Evaluating the performance of the ff99SB force field based on NMR scalar coupling data

Machine-learned molecular mechanics force fields from large-scale quantum chemical data

The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.

Conformational Dynamics of the Nucleosomal Histone H2B Tails Revealed by Molecular Dynamics Simulations

Epigenetic modifications of histone N-terminal tails play a critical role in regulating the chromatin structure and biological processes such as transcription and DNA repair. One of the key post-translational modifications (PTMs) is the acetylation of lysine residues on histone tails. Epigenetic modifications are ubiquitous in the development of diseases, such as cancer and neurological disorders. Histone H2B tails are critical regulators of nucleosome dynamics, biological processes, and certain diseases. Here, we report all-atomistic molecular dynamics (MD) simulations of the nucleosome to demonstrate that acetylation of the histone tails changes their conformational space and interaction with DNA. We perform simulations of H2B tails, critical regulators of gene regulation, in both the lysine-acetylated (ACK) and unacetylated wild type (WT) states. To explore the effects of salt concentration, we use two different NaCl concentrations to perform simulations at microsecond time scales. Salt can modulate the effects of electrostatic interactions between the DNA phosphate backbone and histone tails. Upon acetylation, H2B tails shift their secondary structure helical propensity. The number of contacts between the DNA and the H2B tail decreases. We characterize the conformational dynamics of the H2B tails by principal component analysis (PCA). The ACK tails become more compact at increased salt concentrations, but conformations from the WT tails display the most contacts with DNA at both salt concentrations. Mainly, H2B acetylation may increase the DNA accessibility for regulatory proteins to bind, which can aid in gene regulation and NCP stability.

Multiple spectroscopic and computational studies on binding interaction of 2-phenylamino-4-phenoxyquinoline derivatives with bovine serum albumin

Binding characteristics of potent non-nucleoside HIV-1 reverse transcriptase inhibitors, 4-(2',6'-dimethyl-4'-formylphenoxy)-2-(5″-cyanopyridin-2″ylamino) quinoline (1) and 4-(2',6'-dimethyl-4'-cyanophenoxy)-2-(5″-cyanopyridin-2″ylamino) quinoline (2), to bovine serum albumin (BSA) under simulative physiological conditions were investigated by multiple spectroscopic and computational methods. The experimental results demonstrated that (1) and (2) bound to BSA at site III (subdomain IB), and quenched BSA fluorescence through a static quenching process. The binding interaction of (1) or (2) to BSA forms stable complexes with the binding constants (Kb) at the level of 104 L/mol and the number of binding site was determined to be 1 for both systems, indicating that new synthesized compounds occupied one site in BSA with moderate binding affinities. Based on the analysis of the thermodynamic parameters, it can be indicated that the main binding forces for interaction between BSA and both compounds were hydrogen bonding and van der Waals force. Synchronous fluorescence results revealed that the interaction of two compounds with BSA led to modifications in the microenvironment surrounding tryptophan residue of BSA. Circular dichroism spectra demonstrated alterations in the secondary structure of BSA induced by (1) and (2). Moreover, the experimental data of molecular docking and molecular dynamics (MD) simulations supported the results obtained from multiple spectroscopic techniques, confirming the binding interactions between both compounds and BSA.

Smart carbon-based sensors for the detection of non-coding RNAs associated with exposure to micro(nano)plastics: an artificial intelligence perspective

Micro(nano)plastics (MNPs) are pervasive environmental pollutants that individuals eventually consume. Despite this, little is known about MNP's impact on public health. In this article, we assess the evidence for potentially harmful consequences of MNPs in the human body, concentrating on molecular toxicity and exposure routes. Since MNPs are present in various consumer products, foodstuffs, and the air we breathe, exposure can occur through ingestion, inhalation, and skin contact. MNPs exposure can cause mitochondrial oxidative stress, inflammatory lesions, and epigenetic modifications, releasing specific non-coding RNAs in circulation, which can be detected to diagnose non-communicable diseases. This article examines the most fascinating smart carbon-based nanobiosensors for detecting circulating non-coding RNAs (lncRNAs and microRNAs). Carbon-based smart nanomaterials offer many advantages over traditional methods, such as ease of use, sensitivity, specificity, and efficiency, for capturing non-coding RNAs. In particular, the synthetic methods, conjugation chemistries, doping, and in silico approach for the characterization of synthesized carbon nanodots and their adaptability to identify and measure non-coding RNAs associated with MNPs exposure is discussed. Furthermore, the article provides insights into the use of artificial intelligence tools for designing smart carbon nanomaterials.

Dissecting the geometric and hydrophobic constraints of stapled peptides

Stapled peptides are a promising class of molecules with potential as highly specific probes of protein-protein interactions and as therapeutics. Hydrocarbon stapling affects the peptide properties through the interplay of two factors: enhancing the overall hydrophobicity and constraining the conformational flexibility. By constructing a series of virtual peptides, we study the role of each factor in modulating the structural properties of a hydrocarbon-stapled peptide PM2, which has been shown to enter cells, engage its target Mouse Double Minute 2 (MDM2), and activate p53. Hamiltonian replica exchange molecular dynamics (HREMD) simulations suggest that hydrocarbon stapling favors helical populations of PM2 through a combination of the geometric constraints and the enhanced hydrophobicity of the peptide. To further understand the conformational landscape of the stapled peptides along the binding pathway, we performed HREMD simulations by restraining the peptide at different distances from MDM2. When the peptide approaches MDM2, the binding pocket undergoes dehydration which appears to be greater in the presence of the stapled peptide compared with the linear peptide. In the binding pocket, the helicity of the stapled peptide is increased due to the favorable interactions between the peptide residues as well as the staple and the microenvironment of the binding pocket, contributing to enhanced affinity. The dissection of the multifaceted mechanism of hydrocarbon stapling into individual factors not only deepens fundamental understanding of peptide stapling, but also provides guidelines for the design of new stapled peptides.

