Biomimetic Self-Maturation Mineralization System for Enamel Repair.

Enamel repair is crucial for restoring tooth function and halting dental caries. However, contemporary research often overlooks the retention of organic residues within the repair layer, which hinders the growth of dense crystals and compromises the properties of the repaired enamel. During the maturation of natural enamel, the organic matrix undergoes enzymatic processing to facilitate further crystal growth, resulting in a highly mineralized tissue. Inspired by this process, a biomimetic self-maturation mineralization system is developed, comprising ribonucleic acid-stabilized amorphous calcium phosphate (RNA-ACP) and ribonuclease (RNase). The RNA-ACP induces initial mineralization in the form of epitaxial crystal growth, while the RNase present in saliva automatically triggers a biomimetic self-maturation process. The mechanistic study further indicates that RNA degradation prompts conformational rearrangement of the RNA-ACP, effectively excluding the organic matter introduced earlier. This exclusion process promotes lateral crystal growth, resulting in the generation of denser enamel-like apatite crystals that are devoid of organic residues. This strategy of eliminating organic residues from enamel crystals enhances the mechanical and physiochemical properties of the repaired enamel. The present study introduces a conceptual biomimetic mineralization strategy for effective enamel repair in clinical practice and offers potential insights into the mechanisms of biomineral formation.

Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential.

In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). We first evaluated the combination of these methods (ANI-2X/CG-BS) using two molecule sets. For the 231-molecule set, ab initio calculations were performed at both the ωB97X/6-31G(d) and B3LYP-D3BJ/DZVP levels for accuracy comparison, while for the 8,992-molecule set, ab initio calculations were carried out at the B3LYP-D3BJ/DZVP level. For each molecule in the two molecular sets, up to 10 conformations were generated, which diminish the influence of individual outliers on the performance evaluation. Encouraged by the performance of ANI-2x/CG-BS in these evaluations, we calculated the energy distributions using ANI-2x/CG-BS for more than 27,000 ligands in the protein data bank (PDB). Each ligand has at least one conformation bound to a biological molecule, and this ligand conformation is labeled as a bound conformation. Besides the bound conformations, up to 200 conformations were generated using OpenEye's Omega2 software (https://docs.eyesopen.com/applications/ omega/) for each conformation. We performed a statistical analysis of how the bound conformation energies are distributed in the ensembles for 17,197 PDB ligands that have their bound conformation energies within the energy ranges of the Omega2-generated conformation ensembles. We found that half of the ligands have their relative conformation energy lower than 2.91 kcal/mol for the bound conformations in comparison with the global conformations, and about 90% of the bound conformations are within 10 kcal/mol above the global conformation energies. This information is useful to guide the construction of libraries for shape-based virtual screening and to improve the docking algorithm to efficiently sample bound conformations.

Dissecting the Structural Dynamics of Authentic Cholesteryl Ester Transfer Protein for the Discovery of Potential Lead Compounds: A Theoretical Study.

Current structural and functional investigations of cholesteryl ester transfer protein (CETP) inhibitor design are nearly entirely based on a fully active mutation (CETPMutant) constructed for protein crystallization, limiting the study of the dynamic structural features of authentic CETP involved in lipid transport under physiological conditions. In this study, we conducted comprehensive molecular dynamics (MD) simulations of both authentic CETP (CETPAuthentic) and CETPMutant. Considering the structural differences between the N- and C-terminal domains of CETPAuthentic and CETPMutant, and their crucial roles in lipid transfer, we identified the two domains as binding pockets of the ligands for virtual screening to discover potential lead compounds targeting CETP. Our results revealed that CETPAuthentic displays greater flexibility and pronounced curvature compared to CETPMutant. Employing virtual screening and MD simulation strategies, we found that ZINC000006242926 has a higher binding affinity for the N- and C-termini, leading to reduced N- and C-opening sizes, disruption of the continuous tunnel, and increased curvature of CETP. In conclusion, CETPAuthentic facilitates the formation of a continuous tunnel in the "neck" region, while CETPMutant does not exhibit such characteristics. The ligand ZINC000006242926 screened for binding to the N- and C-termini induces structural changes in the CETP unfavorable to lipid transport. This study sheds new light on the relationship between the structural and functional mechanisms of CETP. Furthermore, it provides novel ideas for the precise regulation of CETP functions.

p38α Kinase Auto-Activation through Its Conformational Transition Induced by Tyr323 Phosphorylation.

