Development of Highly Potent Noncovalent Inhibitors of SARS-CoV-2 3CLpro.

The 3C-like protease (3CLpro) is an essential enzyme for the replication of SARS-CoV-2 and other coronaviruses and thus is a target for coronavirus drug discovery. Nearly all inhibitors of coronavirus 3CLpro reported so far are covalent inhibitors. Here, we report the development of specific, noncovalent inhibitors of 3CLpro. The most potent one, WU-04, effectively blocks SARS-CoV-2 replications in human cells with EC50 values in the 10-nM range. WU-04 also inhibits the 3CLpro of SARS-CoV and MERS-CoV with high potency, indicating that it is a pan-inhibitor of coronavirus 3CLpro. WU-04 showed anti-SARS-CoV-2 activity similar to that of PF-07321332 (Nirmatrelvir) in K18-hACE2 mice when the same dose was administered orally. Thus, WU-04 is a promising drug candidate for coronavirus treatment.

Triaging of DNA-Encoded Library Selection Results by High-Throughput Resynthesis of DNA-Conjugate and Affinity Selection Mass Spectrometry.

DNA encoded library (DEL) technology allows for rapid identification of novel small-molecule ligands and thus enables early-stage drug discovery. DEL technology is well-established, numerous cases of discovered hit molecules have been published, and the technology is widely employed throughout the pharmaceutical industry. Nonetheless, DEL selection results can be difficult to interpret, as library member enrichment may derive from not only desired products, but also DNA-conjugated byproducts and starting materials. Note that DELs are generally produced using split-and-pool combinatorial chemistry, and DNA-conjugated byproducts and starting materials cannot be removed from the library mixture. Herein, we describe a method for high-throughput parallel resynthesis of DNA-conjugated molecules such that byproducts, starting materials, and desired products are produced in a single pot, using the same chemical reactions and reagents as during library production. The low-complexity mixtures of DNA-conjugate are then assessed for protein binding by affinity selection mass spectrometry and the molecular weights of the binding ligands ascertained. This workflow is demonstrated to be a practical tool to triage and validate potential hits from DEL selection data.

A Strategy for the Synthesis of Sulfonamides on DNA.

An efficient method is reported to synthesize sulfonamides on DNA from sulfinic acids or sodium sulfinates and amines in the presence of iodine under mild conditions. This method demonstrates a major expansion of scope of sulfonamide formation on DNA through the utilization of a novel sodium carbonate-sodium sulfinate bifunctional reagent class.

[Study on transmission rate and quantitative structure-property relationships of alkaloids and iridoid compounds on UF membranes with MWCOS of 1000].

OBJECTIVE: Taking alkaloids and iridoid as the study, relationships of quantitative molecular structure and transmission rate on UF membrane were developed initially at exploring the mechanism of UF in Chinese herb decoction and to lay a foundation for the optimal design of membrane technology in the production of Chinese drugs preparation. METHOD: The transmission rate of eight alkaloids and iridoid compounds in three UF membranes was determined, and quantitative structure-property relationships between quantitative molecular structure and transmission rate were developed by the data mining method of PLS or SVM. RESULT: Quantitative structure-property relationship models of three membranes were developed by the data mining method of PLS or SVM, correlation coefficients were all greater than 0.9. The parameters in the models included LUMO, AlogP, CMR, K&H2, K&H3. CONCLUSION: The relationship of UF transmission rate of these eight compounds and their molecular weight are not simply linearly dependent, the main factors which affect the UF transmission rate are capacity of gain or loss electron, hydrophilicity or hydrophobic and compound spatial structure.

Prediction of interactiveness of proteins and nucleic acids based on feature selections.

It is important to identify which proteins can interact with nucleic acids for the purpose of protein annotation, since interactions between nucleic acids and proteins involve in numerous cellular processes such as replication, transcription, splicing, and DNA repair. This research tries to identify proteins that can interact with DNA, RNA, and rRNA, respectively. mRMR (Minimum redundancy and maximum relevance), with its elegant mathematical formulation, has been applied widely in processing biological data and feature analysis since its introduction in 2005. mRMR plus incremental feature selection (IFS) is known to be very efficient in feature selection and analysis, and able to improve both effectiveness and efficiency of a prediction model. IFS is applied to decide how many features should be selected from feature list provided by mRMR. In the end, the selected features of mRMR and IFS are further refined by a conventional feature selection method--forward feature wrapper (FFW), by reordering the features. Each protein is coded by 132 features including amino acid compositions and physicochemical properties. After the feature selection, k-Nearest Neighbor algorithm, the adopted prediction model, is trained and tested. As a result, the optimized prediction accuracies for the DNA, RNA, and rRNA are 82.0, 83.4, and 92.3%, respectively. Furthermore, the most important features that contribute to the prediction are identified and analyzed biologically. The predictor, developed for this research, is available for public access at http://chemdata.shu.edu.cn/protein_na_mrmr/.

Multiple classifier integration for the prediction of protein structural classes.

Supervised classifiers, such as artificial neural network, partition trees, and support vector machines, are often used for the prediction and analysis of biological data. However, choosing an appropriate classifier is not straightforward because each classifier has its own strengths and weaknesses, and each biological dataset has its own characteristics. By integrating many classifiers together, people can avoid the dilemma of choosing an individual classifier out of many to achieve an optimized classification results (Rahman et al., Multiple Classifier Combination for Character Recognition: Revisiting the Majority Voting System and Its Variation, Springer, Berlin, 2002, 167-178). The classification algorithms come from Weka (Witten and Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, 2005) (a collection of software tools for machine learning algorithms). By integrating many predictors (classifiers) together through simple voting, the correct prediction (classification) rates are 65.21% and 65.63% for a basic training dataset and an independent test set, respectively. These results are better than any single machine learning algorithm collected in Weka when exactly the same data are used. Furthermore, we introduce an integration strategy which takes care of both classifier weightings and classifier redundancy. A feature selection strategy, called minimum redundancy maximum relevance (mRMR), is transferred into algorithm selection to deal with classifier redundancy in this research, and the weightings are based on the performance of each classifier. The best classification results are obtained when 11 algorithms are selected by mRMR method, and integrated together through majority votes with weightings. As a result, the prediction correct rates are 68.56% and 69.29% for the basic training dataset and the independent test dataset, respectively. The web-server is available at http://chemdata.shu.edu.cn/protein_st/.