MR2CPPIS: Accurate prediction of protein-protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism.

Proteins play a vital role in various biological processes and achieve their functions through protein-protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming. The development of computational prediction methods for PPI sites offers promising alternatives. Most known deep learning (DL) methods employ layer-wise multi-scale CNNs to extract features from protein sequences. But, these methods usually neglect the spatial positions and hierarchical information embedded within protein sequences, which are actually crucial for PPI site prediction. In this paper, we propose MR2CPPIS, a novel sequence-based DL model that utilizes the multi-scale Res2Net with coordinate attention mechanism to exploit multi-scale features and enhance PPI site prediction capability. We leverage the multi-scale Res2Net to expand the receptive field for each network layer, thus capturing multi-scale information of protein sequences at a granular level. To further explore the local contextual features of each target residue, we employ a coordinate attention block to characterize the precise spatial position information, enabling the network to effectively extract long-range dependencies. We evaluate our MR2CPPIS on three public benchmark datasets (Dset 72, Dset 186, and PDBset 164), achieving state-of-the-art performance. The source codes are available at https://github.com/YyinGong/MR2CPPIS.

A CNN-LSTM Ensemble Model for Predicting Protein-Protein Interaction Binding Sites.

Proteins commonly perform biological functions through protein-protein interactions (PPIs). The knowledge of PPI sites is imperative for the understanding of protein functions, disease mechanisms, and drug design. Traditional biological experimental methods for studying PPI sites still incur considerable drawbacks, including long experimental time and high labor costs. Therefore, many computational methods have been proposed for predicting PPI sites. However, achieving high prediction performance and overcoming severe data imbalance remain challenging issues. In this paper, we propose a new sequence-based deep learning model called CLPPIS (standing for CNN-LSTM ensemble based PPI Sites prediction). CLPPIS consists of CNN and LSTM components, which can capture spatial features and sequential features simultaneously. Further, it utilizes a novel feature group as input, which has 7 physicochemical, biophysical, and statistical properties. Besides, it adopts a batch-weighted loss function to reduce the interference of imbalance data. Our work suggests that the integration of protein spatial features and sequential features provides important information for PPI sites prediction. Evaluation on three public benchmark datasets shows that our CLPPIS model significantly outperforms existing state-of-the-art methods.

Heterologous expression of fungal AcGDH alleviates ammonium toxicity and suppresses photorespiration, thereby improving drought tolerance in rice.

Drought stress can significantly affect plant growth and agricultural productivity. Thus, it is essential to explore and identify the optimal genes for the improvement of crop drought tolerance. Here, a fungal NADP(H)-dependent glutamate dehydrogenase gene (AcGDH) was isolated from Aspergillus candidus, and heterologously expressed in rice. AcGDH has a high affinity for NH4+ and increases the ammonium assimilation in rice. AcGDH transgenic plants exhibited a tolerance to drought and alkali stresses, and their photorespiration was significantly suppressed. Our findings demonstrate that AcGDH alleviates ammonium toxicity and suppresses photorespiration by assimilating excess NH4+ and disturbing the delicate balance of carbon and nitrogen metabolism, thereby improving drought tolerance in rice. Moreover, AcGDH not only improved drought tolerance at the seedling stage but also increased the grain yield under drought stress. Thus, AcGDH is a promising candidate gene for maintaining rice grain yield, and offers an opportunity for improving crop yield under drought stress.