Unraveling the Stability of Plasma Proteins upon Interaction of Synthesized Androstenedione and Its Derivatives-A Biophysical and Computational Approach.

4-Androstene-3-17-dione (4A), also known as androstenedione, is the key intermediate of steroid metabolism. 5ß-Androstane-3-17-dione (5A) and (+)-6-methyl-5ß-androstane-3-17-dione (6M) are the steroid derivatives of androstenedione. The interactions of androstenedione and its derivatives with plasma proteins are important in understanding the distribution and bioavailability of these molecules. In our present study, we have studied the binding affinity of androstenedione and its derivatives with plasma proteins such as human serum albumin (HSA) and α1-acid glycoprotein (AGP). Our results showed that the 4A, 5A, and 6M steroid molecules can form stable complexes with HSA and AGP. The affinity of the studied steroid molecules with HSA is high compared to that with AGP, and the binding constants obtained for 4A, 5A, and 6M with HSA are 5.3 ± 2 × 104, 5.3 ± 1 × 104, and 9.5 ± 0.2 × 104 M-1, respectively. Further, binding sites of these steroid molecules in HSA are identified using molecular displacement and docking studies: it is found that 4A and 5A bind to domain III while 6M binds to domain II of HSA. Furthermore, the circular dichroism data revealed that there is a partial unfolding of the protein while interacting with androstenedione and its derivatives. Also, molecular dynamics simulations were carried out for HSA-androstenedione and its derivative complexes to understand their stability; hence, these results yielded that HSA-androstenedione and its derivative complexes were stabilized after 15 ns and maintained their stable structures.

Perl module and PISE wrappers for the integrated analysis of sequence data and SNP features.

BACKGROUND: There is a need for software scripts and modules for format parsing, data manipulation, statistical analysis and annotation especially for tasks related to marker identification from sequence data and sequence diversity analysis. RESULTS: Here we present several new Perl scripts and a module for sequence data diversity analysis. To enable the use of these software with other public domain tools, we also make available PISE (Pasteur Institute Software Environment) wrappers for these Perl scripts and module. This enables the user to generate pipelines for automated analysis, since PISE is a web interface generator for bioinformatics programmes. CONCLUSION: A new set of modules and scripts for diversity statistic calculation, format parsing and data manipulation are available with PISE wrappers that enable pipelining of these scripts with commonly used contig assembly and sequence feature prediction software, to answer specific sequence diversity related questions.