Collective Molecular Machines: Multidimensionality and Reconfigurability

Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work.During the last 60 years,designing molecular structures capable of generating unidirectional mechanical motion at the nanoscale has been the topic of intense research.Effective progress has been made,attributed to advances in various fields such as supramolecular chemistry,biology and nanotechnology,and informatics.However,individual molecular machines are only capable of producing nanometer work and generally have only a single functionality.In order to address these problems,collective behaviors realized by integrating several or more of these individual mechanical units in space and time have become a new paradigm.In this review,we comprehensively discuss recent developments in the collective behaviors of molecular machines.In particular,collective behavior is divided into two paradigms.One is the appropriate integration of molecular machines to efficiently amplify molecular motions and deformations to construct novel functional materials.The other is the construction of swarming modes at the supramolecular level to perform nanoscale or microscale operations.We discuss design strategies for both modes and focus on the modulation of features and properties.Subsequently,in order to address existing challenges,the idea of transferring experience gained in the field of micro/nano robotics is presented,offering prospects for future developments in the collective behavior of molecular machines.

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

Predicting liposome formulations by the integrated machine learning and molecular modeling approaches

Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability.Due to the complex formulation components and preparation process,formulation screening mostly relies on trial-and-error process with low efficiency.Here liposome formulation prediction models have been built by machine learning(ML)approaches.The important parameters of liposomes,including size,polydispersity index(PDI),zeta potential and encapsulation,are predicted individually by optimal ML algorithm,while the formulation features are also ranked to provide important guidance for formulation design.The analysis of key parameter reveals that drug molecules with logS[-3,-6],molecular complexity[500,1000]and XLogP3(≥2)are priority for preparing liposome with higher encapsulation.In addition,naproxen(NAP)and palmatine HCl(PAL)represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability.The consistency between predicted and experimental value verifies the satisfied accuracy of ML models.As the drug properties are critical for liposome particles,the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations.The modeling structure reveals that NAP molecules could distribute into lipid layer,while most PAL molecules aggregate in the inner aqueous phase of liposome.The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations.In summary,the general prediction models are built to predict liposome formulations,and the impacts of key factors are analyzed by combing ML with molecular modeling.The availability and rationality of these intelligent prediction systems have been proved in this study,which could be applied for liposome formulation development in the future.

Molecular insight into the GaP(110)-water interface using machine learning accelerated molecular dynamics

GaP has been shown to be a promising photoelectrocatalyst for selective CO_(2)reduction to methanol.Due to the relevance of the interface structure to important processes such as electron/proton transfer,a detailed understanding of the GaP(110)-water interfacial structure is of great importance.Ab initio molecular dynamics(AIMD)can be used for obtaining the microscopic information of the interfacial structure.However,the GaP(110)-water interface cannot converge to an equilibrated structure at the time scale of the AIMD simulation.In this work,we perform the machine learning accelerated molecular dynamics(MLMD)to overcome the difficulty of insufficient sampling by AIMD.With the help of MLMD,we unravel the microscopic information of the structure of the GaP(110)-water interface,and obtain a deeper understanding of the mechanisms of proton transfer at the GaP(110)-water interface,which will pave the way for gaining valuable insights into photoelectrocatalytic mechanisms and improving the performance of photoelectrochemical cells.

Ligand Based Virtual Screening of Molecular Compounds in Drug Discovery Using GCAN Fingerprint and Ensemble Machine Learning Algorithm

The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.

Taxonomy of Two New Peritrich Ciliates(Protozoa:Ciliophora)from Inland Saltwaters of China

The inland saltwater lakes harbor exceptional biodiversity.Here,two new species of solitary sessile peritrich ciliates were isolated from Qinghai Lake,the largest inland saltwater lake in China.Their morphology,ciliature,silverline system,and molecular phylogeny were investigated based on live observation,silver staining,and analysis of the small subunit ribosomal DNA(SSU rDNA).Vorticella paraglobosa sp.n.is characterized mainly by its obconical or elongate bell-shaped zooid,C-shaped macronucleus,single ventrally located contractile vacuole,two-rowed infundibular polykinety 3,and 28-38 silverlines between peristome and aboral tro-chal band and 10-15 between aboral trochal band and scopula.Vorticella cotyliformis sp.n.differs from its congeners mainly by its double-layered peristomial lip,cup-shaped zooid,J-shaped macronucleus,single ventrally positioned contractile vacuole,three-rowed infundibular polykinety 3,and 70-85 silverlines between peristome and aboral trochal band and 21-25 between aboral trochal band and scopula.The SSU rDNA sequences of the two new species were obtained,and the subsequent molecular phylogenetic analysis supported their taxonomic classification.