Structural dynamics and mechanistic action guided engineering of lipolytic enzymes

Lipases have been established as important biocatalysts in several industrial applications, owing to their diverse substrate specificity. The availability of data on three-dimensional crystal structures for various lipases offers an opportunity for modulating their structural and functional aspects to design and engineer better versions of lipases. With the aim of investigating the structural components governing the extremophilic behavior of lipases, structural analysis of microbial lipases was performed using advanced bioinformatics and molecular dynamics simulation approaches. In sequences and functionally distinct alkaliphilic and thermophilic lipases were investigated for their functional properties to understand the distinguishing features of their structures. The alkaliphilic lipase from Bacillus subtilis (LipA) showed conformational changes in the loop region Ala132-Met137, subsequently, the active site residue His156 shows two conformations, toward the active site nucleophilic residues Ser77 and away from the Ser77. Interestingly, the active site of LipA is more solvent-exposed and can be correlated with the adoption of an open conformation which might extend and expose the active site region to solvents during the catalysis process. Furthermore, the MD simulation of thermophilic lipase from marine Streptomyces (MAS1) revealed the role of N- and C-terminal regions with disulfide bridges and identified a metal ion binding site that facilitates the enzyme stability. The novel thermo-alkaliphilic lipase can be designed to integrate the stability features of MAS1 into the alkaliphilic LipA. These structural-level intrinsic characteristics can be used for lipase engineering to amend the lipase activity and stability as per the requirements of the industrial processes.

Shorter Alkanesulfonate Carbon Chains Destabilize the Active Site Architecture of SsuD for Desulfonation

Bacteria have evolved to utilize alternative organosulfur sources when sulfur is limiting. The SsuE/SsuD and MsuE/MsuD enzymes expressed when sulfur sources are restricted, are responsible for providing specific bacteria with sulfur in the form of alkanesulfonates. In this study, we evaluated why two structurally and functionally similar FMNH₂-dependent monooxygenase enzymes (MsuD and SsuD) are needed for the acquisition of alkanesulfonates in some bacteria. In desulfonation assays, MsuD was able to utilize the entire range of alkanesulfonates (C1-C10). However, SsuD was not able to utilize smaller alkanesulfonate substrates. Interestingly, SsuD had a similar binding affinity for methanesulfonate (MES) (15 ± 1 μM) as MsuD (12 ± 1 μM) even though SsuD was not able to catalyze the desulfonation of the MES substrate. SsuD and MsuD showed decreased proteolytic susceptibility in the presence of FMNH₂ with MES and octanesulfonate (OCS). Tighter loop closure was observed for the MsuD/FMNH₂ complex with MES and OCS compared to SsuD under comparable conditions. Analysis of the SsuD/FMNH₂/MES structure using accelerated molecular dynamics simulations found three different conformations for MES, demonstrating the instability of the bound structure. Even when MES was bound in a similar fashion to OCS within the active site, the smaller alkane chain resulted in a shift of FMNH₂ so that it was no longer in a position to catalyze the desulfonation of MES. The active site of SsuD requires a longer alkane chain to maintain the appropriate architecture for desulfonation.

A proof-of-concept study of the secondary structure of influenza A, B M2 and MERS- and SARS-CoV E transmembrane peptides using folding molecular dynamics simulations in a membrane mimetic solvent

Here, we have carried out a proof-of-concept molecular dynamics (MD) simulation with adaptive tempering in a membrane mimetic environment to study the folding of single-pass membrane peptides. We tested the influenza A M2 viroporin, influenza B M2 viroporin, and protein E from coronaviruses MERS-Cov-2 and SARS-CoV-2 peptides with known experimental secondary structures in membrane bilayers. The two influenza-derived peptides are significantly different in the peptide sequence and secondary structure and more polar than the two coronavirus-derived peptides. Through a total of more than 50 μs of simulation time that could be accomplished in trifluoroethanol (TFE), as a membrane model, we characterized comparatively the folding behavior, helical stability, and helical propensity of these transmembrane peptides that match perfectly their experimental secondary structures, and we identified common motifs that reflect their quaternary organization and known (or not) biochemical function. We showed that BM2 is organized into two structurally distinct parts: a significantly more stable N-terminal half, and a fast-converting C-terminal half that continuously folds and unfolds between α-helical structures and non-canonical structures, which are mostly turns. In AM2, both the N-terminal half and C-terminal half are very flexible. In contrast, the two coronavirus-derived transmembrane peptides are much more stable and fast helix-formers when compared with the influenza ones. In particular, the SARS-derived peptide E appears to be the fastest and most stable helix-former of all the four viral peptides studied, with a helical structure that persists almost without disruption for the whole of its 10 μs simulation. By comparing the results with experimental observations, we benchmarked TFE in studying the conformation of membrane and hydrophobic peptides. This work provided accurate results suggesting a methodology to run long MD simulations and predict structural properties of biologically important membrane peptides.

JND4135, a New Type II TRK Inhibitor, Overcomes TRK xDFG and Other Mutation Resistance In Vitro and In Vivo

The tropomyosin receptor kinases (TRKs) have been validated as effective targets in anticancer drug discovery. Two first-generation TRK inhibitors have been approved into market and displayed an encouraging therapeutic response in cancer patients harboring TRK fusion proteins. However, acquired resistance mediated by secondary TRK mutations especially in the xDFG motif remains an unsolved challenge in the clinic. Herein, we report the preclinical pharmacological results of JND4135, a new type II pan-TRK inhibitor, in overcoming TRK mutant resistance, including the xDFG mutations in vitro and in vivo. At a low nanomolar level, JND4135 displays a strong activity against wild-type TRKA/B/C and secondary mutations involving xDFG motif substitutions in kinase assays and cellular models; occupies the TRK proteins for an extended time; and has a slower dissociation rate than other TRK inhibitors. Moreover, by intraperitoneal injection, JND4135 exhibits tumor growth inhibition (TGI) of 81.0% at a dose of 40 mg/kg in BaF3-CD74-TRKA-G667C mice xenograft model. Therefore, JND4135 can be considered as a lead compound for drug discovery overcoming the resistance of TRK inhibitor drugs mediated by xDFG mutations.

