DelPhi: a comprehensive suite for DelPhi software and associated resources

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

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

How does the ion concentration affect the functions of kinesin BimC

BimC family proteins are bipolar motor proteins belonging to the kinesin superfamily which promote mitosis by crosslinking and sliding apart antiparallel microtubules. Understanding the binding mechanism between the kinesin and the microtubule is crucial for researchers to make advances in the treatment of cancer and other malignancies. Experimental research has shown that the ion concentration affects the function of BimC significantly. But the insights of the ion-dependent function of BimC remain unclear. By combining molecular dynamics (MD) simulations with a series of computational approaches, we studied the electrostatic interactions at the binding interfaces of BimC and the microtubule under different KCl concentrations. We found the electrostatic interaction between BimC and microtubule is stronger at 0 mM KCl compared to 150 mM KCl, which is consistent with experimental conclusions. Furthermore, important salt bridges and residues at the binding interfaces of the complex were identified, which illustrates the details of the BimC-microtubule interactions. Molecular dynamics analyses of salt bridges identified that the important residues on the binding interface of BimC are positively charged, while those residues on the binding interface of the tubulin heterodimer are negatively charged. The finding in this work reveals some important mechanisms of kinesin-microtubule binding, which helps the future drug design for cancer therapy.

SARS-CoV-2 Omicron Subvariants Do Not Differ Much in Binding Affinity to Human ACE2: A Molecular Dynamics Study

The emergence of the variant of concern Omicron (B.1.1.529) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exacerbates the COVID-19 pandemic due to its high contagious ability. Studies have shown that the Omicron binds human ACE2 more strongly than the wild type. The prevalence of Omicron in new cases of COVID-19 promotes novel lineages with improved receptor binding affinity and immune evasion. To shed light on this open problem, in this work, we investigated the binding free energy of the receptor binding domain of the Omicron lineages BA.2, BA.2.3.20, BA.3, BA4/BA5, BA.2.75, BA.2.75.2, BA.4.6, XBB.1, XBB.1.5, BJ.1, BN.1, BQ.1.1, and CH.1.1 to human ACE2 using all-atom molecular dynamics simulation and the molecular mechanics Poisson-Boltzmann surface area method. The results show that these lineages have increased binding affinity compared to the BA.1 lineage, and BA.2.75 and BA.2.75.2 subvariants bind ACE2 more strongly than others. However, in general, the binding affinities of the Omicron lineages do not differ significantly from each other. The electrostatic force dominates over the van der Waals force in the interaction between Omicron lineages and human cells. Based on our results, we argue that viral evolution does not further improve the affinity of SARS-CoV-2 for ACE2 but may increase immune evasion.

Exciton interactions of chlorophyll tetramer in water-soluble chlorophyll-binding protein BoWSCP

The exciton interaction of four chlorophyll a (Chl a) molecules in a symmetrical tetrameric complex of the water-soluble chlorophyll-binding protein BoWSCP was analyzed in the pH range of 3-11. Exciton splitting ΔE = 232 ± 2 cm-1 of the Qy band of Chl a into two subcomponents with relative intensities of 78.1 ± 0.7 % and 21.9 ± 0.7 % was determined by a joint decomposition of the absorption and circular dichroism spectra into Gaussian functions. The exciton coupling parameters were calculated based on the BoWSCP atomic structure in three approximations: the point dipole model, the distributed atomic monopoles, and direct ab initio calculations in the TDDFT/PCM approximation. The Coulomb interactions of monomers were calculated within the continuum model using three values of optical permittivity. The models based on the properties of free Chl a in solution suffer from significant errors both in estimating the absolute value of the exciton interaction and in the relative intensity of exciton transitions. Calculations within the TDDFT/PCM approximation reproduce the experimentally determined parameters of the exciton splitting and the relative intensities of the exciton bands. The following factors of pigment-protein and pigment-pigment interactions were examined: deviation of the macrocycle geometry from the planar conformation of free Chl; the formation of hydrogen bonds between the macrocycle and water molecules; the overlap of wave functions of monomers at close distances. The most significant factor is the geometrical deformation of the porphyrin macrocycle, which leads to an increase in the dipole moment of Chl monomer from 5.5 to 6.9 D and to a rotation of the dipole moment by 15° towards the cyclopentane ring. The contributions of resonant charge-transfer states to the wave functions of the Chl dimer were determined and the transition dipole moments of the symmetric and antisymmetric charge-transfer states were estimated.

Bridging Eulerian and Lagrangian Poisson-Boltzmann solvers by ESES

The Poisson-Boltzmann (PB) model is a widely used electrostatic model for biomolecular solvation analysis. Formulated as an elliptic interface problem, the PB model can be numerically solved on either Eulerian meshes using finite difference/finite element methods or Lagrangian meshes using boundary element methods. Molecular surface generators, which produce the discretized dielectric interfaces between solutes and solvents, are critical factors in determining the accuracy and efficiency of the PB solvers. In this work, we investigate the utility of the Eulerian Solvent Excluded Surface (ESES) software for rendering conjugated Eulerian and Lagrangian surface representations, which enables us to numerically validate and compare the quality of Eulerian PB solvers, such as the MIBPB solver, and the Lagrangian PB solvers, such as the TABI-PB solver. Furthermore, with the ESES software and its associated PB solvers, we are able to numerically validate an interesting and useful but often neglected source-target symmetric property associated with the linearized PB model.

Poisson-Boltzmann-based machine learning model for electrostatic analysis

Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation free energies for macromolecules due to the nonlinearity, dielectric jumps, charge singularity, and geometric complexity associated with the PB equation. The present work introduces a PB-based machine learning (PBML) model for biomolecular electrostatic analysis. Trained with the second-order accurate MIBPB solver, the proposed PBML model is found to be more accurate and faster than several eminent PB solvers in electrostatic analysis. The proposed PBML model can provide highly accurate PB electrostatic solvation free energy of new biomolecules or new conformations generated by molecular dynamics with much reduced computational cost.

Attenuation of Type IV pili activity by natural products

The virulence factor Type IV pili (T4P) are surface appendages used by the opportunistic pathogen Pseudomonas aeruginosa for twitching motility and adhesion in the environment and during infection. Additionally, the use of these appendages by P. aeruginosa for biofilm formation increases its virulence and drug resistance. Therefore, attenuation of the activity of T4P would be desirable to control P. aeruginosa infections. Here, a computational approach has been pursued to screen natural products that can be used for this purpose. PilB, the elongation ATPase of the T4P machinery in P. aeruginosa, has been selected as the target subunit and virtual screening of FDA-approved drugs has been conducted. Screening identified two natural compounds, ergoloid and irinotecan, as potential candidates for inhibiting this T4P-associated ATPase in P. aeruginosa. These candidate compounds underwent further rigorous evaluation through molecular dynamics (MD) simulations and then through in vitro twitching motility and biofilm inhibition assays. Notably, ergoloid emerged as a particularly promising candidate for weakening the T4P activity by inhibiting the elongation ATPases associated with T4P. This repurposing study paves the way for the timely discovery of antivirulence drugs as an alternative to classical antibiotic treatments to help combat infections caused by P. aeruginosa and related pathogens.Communicated by Ramaswamy H. Sarma.

