pyMSER─An Open-Source Library for Automatic Equilibration Detection in Molecular Simulations.

Automated molecular simulations are used extensively for predicting material properties. Typically, these simulations exhibit two regimes: a dynamic equilibration part, followed by a steady state. For extracting observable properties, the simulations must first reach a steady state so that thermodynamic averages can be taken. However, as equilibration depends on simulation conditions, predicting the optimal number of simulation steps a priori is impossible. Here, we demonstrate the application of the Marginal Standard Error Rule (MSER) for automatically identifying the optimal truncation point in Grand Canonical Monte Carlo (GCMC) simulations. This novel automatic procedure determines the point at which a steady state is reached, ensuring that figures of merit are extracted in an objective, accurate, and reproducible fashion. In the case of GCMC simulations of gas adsorption in metal-organic frameworks, we find that this methodology reduces the computational cost by up to 90%. As MSER statistics are independent of the simulation method that creates the data, this library is, in principle, applicable to any time series analysis in which equilibration truncation is required. The open-source Python implementation of our method, pyMSER, is publicly available for reuse and validation at https://github.com/IBM/pymser.

Metal-Organic Framework with Space-Partition Pores by Fluorinated Anions for Benchmark C2H2/CO2 Separation.

The efficient separation of C2H2 from C2H2/CO2 or C2H2/CO2/CH4 mixtures is crucial for achieving high-purity C2H2 (>99%), essential in producing contemporary commodity chemicals. In this report, we present ZNU-12, a metal-organic framework with space-partitioned pores formed by inorganic fluorinated anions, for highly efficient C2H2/CO2 and C2H2/CO2/CH4 separation. The framework, partitioned by fluorinated SiF62- anions into three distinct cages, enables both a high C2H2 capacity (176.5 cm3/g at 298 K and 1.0 bar) and outstanding C2H2 selectivity over CO2 (13.4) and CH4 (233.5) simultaneously. Notably, we achieve a record-high C2H2 productivity (132.7, 105.9, 98.8, and 80.0 L/kg with 99.5% purity) from C2H2/CO2 (v/v = 50/50) and C2H2/CO2/CH4 (v/v = 1/1/1, 1/1/2, or 1/1/8) mixtures through a cycle of adsorption-desorption breakthrough experiments with high recovery rates. Theoretical calculations suggest the presence of potent "2 + 2" collaborative hydrogen bonds between C2H2 and two hexafluorosilicate (SiF62-) anions in the confined cavities.

Developing semi-empirical water model for efficiently simulating temperature-dependent chemisorption of CO2 in amine solvents.

Classical molecular dynamics (MD) simulations without bond forming/breaking cannot be used to model chemical reactions (CRs) among small molecules. Although the first-principle MD simulation can adequately describe CRs with explicit water molecules, such simulation is normally too costly for most researchers to afford. Generally, water molecules in a solvent can exert hydrophobic forces on reacting molecules, which yields a so-called caging effect that cannot be ignored when constructing a free energy landscape for reacting molecules. Many recently developed semi-empirical methods (such as DFTB, PM6 and xTB) are highly efficient for modeling CRs, however none of them can be directly used to model bulk water properly. Here, we developed a modified xTB approach that enables the simulation of CRs in explicit water. Using the chemisorption of CO2 by amines in water as an example application, we demonstrate that our approach yielded results comparable with the first-principle ones, while only using a limited computing resource. Potentially, our proposed semi-empirical water model can be utilized for the computational study of any CR in water.

Topological Design of Unprecedented Metal-Organic Frameworks Featuring Multiple Anion Functionalities and Hierarchical Porosity for Benchmark Acetylene Separation.

Separation of acetylene (C2 H2 ) from carbon dioxide (CO2 ) or ethylene (C2 H4 ) is industrially important but still challenging so far. Herein, we developed two novel robust metal organic frameworks AlFSIX-Cu-TPBDA (ZNU-8) with znv topology and SIFSIX-Cu-TPBDA (ZNU-9) with wly topology for efficient capture of C2 H2 from CO2 and C2 H4 . Both ZNU-8 and ZNU-9 feature multiple anion functionalities and hierarchical porosity. Notably, ZNU-9 with more anionic binding sites and three distinct cages displays both an extremely large C2 H2 capacity (7.94 mmol/g) and a high C2 H2 /CO2 (10.3) or C2 H2 /C2 H4 (11.6) selectivity. The calculated capacity of C2 H2 per anion (4.94 mol/mol at 1 bar) is the highest among all the anion pillared metal organic frameworks. Theoretical calculation indicated that the strong cooperative hydrogen bonds exist between acetylene and the pillared SiF6 2- anions in the confined cavity, which is further confirmed by in situ IR spectra. The practical separation performance was explicitly demonstrated by dynamic breakthrough experiments with equimolar C2 H2 /CO2 mixtures and 1/99 C2 H2 /C2 H4 mixtures under various conditions with excellent recyclability and benchmark productivity of pure C2 H2 (5.13 mmol/g) or C2 H4 (48.57 mmol/g).