Thermal conductivity of GeTe crystals based on machine learning potentials

GeTe has attracted extensive research interest for thermoelectric applications.In this paper,we first train a neuroevolution potential(NEP)based on a dataset constructed by ab initio molecular dynamics,with the Gaussian approximation potential(GAP)as a reference.The phonon density of states is then calculated by two machine learning potentials and compared with density functional theory results,with the GAP potential having higher accuracy.Next,the thermal conductivity of a GeTe crystal at 300 K is calculated by the equilibrium molecular dynamics method using both machine learning potentials,and both of them are in good agreement with the experimental results;however,the calculation speed when using the NEP potential is about 500 times faster than when using the GAP potential.Finally,the lattice thermal conductivity in the range of 300 K-600 K is calculated using the NEP potential.The lattice thermal conductivity decreases as the temperature increases due to the phonon anharmonic effect.This study provides a theoretical tool for the study of the thermal conductivity of GeTe.

Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach

Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide.Therapeutic failure in lung cancer(LUAD)is heavily influenced by drug resistance.This challenge stems from the diverse cell populations within the tumor,each having unique genetic,epigenetic,and phenotypic profiles.Such variations lead to varied therapeutic responses,thereby contributing to tumor relapse and disease progression.Methods:The Genomics of Drug Sensitivity in Cancer(GDSC)database was used in this investigation to obtain the mRNA expression dataset,genomic mutation profile,and drug sensitivity information of NSCLS.Machine Learning(ML)methods,including Random Forest(RF),Artificial Neurol Network(ANN),and Support Vector Machine(SVM),were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical methods.The most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods,and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer subtype.Finally,the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available datasets.Results:Our analyses yielded 1,564 gene features and 45 mutational features for 46 drugs.Applying the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response(area under the curve[AUC]0.875)using CIT,GAS2L3,STAG3L3,ATP2B4-mut,and IL15RA-mut as molecular features.Furthermore,the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance(AUC 0.780)in Gefitinib with CCL23-mut.Conclusion:This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs.

Electromechanical Transient Modeling Analysis of Large-Scale New Energy Grid Connection

The synchronous virtual machine uses inverter power to imitate the performance of the conventional synchronous machine.It also has the same inertia,damping,frequency,voltage regulation,and other external performance as the generator.It is the key technology to realize new energy grid connections’stable and reliable operation.This project studies a dynamic simulation model of an extensive new energy power system based on the virtual synchronous motor.A new energy storage method is proposed.The mathematical energy storage model is established by combining the fixed rotor model of a synchronous virtual machine with the charge-discharge power,state of charge,operation efficiency,dead zone,and inverter constraint.The rapid conversion of energy storage devices absorbs the excess instantaneous kinetic energy caused by interference.The branch transient of the critical cut set in the system can be confined to a limited area.Thus,the virtual synchronizer’s kinetic and potential energy can be efficiently converted into an instantaneous state.The simulation of power system analysis software package(PSASP)verifies the correctness of the theory and algorithm in this paper.This paper provides a theoretical basis for improving the transient stability of new energy-connected power grids.

Machine learning for molecular thermodynamics

Thermodynamic properties of complex systems play an essential role in developing chemical engineering processes.It remains a challenge to predict the thermodynamic properties of complex systems in a wide range and describe the behavior of ions and molecules in complex systems.Machine learning emerges as a powerful tool to resolve this issue because it can describe complex relationships beyond the capacity of traditional mathematical functions.This minireview will summarize some fundamental concepts of machine learning methods and their applications in three aspects of the molecular thermodynamics using several examples.The first aspect is to apply machine learning methods to predict the thermodynamic properties of a broad spectrum of systems based on known data.The second aspect is to integer machine learning and molecular simulations to accelerate the discovery of materials.The third aspect is to develop machine learning force field that can eliminate the barrier between quantum mechanics and all-atom molecular dynamics simulations.The applications in these three aspects illustrate the potential of machine learning in molecular thermodynamics of chemical engineering.We will also discuss the perspective of the broad applications of machine learning in chemical engineering.

Morphology and molecular phylogeny of Paragorgia rubra sp.nov.(Cnidaria:Octocorallia),a new bubblegum coral species from a seamount in the tropical Western Pacific

A new species of bubblegum coral,Paragorgia rubra sp.nov.,discovered from a seamount at a water depth of 373 m near the Yap Trench is studied using morphological and molecular approaches.Paragorgia rubra sp.nov.is the fourth species of the genus found in the tropical Western Pacific.The new gorgonian is red-colored,uniplanar,and measures approximately 530 mm high and 440 mm wide,with autozooids distributed only on one side of the colony.Paragorgia rubra sp.nov.is most similar to P.kaupeka Sánchez,2005,but differs distinctly in the polyp ovals with large and compound protuberances(vs.small and simple conical protuberances) and the medullar spindles possessing simple conical protuberances(vs.compound protuberances).Moreover,P.rubra sp.nov.differs from P.kaupeka in the smaller length/width ratio of surface radiates(1.53 vs.1.75).The genetic distance of the mtMutS gene between P.rubra sp.nov.and P.kaupeka is 0.66%,while the intraspecific distances within Paragorgia Milne-Edwards & Haime,1857 except the species P.regalis complex are no more than 0.5%,further supporting the establishment of the new species.Furthermore,the ITS2 secondary structure of P.rubra sp.nov.is also different from those of congeners.Phylogenetic analyses indicate Paragorgia rubra sp.nov.and P.kaupeka form a clade,which branched early within Paragorgia and diversified approximately 15 Mya.

Parallel Molecular Dynamics on the Connection Machines

Abstract Abstract:We have demonstrated using vectorized parallel Lennard-Jones fluid program that vectorizing general-purpose parallel molecular package for simulating biomolecules which currently runs on the Connection Machine CM-5 using CMMD message passing would offer a significant improvement over 4 non-vectorized version. Our results indicate that the Lennard-Jones fluid program written in C*/CMNID is five times faster than the same program written in C/CMMD.

Molecular Biomarker Characteristics of the Linxi Formation Source Rocks in the Middle-Western Region of Inner Mongolia:New evidence for late-stage tectonic evolution of the Paleo-Asian Ocean

Objective Two important geological issues have long been controversial in the Xing-Meng area of North China. The first concerns the final closure of Paleo-Asian Ocean in Xing-Meng area, and the other concerns the folding and lifting of the Xing-Meng Trough. The focus of thses issues is the Late Permian sedimentary environment, which is generally considered to be either an exclusively continental environment or from the closed inland sea environment in the Early to Middle stage to continental lacustrine environment in the late stage. In recent years, we have successively discovered abundant typical marine fossils （e.g., bryozoans and calcareous algae） in the Upper Permian thick limestone layer from Linxi County and Ar Horqin Banner in eastern region of Inner Mongolia and Jiutain County in Jilin Province. These significant findings have attracted the attention from fellow academics.

Advances in machine learning-and artificial intelligence-assisted material design of steels

With the rapid development of artificial intelligence technology and increasing material data,machine learning-and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials science.Machine learning methods,based on an interdisciplinary discipline between computer science,statistics and material science,are good at discovering correlations between numerous data points.Compared with the traditional physical modeling method in material science,the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials.This review starts with data preprocessing and the introduction of different machine learning models,including algorithm selection and model evaluation.Then,some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition,structure,processing,and performance.The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed.Finally,the applicability and limitations of machine learning in the material field are summarized,and future directions and prospects are discussed.

Mechanism of micro-wetting of highly hydrophobic coal dust in underground mining and new wetting agent development

The internal mechanism of the high hydrophobicity of the coal samples from the Pingdingshan mining area was studied through industrial,element,and surface functional group analysis.Laboratory testing and molecular dynamics simulations were employed to study the impact of three types of surfactants on the surface adsorption properties and wettability of highly hydrophobic bituminous coal.The results show that the surface of highly hydrophobic bituminous coal is compact,rich in inorganic minerals,and poorly wettable and that coal molecules are dominated by hydrophobic functional groups of aromatic rings and aliphatic structures.The wetting performance of surfactants as the intermediate carrier to connect coal and water molecules is largely determined by the interaction force between surfactants and coal(Fs-c)and the interaction force between surfactants and water(Fs-w),which effectively improve the wettability of modified coal dust via modifying its surface electrical properties and surface energy.A new type of wetting agent with a dust removal rate of 89%has been developed through discovery of a compound wetting agent solution with optimal wetting and settling performance.This paper provides theoretical and technical support for removing highly hydrophobic bituminous coal dust in underground mining.