A novel approach to study multi-domain motions in JAK1's activation mechanism based on energy landscape

The family of Janus Kinases (JAKs) associated with the JAK-signal transducers and activators of transcription signaling pathway plays a vital role in the regulation of various cellular processes. The conformational change of JAKs is the fundamental steps for activation, affecting multiple intracellular signaling pathways. However, the transitional process from inactive to active kinase is still a mystery. This study is aimed at investigating the electrostatic properties and transitional states of JAK1 to a fully activation to a catalytically active enzyme. To achieve this goal, structures of the inhibited/activated full-length JAK1 were modelled and the energies of JAK1 with Tyrosine Kinase (TK) domain at different positions were calculated, and Dijkstra's method was applied to find the energetically smoothest path. Through a comparison of the energetically smoothest paths of kinase inactivating P733L and S703I mutations, an evaluation of the reasons why these mutations lead to negative or positive regulation of JAK1 are provided. Our energy analysis suggests that activation of JAK1 is thermodynamically spontaneous, with the inhibition resulting from an energy barrier at the initial steps of activation, specifically the release of the TK domain from the inhibited Four-point-one, Ezrin, Radixin, Moesin-PK cavity. Overall, this work provides insights into the potential pathway for TK translocation and the activation mechanism of JAK1.

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

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

Development and test of highly accurate endpoint free energy methods. 3: partition coefficient prediction using a Poisson-Boltzmann method combined with a solvent accessible surface area model for SAMPL challenges

Accurately predicting solvation free energy is the key to predict protein-ligand binding free energy. In addition, the partition coefficient (log P), which is an important physicochemical property that determines the distribution of a drug in vivo, can be derived directly from transfer free energies, i.e., the difference between solvation free energies (SFEs) in different solvents. Within the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) 9 challenge, we applied the Poisson-Boltzmann (PB) surface area (SA) approach to predict the toluene/water transfer free energy and partition coefficient (log Ptoluene/water) from SFEs. For each solute, only a single conformation automatically generated by the free software Open Babel was used. The PB calculation directly adopts our previously optimized boundary definition - a set of general AMBER force field 2 (GAFF2) atom-type based sphere radii for solute atoms. For the non-polar SA model, we newly developed the solvent-related molecular surface tension parameters γ and offset b for toluene and cyclohexane targeting experimental SFEs. This approach yielded the highest predictive accuracy in terms of root mean square error (RMSE) of 1.52 kcal mol-1 in transfer free energy for 16 small drug molecules among all 18 submissions in the SAMPL9 blind prediction challenge. The re-evaluation of the challenge set using multi-conformation strategies based on molecular dynamics (MD) simulations further reduces the prediction RMSE to 1.33 kcal mol-1. At the same time, an additional evaluation of our PBSA method on the SAMPL5 cyclohexane/water distribution coefficient (log Dcyclohexane/water) prediction revealed that our model outperformed COSMO-RS, the best submission model with RMSEPBSA = 1.88 versus RMSECOSMO-RS = 2.11 log units. Two external log Ptoluene/water and log Pcyclohexane/water datasets that contain 110 and 87 data points, respectively, are collected for extra validation and provide an in-depth insight into the error source of the PBSA method.

In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling

The "Long-COVID syndrome" has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<-6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-O-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome.

Protein charge transfer far from equilibrium: a theoretical perspective

Potential differences for protein-assisted electron transfer across lipid bilayers or in bio-nano setups can amount to several 100 mV; they lie far outside the range of linear response theory. We describe these situations by Pauli-master equations that are based on Marcus theory of charge transfer between self-trapped electrons and that obey Kirchhoff's current law. In addition, we take on-site blockade effects and a full non-linear response of the local potentials into account. We present analytical and numerical current-potential curves and electron populations for multi-site model systems and biological electron transfer chains. Based on these, we provide empirical rules for electron populations and chemical potentials along the chain. The Pauli-master mean-field results are validated by kinetic Monte Carlo simulations. We briefly discuss the biochemical and evolutionary aspects of our findings.

Commentary on: The actin bundling activity of ITPKA mainly accounts for its migration-promoting effect in lung cancer cells

1,4,5-triphosphate 3-kinase A (ITPKA) was first described and characterized by Irvine et al. in 1986 and cloned by Takazawa et al. in 1990. It is one of the components of the Ca2+ and calmodulin signaling pathway and a substrate for cAMP-dependent kinase (PKA) and protein kinase C (PKC), and is mainly involved in the regulation of intracellular inositol polyphosphate signaling molecules. Through a series of studies, Sabine's team has found that ITPKA expression was up-regulated in a variety of cancer cells, and silencing ITPKA inhibited while overexpressing ITPKA promoted cancer cell migration in vitro and metastasis in vivo. The latest research from Sabine's team has demonstrated that in H1299 lung cancer cells, the mechanism by which ITPKA promoted migration and invasion was predominantly depending on the ability of binding to F-actin, which will induce cancer cells to form a tight flexible actin networks. Small molecule compounds targeting the IP3 kinase activity of ITPKA protein may only inhibit the migration and invasion of cancer cells caused by the enhanced ITPKA kinase activity under ATP stimulation, but not the cytoskeletal remodeling caused by the binding of ITPKA protein to F-actin and the driven migration and invasion of cancer cells. Therefore, targeted therapeutic strategy focusing on blocking the binding of ITPKA to F-actin is indispensable when designing the inhibitors targeting ITPKA protein.

