Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations

Energy, water, and protein folding: A molecular dynamics-based quantitative inventory of molecular interactions and forces that make proteins stable

Protein folding energetics can be determined experimentally on a case-by-case basis but it is not understood in sufficient detail to provide deep control in protein design. The fundamentals of protein stability have been outlined by calorimetry, protein engineering, and biophysical modeling, but these approaches still face great difficulty in elucidating the specific contributions of the intervening molecules and physical interactions. Recently, we have shown that the enthalpy and heat capacity changes associated to the protein folding reaction can be calculated within experimental error using molecular dynamics simulations of native protein structures and their corresponding unfolded ensembles. Analyzing in depth molecular dynamics simulations of four model proteins (CI2, barnase, SNase, and apoflavodoxin), we dissect here the energy contributions to ΔH (a key component of protein stability) made by the molecular players (polypeptide and solvent molecules) and physical interactions (electrostatic, van der Waals, and bonded) involved. Although the proteins analyzed differ in length, isoelectric point and fold class, their folding energetics is governed by the same quantitative pattern. Relative to the unfolded ensemble, the native conformations are enthalpically stabilized by comparable contributions from protein-protein and solvent-solvent interactions, and almost equally destabilized by interactions between protein and solvent molecules. The native protein surface seems to interact better with water than the unfolded one, but this is outweighed by the unfolded surface being larger. From the perspective of physical interactions, the native conformations are stabilized by van de Waals and Coulomb interactions and destabilized by conformational strain arising from bonded interactions. Also common to the four proteins, the sign of the heat capacity change is set by interactions between protein and solvent molecules or, from the alternative perspective, by Coulomb interactions.

Characterization and bioactivities of coffee husks extract encapsulated with polyvinylpyrrolidone

Coffee processing generates large amounts of residues of which a portion still has bioactive properties due to their richness in phenolic compounds. This study aimed to obtain a coffee husks extract (CHE) and to encapsulate it (ECHE) with polyvinylpyrrolidone using a one-step procedure of solid dispersion. The extraction and encapsulation yields were 9.1% and 92%, respectively. Thermal analyses revealed that the encapsulation increased the thermal stability of CHE and dynamic light scattering analyses showed a bimodal distribution of size with 81% of the ECHE particles measuring approximately 711 nm. Trigonelline and caffeine were the main alkaloids and quercetin the main phenolic compound in CHE, and the encapsulation tripled quercetin extraction. The total phenolics content and the antioxidant activity of ECHE, assayed with three different procedures, were higher than those of CHE. The antioxidant activity and the bioaccessibility of the phenolic compounds of ECHE were also higher than those of CHE following simulated gastrointestinal digestion (SGID). Both CHE and ECHE were not toxic against Alliumcepa cells and showed similar capacities for inhibiting the pancreatic α-amylase in vitro. After SGID, however, ECHE became a 1.9-times stronger inhibitor of the α-amylase activity in vitro (IC50 = 8.5 mg/mL) when compared to CHE. Kinetic analysis revealed a non-competitive mechanism of inhibition and in silico docking simulation suggests that quercetin could be contributing significantly to the inhibitory action of both ECHE and CHE. In addition, ECHE (400 mg/kg) was able to delay by 50% the increases of blood glucose in vivo after oral administration of starch to rats. This finding shows that ECHE may be a candidate ingredient in dietary supplements used as an adjuvant for the treatment of diabetes.

In silico λ-dynamics predicts protein binding specificities to modified RNAs

RNA modifications shape gene expression through a smorgasbord of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.

KCNQ1 p.D446E Variant as a Risk Allele for Arrhythmogenic Phenotypes: Electrophysiological Characterization Reveals a Complex Phenotype Affecting the Slow Delayed Rectifier Potassium Current (IKs) Voltage Dependence by Causing a Hyperpolarizing Shift and a Lack of Response to Protein Kinase A Activation

Genetic testing is crucial in inherited arrhythmogenic channelopathies; however, the clinical interpretation of genetic variants remains challenging. Incomplete penetrance, oligogenic, polygenic or multifactorial forms of channelopathies further complicate variant interpretation. We identified the KCNQ1/p.D446E variant in 2/63 patients with long QT syndrome, 30-fold more frequent than in public databases. We thus characterized the biophysical phenotypes of wildtype and mutant IKs co-expressing these alleles with the β-subunit minK in HEK293 cells. KCNQ1 p.446E homozygosity significantly shifted IKs voltage dependence to hyperpolarizing potentials in basal conditions (gain of function) but failed to shift voltage dependence to hyperpolarizing potentials (loss of function) in the presence of 8Br-cAMP, a protein kinase A activator. Basal IKs activation kinetics did not differ among genotypes, but in response to 8Br-cAMP, IKs 446 E/E (homozygous) activation kinetics were slower at the most positive potentials. Protein modeling predicted a slower transition of the 446E Kv7.1 tetrameric channel to the stabilized open state. In conclusion, biophysical and modelling evidence shows that the KCNQ1 p.D446E variant has complex functional consequences including both gain and loss of function, suggesting a contribution to the pathogenesis of arrhythmogenic phenotypes as a functional risk allele.

Elucidating Dynamics of Adenylate Kinase from Enzyme Opening to Ligand Release

This study explores ligand-driven conformational changes in adenylate kinase (AK), which is known for its open-to-close conformational transitions upon ligand binding and release. By utilizing string free energy simulations, we determine the free energy profiles for both enzyme opening and ligand release and compare them with profiles from the apoenzyme. Results reveal a three-step ligand release process, which initiates with the opening of the adenosine triphosphate-binding subdomain (ATP lid), followed by ligand release and concomitant opening of the adenosine monophosphate-binding subdomain (AMP lid). The ligands then transition to nonspecific positions before complete dissociation. In these processes, the first step is energetically driven by ATP lid opening, whereas the second step is driven by ATP release. In contrast, the AMP lid opening and its ligand release make minor contributions to the total free energy for enzyme opening. Regarding the ligand binding mechanism, our results suggest that AMP lid closure occurs via an induced-fit mechanism triggered by AMP binding, whereas ATP lid closure follows conformational selection. This difference in the closure mechanisms provides an explanation with implications for the debate on ligand-driven conformational changes of AK. Additionally, we determine an X-ray structure of an AK variant that exhibits significant rearrangements in the stacking of catalytic arginines, explaining its reduced catalytic activity. In the context of apoenzyme opening, the sequence of events is different. Here, the AMP lid opens first while the ATP lid remains closed, and the free energy associated with ATP lid opening varies with orientation, aligning with the reported AK opening and closing rate heterogeneity. Finally, this study, in conjunction with our previous research, provides a comprehensive view of the intricate interplay between various structural elements, ligands, and catalytic residues that collectively contribute to the robust catalytic power of the enzyme.

Fluoride Transport and Inhibition Across CLC Transporters

The Chloride Channel (CLC) family includes proton-coupled chloride and fluoride transporters. Despite their similar protein architecture, the former exchange two chloride ions for each proton and are inhibited by fluoride, whereas the latter efficiently transport one fluoride in exchange for one proton. The combination of structural, mutagenesis, and functional experiments with molecular simulations has pinpointed several amino acid changes in the permeation pathway that capitalize on the different chemical properties of chloride and fluoride to fine-tune protein function. Here we summarize recent findings on fluoride inhibition and transport in the two prototypical members of the CLC family, the chloride/proton transporter from Escherichia coli (CLC-ec1) and the fluoride/proton transporter from Enterococcus casseliflavus (CLCF-eca).

