NMR resonance assignments for sparsely <Superscript>15</Superscript>N labeled proteins

For larger proteins, and proteins not amenable to expression in bacterial hosts, it is difficult to deduce structures using NMR methods based on uniform ¹³C, ¹⁵N isotopic labeling and observation of just nuclear Overhauser effects (NOEs). In these cases, sparse labeling with selected ¹⁵N enriched amino acids and extraction of a wider variety of backbone-centered structural constraints is providing an alternate approach. A limitation, however, is the absence of resonance assignment strategies that work without uniform ¹⁵N, ¹³C labeling or preparation of numerous samples labeled with pairs of isotopically labeled amino acids. In this paper an approach applicable to a single sample prepared with sparse ¹⁵N labeling in selected amino acids is presented. It relies on correlation of amide proton exchange rates, measured from data on the intact protein and on digested and sequenced peptides. Application is illustrated using the carbohydrate binding protein, Galectin-3. Limitations and future applications are discussed.     

Gene selection using hybrid binary black hole algorithm and modified binary particle swarm optimization

In cancer classification, gene selection is an important data preprocessing technique, but it is a difficult task due to the large search space. Accordingly, the objective of this study is to develop a hybrid meta-heuristic Binary Black Hole Algorithm (BBHA) and Binary Particle Swarm Optimization (BPSO) (4-2) model that emphasizes gene selection. In this model, the BBHA is embedded in the BPSO (4-2) algorithm to make the BPSO (4-2) more effective and to facilitate the exploration and exploitation of the BPSO (4-2) algorithm to further improve the performance. This model has been associated with Random Forest Recursive Feature Elimination (RF-RFE) pre-filtering technique. The classifiers which are evaluated in the proposed framework are Sparse Partial Least Squares Discriminant Analysis (SPLSDA); k-nearest neighbor and Naive Bayes. The performance of the proposed method was evaluated on two benchmark and three clinical microarrays. The experimental results and statistical analysis confirm the better performance of the BPSO (4-2)-BBHA compared with the BBHA, the BPSO (4-2) and several state-of-the-art methods in terms of avoiding local minima, convergence rate, accuracy and number of selected genes. The results also show that the BPSO (4-2)-BBHA model can successfully identify known biologically and statistically significant genes from the clinical datasets.