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Conformational changes underpin function and encode complex biomolecular mechanisms. Gaining atomic-level detail of how such changes occur has the potential to reveal these mechanisms and is of critical importance in identifying drug targets, facilitating rational drug design, and enabling bioengineering applications. While the past two decades have brought Markov State Model techniques to the point where practitioners can regularly use them to glimpse the long-time dynamics of slow conformations in complex systems, many systems are still beyond their reach. In this perspective, we discuss how memory can reduce the computational cost to predict the long-time dynamics in these complex systems by orders of magnitude and with greater accuracy and resolution than state-of-the-art Markov State Models. We illustrate how memory lies at the heart of successful and promising techniques, ranging from the Fokker-Planck and generalized Langevin equations to deep learning recurrent neural networks and generalized master equations. We delineate how these techniques work, identify insights that they can offer in biomolecular systems, and discuss their advantages and disadvantages in practical settings. We show how generalized master equations can enable the investigation of, for example, the gate-opening process in RNA polymerase II and demonstrate how our recent advances tame the deleterious influence of statistical underconvergence of the molecular dynamics simulations used to parameterize these techniques. This represents a significant leap forward that will enable our memory-based techniques to interrogate systems that are currently beyond the reach of even the best Markov State Models. We conclude by discussing some current challenges and future prospects for how exploiting memory will open the door to many exciting opportunities.

The 3D reference interaction site model (3DRISM) provides an efficient grid-based solvation model to compute the structural and thermodynamic properties of biomolecules in aqueous solutions. However, it remains challenging for existing 3DRISM methods to correctly predict water distributions around negatively charged solute molecules. In this paper, we first show that this challenge is mainly due to the orientation of water molecules in the first solvation shell of the negatively charged solute molecules. To properly consider this orientational preference, position-dependent two-body intramolecular correlations of solvent need to be included in the 3DRISM theory, but direct evaluations of these position-dependent two-body intramolecular correlations remain numerically intractable. To address this challenge, we introduce the Ion-Dipole Correction (IDC) to the 3DRISM theory, in which we incorporate the orientation preference of water molecules via an additional solute-solvent interaction term (i.e., the ion-dipole interaction) while keeping the formulism of the 3DRISM equation unchanged. We prove that this newly introduced IDC term is equivalent to an effective direct correlation function which can effectively consider the orientation effect that arises from position dependent two-body correlations. We first quantitatively validate our 3DRISM-IDC theory combined with the PSE3 closure on Cl

The interaction perception between aroma and oral chewing during food consumption has always been a hot topic in exploring consumers’ preferences and purchase desires. A chewing simulation system was set to find out the effect of key saliva components and chewing time on odorants released with grilled eel meat. Odor release did not always enhance with the degree of chewing, or the amount of saliva released. The breaking up of the tissue structure of the fish meat by the teeth encourages the release of odorants and the participation of saliva partially blocks this process. The release of pyrazine, alcohol, and acid compounds in grilled eel meat peaked within 20–60 s after chewing. Sufficient exposure of saliva to grilled eel meat will inhibit aromatic, ketone, ester, hydrocarbon, and sulfur compounds release. 3-methyl-2-butanol contributed to the subtle aroma differences that arise before and after eating grilled eel meat. Naphthalene, 2-acetylthiazole, 2-decenal, 2-undecanone, 5-ethyldihydro-2(3H)-furanone were the main odorants released in large quantities in the early stages of eating grilled eel and affected the top note. Consequently, the results provided the odorants information in aroma perception during grilled eel consumption and benefited the objective evaluation of grilled eel product optimization.

Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack the high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenge. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, like tICA, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets which combines graph neural networks with variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in a high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory that studies Markov processes without forcing detailed balance constraint, which addresses the out-of-equilibrium challenge in self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: aggregation of two hydrophobic molecules and self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely to applied to molecular self-assembly.

The self-balanced loading test is a state-of-art pile testing method, but its suitability to pile bearing capacity determination in transformer substation engineering in mountainous and hilly areas is not yet clear. In this study, a two-dimensional axisymmetric numerical model is established by the PLAXIS software to simulate the behavior and bearing mechanism of shallow rock-socketed short piles based on the self-balanced loading test. The model is first validated by simulating the field tests of two adjacent piles under self-balanced loading. Then the influence factors of the load-displacement curves of piles are analyzed. Thereafter, the mechanical mechanism of the self-balanced loading tests is simulated and compared with the conventional static loading tests. It is observed that the rock modulus, rock-socketed depth of piles, and burial depth of the Osterberg Cell affect the load-displacement significantly, but the cohesion of the rocks affects little. Moreover, compared with the conventional static loading tests, the shear stress of the pile-soil interface distributes less uniformly under self-balanced loading conditions. On this basis, a bearing capacity computation method of shallow rock-socketed short piles based on the self-balanced loading test is proposed.

