Modeling the adaptive immune system: predictions and simulations

Lundegaard, Claus; Lund, Ole; Kesmir, Can; Brunak, Søren; Nielsen, Morten

A large variety of machine-learning techniques are commonly used in the field of immunological bioinformatics ranging from the conventional techniques of position-specific scoring matrices (PSSMs) (Altschul et al., 1997) , Gibbs sampling (Lawrence et al., 1993; Nielsen et al., 2004) , artificial neural networks (ANNs) described in Baldi and Brunak (2001) , hidden Markov models (HMMs) explained in Hughey and Krogh (1996) , and support vector machines (SVMs) described in Cortes and Vapnik (1995) , to more exotic methods like ant colonies (Karpenko et al., 2005) and other motif search algorithms (Bui et al., 2005; Chang et al., 2006; Murugan and Dai, 2005) . ANNs and SVMs and are ideally suited to recognize non-linear patterns, which are believed to contribute to, for instance, peptide-HLA-I interactions (Adams and Koziol, 1995; Brusic et al., 1994; Buus et al., 2003; Gulukota et al., 1997; Nielsen et al., 2003) . In an ANN, information is trained and distributed into a computer network with an input layer, hidden layers and an output layer all connected in a given structure through weighted connections (Baldi and Brunak, 2001) . In a PSSM on the other hand, all positions in the motif are assumed to contribute in an independent manner, and the likelihood for matching a motif is calculated as a sum of individual matrix scores. The Gibbs sampler method is a particular implementation of the PSSM search algorithm, where the optimal PSSM is determined by a search for a sequence alignment that provides maximal information content for a given motif length. Conventionally PSSMs are log-odds matrices (Altschul et al., 1997) , where the weight matrix elements are estimated from the logarithm of the ratio of the observed frequency of a given amino acid to the background frequency of that amino acid. However, many other techniques including the stabilization matrix method (SMM) (Peters and Sette, 2005) , and evolutionary algorithm (Brusic et al., 1998) exist to construct a PSSM. The PSSMs might also be coupled with other information available to compensate for lack of data (Lundegaard et al., 2004) . Finally, HMMs have been used in the field of immunological bioinformatics. These are well suited to characterized biological motifs with an inherent structural composition, and have been used in the field of immunology to predict for instance peptide binding to MHC class I (Mamitsuka, 1998) and class II (Noguchi et al., 2002) molecules. Beside machine-learning techniques, also (empirical) molecular force field modeling techniques (Logean et al., 2001) and 3D Quantitative Structure-Activity Relationship (3D-QSAR) (Doytchinova and Flower, 2002; Zhihua et al., 2004) analysis have been used to predict features of the immune system.