Electrostatics-Based Computational Design and Experimental Analysis of Buforin II Antimicrobial Peptide Variants with Increased DNA Affinities

Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics in the treatment of bacterial infections in part due to their targeting of generic bacterial structures that make it more difficult to develop drug resistance. In this study, we introduce and implement a design workflow to develop more potent AMPs by improving their electrostatic interactions with DNA, which is a putative intracellular target. Using the existing membrane-translocating AMP buforin II (BF2) as a starting point, we use a computational workflow that integrates electrostatic charge optimization, continuum electrostatics, and molecular dynamics simulations to suggest peptide positions at which a neutral BF2 residue could be substituted with arginine to increase DNA-binding affinity either significantly or minimally, with the latter choice done to determine whether AMP binding affinity depends on charge distribution and not just overall monopole. Our analyses predicted that T1R and L8R BF2 variants would yield substantial and minimal increases in DNA-binding affinity, respectively. These predictions were validated with experimental peptide-DNA binding assays with additional computational analyses providing structural insights. Additionally, experimental measurements of antimicrobial potency showed that a design to increase DNA binding can also yield greater potency. As a whole, this study takes initial steps to support the idea that (i) a design strategy aimed to increase AMP binding affinity to DNA by focusing only on electrostatic interactions can improve AMP potency and (ii) the effect on DNA binding of increasing the overall peptide monopole via arginine substitution depends on the position of the substitution. More broadly, this design strategy is a novel way to increase the potency of other membrane-translocating AMPs that target nucleic acids.

SurfPB: A GPU-Accelerated Electrostatic Calculation and Visualization Tool for Biomolecules

In this work, we present SurfPB as a useful tool for the study of biomolecules. It can do many typical calculations, including the molecular surface, electrostatic potential, solvation free energy, entropy, and binding free energy. Among all of the calculations, the entropy calculation is the most time-consuming one. In SurfPB, the calculation can be performed in a vacuum or implicit solvent and accelerated on GPU. The Poisson-Boltzmann equation solver is accelerated on GPU as well. Moreover, we developed a graphical user interface for SurfPB. It allows users to input the parameters and complete the whole calculation in a visual way. The calculated electrostatic potentials are shown on the molecular surface in a three-dimensional scene.

JAK3 Y841 Autophosphorylation Is Critical for STAT5B Activation, Kinase Domain Stability and Dimer Formation

Janus tyrosine kinase 3 (JAK3) is primarily expressed in immune cells and is needed for signaling by the common gamma chain (γc) family of cytokines. Abnormal JAK3 signal transduction can manifest as hematological disorders, e.g., leukemia, severe combined immunodeficiency (SCID) and autoimmune disease states. While regulatory JAK3 phosphosites have been well studied, here a functional proteomics approach coupling a JAK3 autokinase assay to mass spectrometry revealed ten previously unreported autophosphorylation sites (Y105, Y190, Y238, Y399, Y633, Y637, Y738, Y762, Y824, and Y841). Of interest, Y841 was determined to be evolutionarily conserved across multiple species and JAK family members, suggesting a broader role for this residue. Phospho-substitution mutants confirmed that Y841 is also required for STAT5 tyrosine phosphorylation. The homologous JAK1 residue Y894 elicited a similar response to mutagenesis, indicating the shared importance for this site in JAK family members. Phospho-specific Y841-JAK3 antibodies recognized activated kinase from various T-cell lines and transforming JAK3 mutants. Computational biophysics analysis linked Y841 phosphorylation to enhanced JAK3 JH1 domain stability across pH environments, as well as to facilitated complementary electrostatic JH1 dimer formation. Interestingly, Y841 is not limited to tyrosine kinases, suggesting it represents a conserved ubiquitous enzymatic function that may hold therapeutic potential across multiple kinase families.

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

CONTEXT

Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆G_binding, K_d) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆G_binding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆G_binding without involving intermediate structural descriptors.

METHODS

Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆G_binding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Pr_fmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.

Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol-water partition coefficient (logP) for druglike molecules using MM-PBSA method

The logarithm of n-octanol-water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time-consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy-based logP prediction model-FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water to n-octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), thus this method is named as free energy-based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly-used QSPR or machine learning-based logP models, as well as some continuum solvation model-based methods. The root-mean-square error (RMSE) and Pearson correlation coefficient (R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner-up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.

Molecular Basis for Actin Polymerization Kinetics Modulated by Solution Crowding

Actin polymerization drives cell movement and provides cells with structural integrity. Intracellular environments contain high concentrations of solutes, including organic compounds, macromolecules, and proteins. Macromolecular crowding has been shown to affect actin filament stability and bulk polymerization kinetics. However, the molecular mechanisms behind how crowding influences individual actin filament assembly are not well understood. In this study, we investigated how crowding modulates filament assembly kinetics using total internal reflection fluorescence (TIRF) microscopy imaging and pyrene fluorescence assays. The elongation rates of individual actin filaments analyzed from TIRF imaging depended on the type of crowding agent (polyethylene glycol, bovine serum albumin, and sucrose) as well as their concentrations. Further, we utilized all-atom molecular dynamics (MD) simulations to evaluate the effects of crowding molecules on the diffusion of actin monomers during filament assembly. Taken together, our data suggest that solution crowding can regulate actin assembly kinetics at the molecular level.

Phosphoantigen sensing combines TCR-dependent recognition of the BTN3A IgV domain and germline interaction with BTN2A1

Vγ9Vδ2 T cells play critical roles in microbial immunity by detecting target cells exposed to pathogen-derived phosphoantigens (P-Ags). Target cell expression of BTN3A1, the "P-Ag sensor," and BTN2A1, a direct ligand for T cell receptor (TCR) Vγ9, is essential for this process; however, the molecular mechanisms involved are unclear. Here, we characterize BTN2A1 interactions with Vγ9Vδ2 TCR and BTN3A1. Nuclear magnetic resonance (NMR), modeling, and mutagenesis establish a BTN2A1-immunoglobulin V (IgV)/BTN3A1-IgV structural model compatible with their cell-surface association in cis. However, TCR and BTN3A1-IgV binding to BTN2A1-IgV is mutually exclusive, owing to binding site proximity and overlap. Moreover, mutagenesis indicates that the BTN2A1-IgV/BTN3A1-IgV interaction is non-essential for recognition but instead identifies a molecular surface on BTN3A1-IgV essential to P-Ag sensing. These results establish a critical role for BTN3A-IgV in P-Ag sensing, in mediating direct or indirect interactions with the γδ-TCR. They support a composite-ligand model whereby intracellular P-Ag detection coordinates weak extracellular germline TCR/BTN2A1 and clonotypically influenced TCR/BTN3A-mediated interactions to initiate Vγ9Vδ2 TCR triggering.