Rate limiting step of the allosteric activation of the bacterial adhesin FimH investigated by molecular dynamics simulations

The bacterial adhesin FimH is a model for the study of protein allostery because its structure has been resolved in multiple configurations, including the active and the inactive state. FimH consists of a pilin domain (PD) that anchors it to the rest of the fimbria and an allosterically regulated lectin domain (LD) that binds mannose on the surface of infected cells. Under normal conditions, the two domains are docked to each other and LD binds mannose weakly. However, in the presence of tensile force generated by shear the domains separate and conformational changes propagate across LD resulting in a stronger bond to mannose. Recently, the crystallographic structure of a variant of FimH has been resolved, calledFimH FocH , where PD contains 10 mutations near the inter-domain interface. Although the X-ray structures of FimH andFimH FocH are almost identical, experimental evidence shows thatFimH FocH is activated even in the absence of shear. Here, molecular dynamics simulations combined with the Jarzynski equality were used to investigate the discrepancy between the crystallographic structures and the functional assays. The results indicate that the free energy barrier of the unbinding process between LD and PD is drastically reduced inFimH FocH . Rupture of inter-domain hydrogen bonds involving R166 constitutes a rate limiting step of the domain separation process and occurs more readily inFimH FocH than FimH. In conclusion, the mutations inFimH FocH shift the equilibrium toward an equal occupancy of bound and unbound states for LD and PD by reducing a rate limiting step.

Calcium-gated potassium channel blockade via membrane-facing fenestrations

Quaternary ammonium blockers were previously shown to bind in the pore to block both open and closed conformations of large-conductance calcium-activated potassium (BK and MthK) channels. Because blocker entry was assumed through the intracellular entryway (bundle crossing), closed-pore access suggested that the gate was not at the bundle crossing. Structures of closed MthK, a Methanobacterium thermoautotrophicum homolog of BK channels, revealed a tightly constricted intracellular gate, leading us to investigate the membrane-facing fenestrations as alternative pathways for blocker access directly from the membrane. Atomistic free energy simulations showed that intracellular blockers indeed access the pore through the fenestrations, and a mutant channel with narrower fenestrations displayed no closed-state TPeA block at concentrations that blocked the wild-type channel. Apo BK channels display similar fenestrations, suggesting that blockers may use them as access paths into closed channels. Thus, membrane fenestrations represent a non-canonical pathway for selective targeting of specific channel conformations, opening novel ways to selectively drug BK channels.

Calculation of Protein Folding Thermodynamics Using Molecular Dynamics Simulations

Despite advances in artificial intelligence methods, protein folding remains in many ways an enigma to be solved. Accurate computation of protein folding energetics could help drive fields such as protein and drug design and genetic interpretation. However, the challenge of calculating the state functions governing protein folding from first-principles remains unaddressed. We present here a simple approach that allows us to accurately calculate the energetics of protein folding. It is based on computing the energy of the folded and unfolded states at different temperatures using molecular dynamics simulations. From this, two essential quantities (ΔH and ΔCp) are obtained and used to calculate the conformational stability of the protein (ΔG). With this approach, we have successfully calculated the energetics of two- and three-state proteins, representatives of the major structural classes, as well as small stability differences (ΔΔG) due to changes in solution conditions or variations in an amino acid residue.

Two-Step Covalent Docking with Attracting Cavities

Due to their various advantages, interest in the development of covalent drugs has been renewed in the past few years. It is therefore important to accurately describe and predict their interactions with biological targets by computer-aided drug design tools such as docking algorithms. Here, we report a covalent docking procedure for our in-house docking code Attracting Cavities (AC), which mimics the two-step mechanism of covalent ligand binding. Ligand binding to the protein cavity is driven by nonbonded interactions, followed by the formation of a covalent bond between the ligand and the protein through a chemical reaction. To test the performance of this method, we developed a diverse, high-quality, openly accessible re-docking benchmark set of 95 covalent complexes bound by 8 chemical reactions to 5 different reactive amino acids. Combination with structures from previous studies resulted in a set of 304 complexes, on which AC obtained a success rate (rmsd ≤ 2 Å) of 78%, outperforming two state-of-the-art covalent docking codes, genetic optimization for ligand docking (GOLD (66%)) and AutoDock (AD (35%)). Using a more stringent success criterion (rmsd ≤ 1.5 Å), AC reached a success rate of 71 vs 55% for GOLD and 26% for AD. We additionally assessed the cross-docking performance of AC on a set of 76 covalent complexes of the SARS-CoV-2 main protease. On this challenging test set of mainly small and highly solvent-exposed ligands, AC yielded success rates of 58 and 28% for re-docking and cross-docking, respectively, compared to 45 and 17% for GOLD.

Quantifying the Nearly Random Microheterogeneity of Aqueous tert-Butyl Alcohol Solutions Using Vibrational Spectroscopy

The microheterogeneous structure of aqueous tert-butyl alcohol (TBA) solutions is quantified by combining experimental, simulations, and theoretical results. Experimental Raman multivariate curve resolution (Raman-MCR) C-H frequency shift measurements are compared with predictions obtained using combined quantum mechanical and effective fragment potential (QM/EFP) calculations, as well as with molecular dynamics (MD), random mixture (RM), and finite lattice (FL) predictions. The results indicate that the microheterogeneous aggregation in aqueous TBA solutions is slightly less than that predicted by MD simulations performed using either CHARMM generalized force field (CGenFF) or optimized parameters for liquid simulations all atom (OPLS-AA) force fields but slightly more than that in a self-avoiding RM of TBA-like molecules. The results imply that the onset of microheterogeneity in aqueous solutions occurs when solute contact free energies are about an order of magnitude smaller than thermal fluctuations, thus suggesting a fundamental bound of relevance to biological self-assembly.

Structural and thermodynamic framework for PIEZO1 modulation by small molecules

Mechanosensitive PIEZO channels constitute potential pharmacological targets for multiple clinical conditions, spurring the search for potent chemical PIEZO modulators. Among them is Yoda1, a widely used synthetic small molecule PIEZO1 activator discovered through cell-based high-throughput screening. Yoda1 is thought to bind to PIEZO1's mechanosensory arm domain, sandwiched between two transmembrane regions near the channel pore. However, how the binding of Yoda1 to this region promotes channel activation remains elusive. Here, we first demonstrate that cross-linking PIEZO1 repeats A and B with disulfide bridges reduces the effects of Yoda1 in a redox-dependent manner, suggesting that Yoda1 acts by perturbing the contact between these repeats. Using molecular dynamics-based absolute binding free energy simulations, we next show that Yoda1 preferentially occupies a deeper, amphipathic binding site with higher affinity in PIEZO1 open state. Using Yoda1's binding poses in open and closed states, relative binding free energy simulations were conducted in the membrane environment, recapitulating structure-activity relationships of known Yoda1 analogs. Through virtual screening of an 8 million-compound library using computed fragment maps of the Yoda1 binding site, we subsequently identified two chemical scaffolds with agonist activity toward PIEZO1. This study supports a pharmacological model in which Yoda1 activates PIEZO1 by wedging repeats A and B, providing a structural and thermodynamic framework for the rational design of PIEZO1 modulators. Beyond PIEZO channels, the three orthogonal computational approaches employed here represent a promising path toward drug discovery in highly heterogeneous membrane protein systems.

