Transcript slides
Machine Learning Methods of Protein Secondary Structure Prediction Presented by Chao Wang • What is secondary structure? • How to evaluate secondary structure prediction? • How secondary structure prediction affects the accuracy of tertiary structure prediction? • Our perspective: ``elite'' What is secondary structure? • Hydrogen bond: a non-covalent bond A hydrogen bond is identified if E in the following equation is less than -0.5 kcal/mol 8-state annotation by DSSP Prediction • Early methods of secondary-structure prediction were restricted to predicting the three predominate states: helix, sheet, or random coil. These methods were based on the helix- or sheet-forming propensities of individual amino acids, sometimes coupled with rules for estimating the free energy of forming secondary structure elements. Such methods were typically ~60% accurate in predicting which of the three states (helix/sheet/coil) a residue adopts. A significant increase in accuracy (to nearly ~80%) was made by exploiting multiple sequence alignment; knowing the full distribution of amino acids that occur at a position (and in its vicinity, typically ~7 residues on either side) throughout evolution provides a much better picture of the structural tendencies near that position. For illustration, a given protein might have a glycine at a given position, which by itself might suggest a random coil there. However, multiple sequence alignment might reveal that helix-favoring amino acids occur at that position (and nearby positions) in 95% of homologous proteins spanning nearly a billion years of evolution. Moreover, by examining the average hydrophobicity at that and nearby positions, the same alignment might also suggest a pattern of residue solvent accessibility consistent with an α-helix. Taken together, these factors would suggest that the glycine of the original protein adopts α-helical structure, rather than random coil. Several types of methods are used to combine all the available data to form a 3-state prediction, including neural networks, hidden Markov models and support vector machines. Modern prediction methods also provide a confidence score for their predictions at every position. Outline • • • • CNF model by Jinbo Multi-step learning model by Yaoqi Iterative deep learning model by Yaoqi Our perspective: Elite. – A new enperiment to detect how elite affects secondary structure prediction. • Methods – How to model the probability – Feature Selection • Results – vs. other methods – Improvement Protein 8-class secondary structure prediction using conditional neural fields Zhiyong Wang, Feng Zhao, Jian Peng, and Jinbo Xu Proteomics. 2011 Model Training & Prediction Features Training/testing set Results • Outperform SSpro8 on each state • Regularization factor effect: insensitive, optimal when the factor is set to 9. • Neff effective: for SS prediction, it may not be the best strategy to use evolutionary information in as many homologs as possible. Instead, we should use a subset of sequence homologs to build sequence profile when there are many sequence homologs available. J Comput Chem. 2012