Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes...Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.展开更多
This study guides the discovery of sustainable high-performance polymer membranes for organic binary solvent separations.We focus on solvent diffusivity in polymers,a key factor in quantifying solvent transport.Tradit...This study guides the discovery of sustainable high-performance polymer membranes for organic binary solvent separations.We focus on solvent diffusivity in polymers,a key factor in quantifying solvent transport.Traditional experimental and computational methods for determining diffusivity are time-and resource-intensive,while current machine learning(ML)models often lack accuracy outside their training domains.To overcome this,we fuse experimental and simulated diffusivity data to train physics-enforced multi-task ML models,achieving more robust predictions in unseen chemical spaces and outperforming single-task models in data-limited scenarios.Next,we address the challenge of identifying optimal membranes for a model toluene-heptane separation,identifying polyvinyl chloride(PVC)as the optimal membrane among 13,000 polymers,consistent with literature findings,thereby validating our methodology.Expanding our search,we screen 1 million publicly available and 7 million chemically recyclable polymers,identifying greener halogen-free alternatives to PVC.This capability is expected to advance membrane design for solvent separations.展开更多
基金National Natural Science Foundation of China,No.31971180 and No.11474013.
文摘Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.
基金the Office of Naval Research through a multidisciplinary university research initiative(MURI)for their funding support,We would also like to acknowledge Dr.Kuan-Hsuan Shen for their valuable support in building the simulation pipeline.We also extend a thank you to Dr.Lihua Chen for her guidance in the initial stage of the work.This research is supported in part through research cyber-infrastructure resources and services provided by the Partnership for an Advanced Computing Environment(PACE)at the Georgia Institute of Technology and XSEDE/ACCESS for computational support through Grant No.TG-DMR080058N.
文摘This study guides the discovery of sustainable high-performance polymer membranes for organic binary solvent separations.We focus on solvent diffusivity in polymers,a key factor in quantifying solvent transport.Traditional experimental and computational methods for determining diffusivity are time-and resource-intensive,while current machine learning(ML)models often lack accuracy outside their training domains.To overcome this,we fuse experimental and simulated diffusivity data to train physics-enforced multi-task ML models,achieving more robust predictions in unseen chemical spaces and outperforming single-task models in data-limited scenarios.Next,we address the challenge of identifying optimal membranes for a model toluene-heptane separation,identifying polyvinyl chloride(PVC)as the optimal membrane among 13,000 polymers,consistent with literature findings,thereby validating our methodology.Expanding our search,we screen 1 million publicly available and 7 million chemically recyclable polymers,identifying greener halogen-free alternatives to PVC.This capability is expected to advance membrane design for solvent separations.
