Zhen Li

Bio: Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.

Colorimetric Sensing of α-Amino Acids and Its Application for the “Label-Free” Detection of Protease

Abstract: A new indirect approach to explore sensitive colorimetric sensors toward α-amino acids is proposed: the pink solution of 1 and copper ions changed to colorless immediately upon the addition of α-amino acids. As the hydrolysis of bovine serum albumin (BSA) with the aid of trypsin produces α-amino acids, the complex of 1/Cu2+/BSA could act as a label-free, sensitive, selective sensor toward trypsin. The detection process could be visually observed by naked eyes.

Vacancy engineering of Cu2−xSe nanoparticles with tunable LSPR and magnetism for dual-modal imaging guided photothermal therapy of cancer

Abstract: The vacancies in the semiconductor nanocrystals not only induce unique properties, but also provide spaces for engineering them with multifunctions by the introduction of other elements. Herein, the vacancy of Cu2−xSe nanoparticles was tuned by doping with magnetic ferric ions (Fe3+) at room temperature, and the position and intensity of the near-infrared localized surface plasmon resonance (LSPR) in the resultant nanostructure can be finely controlled by altering the feeding amount of Fe3+ ions. The results of the density-functional theory (DFT) calculations show that both doping and replacement reactions are favourable. Owing to its tunable near-infrared absorption and magnetic property, the obtained hybrid nanostructure was demonstrated to be a novel nanotheranostic agent for effective deep-tissue photoacoustic imaging, magnetic resonance imaging, and photothermal therapy of cancer.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Highly accurate protein structure prediction with AlphaFold

Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.