The Markov State Model (MSM) is a powerful tool for modeling long timescale dynamics based on numerous short molecular dynamics (MD) simulation trajectories, which makes it a useful tool for elucidating the conformational changes of biological macromolecules. By partitioning the phase space into discretized states and estimating the probabilities of inter-state transitions based on short MD trajectories, one can construct a kinetic network model that could be used to extrapolate long-timescale kinetics if the Markovian condition is met. However, meeting the Markovian condition often requires hundreds or even thousands of states (microstates), which greatly hinders the comprehension of the conformational dynamics of complex biomolecules. Kinetic lumping algorithms can coarse grain numerous microstates into a handful of metastable states (macrostates), which would greatly facilitate the elucidation of biological mechanisms. In this work, we have developed a reverse-projection-based neural network (RPnet) to lump microstates into macrostates, by making use of a physics-based loss function that is based on the projection operator framework of conformational dynamics. By recognizing that microstate and macrostate transition modes can be related through a projection process, we have developed a reverse-projection scheme to directly compare the microstate and macrostate dynamics. Based on this reverse-projection scheme, we designed a loss function that allows the effective assessment of the quality of a given kinetic lumping. We then make use of a neural network to efficiently minimize this loss function to obtain an optimized set of macrostates. We have demonstrated the power of our RPnet in analyzing the dynamics of a numerical 2D potential, alanine dipeptide, and the clamp opening of an RNA polymerase. In all these systems, we have illustrated that our method could yield comparable or better results than competing methods in terms of state partitioning and reproduction of slow dynamics. We expect that our RPnet holds promise in analyzing the conformational dynamics of biological macromolecules.

An increasing number of mutations associated with devastating human diseases are diagnosed by whole-genome/exon sequencing. Recurrentde novomissense mutations have been discovered in B56δ (encoded byPPP2R5D), a regulatory subunit of protein phosphatase 2A (PP2A), that cause intellectual disabilities (ID), macrocephaly, Parkinsonism, and a broad range of neurological symptoms. Single-particle cryo-EM structures show that the PP2A-B56δ holoenzyme possesses closed latent and open active forms. In the closed form, the long, disordered arms of B56δ termini fold against each other and the holoenzyme core, establishing dual autoinhibition of the phosphatase active site and the substrate-binding protein groove. The resulting interface spans over 190 Å and harbors unfavorable contacts, activation phosphorylation sites, and nearly all residues with ID-associated mutations. Our studies suggest that this dynamic interface is close to an allosteric network responsive to activation phosphorylation and altered globally by mutations. Furthermore, we found that ID mutations perturb the activation phosphorylation rates, and the severe variants significantly increase the mitotic duration and error rates compared to the wild variant.

Wheat powdery mildew (Blumeria graminis f. sp. tritici, Bgt, recently clarified as B. graminis s. str.), is one of the most destructive diseases of wheat. Pm60 is a nucleotide-binding leucine-rich repeat (NLR) gene that confers race-specific resistance to Bgt. Allelic variants (Pm60, Pm60a, and Pm60b) were found in Triticum urartu and T. dicoccoides, the wild progenitors of wheat. In the present study, we studied the diversity of the Pm60 locus in a large set of wheat germplasm and found 20 tetraploid wheats harboring the Pm60 alleles, which correspond to three novel haplotypes (HapI–HapIII). HapI (Pm60 allele) and HapII (Pm60a allele) were present in domesticated tetraploid wheats, whereas HapIII (Pm60a allele) was identified in wild tetraploid T. araraticum. A sequence comparison of HapII and HapIII revealed that they differed by three SNPs and a GCC deletion. Results of the phylogenetic analysis revealed that HapII was more closely related to the functional haplotype MlIW172. Infection tests showed that HapII-carrying lines display a partial resistance response to Bgt#GH, while HapI was susceptible. Our results provide insights into the genetic evolution of the Pm60 locus and potential valuable alleles for powdery mildew resistance breeding.

Conformational changes play an important role for many biomolecules to perform their functions. In recent years, Markov State Model (MSM) has become a powerful tool to investigate these functional conformational changes by predicting long timescale dynamics from many short molecular dynamics (MD) simulations. In MSM, dynamics are modelled by a first-order master equation, in which a biomolecule undergoes Markovian transitions among conformational states at discrete-time intervals, called lag time. The lag time has to be sufficiently long to build a Markovian model, but this parameter is often bound by the length of MD simulations available for estimating the frequency of interstate transitions. To address this challenge, we recently employed the generalized master equation (GME) formalism (e.g., the quasi-Markov State Model or qMSM) to encode non-Markovian dynamics in a time-dependent memory kernel. When applied to study protein dynamics, our qMSM can be built from MD simulations that are an order-of-magnitude shorter than MSM would have required. The construction of qMSM is more complicated than that of MSM, as time-dependent memory kernels need to be properly extracted from the MD simulation trajectories. In this chapter, we will present a step-by-step guide on how to build qMSM from MD simulation datasets, and the accompanying materials are publicly available on Github: https://github.com/ykhdrew/qMSM_tutorial. We hope this tutorial is useful for researchers who want to apply qMSM and study functional conformational changes in biomolecules.

The generalized master equation (GME) provides a powerful approach to study biomolecular dynamics via non-Markovian dynamic models built from molecular dynamics (MD) simulations. Previously, we have implemented the GME for biomolecular dynamics, namely the quasi Markov State Model (qMSM), where we explicitly calculate the memory kernel and propagate protein dynamics using a discretized GME. qMSM can be constructed with much shorter MD simulation trajectories than the Markov State Model (MSM). However, since qMSM needs to explicitly compute the time-dependent memory kernels, it is heavily affected by the numerical fluctuations of simulation data when applied to study complicated conformational changes of biomolecules. This can lead to numerical instability of predicted long-time dynamics, greatly limiting the applicability of the qMSM in complicated molecules. In this paper, we propose a new theory, the Integrative GME (IGME), to overcome the challenges of the qMSM by using the time integrations of memory kernels. The IGME avoids the numerical instability induced by explicit computation of time-dependent memory kernel functions, giving more robust predictions of long-time dynamics. Using our analytical solution of the IGME, we propose a new approach to compute memory kernels and long-time dynamics in a numerically stable, accurate and efficient way. To demonstrate its effectiveness, we have applied the IGME in three biomolecules: the alanine dipeptide, the FIP35 WW domain, and Taq RNAP. In each system, the IGME achieves significantly smaller fluctuations for both memory kernels and long-time dynamics compared to the qMSM. We anticipate that the IGME can be widely applied to investigate complex conformational changes of biomolecules.

