CircDENND4C is a HIF1α-associated circRNA promoting the proliferation of breast cancer cells under hypoxia.

Effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery. Despite the outstanding capability of deep learning in...

<p><b>Abstract:</b> Effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery. Despite the outstanding capability of deep learning in retrosynthesis and forward synthesis, predictions based on small chemical datasets generally result in low accuracy due to an insufficiency of reaction examples. Here, we introduce a new state art of method, which integrates transfer learning with transformer model to predict the outcomes of the Baeyer-Villiger reaction which is a representative small dataset reaction. The results demonstrate that introducing transfer learning strategy markedly improves the top-1 accuracy of the transformer-transfer learning model (81.8%) over that of the transformer-baseline model (58.4%). Moreover, we further introduce data augmentation to the input reaction SMILES, which allows for better performance and improves the accuracy of the transformer-transfer learning model (86.7%). In summary, both transfer learning and data augmentation methods significantly improve the predictive performance of transformer model, which are powerful methods used in chemistry field to eliminate the restriction of limited training data.</p>

Cutinase thermostability is important so that the enzymes can function above the glass transition of what are often rigid polymer substrates. A detailed thermal inactivation analysis was performed for two well-characterized cutinases, Aspergillus oryzae Cutinase (AoC) and Thiellavia terrestris Cutinase (TtC). Both AoC and TtC are prone to thermal aggregation upon unfolding at high temperature, which was found to be a major reason for irreversible loss of enzyme activity. Our study demonstrates that glycosylation stabilizes TtC expressed in Pichia pastoris by inhibiting its thermal aggregation. Based on the comparative thermal inactivation analyses of non-glycosylated AoC, glycosylated (TtC-G), and non-glycosylated TtC (TtC-NG), a unified model for thermal inactivation is proposed that accounts for thermal aggregation and may be applicable to other cutinase homologues. Inspired by glycosylated TtC, we successfully employed glycosylation site engineering to inhibit AoC thermal aggregation. Indeed, the inhibition of thermal aggregation by AoC glycosylation was greater than that achieved by conventional use of trehalose under a typical condition. Collectively, this study demonstrates the excellent potential of implementing glycosylation site engineering for thermal aggregation inhibition, which is one of the most common reasons for the irreversible thermal inactivation of cutinases and many proteins. Biotechnol. Bioeng. 2017;114: 63-73. © 2016 Wiley Periodicals, Inc.

Deep learning models based on NLP, mainly the Transformer family, have been successfully applied to solve many chemistry-related problems, but their applications are mostly limited to chemical reactions. Meanwhile, solvation...

Cutinases are esterases of industrial importance for applications in recycling and surface modification of polyesters. The cutinase from Thielavia terrestris (TtC) is distinct in terms of its ability to retain its stability and activity in acidic pH. Stability and activity in acidic pHs are desirable for esterases as the pH of the reaction tends to go down with the generation of acid. The pH stability and activity are governed by the charged state of the residues involved in catalysis or in substrate binding. In this study, we performed the detailed structural and biochemical characterization of TtC coupled with surface charge analysis to understand its acidic tolerance. The stability of TtC in acidic pH was rationalized by evaluating the contribution of charge interactions to the Gibbs free energy of unfolding at varying pHs. The activity of TtC was found to be limited by substrate binding affinity, which is a function of the surface charge. Additionally, the presence of glycosylation affects the biochemical characteristics of TtC owing to steric interactions with residues involved in substrate binding.

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This paper describes a database framework that enables one to rapidly explore systematics in structure-function relationships associated with new and emerging PFAS chemistries. The data framework maps high dimensional information associated with the SMILES approach of encoding molecular structure with functionality data including bioactivity and physicochemical property. This ‘PFAS-Map’ is a 3-dimensional unsupervised visualization tool that can automatically classify new PFAS chemistries based on current PFAS classification criteria. We provide examples on how the PFAS-Map can be utilized, including the prediction and estimation of yet unmeasured fundamental physical properties of PFAS chemistries, uncovering hierarchical characteristics in existing classification schemes, and the fusion of data from diverse sources.