ALBERT

Description: : Although in silico drug discovery approaches are crucial for the development of pharmaceuticals, their potential advantages in agrochemical industry have not been realized. The challenge for computer-aided methods in agrochemical arena is a lack of sufficient information for both pesticides and their targets. Therefore, it is important to establish such knowledge repertoire that contains comprehensive pesticides’ profiles, which include physicochemical properties, environmental fates, toxicities and mode of actions. Here, we present an integrated platform called Pesticide-Target interaction database (PTID), which comprises a total of 1347 pesticides with rich annotation of ecotoxicological and toxicological data as well as 13 738 interactions of pesticide-target and 4245 protein terms via text mining. Additionally, through the integration of ChemMapper, an in-house computational approach to polypharmacology, PTID can be used as a computational platform to identify pesticides targets and design novel agrochemical products. Availability: http://lilab.ecust.edu.cn/ptid/ . Contact: hlli@ecust.edu.cn ; xhqian@ecust.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Identifying functional modules in protein–protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of ‘higher than expected connectivity’, those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks. Results: In this article, we propose a novel optimization formulation LCP 2 (low two-hop conductance sets) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for LCP 2 by random walk. A spectral approximate algorithm SLCP 2 is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP 2 to a new algorithm (greedy algorithm for LCP 2 ) GLCP 2 to identify overlapping functional modules. We compare SLCP 2 and GLCP 2 with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level Gene Ontology term prediction and especially sparse module detection, has demonstrated that our algorithms based on searching for LCP 2 outperform all other compared algorithms. Availability and implementation: All data and code are available at http://www.cse.usf.edu/~xqian/fmi/slcp2hop/ . Contact: yijie@mail.usf.edu or xqian@ece.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online.