Objective: We aimed to explore the effect of Poria polysaccharide oral liquid (PPOL) on cancer-related fatigue (CRF) in postoperative patients with colorectal cancer. Methods: Fifty patients with colorectal cancer who were diagnosed in our hospital from March 2015 to March 2019 were included in this retrospective study. They were all underwent tumor resection. They were randomly divided into control group (n = 25) and observation group (n = 25). Patients in the control group were treated according to the F0LF0X4 chemotherapy regimen, and those in the observation group were treated with F0LF0X4 chemotherapy plus PPOL (10 ml/time, 3 times/day) for 6 weeks. The quality of life index (QL-index) and brief fatigue scale (BFI) were used to investigate the quality of life and fatigue, respectively. The serum levels of inflammatory factors, IL-6 and TNF-a, were measured. Results: Compared with the control group, the QL-index score in the observation group was significantly better (P< 0.05). Moreover, the BFI in the observation group was significantly lower than that in the control group (P < 0.05). Furthermore, the serum levels of IL-6 and TNF-α in the observation group were notably decreased in comparison with the control group (P < 0.05). Conclusion: PPOL can effectively improve the fatigue state of postoperative patients with colorectal cancer by reducing the levels of IL-6 and TNF- α and improving the quality of life.

As an elementary mesh quality improvement technique, smoothing has been widely used in finite element (FE) analysis. Heuristic smoothing methods and optimization-based smoothing methods are the two main smoothing types. The former is efficient. However, it operates heuristically and may create low-quality elements. In contrast, optimization-based smoothing is very effective at improving mesh quality. However, it suffers from high computational cost since it calculates the optimal position of a free node iteratively. In this paper, we present a new smoothing method. The proposed method imitates the optimization-based smoothing based on a neural network, but it calculates the optimal position of a free node straightforwardly. Hence, the proposed method is more efficient than these optimization-based smoothing methods while being comparable in terms of mesh quality. We present various testing results to illustrate the effectiveness of the proposed method.

Integral equation theory (IET) provides an effective solvation model for chemical and biological systems that balances computational efficiency and accuracy. We present a new software package, the Expanded Package for IET-based Solvation (EPISOL), that performs 3D-reference interaction site model (3D-RISM) calculations to obtain the solvation structure and free energies of solute molecules in different solvents. In EPISOL, we have implemented 22 different closures, multiple free energy functionals, and new variations of 3D-RISM theory, including the recent hydrophobicity-induced density inhomogeneity (HI) theory for hydrophobic solutes and ion-dipole correction (IDC) theory for negatively charged solutes. To speed up the convergence and enhance the stability of the self-consistent iterations, we have introduced several numerical schemes in EPISOL, including a newly developed dynamic mixing approach. We show that these schemes have significantly reduced the failure rate of 3D-RISM calculations compared to AMBER-RISM software. EPISOL consists of both a user-friendly graphic interface and a kernel library that allows users to call its routines and adapt them to other programs. EPISOL is compatible with the force-field and coordinate files from both AMBER and GROMACS simulation packages. Moreover, EPISOL is equipped with an internal memory control to efficiently manage the use of physical memory, making it suitable for performing calculations on large biomolecules. We demonstrate that EPISOL can efficiently and accurately calculate solvation density distributions around various solute molecules (including a protein chaperone consisting of 120,715 atoms) and obtain solvent free energy for a wide range of organic compounds. We expect that EPISOL can be widely applied as a solvation model for chemical and biological systems. EPISOL is available at https://github.com/EPISOLrelease/EPISOL.

The Maillard reaction (MR) is affected by lipid oxidation, the intermediate products of which are key to understanding this process. Herein, nine aliphatic aldehyde–glutathione–ribose models were designed to explore the influence of lipid oxidation products with different structures on the MR. The browning degree, fluorescence degree, and antioxidant activity of the MR products were determined, and the generated volatile organic compounds (VOCs) and nonvolatile compounds were analyzed by gas chromatography-mass spectrometry and ultra-performance liquid chromatography-mass spectrometry. A total of 108 VOCs and 596 nonvolatile compounds were detected. The principal component and hierarchical clustering analyses showed that saturated aldehydes mainly affected the VOCs generated by the MR, while unsaturated aldehydes significantly affected the nonvolatile compounds, which changed the taste attributes of the MR products. Compared with the control group, the addition of unsaturated aldehydes significantly increased the sourness score and decreased the umami score. In addition, the addition of unsaturated aldehydes decreased the antioxidant activity and changed the composition of nonvolatile compounds, especially aryl thioethers and medium chain fatty acids, with a strong correlation with umami and sourness in the electronic tongue analysis (p < 0.05). The addition of aliphatic aldehydes reduces the ultraviolet absorption of the intermediate products of MR browning, whereas saturated aldehydes reduce the browning degree of the MR products. Therefore, the flavor components of processed foods based on the MR can be effectively modified by the addition of lipid oxidation products.

