scholarly journals AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations

2021 ◽  
Author(s):  
Sara Capponi ◽  
Shangying Wang ◽  
Simone Bianco
Keyword(s):  
Binding Affinity ◽  
High Accuracy ◽  
Atomistic Simulations ◽  
Small Subset ◽  
Novel Technique ◽  
Neural Network Algorithm ◽  
Dynamics Simulations ◽  
Simulation Time ◽  
High Binding Affinity ◽  
Human Receptor

AbstractWe present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy mutations with low binding affinity from mutations with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the the SARS-CoV-2 spike protein and the human receptor ACE2.

scholarly journals AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations

2021 ◽  
Vol 44 (10) ◽  
Author(s):  
Sara Capponi ◽  
Shangying Wang ◽  
Erik J. Navarro ◽  
Simone Bianco
Keyword(s):  
Binding Affinity ◽  
High Accuracy ◽  
Atomistic Simulations ◽  
Small Subset ◽  
Novel Technique ◽  
Neural Network Algorithm ◽  
Dynamics Simulations ◽  
Simulation Time ◽  
High Binding Affinity ◽  
Human Receptor

Abstract We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2. Graphic abstract

scholarly journals Single mutations in the ε subunit from thermophilic Bacillus PS3 generate a high binding affinity site for ATP

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5505 ◽  
Author(s):  
Alexander Krah ◽  
Peter J. Bond
Keyword(s):  
Binding Affinity ◽  
Atp Hydrolysis ◽  
Atp Binding ◽  
Evolutionary Analysis ◽  
Binding Affinities ◽  
Wild Type ◽  
Dynamics Simulations ◽  
High Binding Affinity ◽  
Atp Synthases

The ε subunit from ATP synthases acts as an ATP sensor in the bacterial cell to prevent ATP hydrolysis and thus the waste of ATP under conditions of low ATP concentration. However, the ATP binding affinities from various bacterial organisms differ markedly, over several orders of magnitude. For example, the ATP synthases from thermophilic Bacillus PS3 and Escherichia coli exhibit affinities of 4 µM and 22 mM, respectively. The recently reported R103A/R115A double mutant of Bacillus PS3 ATP synthase demonstrated an increased binding affinity by two orders of magnitude with respect to the wild type. Here, we used atomic-resolution molecular dynamics simulations to determine the role of the R103A and R115A single mutations. These lead us to predict that both single mutations also cause an increased ATP binding affinity. Evolutionary analysis reveals R103 and R115 substitutions in the ε subunit from other bacillic organisms, leading us to predict they likely have a higher ATP binding affinity than previously expected.

scholarly journals Herb-target virtual screening and network pharmacology for prediction of molecular mechanism of Danggui Beimu Kushen Wan for prostate cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hong Li ◽  
Andrew Hung ◽  
Angela Wei Hong Yang
Keyword(s):  
Prostate Cancer ◽  
Binding Affinity ◽  
Molecular Mechanisms ◽  
Biological Activities ◽  
Tight Binding ◽  
Experimental Studies ◽  
Network Pharmacology ◽  
High Morbidity ◽  
High Binding ◽  
High Binding Affinity

AbstractProstate cancer (PCa) is a cancer that occurs in the prostate with high morbidity and mortality. Danggui Beimu Kushen Wan (DBKW) is a classic formula for patients with difficult urination including PCa. This study aimed to investigate the molecular mechanisms of DBKW for PCa. We obtained DBKW compounds from our previous reviews. We identified potential targets for PCa from literature search, currently approved drugs and Open Targets database and filtered them by protein–protein interaction network analysis. We selected 26 targets to predict three cancer-related pathways. A total of 621 compounds were screened via molecular docking using PyRx and AutoDock Vina against 21 targets for PCa, producing 13041 docking results. The binding patterns and positions showed that a relatively small number of tight-binding compounds from DBKW were predicted to interact strongly and selectively with three targets. The top five high-binding-affinity compounds were selected to generate a network, indicating that compounds from all three herbs had high binding affinity against the 21 targets and may have potential biological activities with the targets. DBKW contains multi-targeting agents that could act on more than one pathway of PCa simultaneously. Further studies could focus on validating the computational results via experimental studies.