Mechanistic Origin of Different Binding Affinities of SARS-CoV and SARS-CoV-2 Spike RBDs to Human ACE2

The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein (RBD_CoV2) has a higher binding affinity to the human receptor angiotensin-converting enzyme 2 (ACE2) than the SARS-CoV RBD (RBD_CoV). Here, we performed molecular dynamics (MD) simulations, binding free energy (BFE) calculations, and interface residue contact network (IRCN) analysis to explore the mechanistic origin of different ACE2-binding affinities of the two RBDs. The results demonstrate that, when compared to the RBD_CoV2-ACE2 complex, RBD_CoV-ACE2 features enhanced dynamicsand inter-protein positional movements and increased conformational entropy and conformational diversity. Although the inter-protein electrostatic attractive interactions are the primary determinant for the high ACE2-binding affinities of both RBDs, the significantly enhanced electrostatic attractive interactions between ACE2 and RBD_CoV2 determine the higher ACE2-binding affinity of RBD_CoV2 than of RBD_CoV. Comprehensive comparative analyses of the residue BFE components and IRCNs between the two complexes reveal that it is the residue changes at the RBD interface that lead to the overall stronger inter-protein electrostatic attractive force in RBD_CoV2-ACE2, which not only tightens the interface packing and suppresses the dynamics of RBD_CoV2-ACE2, but also enhances the ACE2-binding affinity of RBD_CoV2. Since the RBD residue changes involving gain/loss of the positively/negatively charged residues can greatly enhance the binding affinity, special attention should be paid to the SARS-CoV-2 variants carrying such mutations, particularly those near or at the binding interfaces with the potential to form hydrogen bonds and/or salt bridges with ACE2.

Do molecular dynamics force fields accurately model Ramachandran distributions of amino acid residues in water?

Molecular dynamics (MD) is a powerful tool for studying intrinsically disordered proteins, however, its reliability depends on the accuracy of the force field. We assess Amber ff19SB, Amber ff14SB, OPLS-AA/M, and CHARMM36m with respect to their capacity to capture intrinsic conformational dynamics of 14 guest residues x (=G, A, L, V, I, F, Y, D^P, E^P, R, C, N, S, T) in GxG peptides in water. The MD-derived Ramachandran distribution of each guest residue is used to calculate 5 J-coupling constants and amide I' band profiles to facilitate a comparison to spectroscopic data through reduced χ² functions. We show that the Gaussian model, optimized to best fit the experimental data, outperforms all MD force fields by an order of magnitude. The weaknesses of the MD force fields are: (i) insufficient variability of the polyproline II (pPII) population among the guest residues; (ii) oversampling of antiparallel at the expense of transitional β-strand region; (iii) inadequate sampling of turn-forming conformations for ionizable and polar residues; and (iv) insufficient guest residue-specificity of the Ramachandran distributions. Whereas Amber ff19SB performs worse than the other three force fields with respect to χ² values, it accounts for residue-specific pPII content better than the other three force fields. Additional testing of residue-specific RSFF1 and Amber ff14SB combined with TIP4P/2005 on six guest residues x (=A, I, F, D^P, R, S) reveals that residue specificity derived from protein coil libraries or an improved water model alone do not result in significantly lower χ² values.

Lowering energy consumption for fermentable sugar production from Ramulus mori: Engineered xylanase synergy and improved pretreatment strategy

Biorefinery of Ramulus mori with lower energy consumption through improved enzyme and pretreatment strategies was reported. Directed evolution and saturation mutagenesis were used for the modification of xylanase, the yield of fermentable sugars and the degree of synergy (DS) were determined for different pretreatment (seawater/non-seawater) and enzyme treatment groups (xylanase/cellulase/co-treatment). The dominant mutant I133A/Q143Y of Bispora sp. xylanase XYL10C_ΔN was obtained with improved specific activity (1860 U/mg), catalytic efficiency (1150 mL/s∙mg) at 40 °C, and thermostability (T₅₀ increased by 7 °C). With the pretreatment of seawater immersion, the highest yield of fermentable sugars for Ramulus mori at 40 °C reached 199 μmol/g when hydrolyzed with cellulase and I133A/Q143Y, with the highest DS of 2.6; this was 4.5-fold that of the group hydrolyzed by cellulase alone with non-seawater pretreatment. Thus, bioconversion of reducing sugar from Ramulus mori was improved significantly at lower temperatures, which provides an efficient and energy-saving wayfor biofuel production.

Impact of Electronic Polarization on Preformed, β-Strand Rich Homogenous and Heterogeneous Amyloid Oligomers

Amyloids are a subset of intrinsically disordered proteins (IDPs) that self-assemble into cross-[Formula: see text] oligomers and fibrils. The structural plasticity of amyloids leads to sampling of metastable, low-molecular-weight oligomers that contribute to cytotoxicity. Of interest are amyloid-[Formula: see text] (A[Formula: see text] and islet amyloid polypeptide (IAPP), which are involved in the pathology of Alzheimer’s disease and Type 2 diabetes mellitus, respectively. In addition to forming homogenous oligomers and fibrils, these species have been found to cross-aggregate in heterogeneous structures. Biophysical properties, including electronic effects, that are unique or conserved between homogenous and heterogeneous amyloids oligomers are thus far unexplored. Here, we simulated homogenous and heterogeneous amyloid oligomers of A[Formula: see text] and IAPP[Formula: see text] fragments using the Drude oscillator model to investigate the impact of electronic polarization on the structural morphology and stability of preformed hexamers. Upon simulation of preformed, [Formula: see text]-strand rich oligomers with Drude, structural rearrangement occurred causing some loss of [Formula: see text]-strand structure in favor of random coil content for all oligomers. Homogenous A[Formula: see text] was the most stable system, deriving stability from low polarization in hydrophobic residues and through salt bridge formation. Changes in polarization were observed primarily for A[Formula: see text] residues in heterogeneous cross-amyloid systems, displaying a decrease in charged residue dipole moments and an increase in hydrophobic sidechain dipole moments. This work is the first study utilizing the Drude-2019 force field with amyloid oligomers, providing insight into the impact of electronic effects on oligomer structure and highlighting the importance of different microenvironments on amyloid oligomer stability.

Loop engineering of a thermostable GH10 xylanase to improve low-temperature catalytic performance for better synergistic biomass-degrading abilities

Lignocellulosic biorefining for producing biofuels poses technical challenges. It is usually conducted over a long time using heat, making it energy intensive. In this study, we lowered the energy consumption of this process through an optimized enzyme and pretreatment strategy. First, the dominant mutant M137E/N269G of Bispora sp. MEY-1XYL10C_ΔN was obtained by directed evolution with highcatalytic efficiency (970 mL/s∙mg)and specific activity (2090 U/mg)at 37 °C, and thermostability was improved (T₅₀ increased by5 °C). After pretreatment with seawater immersionfollowing steam explosion,bagasse was co-treated with cellulase and M137E/N269G under mild conditions (37 °C), the resulting highest yield of fermentable sugars reached 219 µmol/g of bagasse,46% higher than that of the non-seawater treatment group, with the highest degree of synergy of 2.0. Pretreatment with seawater following steam explosion and synergistic hydrolysis through high activity xylanase and cellulase helped to achieve low energy degradation of lignocellulosic biomass.