Highly Efficient CO2/C2H2 Separation by Porous Graphene via Quadrupole Gating Mechanism.

Acetylene (C2H2) is an important and widely used raw material in various industries (such as petrochemical). Generally, a product yield is proportional to the purity of C2H2; however, C2H2 from a typical industrial gas-production process is commonly contaminated by CO2. So far, the achievement of high-purity C2H2 separated from a CO2/C2H2 mixture is still challenging due to their very close molecular dimensions and boiling temperatures. Taking advantage of their quadrupoles with opposite signs, here, we show that the graphene membrane embedded with crown ether nanopores can achieve an unprecedented separation efficiency of CO2/C2H2. Combining the molecular dynamics simulation and the density functional theory (DFT) approaches, we discovered that the electrostatic gas-pore interaction favorably allows the fast transport of CO2 through crown ether nanopores while completely prohibiting C2H2 transport, which yields a remarkable permeation selectivity. In particular, the utilized crown ether pore is capable of allowing the individual transport of CO2 while completely rejecting the passage of C2H2, independent of the applied pressures, fed gases ratios, and exerted temperatures, featuring the superiority and robustness of the crown pore in CO2/C2H2 separation. Further, DFT and PMF calculations demonstrate that the transport of CO2 through the crown pore is energetically more favorable than the transport of C2H2. Our findings reveal the potential application of graphene crown pore for CO2 separation with outstanding performance.

CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks.

Grand Canonical Monte Carlo is an important method for performing molecular-level simulations and assisting the study and development of nanoporous materials for gas capture applications. These simulations are based on the use of force fields and partial charges to model the interaction between the adsorbent molecules and the solid framework. The choice of the force field parameters and partial charges can significantly impact the results obtained, however, there are very few databases available to support a comprehensive impact evaluation. Here, we present a database of simulations of CO2 and N2 adsorption isotherms on 690 metal-organic frameworks taken from the CoRE MOF 2014 database. We performed simulations with two force fields (UFF and DREIDING), six partial charge schemes (no charges, Qeq, EQeq, MPNN, PACMOF, and DDEC), and three temperatures (273, 298, 323 K). The resulting isotherms compose the Charge-dependent, Reproducible, Accessible, Forcefield-dependent, and Temperature-dependent Exploratory Database (CRAFTED) of adsorption isotherms.

Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74.

Among various porous solids for gas separation and purification, metal-organic frameworks (MOFs) are promising materials that potentially combine high CO2 uptake and CO2/N2 selectivity. So far, within the hundreds of thousands of MOF structures known today, it remains a challenge to computationally identify the best suited species. First principle-based simulations of CO2 adsorption in MOFs would provide the necessary accuracy; however, they are impractical due to the high computational cost. Classical force field-based simulations would be computationally feasible; however, they do not provide sufficient accuracy. Thus, the entropy contribution that requires both accurate force fields and sufficiently long computing time for sampling is difficult to obtain in simulations. Here, we report quantum-informed machine-learning force fields (QMLFFs) for atomistic simulations of CO2 in MOFs. We demonstrate that the method has a much higher computational efficiency (∼1000×) than the first-principle one while maintaining the quantum-level accuracy. As a proof of concept, we show that the QMLFF-based molecular dynamics simulations of CO2 in Mg-MOF-74 can predict the binding free energy landscape and the diffusion coefficient close to experimental values. The combination of machine learning and atomistic simulation helps achieve more accurate and efficient in silico evaluations of the chemisorption and diffusion of gas molecules in MOFs.

Benchmark single-step ethylene purification from ternary mixtures by a customized fluorinated anion-embedded MOF.