There is little known about kinetic changes in anterior cruciate ligament deficiency (ACLD) combined with meniscal tears during jogging. Therefore, 29 male patients with injured ACLs and 15 healthy male volunteers were recruited for this study to investigate kinetic abnormalities in male patients after ACL deficiency combined with a meniscal injury during jogging. Based on experimental data measured by an optical tracking system, a subject-specific musculoskeletal model was employed to estimate the tibiofemoral joint kinetics during jogging. Between-limb and interpatient differences were compared by the analysis of variance. The results showed that decreased knee joint forces and moments of both legs in ACLD patients were detected during the stance phase compared to the control group. Meanwhile, compared with ACLD knees, significantly fewer contact forces and flexion moments in ACLD combined with lateral and medial meniscal injury groups were found at the mid-stance, and ACLD with medial meniscal injury group showed a lower axial moment in the loading response (p < 0.05). In conclusion, ACLD knees exhibit reduced tibiofemoral joint forces and moments during jogging when compared with control knees. A combination of meniscus injuries in the ACLD-affected side exhibited abnormal kinetic alterations at the loading response and mid-stance phase.

During RNA elongation, the influenza A viral (IAV) RNA-dependent RNA polymerase (RdRp) residues in the active site interact with the triphosphate moiety of nucleoside triphosphate (NTP) for catalysis. The molecular mechanisms by which they control the rate and fidelity of NTP incorporation remain elusive. Here, we demonstrated through enzymology, virology and computational approaches that the R239 and K235 in the PB1 subunit of RdRp are critical to controlling the activity and fidelity of transcription. Contrary to common beliefs that high-fidelity RdRp variants exert a slower incorporation rate, we discovered a first-of-its-kind, single lysine-to-arginine mutation on K235 exhibited enhanced fidelity and activity compared with wild-type. In particular, we employed a single-turnover NTP incorporation assay for the first time on IAV RdRp to show that K235R mutant RdRp possessed a 1.9-fold increase in the transcription activity of the cognate NTP and a 4.6-fold increase in fidelity compared to wild-type. Our all-atom molecular dynamics simulations further elucidated that the higher activity is attributed to the shorter distance between K235R and the triphosphate moiety of NTP compared with wild-type. These results provide novel insights into NTP incorporation and fidelity control mechanisms, which lay the foundation for the rational design of IAV vaccine and antiviral targets.

Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.

Ice nucleation on the surface plays a vital role in diverse areas, ranging from physics and cryobiology to atmospheric science. Compared to ice nucleation in the bulk, the water-surface interactions present in heterogeneous ice nucleation complicate the nucleation process, making heterogeneous ice nucleation less comprehended, especially the relationship between the kinetics and the structures of the critical ice nucleus. Here we combine Markov State Models and transition path theory to elucidate the ensemble pathways of heterogeneous ice nucleation. Our Markov State Models reveal that the classical one-step and non-classical two-step nucleation pathways can surprisingly co-exist with comparable fluxes at T = 230 K. Interestingly, we find that the disordered mixing of rhombic and hexagonal ice leads to a favorable configurational entropy that stabilizes the critical nucleus, facilitating the non-classical pathway. In contrast, the favorable energetics promotes the formation of hexagonal ice, resulting in the classical pathway. Furthermore, we discover that, at elevated temperatures, the nucleation process prefers to proceed via the classical pathway, as opposed to the non-classical pathway, since the potential energy contributions override the configurational entropy compensation. This study provides insights into the mechanisms of heterogeneous ice nucleation and sheds light on the rational designs to control crystallization processes., Classically, ice nucleation on foreign surfaces is considered a one-step process. Here, Li et al. uncover a two-barrier pathway which becomes competitive at lower temperatures, facilitated by synergistic, entropic effects of rhombic and hexagonal ice structures.

Lipid oxidation and the Maillard reaction are two of the most important reactions affecting the flavor of foods that have been heat-processed. To investigate the contribution of lipids to the mussel’s flavor, the mussel’s Maillard reaction products (MRPs) were prepared with polar lipids (mainly phospholipids) and nonpolar lipids (mainly glycerides), respectively. The effects of polar and nonpolar lipids on the flavor of the MRPs were investigated by sensory evaluation, electronic tongue, electronic nose, ultra-performance liquid chromatography-mass-spectrometry (UPLC-MS) and gas chromatography-mass-spectrometry (GC-MS). From the sensory evaluation results, the polar lipid MRPs had the highest scores. The tastes of polar lipid MRPs and nonpolar lipid MRPs were mainly umami, saltiness and sourness, and there were significant differences in their sour tastes. The flavor compounds in the MRPs were mainly inorganic sulfides, organic sulfides and nitrogen oxides. The odor of polar lipid MRPs was stronger than that of nonpolar lipid MRPs, and the seafood flavor was more obvious. A total of 37 volatile compounds were detected by GC-MS, mainly aldehydes, alcohols and ketones. The addition of polar lipids helped the MRPs to produce more volatile compounds. A total of 177 non-volatile compounds (including amino acids and their derivatives and oligopeptides, etc.) were detected in the samples using UPLC-MS. The non-volatile compounds contained in the no-lipid MRPs, polar lipid MRPs and nonpolar lipid MRPs were significantly different. This study provides a theoretical basis and technical support for the production of mussel MRPs.

