ABSTRACT
BACKGROUND: Coiled-coils are found in different proteins like transcription factors, myosin tail domain, tropomyosin, leucine zippers and kinesins. Analysis of various structures containing coiled-coils has revealed the importance of electrostatic and hydrophobic interactions. In such domains, regions of different strength of interactions need to be identified since they could be biologically relevant. FINDINGS: We have updated our coiled-coil validation webserver, now called COILCHECK+, where new features were added to efficiently identify the strength of interaction at the interface region and measure the density of charged residues and hydrophobic residues. We have examined charged residues and hydrophobic ladders, using a new algorithm called CHAHO, which is incorporated within COILCHECK + server. CHAHO permits the identification of spatial charged residue patches and the continuity of hydrophobic ladder which stabilizes and destabilizes the coiled-coil structure. CONCLUSIONS: The availability of such computational tools should be useful to understand the importance of spatial clustering of charged residues and the continuity of hydrophobic residues at the interface region of coiled-coil dimers. COILCHECK + is a structure based tool to validate coiled-coil stability; it can be accessed at http://caps.ncbs.res.in/coilcheckplus.
Subject(s)
Databases, Protein , Molecular Motor Proteins/chemistry , Myosin Heavy Chains/chemistry , Structural Homology, Protein , Algorithms , Amino Acid Motifs , Animals , Humans , Hydrogen Bonding , Hydrophobic and Hydrophilic Interactions , Internet , Models, Molecular , Molecular Motor Proteins/metabolism , Myosin Heavy Chains/metabolism , Pattern Recognition, Automated , Protein Conformation , Protein Stability , Static Electricity , Structure-Activity Relationship , Tropomyosin/chemistryABSTRACT
In this paper we report a novel three-dimensional QSAR approach, kNN-MFA, developed based on principles of the k-nearest neighbor method combined with various variable selection procedures. The kNN-MFA approach was used to generate models for three different data sets and predict the activity of test molecules through each of these models. The three data sets used were the standard steroid benchmark, an antiinflammatory and an anticancerous data set. The study resulted in kNN-MFA models having better statistical parameters than the reported CoMFA models for all the three data sets. It was also found that stochastic methods generate better models resulting in more accurate predictions as compared to stepwise forward selection procedures. Thus, kNN-MFA method represents a good alternative to CoMFA-like methods.