Effect of the Force Field on Molecular Dynamics Simulations of the Multidrug Efflux Protein P-Glycoprotein

Molecular dynamics (MD) simulations have been used extensively to study P-glycoprotein (P-gp), a flexible multidrug transporter that is a key player in the development of multidrug resistance to chemotherapeutics. A substantial body of literature has grown from simulation studies that have employed various simulation conditions and parameters, including AMBER, CHARMM, OPLS, GROMOS, and coarse-grained force fields, drawing conclusions from simulations spanning hundreds of nanoseconds. Each force field is typically parametrized and validated on different data and observables, usually of small molecules and peptides; there have been few comparisons of force field performance on large protein-membrane systems. Here we compare the conformational ensembles of P-gp embedded in a POPC/cholesterol bilayer generated over 500 ns of replicate simulation with five force fields from popular biomolecular families: AMBER 99SB-ILDN, CHARMM 36, OPLS-AA/L, GROMOS 54A7, and MARTINI. We find considerable differences among the ensembles with little conformational overlap, although they correspond to similar extents to structural data obtained from electron paramagnetic resonance and cross-linking studies. Moreover, each trajectory was still sampling new conformations at a high rate after 500 ns of simulation, suggesting the need for more sampling. This work highlights the need to consider known limitations of the force field used (e.g., biases toward certain secondary structures) and the simulation itself (e.g., whether sufficient sampling has been achieved) when interpreting accumulated results of simulation studies of P-gp and other transport proteins.

A fixed multi-site interaction charge model for an accurate prediction of the QM/MM interactions

A fixed multi-site interaction charge (FMIC) model was proposed for the accurate prediction of intermolecular electrostatic interactions based on the quantum mechanical linear response of a molecule to an external electric field. In such a model, some additional off-center interaction sites were added for capturing multipole interactions for a given molecule. By multivariate least-square fitting analysis of the calculated QM/MM interactions of a given molecule with the electrostatic environment and the electrostatic potentials of the environment at the pre-defined distributed interaction sites, the FMIC of the molecule was obtained. The model system of CO in myoglobin (Mb) was utilized to demonstrate the derivation of the FMIC. The accuracy of FMIC in predicting the electrostatic interactions between CO and the Mb environment was investigated using 10 000 different Mb-CO configurations generated from the 400 ps QM/MM MD simulation. In comparison to the QM/MM calculations at the B3LYP/aug-cc-pVTZ/ff99SB level, the mean unsigned error (MUE) of the results based on the FMIC model was merely 0.10 kcal mol^-1, and the root mean square error (RMSE) was only 0.13 kcal mol^-1, which are significantly lower than the results predicted by the ESP charge model (MUE = 1.45 kcal mol^-1, and RMSE = 1.7 kcal mol^-1, respectively). The transferability of FMIC was tested by applying the obtained FMIC in the wild type Mb-CO system to the mutants of V68F and H64L Mb-CO systems. The MUEs of the obtained results for 10 000 different configurations are both smaller than 0.2 kcal mol^-1 for the V68F and H64L Mb-CO systems in comparison to the B3LYP/aug-cc-pVTZ/ff99SB calculations, and the RMSEs are also lower than 0.2 kcal mol^-1 for both mutants. The applications of FMIC were extended to model the electrostatic interactions between a hydrogen fluoride molecule and 492 waters in a truncated octahedron box; our study showed that the FMIC could give satisfactory results with a MUE of 0.12 kcal mol^-1 and a RMSE of 0.16 kcal mol^-1 in comparison to the B3LYP/aug-cc-pVDZ/TIP3P calculations for 10 000 different configurations generated using the 10 ns classical MD simulation. Therefore, the FMIC method provides an accurate and efficient tool for predicting intermolecular electrostatic interactions, which can be utilized in the future development of molecular force fields.

Balanced dual acting compounds targeting aromatase and estrogen receptor α as an emerging therapeutic opportunity to counteract estrogen responsive breast cancer

Breast Cancer (BC) is a leading cause of death in women, currently affecting 13% of female population worldwide. First-line clinical treatments against Estrogen Receptor positive (ER+) BC rely on suppressing estrogen production, by inhibiting the aromatase (AR) enzyme, or on blocking estrogen-dependent pro-oncogenic signaling, by targeting Estrogen Receptor (ER) α with selective Modulators/Degraders (SERMs/SERDs). The development of dual acting molecules targeting AR and ERα represents a tantalizing alternative strategy to fight ER + BC, reducing the incidence of adverse effects and resistance onset that limit the effectiveness of these gold-standard therapies. Here, in silico design, synthesis, biological evaluation and an atomic-level characterization of the binding and inhibition mechanism of twelve structurally related drug-candidates enable the discovery of multiple compounds active on both AR and ERα in the sub-μM range. The best drug-candidate 3a displayed a balanced low-nanomolar IC₅₀ towards the two targets, SERM activity and moderate selectivity towards a BC cell line. Moreover, most of the studied compounds reduced ERα levels, suggesting a potential SERD activity. This study dissects the key structural traits needed to obtain optimal dual acting drug-candidates, showing that multitarget compounds may be a viable therapeutic option to counteract ER + BC.

Configurational Entropy of Folded Proteins and Its Importance for Intrinsically Disordered Proteins

Many pairwise additive force fields are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), some of which modify energetic terms to improve the description of IDPs/IDRs but are largely in disagreement with solution experiments for the disordered states. This work considers a new direction-the connection to configurational entropy-and how it might change the nature of our understanding of protein force field development to equally well encompass globular proteins, IDRs/IDPs, and disorder-to-order transitions. We have evaluated representative pairwise and many-body protein and water force fields against experimental data on representative IDPs and IDRs, a peptide that undergoes a disorder-to-order transition, for seven globular proteins ranging in size from 130 to 266 amino acids. We find that force fields with the largest statistical fluctuations consistent with the radius of gyration and universal Lindemann values for folded states simultaneously better describe IDPs and IDRs and disorder-to-order transitions. Hence, the crux of what a force field should exhibit to well describe IDRs/IDPs is not just the balance between protein and water energetics but the balance between energetic effects and configurational entropy of folded states of globular proteins.

