An activity prediction model using shape-based descriptor method

Similarity searching, the activity of an unknown compound (target) is predicted through the comparison of an unknown compound with a set of known activities of compounds. The known activities of the most similar compounds are assigned to the unknown compound. Different machine learning methods and Multilevel Neighborhoods of Atoms (MNA) structure descriptors have been applied for the activities prediction. In this paper, we introduced a new activity prediction model with Shape-Based Descriptor Method (SBDM). Experimental results show that SBDM-MNA provides a useful method of using the prior knowledge of target class information (active and inactive compounds) of predicting the activity of orphan compounds. To validate our method, we have applied the SBDM-MNA to different established data sets from literature and compare its performance with the classical MNA descriptor for activity prediction

Data updates on Norine, the reference Non-Ribosomal Peptide knowledge base

International audienc

Norine: a powerful resource for novel nonribosomal peptide discovery

International audienceSince its first release in 2008, Norine remains the unique resource completely devoted to nonribosomal peptides (NRPs). They are very attractive microbial secondary metabolites, displaying a remarkable diversity of structure and functions. Norine (http://bioinfo.lifl.fr/NRP) includes a database now containing more than 1160 annotated peptides and user-friendly interfaces enabling the querying of the database, through the annotations or the structure of the peptides. Dedicated tools are associated for structural comparison of the compounds and prediction of their biological activities. In this paper, we start by describing the knowledgebase and the dedicated tools. We then present some user cases to show how useful Norine is for the discovery of novel nonribosomal peptides

A new fingerprint to predict nonribosomal peptides activity.

Bacteria and fungi use a set of enzymes called nonribosomal peptide synthetases to provide a wide range of natural peptides displaying structural and biological diversity. So, nonribosomal peptides (NRPs) are the basis for some efficient drugs. While discovering new NRPs is very desirable, the process of identifying their biological activity to be used as drugs is a challenge. In this paper, we present a novel peptide fingerprint based on monomer composition (MCFP) of NRPs. MCFP is a novel method for obtaining a representative description of NRP structures from their monomer composition in fingerprint form. Experiments with Norine NRPs database and MCFP show high prediction accuracy (>93 %). Also a high recall rate (>82 %) is obtained when MCFP is used for screening NRPs database. From this study it appears that our fingerprint, built from monomer composition, allows an effective screening and prediction of biological activities of NRPs database