Lipid oxidation and the Maillard reaction are two of the most important reactions affecting the flavor of foods that have been heat-processed. To investigate the contribution of lipids to the mussel's flavor, the mussel's Maillard reaction products (MRPs) were prepared with polar lipids (mainly phospholipids) and nonpolar lipids (mainly glycerides), respectively. The effects of polar and nonpolar lipids on the flavor of the MRPs were investigated by sensory evaluation, electronic tongue, electronic nose, ultra-performance liquid chromatography-mass-spectrometry (UPLC-MS) and gas chromatography-mass-spectrometry (GC-MS). From the sensory evaluation results, the polar lipid MRPs had the highest scores. The tastes of polar lipid MRPs and nonpolar lipid MRPs were mainly umami, saltiness and sourness, and there were significant differences in their sour tastes. The flavor compounds in the MRPs were mainly inorganic sulfides, organic sulfides and nitrogen oxides. The odor of polar lipid MRPs was stronger than that of nonpolar lipid MRPs, and the seafood flavor was more obvious. A total of 37 volatile compounds were detected by GC-MS, mainly aldehydes, alcohols and ketones. The addition of polar lipids helped the MRPs to produce more volatile compounds. A total of 177 non-volatile compounds (including amino acids and their derivatives and oligopeptides, etc.) were detected in the samples using UPLC-MS. The non-volatile compounds contained in the no-lipid MRPs, polar lipid MRPs and nonpolar lipid MRPs were significantly different. This study provides a theoretical basis and technical support for the production of mussel MRPs.

IntroductionThe prevalence of heart failure (HF) is increasing. Exercise-based cardiac rehabilitation (CR) reduces mortality and further improves the prognosis of patients with HF. However, the effect of different modes of CR delivery on HF remains unclear. Thus, the purpose of this study is to find out the relative efficacy and safety of different modes of CR delivery for individuals with HF using a network meta-analysis.Methods and analysisWe will perform a systematic review and network meta-analysis of randomised controlled trials which compare different modes of exercise-based CR delivery for patients with HF. Databases including Embase, Medline, the Cochrane Central Register of Controlled Trials and Web of Science will be searched up to May 2022. The primary outcomes will focus on the functional capacity and the health-related quality of life (hr-QOL). Functional capacity will be evaluated by peak oxygen consumption (mL/kg/min) and 6 min walking test (metres). The Minnesota Living with Heart Failure questionnaire, Short Form-36, Psychometric properties of the Kansas City cardiomyopathy questionnaire and EuroQol five dimensions questionnaire will serve as measures of hr-QOL. As secondary outcomes, we will assess hospital admissions (all-cause and cardiac) and all-cause mortality, which required a minimum follow-up of 6 months, as well as adverse events during exercise training. The risk of bias for individual studies will be evaluated according to the Cochrane Handbook. The quality of evidence will be assessed by the Grading of Recommendations, Assessment, Development and Evaluation approach.Ethics and disseminationThis study does not require ethics approval as it is based on published trials. Results of this systematic review and network meta-analysis will be submitted to a peer-reviewed journal for future publication.Trial registration numberCRD42021278351.

High glucose plays a critical role in diabetes. However, the point when high glucose induces diabetes and the organ that triggers the initiation of diabetes remain to be elucidated. The aim of the present study was to clarify the damage induced on different organs of rats, when administered a 2-week infusion of dietary glucose. SD rats (12 weeks old) were randomly divided into normal diet, high glucose infusion (IHG) and oral high glucose (OHG) groups. The levels of fasting blood sugar, tumor necrosis factor (TNF)-α and interleukin (IL)-6 were assessed. Intestine, kidney and liver samples were collected for pathological examination. Feces were collected from the rats for gut microbiota assessment. The results indicated that short-term high glucose induced hyperglycemia that lasted for at least 2 weeks after cessation of high glucose intake. Short-term high glucose also clearly increased the serum levels of IL-6 and TNF-α, led to jejunum mucosa injury and obvious steatosis in hepatocytes, and disturbed the balance of the gut microbiota. OHG led to swelling and necrosis of individual intestinal villi. IHG led to the necrosis and disappearance of cells in the upper layer of the intestinal mucosa. The lesions were confined to the mucosa. A degree of glomerular cell swelling and apoptosis were also observed. Short-term high glucose intake induced lesions in the liver, kidney and intestine, disturbed the balance of the gut microbiota and may consequently induce diabetes complications.

The development of unnatural base pairs (UBPs) has greatly increased the information storage capacity of DNA, allowing for transcription of unnatural RNA by the heterologously expressed T7 RNA polymerase (RNAP) in Escherichia coli. However, little is known about how UBPs are transcribed by cellular RNA polymerases. Here, we investigated how synthetic unnatural nucleotides, NaM and TPT3, are recognized by eukaryotic RNA polymerase II (Pol II) and found that Pol II is able to selectively recognize UBPs with high fidelity when dTPT3 is in the template strand and rNaMTP acts as the nucleotide substrate. Our structural analysis and molecular dynamics simulation provide structural insights into transcriptional processing of UBPs in a stepwise manner. Intriguingly, we identified a novel 3'-RNA binding site after rNaM addition, termed the swing state. These results may pave the way for future studies in the design of transcription and translation strategies in higher organisms with expanded genetic codes.