Role of water in the determination of protonation states of titratable residues

Water is the fundamental unit for living being, and its contribution in variety of crucial cellular functions is widely accepted. The presence of water molecules in protein's environment also accounts for structural optimization, in which highly conserved water molecules ensure structural stability of the biomolecule by providing protein-water (solute-solvent) hydrogen-bond interaction networks. Similarly, protonation states and pKa values of individual amino acid residues are also influenced by neighboring water molecules present in the protein's vicinity. In the present study, we have highlighted the role of water molecules in hydrogen-bond optimization, in determining pKa values and protonation states of titratable residues in JH2 domain of JAK2 apo protein. We found that inclusion or exclusion of water molecules while calculating pKa and assigning protonation states to amino acid residues during the molecular system build-up step resulted in slight differences in pKa values of few titratable residues and alternative protonation states of a certain residue. Accordingly, different protonation states of ionizable residues offer differing interaction patterns. Thus, we inferred that the presence of water optimizes the hydrogen-bond interactions by forming direct protein-water interactions and by linking via protein-protein bridging interactions. However, in the absence of water, the interaction pattern is somewhat disrupted. We assume that water molecules could modulate the plausibility of a particular protonation state of titratable residues on the basis of its fit with the local environment, by utilizing some particular hydrogen-bond contacts that would remain unexploited in the absence of water.

Hybrid methods for combined experimental and computational determination of protein structure

Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.

A molecular dynamics simulation study on the propensity of Asn-Gly-containing heptapeptides towards β-turn structures: Comparison with ab initio quantum mechanical calculations

Both molecular mechanical and quantum mechanical calculations play an important role in describing the behavior and structure of molecules. In this work, we compare for the same peptide systems the results obtained from folding molecular dynamics simulations with previously reported results from quantum mechanical calculations. More specifically, three molecular dynamics simulations of 5 μs each in explicit water solvent were carried out for three Asn-Gly-containing heptapeptides, in order to study their folding and dynamics. Previous data, based on quantum mechanical calculations within the DFT framework have shown that these peptides adopt β-turn structures in aqueous solution, with type I’ β-turn being the most preferred motif. The results from our analyses indicate that at least for the given systems, force field and simulation protocol, the two methods diverge in their predictions. The possibility of a force field-dependent deficiency is examined as a possible source of the observed discrepancy.

The balance between side-chain and backbone-driven association in folding of the α-helical influenza A transmembrane peptide

The correct balance between attractive, repulsive and peptide hydrogen bonding interactions must be attained for proteins to fold correctly. To investigate these important contributors, we sought a comparison of the folding between two 25-residues peptides, the influenza A M2 protein transmembrane domain (M2TM) and the 25-Ala (Ala₂₅ ). M2TM forms a stable α-helix as is shown by circular dichroism (CD) experiments. Molecular dynamics (MD) simulations with adaptive tempering show that M2TM monomer is more dynamic in nature and quickly interconverts between an ensemble of various α-helical structures, and less frequently turns and coils, compared to one α-helix for Ala₂₅ . DFT calculations suggest that folding from the extended structure to the α-helical structure is favored for M2TM compared with Ala₂₅ . This is due to CH⋯O attractive interactions which favor folding to the M2TM α-helix, and cannot be described accurately with a force field. Using natural bond orbital (NBO) analysis and quantum theory atoms in molecules (QTAIM) calculations, 26 CH⋯O interactions and 22 NH⋯O hydrogen bonds are calculated for M2TM. The calculations show that CH⋯O hydrogen bonds, although individually weaker, have a cumulative effect that cannot be ignored and may contribute as much as half of the total hydrogen bonding energy, when compared to NH⋯O, to the stabilization of the α-helix in M2TM. Further, a strengthening of NH⋯O hydrogen bonding interactions is calculated for M2TM compared to Ala₂₅ . Additionally, these weak CH⋯O interactions can dissociate and associate easily leading to the ensemble of folded structures for M2TM observed in folding MD simulations.

Molecular Basis for Endocrine Disruption by Pesticides Targeting Aromatase and Estrogen Receptor

The intensive use of pesticides has led to their increasing presence in water, soil, and agricultural products. Mounting evidence indicates that some pesticides may be endocrine disrupting chemicals (EDCs), being therefore harmful for the human health and the environment. In this study, three pesticides, glyphosate, thiacloprid, and imidacloprid, were tested for their ability to interfere with estrogen biosynthesis and/or signaling, to evaluate their potential action as EDCs. Among the tested compounds, only glyphosate inhibited aromatase activity (up to 30%) via a non-competitive inhibition or a mixed inhibition mechanism depending on the concentration applied. Then, the ability of the three pesticides to induce an estrogenic activity was tested in MELN cells. When compared to 17β-estradiol, thiacloprid and imidacloprid induced an estrogenic activity at the highest concentrations tested with a relative potency of 5.4 × 10⁻¹⁰ and 3.7 × 10⁻⁹, respectively. Molecular dynamics and docking simulations predicted the potential binding sites and the binding mode of the three pesticides on the structure of the two key targets, providing a rational for their mechanism as EDCs. The results demonstrate that the three pesticides are potential EDCs as glyphosate acts as an aromatase inhibitor, whereas imidacloprid and thiacloprid can interfere with estrogen induced signaling.

In Silico Screening to Identify Inhibitors of Growth Factor Receptor 2-Focal Adhesion Kinase Interaction for Therapeutic Treatment of Pathological Cardiac Hypertrophy

The focal adhesion kinase-growth factor receptor 2 (FAK-Grb2) protein-protein interaction is implicated in pathogenesis of stress-induced cardiac hypertrophy. The focal adhesion targeting (FAT) domain of FAK unfolds to form a structural intermediate that interacts with a multibinding hot spot in the SH2 domain of Grb2. Disruption of the Grb2-FAT interaction is a therapeutic strategy for prevention of pathological cardiac hypertrophy. A pharmacophore was generated on the basis of structural and electrostatic properties of FAT bound to FAK using the Forge tool (Cresset). This pharmacophore was used as a query for Blaze server (Cresset) to screen a selectively enriched chemical library of 4,32,508 small molecules. The compounds selected were further filtered by hierarchical flexible docking approach using AutoDock v4. From the favorably docked compounds, five were selected on the basis of good adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties using SwissADME, MedChem Designer v.3, and MOLINSPIRATION. Stability of the binding mode of the inhibitors was further confirmed by molecular dynamic simulation study with AMBER v15 for a simulation time of 50 ns in aqueous environment. PM2307 was identified as the best inhibitor in terms of pharmacophoric features, dock score, and in silico ADMET analysis. The calculated binding affinity of PM2307 was better than that of the FAT-Grb2 complex as well as a previously reported small molecule inhibitor. PM2307 is also a quinolyl derivative sharing a similar scaffold with ofloxacin drugs, asserting its drug-like properties. Thus, it was proposed as a lead compound for development of drugs for pathological cardiac hypertrophy.