scholarly journals The Detection of Bovine Estrus by Lactoferrin Monoclonal Antibody

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1582
Author(s):  
Jihwan Lee ◽  
Suhyun Lee ◽  
Younbae Park ◽  
Seokhyun Lee ◽  
Seungmin Ha ◽  
...  
Keyword(s):  
Monoclonal Antibody ◽  
Binding Affinity ◽  
Reproductive Performance ◽  
Economic Loss ◽  
Cervical Mucus ◽  
Female Rats ◽  
Economic Damage ◽  
Bovine Lactoferrin ◽  
Estrous Synchronization ◽  
High Binding Affinity

To improve reproductive performance in cattle, the accurate detection of estrus and optimization of insemination relative to ovulation are necessary. However, poor heat detection by farm staff leads to a decreased conception rate, thus inflicting economic damage to the beef and dairy industries. This study aimed to develop monoclonal antibodies (mAb) that can specifically bind to the bovine lactoferrin (bLF) protein, which we have previously demonstrated to be overexpressed in bovine cervical mucus during estrus. Female rats were intraperitoneally immunized with bLF protein as the antigen. Anti-bLF mAbs were then purified by affinity chromatography, and their binding affinity for the bLF antigen was examined using ELISA. We found a high binding affinity between mAbs and bLF. Finally, we developed a rapid bovine heat detection kit using the anti-bLF mAbs that we generated and tested on cervical mucus from 12 cows (estrous synchronization, n = 2; natural cycling, n = 10). We found that the kits accurately detected estrus. Overall, our fabricated heat detection kit based on rat anti-bLF mAbs could pave the way for the development of potent tools for heat detection devices for dairy cattle, thereby preventing economic loss.

Dislocation Generation and Crack Propagation in Metals Examined in Molecular Dynamics Simulations

1992 ◽  
Vol 278 ◽  
Author(s):  
J. A. Rifkin ◽  
C. S. Becquart ◽  
D. Kim ◽  
P. C. Clapp
Keyword(s):  
Crack Propagation ◽  
Atomistic Simulations ◽  
Many Body ◽  
Dislocation Generation ◽  
Embedded Atom ◽  
Stress Levels ◽  
Different Temperatures ◽  
The Many ◽  
Dynamics Simulations ◽  
Many Body Interactions

AbstractWe have carried out a series of atomistic simulations on arrays of about 10,000 atoms containing an atomically sharp crack and subjected to increasing stress levels. The ordered stoichiometric alloys B2 NiAl, B2 RuAl and A15 Nb3AI have been studied at different temperatures and stress levels, as well as the elements Al, Ni, Nb and Ru. The many body interactions used in the simulations were derived semi-empirically, using techniques related to the Embedded Atom Method. Trends in dislocation generation rates and crack propagation modes will be discussed and compared to experimental indications where possible, and some of the simulations will be demonstrated in the form of computer movies.

Uncertainties of Thermal Conductivities From Equilibrium Molecular Dynamics Simulations

2016 ◽  
Author(s):  
Zuyuan Wang ◽  
Xiulin Ruan
Keyword(s):  
Molecular Dynamics ◽  
Correlation Time ◽  
Domain Size ◽  
Material System ◽  
Dynamics Simulations ◽  
Increasing Temperature ◽  
Solid Argon ◽  
Simulation Parameters ◽  
Simulation Time ◽  
Thermal Conductivities

The Green-Kubo method in the framework of equilibrium molecular dynamics (EMD) simulations is an effective method that has been widely used to calculate thermal conductivities of materials. The previous studies focused on the thermal conductivity values or the average values from repetitive simulations. Little research has been done to investigate the uncertainties of the thermal conductivities from EMD simulations. In this paper, we use solid argon as the material system to study the factors influencing the uncertainties of the predicted thermal conductivities. We find that the uncertainties decrease with the total simulation time as (ttotal)−α and increase with correlation time as (tcorre)β, where 0.48 < α, β < 0.52. We also find that the uncertainties decrease with increasing temperature, but the simulation domain size has a negligible effect. We propose some guidelines for selecting appropriate simulation parameters (e.g., the correlation time and total simulation time) to achieve a desired level of uncertainty. This work is potentially useful for future studies on calculating the thermal conductivities of materials using EMD simulations.

