Comparative modelling techniques: where are we?

Metagenomic gene discovery: how far have we moved into novel sequence space?

Metagenomics emerged in the late 1990s as a tool for accessing and studying the collective microbial genetic material in the environment. The advent of the technology generated great excitement, as it has provided new opportunities and technologies for studying the wealth of microbial genetic diversity in the environment. Metagenomics has been widely predicted to access new dimensions of protein sequence space. A decade on, we review how far we have actually moved into new sequence space (and other aspects of protein space) using metagenomic tools. While several novel enzyme activities and protein structures have been identified through metagenomic strategies, the greatest advancement has been made in the isolation of novel protein sequences, some of which have no close relatives, form deeply branched lineages and even represent novel families. This is particularly true for glycosyl hydrolases and lipase/esterases, despite the fact that these activities are frequently screened for in metagenomic studies. However, there is much room for improvement in the methods employed and they will need to be addressed so that access to novel biocatalytic activities can be widened.

Fast and accurate methods for predicting short-range constraints in protein models

Protein modeling tools utilize many kinds of structural information that may be predicted from amino acid sequence of a target protein or obtained from experiments. Such data provide geometrical constraints in a modeling process. The main aim is to generate the best possible consensus structure. The quality of models strictly depends on the imposed conditions. In this work we present an algorithm, which predicts short-range distances between Calpha atoms as well as a set of short structural fragments that possibly share structural similarity with a query sequence. The only input of the method is a query sequence profile. The algorithm searches for short protein fragments with high sequence similarity. As a result a statistics of distances observed in the similar fragments is returned. The method can be used also as a scoring function or a short-range knowledge-based potential based on the computed statistics.

Comparative modeling without implicit sequence alignments

MOTIVATION

The number of known protein sequences is about thousand times larger than the number of experimentally solved 3D structures. For more than half of the protein sequences a close or distant structural analog could be identified. The key starting point in a classical comparative modeling is to generate the best possible sequence alignment with a template or templates. With decreasing sequence similarity, the number of errors in the alignments increases and these errors are the main causes of the decreasing accuracy of the molecular models generated. Here we propose a new approach to comparative modeling, which does not require the implicit alignment - the model building phase explores geometric, evolutionary and physical properties of a template (or templates).

RESULTS

The proposed method requires prior identification of a template, although the initial sequence alignment is ignored. The model is built using a very efficient reduced representation search engine CABS to find the best possible superposition of the query protein onto the template represented as a 3D multi-featured scaffold. The criteria used include: sequence similarity, predicted secondary structure consistency, local geometric features and hydrophobicity profile. For more difficult cases, the new method qualitatively outperforms existing schemes of comparative modeling. The algorithm unifies de novo modeling, 3D threading and sequence-based methods. The main idea is general and could be easily combined with other efficient modeling tools as Rosetta, UNRES and others.