p38α is a key serine/threonine kinase that can enable atypical auto-activation through Zap70 phosphorylation and initiate T cell receptor signaling. The auto-activation plays an important role in autoimmune diseases. Although the classical activation mechanism of p38α has been studied in-depth, the atypical activation mechanism of Y323 phosphorylation-induced p38α auto-activation remains largely unexplained, especially the regulatory effects of phosphorylation on different sites (Y323 vs T180). From the X-ray experimental data, we identified the inactive and active states of p38α using principal component analysis. To understand the auto-activation process and the internal driving mechanism, a computational paradigm that couples the targeted molecular dynamics simulations, the String Method, and the umbrella sampling strategy were employed to generate the conformational landscape of p38α, including p38α T180-Y323, p38α T180-pY323, and p38α pT180-pY323 systems (pT180/pY323: phosphorylated T180/Y323). We explored that pY323 could change the conformational distribution and promote the conformational transition of p38α from the inactive state to the active state. Auto-activation of p38α is regulated by pY323 through destabilization of the hydrophobic core structure and aided by R173. This study will further explain the conformational transition of p38α induced by Y323 phosphorylation and provide insights into the universal molecular auto-activation mechanism of the p38 subfamily at the atomic level.

Sequence Matching between Hemagglutinin and Neuraminidase through Sequence Analysis Using Machine Learning.

To date, many experiments have revealed that the functional balance between hemagglutinin (HA) and neuraminidase (NA) plays a crucial role in viral mobility, production, and transmission. However, whether and how HA and NA maintain balance at the sequence level needs further investigation. Here, we applied principal component analysis and hierarchical clustering analysis on thousands of HA and NA sequences of A/H1N1 and A/H3N2. We discovered significant coevolution between HA and NA at the sequence level, which is closely related to the type of host species and virus epidemic years. Furthermore, we propose a sequence-to-sequence transformer model (S2STM), which mainly consists of an encoder and a decoder that adopts a multi-head attention mechanism for establishing the mapping relationship between HA and NA sequences. The training results reveal that the S2STM can effectively realize the "translation" from HA to NA or vice versa, thereby building a relationship network between them. Our work combines unsupervised and supervised machine learning methods to identify the sequence matching between HA and NA, which will advance our understanding of IAVs' evolution and also provide a novel idea for sequence analysis methods.

Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials.

An efficient yet accurate method for producing a large amount of energy data for molecular mechanical force field (MMFF) parameterization is on demand, especially for torsional angle parameters which are typically derived to reproduce ab initio rotational profiles or torsional potential energy surfaces (PESs). Recently, an active learning potential (ANI-1x) for organic molecules which can produce smooth and physically meaningful PESs has been developed. The high efficiency and accuracy make ANI-1x especially attractive for geometry optimization at low cost. To apply the ANI-1x potential in MMFF parameterization, one needs to perform constrained geometry optimization. In this work, we first developed a computational protocol to constrain rotatable torsional angles and other geometric parameters for a molecule whose geometry is described by Cartesian coordinates. The constraint is successfully achieved by force projection for the two conjugated gradient (CG) algorithms. We then conducted large-scale assessments on ANI-1x along with four different optimization algorithms in reproducing DFT energies and geometries for two CG algorithms, CG backtracking line search (CG-BS) and CG Wolfe line search (CG-WS), and two quasi-Newton algorithms, Broyden-Fletcher-Goldfarb-Shanno (BFGS) and low-memory BFGS (L-BFGS). Note that CG-BS is a new algorithm we developed in this work. All four algorithms take the ANI energies and forces to optimize a molecule geometry. Last, we conducted a large-scale assessment of applying ANI-1x in MMFF development in three aspects. First, we performed full optimizations for 100 drug molecules, each consisting of five distinct conformations. The average root-mean-square error (RMSE) between ANI-1x and DFT is about 1.3 kcal/mol, and the root-mean-square displacement (RMSD) of heavy atoms is about 0.35 Å. Second, we generated torsional PESs for 160 organic molecules, and constrained optimizations were performed for up to 18 conformations for each PES. We found that the RMSE of all the conformers is 1.23 kcal/mol. Last, we carried out constrained optimizations for alanine dipeptide with both ϕ and φ angles being frozen. The Ramachandran plots indicate that the two CG algorithms in conjunction with the ANI-1x potential could well reproduce the DFT-optimized geometries and torsional PESs. We concluded that CG-BS and CG-WS are good choices for generating PESs, while CG-WS or BFGS is ideal for performing full geometry optimization. With the continuously increased quality of ANI, it is expected that the computational algorithms and protocols presented in this work will have great applications in improving the quality of an existing small-molecule MMFF.