Phosphorylation of Tyrosine 841 Plays a Significant Role in JAK3 Activation

Janus Kinase 3 (JAK3) plays a key role in the development, proliferation, and differentiation of various immune cells. It regulates gene expression by phosphorylation of Signal Transducers and Activators of Transcriptions (STATs) via the JAK/STAT pathway. Recently, we found a new JAK3 phosphorylation site, tyrosine 841 (Y841). The results showed that pY841 helps the kinase domain flip around the pseudo kinase domain, which may cause JAK3 conformational changes. It also reduces the size of the cleft between the N-lobe and the C-lobe of the JAK3 kinase domain. However, pY841 was found to enlarge the cleft when ATP/ADP was bound to the kinase. The increase in the cleft size suggested that pY841 enhanced the elasticity of the kinase domain. For unphosphorylated JAK3 (JAK3-Y841), the binding forces between the kinase domain and ATP or ADP were similar. After phosphorylation of Y841, JAK3-pY841 exhibited more salt bridges and hydrogen bonds between ATP and the kinase than between ADP and the kinase. Consequently, the electrostatic binding force between ATP and the kinase was higher than that between ADP and the kinase. The result was that compared to ADP, ATP was more attractive to JAK3 when Y841 was phosphorylated. Therefore, JAK3-pY841 tended to bind ATP rather than ADP. This work provides new insights into the role of phosphorylation in kinase activation and ATP hydrolysis and sheds light on the importance of understanding the molecular mechanisms that regulate the kinase function.

Linear scaling computation of forces for the domain-decomposition linear Poisson-Boltzmann method

The Linearized Poisson-Boltzmann (LPB) equation is a popular and widely accepted model for accounting solvent effects in computational (bio-) chemistry. In the present article, we derive the analytical forces using the domain-decomposition-based LPB-method with a van-der Waals or solvent-accessible surface. We present an efficient strategy to compute the forces and its implementation, allowing linear scaling of the method with respect to the number of atoms using the fast multipole method. Numerical tests illustrate the accuracy of the computation of the analytical forces and compare the efficiency with other available methods.

Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson-Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein-ligand binding free energies using the MM-PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM-PBSA method.

Towards Quantum-Chemical Level Calculations of SARS-CoV-2 Spike Protein Variants of Concern by First Principles Density Functional Theory

The spike protein (S-protein) is a crucial part of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with its many domains responsible for binding, fusion, and host cell entry. In this review we use the density functional theory (DFT) calculations to analyze the atomic-scale interactions and investigate the consequences of mutations in S-protein domains. We specifically describe the key amino acids and functions of each domain, which are essential for structural stability as well as recognition and fusion processes with the host cell; in addition, we speculate on how mutations affect these properties. Such unprecedented large-scale ab initio calculations, with up to 5000 atoms in the system, are based on the novel concept of amino acid-amino acid-bond pair unit (AABPU) that allows for an alternative description of proteins, providing valuable information on partial charge, interatomic bonding and hydrogen bond (HB) formation. In general, our results show that the S-protein mutations for different variants foster an increased positive partial charge, alter the interatomic interactions, and disrupt the HB networks. We conclude by outlining a roadmap for future computational research of biomolecular virus-related systems.

De novo design of a polycarbonate hydrolase

Enzymatic degradation of plastics is currently limited to the use of engineered natural enzymes. As of yet, all engineering approaches applied to plastic degrading enzymes retain the natural $\alpha /\beta $-fold. While mutations can be used to increase thermostability, an inherent maximum likely exists for the $\alpha /\beta $-fold. It is thus of interest to introduce catalytic activity toward plastics in a different protein fold to escape the sequence space of plastic degrading enzymes. Here, a method for designing highly thermostable enzymes that can degrade plastics is described. With the help of Rosetta an active site catalysing the hydrolysis of polycarbonate is introduced into a set of thermostable scaffolds. Through computational evaluation, a potential PCase was selected and produced recombinantly in Escherichia coli. Thermal analysis suggests that the design has a melting temperature of >95$^{\circ }$C. Activity toward polycarbonate was confirmed using atomic force spectroscopy (AFM), proving the successful design of a PCase.

Computational Exploration of Anti-cancer Potential of Flavonoids against Cyclin-Dependent Kinase 8: An In Silico Molecular Docking and Dynamic Approach

Over the centuries, cancer has been considered one of the significant health threats. It holds the position in the list of deadliest diseases over the globe. In women, breast cancer is the most common among many cancers and is the second most common cancer all over the world, while lung cancer is the first. Cyclin-dependent kinase 8 (CDK8) has been identified as a critical oncogenic driver that is found in breast cancer and associated with tumor progression. Flavonoids were virtually screened against CDK8 using molecular docking, drug-likeness, ADMET prediction, and a molecular dynamics (MD) simulation approach to determine the potential flavonoid structure against CDK8. The results indicated that ZINC000005854718 showed the highest negative binding affinity of -10.7 kcal/mol with the targeted protein and passed all the drug-likeness parameters. Performed molecular dynamics simulation showed that docked complex systems have good conformational stability over 100 ns in different temperatures (298, 300, 305, 310, and 320 K). The comparison between calculated binding free energy via MM/PB(GB)SA methods and binding affinity calculated via molecular docking suggested tight binding of ZINC000005854718 with targeted protein. The results concluded that ZINC000005854718 has drug-like properties with tight and stable binding with the targeted protein.

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

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

Overcoming pancreatic cancer immune resistance by codelivery of CCR2 antagonist using a STING-activating gemcitabine-based nanocarrier

STING agonist has recently gained much attention for cancer treatment, but the therapeutic potential of STING agonist is hampered by STING-associated tumor immune resistance. In this work, guided by both bioinformatics and computer modeling, we rationally designed a "one stone hits two birds" nanoparticle-based strategy to simultaneously activate STING innate immune response while eliminating STING-associated immune resistance for the treatment of pancreatic ductal adenocarcinoma (PDAC). We discovered that the ultra-small sized micellar system based on gemcitabine-conjugated polymer (PGEM), which showed superior capacity of penetration in pancreatic tumor spheroid model and orthotopic tumor model, could serve as a novel "STING agonist". The activation of STING signaling in dendritic cells (DCs) by PGEM increased both innate nature killer (NK) and adaptive anti-tumor T cell response. However, activation of STING signaling by PGEM in tumor cells also drove the induction of chemokines CCL2 and CCL7, resulting in immune resistance by recruiting tumor associated macrophage (TAM) and myeloid-derived suppressor cells (MDSCs). Through the combination of computer modeling and experimental screening, we developed a dual delivery modality by incorporating a CCR2 (the receptor shared by both CCL2 and CCL7) antagonist PF-6309 (PF) into PGEM micellar system. Our studies demonstrated that PGEM/PF formulation significantly reduced pancreatic tumor burden and induced potent anti-tumor immunity through reversing the CCL2/CCL7-mediated immunosuppression. Moreover, PGEM/PF sensitized PDAC tumors to anti-PD-1 therapy, leading to complete suppression/eradication of the tumors. Our work has shed light to the multi-faceted role of STING activation and provided a novel immunotherapy regimen to maximize the benefit of STING activation for PDAC treatment. In addition, this work paved a new way for bioinformatics and computer modeling-guided rational design of nanomedicine.

