MLCV: Bridging Machine-Learning-Based Dimensionality Reduction and Free-Energy Calculation.

Importance-sampling algorithms leaning on the definition of a model reaction coordinate (RC) are widely employed to probe processes relevant to chemistry and biology alike, spanning time scales not amenable to common, brute-force molecular dynamics (MD) simulations. In practice, the model RC often consists of a handful of collective variables (CVs) chosen on the basis of chemical intuition. However, constructing manually a low-dimensional RC model to describe an intricate geometrical transformation for the purpose of free-energy calculations and analyses remains a daunting challenge due to the inherent complexity of the conformational transitions at play. To solve this issue, remarkable progress has been made in employing machine-learning techniques, such as autoencoders, to extract the low-dimensional RC model from a large set of CVs. Implementation of the differentiable, nonlinear machine-learned CVs in common MD engines to perform free-energy calculations is, however, particularly cumbersome. To address this issue, we present here a user-friendly tool (called MLCV) that facilitates the use of machine-learned CVs in importance-sampling simulations through the popular Colvars module. Our approach is critically probed with three case examples consisting of small peptides, showcasing that through hard-coded neural network in Colvars, deep-learning and enhanced-sampling can be effectively bridged with MD simulations. The MLCV code is versatile, applicable to all the CVs available in Colvars, and can be connected to any kind of dense neural networks. We believe that MLCV provides an effective, powerful, and user-friendly platform accessible to experts and nonexperts alike for machine-learning (ML)-guided CV discovery and enhanced-sampling simulations to unveil the molecular mechanisms underlying complex biochemical processes.

Sulfammox forwarding thiosulfate-driven denitrification and anammox process for nitrogen removal.

The coupled process of thiosulfate-driven denitrification (NO3-→NO2-) and Anammox (TDDA) was a promising process for the treatment of wastewater containing NH4+-N and NO3--N. However, the high concentration of SO42- production limited its application, which needs to be alleviated by an economical and effective way to promote the application of TDDA process. In this study, TDDA process was started in a relatively short time by stepwise replacing nitrite with nitrate and operated continuously for 146 days. Results presented that the average total nitrogen removal efficiency of 82.18% can be acquired at a high loading rate of 1.98 kg N/(m3·d) with maximum nitrogen removal efficiency up to 87.04%. It was observed that the increase of S/N ratio improved the denitrification efficiency and slightly inhibit the Anammox process. Batch tests showed that Sulfammox process appeared in TDDA process under certain conditions, further contributing 2.59% nitrogen removal and 10.46% sulfur removal (14.42 mg/L NH4+-N and 37.68 mg/L SO42--S were removed). This finding was mainly attributed to the reduction of sulfate in TDDA system to elemental S0 or HS-, which subsequently was used as an electron donor to realize the recycling of sulfate (SO42--S) pollutants and promote the sulfur-nitrogen (S-N) cycle. High-throughput analysis displayed that Anammox bacteria (Candidatus_Kuenenia), Sulfur-oxidizing bacteria (Thiobacillus) with relatively high abundance of 5.37%, 7.74%, respectively, guaranteeing the excellent nitrogen and sulfate removal performance in the reactor. The enrichment of phyla Chloroflexi (31.79%), Proteobacteria (31.82%), class Ignavibacteriales (10.55%), genus Planctomycetes (13.57%) further verified the exitence of Sulfammox process in the TDDA reactor. This study provides a new perspective for the practical application of TDDA in terms of reducing the production of high concentration SO42- and saving operational cost and strengthening deeply nitrogen removal.

Silencing of B7-H4 induces intracellular oxidative stress and inhibits cell viability of breast cancer cells via downregulating PRDX3.

