Excited state properties of 5-fluoro-4-thiouridine derivative†.

The excited state properties of thionated 5-fluorouridine (2',3',5'-tri-O-acetyl-5-fluoro-4-thiouridine; ta5F4TUrd), synthesized with Lawesson's reagent, have been intensively investigated with nanosecond transient absorption spectroscopy, time-resolved thermal lensing, near-infrared emission, and quantum chemical calculation. The intrinsic triplet lifetime of ta5F4TUrd was determined to be 4.2 ± 0.7 µs in acetonitrile, and the formation quantum yield of the excited triplet state was as large as 0.79 ± 0.01 . The quenching rate constants of the triplet ta5F4TUrd by the dissolved oxygen molecule and by the self-quenching process were found to be nearly equal to the diffusion-controlled rate of acetonitrile. The quantum yield of the singlet molecular oxygen produced through energy transfer between the triplet ta5F4TUrd and the dissolved oxygen, Φ Δ , was successfully determined to be 0.61 ± 0.02 under the oxygen-saturated condition. From the oxygen concentration dependence of the Φ Δ value, the fraction of triplet ta5F4TUrd quenched by dissolved oxygen which gives rise to the 1 O2 * formation, S Δ , was successfully obtained to be 0.78 ± 0.01 , which was the largest among the thionucleobases and the thionucleosides reported so far. This could be due to the lower energy and/or the ππ* character of the triplet state.

Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network.

In protein tertiary structure prediction, model quality assessment programs (MQAPs) are often used to select the final structural models from a pool of candidate models generated by multiple templates and prediction methods. The 3-dimensional convolutional neural network (3DCNN) is an expansion of the 2DCNN and has been applied in several fields, including object recognition. The 3DCNN is also used for MQA tasks, but the performance is low due to several technical limitations related to protein tertiary structures, such as orientation alignment. We proposed a novel single-model MQA method based on local structure quality evaluation using a deep neural network containing 3DCNN layers. The proposed method first assesses the quality of local structures for each residue and then evaluates the quality of whole structures by integrating estimated local qualities. We analyzed the model using the CASP11, CASP12, and 3D-Robot datasets and compared the performance of the model with that of the previous 3DCNN method based on whole protein structures. The proposed method showed a significant improvement compared to the previous 3DCNN method for multiple evaluation measures. We also compared the proposed method to other state-of-the-art methods. Our method showed better performance than the previous 3DCNN-based method and comparable accuracy as the current best single-model methods; particularly, in CASP11 stage2, our method showed a Pearson coefficient of 0.486, which was better than those of the best single-model methods (0.366-0.405). A standalone version of the proposed method and data files are available at https://github.com/ishidalab-titech/3DCNN_MQA.