Arf GTPase activates the WAVE regulatory complex through a distinct binding site

Cross-talk between Rho- and Arf-family guanosine triphosphatases (GTPases) plays an important role in linking the actin cytoskeleton to membrane protrusions, organelle morphology, and vesicle trafficking. The central actin regulator, WAVE regulatory complex (WRC), integrates Rac1 (a Rho-family GTPase) and Arf signaling to promote Arp2/3-mediated actin polymerization in many processes, but how WRC senses Arf signaling is unknown. Here, we have reconstituted a direct interaction between Arf and WRC. This interaction is greatly enhanced by Rac1 binding to the D site of WRC. Arf1 binds to a previously unidentified, conserved surface on the Sra1 subunit of WRC, which, in turn, drives WRC activation using a mechanism distinct from that of Rac1. Mutating the Arf binding site abolishes Arf1-WRC interaction, disrupts Arf1-mediated WRC activation, and impairs lamellipodia formation and cell migration. This work uncovers a new mechanism underlying WRC activation and provides a mechanistic foundation for studying how WRC-mediated actin polymerization links Arf and Rac signaling in cells.

Understanding protein diffusion on force-induced stretched DNA conformation

DNA morphology is subjected to environmental conditions and is closely coupled with its function. For example, DNA experiences stretching forces during several biological processes, including transcription and genome transactions, that significantly alter its conformation from that of B-DNA. Indeed, a well-defined 1.5 times extended conformation of dsDNA, known as Σ-DNA, has been reported in DNA complexes with proteins such as Rad51 and RecA. A striking feature in Σ-DNA is that the nucleobases are partitioned into triplets of three locally stacked bases separated by an empty rise gap of ∼ 5 Å. The functional role of such a DNA base triplet was hypothesized to be coupled with the ease of recognition of DNA bases by DNA-binding proteins (DBPs) and the physical origin of three letters (codon/anti-codon) in the genetic code. However, the underlying mechanism of base-triplet formation and the ease of DNA base-pair recognition by DBPs remain elusive. To investigate, here, we study the diffusion of a protein on a force-induced stretched DNA using coarse-grained molecular dynamics simulations. Upon pulling at the 3' end of DNA by constant forces, DNA exhibits a conformational transition from B-DNA to a ladder-like S-DNA conformation via Σ-DNA intermediate. The resulting stretched DNA conformations exhibit non-uniform base-pair clusters such as doublets, triplets, and quadruplets, of which triplets are energetically more stable than others. We find that protein favors the triplet formation compared to its unbound form while interacting non-specifically along DNA, and the relative population of it governs the ruggedness of the protein-DNA binding energy landscape and enhances the efficiency of DNA base recognition. Furthermore, we analyze the translocation mechanism of a DBP under different force regimes and underscore the significance of triplet formation in regulating the facilitated diffusion of protein on DNA. Our study, thus, provides a plausible framework for understanding the structure-function relationship between triplet formation and base recognition by a DBP and helps to understand gene regulation in complex regulatory processes.

ES-Screen: A Novel Electrostatics-Driven Method for Drug Discovery Virtual Screening

Electrostatic interactions drive biomolecular interactions and associations. Computational modeling of electrostatics in biomolecular systems, such as protein-ligand, protein-protein, and protein-DNA, has provided atomistic insights into the binding process. In drug discovery, finding biologically plausible ligand-protein target interactions is challenging as current virtual screening and adjuvant techniques such as docking methods do not provide optimal treatment of electrostatic interactions. This study describes a novel electrostatics-driven virtual screening method called 'ES-Screen' that performs well across diverse protein target systems. ES-Screen provides a unique treatment of electrostatic interaction energies independent of total electrostatic free energy, typically employed by current software. Importantly, ES-Screen uses initial ligand pose input obtained from a receptor-based pharmacophore, thus independent of molecular docking. ES-Screen integrates individual polar and nonpolar replacement energies, which are the energy costs of replacing the cognate ligand for a target with a query ligand from the screening. This uniquely optimizes thermodynamic stability in electrostatic and nonpolar interactions relative to an experimentally determined stable binding state. ES-Screen also integrates chemometrics through shape and other physicochemical properties to prioritize query ligands with the greatest physicochemical similarities to the cognate ligand. The applicability of ES-Screen is demonstrated with in vitro experiments by identifying novel targets for many drugs. The present version includes a combination of many other descriptor components that, in a future version, will be purely based on electrostatics. Therefore, ES-Screen is a first-in-class unique electrostatics-driven virtual screening method with a unique implementation of replacement electrostatic interaction energies with broad applicability in drug discovery.

Electrostatics in Computational Biophysics and Its Implications for Disease Effects

This review outlines the role of electrostatics in computational molecular biophysics and its implication in altering wild-type characteristics of biological macromolecules, and thus the contribution of electrostatics to disease mechanisms. The work is not intended to review existing computational approaches or to propose further developments. Instead, it summarizes the outcomes of relevant studies and provides a generalized classification of major mechanisms that involve electrostatic effects in both wild-type and mutant biological macromolecules. It emphasizes the complex role of electrostatics in molecular biophysics, such that the long range of electrostatic interactions causes them to dominate all other forces at distances larger than several Angstroms, while at the same time, the alteration of short-range wild-type electrostatic pairwise interactions can have pronounced effects as well. Because of this dual nature of electrostatic interactions, being dominant at long-range and being very specific at short-range, their implications for wild-type structure and function are quite pronounced. Therefore, any disruption of the complex electrostatic network of interactions may abolish wild-type functionality and could be the dominant factor contributing to pathogenicity. However, we also outline that due to the plasticity of biological macromolecules, the effect of amino acid mutation may be reduced, and thus a charge deletion or insertion may not necessarily be deleterious.

