MOTIVATION: The membrane-lytic mechanism of antimicrobial peptides (AMPs) is often overlooked during their in silico discovery process, largely due to the lack of a suitable metric for the membrane-binding propensity of peptides. Previously, we proposed a characteristic called membrane contact probability (MCP) and applied it to the identification of membrane proteins and membrane-lytic AMPs. However, previous MCP predictors were not trained on short peptides targeting bacterial membranes, which may result in unsatisfactory performance for peptide studies. RESULTS: In this study, we present PepMCP, a peptide-tailored model for predicting MCP values of short peptides. We collected more than 500 membrane-lytic AMPs from the literature, conducted coarse-grained molecular dynamics (MD) simulations for these AMPs, and extracted their residue MCP labels from MD trajectories to train PepMCP. PepMCP employs the GraphSAGE framework to address this node regression task, encoding each peptide sequence as a graph with 4-hop edges. PepMCP achieved a Pearson correlation coefficient of 0.883 and an RMSE of 0.123 on the node-level test set. It can recognize membrane-lytic AMPs with the predicted MCP values for each sequence, thereby facilitating mechanism-driven AMP discovery. Additionally, we provide a database, MemAMPdb, which includes the membrane-lytic AMPs, as well as the PepMCP web server for easy access. AVAILABILITY AND IMPLEMENTATION: The code and data are available at https://github.com/ComputBiophys/PepMCP.

With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using traditional methods is a time-consuming and costly process. Deep learning has been applied to the de novo design of AMPs and address AMP classification with high efficiency. In this study, several natural language processing models were combined to design and identify AMPs, i.e. sequence generative adversarial nets, bidirectional encoder representations from transformers and multilayer perceptron. Then, six candidate AMPs were screened by AlphaFold2 structure prediction and molecular dynamic simulations. These peptides show low homology with known AMPs and belong to a novel class of AMPs. After initial bioactivity testing, one of the peptides, A-222, showed inhibition against gram-positive and gram-negative bacteria. The structural analysis of this novel peptide A-222 obtained by nuclear magnetic resonance confirmed the presence of an alpha-helix, which was consistent with the results predicted by AlphaFold2. We then performed a structure-activity relationship study to design a new series of peptide analogs and found that the activities of these analogs could be increased by 4-8-fold against Stenotrophomonas maltophilia WH 006 and Pseudomonas aeruginosa PAO1. Overall, deep learning shows great potential in accelerating the discovery of novel AMPs and holds promise as an important tool for developing novel AMPs.

International audience

As today’s manufacturing domain is becoming more and more knowledge-intensive, knowledge-based systems (KBS) are widely applied in the predictive maintenance domain to detect and predict anomalies in machines and machine components. Within a KBS, decision rules are a comprehensive and interpretable tool for classification and knowledge discovery from data. However, when the decision rules incorporated in a KBS are extracted from heterogeneous sources, they may suffer from several rule quality issues, which weakens the performance of a KBS. To address this issue, in this paper, we propose a rule base refinement approach with considering rule quality measures. The proposed approach is based on a rule integration method for integrating the expert rules and the rules obtained from data mining. Within the integration process, rule accuracy, coverage, redundancy, conflict, and subsumption are the quality measures that we use to refine the rule base. A case study on a real-world data set shows the approach in detail.

Within manufacturing processes, anomalies such as machinery faults and failures may lead to the outage situation of production lines. The outage of production lines is detrimental for the availability of production systems and may cause severe economic loss. To avoid the economic loss that may be caused by the outage situation, the prediction of anomalies on production lines is a crucial concern for manufacturers. Recently, data mining techniques have been applied to the manufacturing domain for predicting occurrence time of anomalies, such as the moment of machinery failure. However, existing predictive maintenance approaches have been limited to the prediction of the time of occurrence of machinery failures, while lacking the capability for identifying the criticality of the failures. This may lead to inappropriate maintenance plans and strategies. In this context, in this paper, we introduce a novel ontology-based approach to facilitate predictive maintenance in industry. The proposed approach is a combination use of fuzzy clustering and semantic technologies, where fuzzy clustering techniques are used to learn the criticality of failures based on machine historical data, and semantic technologies use the results of fuzzy clustering to predict the time of failures and the criticality of them. As results, a domain ontology for modeling predictive maintenance knowledge is developed, and a set of Semantic Web Rule Language (SWRL) predictive rules are proposed to reason about the time and criticality of machinery failures. A case study on a real-world industrial data set is followed to evaluate the usefulness and effectiveness of the proposed approach.

Following the trend of Industry 4.0, automation in different manufacturing processes has triggered the use of intelligent condition monitoring systems, which are crucial for improving productivity and availability of production systems. To develop such an intelligent system, semantic technologies are of paramount importance. This paper introduces an ontology that will be used to develop an intelligent condition monitoring system. The proposed ontology formalizes domain knowledge related to condition monitoring tasks of manufacturing processes. After introducing the ontology in detail, we evaluate the proposed ontology by instantiating it with a case study: a conditional maintenance task of bearings in rotating machinery.