Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate.
Understanding complex biological systems requires extensive support from software tools. Such tools are needed at each step of a systems biology computational workflow, which typically consists of data handling, network inference, deep curation, dynamical simulation and model analysis. In addition, there are now efforts to develop integrated software platforms, so that tools that are used at different stages of the workflow and by different researchers can easily be used together. This Review describes the types of software tools that are required at different stages of systems biology research and the current options that are available for systems biology researchers. We also discuss the challenges and prospects for modelling the effects of genetic changes on physiology and the concept of an integrated platform.
We present systemsDock, a web server for network pharmacology-based prediction and analysis, which permits docking simulation and molecular pathway map for comprehensive characterization of ligand selectivity and interpretation of ligand action on a complex molecular network. It incorporates an elaborately designed scoring function for molecular docking to assess protein–ligand binding potential. For large-scale screening and ease of investigation, systemsDock has a user-friendly GUI interface for molecule preparation, parameter specification and result inspection. Ligand binding potentials against individual proteins can be directly displayed on an uploaded molecular interaction map, allowing users to systemically investigate network-dependent effects of a drug or drug candidate. A case study is given to demonstrate how systemsDock can be used to discover a test compound's multi-target activity. systemsDock is freely accessible at http://systemsdock.unit.oist.jp/.
A database with details of the geometry of metal sites in proteins has been set up. The data are derived from metalloprotein structures that are in the Protein Data Bank [PDB; Berman, Henrick, Nakamura & Markley (2006). Nucleic Acids Res. 35, D301-D303] and have been determined at 2.5 Å resolution or better. The database contains all contacts within the crystal asymmetric unit considered to be chemical bonds to any of the metals Na, Mg, K, Ca, Mn, Fe, Co, Ni, Cu or Zn. The stored information includes PDB code, crystal data, resolution of structure determination, refinement program and R factor, protein class (from PDB header), contact distances, atom names of metal and interacting atoms as they appear in the PDB, site occupancies, B values, coordination numbers, information on coordination shapes, and metal-metal distances. This may be accessed by SQL queries, or by a user-friendly web interface which searches for contacts between specified types of atoms [for example Ca and carboxylate O of aspartate, Co and imidazole N of histidine] or which delivers details of all the metal sites in a specified protein. The web interface allows graphical display of the metal site, on its own or within the whole protein molecule, and may be accessed at http://eduliss.bch.ed.ac.uk/MESPEUS/. Some applications are briefly described, including a study of the characteristics of Mg sites that bind adenosine triphosphate, the derivation of an average Mg-O phosphate distance and some problems that arise when average bond distances with high precision are required.
A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.
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