Breast cancer (BC) is the most common malignancy in women worldwide, accounting for 15.5% of total cancer deaths. B7-H4 belongs to the B7 family members and plays an important role in the development of a variety of cancers, while Peroxiredoxin III (PRDX3) is an antioxidant protein found in mitochondria. Aberrant expression of B7-H4 or PRDX3 has been implicated in the tumorigenesis of various cancers. However, the functional roles of B7-H4 and PRDX3 in BC and the underlying mechanisms remain unclear. In this research, we found that silencing of B7-H4 by siRNA could lead to not only cell viability inhibition but also the downregulation of PRDX3 in MCF-7 and T47D cells. In order to reveal the roles of PRDX3 in the B7-H4 pathway, we firstly transfected siRNA specifically targeting PRDX3 into MCF-7 and T47D cells, and the results showed that silencing of PRDX3 also inhibited the viability of MCF-7 and T47D cells significantly, accompanied by the increase of reactive oxygen species (ROS) levels. Then we overexpressed the expression of PRDX3 by transfecting PRDX3 expression plasmids into B7-H4 knocking-down cells of MCF-7 and T47D. The results showed that compared with the control groups (MCF-7 or T47D/siNC+pcDNA3.1 vector), cell viabilities were significantly inhibited in RNAi groups (MCF-7 or T47D/siB7-H4+pcDNA3.1 vector), and mildly inhibited in revertant groups (MCF-7 or T47D/siB7-H4+pcDNA3.1 PRDX3), meanwhile, ROS levels significantly elevated in RNAi groups and had no significant changes in revertant groups. All these results indicate that silencing of B7-H4 increases intracellular ROS levels and affects cell viability by modulating the expression of PRDX3 in BC cells, which may provide a potential strategy and therapeutic target for the treatment of BC.

BFEE2: Automated, Streamlined, and Accurate Absolute Binding Free-Energy Calculations.

Accurate absolute binding free-energy estimation in silico, following either an alchemical or a geometrical route, involves several subprocesses and requires the introduction of geometric restraints. Human intervention, for instance, to define the necessary collective variables, prepare the input files, monitor the simulation, and perform post-treatments is, however, tedious, cumbersome, and prone to errors. With the aim of automating and streamlining free-energy calculations, especially for nonexperts, version 2.0 of the binding free energy estimator (BFEE2) provides both standardized alchemical and geometrical workflows and obviates the need for extensive human intervention to guarantee complete reproducibility of the results. To achieve the largest gamut of protein-ligand and, more generally, of host-guest complexes, BFEE2 supports most academic force fields, such as CHARMM, Amber, OPLS, and GROMOS. Configurational files are generated in the NAMD and Gromacs formats, and all the post-treatments are performed in an automated fashion. Moreover, convergence of the free-energy calculation can be monitored from the intermediate files generated during the simulation. All in all, BFEE2 is a foolproof, versatile tool for accurate absolute binding free-energy calculations, assisting the end-user over a broad range of applications.

The benefits of autotrophic nitrogen removal from high concentration of urea wastewater through a process of urea hydrolysis and partial nitritation in sequencing batch reactor.

For the sake of high efficiency and saving operational cost for high-concentration urea wastewater treatment, a novel two-stage partial nitritation (PN)-anammox process containing simultaneous urea hydrolysis and PN in sequencing batch reactor (SBR) was investigated. Although the influent urea concentration increased from 500 to 1200 mg/L, the SBR simultaneously achieved urea removal efficiency higher than 98% and stable PN with effluent NO2--N/NH4+-N ratio of 1.0-1.3 without any extra alkalinity addition. The intracellular hydrolysis was the dominant mechanism for urea removal and persistent free ammonia inhibition on nitrite-oxidizing bacteria was the main reason for nitrite accumulation of 97.92% in SBR. The subsequent anammox reactor showed efficient nitrogen removal performance with average ammonium removal efficiency, nitrogen removal efficiency and maximum nitrogen removal loading rate of 98.08%, 81.45% and 1.05 kg N·m-3·d-1 respectively. High-throughput sequencing results indicated Gemmatimonadetes became the most abundant bacterial phylum related to potential intracellular urea hydrolysis and displayed obvious ammonium-oxidizing bacteria enrichment and nitrite-oxidizing bacteria inhibition in SBR, and the dominant anammox bacteria (Candidatus_Kuenenia) in anammox reactor. The proposed process was proven to be promising for high-concentration urea wastewater treatment, facilitating the sustainable development of the urea industry in the future.

