Tannases produced by filamentous fungi are in a family of important hydrolases of gallotannins and have broad industry applications.But until now,the 3-D structures of fungi tannases have not been reported.The protein...Tannases produced by filamentous fungi are in a family of important hydrolases of gallotannins and have broad industry applications.But until now,the 3-D structures of fungi tannases have not been reported.The protein sequence deduced from the cDNA sequence obtained using RT-PCR amplification was identified as tannase through sequence alignment and phylogenetic analysis.Structure models based on the tannase sequence were collected using I-TASSER,and the model with the best match to the surface charge density-pH titration profile was selected as the final structure for tannase from Aspergillusniger N5-5.This work provides an effective method for protein structure research.The structure constructed in this work should be very important to understand the enzyme bioactivities and further developments of fungi tannases.展开更多
Data-driven innovation has shown great power in solving problems in multifactor correlation,convergence and optimization,synergistic and antagonistic effects,pattern and boundary identification,critical behavior and p...Data-driven innovation has shown great power in solving problems in multifactor correlation,convergence and optimization,synergistic and antagonistic effects,pattern and boundary identification,critical behavior and phase transition,which are ubiquitous in polymer science.Either for the in-depth understanding of physical problems or in the discovery of new polymer materials,integrating data and machine learning into conventional experimental,theoritical,modeling and simulation approaches becomes blooming.Here we present a perspective based on our research interests,highlight some key issues and provide a prospection in this emerging direction.We focus on a number of typical advances in the description and identification of polymer conformation and structures,and the interpretation and prediction of structureproperty correlations,that have applied data and machine learning in polymer science.展开更多
Bridging the gap between the computation of mechanical properties and the chemical structure of elastomers is a long-standing challenge.To fill the gap,we create a raw dataset and build predictive models for Young’s ...Bridging the gap between the computation of mechanical properties and the chemical structure of elastomers is a long-standing challenge.To fill the gap,we create a raw dataset and build predictive models for Young’s modulus,tensile strength,and elongation at break of polyurethane elastomers(PUEs).We then construct a benchmark dataset with 50.4%samples remained from the raw dataset which suffers from the intrinsic diversity problem,through a newly proposed recursive data elimination protocol.The coefficients of determination(R^(2)s)from predictions are improved from 0.73-0.78 to 0.85-0.91 based on the raw and the benchmark datasets.The fitting of stress-strain curves using the machine learning model shows a slightly better performance than that for one of the well-performed constitutive models(e.g.,the Khiêm-Itskov model).It confirmed that the black-box machine learning models are feasible to bridge the gap between the mechanical properties of PUEs and multiple factors for their chemical structures,composition,processing,and measurement settings.While accurate prediction for these curves is still a challenge.We release the raw dataset and the most representative benchmark dataset so far to call for more attention to tackle the longstanding gap problem.展开更多
A clear diagram for the unfolding of protein induced by denaturant is a classical but still unsolved challenge. To explore the unfolded conformations of ubiquitin under different urea concentrations, we performed hybr...A clear diagram for the unfolding of protein induced by denaturant is a classical but still unsolved challenge. To explore the unfolded conformations of ubiquitin under different urea concentrations, we performed hybrid Monte Carlo-molecular dynamics simulations (MC-MD) guided by small angle X-ray scattering (SAXS) structural information. Conformational ensembles sampled by the hybrid MC-MD algorithm exhibited typical 3D structures at different urea concentrations. These typical structures suggested that ubiquitin was subjected to a sequential unfolding, where the native contacts between adjacent β-sheets at first were disrupted together with the exposure of hydrophobic core, followed by the conversion of remaining β-strands and helices into random coils. Ubiquitin in 8 mol·L?1 urea is almost a random coil. With the disruption of native structure, urea molecules are enriched at protein hydrated layer to stabilize newly exposed residues. Compared with water, urea molecules prefer to form hydrogen bonds with the backbone of ubiquitin, thus occupying nodes of the hydrogen bonding network that construct the secondary structure of proteins. Meanwhile, we also found that the slow dynamics of urea molecules was almost unchanged while the dynamics of water was accelerated in the hydration shell when more residues were unfolded and exposed. The former was also responsible for the stabilization of unfolded structures.展开更多
