1. PROTEIN SECONDARY STRUCTURE PREDICTION WITH NEURAL NETWORKS

2. Neural Networks Class of algorithms modelled after a biological brain Can be used for both supervised and unsupervised learning Can find statistical correlations in data sets through training

3. The Artificial Neuron A number of input signals One output signal Activation or transfer function The output of a artificial neuron is the activation function of the weighted sum of its inputs: f(∑x i w ij -  )

4. Artificial Neural Networks Neurons are ordered in a layered network Neurons are usually fully interconnetcted between layers The activation function is often the same in the entire network

5. Training a neural network Training means to update the weights of the network Gradient descent learning Tries to alter the wieghts to Mimimize some error function

6. Protein structure prediction Use a neural network to predict the secondary structure of a given amino acid sequence. Predictions often made into 3 classes: alpha, beta or other ASCII-characters not very well suited for input representation of amino acids Orthogonal alphabet: X1 = [1,0,…,0] X2 = [0,1,…,0] Xn = [0,0,…,1]

7. Success of predictions Straightforward implementation, feed-forward network prediction into three classes: 60% Best general prediction: 78% Transmembrane prediction: 95% The key to success lies not in neural networks themselfes, but in combining serveral methods, extensive post-prediction processing, use of multiple alignments of input data etc