Dramatic Differences between the Structural Susceptibility of the S1 Pre- and S2 Postfusion States of the SARS-CoV-2 Spike Protein to External Electric Fields Revealed by Molecular Dynamics Simulations

In its prefusion state, the SARS-CoV-2 spike protein (similarly to other class I viral fusion proteins) is metastable, which is considered to be an important feature for optimizing or regulating its functions. After the binding process of its S1 subunit (S1) with ACE2, the spike protein (S) undergoes a dramatic conformational change where S1 splits from the S2 subunit, which then penetrates the membrane of the host cell, promoting the fusion of the viral and cell membranes. This results in the infection of the host cell. In a previous work, we showed-using large-scale molecular dynamics simulations-that the application of external electric fields (EFs) induces drastic changes and damage in the receptor-binding domain (RBD) of the wild-type spike protein, as well of the Alpha, Beta, and Gamma variants, leaving a structure which cannot be recognized anymore by ACE2. In this work, we first extend the study to the Delta and Omicron variants and confirm the high sensitivity and extreme vulnerability of the RBD of the prefusion state of S to moderate EF (as weak as 104 V/m), but, more importantly, we also show that, in contrast, the S2 subunit of the postfusion state of the spike protein does not suffer structural damage even if electric field intensities four orders of magnitude higher are applied. These results provide a solid scientific basis to confirm the connection between the prefusion-state metastability of the SARS-CoV-2 spike protein and its susceptibility to be damaged by EF. After the virus docks to the ACE2 receptor, the stable and robust postfusion conformation develops, which exhibits a similar resistance to EF (damage threshold higher than 108 V/m) like most globular proteins.

Exploring the Substrate-Assisted Dehydration of Chorismate Catalyzed by Dehydratase MqnA from QM/MM Calculations: The Role of Pocket Residues and the Hydrolysis Mechanism of N17D Mutant

MqnA is the first enzyme on the futalosine pathway to menaquinone, which catalyzes the dehydration of chorismate to yield 3-enolpyruvyl-benzoate (3-EPB). MqnA is also the only chorismate dehydratase known so far. In this work, based on the recently determined crystal structures, we constructed the enzyme-substrate complex models and conducted quantum mechanics/molecular mechanics (QM/MM) calculations to elucidate the reaction details of MqnA and the critical roles of pocket residues. The calculation results confirm that the MqnA-catalyzed dehydration of chorismate follows the substrate-assisted E1cb mechanism, in which the enol carboxylate in the side chain of the substrate is responsible for deprotonating the C3 of chorismate. This proton transfer process is much slower than C4-OH departure. Calculations on different mutants reveal that S86 and N17 are important for anchoring the enol carboxylate of the substrate in a favorable conformation to extract the C3-proton. The strong H-bonds formed between the enol carboxylate of chorismate and S86/N17 play a key role in stabilizing the reaction intermediate. Consistent with the experimental observations, our calculations demonstrate that the MqnA N17D mutant also shows hydrolase activity and the typical enzyme-catalyzed hydrolysis mechanism is elucidated. The protonated D17 is responsible for saturating the methylene group of chorismate to start the hydrolysis reaction. The orientation of the carboxyl group of D17 is key in determining MqnA to be a dehydratase or hydrolase.

An inorganic mineral-based protocell with prebiotic radiation fitness

Protocell fitness under extreme prebiotic conditions is critical in understanding the origin of life. However, little is known about protocell's survival and fitness under prebiotic radiations. Here we present a radioresistant protocell model based on assembly of two types of coacervate droplets, which are formed through interactions of inorganic polyphosphate (polyP) with divalent metal cation and cationic tripeptide, respectively. Among the coacervate droplets, only the polyP-Mn droplet is radiotolerant and provides strong protection for recruited proteins. The radiosensitive polyP-tripeptide droplet sequestered with both proteins and DNA could be encapsulated inside the polyP-Mn droplet, and form into a compartmentalized protocell. The protocell protects the inner nucleoid-like condensate through efficient reactive oxygen species' scavenging capacity of intracellular nonenzymic antioxidants including Mn-phosphate and Mn-peptide. Our results demonstrate a radioresistant protocell model with redox reaction system in response to ionizing radiation, which might enable the protocell fitness to prebiotic radiation on the primitive Earth preceding the emergence of enzyme-based fitness. This protocell might also provide applications in synthetic biology as bioreactor or drug delivery system.

Allosteric Competition and Inhibition in AMPA Receptors

Excitatory neurotransmission is principally mediated by AMPA-subtype ionotropic glutamate receptors (AMPARs). Dysregulation of AMPARs is the cause of many neurological disorders and how therapeutic candidates such as negative allosteric modulators inhibit AMPARs is unclear. Here, we show that non-competitive inhibition desensitizes AMPARs to activation and prevents positive allosteric modulation. We dissected the noncompetitive inhibition mechanism of action by capturing AMPARs bound to glutamate and the prototypical negative allosteric modulator, GYKI-52466, with cryo-electron microscopy. Noncompetitive inhibition by GYKI-52466, which binds in the transmembrane collar region surrounding the ion channel, negatively modulates AMPARs by decoupling glutamate binding in the ligand binding domain from the ion channel. Furthermore, during allosteric competition between negative and positive modulators, negative allosteric modulation by GKYI-52466 outcompetes positive allosteric modulators to control AMPAR function. Our data provide a new framework for understanding allostery of AMPARs and foundations for rational design of therapeutics targeting AMPARs in neurological diseases.

Effect of Main and Side Chains on the Folding Mechanism of the Trp-Cage Miniprotein

Proteins that do not fold into their functional native state have been linked to diseases. In this study, the influence of the main and side chains of individual amino acids on the folding of the tryptophan cage (Trp-cage), a designed 20-residue miniprotein, was analyzed. For this purpose, we calculated the solvation free energy (SFE) contributions of individual atoms by using the 3D-reference interaction site model with the atomic decomposition method. The mechanism by which the Trp-cage is stabilized during the folding process was examined by calculating the total energy, which is the sum of the conformational energy and SFE. The folding process of the Trp-cage resulted in a stable native state, with a total energy that was 62.4 kcal/mol lower than that of the unfolded state. The solvation entropy, which is considered to be responsible for the hydrophobic effect, contributed 31.3 kcal/mol to structural stabilization. In other words, the contribution of the solvation entropy accounted for approximately half of the total contribution to Trp-cage folding. The hydrophobic core centered on Trp6 contributed 15.6 kcal/mol to the total energy, whereas the solvation entropy contribution was 6.3 kcal/mol. The salt bridge formed by the hydrophilic side chains of Asp9 and Arg16 contributed 10.9 and 5.0 kcal/mol, respectively. This indicates that not only the hydrophobic core but also the salt bridge of the hydrophilic side chains gain solvation entropy and contribute to stabilizing the native structure of the Trp-cage.