Prevalence, serotypes, and antimicrobial resistance of Salmonella isolates from patients with diarrhea in Shenzhen, China.

BACKGROUND: Salmonella is one of the main causative agents of diarrhea which results in substantial disease burden. To determine the prevalence, serotype distribution, and antimicrobial resistance profiles of clinical Salmonella isolates in Shenzhen, a 6-year surveillance study was conducted. RESULTS: A total of 297 (5.7%) Salmonella strains were isolated from stool samples from 5239 patients. Among the 42 serotypes identified, serotype Typhimurium was the most common one which represented 39.7% of the isolates (118), followed by serotype Enteritidis (71, 23.9%), London (12, 4.0%), 4, 5, 12: i: - (11, 3.7%), and Senftenberg (8, 2.7%). A high frequency of resistance was found in ampicillin (70.6%), piperacillin (64.5%), tetracycline (63.5%), and streptomycin (54.3%). Resistance to ampicillin and tetracycline was observed in 95.3% of S. Typhimurium isolates; and nalidixic acid in 93.1% of S. Enteritidis isolates. Resistance to 5 or more antimicrobial agents was found in 78.8% of S. Typhimurium and 69.0% of S. Enteritidis isolates. A decreased susceptibility to ciprofloxacin and levofloxacin was associated with amino acid alteration in gyrA gene. Point mutations without amino acid changes were seen in gyrB, parC, and parE genes. CONCLUSIONS: A broad range of serotypes are responsible for Salmonellosis in Shenzhen, with Enteritidis and Typhimurium being the most common serotypes. The high level of antibiotic resistance is of public health significance and ongoing monitoring combined with rational use of antibiotics are recommended. Point mutations in gyrA gene might play an important role in the resistance to fluoroquinolones.

Finding an Optimal Pathway on a Multidimensional Free-Energy Landscape.

An ad-hoc, yet widely adopted approach to investigate complex molecular objects in motion using importance-sampling schemes involves two steps, namely (i) mapping the multidimensional free-energy landscape that characterizes the movements in the molecular object at hand and (ii) finding the most probable transition path connecting basins of the free-energy hyperplane. To achieve this goal, we turn to an importance-sampling algorithm, coined well-tempered metadynamics-extended adaptive biasing force (WTM-eABF), aimed at mapping rugged free-energy landscapes, combined with a path-searching algorithm, which we call multidimensional lowest energy (MULE), to identify the underlying minimum free-energy pathway in the collective-variable space of interest. First, the well-tempered feature of the importance-sampling scheme confers to the latter an asymptotic convergence, while the overall algorithm inherits the advantage of high sampling efficiency of its predecessor, meta-eABF, making its performance less sensitive to user-defined parameters. Second, the Dijkstra algorithm implemented in MULE is able to identify with utmost efficiency a pathway that satisfies minimum free energy of activation among all the possible routes in the multidimensional free-energy landscape. Numerical simulations of three molecular assemblies indicate that association of WTM-eABF and MULE constitutes a reliable, efficient and robust approach for exploring coupled movements in complex molecular objects. On account of its ease of use and intrinsic performance, we expect WTM-eABF and MULE to become a tool of choice for both experts and nonexperts interested in the thermodynamics and the kinetics of processes relevant to chemistry and biology.

Boosting Free-Energy Perturbation Calculations with GPU-Accelerated NAMD.

