Dong-Qing Wei

Bio: Dong-Qing Wei is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Medicine & Density functional theory. The author has an hindex of 48, co-authored 418 publications receiving 7839 citations. Previous affiliations of Dong-Qing Wei include Henan University of Technology & Beijing Institute of Technology.

Higher infectivity of the SARS-CoV-2 new variants is associated with K417N/T, E484K, and N501Y mutants: An insight from structural data.

Abstract: The evolution of the SARS-CoV-2 new variants reported to be 70% more contagious than the earlier one is now spreading fast worldwide. There is an instant need to discover how the new variants interact with the host receptor (ACE2). Among the reported mutations in the Spike glycoprotein of the new variants, three are specific to the receptor-binding domain (RBD) and required insightful scrutiny for new therapeutic options. These structural evolutions in the RBD domain may impart a critical role to the unique pathogenicity of the SARS-CoV-2 new variants. Herein, using structural and biophysical approaches, we explored that the specific mutations in the UK (N501Y), South African (K417N-E484K-N501Y), Brazilian (K417T-E484K-N501Y), and hypothetical (N501Y-E484K) variants alter the binding affinity, create new inter-protein contacts and changes the internal structural dynamics thereby increases the binding and eventually the infectivity. Our investigation highlighted that the South African (K417N-E484K-N501Y), Brazilian (K417T-E484K-N501Y) variants are more lethal than the UK variant (N501Y). The behavior of the wild type and N501Y is comparable. Free energy calculations further confirmed that increased binding of the spike RBD to the ACE2 is mainly due to the electrostatic contribution. Further, we find that the unusual virulence of this virus is potentially the consequence of Darwinian selection-driven epistasis in protein evolution. The triple mutants (South African and Brazilian) may pose a serious threat to the efficacy of the already developed vaccine. Our analysis would help to understand the binding and structural dynamics of the new mutations in the RBD domain of the Spike protein and demand further investigation in in vitro and in vivo models to design potential therapeutics against the new variants.

Orientational order in simple dipolar liquids: Computer simulation of a ferroelectric nematic phase.

Abstract: Molecular-dynamics simulations are used to show that strongly interacting dipolar spheres can form a ferroelectric nematic phase. This is the first demonstration that dipolar forces alone can create an orientationally ordered liquid state. It is also the first time that the existence of a ferroelectric nematic phase has been established for a model fluid

Hydrated proton clusters and solvent effects on the proton transfer barrier: A density functional study

Abstract: The density functional calculations using the Perdew nonlocal corrections to exchange and correlation have been carried out for a sequence of hydrated proton clusters. The optimized structures were obtained up to H13O+6. It is found that H3O+ is indeed the central unit in all the lowest energy structures we found. Our results support the argument that the structure with a four‐coordinate first solvation shell is very unlikely in small hydrated proton clusters. The density functional calculations with the Perdew nonlocal corrections to exchange and correlation give somewhat shorter hydrogen bond lengths, but slightly longer chemical bond lengths as compared with the post‐Hartree–Fock calculations. The harmonic vibrational frequencies and IR intensities of various vibrational modes have been generated for all the structures optimized. Results for small clusters are compared with the high resolution experimental spectroscopy studies of Yeh et al. and Begemann et al. Results for larger clusters are used to in...

DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features

Abstract: Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases. The data sets and source code are freely available at https://github.com//a96123155/DTI-CDF.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

A Chemist's Guide to Density Functional Theory

Abstract: "Chemists familiar with conventional quantum mechanics will applaud and benefit greatly from this particularly instructive, thorough and clearly written exposition of density functional theory: its basis, concepts, terms, implementation, and performance in diverse applications. Users of DFT for structure, energy, and molecular property computations, as well as reaction mechanism studies, are guided to the optimum choices of the most effective methods. Well done!" Paul von RaguE Schleyer "A conspicuous hole in the computational chemist's library is nicely filled by this book, which provides a wide-ranging and pragmatic view of the subject.[...It] should justifiably become the favorite text on the subject for practioneers who aim to use DFT to solve chemical problems." J. F. Stanton, J. Am. Chem. Soc. "The authors' aim is to guide the chemist through basic theoretical and related technical aspects of DFT at an easy-to-understand theoretical level. They succeed admirably." P. C. H. Mitchell, Appl. Organomet. Chem. "The authors have done an excellent service to the chemical community. [...] A Chemist's Guide to Density Functional Theory is exactly what the title suggests. It should be an invaluable source of insight and knowledge for many chemists using DFT approaches to solve chemical problems." M. Kaupp, Angew. Chem.