scholarly journals AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2

2021 ◽  
Vol 118 (26) ◽  
pp. e2025879118
Author(s):  
Sheng Ye ◽  
Guozhen Zhang ◽  
Jun Jiang
Keyword(s):  
Machine Learning ◽  
Ir Spectra ◽  
Real Time ◽  
Structural Information ◽  
Protein Complexes ◽  
Time Resolved ◽  
S Protein ◽  
Spectroscopic Monitoring ◽  
Quantum Chemistry Calculations ◽  
Novel Coronavirus

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), invades a human cell via human angiotensin-converting enzyme 2 (hACE2) as the entry, causing the severe coronavirus disease (COVID-19). The interactions between hACE2 and the spike glycoprotein (S protein) of SARS-CoV-2 hold the key to understanding the molecular mechanism to develop treatment and vaccines, yet the dynamic nature of these interactions in fluctuating surroundings is very challenging to probe by those structure determination techniques requiring the structures of samples to be fixed. Here we demonstrate, by a proof-of-concept simulation of infrared (IR) spectra of S protein and hACE2, that time-resolved spectroscopy may monitor the real-time structural information of the protein−protein complexes of interest, with the help of machine learning. Our machine learning protocol is able to identify fine changes in IR spectra associated with variation of the secondary structures of S protein of the coronavirus. Further, it is three to four orders of magnitude faster than conventional quantum chemistry calculations. We expect our machine learning protocol would accelerate the development of real-time spectroscopy study of protein dynamics.

scholarly journals AI-based Spectroscopic Monitoring of Real-time Interactions between SARS-CoV-2 and Human ACE2

2020 ◽  
Author(s):  
Sheng Ye ◽  
Guozhen Zhang ◽  
Jun Jiang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Protein Dynamics ◽  
Structural Information ◽  
Protein Complexes ◽  
Spike Protein ◽  
Proof Of Concept ◽  
Spectroscopy Study ◽  
Time Resolved ◽  
Spectroscopic Monitoring

<div> <p>Here we demonstrate by a proof-of-concept simulation of IR spectra of complex of spike protein of SARS-CoV-2 and human ACE2, that a time-resolved spectroscopy may monitor the real-time structural information of the protein-protein complexes of interest, with the help of a machine learning protocol. The significant speedup of our approach relative to conventional quantum chemistry approach suggests a promising way of accelerating the development of real-time spectroscopy study of protein dynamics.</p> </div>

scholarly journals AI-based Spectroscopic Monitoring of Real-time Interactions between SARS-CoV-2 and Human ACE2

2020 ◽  
Author(s):  
Sheng Ye ◽  
Guozhen Zhang ◽  
Jun Jiang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Protein Dynamics ◽  
Structural Information ◽  
Protein Complexes ◽  
Spike Protein ◽  
Proof Of Concept ◽  
Spectroscopy Study ◽  
Time Resolved ◽  
Spectroscopic Monitoring

<div> <p>Here we demonstrate by a proof-of-concept simulation of IR spectra of complex of spike protein of SARS-CoV-2 and human ACE2, that a time-resolved spectroscopy may monitor the real-time structural information of the protein-protein complexes of interest, with the help of a machine learning protocol. The significant speedup of our approach relative to conventional quantum chemistry approach suggests a promising way of accelerating the development of real-time spectroscopy study of protein dynamics.</p> </div>

scholarly journals Structural and Drug Screening Analysis of the Non-structural Proteins of Severe Acute Respiratory Syndrome Coronavirus 2 Virus Extracted From Indian Coronavirus Disease 2019 Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Nupur Biswas ◽  
Krishna Kumar ◽  
Priyanka Mallick ◽  
Subhrangshu Das ◽  
Izaz Monir Kamal ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Drug Screening ◽  
Structural Information ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Host Protein ◽  
Structural Proteins ◽  
Effective Vaccine ◽  
Indian Patients ◽  
Novel Coronavirus

The novel coronavirus 2 (nCoV2) outbreaks took place in December 2019 in Wuhan City, Hubei Province, China. It continued to spread worldwide in an unprecedented manner, bringing the whole world to a lockdown and causing severe loss of life and economic stability. The coronavirus disease 2019 (COVID-19) pandemic has also affected India, infecting more than 10 million till 31st December 2020 and resulting in more than a hundred thousand deaths. In the absence of an effective vaccine, it is imperative to understand the phenotypic outcome of the genetic variants and subsequently the mode of action of its proteins with respect to human proteins and other bio-molecules. Availability of a large number of genomic and mutational data extracted from the nCoV2 virus infecting Indian patients in a public repository provided an opportunity to understand and analyze the specific variations of the virus in India and their impact in broader perspectives. Non-structural proteins (NSPs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus play a major role in its survival as well as virulence power. Here, we provide a detailed overview of the SARS-CoV2 NSPs including primary and secondary structural information, mutational frequency of the Indian and Wuhan variants, phylogenetic profiles, three-dimensional (3D) structural perspectives using homology modeling and molecular dynamics analyses for wild-type and selected variants, host-interactome analysis and viral–host protein complexes, and in silico drug screening with known antivirals and other drugs against the SARS-CoV2 NSPs isolated from the variants found within Indian patients across various regions of the country. All this information is categorized in the form of a database named, Database of NSPs of India specific Novel Coronavirus (DbNSP InC), which is freely available at http://www.hpppi.iicb.res.in/covid19/index.php.

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Rapid Online Buffer Exchange: A Method for Screening of Proteins, Protein Complexes, and Cell Lysates by Native Mass Spectrometry

2019 ◽  
Author(s):  
Zachary VanAernum ◽  
Florian Busch ◽  
Benjamin J. Jones ◽  
Mengxuan Jia ◽  
Zibo Chen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Speed ◽  
Structural Information ◽  
Protein Complexes ◽  
High Sensitivity ◽  
Native Mass Spectrometry ◽  
Structural Features ◽  
Consumer Products ◽  
Cell Lysates ◽  
Protein Expression And Purification

It is important to assess the identity and purity of proteins and protein complexes during and after protein purification to ensure that samples are of sufficient quality for further biochemical and structural characterization, as well as for use in consumer products, chemical processes, and therapeutics. Native mass spectrometry (nMS) has become an important tool in protein analysis due to its ability to retain non-covalent interactions during measurements, making it possible to obtain protein structural information with high sensitivity and at high speed. Interferences from the presence of non-volatiles are typically alleviated by offline buffer exchange, which is timeconsuming and difficult to automate. We provide a protocol for rapid online buffer exchange (OBE) nMS to directly screen structural features of pre-purified proteins, protein complexes, or clarified cell lysates. Information obtained by OBE nMS can be used for fast (<5 min) quality control and can further guide protein expression and purification optimization.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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