Multi-agent differential games usually include tracking policies and escaping policies. To obtain the proper policies in unknown environments, agents can learn through reinforcement learning. This typically requires a large amount of interaction with the environment, which is time-consuming and inefficient. However, if one can obtain an estimated model based on some prior knowledge, the control policy can be obtained based on suboptimal knowledge. Although there exists an error between the estimated model and the environment, the suboptimal guided policy will avoid unnecessary exploration; thus, the learning process can be significantly accelerated. Facing the problem of tracking policy optimization for multiple pursuers, this study proposed a new form of fuzzy actor–critic learning algorithm based on suboptimal knowledge (SK-FACL). In the SK-FACL, the information about the environment that can be obtained is abstracted as an estimated model, and the suboptimal guided policy is calculated based on the Apollonius circle. The guided policy is combined with the fuzzy actor–critic learning algorithm, improving the learning efficiency. Considering the ground game of two pursuers and one evader, the experimental results verified the advantages of the SK-FACL in reducing tracking error, adapting model error and adapting to sudden changes made by the evader compared with pure knowledge control and the pure fuzzy actor–critic learning algorithm.

The ongoing pandemic caused by SARS-CoV-2 emphasizes the need for effective therapeutics. Inhibition of SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) by nucleotide analogs provides a promising antiviral strategy. One common group of RdRp inhibitors, 2'-modified nucleotides, are reported to exhibit different behaviors in the SARS-CoV-2 RdRp transcription assay. Three of these analogs, 2'-O-methyl UTP, Sofosbuvir, and 2'-methyl CTP, act as effective inhibitors in previous biochemical experiments, while Gemcitabine and ara-UTP show no inhibitory activity. To understand the impact of the 2'-modification on their inhibitory effects, we conducted extensive molecular dynamics simulations and relative binding free energy calculations using the free energy perturbation method on SARS-CoV-2 replication-transcription complex (RTC) with these five nucleotide analogs. Our results reveal that the five nucleotide analogs display comparable binding affinities to SARS-CoV-2 RdRp and they can all be added to the nascent RNA chain. Moreover, we examine how the incorporation of these nucleotide triphosphate (NTP) analogs will impact the addition of the next nucleotide. Our results indicate that 2'-O-methyl UTP can weaken the binding of the subsequent NTP and consequently lead to partial chain termination. Additionally, Sofosbuvir and 2'-methyl CTP can cause immediate termination due to the strong steric hindrance introduced by their bulky 2'-methyl groups. In contrast, nucleotide analogs with smaller substitutions, such as the fluorine atoms and the ara-hydroxyl group in Gemcitabine and ara-UTP, have a marginal impact on the polymerization process. Our findings are consistent with experimental observations, and more importantly, shed light on the detailed molecular mechanism of SARS-CoV-2 RdRp inhibition by 2'-substituted nucleotide analogs, and may facilitate the rational design of antiviral agents to inhibit SARS-CoV-2 RdRp.

Herein, we report the development of a facile synthetic strategy for constructing diverse peptide structural architectures via chemoselective peptide ligation. The key advancement involved is to utilize the benzofuran moiety as the peptide salicylaldehyde ester surrogate, and Dap–Ser/Lys–Ser dipeptide as the hydroxyl amino functionality, which could be successfully introduced at the side chain of peptides enabling peptide ligation. With this method, the side chain-to-side chain cyclic peptide, branched/bridged peptides, tailed cyclic peptides and multi-cyclic peptides have been designed and successfully synthesized with native peptidic linkages at the ligation sites. This strategy has provided an alternative strategic opportunity for synthetic peptide development. It also serves as an inspiration for the structural design of PPI inhibitors with new modalities.

Understanding the mechanisms of enzymatic catalysis requires a detailed understanding of the complex interplay of structure and dynamics of large systems that is a challenge for both experimental and computational approaches. More importantly, the computational demands of QM/MM simulations mean that the dynamics of the reaction can only be considered on a timescale of nanoseconds even though the conformational changes needed to reach the catalytically active state happen on a much slower timescale. Here we demonstrate an alternative approach that uses transition state force fields (TSFFs) derived by the quantum-guided molecular mechanics (Q2MM) method that provides a consistent treatment of the entire system at the classical molecular mechanics level and allows simulations at the microsecond timescale. Application of this approach to the second hydride transfer transition state of HMG-CoA reductase from Pseudomonas mevalonii (PmHMGR) identified three remote residues, R396, E399 and L407, (15–27 Å away from the active site) that have a remote dynamic effect on enzyme activity. The predictions were subsequently validated experimentally via site-directed mutagenesis. These results show that microsecond timescale MD simulations of transition states are possible and can predict rather than just rationalize remote allosteric residues., Transition state force fields enable MD simulations at the transition state of HMGCoA reductase that sample the transition state ensemble on the μs timescale to identify remote residues that affect the reaction rate.

COVID-19 has recently caused a global health crisis and an effective interventional therapy is urgently needed. Remdesivir is one effective inhibitor for SARS-CoV-2 viral RNA replication. It supersedes other NTP analogues because it not only terminates the polymerization activity of RNA-dependent RNA polymerase (RdRp), but also inhibits the proofreading activity of intrinsic exoribonuclease (ExoN). Even though the static structure of Remdesivir binding to RdRp has been solved and biochemical experiments have suggested it to be a "delayed chain terminator", the underlying molecular mechanisms is not fully understood. Here, we performed all-atom molecular dynamics (MD) simulations with an accumulated simulation time of 24 microseconds to elucidate the inhibitory mechanism of Remdesivir on nucleotide addition and proofreading. We found that when Remdesivir locates at an upstream site in RdRp, the 1'-cyano group experiences electrostatic interactions with a salt bridge (Asp865-Lys593), which subsequently halts translocation. Our findings can supplement the current understanding of the delayed chain termination exerted by Remdesivir and provide an alternative molecular explanation about Remdesivir's inhibitory mechanism. Such inhibition also reduces the likelihood of Remdesivir to be cleaved by ExoN acting on 3'-terminal nucleotides. Furthermore, our study also suggests that Remdesivir's 1'-cyano group can disrupt the cleavage site of ExoN via steric interactions, leading to a further reduction in the cleavage efficiency. Our work provides plausible and novel mechanisms at the molecular level of how Remdesivir inhibits viral RNA replication, and our findings may guide rational design for new treatments of COVID-19 targeting viral replication.