Effect of Delta and Omicron Mutations on the RBD-SD1 Domain of the Spike Protein in SARS-CoV-2 and the Omicron Mutations on RBD-ACE2 Interface Complex

The receptor-binding domain (RBD) is the essential part in the Spike-protein (S-protein) of SARS-CoV-2 virus that directly binds to the human ACE2 receptor, making it a key target for many vaccines and therapies. Therefore, any mutations at this domain could affect the efficacy of these treatments as well as the viral-cell entry mechanism. We introduce ab initio DFT-based computational study that mainly focuses on two parts: (1) Mutations effects of both Delta and Omicron variants in the RBD-SD1 domain. (2) Impact of Omicron RBD mutations on the structure and properties of the RBD-ACE2 interface system. The in-depth analysis is based on the novel concept of amino acid-amino acid bond pair units (AABPU) that reveal the differences between the Delta and/or Omicron mutations and its corresponding wild-type strain in terms of the role played by non-local amino acid interactions, their 3D shapes and sizes, as well as contribution to hydrogen bonding and partial charge distributions. Our results also show that the interaction of Omicron RBD with ACE2 significantly increased its bonding between amino acids at the interface providing information on the implications of penetration of S-protein into ACE2, and thus offering a possible explanation for its high infectivity. Our findings enable us to present, in more conspicuous atomic level detail, the effect of specific mutations that may help in predicting and/or mitigating the next variant of concern.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

SARS-CoV-2 Omicron Variant Binds to Human Cells More Strongly than the Wild Type: Evidence from Molecular Dynamics Simulation

The emergence of the variant of concern Omicron (B.1.1.529) of the severe acute respiratory syndrome coronavirus 2 has aggravated the Covid-19 pandemic due to its very contagious ability. The high infection rate may be due to the high binding affinity of Omicron to human cells, but both experimental and computational studies have yielded conflicting results on this issue. Some studies have shown that the Omicron variant binds to human angiotensin-converting enzyme 2 (hACE2) more strongly than the wild type (WT), but other studies have reported comparable binding affinities. To shed light on this open problem, in this work, we calculated the binding free energy of the receptor binding domain (RBD) of the WT and Omicron spike protein to hACE2 using all-atom molecular dynamics simulation and the molecular mechanics Poisson-Boltzmann surface area method. We showed that Omicron binds to human cells more strongly than the WT due to increased RBD charge, which enhances electrostatic interaction with negatively charged hACE2. N440K, T478K, E484A, Q493R, and Q498R mutations in the RBD have been found to play a critical role in the stability of the RBD-hACE2 complex. The effect of homogeneous and heterogeneous models of glycans coating the viral RBD and the peptidyl domain of hACE2 was examined. Although the total binding free energy is not sensitive to the glycan model, the distribution of per-residue interaction energies depends on it. In addition, glycans have a little effect on the binding affinity of the WT RBD to hACE2.

In silico studies of the human IAPP in the presence of osmolytes

The human islet amyloid polypeptide or amylin is secreted along with insulin by pancreatic islets. Under the drastic environmental conditions, amylin can aggregate to form amyloid fibrils. This amyloid plaque of hIAPP in the pancreatic cells is the cause of type II diabetes. Early stages of amylin aggregates are more cytotoxic than the matured fibrils. Here, we have used the all-atom molecular dynamic simulation to see the effect of water, TMAO, urea and urea/TMAO having ratio 2:1 of different concentrations on the amylin protein. Our study suggest that the amylin protein forms β-sheets in its monomeric form and may cause the aggregation of protein through the residue 13-17 and the C-terminal region. α-Helical content of protein increases with an increase in TMAO concentration by decreasing the SASA value of protein, increase in intramolecular hydrogen bonds and on making the short-range hydrophobic interactions. Electrostatic potential surfaces show that hydrophobic groups are buried and normalised configurational entropy of backbone, and side-chain atoms is lesser in the presence of TMAO, whereas opposite behaviour is obtained in the case of urea. Counteraction effect of TMAO using Kast model towards urea is also observed in ternary solution of urea/TMAO.

Protposer: the web server that readily proposes protein stabilizing mutations with high PPV

Protein stability is a requisite for most biotechnological and medical applications of proteins. As natural proteins tend to suffer from a low conformational stability ex vivo, great efforts have been devoted toward increasing their stability through rational design and engineering of appropriate mutations. Unfortunately, even the best currently used predictors fail to compute the stability of protein variants with sufficient accuracy and their usefulness as tools to guide the rational stabilisation of proteins is limited. We present here Protposer, a protein stabilising tool based on a different approach. Instead of quantifying changes in stability, Protposer uses structure- and sequence-based screening modules to nominate candidate mutations for subsequent evaluation by a logistic regression model, carefully trained to avoid overfitting. Thus, Protposer analyses PDB files in search for stabilization opportunities and provides a ranked list of promising mutations with their estimated success rates (eSR), their probabilities of being stabilising by at least 0.5 kcal/mol. The agreement between eSRs and actual positive predictive values (PPV) on external datasets of mutations is excellent. When Protposer is used with its Optimal kappa selection threshold, its PPV is above 0.7. Even with less stringent thresholds, Protposer largely outperforms FoldX, Rosetta and PoPMusiC. Indicating the PDB file of the protein suffices to obtain a ranked list of mutations, their eSRs and hints on the likely source of the stabilization expected. Protposer is a distinct, straightforward and highly successful tool to design protein stabilising mutations, and it is freely available for academic use at http://webapps.bifi.es/the-protposer.

Protein Electrostatics Investigated through Paramagnetic NMR for Nonpolar Groups

Experimental validation of theoretical models for protein electrostatics remains rare. Recently, we have developed a paramagnetic NMR-based method for de novo determination of effective near-surface electrostatic potentials, which allows for straightforward examination of electrostatic models for biomolecules. In the current work, we expand this method and demonstrate that effective near-surface electrostatic potentials can readily be determined from ¹H paramagnetic relaxation enhancement (PRE) data for protein C_αH and CH₃ groups. The experimental data were compared with those predicted from the Poisson-Boltzmann theory. The impact of structural dynamics on the effective near-surface electrostatic potentials was also assessed. The agreement between the experimental and theoretical data was particularly good for methyl ¹H nuclei. Compared to the conventional pK_a-based validation, our paramagnetic NMR-based approach can provide a far larger number of experimental data that can directly be used to examine the validity of theoretical electrostatic models for proteins.

HIT web server: a hybrid method to improve electrostatic calculations for biomolecules

The electrostatic features of highly charged biomolecules are crucial and challenging tasks in computational biophysics. The electrostatic calculations by traditional implicit solvent methods are efficient but have difficulties on highly charged biomolecules. We have developed a Hybridizing Ion Treatment (HIT) tool, which successfully hybridizes the explicit ions and implicit solvation model to accurately calculate the electrostatic potential for highly charged biomolecules. Here we implemented the HIT tool into a web server. In this study, a training set was prepared to optimize the number of frames for the HIT web server. The results on tubulins, DNAs, and RNAs, reveal the mechanisms for the motor proteins, DNA of HIV, and tRNA. This HIT web server can be widely used to study highly charged biomolecules, including DNAs, RNAs, molecular motors, and other highly charged biomolecules.