Ethylene (C2H4) purification from multi-component mixtures by physical adsorption is a great challenge in the chemical industry. Herein, we report a GeF62- anion embedded MOF (ZNU-6) with customized pore structure and pore chemistry for benchmark one-step C2H4 recovery from C2H2 and CO2. ZNU-6 exhibits significantly high C2H2 (1.53 mmol/g) and CO2 (1.46 mmol/g) capacity at 0.01 bar. Record high C2H4 productivity is achieved from C2H2/CO2/C2H4 mixtures in a single adsorption process under various conditions. The separation performance is retained over multiple cycles and under humid conditions. The potential gas binding sites are investigated by density functional theory (DFT) calculations, which suggest that C2H2 and CO2 are preferably adsorbed in the interlaced narrow channel with high aff0inity. In-situ single crystal structures with the dose of C2H2, CO2 or C2H4 further reveal the realistic host-guest interactions. Notably, rare C2H2 clusters are formed in the narrow channel while two distinct CO2 adsorption locations are observed in the narrow channel and the large cavity with a ratio of 1:2, which accurately account for the distinct adsorption heat curves.

Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.

Spike protein-independent attenuation of SARS-CoV-2 Omicron variant in laboratory mice.

Despite being more transmissible, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant only causes milder diseases in laboratory animals, often accompanied by a lower viral load compared with previous variants of concern. In this study, we report the structural basis for a robust interaction between the receptor-binding domain of the Omicron spike protein and mouse ACE2. We show that pseudovirus bearing the Omicron spike protein efficiently utilizes mouse ACE2 for entry. By comparing viral load and disease severity among laboratory mice infected by a natural Omicron variant or recombinant ancestral viruses bearing either the entire Omicron spike or only the N501Y/Q493R mutations in its spike, we find that mutations outside the spike protein in the Omicron variant may be responsible for the observed lower viral load. Together, our results imply that a post-entry block to the Omicron variant exists in laboratory mice.

Benchmarking various types of partial atomic charges for classical all-atom simulations of metal-organic frameworks.

The density derived electrostatic and chemical (DDEC) approach for calculating the charges of atoms in a metal-organic framework (MOF) is considered to be the most accurate (yet computationally costly) one among many charge-assignment methods. Here, we conducted a comparative study on five different types of atomic partial charges (namely CM5, Mulliken, Qeq, EQeq and PACMOF) prepared for a subset of MOFs with affordable computational costs and benchmarked them with respect to the DDEC charges, which is particularly relevant because currently most databases lack MOFs with pre-calculated DDEC charges. To find a suitable charge type alternative to the DDEC approach, we statistically ranked the five charge types based on two metrics, the relative standard deviation of charges and relative dipole moment difference, based on which we provide general guidance as well as suggestions for specific MOFs according to bond polarity analyses. Finally, we recommend a possible and more accurate parametrization scheme for future studies.

Understanding interactions between biomolecules and two-dimensional nanomaterials using in silico microscopes.

Two-dimensional (2D) nanomaterials such as graphene are increasingly used in research and industry for various biomedical applications. Extensive experimental and theoretical studies have revealed that 2D nanomaterials are promising drug delivery vehicles, yet certain materials exhibit toxicity under biological conditions. So far, it is known that 2D nanomaterials possess strong adsorption propensities for biomolecules. To mitigate potential toxicity and retain favorable physical and chemical properties of 2D nanomaterials, it is necessary to explore the underlying mechanisms of interactions between biomolecules and nanomaterials for the subsequent design of biocompatible 2D nanomaterials for nanomedicine. The purpose of this review is to integrate experimental findings with theoretical observations and facilitate the study of 2D nanomaterial interaction with biomolecules at the molecular level. We discuss the current understanding and progress of 2D nanomaterial interaction with proteins, lipid membranes, and DNA based on molecular dynamics (MD) simulation. In this review, we focus on the 2D graphene nanosheet and briefly discuss other 2D nanomaterials. With the ever-growing computing power, we can image nanoscale processes using MD simulation that are otherwise not observable in experiment. We expect that molecular characterization of the complex behavior between 2D nanomaterials and biomolecules will help fulfill the goal of designing effective 2D nanomaterials as drug delivery platforms.

Crown Nanopores in Graphene for CO2 Capture and Filtration.