Harnessing the power of graphics processing units (GPUs) to accelerate molecular dynamics (MD) simulations in the context of free-energy calculations has been a longstanding effort toward the development of versatile, high-performance MD engines. We report a new GPU-based implementation in NAMD of free-energy perturbation (FEP), one of the oldest, most popular importance-sampling approaches for the determination of free-energy differences that underlie alchemical transformations. Compared to the CPU implementation available since 2001 in NAMD, our benchmarks indicate that the new implementation of FEP in traditional GPU code is about four times faster, without any noticeable loss of accuracy, thereby paving the way toward more affordable free-energy calculations on large biological objects. Moreover, we have extended this new FEP implementation to a code path highly optimized for a single-GPU node, which proves to be up to nearly 30 times faster than the CPU implementation. Through optimized GPU performance, the present developments provide the community with a cost-effective solution for conducting FEP calculations. The new FEP-enabled code has been released with NAMD 3.0.

ELF: An Extended-Lagrangian Free Energy Calculation Module for Multiple Molecular Dynamics Engines.

Extended adaptive biasing force (eABF), a collective variable (CV)-based importance-sampling algorithm, has proven to be very robust and efficient compared with the original ABF algorithm. Its implementation in Colvars, a software addition to molecular dynamics (MD) engines, is, however, currently limited to NAMD and LAMMPS. To broaden the scope of eABF and its variants, like its generalized form (egABF), and make them available to other MD engines, e.g., GROMACS, AMBER, CP2K, and openMM, we present a PLUMED-based implementation, called extended-Lagrangian free energy calculation (ELF). This implementation can be used as a stand-alone gradient estimator for other CV-based sampling algorithms, such as temperature-accelerated MD (TAMD) and extended-Lagrangian metadynamics (MtD). ELF provides the end user with a convenient framework to help select the best-suited importance-sampling algorithm for a given application without any commitment to a particular MD engine.

BFEE: A User-Friendly Graphical Interface Facilitating Absolute Binding Free-Energy Calculations.

Quantifying protein-ligand binding has attracted the attention of both theorists and experimentalists for decades. Many methods for estimating binding free energies in silico have been reported in recent years. Proper use of the proposed strategies requires, however, adequate knowledge of the protein-ligand complex, the mathematical background for deriving the underlying theory, and time for setting up the simulations, bookkeeping, and postprocessing. Here, to minimize human intervention, we propose a toolkit aimed at facilitating the accurate estimation of standard binding free energies using a geometrical route, coined the binding free-energy estimator (BFEE), and introduced it as a plug-in of the popular visualization program VMD. Benefitting from recent developments in new collective variables, BFEE can be used to generate the simulation input files, based solely on the structure of the complex. Once the simulations are completed, BFEE can also be utilized to perform the post-treatment of the free-energy calculations, allowing the absolute binding free energy to be estimated directly from the one-dimensional potentials of mean force in simulation outputs. The minimal amount of human intervention required during the whole process combined with the ergonomic graphical interface makes BFEE a very effective and practical tool for the end-user.

Effective recovery of the nitritation process through hydrogen peroxide.

This study successfully achieved stable nitritation by adding hydrogen peroxide (H2O2) to the nitrification sludge whose nitritation stability had been destroyed. The batch experiment demonstrated that, the activity of ammonia-oxidizing bacteria (AOB) was restored more rapidly than that of nitrite oxidizing bacteria (NOB) after the addition of H2O2, thereby selectively promoting AOB enrichment and NOB washout. When the H2O2 concentration was 6.25 mg/L, the NOB activity was significantly reduced and the nitrite accumulation rate (NAR) was more than 95% after 18 cycles of nitrifying sludge restoration. As a result, H2O2 treatment enabled a nitrifying reactor to recover stable nitritation performance via H2O2 treatment, with the NAR and ammonia removal efficiency (ARE) both exceeding 90%. High-throughput sequencing analysis revealed that H2O2 treatment was successful in restoring nitritation, as the relative abundance of Nitrosomonas in the nitrifying reactor increased from 6.43% to 41.97%, and that of Nitrolancea decreased from 17.34% to 2.37%. Recovering nitritation by H2O2 inhibition is a low operational cost, high efficiency, and non-secondary pollution nitritation performance stabilization method. By leveraging the varying inhibition degrees of H2O2 on AOB and NOB, stable nitrification can be efficiently restored at a low cost and without causing secondary pollution.