Mechanism of Glycans Modulating Cholesteryl Ester Transfer Protein: Unveiled by Molecular Dynamics Simulation.

Inhibition of the cholesteryl ester transfer protein (CETP) has been considered as a promising way for the treatment of cardiovascular disease (CVD) for three decades. However, clinical trials of several CETP inhibitors with various potencies have been marginally successful at best, raising doubts on the target drugability of CETP. The in-depth understanding of the glycosylated CETP structure could be beneficial to more definitive descriptions of the CETP function and the underlying mechanism. In this work, large-scale molecular dynamics simulations were performed to thoroughly explore the mechanism of glycans modulating CETP. Here, the extensive simulation results intensely suggest that glycan88 tends to assist CETP in forming a continuous tunnel throughout interacting with the upper-right region of the N-barrel, while it also could prevent the formation of a continuous tunnel by swinging toward the right-rear of the N-barrel. Furthermore, glycan240 formed stable H-bonds with Helix-B and might further stabilize the central cavity of CETP. Furthermore, the nonspecific involvement of the hydroxyl groups from the various glycans with protein core interactions and the similar influence of different glycans trapped at similar regions on the protein structure suggest that physiological glycan may lead to a similar effect. This study would provide valuable insights into devising novel methods for CVD treatment targeting CETP and functional studies about glycosylation for other systems.

Extracellular DNA: A Missing Link in the Pathogenesis of Ectopic Mineralization.

Although deoxyribonucleic acid (DNA) is the genetic coding for the very essence of life, these macromolecules or components thereof are not necessarily lost after a cell dies. There appears to be a link between extracellular DNA and biomineralization. Here the authors demonstrate that extracellular DNA functions as an initiator of collagen intrafibrillar mineralization. This is confirmed with in vitro and in vivo biological mineralization models. Because of their polyanionic property, extracellular DNA molecules are capable of stabilizing supersaturated calcium phosphate solution and mineralizing 2D and 3D collagen matrices completely as early as 24 h. The effectiveness of extracellular DNA in biomineralization of collagen is attributed to the relatively stable formation of amorphous liquid droplets triggered by attraction of DNA to the collagen fibrils via hydrogen bonding. These findings suggest that extracellular DNA is biomimetically significant for fabricating inorganic-organic hybrid materials for tissue engineering. DNA-induced collagen intrafibrillar mineralization provides a clue to the pathogenesis of ectopic mineralization in different body tissues. The use of DNase for targeting extracellular DNA at destined tissue sites provides a potential solution for treatment of diseases associated with ectopic mineralization.

Multifunctional Nanomachinery for Enhancement of Bone Healing.

The visionary idea that RNA adopts nonbiological roles in today's nanomaterial world has been nothing short of phenomenal. These RNA molecules have ample chemical functionality and self-assemble to form distinct nanostructures in response to external stimuli. They may be combined with inorganic materials to produce nanomachines that carry cargo to a target site in a controlled manner and respond dynamically to environmental changes. Comparable to biological cells, programmed RNA nanomachines have the potential to replicate bone healing in vitro. Here, an RNA-biomineral nanomachine is developed, which accomplishes intrafibrillar and extrafibrillar mineralization of collagen scaffolds to mimic bone formation in vitro. Molecular dynamics simulation indicates that noncovalent hydrogen bonding provides the energy source that initiates self-assembly of these nanomachines. Incorporation of the RNA-biomineral nanomachines into collagen scaffolds in vivo creates an osteoinductive microenvironment within a bone defect that is conducive to rapid biomineralization and osteogenesis. Addition of RNA-degrading enzymes into RNA-biomineral nanomachines further creates a stop signal that inhibits unwarranted bone formation in tissues. The potential of RNA in building functional nanostructures has been underestimated in the past. The concept of RNA-biomineral nanomachines participating in physiological processes may transform the nanoscopic world of life science.

Molecular insights into the binding variance of the SARS-CoV-2 spike with human, cat and dog ACE2 proteins.

