Insight into the Underlying Synergy between Exo–Lytic Cellulases

Abstract Lignocellulosic biomass can be converted into biofuels and biochemicals, through a synergetic degradation by cellulases. The underlying cooperative mechanism is of paramount importance for the development of efficient enzyme cocktails. The synergy between exo–lytic cellulases, however, still remains poorly understood. In the present contribution, we decipher the synergism by investigation of the enzyme–enzyme interactions (EEIs) between two exo–lytic cellulases––Talaromyces emersonii Cel7A (TeCel7A) and Trichoderma reesei Cel6A (TrCel6A), and between TeCel7A and other main components in the cocktails, and propose a novel synergistic way. The enzymes are found to be apt to bind around the eight substrate–enclosing loops (SELs) of TeCel7A, of which the TrCel6A possesses the strongest binding energy with TeCel7A. The combination between TeCel7A and TrCel6A is further investigated experimentally by Microscale Thermophoresis, confirming the existence of their interactions. Due to the EEIs, the flexibility of the SELs, which mediate dissociation of TeCel7A from cellulose, is increased. We further found that the improved flexibility of loop B3 is pivotal to accelerate dethreading process, helping improving enzymatic hydrolytic efficiency. In view of our theoretical and experimental results here and previous experimental phenomena, the carbohydrate binding modules of the exo–lytic enzymes, which enable them to absorb on the same plane of the substrate, are conjectured to enhance the degree of the EEIs. This work brings to light an underlying synergy between exo–lytic cellulases, and is conducive to a systematic understanding of the synergetic actions in cellulase cocktails.</jats:p

Targeting against HIV/HCV Co-infection using Machine Learning-based multitarget-quantitative structure-activity relationships (mt-QSAR) Methods

ABSTRACTCo-infection between HIV-1 and HCV is common today in certain populations. However, treatment of co-infection is full of challenges with special consideration for potential hepatic safety and drug-drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV co-infection. However, identification of one molecule acting on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining naive Bayesian (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints (MACCS and ECFP6), 60 classification models were constructed to predict the active compounds toward 11 HIV-1 targets and 4 HCV targets based on the multitarget-quantitative structure-activity relationships (mt-QSAR). 5-fold cross-validation and test set validation was performed to confirm the performance of 60 classification models. Our results show that 60 mt-QSAR models appeared to have high classification accuracy in terms of ROC-AUC values ranging from 0.83 to 1 with a mean value of 0.97 for HIV-1 models, and ROC-AUC values ranging from 0.84 to 1 with a mean value of 0.96 for HCV. Furthermore, the 60 models were applied to comprehensively predict the potential targets for additional 46 compounds including 27 approved HIV-1 drugs, 10 approved HCV drugs and 9 selected compounds known to be active on one or more targets of HIV-1 or those of HCV. Finally, 18 hits including 7 HIV-1 approved drugs, 4 HCV approved drugs and 7 compounds were predicted to be HIV/HCV co-infection multitarget inhibitors. The reported bioactivity data confirmed that 7 compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. Of those remaining predicted hits and chemical-protein interaction pairs involving the potential ability to suppress HIV/HCV co-infection deserve further investigation by experiments. This investigation shows that the mt-QSAR method is available to predict chemical-protein interaction for discovering multitarget inhibitors and provide a unique perspective on HIV/HCV co-infection treatment.</jats:p

Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method

Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug&ndash;drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Na&iuml;ve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure&ndash;activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection