Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force fields with high accuracy to achieve correct folding. However, the current force fields are inaccurate, inapplicable and inefficient. We propose a machine learning protocol, the inductive transfer learning force field (ITLFF), to construct protein force fields in seconds with any level of accuracy from a small dataset. This process is achieved by incorporating an inductive transfer learning algorithm into deep neural networks, which learn knowledge of any high-level calculations from a large dataset of low-level method. Here, we use a double-hybrid density functional theory (DFT) as a case functional, but ITLFF is suitable for any high-precision functional. The performance of the selected 18 proteins indicates that compared with the fragment-based double-hybrid DFT algorithm, the force field constructed by ITLFF achieves considerable accuracy with a mean absolute error of 0.0039 kcal/mol/atom for energy and a root mean square error of 2.57 $\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more than 30 000 times faster and obtains more significant efficiency benefits as the system increases. The outstanding performance of ITLFF provides promising prospects for accurate and efficient protein dynamic simulations and makes an important step toward protein folding simulation. Due to the ability of ITLFF to utilize the knowledge acquired in one task to solve related problems, it is also applicable for various problems in biology, chemistry and material science.
Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high-precision and knowledge learned from large amount of low-level calculations. The high-level double-hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01 kcal/mol/atom for potential energy and an average root mean square error of 1.47 kcal/mol/$ {\rm A^{^{ \!\!\!o}}} $ for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.
Excessive consumption of Δ9-tetrahydrocannabinol
(THC) severely
endangers human health and has raised public safety concerns. However,
its quantification by readily rapid tools with simplicity and low
cost is still challenging. Herein, we found that a G-rich THC aptamer
(THC1.2) can tightly bind to thioflavin T (ThT) with strong fluorescence,
which would be specifically quenched in the presence of THC. Based
on that, a label-free ratiometric fluorescent sensor for the sensing
of THC and its metabolite (THC-COOH) based on THC1.2/ThT as a color
emitter and red CdTe quantum dots as reference fluorescence was constructed.
Notably, a transition of the fluorescent color of the ratiometric
probe from green to red can be instantly observed upon the increased
concentration of THC and THC-COOH. Furthermore, a portable smartphone-based
fluorescence device integrated with a self-programmed Python program
was fabricated and used to accomplish on-site monitoring of THC and
THC-COOH within 5 min. Under optimized conditions, this ratiometric
fluorescent sensor allowed for an instant response toward THC and
its metabolite with considerable limits of detection of 97 and 254
nM, respectively. The established sensor has been successfully applied
to urine and saliva samples and exhibited satisfactory recoveries
(88–116%). This ratiometric fluorescent sensor can be used
for the simultaneous detection of THC and THC-COOH with the advantages
of rapidness, low cost, ease of operation, and portability, providing
a promising strategy for on-site detection and facilitating law enforcement
regulation and roadside control of THC.
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