Publications (3) View all
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Article: Structural attributes for the recognition of weak and anomalous regions in coiled-coils of myosins and other motor proteins.
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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.BMC Research Notes 09/2012; 5(1):530. -
Article: Group‐Based QSAR (G‐QSAR): Mitigating Interpretation Challenges in QSAR
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ABSTRACT: Several approaches are widely being used as important tools for drug discovery. These approaches include Hansch method, Free-Wilson method and conventional 2-D/3-D QSAR methods. The Hansch analysis assumes that substituents are independent of each other and does not include explicit interactions of groups. In the conventional QSAR method, the interpretation of model generated is rather difficult, as one does not a get clear direction about the site for improvement. A new Group-Based QSAR (G-QSAR) method is proposed which uses descriptors evaluated for the fragments of the molecules generated using specific fragmentation rules defined for a given dataset. Herein, we describe the application of G-QSAR method on two different datasets belonging to a simple congeneric series and a complex noncongeneric series. This method provides models with predictive ability similar or better to conventional methods and in addition provides hints for sites of improvement in the molecules.QSAR & Combinatorial Science 11/2008; 28(1):36 - 51. · 1.55 Impact Factor -
Article: Three-dimensional QSAR using the k-nearest neighbor method and its interpretation.
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ABSTRACT: 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.Journal of Chemical Information and Modeling 46(1):24-31. · 4.68 Impact Factor
