OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer.

Accurate protein side-chain modeling is crucial for protein folding and design. This is particularly true for molecular docking as ligands primarily interact with side chains. In this study, we introduce a two-stage side-chain modeling approach called OPUS-Rota5. It leverages a modified 3D-Unet to capture the local environmental features, including ligand information of each residue, and then employs the RotaFormer module to aggregate various types of features. Evaluation on three test sets, including recently released targets from CAMEO and CASP15, shows that OPUS-Rota5 significantly outperforms some other leading side-chain modeling methods. We also employ OPUS-Rota5 to refine the side chains of 25 G protein-coupled receptor targets predicted by AlphaFold2 and achieve a significantly improved success rate in a subsequent "back" docking of their natural ligands. Therefore, OPUS-Rota5 is a useful and effective tool for molecular docking, particularly for targets with relatively accurate predicted backbones but not side chains such as high-homology targets.

Aryldiazonium-Salt-Triggered Carboxylative Azotization of Pyrroles or Indoles with Polyhalomethanes via Halogen-Atom Transfer (XAT).

A halogen-atom-transfer (XAT)-based method for carbonylazotization of pyrroles or indoles with aryldiazonium salts and polyhalomethanes via dual C(sp2)-H bond functionalization is described. Using aryldiazonium salts realizes carbonylation/azotization of pyrroles or indoles via polyhalomethyl-radical-mediated and electrophilic substitution, thus providing a green, efficient, and step-economy approach for synthesis of multifunctional pyrroles or indoles from the easily available substrates. Notably, this strategy relies on the use of aryldiazonium salts to extend the well-established iodine atom transfer to bromine or chlorine atom transfer.

OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis.

Cryo-electron microscopy (cryo-EM) captures snapshots of dynamic macromolecules, collectively illustrating the involved structural landscapes. This provides an exciting opportunity to explore the structural variations of macromolecules under study. However, traditional cryo-EM single-particle analysis often yields static structures. Here we describe OPUS-DSD, an algorithm capable of efficiently reconstructing the structural landscape embedded in cryo-EM data. OPUS-DSD uses a three-dimensional convolutional encoder-decoder architecture trained with cryo-EM images, thereby encoding structural variations into a smooth and easily analyzable low-dimension space. This space can be traversed to reconstruct continuous dynamics or clustered to identify distinct conformations. OPUS-DSD can offer meaningful insights into the structural variations of macromolecules, filling in the gaps left by traditional cryo-EM structural determination, and potentially improves the reconstruction resolution by reliably clustering similar particles within the dataset. These functionalities are especially relevant to the study of highly dynamic biological systems. OPUS-DSD is available at https://github.com/alncat/opusDSD .

OPUS-Fold3: a gradient-based protein all-atom folding and docking framework on TensorFlow.

For refining and designing protein structures, it is essential to have an efficient protein folding and docking framework that generates a protein 3D structure based on given constraints. In this study, we introduce OPUS-Fold3 as a gradient-based, all-atom protein folding and docking framework, which accurately generates 3D protein structures in compliance with specified constraints, such as a potential function as long as it can be expressed as a function of positions of heavy atoms. Our tests show that, for example, OPUS-Fold3 achieves performance comparable to pyRosetta in backbone folding and significantly better in side-chain modeling. Developed using Python and TensorFlow 2.4, OPUS-Fold3 is user-friendly for any source-code level modifications and can be seamlessly combined with other deep learning models, thus facilitating collaboration between the biology and AI communities. The source code of OPUS-Fold3 can be downloaded from http://github.com/OPUS-MaLab/opus_fold3. It is freely available for academic usage.

Degradation mechanism of ammonia nitrogen synergistic with bromate under UV or UV/TiO2.

Bromate (BrO3-) and ammonia nitrogen (NH4+) are both typical environmental pollutants: BrO3- has been categorized as one of the Group 2B carcinogen by IARC; an excess of NH4+ might result in the eutrophication of water. The existence of NH4+ could inhibit the transformation of bromide (Br-) to bromate (BrO3-). However, the interaction of NH4+ and BrO3- during the removal process is not clear. This study intends to disclose the mutual relationships of ammonia nitrogen and bromate ions under UV irradiation or UV/TiO2 conditions. Without UV irradiation, BrO3- and NH4+ were both stable even under the presentation of each other. Under UV irradiation or UV/TiO2 conditions, BrO3- and NH4+ promoted the degradation of each other, showing the synergistic degradation mechanism. In the neutral environment, both of BrO3- and NH4+ could be transformed effectively. Furthermore, NH4+ accelerated the transformation of BrO3- to Br- at the reaction beginning and the existence of BrO3- is beneficial for the transformation of NH4+ to N2.

Sparseness and Smoothness Regularized Imaging for improving the resolution of Cryo-EM single-particle reconstruction.

In this paper, we present a refinement method for cryo-electron microscopy (cryo-EM) single-particle reconstruction, termed as OPUS-SSRI (Sparseness and Smoothness Regularized Imaging). In OPUS-SSRI, spatially varying sparseness and smoothness priors are incorporated to improve the regularity of electron density map, and a type of real space penalty function is designed. Moreover, we define the back-projection step as a local kernel regression and propose a first-order method to solve the resulting optimization problem. On the seven cryo-EM datasets that we tested, the average improvement in resolution by OPUS-SSRI over that from RELION 3.0, the commonly used image-processing software for single-particle cryo-EM, was 0.64 Å, with the largest improvement being 1.25 Å. We expect OPUS-SSRI to be an invaluable tool to the broad field of cryo-EM single-particle analysis. The implementation of OPUS-SSRI can be found at https://github.com/alncat/cryoem.

A Hybrid Method for Predicting Traffic Congestion during Peak Hours in the Subway System of Shenzhen.

Traffic congestion, especially during peak hours, has become a challenge for transportation systems in many metropolitan areas, and such congestion causes delays and negative effects for passengers. Many studies have examined the prediction of congestion; however, these studies focus mainly on road traffic, and subway transit, which is the main form of transportation in densely populated cities, such as Tokyo, Paris, and Beijing and Shenzhen in China, has seldom been examined. This study takes Shenzhen as a case study for predicting congestion in a subway system during peak hours and proposes a hybrid method that combines a static traffic assignment model with an agent-based dynamic traffic simulation model to estimate recurrent congestion in this subway system. The homes and work places of the residents in this city are collected and taken to represent the traffic demand for the subway system of Shenzhen. An origin-destination (OD) matrix derived from the data is used as an input in this method of predicting traffic, and the traffic congestion is presented in simulations. To evaluate the predictions, data on the congestion condition of subway segments that are released daily by the Shenzhen metro operation microblog are used as a reference, and a comparative analysis indicates the appropriateness of the proposed method. This study could be taken as an example for similar studies that model subway traffic in other cities.