Host Adaptation of Codon Usage in SARS-CoV-2 From Mammals Indicate Natural Selection

2021 ◽  
Author(s):  
Yanan Fu ◽  
Yanping Huang ◽  
Jingjing Rao ◽  
Feng Zeng ◽  
Ruiping Yang ◽  
...  
Keyword(s):  
Natural Selection ◽  
Codon Usage ◽  
Binding Affinity ◽  
Codon Bias ◽  
Synonymous Codon ◽  
Host Adaptation ◽  
Synonymous Codon Usage ◽  
Mutation Pressure ◽  
Usage Analysis ◽  
Human Receptor

Abstract The outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, spread across hosts from humans to animals, transmitting particularly effectively in mink. How SARS-CoV-2 selects and evolves in the host, and the differences in the evolution of different animals are still unclear. To analysis the mutation and codon usage bias of SARS-CoV-2 in infected humans and animals. The SARS-CoV-2 sequence in mink (Mink-SARS2) and binding energy with receptor were calculated compared with human. The relative synonymous codon usage of viral encoded gene was analyzed to characterize the differences and the evolutionary characteristics. A synonymous codon usage analysis showed that SARS-CoV-2 is optimized to adapt in the animals in which it is currently reported, and all of the animals showed decreased adaptability relative to that of humans, except for mink. The neutrality plot showed that the effect of natural selection on different SARS-CoV-2 sequences is stronger than mutation pressure. A binding affinity analysis indicated that the spike protein of the SARS-CoV-2 variant in mink showed a greater preference for binding with the mink receptor ACE2 than with the human receptor, especially as the mutation Y453F and N501T in Mink-SARS2 lead to improvement of binding affinity for mink receptor. In summary, mutations Y453F and N501T in Mink-SARS2 lead to improvement of binding affinity with mink receptor, indicating possible natural selection and current host adaptation. Monitoring the variation and codon bias of SARS-CoV-2 provides a theoretical basis for tracing the epidemic, evolution and cross-species spread of SARS-CoV-2.

Real-time engine model development based on time complexity analysis

2021 ◽  
pp. 146808742110397
Author(s):  
Haotian Chen ◽  
Kun Zhang ◽  
Kangyao Deng ◽  
Yi Cui
Keyword(s):  
Real Time ◽  
Time Complexity ◽  
Complexity Analysis ◽  
High Accuracy ◽  
The Real ◽  
Cycle Simulation ◽  
Crank Angle ◽  
Time Requirements ◽  
Improved Model ◽  
Simulation Time

Real-time simulation models play an important role in the development of engine control systems. The mean value model (MVM) meets real-time requirements but has limited accuracy. By contrast, a crank-angle resolved model, such as the filling -and-empty model, can be used to simulate engine performance with high accuracy but cannot meet real-time requirements. Time complexity analysis is used to develop a real-time crank-angle resolved model with high accuracy in this study. A method used in computer science, program static analysis, is used to theoretically determine the computational time for a multicylinder engine filling-and-empty (crank-angle resolved) model. Then, a prediction formula for the engine cycle simulation time is obtained and verified by a program run test. The influence of the time step, program structure, algorithm and hardware on the cycle simulation time are analyzed systematically. The multicylinder phase shift method and a fast calculation method for the turbocharger characteristics are used to improve the crank-angle resolved filling-and-empty model to meet real-time requirements. The improved model meets the real-time requirement, and the real-time factor is improved by 3.04 times. A performance simulation for a high-power medium-speed diesel engine shows that the improved model has a max error of 5.76% and a real-time factor of 3.93, which meets the requirement for a hardware-in-the-loop (HIL) simulation during control system development.

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