Cooperative Regulation of the Activity of Factor Xa within Prothrombinase by Discrete Amino Acid Regions from Factor Va Heavy Chain†

ABSTRACT: The prothrombinase complex catalyzes the activation of prothrombin to R-thrombin. We have repetitively shown that amino acid region 695DYDY698 from the COOH terminus of the heavy chain of factor Va regulates the rate of cleavage of prothrombin at Arg271 by prothrombinase. We have also recently demonstrated that amino acid region 334DY335 is required for the optimal activity of prothrombinase. To assess the effect of these six amino acid residues on cofactor activity, we created recombinant factor Va molecules combining mutations at amino acid regions 334–335 an

Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>The amount of data on protein-protein interactions (PPIs) available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine.</p> <p>Results</p> <p>To address this need, we have developed the 'Druggable Protein-protein Interaction Assessment System' (Dr. PIAS). Dr. PIAS has a meta-database that stores various types of information (tertiary structures, drugs/chemicals, and biological functions associated with PPIs) retrieved from public sources. By integrating this information, Dr. PIAS assesses whether a PPI is druggable as a target for small chemical ligands by using a supervised machine-learning method, support vector machine (SVM). Dr. PIAS holds not only known druggable PPIs but also all PPIs of human, mouse, rat, and human immunodeficiency virus (HIV) proteins identified to date.</p> <p>Conclusions</p> <p>The design concept of Dr. PIAS is distinct from other published PPI databases in that it focuses on selecting the PPIs most likely to make good drug targets, rather than merely collecting PPI data.</p