Polymer-solvent interaction is a fundamentally important concept routinely described by the Flory-Huggins interaction(χ),Hildebrand solubility(Δδ)and the relative energy difference(RED)determined from Hansen solubi...Polymer-solvent interaction is a fundamentally important concept routinely described by the Flory-Huggins interaction(χ),Hildebrand solubility(Δδ)and the relative energy difference(RED)determined from Hansen solubility in experimental,theoretical and simulation studies.Here we performed a machine learning study based on a comprehensive and representative dataset covering the interaction pairs from 81polymers and 1221 solvents.The regression models provide the coefficients of determination in the range of 0.86-0.94 and the classification models deliver the area under the receiver operating characteristic curve(AUCs)better than 0.93.These models were integrated into a newly developed software polySML-PSI.Important features including Log P,molar volume and dipole are identified,and their non-linear,nonmonotonic contributions to polymer-solvent interactions are presented.The widely known“like-dissolve-like”rule and two broadly used empirical equations to estimateχas a function of temperature or Hansen solubility are also evaluated,and the polymer-specified constants are presented.This study provides a quantitative reference and a tool to understand and utilize the concept of polymer-solvent interactions.展开更多
Conformation and dynamical evolution of block copolymers in shear flow is an important topic in polymer physics that underscores the forming process of various materials.We explored deformation and dynamics of copolym...Conformation and dynamical evolution of block copolymers in shear flow is an important topic in polymer physics that underscores the forming process of various materials.We explored deformation and dynamics of copolymers composed of rigid or flexible blocks in simple shear flow by employing multiparticle collision dynamics integrated with molecular dynamics simulations.We found that compared with the proportion between rigid and flexible blocks,the type of the central blocks plays more important role in the conformational and dynamical evolution of copolymers.That is,if the central block is a coil,the copolymer chain takes end-over-end tumbling motion,while if the central block is a rod,the copolymer chain undergoes U-shape or S-shape deformation at mid shear rate.As the shear strength increases,all copolymers behave similar to flexible polymers at high shear rate.This can be attributed to the fact that shear flow is strong enough to overcome the buckling force of the rigid blocks.These results provide a deeper understanding of the roles played by rod and coil blocks in copolymers for phase interface during forming processing.展开更多
基金the National Natural Science Foundation of China (No. 21374117)the 100 Talents Program of Chinese Academy of Sciences for financial support
文摘Tannases produced by filamentous fungi are in a family of important hydrolases of gallotannins and have broad industry applications.But until now,the 3-D structures of fungi tannases have not been reported.The protein sequence deduced from the cDNA sequence obtained using RT-PCR amplification was identified as tannase through sequence alignment and phylogenetic analysis.Structure models based on the tannase sequence were collected using I-TASSER,and the model with the best match to the surface charge density-pH titration profile was selected as the final structure for tannase from Aspergillusniger N5-5.This work provides an effective method for protein structure research.The structure constructed in this work should be very important to understand the enzyme bioactivities and further developments of fungi tannases.
基金financial support from the National Natural Science Foundation of China(NSFC)(Nos.51988102 and 22173094)support from NSFC(No.22073004)+4 种基金financial support from NSFC(No.22173030)financial support from NSFC(No.21973018)CAS Key Research Program of Frontier Sciences(No.QYZDY-SSW-SLH027)the Fundamental Research Funds for the Central UniversitiesShanghai Scientific and Technological Innovation Projects(No.22ZR1417500)。
文摘Data-driven innovation has shown great power in solving problems in multifactor correlation,convergence and optimization,synergistic and antagonistic effects,pattern and boundary identification,critical behavior and phase transition,which are ubiquitous in polymer science.Either for the in-depth understanding of physical problems or in the discovery of new polymer materials,integrating data and machine learning into conventional experimental,theoritical,modeling and simulation approaches becomes blooming.Here we present a perspective based on our research interests,highlight some key issues and provide a prospection in this emerging direction.We focus on a number of typical advances in the description and identification of polymer conformation and structures,and the interpretation and prediction of structureproperty correlations,that have applied data and machine learning in polymer science.
基金financially supported by the National Natural Science Foundation of China(Nos.51988102 and 22173094)CAS Key Research Program of Frontier Sciences(No.QYZDYSSW-SLH027)+1 种基金Network and Computing Center,Changchun Institute of Applied Chemistry for essential supportthe financial support of Major Science and Technology Project in Yunnan Province(No.202002AB080001-1)。
文摘Bridging the gap between the computation of mechanical properties and the chemical structure of elastomers is a long-standing challenge.To fill the gap,we create a raw dataset and build predictive models for Young’s modulus,tensile strength,and elongation at break of polyurethane elastomers(PUEs).We then construct a benchmark dataset with 50.4%samples remained from the raw dataset which suffers from the intrinsic diversity problem,through a newly proposed recursive data elimination protocol.The coefficients of determination(R^(2)s)from predictions are improved from 0.73-0.78 to 0.85-0.91 based on the raw and the benchmark datasets.The fitting of stress-strain curves using the machine learning model shows a slightly better performance than that for one of the well-performed constitutive models(e.g.,the Khiêm-Itskov model).It confirmed that the black-box machine learning models are feasible to bridge the gap between the mechanical properties of PUEs and multiple factors for their chemical structures,composition,processing,and measurement settings.While accurate prediction for these curves is still a challenge.We release the raw dataset and the most representative benchmark dataset so far to call for more attention to tackle the longstanding gap problem.
