Simultaneous determination of folic acid photolysis products and oxidized guanine derivatives in plasma by liquid chromatography-tandem mass spectrometry.

Folic acid (FA) is easily photodegraded to yield 6-formylpterin and pterin-6-carboxylic acid, which can generate reactive oxygen species and result in the formation of oxidized guanine derivatives such as 8-hydroxy-2'-deoxyguanosine and 8-hydroxy-guanosine. In this study, we developed a simple, rapid, and sensitive liquid chromatography-tandem mass spectrometry strategy for the simultaneous determination of FA photolysis products and oxidized guanine derivatives in plasma samples. Chromatographic separation was performed on a Waters HSS T3 column (2.1 × 100 mm, 5.0 µm) with gradient elution at a flow rate of 0.25 mL/min. Plasma samples were first pretreated with 1% formic acid, followed by protein precipitation with methanol. The developed method showed good linear relationships between 1 and 2000 ng/mL (r2 > 0.99). The intra- and inter-day precisions ranged from 2.6% to 7.5% and from 2.5% to 6.5%, respectively. Recoveries of the analytes were between 75.4% and 112.4% with the relative standard deviation < 9.1%. Finally, the method was applied to quantify FA photolysis products and oxidized guanine derivatives in rats with light and non-light conditions.

Mechanistic Insights into Water Autoionization through Metadynamics Simulation Enhanced by Machine Learning.

Characterizing the free energy landscape of water ionization has been a great challenge due to the limitations from expensive ab initio calculations and strong rare-event features. Lacking equilibrium sampling of the ionization pathway will cause ambiguities in the mechanistic study. Here, we obtain convergent free energy surfaces through nanosecond timescale metadynamics simulations with classical nuclei enhanced by atomic neural network potentials, which yields good reproduction of the equilibrium constant (pK_{w}=14.14) and ionization rate constant (1.369×10^{-3} s^{-1}). The character of transition state unveils the triple-proton transfer occurs through a concerted but asynchronous mechanism. Conditional ensemble average analyses establish the dual-presolvation mechanism, where a pair of hypercoordinated and undercoordinated waters bridged by one H_{2}O cooperatively constitutes the initiation environment for autoionization, and contributes extremely to the local electric field fluctuation to promote water dissociation.

A new family of proteins is required for tethering of Casparian strip membrane domain and nutrient homoeostasis in rice.

Cell-cell junctions are essential for multicellular organisms to maintain nutrient homoeostasis. A plant-type tight junction, the Casparian strip (CS)-Casparian strip membrane domain (CSD) that seals the paracellular space between adjacent endodermal cells, has been known for more than one hundred years. However, the molecular basis of this structure remains unknown. Here we report that a new family of proteins containing a glycine/alanine/proline-rich domain, a lectin domain and a secretory signal peptide (GAPLESS) mediates tethering of the plasma membrane to the CS in rice. The GAPLESS proteins are specifically localized in the CS of root endodermal cells, and loss of their functions results in a disabled cell-cell junction and disrupted nutrient homoeostasis. The GAPLESS protein forms a tight complex with OsCASP1 in the plasma membrane, thereby mediating the CS-CSD junction. This study provides valuable insights into the junctional complex of plant endodermal cells, shedding light on our understanding of nutrient homoeostasis in crops and the cell junctions of eukaryotes.

Kylin 1.0: An ab-initio density matrix renormalization group quantum chemistry program.

The accurate evaluation of electron correlations is highly necessary for the proper descriptions of the electronic structures in strongly correlated molecules, ranging from bond-dissociating molecules, polyradicals, to large conjugated molecules and transition metal complexes. For this purpose, in this paper, a new ab-initio quantum chemistry program Kylin 1.0 for electron correlation calculations at various quantum many-body levels, including configuration interaction (CI), perturbation theory (PT), and density matrix renormalization group (DMRG), is presented. Furthermore, fundamental quantum chemistry methods such as Hartree-Fock self-consistent field (HF-SCF) and the complete active space SCF (CASSCF) are also implemented. The Kylin 1.0 program possesses the following features: (1) a matrix product operator (MPO) formulation-based efficient DMRG implementation for describing static electron correlation within a large active space composed of more than 100 orbitals, supporting both U 1 n × U 1 S z and U 1 n × SU 2 S symmetries; (2) an efficient second-order DMRG-self-consistent field (SCF) implementation; (3) an externally contracted multi-reference CI (MRCI) and Epstein-Nesbet PT with DMRG reference wave functions for including the remaining dynamic electron correlation outside the large active spaces. In this paper, we introduce the capabilities and numerical benchmark examples of the Kylin 1.0 program.

Upregulation of PIK3IP1 monitors the anti-cancer activity of PI3Kα inhibitors in gastric cancer cells.