With growing concerns about global warming, it has become urgent and critical to capture carbon from various emission sources (such as power plants) and even directly from air. Recent advances in materials research permit the design of various efficient approaches for capturing CO2 with high selectivity over other gases. Here, we show that crown nanopores (resembling crown ethers) embedded in graphene can efficaciously allow CO2 to pass and block other flue gas components (such as N2 and O2). We carried out extensive density functional theory-based calculations as well as classical and ab initio molecular dynamics simulations to reveal the energetics and dynamics of gas transport through crown nanopores. Our results highlight that the designed crown nanopores in graphene possess not only an excellent selectivity for CO2 separation/capture but also fast transport (flow) rates, which are ideal for the treatment of flue gas in power plants.

Stable Cell Clones Harboring Self-Replicating SARS-CoV-2 RNAs for Drug Screen.

The development of antivirals against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been hampered by the lack of efficient cell-based replication systems that are amenable to high-throughput screens in biosafety level 2 laboratories. Here we report that stable cell clones harboring autonomously replicating SARS-CoV-2 RNAs without spike (S), membrane (M), and envelope (E) genes can be efficiently derived from the baby hamster kidney (BHK-21) cell line when a pair of mutations were introduced into the non-structural protein 1 (Nsp1) of SARS-CoV-2 to ameliorate cellular toxicity associated with virus replication. In a proof-of-concept experiment we screened a 273-compound library using replicon cells and identified three compounds as novel inhibitors of SARS-CoV-2 replication. Altogether, this work establishes a robust, cell-based system for genetic and functional analyses of SARS-CoV-2 replication and for the development of antiviral drugs. IMPORTANCE SARS-CoV-2 replicon systems that have been reported up to date were unsuccessful in deriving stable cell lines harboring non-cytopathic replicons. The transient expression of viral sgmRNA or a reporter gene makes it impractical for industry-scale screening of large compound libraries using these systems. Here, for the first time, we derived stable cell clones harboring the SARS-CoV-2 replicon. These clones may now be conveniently cultured in a standard BSL-2 laboratory for high throughput screen of compound libraries. Additionally, our stable replicon cells represent a new model system to study SARS-CoV-2 replication.

Crystal-structures-guided design of fragment-based drugs for inhibiting the main protease of SARS-CoV-2.

Since the beginning of the COVID-19 pandemic, scientists across the globe are racing to find a cure for the highly contagious infectious disease caused by the SARS-CoV-2 virus. Despite many promising ongoing progress, there are currently no FDA approved drug to treat infected patients. Recently, the crowdsourcing of drug discovery for inhibiting the main protease (Mpro) of SARS-CoV-2 have yielded a plenty of drug fragments resolved inside the active site of Mpro via the crystallography method. Following the principle of fragment-based drug design (FBDD), we are motivated to design a potent drug candidate (named B19) by merging three fragments JFM, U0P, and HWH. Through extensive all-atom molecular dynamics simulation and molecular docking, we found that B19 among all designed ones is most stable inside the Mpro's active site and the binding free energy of B19 is comparable to or even a little better than that of a native protein ligand processed by Mpro. Our promising results suggest that B19 and its derivatives can potentially be efficacious drug candidates for COVID-19.

Insights into SARS-CoV-2's Mutations for Evading Human Antibodies: Sacrifice and Survival.

The highly infectious SARS-CoV-2 variant B.1.351 that first emerged in South Africa with triple mutations (N501Y, K417N, and E484K) is globally worrisome. It is known that N501Y and E484K can enhance binding between the coronavirus receptor domain (RBD) and human ACE2. However, the K417N mutation appears to be unfavorable as it removes one interfacial salt bridge. Here, we show that despite the decrease in binding affinity (1.48 kcal/mol) between RBD and ACE2, the K417N mutation abolishes a buried interfacial salt bridge between the RBD and neutralizing antibody CB6. This substantially reduces their binding energy by 9.59 kcal/mol, thus facilitating the process by which the variant efficiently eludes CB6 (including many other antibodies). Our theoretical predictions agree with existing experimental findings. Harnessing the revealed molecular mechanisms makes it possible to redesign therapeutic antibodies, thus making them more efficacious.

Nanopores in Atomically Thin 2D Nanosheets Limit Aqueous Single-Stranded DNA Transport.

Nanopores in 2D materials are highly desirable for DNA sequencing, yet achieving single-stranded DNA (ssDNA) transport through them is challenging. Using density functional theory calculations and molecular dynamics simulations we show that ssDNA transport through a pore in monolayer hexagonal boron nitride (h-BN) is marked by a basic nanomechanical conflict. It arises from the notably inhomogeneous flexural rigidity of ssDNA and causes high friction via transient DNA desorption costs exacerbated by solvation effects. For a similarly sized pore in bilayer h-BN, its self-passivated atomically smooth edge enables continuous ssDNA transport. Our findings shed light on the fundamental physics of biopolymer transport through pores in 2D materials.