基金financially supported by the National Natural Science Foundation of China (Nos. 21504092 and U1832177)One Hundred Person Project of the Chinese Academy of Sciences+1 种基金Computing Center of Jilin ProvinceHenan Province Supercomputer Center for essential support
文摘A clear diagram for the unfolding of protein induced by denaturant is a classical but still unsolved challenge. To explore the unfolded conformations of ubiquitin under different urea concentrations, we performed hybrid Monte Carlo-molecular dynamics simulations (MC-MD) guided by small angle X-ray scattering (SAXS) structural information. Conformational ensembles sampled by the hybrid MC-MD algorithm exhibited typical 3D structures at different urea concentrations. These typical structures suggested that ubiquitin was subjected to a sequential unfolding, where the native contacts between adjacent β-sheets at first were disrupted together with the exposure of hydrophobic core, followed by the conversion of remaining β-strands and helices into random coils. Ubiquitin in 8 mol·L?1 urea is almost a random coil. With the disruption of native structure, urea molecules are enriched at protein hydrated layer to stabilize newly exposed residues. Compared with water, urea molecules prefer to form hydrogen bonds with the backbone of ubiquitin, thus occupying nodes of the hydrogen bonding network that construct the secondary structure of proteins. Meanwhile, we also found that the slow dynamics of urea molecules was almost unchanged while the dynamics of water was accelerated in the hydration shell when more residues were unfolded and exposed. The former was also responsible for the stabilization of unfolded structures.
基金financially supported by the National Natural Science Foundation of China(Nos.21774128,U1832177,22173094,51988102)CAS Key Research Program of Frontier Sciences(No.QYZDY-SSW-SLH027)Network and Computing Center,Changchun Institute of Applied Chemistry for essential support。
文摘Polymer-solvent interaction is a fundamentally important concept routinely described by the Flory-Huggins interaction(χ),Hildebrand solubility(Δδ)and the relative energy difference(RED)determined from Hansen solubility in experimental,theoretical and simulation studies.Here we performed a machine learning study based on a comprehensive and representative dataset covering the interaction pairs from 81polymers and 1221 solvents.The regression models provide the coefficients of determination in the range of 0.86-0.94 and the classification models deliver the area under the receiver operating characteristic curve(AUCs)better than 0.93.These models were integrated into a newly developed software polySML-PSI.Important features including Log P,molar volume and dipole are identified,and their non-linear,nonmonotonic contributions to polymer-solvent interactions are presented.The widely known“like-dissolve-like”rule and two broadly used empirical equations to estimateχas a function of temperature or Hansen solubility are also evaluated,and the polymer-specified constants are presented.This study provides a quantitative reference and a tool to understand and utilize the concept of polymer-solvent interactions.
基金supported by the National Natural Science Foundation of China(Nos.21774128,U1832177)Key Research Program of Frontier Sciences(No.QYZDY-SSWSLH027)NSFC Resource and Ecology Based Synthetic Polymeric Materials(No.51988102)。
文摘Conformation and dynamical evolution of block copolymers in shear flow is an important topic in polymer physics that underscores the forming process of various materials.We explored deformation and dynamics of copolymers composed of rigid or flexible blocks in simple shear flow by employing multiparticle collision dynamics integrated with molecular dynamics simulations.We found that compared with the proportion between rigid and flexible blocks,the type of the central blocks plays more important role in the conformational and dynamical evolution of copolymers.That is,if the central block is a coil,the copolymer chain takes end-over-end tumbling motion,while if the central block is a rod,the copolymer chain undergoes U-shape or S-shape deformation at mid shear rate.As the shear strength increases,all copolymers behave similar to flexible polymers at high shear rate.This can be attributed to the fact that shear flow is strong enough to overcome the buckling force of the rigid blocks.These results provide a deeper understanding of the roles played by rod and coil blocks in copolymers for phase interface during forming processing.