Gastric cancer remains one of the most malignant cancers in the world. The target-based drugs approved by FDA for gastric cancer treatment include only three targets and benefit a small portion of gastric cancer patients. PIK3CA, a confirmed oncogene, mutates in 7-25% gastric cancer patients. PI3Kα inhibitor BYL719 has been approved for treating specific breast cancer. However, there is no comprehensive study about PI3Kα inhibitor in gastric cancer. In this study, we found pharmacological inhibition or knockdown of PI3Kα effectively inhibited the proliferation of partial gastric cancer cells. Then, we systematically explored the potential biomarkers for predicting or monitoring treatment response according to previous reports and found that basal expression of several receptor tyrosine kinases were related with the sensitivity of gastric cancer cells to BYL719. Next, RNA-seq technique was utilized and showed that BYL719 inhibited Myc targets V2 gene set in sensitive gastric cancer cells, and western blotting further verified that c-Myc was only inhibited in sensitive gastric cancer cells. More importantly, we firstly found BYL719 significantly elevated the expression of PIK3IP1 in sensitive gastric cancer cells, which was also observed in NCI-N87 cell derived xenograft mice models. Meanwhile, knockdown of PIK3IP1 partially rescued the cell growth inhibited by BYL719 in sensitive gastric cancer cells, suggesting the important role of PIK3IP1 in the antitumor activity of BYL719. In conclusion, our study provides biological evidence that PI3Kα is a promising target in specific gastric cancer and the elevation of PIK3IP1 could supply as a biomarker that monitoring treatment response.

Mixed-Precision Implementation of the Density Matrix Renormalization Group.

The mixed-precision optimization is an effective emerging technique for quantum chemistry methods to obtain better computational performance and maintain the chemical accuracy. Here, we developed a two-level mixed-precision implementation for the density matrix renormalization group (DMRG) method. This implementation is based on the idea that the DMRG process is an iterative process. Therefore, the first several iteration steps can be executed in single precision. A feasible single-precision DMRG may generate moderate accuracy, and when a few double-precision cleanup sweeps are added, the double-precision accuracy will be recovered. In the double-precision sweeps, we developed a mixed-precision diagonalization method that can run the most time-consuming step in single precision and maintain the double-precision accuracy. By combining these two mixed-precision schemes, we implemented our mixed-precision DMRG method. The benchmark result shows that our mixed-precision implementation achieved a good performance. A speed-up of up to 2.31 is achieved, and the accuracy is preserved within 0.01 kcal/mol.

Optimizing two-electron repulsion integral calculations with McMurchie-Davidson method on graphic processing unit.

In this article, several optimization methods of two-electron repulsion integral calculations on a graphic processing unit (GPU) are presented. These methods are based on the investigations of the method presented by McMurchie and Davidson (MD). A new Boys function evaluation method for the GPU calculation is introduced. The series summation, the error function, and the finite sum formula method are combined; thus, good performance on the GPU can be achieved. By taking some theoretical study of the McMurchie-Davidson recurrence relations, three major optimization approaches are derived from the deduction of the general term formula for the Hermite expansion coefficient. The three approaches contain a new form of the Hermite expansion coefficients with corresponding recurrence relations, which is more efficient for one-electron integrals and [ss|∗∗] or [∗∗|ss] type two-electron integrals. In addition, a simple yet efficient new recurrence formula for the coefficient evaluation is derived, which is more efficient both in float operations and memory operations than its original one. In average, the new recurrence relation can save 26% float operations and 37% memory operations. Finally, a common sub-expression elimination (CSE) method is implemented. This CSE method is directly generated from some equalities we discovered from the general term formula other than by computer algebra system software. This optimized method achieved up to 3.09 speedups compared to the original MD method on the GPU and up to 92.75 speedups compared to the GAMESS calculation on the central processing unit.

Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse Ansatz via Machine Learning and Cheminformatics.

With the view of achieving a better performance in task assignment and load-balancing, a top-level designed forecasting system for predicting computational times of density-functional theory (DFT)/time-dependent DFT (TDDFT) calculations is presented. The computational time is assumed as the intrinsic property for the molecule. Based on this assumption, the forecasting system is established using the "reinforced concrete", which combines the cheminformatics, several machine-learning (ML) models, and the framework of many-world interpretation (MWI) in multiverse ansatz. Herein, the cheminformatics is used to recognize the topological structure of molecules, the ML models are used to build the relationships between topology and computational cost, and the MWI framework is used to hold various combinations of DFT functionals and basis sets in DFT/TDDFT calculations. Calculated results of molecules from the DrugBank dataset show that (1) it can give quantitative predictions of computational costs, typical mean relative errors can be less than 0.2 for DFT/TDDFT calculations with derivations of ±25% using the exactly pretrained ML models and (2) it can also be employed to various combinations of DFT functional and basis set cases without exactly pretrained ML models, while only slightly enlarge predicting errors.