Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906–0.910) and 2–17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.

Xanthoxyline ( 1 ), a small natural methyl ketone, was previously reported as a plant growth inhibitor. In this research, related methyl ketones bearing electron-donating and electron-withdrawing groups, together with heteroaromatics, were investigated against seed germination and seedling growth of Chinese amaranth ( Amaranthus tricolor L. ) and barnyard grass [ Echinochloa crus-galli (L.) Beauv ]. The structure–activity relationships (SARs) of methyl ketone herbicides were clarified, and which types and positions of substituents were crucially important for activity were also clarified. Indole derivatives, namely, 3-acetylindole ( 43 ) and 3-acetyl-7-azaindole ( 44 ) were found to be the most active methyl ketones that highly suppressed plant growth at low concentrations. The molecular docking on the 4-hydroxyphenylpyruvate dioxygenase (HPPD) enzyme indicated that carbonyl, aromatic, and azaindole were key interactions of HPPD inhibitors. This finding would be useful for the development of small ketone herbicides.

Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based metapredictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http:// camt. pytho nanyw here. com/ Stack HCV. It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.

The isocitrate lyase (ICL) superfamily catalyzes the cleavage of the C(2)-C(3) bond of various α-hydroxy acid substrates. Members of the family are found in bacteria, fungi, and plants and include ICL itself, oxaloacetate hydrolase (OAH), 2-methylisocitrate lyase (MICL), and (2R,3S)-dimethylmalate lyase (DMML) among others. ICL and related targets have been the focus of recent studies to treat bacterial and fungal infections, including tuberculosis. The catalytic process by which this family achieves C(2)-C(3) bond breaking is still not clear. Extensive structural studies have been performed on this family, leading to a number of plausible proposals for the catalytic mechanism. In this paper, we have applied quantum mechanical/molecular mechanical (QM/MM) methods to the most recently reported family member, DMML, to assess whether any of the mechanistic proposals offers a clear energetic advantage over the others. Our results suggest that Arg161 is the general base in the reaction and Cys124 is the general acid, giving rise to a rate-determining barrier of approximately 10 kcal/mol.

No abstract