To develop a timesaving and easy operating Reverse Phase (RP) chromatography method, we adopted Thermo Pierce RP C18 Tip to separate small amount hippocampus peptide mixtures and to compare with high performance liquid chromatography (HPLC). According to the separation performance of 4 ACN gradient optimization methods, we determined the best ACN concentration gradient. The results showed that, the experiment took only 10 min by separating with eight ACN concentration gradient, which accounted 1/4 for HPLC. But as for the identified proteins, RP C18 Tip accounted 85.5% for HPLC. ACN gradient of 5%, 15%, 20% and 90% had best repeatability (P = 0.429) and result for separating 30 μg peptides. This method is easy to operate, timesaving and has low cost. It could be used into pretreatment of small amount complex proteomic samples.
Chromatography, High Pressure Liquid ; Chromatography, Reverse-Phase ; methods ; Peptides ; Proteins ; isolation & purification ; Proteomics ; methods

The prediction of compound-protein interaction (CPI) is a critical technological tool for discovering lead compounds and drug repurposing during the process of drug development.In recent years, deep learning has been widely used in CPI research, which has accelerated the development of CPI prediction in drug discovery.This review focuses on feature-based CPI prediction models.First, we described the datasets, as well as typical feature representation methods commonly used for compounds and proteins in CPI prediction.Based on the critical problems in modeling, we discussed models for CPI prediction from two perspectives: multimodal features and attention mechanisms.Then, the performance of 12 selected models was evaluated on 3 benchmark datasets for both classification and regression tasks.Finally, the review summarizes the existing challenges in this field and prospects for future directions.We believe that this investigation will provide some reference and insight for further research on CPI prediction.

Abstract: Kirsten rat sarcoma viral oncogene homolog (KRAS) gene is one of the most commonly mutated oncogenes. It has been found that KRAS inhibitors have the potential therapeutic effect on cancer patients with this gene mutation. In this study, machine learning was applied to develop a QSAR(quantitative structure-activity relationship) model for KRAS small molecule inhibitors. A total of 1857data points of IC50 and SMILES(simplified molecular input line entry system) for KRAS inhibitors were collected from three databases: ChEMBL, BindingDB, and PubChem. And nine different classifiers were constructed using three different feature screening methods combined with three machine learning models, namely, random forest, support vector machine, and extreme gradient boosting machine. The results showed that the SVM model combined with mutual information feature selection exhibited the best performance: AUCtest=0.912, ACCtest=0.859, F1test=0.890. Moreover, it also demonstrated good predictive performance on the external validation set(AUCExt=0.944, RecallExt=0.856, FPRExt=0.111). This study provides a new technical route for KRAS inhibitor screening in natural product databases using artificial intelligence methods.

OBJECTIVE To investigate the impact of the methionine synthase reductase （MTRR） rs10380 C＞T gene polymorphism on methotrexate （MTX） plasma concentration， adverse drug reaction， and prognosis in children with intracranial tumors. METHODS Peripheral blood was collected from children with intracranial tumors， and genomic DNA was extracted. The MTRR rs10380 C＞T genotype was analyzed using matrix-assisted laser desorption/ionization-time of flight-mass spectrometry. The association of the MTRR rs10380 C＞T gene polymorphism with the ratio of MTX plasma concentration to dose （C/D ratio）， adverse drug reaction， tumor recurrence， and metastasis was analyzed. Bioinformatics analysis was used to explore the association of the rs10380 genotype and MTRR gene expression and its possible mechanisms. RESULTS A total of 75 children were included in the study. The distribution frequencies of the wild-type CC genotype and C allele of rs10380 were 62.67% and 81.33%， respectively， while the distribution frequencies of the variant CT genotype and T allele were 37.33% and 18.67%， respectively， which were in accordance with Hardy-Weinberg equilibrium（P＞0.05）. The incidence of electrolyte disorders （51.06%） and tumor metastasis rate （57.45%） in children with the CC genotype were significantly higher than those with the CT genotype （P＜0.05）. No significant differences were observed in the 24-hour and 42-hour C/D ratios and recurrence rates between the two genotypes of children （P＞0.05）. Bioinformatics analysis showed that MTRR protein mainly works in conjunction with 10 proteins， including MMAA， and was involved in various biological processes such as sulfur amino acid biosynthesis. CONCLUSIONS The MTRR rs10380 CC genotype may be a risk factor for electrolyte disorders and tumor metastasis in children with intracranial tumors after MTX chemotherapy.

OBJECTIVES: With the increasing detection rate of lung nodules, the qualitative problem of lung nodules has become one of the key clinical issues. This study aims to evaluate the value of combining dynamic contrast-enhanced (DCE) MRI based on time-resolved imaging with interleaved stochastic trajectories-volume interpolated breath hold examination (TWIST-VIBE) with T₁ weighted free-breathing star-volumetric interpolated breath hold examination (T₁WI star-VIBE) in identifying benign and malignant lung nodules. METHODS: We retrospectively analyzed 79 adults with undetermined lung nodules before the operation. All nodules of patients included were classified into malignant nodules (n=58) and benign nodules (n=26) based on final diagnosis. The unenhanced T₁WI-VIBE, the contrast-enhanced T₁WI star-VIBE, and the DCE curve based on TWIST-VIBE were performed. The corresponding qualitative [wash-in time, wash-out time, time to peak (TTP), arrival time (AT), positive enhancement integral (PEI)] and quantitative parameters [volume transfer constant (Ktrans), interstitium-to-plasma rate constant (Kep), and fractional extracellular space volume (Ve)] were evaluated. Besides, the diagnostic efficacy (sensitivity and specificity) of enhanced CT and MRI were compared. RESULTS: There were significant differences in unenhanced T₁WI-VIBE hypo-intensity, and type of A, B, C DCE curve type between benign and malignant lung nodules (all P<0.001). Pulmonary malignant nodules had a shorter wash-out time than benign nodules (P=0.001), and the differences of the remaining parameters were not statistically significant (all P>0.05). After T₁WI star-VIBE contrast-enhanced MRI, the image quality was further improved. Compared with enhanced CT scan, the sensitivity (82.76% vs 80.50%) and the specificity (69.23% vs 57.10%) based on MRI were higher than that of CT (both P<0.001). CONCLUSIONS T₁WI star-VIBE and dynamic contrast-enhanced MRI based on TWIST-VIBE were helpful to improve the image resolution and provide more information for clinical differentiation between benign and malignant lung nodules.
Adult ; Humans ; Retrospective Studies ; Magnetic Resonance Imaging ; Plasma ; Tomography, X-Ray Computed ; Lung