RGCNPPIS: A Residual Graph Convolutional Network for Protein-Protein Interaction Site Prediction.

Accurate identification of protein-protein interaction (PPI) sites is crucial for understanding the mechanisms of biological processes, developing PPI networks, and detecting protein functions. Currently, most computational methods primarily concentrate on sequence context features and rarely consider the spatial neighborhood features. To address this limitation, we propose a novel residual graph convolutional network for structure-based PPI site prediction (RGCNPPIS). Specifically, we use a GCN module to extract the global structural features from all spatial neighborhoods, and utilize the GraphSage module to extract local structural features from local spatial neighborhoods. To the best of our knowledge, this is the first work utilizing local structural features for PPI site prediction. We also propose an enhanced residual graph connection to combine the initial node representation, local structural features, and the previous GCN layer's node representation, which enables information transfer between layers and alleviates the over-smoothing problem. Evaluation results demonstrate that RGCNPPIS outperforms state-of-the-art methods on three independent test sets. In addition, the results of ablation experiments and case studies confirm that RGCNPPIS is an effective tool for PPI site prediction.

Construction of a PbII coordination polymer from a semi-rigid ditopic 2,2-biimidazole derivative: synthesis, crystal structure and characterization.

A new PbII coordination polymer, poly[0.75(aqua)[µ3-4,4'-(1H,1'H-[2,2'-biimidazole]-1,1'-diyl)dibenzoato-κ5O,O';N;O'',O''']]lead(II)] 1.25-hydrate], {[Pb(C20H12N4O4)(H2O)0.75]·1.25H2O}n or {[Pb(L)(H2O)0.75]·1.25H2O}n (1) [H2L = 4,4'-(1H,1'H-[2,2'-biimidazole]-1,1'-diyl)dibenzoic acid], was synthesized under solvothermal reaction conditions and characterized using microanalysis, IR spectroscopy and thermogravimetric analysis. Single-crystal structure analysis reveals that a two-dimensional corrugated layer structure is formed in 1 and that neighbouring layers are further extended into a three-dimensional structure by hydrogen-bonding interactions. In addition, a fluorescence sensing experiment towards Cu2+ based on the polymeric PbII complex was carried out.

SCN-MLTPP: A Multi-Label Classifier for Predicting Therapeutic Properties of Peptides Using the Stacked Capsule Network.

Identifying the function of therapeutic peptides is an important issue in the development of novel drugs. To reduce the time and labor costs required to identify therapeutic peptides, computational methods are increasingly required. However, most of the existing peptide therapeutic function prediction models are used for predicting a single therapeutic function, ignoring the fact that a bioactive peptide might simultaneously consist of multi-activities. Furthermore, in the few existing multi-label classification models, the feature extraction procedures are still rough. We propose a multi-label framework, called SCN-MLTPP, with a stacked capsule network for predicting the therapeutic properties of peptides. Instead of using peptide sequence vectors alone, SCN-MLTPP extracts different view representation vectors from the therapeutic peptides and learns the contributions of different views to the properties of therapeutic peptides based on the dynamic routing mechanism. Benchmarking results show that as compared with existing multi-label predictors, SCN-MLTPP achieves better and more robust performance for different peptides. In addition, some visual analyses and case studies also demonstrate the model can reliably capture features from multi-view data and predict different peptides.

Quantitative evaluation of effects of different cathode materials on performance in Cd(II)-reduced microbial electrolysis cells.

Three materials including stainless steel woven mesh (SSM), nickel foam (NF) and carbon cloth (CC) were conducted as cathode in Cd(II)-reduced microbial electrolysis cells (MECs), respectively. By using electrode potential slope (EPS) method, the experimental open circuit potentials of three cathodes were similar, while the SSM cathode showed the smallest resistance (6 ± 1 mΩ m2), following by NF cathode (18 ± 2 mΩ m2) and CC cathode (32 ± 5 mΩ m2). These values were analyzed to predicte higher current density and more positive cathode potential in the MEC with SSM cathode under subsequent operating conditions. Electrochemical performance was more likely to be limited by current density than cathode potential. Accordingly, the MEC with SSM cathode obtained better system performance than that with other cathodes. This study further expands the application of EPS method that quantitatively evaluating and effectively selecting cathode materials for better system performance in Cd(II)-reduced MECs.