Statistical Analysis of Terminal Extensions of Protein β-Strand Pairs

Shaking the β-Bulges

β-bulges are irregularities inside the β-sheets. They represent more than 3 percent of the protein residues, i.e., they are as frequent as 3.₁₀ helices. In terms of evolution, β-bulges are not more conserved than any other local protein conformations within homologous protein structures. In a first of its kind study, we have investigated the dynamical behaviour of β-bulges using the largest known set of protein molecular dynamics simulations. We observed that more than 50 percent of the existing β-bulges in protein crystal structures remained stable during dynamics while more than1/6^th were not stable at all and disappeared entirely. Surprisingly, 1.1 percent of β-bulges that appeared remained stable. β-bulges have been categorized in different subtypes. The most common β-bulges' types are the smallest insertion in β-strands (namely AC and AG); they are found as stable as the whole β-bulges dataset. Low occurring types (namely PC and AS), that have the largest insertions, are significantly more stable than expected. Thus, this pioneer study allowed to precisely quantify the stability of the β-bulges, demonstrating their structural robustness, with few unexpected cases raising structural questions.

Continuous-time Markov chain-based flux analysis in metabolism

Metabolic flux analysis (MFA), a key technology in bioinformatics, is an effective way of analyzing the entire metabolic system by measuring fluxes. Many existing MFA approaches are based on differential equations, which are complicated to be solved mathematically. So MFA requires some simple approaches to investigate metabolism further. In this article, we applied continuous-time Markov chain to MFA, called MMFA approach, and transformed the MFA problem into a set of quadratic equations by analyzing the transition probability of each carbon atom in the entire metabolic system. Unlike the other methods, MMFA analyzes the metabolic model only through the transition probability. This approach is very generic and it could be applied to any metabolic system if all the reaction mechanisms in the system are known. The results of the MMFA approach were compared with several chemical reaction equilibrium constants from early experiments by taking pentose phosphate pathway as an example.