Structure-Function Analysis of Resistance to Bamlanivimab by SARS-CoV-2 Variants Kappa, Delta, and Lambda.

The newly emerging Kappa, Delta, and Lambda SARS-CoV-2 variants are worrisome, characterized with the double mutations E484Q/L452R, T478K/L452R, and F490S/L452Q, respectively, in their receptor binding domains (RBDs) of the spike proteins. As revealed in crystal structures, most of these residues (e.g., 452 and 484 in RBDs) are not in direct contact with interfacial residues in the angiotensin-converting enzyme 2 (ACE2). This suggests that albeit there are some possibly nonlocal effects, these mutations might not significantly affect RBD's binding with ACE2, which is an important step for viral entry into host cells. Thus, without knowing the molecular mechanism, these successful mutations (from the point of view of SARS-CoV-2) may be hypothesized to evade human antibodies. Using all-atom molecular dynamics (MD) simulation, here, we show that the E484Q/L452R mutations significantly reduce the binding affinity between the RBD of the Kappa variant and the antibody LY-CoV555 (also named as Bamlanivimab), which was efficacious for neutralizing the wild-type SARS-CoV-2. To verify simulation results, we further carried out experiments with both pseudovirions- and live virus-based neutralization assays and demonstrated that LY-CoV555 completely lost neutralizing activity against the L452R/E484Q mutant. Similarly, we show that mutations in the Delta and Lambda variants can also destabilize the RBD's binding with LY-CoV555. With the revealed molecular mechanism on how these variants evade LY-CoV555, we expect that more specific therapeutic antibodies can be accordingly designed and/or a precise mixing of antibodies can be achieved as a cocktail treatment for patients infected with these variants.

Field-Dependent Dehydration and Optimal Ionic Escape Paths for C2N Membranes.

Most analytic theories describing electrostatically driven ion transport through water-filled nanopores assume that the corresponding permeation barriers are bias-independent. While this assumption may hold for sufficiently wide pores under infinitely small bias, transport through subnanometer pores under finite bias is difficult to interpret analytically. Given recent advances in subnanometer pore fabrication and the rapid progress in detailed computer simulations, it is important to identify and understand the specific field-induced phenomena arising during ion transport. Here we consider an atomistic model of electrostatically driven ion permeation through subnanoporous C2N membranes. We analyze probability distributions of ionic escape trajectories and show that the optimal escape path switches between two different configurations depending on the bias magnitude. We identify two distinct mechanisms contributing to field-induced changes in transport-opposing barriers: a weak one arising from field-induced ion dehydration and a strong one due to the field-induced asymmetry of the hydration shells. The simulated current-voltage characteristics are compared with the solution of the 1D Nernst-Planck model. Finally, we show that the deviation of simulated currents from analytic estimates for large fields is consistent with the field-induced barriers and the observed changes in the optimal ion escape path.

Role of intercalation in the electrical properties of nucleic acids for use in molecular electronics.

Intercalating ds-DNA/RNA with small molecules can play an essential role in controlling the electron transmission probability for molecular electronics applications such as biosensors, single-molecule transistors, and data storage. However, its applications are limited due to a lack of understanding of the nature of intercalation and electron transport mechanisms. We addressed this long-standing problem by studying the effect of intercalation on both the molecular structure and charge transport along the nucleic acids using molecular dynamics simulations and first-principles calculations coupled with the Green's function method, respectively. The study on anthraquinone and anthraquinone-neomycin conjugate intercalation into short nucleic acids reveals some universal features: (1) the intercalation affects the transmission by two mechanisms: (a) inducing energy levels within the bandgap and (b) shifting the location of the Fermi energy with respect to the molecular orbitals of the nucleic acid, (2) the effect of intercalation was found to be dependent on the redox state of the intercalator: while oxidized anthraquinone decreases, reduced anthraquinone increases the conductance, and (3) the sequence of the intercalated nucleic acid further affects the transmission: lowering the AT-region length was found to enhance the electronic coupling of the intercalator with GC bases, hence yielding an increase of more than four times in conductance. We anticipate our study to inspire designing intercalator-nucleic acid complexes for potential use in molecular electronics via creating a multi-level gating effect.