Chasing collective variables using temporal data-driven strategies.

The convergence of free-energy calculations based on importance sampling depends heavily on the choice of collective variables (CVs), which in principle, should include the slow degrees of freedom of the biological processes to be investigated. Autoencoders (AEs), as emerging data-driven dimension reduction tools, have been utilised for discovering CVs. AEs, however, are often treated as black boxes, and what AEs actually encode during training, and whether the latent variables from encoders are suitable as CVs for further free-energy calculations remains unknown. In this contribution, we review AEs and their time-series-based variants, including time-lagged AEs (TAEs) and modified TAEs, as well as the closely related model variational approach for Markov processes networks (VAMPnets). We then show through numerical examples that AEs learn the high-variance modes instead of the slow modes. In stark contrast, time series-based models are able to capture the slow modes. Moreover, both modified TAEs with extensions from slow feature analysis and the state-free reversible VAMPnets (SRVs) can yield orthogonal multidimensional CVs. As an illustration, we employ SRVs to discover the CVs of the isomerizations of N-acetyl-N'-methylalanylamide and trialanine by iterative learning with trajectories from biased simulations. Last, through numerical experiments with anisotropic diffusion, we investigate the potential relationship of time-series-based models and committor probabilities.

Discovering Reaction Pathways, Slow Variables, and Committor Probabilities with Machine Learning.

A significant challenge faced by atomistic simulations is the difficulty, and often impossibility, to sample the transitions between metastable states of the free-energy landscape associated with slow molecular processes. Importance-sampling schemes represent an appealing option to accelerate the underlying dynamics by smoothing out the relevant free-energy barriers, but require the definition of suitable reaction-coordinate (RC) models expressed in terms of compact low-dimensional sets of collective variables (CVs). While most computational studies of slow molecular processes have traditionally relied on educated guesses based on human intuition to reduce the dimensionality of the problem at hand, a variety of machine-learning (ML) algorithms have recently emerged as powerful alternatives to discover meaningful CVs capable of capturing the dynamics of the slowest degrees of freedom. Considering a simple paradigmatic situation in which the long-time dynamics is dominated by the transition between two known metastable states, we compare two variational data-driven ML methods based on Siamese neural networks aimed at discovering a meaningful RC model─the slowest decorrelating CV of the molecular process, and the committor probability to first reach one of the two metastable states. One method is the state-free reversible variational approach for Markov processes networks (VAMPnets), or SRVs─the other, inspired by the transition path theory framework, is the variational committor-based neural networks, or VCNs. The relationship and the ability of these methodologies to discover the relevant descriptors of the slow molecular process of interest are illustrated with a series of simple model systems. We also show that both strategies are amenable to importance-sampling schemes through an appropriate reweighting algorithm that approximates the kinetic properties of the transition.

Improving Speed and Affordability without Compromising Accuracy: Standard Binding Free-Energy Calculations Using an Enhanced Sampling Algorithm, Multiple-Time Stepping, and Hydrogen Mass Repartitioning.

Accurate evaluation of protein-ligand binding free energies in silico is of paramount importance for understanding the mechanisms of biological regulation and providing a theoretical basis for drug design and discovery. Based on a series of atomistic molecular dynamics simulations in an explicit solvent, using well-tempered metadynamics extended adaptive biasing force (WTM-eABF) as an enhanced sampling algorithm, the so-called "geometrical route" offers a rigorous theoretical framework for binding affinity calculations that match experimental values. However, although robust, this strategy remains expensive, requiring substantial computational time to achieve convergence of the simulations. Improving the efficiency of the geometrical route, while preserving its reliability through improved ergodic sampling, is, therefore, highly desirable. In this contribution, having identified the computational bottleneck of the geometrical route, to accelerate the calculations we combine (i) a longer time step for the integration of the equations of motion with hydrogen-mass repartitioning (HMR), and (ii) multiple time-stepping (MTS) for collective-variable and biasing-force evaluation. Altogether, we performed 50 independent WTM-eABF simulations in triplicate for the "physical" separation of the Abl kinase-SH3 domain:p41 complex, following different HMR and MTS schemes, while tuning, in distinct protocols, the parameters of the enhanced-sampling algorithm. To demonstrate the consistency and reliability of the results obtained with the best-performing setups, we carried out quintuple simulations. Furthermore, we demonstrated the transferability of our method to other complexes by triplicating a 200 ns separation simulation of nine chosen protocols for the MDM2-p53:NVP-CGM097 complex. [Holzer et al. J. Med. Chem. 2015, 58, 6348-6358.] Our results, based on an aggregate simulation time of 14.4 µs, allowed an optimal set of parameters to be identified, able to accelerate convergence by a factor of three without any noticeable loss of accuracy.