SwissParam 2023: A Modern Web-Based Tool for Efficient Small Molecule Parametrization

Most steps of drug discovery are now routinely supported and accelerated by computer-aided drug design tools. Among them, structure-based approaches use the three-dimensional structure of the targeted biomacromolecule as a major source of information. When it comes to calculating the interactions of small molecules with proteins using the equations of molecular mechanics, topologies, atom typing, and force field parameters are required. However, generating parameters for small molecules remains challenging due to the large number of existing chemical groups. The SwissParam web tool was first released in 2011 with the aim of generating parameters and topologies for small molecules based on the Merck molecular force field (MMFF) while being compatible with the CHARMM22/27 force field. Here, we present an updated version of SwissParam, providing various new features, including the possibility to setup covalent ligands. Molecules can now be imported from different file formats or via a molecular sketcher. The MMFF-based approach has been updated to provide parameters and topologies compatible with the CHARMM36 force field. An option was added to generate small molecule parametrizations following the CHARMM General Force Field via the multipurpose atom-typer for CHARMM (MATCH) approach. Additionally, SwissParam now generates information on probable alternative tautomers and protonation states of the query molecule so that the user can consider all microspecies relevant to its compound. The new version of SwissParam is freely available at www.swissparam.ch and can also be accessed through a newly implemented command-line interface.

Neutralizing Antibody Response to Genotypically Diverse Measles Viruses in Clinically Suspected Measles Cases

The neutralizing antibody (Nt-Ab) response to vaccine and wild-type measles viruses (MeV) was studied in suspected measles cases reported during the years 2012-2016. The neutralization activity against MeV A, D4 and D8 genotypes was studied on sera (Panel A; n = 68 (measles-immunized) and Panel B; n = 50 (unvaccinated)) that were either laboratory confirmed or not confirmed by the presence of IgM antibodies. Additionally, the Nt-Ab response in Panel A was measured against the MeV vaccine and four wild-type viruses. Neutralization results were compared using homology modeling and molecular dynamics simulation (MDS) of MeV-hemagglutinin (H) and fusion (F) proteins. Overall, the Nt-Ab titres for MeV-A were found to be significantly lower than MeV-D4 and MeV-D8 viruses for Panel A. No major difference was noted in Nt-Ab titres between MeV-D8 viruses (Jamnagar and New Delhi), whereas MeV-D4 (Sindhudurg and Bagalkot (BGK) viruses) showed significant differences between Nt-Ab titres for Panel B. Interestingly, the substitutions observed in epitopes of H-protein, L249P and G316A are observed to be unique to MeV-BGK. MDS of H-protein revealed significant fluctuations in neutralizing epitopes due to L249P substitution. The majority of the clinically suspected cases showed Nt-Abs to MeV wild-types. Higher IgG antibody avidity and Nt-Ab titres were noted in IgM-negatives than in IgM-positives cases, indicating reinfection or breakthrough. MDS revealed reduced neutralization due to decreased conformational flexibility in the H-epitope.

Development of mechanically-consistent coarse-grained molecular dynamics model: case study of mechanics of spider silk

Understanding mechanics of spider silk holds immense importance due to its potential to drive innovation in the development of materials with exceptional mechanical characteristics suited for a wide range of applications. Coarse-grained (CG) molecular simulations plays a particularly valuable role in this endeavor, allowing for the efficient investigation of spider silk's mechanical properties. Our research is centered on the examination of spider silk, which comprises major ampullate silk protein (MaSp1). To achieve this, we developed a CG molecular dynamics model. Our investigation began with a focus on MaSp1 chains subjected to uniaxial tensile load, with comparisons made between the CG model results and all-atom simulations. Subsequently, we extended our simulations to encompass more extensive systems, including fully-ordered MaSp1 bundles undergoing uniaxial static stretching. Through comparison with existing literature, we assess how well the CG model reproduces the mechanical properties of spider silk in highly ordered structures. Furthermore, we explored a scenario where MaSp1 bundles were randomly positioned and stretched, providing valuable insights into silk behavior when the initial structure lacks order. Another simulation involved random positioning, but with some degree of orientation in the loading direction, allowing for a closer examination of the initial structure's influence.

Energy coupling and stoichiometry of Zn2+/H+ antiport by the prokaryotic cation diffusion facilitator YiiP

YiiP from Shewanella oneidensis is a prokaryotic Zn2+/H+ antiporter that serves as a model for the Cation Diffusion Facilitator (CDF) superfamily, members of which are generally responsible for homeostasis of transition metal ions. Previous studies of YiiP as well as related CDF transporters have established a homodimeric architecture and the presence of three distinct Zn2+ binding sites named A, B, and C. In this study, we use cryo-EM, microscale thermophoresis and molecular dynamics simulations to address the structural and functional roles of individual sites as well as the interplay between Zn2+ binding and protonation. Structural studies indicate that site C in the cytoplasmic domain is primarily responsible for stabilizing the dimer and that site B at the cytoplasmic membrane surface controls the structural transition from an inward facing conformation to an occluded conformation. Binding data show that intramembrane site A, which is directly responsible for transport, has a dramatic pH dependence consistent with coupling to the proton motive force. A comprehensive thermodynamic model encompassing Zn2+ binding and protonation states of individual residues indicates a transport stoichiometry of 1 Zn2+ to 2-3 H+ depending on the external pH. This stoichiometry would be favorable in a physiological context, allowing the cell to use the proton gradient as well as the membrane potential to drive the export of Zn2+.

Beyond structural bioinformatics for genomics with dynamics characterization of an expanded KRAS mutational landscape

Current capabilities in genomic sequencing outpace functional interpretations. Our previous work showed that 3D protein structure calculations enhance mechanistic understanding of genetic variation in sequenced tumors and patients with rare diseases. The KRAS GTPase is among the critical genetic factors driving cancer and germline conditions. Because KRAS-altered tumors frequently harbor one of three classic hotspot mutations, nearly all studies have focused on these mutations, leaving significant functional ambiguity across the broader KRAS genomic landscape observed in cancer and non-cancer diseases. Herein, we extend structural bioinformatics with molecular simulations to study an expanded landscape of 86 KRAS mutations. We identify multiple coordinated changes strongly associated with experimentally established KRAS biophysical and biochemical properties. The patterns we observe span hotspot and non-hotspot alterations, which can all dysregulate Switch regions, producing mutation-restricted conformations with different effector binding propensities. We experimentally measured mutation thermostability and identified shared and distinct patterns with simulations. Our results indicate mutation-specific conformations, which show potential for future research into how these alterations reverberate into different molecular and cellular functions. The data we present is not predictable using current genomic tools, demonstrating the added functional information derived from molecular simulations for interpreting human genetic variation.

New Benzamides as Multi-Targeted Compounds: A Study on Synthesis, AChE and BACE1 Inhibitory Activity and Molecular Docking

The synthesis of eleven new and previously undescribed benzamides was designed. These compounds were specifically projected as potential inhibitors of the enzymes acetylcholinesterase (AChE) and β-secretase (BACE1). N,N'-(1,4-phenylene)bis(3-methoxybenzamide) was most active against AChE, with an inhibitory concentration of AChE IC50 = 0.056 µM, while the IC50 for donepezil was 0.046 µM. This compound was also the most active against the BACE1 enzyme. The IC50 value was 9.01 µM compared to that for quercetin, with IC50 = 4.89 µM. Quantitative results identified this derivative to be the most promising. Molecular modeling was performed to elucidate the potential mechanism of action of this compound. Dynamic simulations showed that new ligands only had a limited stabilizing effect on AChE, but all clearly reduced the flexibility of the enzyme. It can, therefore, be concluded that a possible mechanism of inhibition increases the stiffness and decreases the flexibility of the enzyme, which obviously impedes its proper function. An analysis of the H-bonding patterns suggests a different mechanism (from other ligands) when interacting the most active derivative with the enzyme.

