Summary of the Ph.D. work
        The thesis consists of two parts. Part I, deals with the crystal structure analysis of glycinin, a legume class seed storage protein from peanut (Arachis hypogaea). Part II, describes the work on the protein stability and folding rate of two state proteins giving importance to secondary structure and solvent accessibility. In addition, a novel method is proposed which acts as a tool for predicting the stability of protein upon mutants as well as for assessing the performance of other protein mutant stability prediction algorithms.  

Crystal Structure of Glycinin: a seed storage protein from Peanut (Arachis hypogaea):

          Seed proteins are the important component of nutrition for both humans and animals. Plant proteins are the cheapest available sources of protein in many countries, hence form an important part of human diet. The principal function of seed storage proteins is to serve as the major nitrogen source for the development of the plant.

          In order to determine the three-dimensional structure of the 11S protein glycinin, purification, crystallization and X-ray diffraction data collection were made from the peanut source. Peanut (
Arachis hypogaea L.), a legume with 24% protein, is a major source of plant protein in most tropical and subtropical regions of the world. Glycinin is a hexamer protein with a molecular weight of 300-380 kDa. The protein was extracted, purified and the diffraction quality crystals were obtained after 7 days by hanging drop vapor diffusion method. X-ray diffraction data upto 3.5 A resolution were collected using mar345 image plate detector system. Crystals of glycinin are rhombohedral (hexagonal obverse setting) with space group R3 and the cell dimensions are a = b = 190.51, c = 232.40 A.  The structure was solved by molecular replacement method using the structure of glycinin from soybean as starting model. Merohedral twin was detected to be 0.48 in the indices h,-h-k,-l. Twin factor was applied for the further refinement which yielded the final R-factor of 29.6% (Rfree = 33.5%). The final refined model consists of two chains, A and B; each containing 383 residues and four disordered regions. The Ramachandran plot for the final model of glycinin shows that 98.3% of the residues are in the allowed regions. Chain A consists of 24 strands and 4 helices and Chain B consists of 21 strands and 4 helices folded into two beta-barrel (jelly roll domains) and two helix domains. The two beta-barrels are named as acidic and basic regions. The overall fold observed in the present model is consistent with the storage protein structures and this fold comes under the cupin superfamily domain.


Stability and Folding Studies of some Selected Proteins:

          This part addresses two important problems in molecular biology, i.e., protein stability and folding. A series of computational analyses were performed to elucidate the factors responsible for it.

          The significance of secondary structure and solvent accessibility to the stability of protein mutants with the help of T4 and human lysozymes were analysed. The properties reflecting hydrophobicity and hydrophobic free energy play a major role to distinguish the stabilizing and destabilizing mutants. The subgroup classification based on secondary structure and the information about its location in the structure yielded good relationship with the experimental DTm (thermal stability). The results reveal that the secondary structure information is equally or even more important than solvent accessibility for understanding the stability of protein mutants.

            In order to identify the reasons for the stability of thermophilic proteins, the analyses were made on good resolution structures of thermophilic and their mesophilic homologous from 23 different families. It was found that a vast majority of thermophilic proteins contribute slightly lower free energy to each energy term than its mesophilic counterpart. The major observation noted from this study is the lower hydrophobic free energy contribution due to carbon atoms and main chain nitrogen atoms in all the thermophilic proteins.

           To identify the mutation-induced changes in protein stability the analysis was performed on 1531 single mutants, for which the stability was identified based on thermal stability (DTm). The parameters which are thought to be associated with thermal stability i.e., the surrounding hydrophobicity, long-range order, stabilization centers and amino acid conservation score are calculated for each mutant and compared with stability. The important conclusion arrived at from the study is that the secondary structural information of the particular mutant is more important to identify its stabilizing behaviour.

           The importance of non-covalent interactions, secondary structural and topological parameters in determining the folding rate of two-state proteins was analysed. It was found that the hydrophobic free energy due to carbon atoms and the hydrogen bonding free energy play important roles in determining the folding rate in combination with other free energies. The comparisons of physico-chemical properties of amino acids with free energy terms indicate that the energetic terms explain the folding rate better rather than amino acid properties. Further the inclusions of secondary structural content and then the topological parameters improved the correlations which demonstrate the importance of topological parameters in determining the folding rates of two-sate proteins.

           The stability of protein mutants were analyzed using three different data sets of 1791, 1396 and 2204 mutants, respectively for thermal stability (DTm), free energy change due to thermal (DDG) and denaturant denaturations (DDGH2O). The mutants were classified into 380 possible substitutions and its stability was assigned using the information obtained with similar type of mutations, based on average assignment method. Analysis showed that this assignment could distinguish the stabilizing and destabilizing mutants at an accuracy of 78-83% at different measures of stability. The nine sub-classifications based on three secondary structures and solvent accessibilities improved the accuracy of assigning stabilizing/destabilizing mutants with an accuracy of 87-91%. From this work it can be concluded that this method could be used for predicting the stability of protein mutants and can be further used as a baseline for assessing new methods for predicting the stability of proteins upon mutations.
Some useful links
About me
Publications
Education Research area
Hosted by www.Geocities.ws

1