dbMPIKT: A web resource for the kinetic and thermodynamic database of mutant protein interactions

Protein-protein interactions (PPIs) perform important roles on biological functions. Researches of mutants on protein interactions can further understand PPIs. In the past, many researchers have developed databases that stored mutants on protein interactions, which are old and not updated till now. To address the issue, we developed a kinetic and thermodynamic database of mutant protein interactions (dbMPIKT) that can be freely accessible at our website. This database contains 5291 mutants that integrated data from previous databases and data from literatures for nearly three years. Furthermore, the data were analyzed, involving mutation number, mutation type, protein pair source and network map construction. On the whole, the database provides new data to further improve the study on PPIs. Website: http://210.45.212.128/lqy/index.ph

Using Physicochemical Properties of Amino Acids to induce Graphical Models of Residue Couplings

Residue coupling in protein families is an important indicator for structural and functional conservation. Two residues are coupled if changes of amino acid at one residue location are correlated with changes in the other. Many algorithmic techniques have been proposed to discover couplings in protein families. These approaches discover couplings over amino acid combinations but do not yield mechanistic or other explanations for such couplings. We propose to study couplings in terms of amino acid classes such as polarity, hydrophobicity, size, and reactivity, and present two algorithms for learning probabilistic graphical models of amino acid class-based residue couplings. Our probabilistic graphical models provide a sound basis for predictive, diagnostic, and abductive reasoning. Further, our methods can take optional structural priors into account for building graphical models. The resulting models are useful in assessing the likelihood of a new protein to be a member of a family and for designing new protein sequences by sampling from the graphical model. We apply our approaches to understand couplings in two protein families: Nickel-responsive transription factors (NikR) and G-protein coupled receptors (GPCRs). The results demonstrate that our graphcial models based on sequences, physicochemical properties, and protein structure are capable of detecting amino acid classbased couplings between important residues that play roles in activities of these two families