Involvement of the alpha-subunit N-terminus in the mechanism of the Na+,K+-ATPase

Previous studies have shown that cytoplasmic K+ release and the associated E2 → E1 conformational change of the Na+,K+-ATPase is a major rate-determining step of the enzyme's ion pumping cycle and hence a prime site of acute regulatory intervention. From the ionic strength dependence of the enzyme's distribution between the E2 and E1 states, it has also been found that E2 is stabilized by an electrostatic attraction. Any disruption of this electrostatic attraction would, thus, have profound effects on the rate of ion pumping. The aim of this paper is to identify the location of this interaction. Using enhanced-sampling molecular dynamics simulations with a predicted N-terminal structure added to the X-ray crystal structure of the Na+,K+-ATPase, a previously postulated salt bridge between Lys32 and Glu233 (rat sequence numbering) of the enzyme's α-subunit can be excluded. The residues never approach closely enough to form a salt bridge. In contrast, strong interactions with anionic lipid head groups were seen. To investigate the possibility of a protein-lipid interaction experimentally, the surface charge density of Na+,K+-ATPase-containing membrane fragments was estimated from zeta potential measurements to be 0.019 (± 0.001) C m-2. This is in good agreement with the charge density previously determined to be responsible for stabilization of the E2 state of 0.023 (± 0.009) C m-2 and the membrane charge density estimated here from published electron-microscopic images of 0.018C m-2. The results are, therefore, consistent with an interaction of the Na+,K+-ATPase α-subunit N-terminus with negatively-charged lipid head groups of the neighbouring cytoplasmic membrane surface as the origin of the electrostatic interaction stabilising the E2 state.

Toward a structural identification of metastable molecular conformations

Interpreting high-dimensional data from molecular dynamics simulations is a persistent challenge. In this paper, we show that for a small peptide, deca-alanine, metastable states can be identified through a neural net based on structural information alone. While processing molecular dynamics data, dimensionality reduction is a necessary step that projects high-dimensional data onto a low-dimensional representation that, ideally, captures the conformational changes in the underlying data. Conventional methods make use of the temporal information contained in trajectories generated through integrating the equations of motion, which forgoes more efficient sampling schemes. We demonstrate that EncoderMap, an autoencoder architecture with an additional distance metric, can find a suitable low-dimensional representation to identify long-lived molecular conformations using exclusively structural information. For deca-alanine, which exhibits several helix-forming pathways, we show that this approach allows us to combine simulations with different biasing forces and yields representations comparable in quality to other established methods. Our results contribute to computational strategies for the rapid automatic exploration of the configuration space of peptides and proteins.

A Proteorhodopsin-Related Photosensor Expands the Repertoire of Structural Motifs Employed by Sensory Rhodopsins

Microbial rhodopsins are light-activated retinal-binding membrane proteins that perform a variety of ion transport and photosensory functions. They display several cases of convergent evolution where the same function is present in unrelated or very distant protein groups. Here we report another possible case of such convergent evolution, describing the biophysical properties of a new group of sensory rhodopsins. The first representative of this group was identified in 2004 but none of the members had been expressed and characterized. The well-studied haloarchaeal sensory rhodopsins interacting with methyl-accepting Htr transducers are close relatives of the halobacterial proton pump bacteriorhodopsin. In contrast, the sensory rhodopsins we describe here are relatives of proteobacterial proton pumps, proteorhodopsins, but appear to interact with Htr-like transducers likewise, even though they do not conserve the residues important for the interaction of haloarchaeal sensory rhodopsins with their transducers. The new sensory rhodopsins display many unusual amino acid residues, including those around the retinal chromophore; most strikingly, a tyrosine in place of a carboxyl counterion of the retinal Schiff base on helix C. To characterize their unique sequence motifs, we augment the spectroscopy and biochemistry data by structural modeling of the wild-type and three mutants. Taken together, the experimental data, bioinformatics sequence analyses, and structural modeling suggest that the tyrosine/aspartate complex counterion contributes to a complex water-mediated hydrogen-bonding network that couples the protonated retinal Schiff base to an extracellular carboxylic dyad.

The solution structure of the unbound IgG Fc receptor CD64 resembles its crystal structure: Implications for function

FcγRI (CD64) is the only high-affinity Fcγ receptor found on monocytes, macrophages, eosinophils, neutrophils and dendritic cells. It binds immunoglobulin G (IgG) antibody-antigen complexes at its Fc region to trigger key immune responses. CD64 contains three immunoglobulin-fold extracellular domains (D1, D2 and D3) and a membrane-spanning region. Despite the importance of CD64, no solution structure for this is known to date. To investigate this, we used analytical ultracentrifugation, small-angle X-ray scattering, and atomistic modelling. Analytical ultracentrifugation revealed that CD64 was monomeric with a sedimentation coefficient s020,w of 2.53 S, together with some dimer. Small-angle X-ray scattering showed that its radius of gyration RG was 3.3-3.4 nm and increased at higher concentrations to indicate low dimerization. Monte Carlo modelling implemented in the SASSIE-web package generated 279,162 physically-realistic trial CD64 structures. From these, the scattering best-fit models at the lowest measured concentrations that minimised dimers revealed that the D1, D2 and D3 domains were structurally similar to those seen in three CD64 crystal structures, but showed previously unreported flexibility between D1, D2 and D3. Despite the limitations of the scattering data, the superimposition of the CD64 solution structures onto crystal structures of the IgG Fc-CD64 complex showed that the CD64 domains do not sterically clash with the IgG Fc region, i.e. the solution structure of CD64 was sufficiently compact to allow IgG to bind to its high-affinity Fcγ receptor. This improved understanding may result in novel approaches to inhibit CD64 function, and opens the way for the solution study of the full-length CD64-IgG complex.

s-Triazine Derivatives Functionalized with Alkylating 2-Chloroethylamine Fragments as Promising Antimicrobial Agents: Inhibition of Bacterial DNA Gyrases, Molecular Docking Studies, and Antibacterial and Antifungal Activity

The spectrum of biological properties of s-triazine derivatives is broad and includes anti-microbial, anti-cancer, and anti-neurodegenerative activities, among others. The s-triazine molecule, due to the possibility of substituting three substituents, offers many opportunities to obtain hybrid compounds with a wide variety of activities. A group of 1,3,5 triazine derivatives containing a dipeptide, 2-ethylpiperazine, and a methoxy group as substituents was screened for their antimicrobial activity. An in vitro study was conducted on pathogenic bacteria (E. coli, S. aureus, B. subtilis, and M. luteus), yeasts (C. albicans), and filamentous fungi (A. fumigatus, A. flavus, F. solani, and P. citrinum) via microdilution in broth, and the results were compared with antibacterial (Streptomycin) and antifungal (Ketoconazole and Nystatin) antibiotics. Several s-triazine analogues have minimal inhibitory concentrations lower than the standard. To confirm the inhibitory potential of the most active compounds against gyrases E. coli and S. aureus, a bacterial gyrases inhibition assay, and molecular docking studies were performed. The most active s-triazine derivatives contained the -NH-Trp(Boc)-AlaOMe, -NH-Asp(OtBu)-AlaOMe, and -NH-PheOMe moieties in their structures.