A Rigorous Framework for Calculating Protein-Protein Binding Affinities in Membranes.

Calculating the binding free energy of integral transmembrane (TM) proteins is crucial for understanding the mechanisms by which they recognize one another and reversibly associate. The glycophorin A (GpA) homodimer, composed of two α-helical segments, has long served as a model system for studying TM protein reversible association. The present work establishes a methodological framework for calculating the binding affinity of the GpA homodimer in the heterogeneous environment of a membrane. Our investigation carefully considered a variety of protocols, including the appropriate choice of the force field, rigorous standardization reflecting the experimental conditions, sampling algorithm, anisotropic environment, and collective variables, to accurately describe GpA dimerization via molecular dynamics-based approaches. Specifically, two strategies were explored: (i) an unrestrained potential mean force (PMF) calculation, which merely enhances sampling along the separation of the two binding partners without any restraint, and (ii) a so-called "geometrical route", whereby the α-helices are progressively separated with imposed restraints on their orientational, positional, and conformational degrees of freedom to accelerate convergence. Our simulations reveal that the simplified, unrestrained PMF approach is inadequate for the description of GpA dimerization. Instead, the geometrical route, tailored specifically to GpA in a membrane environment, yields excellent agreement with experimental data within a reasonable computational time. A dimerization free energy of -10.7 kcal/mol is obtained, in fairly good agreement with available experimental data. The geometrical route further helps elucidate how environmental forces drive association before helical interactions stabilize it. Our simulations also brought to light a distinct, long-lived spatial arrangement that potentially serves as an intermediate state during dimer formation. The methodological advances in the generalized geometrical route provide a powerful tool for accurate and efficient binding-affinity calculations of intricate TM protein complexes in inhomogeneous environments.

Enhancing sampling with free-energy calculations.

In recent years, considerable progress has been made to enhance sampling and help address biological questions, including, but not limited to conformational transitions in biomolecules and protein-ligand reversible binding, hitherto intractable by brute-force computer simulations. Many of these advances result from the development of a palette of methods aimed at exploring rare events through reliable free-energy calculations. The advent of new, often conceptually related methods has also rendered difficult the choice of the best suited option for a given problem. Here, we focus on geometrical transformations and algorithms designed to enhance sampling along adequately chosen progress variables, tracing their theoretical foundations, and showing how they are connected and can be blended together for improved performance.

Hazardous Shortcuts in Standard Binding Free Energy Calculations.

Calculating the standard binding free energies of protein-protein and protein-ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called "geometrical route". In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein-ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein-protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.

When the Dust Has Settled: Calculation of Binding Affinities from First Principles for SARS-CoV-2 Variants with Quantitative Accuracy.