Evolutionary information helps understand distinctive features of the angiotensin II receptors AT1 and AT2 in amniota

In vertebrates, the octopeptide angiotensin II (AngII) is an important in vivo regulator of the cardiovascular system. It acts mainly through two G protein-coupled receptors, AT1 and AT2. To better understand distinctive features of these receptors, we carried out a phylogenetic analysis that revealed a mirror evolution of AT1 and AT2, each one split into two clades, separating fish from terrestrial receptors. It also revealed that hallmark mutations occurred at, or near, the sodium binding site in both AT1 and AT2. Electrostatics computations and molecular dynamics simulations support maintained sodium binding to human AT1 with slow ingress from the extracellular side and an electrostatic component of the binding free energy around -3kT, to be compared to around -2kT for human AT2 and the δ opioid receptor. Comparison of the sodium binding modes in wild type and mutated AT1 and AT2 from humans and eels indicates that the allosteric control by sodium in both AT1 and AT2 evolved during the transition from fish to amniota. The unusual S7.46N mutation in AT1 is mirrored by a L3.36M mutation in AT2. In the presence of sodium, the N7.46 pattern in amniota AT1 stabilizes the inward orientation of N3.35 in the apo receptor, which should contribute to efficient N3.35 driven biased signaling. The M3.36 pattern in amniota AT2 favours the outward orientation of N3.35 and the receptor promiscuity. Both mutations have physiological consequences for the regulation of the renin-angiotensin system.

The analysis of protein sequences from different species can reveal interesting trends in the structural and functional evolution of a protein family. Here, we analyze the evolution of two G protein-coupled receptors, AT1 and AT2, which bind the angiotensin II peptide and are important regulators of the cardiovascular system. We show that these receptors underwent a mirror evolution. Specific mutations at, or near, the sodium binding pocket occurred in both AT1 and AT2 during the transition to terrestrial life. We carried out electrostatics computations and molecular dynamics simulations to decipher the details of the sodium binding mode in eel and human receptors, as prototypes of fish and amniota receptors. Our results indicate that sodium binding is kinetically slow but thermodynamically stable. Comparison of the sodium binding modes in eel and human receptors reveals that an unusual mutation in the sodium binding pocket of AT1 is critical for biased signaling of amniota AT1 whereas a mutation in AT2 promotes promiscuity of amniota AT2. In turn, these data indicate that a few mutations at a strategic position (here the sodium binding pocket) are an efficient way to gain functional evolution.

Computational Study on E-Hooks of Tubulins in the Binding Process with Kinesin

Cargo transport within cells is essential to healthy cells, which requires microtubules-based motors, including kinesin. The C-terminal tails (E-hooks) of alpha and beta tubulins of microtubules have been proven to play important roles in interactions between the kinesins and tubulins. Here, we implemented multi-scale computational methods in E-hook-related analyses, including flexibility investigations of E-hooks, binding force calculations at binding interfaces between kinesin and tubulins, electrostatic potential calculations on the surface of kinesin and tubulins. Our results show that E-hooks have several functions during the binding process: E-hooks utilize their own high flexibilities to increase the chances of reaching a kinesin; E-hooks help tubulins to be more attractive to kinesin. Besides, we also observed the differences between alpha and beta tubulins: beta tubulin shows a higher flexibility than alpha tubulin; beta tubulin generates stronger attractive forces (about twice the strengths) to kinesin at different distances, no matter with E-hooks in the structure or not. Those facts may indicate that compared to alpha tubulin, beta tubulin contributes more to attracting and catching a kinesin to microtubule. Overall, this work sheds the light on microtubule studies, which will also benefit the treatments of neurodegenerative diseases, cancer treatments, and preventions in the future.

The pH Effects on SARS-CoV and SARS-CoV-2 Spike Proteins in the Process of Binding to hACE2

COVID-19 has been threatening human health since the late 2019, and has a significant impact on human health and economy. Understanding SARS-CoV-2 and other coronaviruses is important to develop effective treatments for COVID-19 and other coronavirus-caused diseases. In this work, we applied multi-scale computational approaches to study the electrostatic features of spike (S) proteins for SARS-CoV and SARS-CoV-2. From our results, we found that SARS-CoV and SARS-CoV-2 have similar charge distributions and electrostatic features when binding with the human angiotensin-converting enzyme 2 (hACE2). Energy pH-dependence calculations revealed that the complex structures of hACE2 and the S proteins of SARS-CoV/SARS-CoV-2 are stable at pH values ranging from 7.5 to 9. Three independent 100 ns molecular dynamics (MD) simulations were performed using NAMD to investigate the hydrogen bonds between S proteins RBD and hACE2 RBD. From MD simulations, we found that SARS-CoV-2 forms 19 pairs (average of three simulations) of hydrogen bonds with high occupancy (>50%) to hACE2, compared to 16 pairs between SARS-CoV and hACE2. Additionally, SARS-CoV viruses prefer sticking to the same hydrogen bond pairs, while SARS-CoV-2 tends to have a larger range of selections on hydrogen bonds acceptors. We also labelled key residues involved in forming the top five hydrogen bonds that were found in all three independent 100 ns simulations. This identification is important to potential drug designs for COVID-19 treatments. Our work will shed the light on current and future coronavirus-caused diseases.

Exploration of plant-derived natural polyphenols toward COVID-19 main protease inhibitors: DFT, molecular docking approach, and molecular dynamics simulations

Recent outbreaks of coronavirus have brought serious challenges to public health around the world, and it is essential to find effective treatments. In this study, the 3C-like proteinase (3CLpro) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has been considered as an important drug target because of its role in viral replication. We initially optimized 251 compounds at the PM7 level of theory for docking with 3CLpro, and then we selected the top 12 compounds for further optimization with the B3LYP-D3/6-311G** method and obtained the top four compounds by further molecular docking. Quantum chemistry calculations were performed to predict molecular properties, such as the electrostatic potential and some CDFT descriptors. We also performed molecular dynamics simulations and free energy calculations to determine the relative stability of the selected four potential compounds. We have identified key residues controlling the 3CLpro/ligand binding from per-residue based decomposition of the binding free energy. Convincingly, the comprehensive results support the conclusion that the compounds have the potential to become a candidate for anti-coronavirus treatment.

The combination of molecular dynamics simulations and quantitative calculations as a powerful tool for screening molecules.

