A hybrid method for protein-protein interface prediction

A Hybrid Method for Protein Secondary Structure Prediction

Protein secondary structure can be used to help determine the tertiary structure via the fold recognition. Predicting the secondary structure from the protein sequence has attracted the attention of many researchers. Support Vector Machine (SVM) is a new learning algorithm based on statistical learning theory that has been successfully applied to the protein secondary structure prediction probl...

Prediction of protein crystallization outcome using a hybrid method.

The great power of protein crystallography to reveal biological structure is often limited by the tremendous effort required to produce suitable crystals. A hybrid crystal growth predictive model is presented that combines both experimental and sequence-derived data from target proteins, including novel variables derived from physico-chemical characterization such as R(30), the ratio between a ...

Protein-protein interface: database, analysis and prediction

The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks. Support vector machines (SVM) and related kernel methods offer an attractive approach to predicting pro...

iWRAP: An interface threading approach for protein-protein interaction prediction

A knowledge-based hybrid method for protein secondary structure prediction based on local prediction confidence

Motivation: In our previous approach, we proposed a hybrid method for protein secondary structure prediction, called HYPROSP, which combined our proposed knowledge-based prediction algorithm PROSP and PSIPRED. The knowledge base constructed for PROSP contains small peptides together with their secondary structural information. The hybrid strategy of HYPROSP uses a global quantitative measure, m...