Self-organizing hierarchic networks for pattern recognition in protein sequence

Minimum energy configurations of the 2-dimensional HP-model of proteins by self-organizing networks

We use self-organizing maps (SOM) as an efficient tool to find the minimum energy configurations of the 2-dimensional HP-models of proteins. The usage of the SOM for the protein folding problem is similar to that for the Traveling Salesman Problem. The lattice nodes represent the cities whereas the neurons in the network represent the amino acids moving towards the closest cities, subject to the HH interactions. The valid path that maximizes the HH contacts corresponds to the minimum energy configuration of the protein. We report promising results for the cases when the protein completely fills a lattice and discuss the current problems and possible extensions. In all the test sequences up to 36 amino acids, the algorithm was able to find the global minimum and its degeneracies.

Self-organizing tree-growing network for the classification of protein sequences

The self-organizing tree algorithm (SOTA) was recently introduced to construct phylogenetic trees from biological sequences, based on the principles of Kohonen's self-organizing maps and on Fritzke's growing cell structures. SOTA is designed in such a way that the generation of new nodes can be stopped when the sequences assigned to a node are already above a certain similarity threshold. In this way a phylogenetic tree resolved at a high taxonomic level can be obtained. This capability is especially useful to classify sets of diversified sequences. SOTA was originally designed to analyze pre-aligned sequences. It is now adapted to be able to analyze patterns associated to the frequency of residues along a sequence, such as protein dipeptide composition and other n-gram compositions. In this work we show that the algorithm applied to these data is able to not only successfully construct phylogenetic trees of protein families, such as cytochrome c, triosephophate isomerase, and hemoglobin alpha chains, but also classify very diversified sequence data sets, such as a mixture of interleukins and their receptors.

Artificial neural networks for computer-based molecular design

The theory of artificial neural networks is briefly reviewed focusing on supervised and unsupervised techniques which have great impact on current chemical applications. An introduction to molecular descriptors and representation schemes is given. In addition, worked examples of recent advances in this field are highlighted and pioneering publications are discussed. Applications of several types of artificial neural networks to compound classification, modelling of structure-activity relationships, biological target identification, and feature extraction from biopolymers are presented and compared to other techniques. Advantages and limitations of neural networks for computer-aided molecular design and sequence analysis are discussed.

Can functional regions of proteins be predicted from their coding sequences? The case study of G-protein coupled receptors

A filter based on a set of unsupervised neural networks trained with a winner-take-all strategy discloses signals along the coding sequences of G-protein coupled receptors. By comparing with the existing experimental data it appears that these signals correlate with putative functional domains of the proteins. After protein alignment within subfamilies, signals cluster in protein regions which, according to the presently available experimental results, are described as possible functional domains of the folded proteins. The mapping procedure reveals characteristic regions in the coding sequences common and/or characteristic of the receptor subtype. This is particularly noticeable for the third cytoplasmic loop, which is likely to be involved in the molecular coupling of all the subfamilies with G-proteins. The results indicate that our mapping can highlight intrinsic representative features of the coding sequences which, in the case of G-protein coupled receptors, are characteristic of protein functional regions and suggest a possible application of the filter for predicting functional determinants in proteins starting from the coding sequence.

Global self-organization of all known protein sequences reveals inherent biological signatures

A global classification of all currently known protein sequences is performed. Every protein sequence is partitioned into segments of 50 amino acid residues and a dynamic programming distance is calculated between each pair of segments. This space of segments is initially embedded into Euclidean space. The algorithm that we apply embeds every finite metric space into Euclidean space so that (1) the dimension of the host space is small, (2) the metric distortion is small. A novel self-organized, cross-validated clustering algorithm is then applied to the embedded space with Euclidean distances. We monitor the validity of our clustering by randomly splitting the data into two parts and performing an hierarchical clustering algorithm independently on each part. At every level of the hierarchy we cross-validate the clusters in one part with the clusters in the other. The resulting hierarchical tree of clusters offers a new representation of protein sequences and families, which compares favorably with the most updated classifications based on functional and structural data about proteins. Some of the known families clustered into well distinct clusters. Motifs and domains such as the zinc finger, EF hand, homeobox, EGF-like and others are automatically correctly identified, and relations between protein families are revealed by examining the splits along the tree. This clustering leads to a novel representation of protein families, from which functional biological kinship of protein families can be deduced, as demonstrated for the transporter family. Finally, we introduce a new concise representation for complete proteins that is very useful in presenting multiple alignments, and in searching for close relatives in the database. The self-organization method presented is very general and applies to any data with a consistent and computable measure of similarity between data items.