Molecular Insights Into The Interaction Between Human Nicotinamide Phosphoribosyltransferase and Toll-Like Receptor 4

The secreted form of the enzyme nicotinamide phosphoribosyltransferase (NAMPT), which catalyzes a key reaction in intracellular NAD biosynthesis, acts as a damage-associated molecular pattern triggering Toll-like receptor 4 (TLR4)-mediated inflammatory responses. However, the precise mechanism of interaction is unclear. Using an integrated approach combining bioinformatics and functional and structural analyses, we investigated the interaction between NAMPT and TLR4 at the molecular level. Starting from previous evidence that the bacterial ortholog of NAMPT cannot elicit the inflammatory response, despite a high degree of structural conservation, two positively charged areas unique to the human enzyme (the α1-α2 and β1-β2 loops) were identified as likely candidates for TLR4 binding. However, alanine substitution of the positively charged residues within these loops did not affect either the oligomeric state or the catalytic efficiency of the enzyme. The kinetics of the binding of wildtype and mutated NAMPT to biosensor-tethered TLR4 was analyzed. We found that mutations in the α1-α2 loop strongly decreased the association rate, increasing the K_D value from 18 nM, as determined for the wildtype, to 1.3 μM. In addition, mutations in the β1-β2 loop or its deletion increased the dissociation rate, yielding K_D values of 0.63 and 0.22 μM, respectively. Mutations also impaired the ability of NAMPT to trigger the NF-κB inflammatory signaling pathway in human cultured macrophages. Finally, the involvement of the two loops in receptor binding was supported by NAMPT-TLR4 docking simulations. This study paves the way for future development of compounds that selectively target eNAMPT/TLR4 signaling in inflammatory disorders.

The effects of molecular weight and orientation on the membrane permeation and partitioning of polycyclic aromatic hydrocarbons: a computational study

Membrane permeation and the partitioning of polycyclic aromatic hydrocarbons (PAHs) are crucial aspects affecting their carcinogenicity and mutagenicity. However, a clear understanding of these processes is still rare due to the difficulty of determining the details experimentally. Here, the interactions between PAHs and lipid bilayers were studied by molecular simulations, mainly to check the influence of molecular weight and orientation. The liposome-water partition coefficient (K_LW), transmembrane time (τ), and permeability coefficient (P) of the PAHs were calculated by integrating free energy profiles from umbrella sampling. For selected PAHs, the membrane adsorption is a spontaneous process. The preferred location is near the CC bond and the orientation is related to the molecular structure. The P values of all the PAHs are basically the same order of magnitude, which means that the molecular weight contributes little to the process. As for K_LW and τ, they show obvious increases with different molecular weights. Unconstrained simulations showed that a flat orientation on the membrane surface would prevent PAHs from being transported through the membrane. Highly hydrophobic driving forces are not always good for the absorption of PAHs, especially the formation of aggregates. In addition, the orientations and energetic barriers of PAHs near the midplane of the lipid bilayer explain the different transitions of high- and low-weight PAHs. This work provides molecular level details relating to the interactions of PAHs with lipid membranes, with significance for understanding the health effects of PAHs.

Molecular mechanisms of cardiac actomyosin transforming from rigor state to post-rigor state

Sudden cardiac death contributed to half of all deaths from cardiovascular diseases. The mechanism of the kinetic cycle of cardiac myosin is crucial for heart protection and drug development. The state change in the myosin kinetic cycle from the rigor state to the post-rigor state is fundamental to explain binding and dissociation. Here, we used β-cardiac myosin in the rigor and post-rigor states to model the actomyosin complexes. Molecular dynamics simulations, electrostatic analysis, and energetic analysis of actomyosin complexes were performed in this work. The results showed that there are fewer interactions and lower electrostatic binding strength in the post-rigor state than in the rigor state. In the post-rigor state, there were higher free binding energy, fewer salt bridges, and fewer hydrogen bonds. The results showed a lower binding affinity in the post-rigor state than in the rigor state. The decrease in the binding affinity provided important conditions for dissociation of the myosin from the actin filament. Although previous studies focused mostly on the binding process, this study provides evidence of dissociation, which is even more important in the myosin kinetic cycle. This research on the mechanism of myosin kinetic cycles provides a novel direction for future genetic disease studies.

Using a comprehensive approach to investigate the interaction between Kinesin-5/Eg5 and the microtubule

Kinesins are microtubule-based motor proteins that play important roles ranging from intracellular transport to cell division. Human Kinesin-5 (Eg5) is essential for mitotic spindle assembly during cell division. By combining molecular dynamics (MD) simulations with other multi-scale computational approaches, we systematically studied the interaction between Eg5 and the microtubule. We find the electrostatic feature on the motor domains of Eg5 provides attractive interactions to the microtubule. Additionally, the folding and binding energy analysis reveals that the Eg5 motor domain performs its functions best when in a weak acidic environment. Molecular dynamics analyses of hydrogen bonds and salt bridges demonstrate that, on the binding interfaces of Eg5 and the tubulin heterodimer, salt bridges play the most significant role in holding the complex. The salt bridge residues on the binding interface of Eg5 are mostly positive, while salt bridge residues on the binding interface of tubulin heterodimer are mostly negative. Such salt bridge residue distribution is consistent with electrostatic potential calculations. In contrast, the interface between α and β-tubulins is dominated by hydrogen bonds rather than salt bridges. Compared to the Eg5/α-tubulin interface, the Eg5/β-tubulin interface has a greater number of salt bridges and higher occupancy for salt bridges. This asymmetric salt bridge distribution may play a significant role in Eg5′s directionality. The residues involved in hydrogen bonds and salt bridges are identified in this work and may be helpful for anticancer drug design.

Molecular Simulation of Stapled Peptides

Constrained peptides represent a relatively new class of biologic therapeutics, which have the potential to overcome several limitations of small-molecule drugs, and of designed antibodies. Because of their modest size, the rational design of such peptides is becoming increasingly amenable to computer simulation; multi-microsecond molecular dynamic (MD) simulations are now routinely possible on consumer-grade graphical processors (GPUs). Here, we describe the procedures for performing and analyzing MD simulations of hydrocarbon-stapled peptides using the CHARMM energy function, in isolation and in complex with a binding partner, to investigate their conformational properties and to compute changes in their binding affinity upon mutation.

MLIMC: Machine Learning-Based Implicit-Solvent Monte Carlo

Monte Carlo (MC) methods are important computational tools for molecular structure optimizations and predictions. When solvent effects are explicitly considered, MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions. Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule. The two most popular implicit-solvent models are the Poisson-Boltzmann (PB) model and the Generalized Born (GB) model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time. In this work, we develop a machine learning-based implicit-solvent Monte Carlo (MLIMC) method by combining the advantages of both implicit solvent models in accuracy and efficiency. Specifically, the MLIMC method uses a fast and accurate PB-based machine learning (PBML) scheme to compute the electrostatic solvation free energy at each step. We validate our MLIMC method by using a benzene-water system and a protein-water system. We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.