Some studies on protein structure alignment algorithms

The alignment of two protein structures is a fundamental problem in structural bioinformatics.Their structural similarity carries with it the connotation of similar functional behavior that couldbe exploited in various applications. A plethora of algorithms, including one by us, is a testamentto the importance of the problem. In this thesis, we propose a novel approach to measure theeectiveness of a sample of four such algorithms, DALI, TM-align, CE and EDAlignsse, for de-tecting structural similarities among proteins. The underlying premise is that structural proximityshould translate into spatial proximity. To verify this, we carried out extensive experiments withve dierent datasets, each consisting of proteins from two to six dierent families.In further addition to our work, we have focused on the area of computational methods foraligning multiple protein structures. This problem is known for its np-complete nature. Therefore,there are many ways to come up with a solution which can be better than the existing ones or atleast as good as them. Such a solution is presented here in this thesis. We have used a heuristicalgorithm which is the Progressive Multiple Alignment approach, to have the multiple sequencealignment. We used the root mean square deviation (RMSD) as a measure of alignment quality andreported this measure for a large and varied number of alignments. We also compared the executiontimes of our algorithm with the well-known algorithm MUSTANG for all the tested alignments

Rebooting life: engineering non-natural nucleic acids, proteins and metabolites in microorganisms

The surging demand of value-added products has steered the transition of laboratory microbes to microbial cell factories (MCFs) for facilitating production of large quantities of important native and non-native biomolecules. This shift has been possible through rewiring and optimizing different biosynthetic pathways in microbes by exercising frameworks of metabolic engineering and synthetic biology principles. Advances in genome and metabolic engineering have provided a fillip to create novel biomolecules and produce non-natural molecules with multitude of applications. To this end, numerous MCFs have been developed and employed for production of non-natural nucleic acids, proteins and different metabolites to meet various therapeutic, biotechnological and industrial applications. The present review describes recent advances in production of non-natural amino acids, nucleic acids, biofuel candidates and platform chemicals

Reference centile charts of first-trimester aneuploidy screening & Doppler parameters for Indian population

Background & objectives: The risk estimation for foetal aneuploidies in the first trimester of pregnancy uses reference curves based on western data. The objective of this study was to construct the reference curves of first-trimester foetal aneuploidy screening parameters for the Indian women. Methods: Cross-sectional data were obtained from 1204 singleton pregnancies between the crown-rump length (CRL) of 40-84 mm. Linear regression models were constructed; the mean, median and standard deviation were derived as a function of CRL. Results: The mean value of CRL was 61.3 mm. The regression analysis showed a significant correlation between all variables and CRL (P< 0.001). There was a positive correlation of CRL with nuchal translucency (NT) (y=0.010x+0.629, R2=0.116) and pregnancy-associated plasma protein-A (PAPP-A) (y=0.107x−1.079, R2=0.173), whereas inverse correlation was seen with free β-human chorionic gonadotropin (β-hCG) (y=−0.409x+75.025, R2=0.018) and Doppler parameters pulsatility index (PI) (y=−0.008x+1.924 R2=0.053). The centile charts of NT, PAPP-A, free β-hCG and uterine artery (Ut A) Doppler PI were constructed. Interpretation & conclusions: The reference centile charts of first trimester aneuploidy screening along with Doppler parameters were derived in Indian pregnant women. These centile charts may be used as a reference for clinical use in Indian population