CorNet: Assigning function to networks of co-evolving residues by automated literature mining.

CorNet is a web-based tool for the analysis of co-evolving residue positions in protein super-family sequence alignments. CorNet projects external information such as mutation data extracted from literature on interactively displayed groups of co-evolving residue positions to shed light on the functions associated with these groups and the residues in them. We used CorNet to analyse six enzyme super-families and found that groups of strongly co-evolving residues tend to consist of residues involved in a same function such as activity, specificity, co-factor binding, or enantioselectivity. This finding allows to assign a function to residues for which no data is available yet in the literature. A mutant library was designed to mutate residues observed in a group of co-evolving residues predicted to be involved in enantioselectivity, but for which no literature data is available yet. The resulting set of mutations indeed showed many instances of increased enantioselectivity.

Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces.

BACKGROUND: Many newly detected point mutations are located in protein-coding regions of the human genome. Knowledge of their effects on the protein's 3D structure provides insight into the protein's mechanism, can aid the design of further experiments, and eventually can lead to the development of new medicines and diagnostic tools. RESULTS: In this article we describe HOPE, a fully automatic program that analyzes the structural and functional effects of point mutations. HOPE collects information from a wide range of information sources including calculations on the 3D coordinates of the protein by using WHAT IF Web services, sequence annotations from the UniProt database, and predictions by DAS services. Homology models are built with YASARA. Data is stored in a database and used in a decision scheme to identify the effects of a mutation on the protein's 3D structure and function. HOPE builds a report with text, figures, and animations that is easy to use and understandable for (bio)medical researchers. CONCLUSIONS: We tested HOPE by comparing its output to the results of manually performed projects. In all straightforward cases HOPE performed similar to a trained bioinformatician. The use of 3D structures helps optimize the results in terms of reliability and details. HOPE's results are easy to understand and are presented in a way that is attractive for researchers without an extensive bioinformatics background.

Correlated mutation analyses on super-family alignments reveal functionally important residues.

Correlated mutation analyses (CMA) on multiple sequence alignments are widely used for the prediction of the function of amino acids. The accuracy of CMA-based predictions is mainly determined by the number of sequences, by their evolutionary distances, and by the quality of the alignments. These criteria are best met in structure-based sequence alignments of large super-families. So far, CMA-techniques have mainly been employed to study the receptor interactions. The present work shows how a novel CMA tool, called Comulator, can be used to determine networks of functionally related residues in enzymes. These analyses provide leads for protein engineering studies that are directed towards modification of enzyme specificity or activity. As proof of concept, Comulator has been applied to four enzyme super-families: the isocitrate lyase/phoshoenol-pyruvate mutase super-family, the hexokinase super-family, the RmlC-like cupin super-family, and the FAD-linked oxidases super-family. In each of those cases networks of functionally related residue positions were discovered that upon mutation influenced enzyme specificity and/or activity as predicted. We conclude that CMA is a powerful tool for redesigning enzyme activity and selectivity.

Structure and function of 2,3-dimethylmalate lyase, a PEP mutase/isocitrate lyase superfamily member.

The Aspergillus niger genome contains four genes that encode proteins exhibiting greater than 30% amino acid sequence identity to the confirmed oxaloacetate acetyl hydrolase (OAH), an enzyme that belongs to the phosphoenolpyruvate mutase/isocitrate lyase superfamily. Previous studies have shown that a mutant A. niger strain lacking the OAH gene does not produce oxalate. To identify the function of the protein sharing the highest amino acid sequence identity with the OAH (An07g08390, Swiss-Prot entry Q2L887, 57% identity), we produced the protein in Escherichia coli and purified it for structural and functional studies. A focused substrate screen was used to determine the catalytic function of An07g08390 as (2R,3S)-dimethylmalate lyase (DMML): k(cat)=19.2 s(-1) and K(m)=220 microM. DMML also possesses significant OAH activity (k(cat)=0.5 s(-1) and K(m) =220 microM). DNA array analysis showed that unlike the A. niger oah gene, the DMML encoding gene is subject to catabolite repression. DMML is a key enzyme in bacterial nicotinate catabolism, catalyzing the last of nine enzymatic steps. This pathway does not have a known fungal counterpart. BLAST analysis of the A. niger genome for the presence of a similar pathway revealed the presence of homologs to only some of the pathway enzymes. This and the finding that A. niger does not thrive on nicotinamide as a sole carbon source suggest that the fungal DMML functions in a presently unknown metabolic pathway. The crystal structure of A. niger DMML (in complex with Mg(2+) and in complex with Mg(2+) and a substrate analog: the gem-diol of 3,3-difluoro-oxaloacetate) was determined for the purpose of identifying structural determinants of substrate recognition and catalysis. Structure-guided site-directed mutants were prepared and evaluated to test the contributions made by key active-site residues. In this article, we report the results in the broader context of the lyase branch of the phosphoenolpyruvate mutase/isocitrate lyase superfamily to provide insight into the evolution of functional diversity.