Colony formation is an essential stage of cyanobacterial blooms. High calcium concentration can promote Microcystis aeruginosa aggregation behavior, but the mechanism of colony formation caused by calcium has rarely been reported. In this study, high calcium-induced colony formation was identified as a shift from cell adhesion to cell division, rather than only cell adhesion as previously thought. Algae responded to this calcium-induced environmental pressure by aggregating and forming colonies. Algal cells initially secreted large quantities of extracellular polysaccharides (EPS) and rapidly aggregated by cell adhesion. The highest aggregation proportion was up to 68.93%. However, high calcium concentrations cannot completely inhibit algal cell growth, but only delay the algae into the rapid growth phase. With adaption to calcium and existing high EPS content, the daughter cells reduced EPS synthesis and the aggregation proportion decreased. The increasing growth rate was also responsible for the decreased xylose content in EPS. The mechanism of colony formation changed to cell division. The downregulation of genes related to EPS secretion also supported this hypothesis. Overall, these results can benefit for our understanding of cyanobacterial bloom formation.

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through CRISPR screening, a knowledge graph about human SLs, and new web interface, etc. Over 16,000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14,100 genes, 53 cancers, and 1,898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful APIs. The database is accessible online at http://synlethdb.sist.shanghaitech.edu.cn/v2.

With the wide application of plastic products, microplastic pollution has become a major environmental issue of global concern. Microplastics in aquatic environments can interact with organic pollutants, causing a combined effect on submerged macrophytes. This study investigated the response mechanisms of the submerged plant Myriophyllum verticillatum and epiphytic biofilm to the antibiotic enrofloxacin, microplastics, and their combined exposure in a high nitrogen and phosphorus environment. The results indicated that Myriophyllum verticillatum was not sensitive to enrofloxacin of 1 mg L

Aberrant expression of circular RNA (circRNA) is involved in the occurrence of various diseases and tumor development, in which plays a vital role, including hepatocellular carcinoma (HCC). Nevertheless, the regulation mechanism and biological function of circITCH in hepatocellular carcinoma (HCC) remain unclear. The expression level of circular RNA itchy E3 ubiquitin protein ligase (circ-ITCH) was identified and validated by real-time polymerase-chain reaction (RT-qPCR) in HCC cell lines. The stability of circITCH was confirmed by Ribonuclease R (RNase R) assay. Subsequently, through silencing and overexpression of circITCH to investigate the functional roles of circITCH in HCC proliferation, invasion, and apoptosis. We also carried out bioinformatics analysis, luciferase reporter assays to define the relationship between microRNA (miR)-184 and circITCH. Moreover, xenograft mouse models and immunohistochemistry were employed to assess the function of circITCH in HCC. CircITCH (hsa_circ_0001141) was a stable circRNA and downregulated in HCC cells. Overexpression of circITCH inhibited cell proliferation, migration, invasion, and promoted apoptosis in vitro and in vivo, whereas knockdown of circITCH had the opposite effects. Mechanistically, miR-184 could be sponged by circITCH, and its overexpression could mitigate the suppressive effects of circITCH overexpression on HCC progression. Through biological website to predict the target genes of miR-184 may be combined. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to investigate mRNAs with significant functional enrichment and pathways, also which its relationship with HCC-related pathway and immune cells. Our findings reveal that circITCH served as a repressor to restrain HCC malignancy via miR-184. Therefore, circITCH may serve as a potential prognostic marker and therapeutic target for HCC.

Serine-based β-lactamases of Class A, C and D all rely on a key water molecule to hydrolyze and inactivate β-lactam antibiotics. This process involves two conserved catalytic steps. In the first acylation step, the β-lactam antibiotic forms an acyl-enzyme intermediate (ES*) with the catalytic serine residue. In the second deacylation step, an activated water molecule serves as nucleophile (WAT_Nu) to attack ES* and release the inactivated β-lactam. The coordination and activation of WAT_Nu is not fully understood. Using time-resolved x-ray crystallography and QM/MM simulations, we analyzed three intermediate structures of Class A β-lactamase PenP as it slowly hydrolyzed cephaloridine. WAT_Nu is centrally located in the apo structure but becomes slightly displaced away by ES* in the post-acylation structure. In the deacylation structure, WAT_Nu moves back and is positioned along the Bürgi–Dunitz trajectory with favorable energetic profile to attack ES*. Unexpectedly, WAT_Nu is also found to adopt a catalytically incompetent conformation in the deacylation structure forming a hydrogen bond with ES*. Our results reveal that ES* plays a significant role in coordinating and activating WAT_Nu through subtle yet distinct interactions at different stages of the catalytic process. These interactions may serve as potential targets to circumvent β-lactamase-mediated antibiotic resistance.

