dimer interface. This study also adds to the growing list of examples where signaling does not depend solely on backbone deviations. In general, this study provides an approach to filter out conserved patterns in protein dynamics.

Structural Adaptation of Secondary p53 Binding Sites on MDM2 and MDMX.

The thermodynamics of secondary p53 binding sites on MDM2 and MDMX were evaluated using p53 peptides containing residues 16-29, 17-35, and 1-73. All the peptides had large, negative heat capacity (ΔCp), consistent with the burial of p53 residues F19, W23, and L26 in the primary binding sites of MDM2 and MDMX. MDMX has a higher affinity and more negative ΔCp than MDM2 for p5317-35, which is due to MDMX stabilization and not additional interactions with the secondary binding site. ΔCp measurements show binding to the secondary site is inhibited by the disordered tails of MDM2 for WT p53 but not a more helical mutant where proline 27 is changed to alanine. This result is supported by all-atom molecular dynamics simulations showing that p53 residues 30-35 turn away from the disordered tails of MDM2 in P27A17-35 and make direct contact with this region in p5317-35. Molecular dynamics simulations also suggest that an intramolecular methionine-aromatic motif found in both MDM2 and MDMX structurally adapts to support multiple p53 binding modes with the secondary site. ΔCp measurements also show that tighter binding of the P27A mutant to MDM2 and MDMX is due to increased helicity, which reduces the energetic penalty associated with coupled folding and binding. Our results will facilitate the design of selective p53 inhibitors for MDM2 and MDMX.