Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming

Decision-Making Method based on Mixed Integer Linear Programming and Rough Set: A Case Study of Diesel Engine Quality and Assembly Clearance Data

The purpose of this paper is to establish a decision-making system for assembly clearance parameters and machine quality level by analyzing the data of assembly clearance parameters of diesel engine. Accordingly, we present an extension of the rough set theory based on mixed-integer linear programming (MILP) for rough set-based classification (MILP-FRST). Traditional rough set theory has two shortcomings. First, it is sensitive to noise data, resulting in a low accuracy of decision systems based on rough sets. Second, in the classification problem based on rough sets, the attributes cannot be automatically determined. MILP-FRST has the advantages of MILP in resisting noisy data and has the ability to select attributes flexibly and automatically. In order to prove the validity and advantages of the proposed model, we used the machine quality data and assembly clearance data of 29 diesel engines of a certain type to validate the proposed model. Experiments show that the proposed decision-making method based on MILP-FRST model can accurately determine the quality level of the whole machine according to the assembly clearance parameters.

Mutations in protein-binding hot-spots on the hub protein Smad3 differentially affect its protein interactions and Smad3-regulated gene expression

Background

Hub proteins are connected through binding interactions to many other proteins. Smad3, a mediator of signal transduction induced by transforming growth factor beta (TGF-β), serves as a hub protein for over 50 protein-protein interactions. Different cellular responses mediated by Smad3 are the product of cell-type and context dependent Smad3-nucleated protein complexes acting in concert. Our hypothesis is that perturbation of this spectrum of protein complexes by mutation of single protein-binding hot-spots on Smad3 will have distinct consequences on Smad3-mediated responses.

Methodology/Principal Findings

We mutated 28 amino acids on the surface of the Smad3 MH2 domain and identified 22 Smad3 variants with reduced binding to subsets of 17 Smad3-binding proteins including Smad4, SARA, Ski, Smurf2 and SIP1. Mutations defective in binding to Smad4, e.g., D408H, or defective in nucleocytoplasmic shuttling, e.g., W406A, were compromised in modulating the expression levels of a Smad3-dependent reporter gene or six endogenous Smad3-responsive genes: Mmp9, IL11, Tnfaip6, Fermt1, Olfm2 and Wnt11. However, the Smad3 mutants Y226A, Y297A, W326A, K341A, and E267A had distinct differences on TGF-β signaling. For example, K341A and Y226A both reduced the Smad3-mediated activation of the reporter gene by ∼50% but K341A only reduced the TGF-β inducibilty of Olfm2 in contrast to Y226A which reduced the TGF-β inducibility of all six endogenous genes as severely as the W406A mutation. E267A had increased protein binding but reduced TGF-β inducibility because it caused higher basal levels of expression. Y297A had increased TGF-β inducibility because it caused lower Smad3-induced basal levels of gene expression.

Conclusions/Significance

Mutations in protein binding hot-spots on Smad3 reduced the binding to different subsets of interacting proteins and caused a range of quantitative changes in the expression of genes induced by Smad3. This approach should be useful for unraveling which Smad3 protein complexes are critical for specific biological responses.