Structural and molecular insight into the pH-induced low-permeability of the voltage-gated potassium channel Kv1.2 through dewetting of the water cavity

Understanding the gating mechanism of ion channel proteins is key to understanding the regulation of cell signaling through these channels. Channel opening and closing are regulated by diverse environmental factors that include temperature, electrical voltage across the channel, and proton concentration. Low permeability in voltage-gated potassium ion channels (Kv) is intimately correlated with the prolonged action potential duration observed in many acidosis diseases. The Kv channels consist of voltage-sensing domains (S1-S4 helices) and central pore domains (S5-S6 helices) that include a selectivity filter and water-filled cavity. The voltage-sensing domain is responsible for the voltage-gating of Kv channels. While the low permeability of Kv channels to potassium ion is highly correlated with the cellular proton concentration, it is unclear how an intracellular acidic condition drives their closure, which may indicate an additional pH-dependent gating mechanism of the Kv family. Here, we show that two residues E327 and H418 in the proximity of the water cavity of Kv1.2 play crucial roles as a pH switch. In addition, we present a structural and molecular concept of the pH-dependent gating of Kv1.2 in atomic detail, showing that the protonation of E327 and H418 disrupts the electrostatic balance around the S6 helices, which leads to a straightening transition in the shape of their axes and causes dewetting of the water-filled cavity and closure of the channel. Our work offers a conceptual advancement to the regulation of the pH-dependent gating of various voltage-gated ion channels and their related biological functions.

Probing inclusion complexes of 2-hydroxypropyl-β-cyclodextrin with mono-amino mono-carboxylic acids: physicochemical specification, characterization and molecular modeling

Density (ρ), viscosity (η) and surface tension (γ) of three amino acids (valine, alanine, and glycine) have been measured at a different mass fraction (0.002 - 0.009) of aqueous hydroxypropyl-β-cyclodextrin (HPβCD) mixtures and different temperatures (278.15 - 295.15 K). The formation of inclusion complexes has been analyzed via evaluating the amounts of apparent and limiting apparent molar volumes, limiting apparent molar expansibilities, activation energy, kinematic, relative, intrinsic, spatial, and dynamic viscosities. The surface tension studies indicated that the inclusion complexes have been formed with 1:1 stoichiometry and mediated by hydrophobic effects and electrostatic forces. Additionally, the ρ and η parameters were evaluated by molecular modeling experiments to provide more details on the mechanisms of the complexation.

Homology modeling, structural insights and in-silico screening for selective inhibitors of mycobacterial CysE

Tuberculosis posses a major threat for health practitioners due to lengthy treatment regimen, increase in the drug-resistant strains of Mycobacterium tuberculosis (M. tb) and unavailability of drugs for its persistent form. Therefore, there is an urgent need for discovery of new and improved anti-tubercular drugs. In M. tb, the two step de novo biosynthesis of L-cysteine, an essential metabolic pathway is reported to be up-regulated in the persistent phase of the organism, involves two enzymes CysE and CysK. Although, structural insights for rational drug discovery are available for the later, not much information is known for the former. This study proposes a 3-dimensional model of M. tb CysE followed by in-silico screening of 67,030 anti-tuberculosis bioactive compounds. Subsequently, post-processing of 1000 best hits was carried out and top 200 compounds thus obtained were docked into the active site cleft of E. coli homologue as a control, but revealed unexpected results. Differences in the active site architectures and comparative analysis of molecular electrostatic potentials between the two CysEs provide molecular basis for the compounds C1, C3, C4 and C7 exhibiting preferential binding for M. tb CysE. In addition, shorter N-terminus along with positive and irregular trimeric base of M. tb CysE indicates its biological assembly as trimer. Based on mapping of residues involved in cysteine sensitivity on to the model structure of M. tb CysE, it is hypothesized that feedback inhibition of this homologue by cysteine may be affected.Communicated by Ramaswamy H. Sarma.

Hamiltonian Reweighing To Refine Protein Backbone Dihedral Angle Parameters in the GROMOS Force Field

Molecular dynamics simulations of proteins depend critically on the underlying force field, which may be parameterized against experimental data or high-quality quantum calculations. Here, we develop search algorithms based on Monte Carlo and steepest descent calculations to optimize the backbone dihedral angle parameters from a single reference simulation. We apply these tools to improve the agreement between simulations of single, capped amino acids and experimentally determined J values and secondary structure propensities of these molecules. The parameters are further refined based on simulations of a set of seven proteins and finally validated in simulations on a large set of 52 protein structures. Improvements in the dihedral angle distributions are observed, and structural propensities of the proteins are reproduced very well. Overall, the GROMOS 54A8_bb parameter set forms an improvement to previous parameter sets, both for small molecules and for protein simulations.

Desolvation of the substrate-binding protein TauA dictates ligand specificity for the alkanesulfonate ABC importer TauABC

Under limiting sulfur availability, bacteria can assimilate sulfur from alkanesulfonates. Bacteria utilize ATP-binding cassette (ABC) transporters to internalise them for further processing to release sulfur. In gram-negative bacteria the TauABC and SsuABC ensure internalization, although, these two systems have common substrates, the former has been characterized as a taurine specific system. TauA and SsuA are substrate-binding proteins (SBPs) that bind and bring the alkanesulfonates to the ABC importer for transport. Here, we have determined the crystal structure of TauA and have characterized its thermodynamic binding parameters by isothermal titration calorimetry in complex with taurine and different alkanesulfonates. Our structures revealed that the coordination of the alkanesulfonates is conserved, with the exception of Asp205 that is absent from SsuA, but the thermodynamic parameters revealed a very high enthalpic penalty cost for binding of the other alkanesulfonates relative to taurine. Our molecular dynamic simulations indicated that the different levels of hydration of the binding site contributed to the selectivity for taurine over the other alkanesulfonates. Such selectivity mechanism is very likely to be employed by other SBPs of ABC transporters.

ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution

Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled φ/ψ parameters using 2D φ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in aqueous solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, and to compare to results using other Amber models, we have performed a total of ∼5 ms MD simulations in explicit solvent. Our results show that after amino-acid-specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino-acid-specific Protein Data Bank (PDB) Ramachandran maps better but also shows significantly improved capability to differentiate amino-acid-dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated for by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. Of the explicit water models tested here, we recommend use of OPC with ff19SB.

Using Schematic Models to Understand the Microscopic Basis for Inverted Solubility in γD-Crystallin

Inverted solubility-melting a crystal by cooling-is observed in a handful of proteins, such as carbomonoxy hemoglobin C and γD-crystallin. In human γD-crystallin, the phenomenon is associated with the mutation of the 23rd residue, a proline, to a threonine, serine, or valine. One proposed microscopic mechanism entails an increase in surface hydrophobicity upon mutagenesis. Recent crystal structures of a double mutant that includes the P23T mutation allow for a more careful investigation of this proposal. Here, we first measure the surface hydrophobicity of various mutant structures of γD-crystallin and discern no notable increase in hydrophobicity upon mutating the 23rd residue. We then investigate the solubility inversion regime with a schematic patchy particle model that includes one of three variants of temperature-dependent patch energies: two of the hydrophobic effect, and one of a more generic nature. We conclude that, while solubility inversion due to the hydrophobic effect may be possible, microscopic evidence to support it in γD-crystallin is weak. More generally, we find that solubility inversion requires a fine balance between patch strengths and their temperature-dependent component, which may explain why inverted solubility is not commonly observed in proteins. We also find that the temperature-dependent interaction has only a negligible impact on liquid-liquid phase boundaries of γD-crystallin, in line with previous experimental observations.

Structural, Thermodynamic, and Kinetic Traits of Antiestrogen-Compounds Selectively Targeting the Y537S Mutant Estrogen Receptor α Transcriptional Activity in Breast Cancer Cell Lines

The most frequently diagnosed cancers in women are the estrogen receptor (ER)-positive breast cancer subtypes, which are characterized by estrogen dependency for their growth. The mainstay of clinical treatment for this tumor relies on the modulation of ERα action or on the suppression of estrogen biosynthesis via the administration of Selective ERα Modulators/Down-regulators (SERMs/SERDs) or aromatase inhibitors, respectively. Nevertheless, de novo and acquired resistance to these therapies frequently occurs and represents a major clinical concern for patient survival. Recently, somatic mutations affecting the hormone-binding domain of ERα (i.e., Y537S, Y537N, D538G) have been associated with endocrine resistance, disease relapse and increased mortality rates. Hence, devising novel therapies against these ERα isoforms represents a daunting challenge. Here, we identified five molecules active on recurrent Y537S ERα polymorphism by employing in silico virtual screening on commercial databases of molecules, complemented by ER-transactivation and MTT assays in MCF7 and MDA-MB-231 breast cancer cells expressing wild type or mutated ERα. Among them, one molecule selectively targets Y537S ERα without inducing any cytotoxicity in breast cell lines. Multi-microseconds (4.5 μs) of biased and unbiased molecular dynamics provided an atomic-level picture of the structural, thermodynamics (i.e., binding free energies) and the kinetic (i.e., dissociation free energy barriers) of these active ligands as compared to clinically used SERM/SERDs upon binding to wild type and distinct ERα variants (Y537S, Y537N, D538G). This study contributes to a dissection of the key molecular traits needed by drug-candidates to hamper the agonist (active)-like conformation of ERα, normally selected by those polymorphic variants. This information can be useful to discover mutant specific drug-candidates, enabling to move a step forward toward tailored approaches for breast cancer treatment.

Molecular basis for functional diversity among microbial Nep1-like proteins

Necrosis and ethylene-inducing peptide 1 (Nep1)-like proteins (NLPs) are secreted by several phytopathogenic microorganisms. They trigger necrosis in various eudicot plants upon binding to plant sphingolipid glycosylinositol phosphorylceramides (GIPC). Interestingly, HaNLP3 from the obligate biotroph oomycete Hyaloperonospora arabidopsidis does not induce necrosis. We determined the crystal structure of HaNLP3 and showed that it adopts the NLP fold. However, the conformations of the loops surrounding the GIPC headgroup-binding cavity differ from those of cytotoxic Pythium aphanidermatum NLPPya. Essential dynamics extracted from μs-long molecular dynamics (MD) simulations reveals a limited conformational plasticity of the GIPC-binding cavity in HaNLP3 relative to toxic NLPs. This likely precludes HaNLP3 binding to GIPCs, which is the underlying reason for the lack of toxicity. This study reveals that mutations at key protein regions cause a switch between non-toxic and toxic phenotypes within the same protein scaffold. Altogether, these data provide evidence that protein flexibility is a distinguishing trait of toxic NLPs and highlight structural determinants for a potential functional diversification of non-toxic NLPs utilized by biotrophic plant pathogens.

Post-Translational Regulation of CYP450s Metabolism As Revealed by All-Atoms Simulations of the Aromatase Enzyme

Phosphorylation by kinases enzymes is a widespread regulatory mechanism able of rapidly altering the function of target proteins. Among these are cytochrome P450s (CYP450), a superfamily of enzymes performing the oxidation of endogenous and exogenous substrates thanks to the electron supply of a redox partner. In spite of its pivotal role, the molecular mechanism by which phosphorylation modulates CYP450s metabolism remains elusive. Here by performing microsecond-long all-atom molecular dynamics simulations, we disclose how phosphorylation regulates estrogen biosynthesis, catalyzed by the Human Aromatase (HA) enzyme. Namely, we unprecedentedly propose that HA phosphorylation at Y361 markedly stabilizes its adduct with the flavin mononucleotide domain of CYP450s reductase (CPR), the redox partner of microsomal CYP450s, and a variety of other proteins. With CPR present at physiological conditions in a limiting ratio with respect to its multiple oxidative partners, the enhanced stability of the CPR/HA adduct may favor HA in the competition with the other proteins requiring CPR's electron supply, ultimately facilitating the electron transfer and estrogen biosynthesis. As a result, our work elucidates at atomic-level the post-translational regulation of CYP450s catalysis. Given the potential for rational clinical management of diseases associated with steroid metabolism disorders, unraveling this mechanism is of utmost importance, and raises the intriguing perspective of exploiting this knowledge to devise novel therapies.

