Computational approaches for predicting mutant protein stability

Mutations in the protein affect not only the structure of protein, but also its function and stability. Prediction of mutant protein stability with accuracy is desired for uncovering the molecular aspects of diseases and design of novel proteins. Many advanced computational approaches have been developed over the years, to predict the stability and function of a mutated protein. These approaches based on structure, sequence features and combined features (both structure and sequence features) provide reasonably accurate estimation of the impact of amino acid substitution on stability and function of protein. Recently, consensus tools have been developed by incorporating many tools together, which provide single window results for comparison purpose. In this review, a useful guide for the selection of tools that can be employed in predicting mutated proteins’ stability and disease causing capability is provided.     

AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors

We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes with known structures and binding affinities. The AADS methodology is implemented on an 80 processor cluster and presented as a freely accessible, easy to use tool at http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp .     

Novel atomic-level-based AI topological descriptors: application to QSPR/QSAR modeling

Novel atomic level AI topological indexes based on the adjacency matrix and distance matrix of a graph is used to code the structural environment of each atomic type in a molecule. These AI indexes, along with Xu index, are successfully extended to compounds with heteroatoms in terms of novel vertex degree v(m), which is derived from the valence connectivity delta(v) of Kier-Hall to resolve the differentiation of heteroatoms in molecular graphs. The multiple linear regression (MLR) is used to develop the structure-property/activity models based on the modified Xu and AI indices. The efficiency of these indices is verified by high quality QSPR/QSAR models obtained for several representative physical properties and biological activities of several data sets of alcohols with a wide range of non-hydrogen atoms. The results indicate that the physical properties studied are dominated by molecular size, but other atomic types or groups have small influences dependent on the studied properties. Among all atomic types, -OH groups seem to be most important due to hydrogen-bonding interactions. On the contrary, -OH groups play a dominant role in biological activities studied, although molecular size is also an important factor. These results indicate that both Xu and AI indices are useful model parameters for QSPR/QSAR analysis of complex compounds.     

Computing a new family of shape descriptors for protein structures

The large-scale 3D structure of a protein can be represented by the polygonal curve through the carbon alpha atoms of the protein backbone. We introduce an algorithm for computing the average number of times that a given configuration of crossings on such polygonal curves is seen, the average being taken over all directions in space. Hereby, we introduce a new family of global geometric measures of protein structures, which we compare with the so-called generalized Gauss integrals.     

Genetic approaches to protein purification

A gene fusion system based on the protein A gene from Staphylococcus aureus has been developed to facilitate purification of recombinant proteins, both in large and small scale. Due to the strong interaction with IgG, it is possible to recover gene products fused to various protein A derivatives, in a one-step procedure with high yield and in purity. Site-directed mutagenesis was used to introduce enzymatic and chemical cleavage sites at the fusion point between the protein A derivative and the desired protein. The protein A “tail” can thereby be removed from the affinity purified fusion protein by the appropriate cleavage, releasing biologically active molecules. Recently, the system was improved by designing a synthetic DNA fragment encoding two IgG-binding domains derived from staphylococcal protein A which are resistant to various chemical cleavages. The gene fusion product is secreted to the culture medium of E.coli and can be recovered simply by passing the clarified culture medium through an IgG Fast Flow Sepharose. The system has been used to immobilize enzymes, to obtain monoclonal and polyclonal antibodies and to produce biologically active human peptide hormones in pilot plant scale.