SARS-CoV-2 has recently caused an epidemic in humans and poses a huge threat to global public health. As a primary receptor of SARS-CoV-2, angiotensin-converting enzyme 2 (ACE2) exists in different hosts that are in close contact with humans, especially cats and dogs. However, the underlying mechanism of how the spike receptor binding domain (RBD) of SARS-CoV-2 cooperates with human ACE2 (hACE2), cat ACE2 (cACE2) and dog ACE2 (dACE2) and the variation in binding remains largely unsolved. Therefore, we explored the binding behavior of the spike RBD with cACE2, dACE2 and hACE2 via all-atom molecular dynamics simulations. In accordance with the binding free energies and residue interactions, the spike RBD has respective binding specificities with cACE2, dACE2 and hACE2, and the binding affinities decrease in the order of hACE2, cACE2, dACE2, mainly due to changes in the amino acids Q24L, H34Y, and M82T in cACE2 or dACE2. Furthermore, alanine scanning analysis results validated some key residues of the spike RBD interact with ACE2 and provided clues to the variation of amino acid that could influence the transmissibility or immune responses of SARS-CoV-2. Decreasing dynamic correlations strengths of ACE2 with the RBD were found in all hACE2-RBD, cACE2-RBD and dACE2-RBD systems. The ACE2 protein shows variable motion modes across the zinc metallopeptidase domain, which induces different interactions between ACE2 and the RBD. Our studies reveal that the motion pattern of the zinc metallopeptidase domain is critical to the binding behavior of RBD with ACE2. These findings could aid our understanding of selective recognition involving various ACE2 with the SARS-CoV-2 spike and shed further light on the binding mechanisms.

How Well Does the Extended Linear Interaction Energy Method Perform in Accurate Binding Free Energy Calculations?

With continually increased computer power, molecular mechanics force field-based approaches, such as the endpoint methods of molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA), have been routinely applied in both drug lead identification and optimization. However, the MM-PB/GBSA method is not as accurate as the pathway-based alchemical free energy methods, such as thermodynamic integration (TI) or free energy perturbation (FEP). Although the pathway-based methods are more rigorous in theory, they suffer from slow convergence and computational cost. Moreover, choosing adequate perturbation routes is also crucial for the pathway-based methods. Recently, we proposed a new method, coined extended linear interaction energy (ELIE) method, to overcome some disadvantages of the MM-PB/GBSA method to improve the accuracy of binding free energy calculation. In this work, we have systematically assessed this approach using in total 229 protein-ligand complexes for eight protein targets. Our results showed that ELIE performed much better than the molecular docking and MM-PBSA method in terms of root-mean-square error (RMSE), correlation coefficient (R), predictive index (PI), and Kendall's τ. The mean values of PI, R, and τ are 0.62, 0.58, and 0.44 for ELIE calculations. We also explored the impact of the length of simulation, ranging from 1 to 100 ns, on the performance of binding free energy calculation. In general, extending simulation length up to 25 ns could significantly improve the performance of ELIE, while longer molecular dynamics (MD) simulation does not always perform better than short MD simulation. Considering both the computational efficiency and achieved accuracy, ELIE is adequate in filling the gap between the efficient docking methods and computationally demanding alchemical free energy methods. Therefore, ELIE provides a practical solution for the routine ranking of compounds in lead optimization.

Computational identification of potential chemoprophylactic agents according to dynamic behavior of peroxisome proliferator-activated receptor gamma.

Peroxisome proliferator-activated receptor gamma (PPARγ) is an attractive target for chemoprevention of lung carcinoma, however its highly dynamic nature has plagued drug development for decades, with difficulties in receptor modeling for structure-based design. In this work, an integrated receptor-based virtual screening (VS) strategy was applied to identify PPARγ agonists as chemoprophylactic agents by using extensive docking and conformational sampling methods. Our results showed that the conformational plasticity of PPARγ, especially the H2 & S245 loop, H2' & Ω loop and AF-2 surface, is markedly affected by binding of full/partial agonists. To fully take the dynamic behavior of PPARγ into account, the VS approach effectively sorts out five commercial agents with reported antineoplastic properties. Among them, ZINC03775146 (gusperimus) and ZINC14087743 (miltefosine) might be novel PPARγ agonists with the potential for chemoprophylaxis, that simultaneously take part in a flexible switch of the AF-2 surface and state change of the Ω loop. Furthermore, the dynamic structural coupling between the H2 & S245 and H2' & Ω loops offers enticing hope for PPARγ-targeted therapeutics, by blocking kinase accessibility to PPARγ. These results might aid the development of chemopreventive drugs, and the integrated VS strategy could be conducive to drug design for highly flexible biomacromolecules.