In a typical biomolecular simulation using Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field, the vast majority molecules in the simulation box consist of water, and these water molecules consume the most CPU power due to the explicit mutual induction effect. To improve the computational efficiency, we here develop two new nonpolarizable water models (with flexible bonds and fixed charges) that are compatible with AMOEBA solute: the 3-site AW3C and 5-site AW5C. To derive the force-field parameters for AW3C and AW5C, we fit to six experimental liquid thermodynamic properties: liquid density, enthalpy of vaporization, dielectric constant, isobaric heat capacity, isothermal compressibility and thermal expansion coefficient, at a broad range of temperatures from 261.15 to 353.15 K under 1.0 atm pressure. We further validate our AW3C and AW5C water models by showing that they can well reproduce the radial distribution function g(r), self-diffusion constant D, and hydration free energy from the AMOEBA03 water model and the experimental observations. Furthermore, we show that our AW3C and AW5C water models can greatly accelerate (>5 times) the bulk water as well as biomolecular simulations when compared to AMOEBA water. Specifically, we demonstrate that the applications of AW3C and AW5C water models to simulate a DNA duplex lead to a threefold acceleration, and in the meanwhile well maintain the structural properties as the fully polarizable AMOEBA water. We expect that our AW3C and AW5C water models hold great promise to be widely applied to simulate complex bio-molecules using the AMOEBA force field.

Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally categorized into two basic classes, i.e., backpropagation-like training methods and plasticity-based learning methods. The former methods are dependent on energy-inefficient real-valued computation and non-local transmission, as also required in artificial neural networks (ANNs), whereas the latter are either considered to be biologically implausible or exhibit poor performance. Hence, biologically plausible (bio-plausible) high-performance supervised learning (SL) methods for SNNs remain deficient. In this paper, we proposed a novel bio-plausible SNN model for SL based on the symmetric spike-timing dependent plasticity (sym-STDP) rule found in neuroscience. By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset). To reveal the underlying mechanism of our SL model, we visualized both layer-based activities and synaptic weights using the t-distributed stochastic neighbor embedding (t-SNE) method after training and found that they were well clustered, thereby demonstrating excellent classification ability. Furthermore, to verify the robustness of our model, we trained it on another more realistic dataset (Fashion-MNIST), which also showed good performance. As the learning rules were bio-plausible and based purely on local spike events, our model could be easily applied to neuromorphic hardware for online training and may be helpful for understanding SL information processing at the synaptic level in biological neural systems., Comment: 29 pages, 6 figures

Tigecycline is a broad-spectrum antibacterial agent. As the incidence of multidrug-resistant bacterial infections has increased in intensive care units (ICUs) over the past decades, tigecycline is often used in ICUs. Information about tigecycline-associated hepatotoxicity in ICU patients is limited. To investigate the potential risk factors for tigecycline-associated hepatotoxicity in ICU patients, 148 patients from 2 centers who had received tigecycline for at least 4 days were retrospectively analyzed. Hepatotoxicity was classified according to the National Cancer Institute Common Terminology Criteria for Adverse Events (5.0) grading system. As a result, 33.8% of patients experienced hepatotoxicity events in the ICU. The multivariate analysis showed that an albumin concentration25 g/L at baseline (odds ratio, 3.714; 95%CI, 1.082-12.744; P = .037) and treatment duration (odds ratio, 1.094; 95%CI, 1.032-1.160; P = .003) were significantly correlated with tigecycline-associated hepatotoxicity. The median time to onset of hepatotoxicity was 8.0 days. The median duration ICU stay and the in-hospital mortality rate were not different between the hepatotoxicity group and the nonhepatotoxicity group (33.5 days (interquartile range, 21.0-72.0) vs 31.0 days (interquartile range, 21-62.5), P = .850; 38.0% vs 43.8%; P = .504). Therefore, close monitoring of liver function is recommended for patients with baseline albumin concentrations25 g/L or for patients who receive tigecycline therapy for8 days.

Here, we report the use of an amphiphilic Pt(II) complex, K[Pt{(O3SCH2CH2CH2)2bzimpy}Cl] (PtB), as a model to elucidate the key role of Pt···Pt interactions in directing self-assembly by combining temperature-dependent ultraviolet-visible (UV-Vis) spectroscopy, stopped-flow kinetic experiments, quantum mechanics (QM) calculations, and molecular dynamics (MD) simulations. Interestingly, we found that the self-assembly mechanism of PtB in aqueous solution follows a nucleation-free isodesmic model, as revealed by the temperature-dependent UV-Vis experiments. In contrast, a cooperative growth is found for the self-assembly of PtB in acetone–water (7:1, vol/vol) solution, which is further verified by the stopped-flow experiments, which clearly indicates the existence of a nucleation phase in the acetone–water (7:1, vol/vol) solution. To reveal the underlying reasons and driving forces for these self-assembly processes, we performed QM calculations and show that the Pt···Pt interactions arising from the interaction between the pz and dz2 orbitals play a crucial role in determining the formation of ordered self-assembled structures. In subsequent oligomer MD simulations, we demonstrate that this directional Pt···Pt interaction can indeed facilitate the formation of linear structures packed in a helix-like fashion. Our results suggest that the self-assembly of PtB in acetone–water (7:1, vol/vol) solution is predominantly driven by the directional noncovalent Pt···Pt interaction, leading to the cooperative growth and the formation of fibrous nanostructures. On the contrary, the self-assembly in aqueous solution forms spherical nanostructures of PtB, which is primarily due to the predominant contribution from the less directional hydrophobic interactions over the directional Pt···Pt and π−π interactions that result in an isodesmic growth.