Modeling of transition state by molecular dynamics. Prediction of catalytic efficiency of the mutants of mandelate racemase

Modeling of transition state by molecular dynamic method often requires modification of the force field parameters to describe energy profile accurately. In this work, we avoided the modification by modeling a series of mutants at binding-related site. In predicting the catalytic efficiency (k(cat)/K(m)) of the mutants of mandelate racemase (MR), the prediction performance of three energy subsets was investigated. It was indicated that the interaction-energy subset exhibited better prediction performance than whole-system subset and binding-site subset in both quantity and trend. When prediction error (PE) criterion was equal to 5%, 10 out of 12 samples were predicted correctly within interaction-energy subset, which demonstrated a great application potential of this method in prediction of enzyme catalytic efficiency and enzyme rational design.     

Modeling of Transition State by Molecular Dynamics. Prediction of Catalytic Efficiency of the Mutants of Mandelate Racemase

Abstract

Modeling of transition state by molecular dynamic method often requires modification of the force field parameters to describe energy profile accurately. In this work, we avoided the modification by modeling a series of mutants at binding-related site. In predicting the catalytic efficiency (k _cat /K _m ) of the mutants of mandelate racemase (MR), the prediction performance of three energy subsets was investigated. It was indicated that the interaction-energy subset exhibited better prediction performance than whole-system subset and binding-site subset in both quantity and trend. When prediction error (PE) criterion was equal to 5%, 10 out of 12 samples were predicted correctly within interaction-energy subset, which demonstrated a great application potential of this method in prediction of enzyme catalytic efficiency and enzyme rational design.     

NOTCH1 signaling promotes chemoresistance via regulating ABCC1 expression in prostate cancer stem cells

Chemotherapy is a strategy for patients with advanced prostate cancer, especially those with castration-resistant prostate cancer. Prostate cancer stem cells (PCSCs) are believed to be the origin of cancer recurrence following therapy intervention, including chemotherapy. The mechanisms underlying the chemoresistance of PCSCs are still poorly understood. In the present study, fluorescence-activated cell sorting was used to isolate PCSCs from LNCaP and PC3 cell lines. 3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide was used to measure the cell viability. Quantitative real-time PCR and western blotting were utilized to evaluate the mRNA and protein levels. ShRNA was employed to knock down target gene expression. Chromatin immunoprecipitation (ChIP) was performed to explore the detailed mechanism underlying ABCC1 expression. Our results revealed that the sorted PCSCs showed enhanced chemoresistance ability than matched non-PCSCs. Protein level of activated form of NOTCH1(ICN1) was significantly higher in PCSCs. Inhibition of NOTCH1 with shRNA could decrease ABCC1 expression, and improve chemosensitivity in PCSCs. Finally, ChIP–PCR showed ICN1 could directly bind to the promoter region of ABCC1. In conclusion, NOTCH1 signaling could transactivate ABCC1, resulting in higher chemoresistance ability of PCSCs, which might be one of the important mechanisms underlying the chemoresistance of PCSCs.     

Reversed Hyperbolic Plasmonic Responses in Phosphorene Under Uniaxial Strain

In this paper, hyperbolic plasmonic responses of phosphorene under uniaxial strains have been explored within density functional theory. In the hyperbolic regime, plasmonic slab waveguide modes are found only along armchair direction. Then, uniaxial strains up to 10% have been applied along zigzag and armchair directions, which can significantly modify its plasmonic responses. Under appropriate strain, the signs of permittivities along two in-plane directions can be even reversed, causing switching of the propagating direction of the plasmonic modes into zigzag direction. Our investigations may give a general idea about how to control the hyperbolic plasmonic modes in phosphorene via strain.

     

Loop optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade-off through cross-strategy between machine learning and the B-factor analysis

Trichoderma reesei endoglucanases (EGs) have limited industrial applications due to its low thermostability and activity. Here, we aimed to improve the thermostability of EGs from T. reesei without reducing its activity counteracting the activity–stability trade-off. A cross-strategy combination of machine learning and B-factor analysis was used to predict beneficial amino acid substitution in EG loop optimization. Experimental validation showed single-site mutated EG concomitantly improved enzymatic activity and thermal properties by 17.21%–18.06% and 49.85%–62.90%, respectively, compared with wild-type EGs. Furthermore, the mechanism explained mutant variants had lower root mean square deviation values and a more stable overall structure than the wild type. According to this study, EGs loop optimization is crucial for balancing the activity–stability trade-off, which may provide new insights into how loop region function interacts with enzymatic characteristics. Moreover, the cross-strategy between machine learning and B-factor analysis improved superior enzyme activity–stability performance, which integrated structure-dependent and sequence-dependent information.