Antiviral activity of Cenostigma pluviosum var. peltophoroides extract and fractions against SARS-CoV-2

Few extracts of plant species from the Brazilian flora have been validated from a pharmacological and clinical point of view, and it is important to determine whether their traditional use is proven by pharmacological effects. Cenostigma pluviosum var. peltophoroides is one of those plants, which belongs to the Fabaceae family that is widely used in traditional medicine and is very rich in tannins. Due to the lack of effective drugs to treat severe cases of Covid-19, the main protease of SARS-CoV-2 (Mpro) becomes an attractive target in the research for new antivirals since this enzyme is crucial for virus replication and does not have homologs in humans. This study aimed to prospect inhibitor candidates among the compounds from C. pluviosum extract, by virtual screening simulations using SARS-CoV-2 Mpro as target. Experimental validation was made by inhibitory proteolytic assays of recombinant Mpro and by antiviral activity with infected Vero cells. Docking simulations identify four compounds with potential inhibitory activity of Mpro present in the extract. The compound pentagalloylglucose showed the best result in proteolytic kinetics experiments, with suppression of recombinant Mpro activity by approximately 60%. However, in experiments with infected cells ethyl acetate fraction and sub-fractions, F2 and F4 of C. pluviosum extract performed better than pentagalloylglucose, reaching close to 100% of antiviral activity. The prominent activity of the extract fractions in infected cells may be a result of a synergistic effect from the different hydrolyzable tannins present, performing simultaneous action on Mpro and other targets from SARS-CoV-2 and host.Communicated by Ramaswamy H. Sarma.

Fluoride permeation mechanism of the Fluc channel in liposomes revealed by solid-state NMR

Solid-state nuclear magnetic resonance (ssNMR) methods can probe the motions of membrane proteins in liposomes at the atomic level and propel the understanding of biomolecular processes for which static structures cannot provide a satisfactory description. In this work, we report our study on the fluoride channel Fluc-Ec1 in phospholipid bilayers based on ssNMR and molecular dynamics simulations. Previously unidentified fluoride binding sites in the aqueous vestibules were experimentally verified by 19F-detected ssNMR. One of the two fluoride binding sites in the polar track was identified as a water molecule by 1H-detected ssNMR. Meanwhile, a dynamic hotspot at loop 1 was observed by comparing the spectra of wild-type Fluc-Ec1 in variant buffer conditions or with its mutants. Therefore, we propose that fluoride conduction in the Fluc channel occurs via a "water-mediated knock-on" permeation mechanism and that loop 1 is a key molecular determinant for channel gating.

Graph-Based Analyses of Dynamic Water-Mediated Hydrogen-Bond Networks in Phosphatidylserine: Cholesterol Membranes

Phosphatidylserine lipids are anionic molecules present in eukaryotic plasma membranes, where they have essential physiological roles. The altered distribution of phosphatidylserine in cells such as apoptotic cancer cells, which, unlike healthy cells, expose phosphatidylserine, is of direct interest for the development of biomarkers. We present here applications of a recently implemented Depth-First-Search graph algorithm to dissect the dynamics of transient water-mediated lipid clusters at the interface of a model bilayer composed of 1-palmytoyl-2-oleoyl-sn-glycero-2-phosphatidylserine (POPS) and cholesterol. Relative to a reference POPS bilayer without cholesterol, in the POPS:cholesterol bilayer there is a somewhat less frequent sampling of relatively complex and extended water-mediated hydrogen-bond networks of POPS headgroups. The analysis protocol used here is more generally applicable to other lipid:cholesterol bilayers.

Embedding Nonrigid Solutes in an Averaged Environment: A Case Study on Rhodopsins

Many simulation methods concerning solvated molecules are based on the assumption that the solvated species and the solvent can be characterized by some representative structures of the solute and some embedding potential corresponding to this structure. While the averaging of the solvent configurations to obtain an embedding potential has been studied in great detail, this hinges on a single solute structure representation. This assumption is re-examined and generalized for conformationally flexible solutes and tested on 4 nonrigid systems. In this generalized approach, the solute is characterized by a set of representative structures and the corresponding embedding potentials. The representative structures are identified by means of subdividing the statistical ensemble, which in this work is generated by a constant-temperature molecular dynamics simulation. The embedding potential defined in the Frozen-Density Embedding Theory is used to characterize the average effect of the solvent in each subensemble. The numerical examples concern the vertical excitation energies of protonated retinal Schiff bases in protein environments. It is comprehensively shown that subensemble averaging leads to huge computational savings compared with explicit averaging of the excitation energies in the whole ensemble while introducing only minor errors in the case of the systems examined.

Energy Coupling and Stoichiometry of Zn2+/H+ Antiport by the Cation Diffusion Facilitator YiiP

YiiP is a prokaryotic Zn2+/H+ antiporter that serves as a model for the Cation Diffusion Facilitator (CDF) superfamily, members of which are generally responsible for homeostasis of transition metal ions. Previous studies of YiiP as well as related CDF transporters have established a homodimeric architecture and the presence of three distinct Zn2+ binding sites named A, B, and C. In this study, we use cryo-EM, microscale thermophoresis and molecular dynamics simulations to address the structural and functional roles of individual sites as well as the interplay between Zn2+ binding and protonation. Structural studies indicate that site C in the cytoplasmic domain is primarily responsible for stabilizing the dimer and that site B at the cytoplasmic membrane surface controls the structural transition from an inward facing conformation to an occluded conformation. Binding data show that intramembrane site A, which is directly responsible for transport, has a dramatic pH dependence consistent with coupling to the proton motive force. A comprehensive thermodynamic model encompassing Zn2+ binding and protonation states of individual residues indicates a transport stoichiometry of 1 Zn2+ to 2-3 H+ depending on the external pH. This stoichiometry would be favorable in a physiological context, allowing the cell to use the proton gradient as well as the membrane potential to drive the export of Zn2+.

Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein

Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fields. Currently, molecular mechanical (MM) force fields are mainly utilized in MD simulations because of their low computational cost. Quantum mechanical (QM) calculation has high accuracy, but it is exceedingly time consuming for protein simulations. Machine learning (ML) provides the capability for generating accurate potential at the QM level without increasing much computational effort for specific systems that can be studied at the QM level. However, the construction of general machine learned force fields, needed for broad applications and large and complex systems, is still challenging. Here, general and transferable neural network (NN) force fields based on CHARMM force fields, named CHARMM-NN, are constructed for proteins by training NN models on 27 fragments partitioned from the residue-based systematic molecular fragmentation (rSMF) method. The NN for each fragment is based on atom types and uses new input features that are similar to MM inputs, including bonds, angles, dihedrals, and non-bonded terms, which enhance the compatibility of CHARMM-NN to MM MD and enable the implementation of CHARMM-NN force fields in different MD programs. While the main part of the energy of the protein is based on rSMF and NN, the nonbonded interactions between the fragments and with water are taken from the CHARMM force field through mechanical embedding. The validations of the method for dipeptides on geometric data, relative potential energies, and structural reorganization energies demonstrate that the CHARMM-NN local minima on the potential energy surface are very accurate approximations to QM, showing the success of CHARMM-NN for bonded interactions. However, the MD simulations on peptides and proteins indicate that more accurate methods to represent protein-water interactions in fragments and non-bonded interactions between fragments should be considered in the future improvement of CHARMM-NN, which can increase the accuracy of approximation beyond the current mechanical embedding QM/MM level.

Automated simulation-based membrane protein refinement into cryo-EM data

The resolution revolution has increasingly enabled single-particle cryogenic electron microscopy (cryo-EM) reconstructions of previously inaccessible systems, including membrane proteins-a category that constitutes a disproportionate share of drug targets. We present a protocol for using density-guided molecular dynamics simulations to automatically refine atomistic models into membrane protein cryo-EM maps. Using adaptive force density-guided simulations as implemented in the GROMACS molecular dynamics package, we show how automated model refinement of a membrane protein is achieved without the need to manually tune the fitting force ad hoc. We also present selection criteria to choose the best-fit model that balances stereochemistry and goodness of fit. The proposed protocol was used to refine models into a new cryo-EM density of the membrane protein maltoporin, either in a lipid bilayer or detergent micelle, and we found that results do not substantially differ from fitting in solution. Fitted structures satisfied classical model-quality metrics and improved the quality and the model-to-map correlation of the x-ray starting structure. Additionally, the density-guided fitting in combination with generalized orientation-dependent all-atom potential was used to correct the pixel-size estimation of the experimental cryo-EM density map. This work demonstrates the applicability of a straightforward automated approach to fitting membrane protein cryo-EM densities. Such computational approaches promise to facilitate rapid refinement of proteins under different conditions or with various ligands present, including targets in the highly relevant superfamily of membrane proteins.

Dimerization of European Robin Cryptochrome 4a

Homo-dimer formation is important for the function of many proteins. Although dimeric forms of cryptochromes (Cry) have been found by crystallography and were recently observed in vitro for European robin Cry4a, little is known about the dimerization of avian Crys and the role it could play in the mechanism of magnetic sensing in migratory birds. Here, we present a combined experimental and computational investigation of the dimerization of robin Cry4a resulting from covalent and non-covalent interactions. Experimental studies using native mass spectrometry, mass spectrometric analysis of disulfide bonds, chemical cross-linking, and photometric measurements show that disulfide-linked dimers are routinely formed, that their formation is promoted by exposure to blue light, and that the most likely cysteines are C317 and C412. Computational modeling and molecular dynamics simulations were used to generate and assess a number of possible dimer structures. The relevance of these findings to the proposed role of Cry4a in avian magnetoreception is discussed.

Molecular Crowders Can Induce Collapse in Hydrophilic Polymers via Soft Attractive Interactions

A comprehensive understanding of protein folding and biomolecular self-assembly in the intracellular environment requires obtaining a microscopic view of the crowding effects. The classical view of crowding explains biomolecular collapse in such an environment in terms of the entropic solvent excluded volume effects subjected to hard-core repulsions exerted by the inert crowders, neglecting their soft chemical interactions. In this study, the effects of nonspecific, soft interactions of molecular crowders in regulating the conformational equilibrium of hydrophilic (charged) polymers are examined. Using advanced molecular dynamics simulations, collapse free energies of an uncharged, a negatively charged, and a charge-neutral 32-mer generic polymer are computed. The strength of the polymer-crowder dispersion energy is modulated to examine its effect on polymer collapse. The results show that the crowders preferentially adsorb and drive the collapse of all three polymers. The uncharged polymer collapse is opposed by the change in solute-solvent interaction energy but is overcompensated by the favorable change in the solute-solvent entropy as observed in hydrophobic collapse. However, the negatively charged polymer collapses with a favorable change in solute-solvent interaction energy due to reduction in the dehydration energy penalty as the crowders partition to the polymer interface and shield the charged beads. The collapse of a charge-neutral polymer is opposed by the solute-solvent interaction energy but is overcompensated by the solute-solvent entropy change. However, for the strongly interacting crowders, the overall energetic penalty decreases since the crowders interact with polymer beads via cohesive bridging attractions to induce polymer collapse. These bridging attractions are found to be sensitive to the binding sites of the polymer, since they are absent in the negatively charged or uncharged polymers. These interesting differences in thermodynamic driving forces highlight the crucial role of the chemical nature of the macromolecule as well as of the crowder in determining the conformational equilibria in a crowded milieu. The results emphasize that the chemical interactions of the crowders should be explicitly considered to account for the crowding effects. The findings have implications in understanding the crowding effects on the protein free energy landscapes.

Molecular Simulations of Conformational Transitions within the Insulin Receptor Kinase Reveal Consensus Features in a Multistep Activation Pathway

Modulating the transitions between active and inactive conformations of protein kinases is the primary means of regulating their catalytic activity, achieved by phosphorylation of the activation loop (A-loop). To elucidate the mechanism of this conformational activation, we applied the string method to determine the conformational transition path of insulin receptor kinase between the active and inactive conformations and the corresponding free-energy profiles with and without A-loop phosphorylation. The conformational change was found to proceed in three sequential steps: first, the flipping of the DFG motif of the active site; second, rotation of the A-loop; finally, the inward movement of the αC helix. The main energetic bottleneck corresponds to the conformational change in the A-loop, while changes in the DFG motif and αC helix occur before and after A-loop conformational change, respectively. In accordance with this, two intermediate states are identified, the first state just after the DFG flipping and the second state after the A-loop rotation. These intermediates exhibit structural features characteristic of the corresponding inactive and active conformations of other protein kinases. To understand the impact of A-loop phosphorylation on kinase conformation, the free energies of A-loop phosphorylation were determined at several states along the conformational transition path using the free-energy perturbation simulations. The calculated free energies reveal that while the unphosphorylated kinase interconverts between the inactive and active conformations, A-loop phosphorylation restricts access to the inactive conformation, thereby increasing the active conformation population. Overall, this study suggests a consensus mechanism of conformational activation between different protein kinases.

Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach

Better detectors and automated data collection have generated a flood of high-resolution cryo-EM maps, which in turn has renewed interest in improving methods for determining structure models corresponding to these maps. However, automatically fitting atoms to densities becomes difficult as their resolution increases and the refinement potential has a vast number of local minima. In practice, the problem becomes even more complex when one also wants to achieve a balance between a good fit of atom positions to the map, while also establishing good stereochemistry or allowing protein secondary structure to change during fitting. Here, we present a solution to this challenge using a maximum likelihood approach by formulating the problem as identifying the structure most likely to have produced the observed density map. This allows us to derive new types of smooth refinement potential-based on relative entropy-in combination with a novel adaptive force scaling algorithm to allow balancing of force-field and density-based potentials. In a low-noise scenario, as expected from modern cryo-EM data, the relative-entropy based refinement potential outperforms alternatives, and the adaptive force scaling appears to aid all existing refinement potentials. The method is available as a component in the GROMACS molecular simulation toolkit.

Attracting Cavities 2.0: Improving the Flexibility and Robustness for Small-Molecule Docking

Molecular docking is a computational approach for predicting the most probable position of a ligand in the binding site of a target macromolecule. Our docking algorithm Attracting Cavities (AC) has been shown to compare favorably to other widely used docking algorithms [Zoete, V.; et al. J. Comput. Chem. 2016, 37, 437]. Here we describe several improvements of AC, making the sampling more robust and providing more flexibility for either fast or high-accuracy docking. We benchmark the performance of AC 2.0 using the 285 complexes of the PDBbind Core set, version 2016. For redocking from randomized ligand conformations, AC 2.0 reaches a success rate of 73.3%, compared to 63.9% for GOLD and 58.0% for AutoDock Vina. Due to its force-field-based scoring function and its thorough sampling procedure, AC 2.0 also performs well for blind docking on the entire receptor surface. The accuracy of its scoring function allows for the detection of problematic experimental structures in the benchmark set. For cross-docking, the AC 2.0 success rate is about 30% lower than for redocking (42.5%), similar to GOLD (42.8%) and better than AutoDock Vina (33.1%), and it can be improved by an informed choice of flexible protein residues. For selected targets with a high success rate in cross-docking, AC 2.0 also achieves good enrichment factors in virtual screening.