Bindings of PPARγ ligand-binding domain with 5-cholesten-3ß, 25-diol, 3-sulfate: accurate prediction by molecular simulation.

Peroxisome proliferator-activated receptor gamma (PPARγ) has recently been identified as an attractive target for atherosclerosis intervention. Given potential relevance of 5-cholesten-3ß, 25-diol, 3-sulphate (CHOS) and PPARγ, an integrated docking method was used to study their interaction mechanisms, with the full considerations to distinct CHOS conformations and dynamic ensembles of PPARγ ligand-binding domain (PPARγ-LBD). The results revealed that this novel platform is satisfactory to the accurate determination of binding profiles, and the binding pattern of CHOS is rather similar as those of current PPARγ full/partial agonists. CHOS contributes to the stabilization of the AF2 and ß-sheet surfaces of PPARγ-LBD and promotes the configuration adjustment of Ω loop, in order to inhibit the Cdk5-mediated PPARγ phosphorylation. Nonetheless, there are clear differences in term of occupation of full or partial agonist-like binding models. The energetic and geometric analyses further revealed that CHOS may be fond of partial agonist-like binding, and its sulfonic group and carbon skeleton are helpful for the binding process. We hope that the results will aid our understanding of recognitions involving CHOS with PPARγ-LBD and warrant the further aspects to pharmacological experiments.Communicated by Ramaswamy H. Sarma.

Structure-based methoxyflavone derivatives with potent inhibitory activity against various influenza neuraminidases.

Communicated by Ramaswamy H. Sarma.

Role of glycans in cholesteryl ester transfer protein revealed by molecular dynamics simulation.

Current cholesteryl ester transfer protein (CETP) inhibitors are designed based on the unglycosylated crystal structure, and most of them have failed to cure cardiovascular disease (CVD). It is particularly important for us to investigate the glycosylation structure of CETP (CETP-G) and effect of glycans on the structure and function of CETP. Here, we used a total of 3.0-µs molecular dynamics (MD) trajectories of nascent structure of CETP (CETP-N) and CETP-G to study their structural differentiations, to shed new light on the CETP-mediated lipid exchange. In accordance with our simulations and previous mutation studies, relative to CETP-N, CETP-G adopts a more stretched shape with higher hydrophobic and hydrophilic solvent-accessible surface area (SASA) of N-terminal oscillating with larger amplitude, in which Glycan88 provides partial assistance for CEs through the N-terminal. Glycan341 reduces the flexibility of neck flap, with the interference of CEs through the neck region. Besides, Glycan240 reduces the flexibility of Helix-X to interfere the CEs transfer. Glycan396 decreases the flexibility and increases the hydrophobic SASA of C-terminal. Overall, these glycans affect the dynamics and structure of CETP through forming H-bonds with surrounding residues, and the sampled conformations of glycan is also affected by its surrounding residues. Thus, glycans are an integral part of CETP, further studies on the CETP inhibition and treatment of CVD should fully consider the effect of glycans.

Binding profiles of cholesterol ester transfer protein with current inhibitors: a look at mechanism and drawback.

Although the pharmacological inhibition of cholesterol ester transport protein (CETP) has been proposed as a method of preventing and treating cardiovascular disease (CVD), the adverse effects of current inhibitors have cast doubt on the interaction mechanisms of inhibitors and CETP. In response, a molecular dynamics simulation was used to investigate their interaction and shed light on the lipid exchange mechanism of CETP. Results showed that torcetrapib, anacetrapib, and evacetrapib can induce the incremental rigidity of CETP, yet decrease the stability of Helix X and the hydrophobic tunnel of CETP, with passable binding abilities (ΔGbind, -61.08, -64.23, and -61.57 kcal mol-1). During their binding processes, Van der Waals components (ΔEvdw + ΔGSA) play a dominant role, and the inhibitory effects closely correlated with residues Cys13, Val198, Gln199, Ser230, His232, and Phe263, which could reduce the flexibility of N- and C- termini and Helix X, as well as the stability of hydrophobic tunnel, into which the three inhibitors could enter and promote the formation of intramolecular H-bonds such as Thr138-Asn192 and Arg37-Glu186. Additionally, the three inhibitors could restrain the formation of an opening at the CETP N-terminal, which given the other findings suggests the tunneling mechanism of CETP transfer. The paper closes with an explanation of conceivable causes of the insufficient efficacy of the inhibitors, and puts forward the rationality in targeting the CETP distal end for CVD therapies.