Accurate determination of binding free energy is pivotal for the study of many biological processes and has been applied in a number of theoretical investigations to compare the affinity of severe acute respiratory syndrome coronavirus 2 variants toward the host cell. Diversity of these variants challenges the development of effective general therapies, their transmissibility relying either on an increased affinity toward their dedicated human receptor, the angiotensin-converting enzyme 2 (ACE2), or on escaping the immune response. Now that robust structural data are available, we have determined with utmost accuracy the standard binding free energy of the receptor-binding domain to the most widespread variants, namely, Alpha, Beta, Delta, and Omicron BA.2, as well as the wild type (WT) in complex either with ACE2 or with antibodies, namely, S2E12 and H11-D4, using a rigorous theoretical framework that combines molecular dynamics and potential-of-mean-force calculations. Our results show that an appropriate starting structure is crucial to ensure appropriate reproduction of the binding affinity, allowing the variants to be compared. They also emphasize the necessity to apply the relevant methodology, bereft of any shortcut, to account for all the contributions to the standard binding free energy. Our estimates of the binding affinities support the view that while the Alpha and Beta variants lean on an increased affinity toward the host cell, the Delta and Omicron BA.2 variants choose immune escape. Moreover, the S2E12 antibody, already known to be active against the WT (Starr et al., 2021; Mlcochova et al., 2021), proved to be equally effective against the Delta variant. In stark contrast, H11-D4 retains a low affinity toward the WT compared to that of ACE2 for the latter. Assuming robust structural information, the methodology employed herein successfully addresses the challenging protein-protein binding problem in the context of coronavirus disease 2019 while offering promising perspectives for predictive studies of ever-emerging variants.

A Companion Guide to the String Method with Swarms of Trajectories: Characterization, Performance, and Pitfalls.

The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathway─a one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dynamics simulations spawned at different locations of the discretized path, from whence an average dynamic drift is determined to evolve the string toward an optimal pathway. However conceptually simple in both its theoretical formulation and practical implementation, the SMwST algorithm is computationally intensive and requires a careful choice of parameters for optimal cost-effectiveness in applications to challenging problems in chemistry and biology. In this contribution, the SMwST algorithm is presented in a self-contained manner, discussing with a critical eye its theoretical underpinnings, applicability, inherent limitations, and use in the context of path-following free-energy calculations and their possible extension to kinetics modeling. Through multiple simulations of a prototypical polypeptide, combining the search of the transition pathway and the computation of the potential of mean force along it, several practical aspects of the methodology are examined with the objective of optimizing the computational effort, yet without sacrificing accuracy. In light of the results reported here, we propose some general guidelines aimed at improving the efficiency and reliability of the computed pathways and free-energy profiles underlying the conformational transitions at hand.

[Promoting Nitrogen Removal in ANAMMOX Biofilm Reactor by Fe2+ Under Low Nitrogen Concentration].

The feasibility for nitrogen removal in a two-stage ANAMMOX biofilm reactor promoted by Fe2+ under low nitrogen concentration was investigated. The results showed that the ANAMMOX reaction could be effectively promoted by a ρ(Fe2+) of 5, 10, and 15 mg·L-1. A ρ(Fe2+) of 10 mg·L-1 presented the highest promotion for the ANAMMOX reaction, with the highest nitrogen removal efficiency (NRE) of 81.71% under a ρ(TN) of 150 mg·L-1and a nitrogen loading rate (NLR) of 0.62 kg·(m3·d)-1. Fe2+ promoted the secretion of extracellular polymeric substance (EPS) and the synthesis of heme c in the ANAMMOX system. Batch test results further verified the positive effects by Fe2+on the activity of anaerobic ammonium oxidizing bacteria (AnAOB). The specific ANAMMOX activity (SAA) of 10 mg·L-1 ρ(Fe2+) was 3.6 times as high as that of the control group[ρ(Fe2+)=0 mg·L-1], whereas the activity of AnAOB was significantly inhibited with ρ(Fe2+) increased to 20 mg·L-1. High-throughput sequencing results showed that the addition of Fe2+ increased the abundance of Candidatus_Kuenenia. When ρ(Fe2+) was 10 mg·L-1, the relative abundance of Candidatus_Kuenenia in reactor 1 and reactor 2 increased to 16.18% and 4.22%, respectively. The stable operation of the two-stage ANAMMOX biofilm process promoted by Fe2+provides an alternative technology for low-strength nitrogen wastewater.