Multiple Data Sources Reveal a New Asian Leaf Litter Toad of Leptobrachella Smith, 1925 (Anura, Megophryidae) from Southwestern China

A new species of Leptobrachella is described from Sichuan Province and Chongqing Municipality,China.Molecular phylogenetic analyses based on mitochondrial and nuclear gene sequences indicated that the new species is genetically divergent from its congeners.It could be identified from its congeners by a combination of followings characters:body size of male 29.1-34.1 mm(n=14),female 34.1-34.9 mm(n=4);dorsal skin rough with large tubercles in size of humeral glands,without conical spines;fringes on toes narrow;ventral body basically floral white with deep grey pigments all over;dorsal body deep greyish brown with smoky black markings;iris gold above,gradually silver bellow;tibia-tarsal articulation reaches the level of the middle of the eye when leg being stretched forward;the main call type with dominant frequency4.08 ± 0.16 kHz(14.1-14.9℃),call duration 170.35± 15.19ms,the number of pulses for the first note in a call 3.50±0.89,and the number of pulses for the second note in a call 5.08±0.77.

The First Congress of New Development on Molecular Imaging Dec 22 to 24,2011,Guangzhou,China

"The First Congress of New Development on Molecular Imaging" will be held in Guangdong Hotel in Guangzhou, Guangdong province, Dec 22 to 24, 2011. This conference is hosted by Ultrasound Medical Branch of Guangdong Medical Association, Ultrasound Medical Branch of Guangzhou Medical Association, is undertaken by

New Model Die Set for PET Plastic Jet-Moulding Machine

At the China International Food Packing Machinery Exhibition, the new model die set for PET plastic jet-mouldingmachine developed by the Zhejiang Province Taizhou Municipality Huangyan Sanyou Plastics Factory attracted the attention of numerous domestic and foreign clients. They rushed to the stand in great numbers for consultation and talks on ordering. According to the evaluation of the experts concerned, the die set is the most advanced one nationwide for PET plastic jet-moulding machinery.

A New Inertial Sensing System for Dynamic Measurement of Parallel Machines

One way to solve the problem of measurement precision caused by deformity for thermal expansion, friction and load etc is to use an inertial sensor to measure a change in the length of the rod on a parallel machine. However, the characteristic of dynamic measurement in the inertial sensing system and the effects of the machine＇s working environment, bias error, misalignment and wide band random noise in inertial measurement data results in the in-accuracy of system measurement. Therefore, on the basis of the measurement system a new inertial sensing system is proposed; the drifting of error is restrained with a method of inertial error correction and the system＇s position and the velocity state variables are predicted by the data fusion. After measuring the whole 300mm movement in an experiment, the analyses of the experimental result showed that the application of the new inertial sensing system can improve the positional accuracy about 61% and the movement precision more than 20%. Measurement results also showed that the application of the new inertial sensing system for dynamic measurement was a feasible method to improve the machine＇s dynamic positioning precision. And with the further improvement of the low-cost solid-stateacceleramenter technology, the application of the machine can take a higher position and make the speed dynamic accuracy possible.

Molecular markers for identifying a new selected variety of Pacific white shrimp Litopenaeus vannamei

Selective breeding of the Pacific white shrimp Litopenaeus vannamei during the last decade has produced new varieties exhibiting high growth rates and disease resistance.However,the identification of new varieties of shrimps from their phenotypic characters is difficult.This study introduces a new approach for identifying varieties of shrimps using molecular markers of microsatellites and mitochondrial control region sequences.The method was employed to identify a new selected variety,Kehai No.1(KH-1),from three representative stocks(control group):Zhengda;Tongwei;and a stock collected from Fujian Province,which is now cultured in China's Mainland.By pooled genotyping of KH-1 and the control group,five microsatellites showing differences between KH-1 and the control group were screened out.Individual genotyping data confirmed the results from pooled genotyping.The genotyping data for the five microsatellites were applied to the assignment analysis of the KH-1 group and the control group using the partial Bayesian assignment method in GENECLASS2.By sequencing the mitochondrial control regions of individuals from the KH-1 and control group,four haplotypes were observed in the KH-1 group,whereas14 haplotypes were obtained in the control group.By combining the microsatellite assignment analysis with mitochondrial control region analysis,the average accuracy of identification of individuals in the KH-1group and control group reached 89%.The five selected microsatellite loci and mitochondrial control region sequences were highly polymorphic and could be used to distinguish new selected varieties of L.vannamei from other populations cultured in China.