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CSCE555 Bioinformatics Lecture 18 Protein Bioinforamtics and Protein Secondary Structure Prediction Meeting: MW 4:00PM-5:15PM SWGN2A21 Instructor: Dr. Jianjun Hu Course page: http://www.scigen.org/csce555 University of South Carolina Department of Computer Science and Engineering 2008 www.cse.sc.edu. Outline Understanding Protein Structures Protein bioinformatics: what and why? Protein Secondary Structure Prediction: problem & algorithm Summary Proteins Large organic compounds made of amino acids Proteins play a crucial role in virtually all biological processes with a broad range of functions. The activity of an enzyme or the function of a protein is governed by the three-dimensional structure How Proteins Are Generated folding Protein Bioinformatics Analysis and prediction of protein structures (Structural Bioinformatics) ◦ Protein Design: design a sequence that will fold into a designated structure Assist experimental biology in assigning functions or suggesting functional hypotheses for all known proteins. Protein Bioinformatics Protein structure databases Gene expression database transcription DNA Genomic DNA Databases translation RNA cDNA ESTs UniGene protein Protein sequence databases phenotype TOP 10 Most Wanted solutions in protein bioinformatics 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Protein sequence alignment Predicting protein features from sequence Function prediction Protein structure prediction Membrane proteins Functional site identification Protein-protein interaction Protein-small molecule interaction (Docking) Protein design Protein engineering Why Protein Bioinformatics? Function = S interactions Disease Mechanism, Gene regulation, Drug design… Relevance of Protein Structure in the Post-Genome Era structure medicine sequence function Protein Structure Example Beta Sheet Helix Loop 2 chains Proteins Structure is Hierarchical Single peptide chain Sequence Local Folding Multiple peptide chains Long-range Folding Multi-meric organization How to Obtain Protein Structures Experimental methods (>50,000) X-ray crystallography or NMR (Nuclear magnetic resonance) spectrometry limitation: protein size, require crystallized proteins Difficult to get crystallized for membrane proteins Computational methods (predictive methods) 2-D structure (secondary structure) 3-D structure (tertiary structure) CASP competition: Critical Assessment of Techniques for Protein Structure Prediction Protein Structure Prediction Problem Given the amino acid sequence of a protein, what’s its shape in threedimensional space? ◦ Sequence → secondary structure → 3D structure → function Why Prediction Needed? The functions of a protein is determined by its structure. Experimental methods to determine protein structure are time-consuming and expensive. Big gap between the available protein sequences and structures. Growth of Protein Sequences and Structures 30000*X species 50,000 as 2008 Data from http://www.dna.affrc.go.jp What determines structures: Inter-atomic Forces Covalent bond (short range, very strong) ◦ Binds atoms into molecules / macromolecules Hydrogen bond (short range, strong) ◦ Binds two polar groups (hydrogen + electronegative atom) Disulfide bond / bridge (short range, very strong) ◦ Covalent bond between sulfhydryl (sulfur + hydrogen) groups Hydrophobic / hydrophillic interaction (weak) ◦ Hydrogen bonding w/ H2O in solution Van der Waal’s interaction (very weak) ◦ Nonspecific electrostatic attractive force Electrostatic forces: ◦ oppositely charged side chains form salt bridges Secondary Structure Predication (2D) For each residues in a protein structure, three possible states: a (a-helix), ß (ß-strand), t (others). amino acid sequence Secondary structure sequence Currently the accuracy of secondary structure methods is nearly 80-82% (2006). Theoretical uplimit is 90% due to uncertainty 10% in real proteins Secondary structure prediction can provide useful information to improve other sequence and structure analysis methods, such as sequence alignment and 3-D modeling. http://bioinf.cs.ucl.ac.uk/psipred/psiform.html PSSP: Protein Secondary Structure Prediction Three Generations • Based on statistical information of single amino acids • Based on local amino acid interaction (segments). Typically a segment containes 11-21 aminoacids • Based on evolutionary information of the homology sequences Formulate PSSP as a machine learning classification problem Using a sliding window to move along the amino acid sequence ◦ Each window denotes an instance ◦ Each amino acid inside the window denotes an attribute ◦ The known secondary structure of the central amino acid is the class label How to generalize protein secondary prediction as a machine learning problem? A set of “examples” are generated from sequence with known secondary structures Examples form a training set Build a neural network classifier Apply the classifier to a sequence with unknown secondary structure