Unraveling the Role of Hydrogen Bonds in Thrombin via Two Machine Learning Methods

Hydrogen bonds play a critical role in the folding and stability of proteins, such as proteins and nucleic acids, by providing strong and directional interactions. They help to maintain the secondary and 3D structure of proteins, and structural changes in these molecules often result from the formation or breaking of hydrogen bonds. To gain insights into these hydrogen bonding networks, we applied two machine learning models - a logistic regression model and a decision tree model - to study four variants of thrombin: wild-type, ΔK9, E8K, and R4A. Our results showed that both models have their unique advantages. The logistic regression model highlighted potential key residues (GLU295) in thrombin's allosteric pathways, while the decision tree model identified important hydrogen bonding motifs. This information can aid in understanding the mechanisms of folding in proteins and has potential applications in drug design and other therapies. The use of these two models highlights their usefulness in studying hydrogen bonding networks in proteins.

Using classifiers to understand coarse-grained models and their fidelity with the underlying all-atom systems

Bottom-up coarse-grained (CG) molecular dynamics models are parameterized using complex effective Hamiltonians. These models are typically optimized to approximate high dimensional data from atomistic simulations. However, human validation of these models is often limited to low dimensional statistics that do not necessarily differentiate between the CG model and said atomistic simulations. We propose that classification can be used to variationally estimate high dimensional error and that explainable machine learning can help convey this information to scientists. This approach is demonstrated using Shapley additive explanations and two CG protein models. This framework may also be valuable for ascertaining whether allosteric effects at the atomistic level are accurately propagated to a CG model.

Albumin Is a Component of the Esterase Status of Human Blood Plasma

The esterase status of blood plasma can claim to be one of the universal markers of various diseases; therefore, it deserves attention when searching for markers of the severity of COVID-19 and other infectious and non-infectious pathologies. When analyzing the esterase status of blood plasma, the esterase activity of serum albumin, which is the major protein in the blood of mammals, should not be ignored. The purpose of this study is to expand understanding of the esterase status of blood plasma and to evaluate the relationship of the esterase status, which includes information on the amount and enzymatic activity of human serum albumin (HSA), with other biochemical parameters of human blood, using the example of surviving and deceased patients with confirmed COVID-19. In experiments in vitro and in silico, the activity of human plasma and pure HSA towards various substrates was studied, and the effect of various inhibitors on this activity was tested. Then, a comparative analysis of the esterase status and a number of basic biochemical parameters of the blood plasma of healthy subjects and patients with confirmed COVID-19 was performed. Statistically significant differences have been found in esterase status and biochemical indices (including albumin levels) between healthy subjects and patients with COVID-19, as well as between surviving and deceased patients. Additional evidence has been obtained for the importance of albumin as a diagnostic marker. Of particular interest is a new index, [Urea] × [MDA] × 1000/(BChEb × [ALB]), which in the group of deceased patients was 10 times higher than in the group of survivors and 26 times higher than the value in the group of apparently healthy elderly subjects.

Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field

We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.

Dissociation Rate Calculation via Constant-Force Steered Molecular Dynamics Simulation

Steered molecular dynamics (SMD) simulations are used to study molecular dissociation events by applying a harmonic force to the molecules and pulling them at a constant velocity. Instead of constant-velocity pulling, we use a constant force: the constant-force SMD (CF-SMD) simulation. The CF-SMD simulation employs a constant force to reduce the activation barrier of molecular dissociation, thereby enhancing the dissociation event. Here, we present the capability of the CF-SMD simulation to estimate the dissociation time at equilibrium. We performed all-atom CF-SMD simulations for NaCl and protein-ligand systems, producing dissociation time at various forces. We extrapolated these values to the dissociation rate without a constant force using Bell's model or the Dudko-Hummer-Szabo model. We demonstrate that the CF-SMD simulations with the models predicted the dissociation time in equilibrium. A CF-SMD simulation is a powerful tool for estimating the dissociation rate in a direct and computationally efficient manner.

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Basis for Accurate Protein pKa Prediction with Machine Learning

pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by pK_a's. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pK_a prediction is crucial. Here we present a theoretical pK_a data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pK_a orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pK_a prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pK_a predictor and thus can be applied immediately to, for example, pK_a database construction, protein design, drug discovery, and so on.

Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials

While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.

Computational and Experimental Determination of the Properties, Structure, and Stability of Peptoid Nanosheets and Nanotubes

Peptoids (N-substituted glycines) are a group of highly controllable peptidomimetic polymers. Amphiphilic diblock peptoids have been engineered to assemble crystalline nanospheres, nanofibrils, nanosheets, and nanotubes with biochemical, biomedical, and bioengineering applications. The mechanical properties of peptoid nanoaggregates and their relationship to the emergent self-assembled morphologies have been relatively unexplored and are critical for the rational design of peptoid nanomaterials. In this work, we consider a family of amphiphilic diblock peptoids consisting of a prototypical tube-former (Nbrpm6Nc6, a NH₂-capped hydrophobic block of six N-((4-bromophenyl)methyl)glycine residues conjugated to a polar NH₃(CH₂)₅CO tail), a prototypical sheet-former (Nbrpe6Nc6, where the hydrophobic block comprises six N-((4-bromophenyl)ethyl)glycine residues), and an intermediate sequence that forms mixed structures ((NbrpeNbrpm)3Nc6). We combine all-atom molecular dynamics simulations and atomic force microscopy to determine the mechanical properties of the self-assembled 2D crystalline nanosheets and relate these properties to the observed self-assembled morphologies. We find good agreement between our computational predictions and experimental measurements of Young's modulus of crystalline nanosheets. A computational analysis of the bending modulus along the two axes of the planar crystalline nanosheets reveals bending to be more favorable along the axis in which the peptoids stack by interdigitation of the side chains compared to that in which they form columnar crystals with π-stacked side chains. We construct molecular models of nanotubes of the Nbrpm6Nc6 tube-forming peptoid and predict a stability optimum in good agreement with experimental measurements. A theoretical model of nanotube stability suggests that this optimum is a free energy minimum corresponding to a "Goldilocks" tube radius at which capillary wave fluctuations in the tube wall are minimized.

Applicability of perturbed matrix method for charge transfer studies at bio/metallic interfaces: a case of azurin

As the field of nanoelectronics based on biomolecules such as peptides and proteins rapidly grows, there is a need for robust computational methods able to reliably predict charge transfer properties at bio/metallic interfaces. Traditionally, hybrid quantum-mechanical/molecular-mechanical techniques are employed for systems where the electron hopping transfer mechanism is applicable to determine physical parameters controlling the thermodynamics and kinetics of charge transfer processes. However, these approaches are limited by a relatively high computational cost when extensive sampling of a configurational space is required, like in the case of soft biomatter. For these applications, semi-empirical approaches such as the perturbed matrix method (PMM) have been developed and successfully used to study charge-transfer processes in biomolecules. Here, we explore the performance of PMM on prototypical redox-active protein azurin in various environments, from solution to vacuum interfaces with gold surfaces and protein junction. We systematically benchmarked the robustness and convergence of the method with respect to the quantum-centre size, size of the Hamiltonian, number of samples, and level of theory. We show that PMM can adequately capture all the trends associated with the structural and electronic changes related to azurin oxidation at bio/metallic interfaces.