Molecular simulations minimally restrained by experimental data

One popular approach to incorporating experimental data into molecular simulations is to restrain the ensemble average of observables to their experimental values. Here, I derive equations for the equilibrium distributions generated by restrained ensemble simulations and the corresponding expected values of observables. My results suggest a method to restrain simulations so that they generate distributions that are minimally perturbed from the unbiased distributions while reproducing the experimental values of the observables within their measurement uncertainties.

Development of Polarizable Gaussian Model for Molecular Mechanical Calculations I: Atomic Polarizability Parameterization To Reproduce ab Initio Anisotropy

A set of atomic polarizability parameters for a new polarizable Gaussian model (pGM) has been developed with the goal to accurately reproduce the polarizability anisotropy, taking advantage of its ability to attenuate all short-range electrostatic interactions, by fitting the ab initio molecular polarizability tensors ( A _pq) calculated at the B3LYP/aug-cc-pVTZ level. For comparison, we also rederived the parameters for three Thole models in which the 1-2 (bonded), 1-3 (separated by two bonds), and 1-4 (separated by three bonds) interactions are fully included. The average percent errors (APEs) of molecular polarizability tensors for 4842 molecules or dimers are 2.98, 3.76, 3.28, and 3.82% for the pGM, Thole linear, Thole exponential, and Thole Amoeba models, respectively, with atom-type independent, universal screening factors (USF). The APEs are reduced further to 2.30, 2.69, 2.25, and 2.48% for the four corresponding polarizable models with atom-type dependent, variable screening factors (VSF). It is encouraging that the pGM with variable screening factors achieved APEs of 1.83 for 1155 amino acid analogs, dipeptides, and tetrapeptides, 1.39 for 28 nucleic acid bases, 0.708 for 1464 water clusters, and 1.99 for 85 dimers of water and biological building blocks. Compared to the new set of models, the APEs of the old Thole models that were fitted to isotropic molecular polarizabilities are 8.7% for set A (without the 1-2 and 1-3 interactions) and 6.3% for set D (with the 1-2 and 1-3 interactions) models, respectively. MPAD, a metric of molecular polarization anisotropy difference based on the diagonal terms of molecular polarizability tensors was defined and applied to assess the polarizable models in reproducing the ab initio molecular polarization anisotropy. The MPADs are 3.71, 4.70, 4.11, and 4.77% for the pGM, Thole linear, Thole exponential, and Thole Amoeba USF models, respectively. The APEs are reduced further to 2.85, 3.58, 2.90, and 3.15% for the four corresponding VSF models. Thus, the new pGM and Thole models notably improve molecular polarization anisotropy. Since pGM attenuates all short-range electrostatic interactions, its application is expected to improve stability in charge fitting, energy, and force calculations and the accuracy of multibody polarization.

Fast Pairwise Approximation of Solvent Accessible Surface Area for Implicit Solvent Simulations of Proteins on CPUs and GPUs

We propose a pairwise and readily parallelizable SASA-based nonpolar solvation approach for protein simulations, inspired by our previous pairwise GB polar solvation model development. In this work, we developed a novel function to estimate the atomic and molecular SASAs of proteins, which results in comparable accuracy as the LCPO algorithm in reproducing numerical icosahedral-based SASA values. Implemented in Amber software and tested on consumer GPUs, our pwSASA method reasonably reproduces LCPO simulation results, but accelerates MD simulations up to 30 times compared to the LCPO implementation, which is greatly desirable for protein simulations facing sampling challenges. The value of incorporating the nonpolar term in implicit solvent simulations is explored on a peptide fragment containing the hydrophobic core of HP36 and evaluating thermal stability profiles of four small proteins.

Complete Coupled Binding-Folding Pathway of the Intrinsically Disordered Transcription Factor Protein Brinker Revealed by Molecular Dynamics Simulations and Markov State Modeling

Intrinsically disordered proteins (IDPs) make up a large class of proteins that lack stable structures in solution, existing instead as dynamic conformational ensembles. To perform their biological functions, many IDPs bind to other proteins or nucleic acids. Although IDPs are unstructured in solution, when they interact with binding partners, they fold into defined three-dimensional structures via coupled binding-folding processes. Because they frequently underlie IDP function, the mechanisms of this coupled binding-folding process are of great interest. However, given the flexibility inherent to IDPs and the sparse populations of intermediate states, it is difficult to reveal binding-folding pathways at atomic resolution using experimental methods. Computer simulations are another tool for studying these pathways at high resolution. Accordingly, we have applied 40 μs of unbiased molecular dynamics simulations and Markov state modeling to map the complete binding-folding pathway of a model IDP, the 59-residue C-terminal portion of the DNA binding domain of Drosophila melanogaster transcription factor Brinker (BrkDBD). Our modeling indicates that BrkDBD binds to its cognate DNA and folds in ∼50 μs by an induced fit mechanism, acquiring most of its stable secondary and tertiary structure only after it reaches the final binding site on the DNA. The protein follows numerous pathways en route to its bound and folded conformation, occasionally becoming stuck in kinetic traps. Each binding-folding pathway involves weakly bound, increasingly folded intermediate states located at different sites on the DNA surface. These findings agree with experimental data and provide additional insight into the BrkDBD folding mechanism and kinetics.

Discovery of Highly Potent Pinanamine-Based Inhibitors against Amantadine- and Oseltamivir-Resistant Influenza A Viruses

Influenza pandemic is a constant major threat to public health caused by influenza A viruses (IAVs). IAVs are subcategorized by the surface proteins hemagglutinin (HA) and neuraminidase (NA), in which they are both essential targets for drug discovery. While it is of great concern that NA inhibitor oseltamivir resistant strains are frequently identified from human or avian influenza virus, structural and functional characterization of influenza HA has raised hopes for new antiviral therapies. In this study, we explored a structure-activity relationship (SAR) of pinanamine-based antivirals and discovered a potent inhibitor M090 against amantadine-resistant viruses, including the 2009 H1N1 pandemic strains, and oseltamivir-resistant viruses. Mechanism of action studies, particularly hemolysis inhibition, indicated that M090 targets influenza HA and it occupied a highly conserved pocket of the HA₂ domain and inhibited virus-mediated membrane fusion by "locking" the bending state of HA₂ during the conformational rearrangement process. This work provides new binding sites within the HA protein and indicates that this pocket may be a promising target for broad-spectrum anti-influenza A drug design and development.