Introduction to Neural Network What is an Artificial Neural Network? ◦ An extremely simplified model of the brain Essentially a function approximator Transforms inputs into outputs to the best of its ability How do Neural Network Work? A neuron (perceptron) is a single layer NN The output of a neuron is a function of the weighted sum of the inputs plus a bias Activation Function Binary active function ◦ f(x)=1 if x>=0 ◦ f(x)=0 otherwise The most common sigmoid function used is the logistic function ◦ f(x) = 1/(1 + e-x) Multi-Layer Feedforward NN Example XOR problem (nonlinear classification capable) Where Do The Weights Come From? The weights in a neural network are the most important factor in determining its function Training is the act of presenting the network with some sample data and modifying the weights to better approximate the desired function (class labels) ◦ Supervised Training Supplies the neural network with inputs and the desired outputs Response of the network to the inputs is measured The weights are modified to reduce the difference between the actual and desired outputs Training in Perceptron Neural Net Training a perceptron: Find the weights W that minimizes the error function: E F ( X .W ) t ( X ) P i i i 1 Use steepest descent: - compute gradient: - update weight vector: 2 P: number of training data Xi: training vectors F(W.Xi): output of the perceptron t(Xi) : target value for Xi E E E E E , , ,..., wN w1 w2 w3 Wnew Wold E - iterate (e: learning rate) Back-propagation algorithm For Mult-layer NN, the errors of hidden layers are not known Searches for weight values that minimize the total error of the network over the set of training examples ◦ Forward pass: Compute the outputs of all units in the network, and the error of the output layers. ◦ Backward pass:The network error is backpropogated for updating the weights (credit assignment problem). Feedforward Network Training by Backpropagation: Process Summary Select an architecture Randomly initialize weights While error is too large ◦ Select training pattern and feedforward to find actual network output ◦ Calculate errors and backpropagate error signals ◦ Adjust weights Evaluate performance using the test set 5/23/2017 Copyright G. A. Tagliarini, PhD 28 NN for Protein Secondary Structure Prediction 0 How to Encode Each Amino Acid? 20 bit binary sequence 10000000000000000000-----A 01000000000000000000-----R 00100000000000000000-----N … 00000000000000000001-----V Evaluation of Performance: Accuracy(Q3) ALHEASGPSVILFGSDVTVPPASNAEQAK hhhhhooooeeeeoooeeeooooohhhhh Amino acid sequence ohhhooooeeeeoooooeeeooohhhhhh Q3=22/29=76% Actual Secondary Structure Q3 for random prediction is 33% Secondary structure assignment in real proteins is uncertain to about 10%; Therefore, a “perfect” prediction would have Q3=90%. Performances(CASP) CASP CASP1 YEAR 1994 # of Targets 6 <Q3> Group 63% Rost and Sander Rost CASP2 1996 24 70% CASP3 1998 18 75% Jones CASP4 2000 28 80% Jones Summary Protein bioinformatics is a very important area with many interesting problems Computational methods can have big impact in medicine and molecular biology Secondary protein structure prediction algorithms are very strong Slides Acknowledgements Jinbo Xu University of Waterloo Xingquan Zhu Why predict structure: Can Label Proteins by Dominant Structure Protein classification, Structural Blasting Amino Acids Side chain Each amino acid is identified by its side chain, which determines the properties of this amino acid. Side Chain Properties hydrophobic V, L, I, M, F Hydrophilic N, E, Q, H, K, R, D In-between G, A, S, T, Y, W, C, P Positively charged R, H, L Negatively charged D, E Polar but not charged N, Q, S, T nonpolar A, G, I, L, M, P, V Aromatic F, W, Y Hydrophobic amino acids stay inside of a protein, while Hydrophilic ones tend to stay in the exterior of a protein. Oppositely charged amino acids can form salt bridge. Polar amino acids can participate hydrogen bonding Alpha Helix Examples Beta Sheet Examples Parallel beta sheet Anti-parallel beta sheet Calculate Outputs For Each Neuron Based On The Pattern The output from neuron j for pattern p is Opj where Feedforward 1 pj (net j ) net j 1 e O k ranges the input indices net jover bias *Wbias and Wjk is the weight on the connection from input k to k neuron j 5/23/2017 W jk pk Copyright G. A. Tagliarini, PhD Outputs and O Inputs 40 Calculate The Error Signal For Each Output Neuron The output neuron error signal dpj is given by dpj=(Tpj-Opj) Opj (1-Opj) Tpj is the target value of output neuron j for pattern p Opj is the actual output value of output neuron j for pattern p 5/23/2017 Copyright G. A. Tagliarini, PhD 41 Calculate The Error Signal For Each Hidden Neuron The hidden neuron error signal dpj is given by d pj O pj (1 O pj ) d pkWkj k where dpk is the error signal of a postsynaptic neuron k and Wkj is the weight of the connection from hidden neuron j to the post-synaptic neuron k 5/23/2017 Copyright G. A. Tagliarini, PhD 42