The concept of consensus in multiple sequence alignments (MSAs) has been used to design and engineer proteins previously with some success. However, consensus design implicitly assumes that all amino acid positions function independently, whereas in reality, the amino acids in a protein interact with each other and work cooperatively to produce the optimum structure required for its function. Correlation analysis is a tool that can capture the effect of such interactions. In a previously published study, we made consensus variants of the triosephosphate isomerase (TIM) protein using MSAs that included sequences form both prokaryotic and eukaryotic organisms. These variants were not completely native-like and were also surprisingly different from each other in terms of oligomeric state, structural dynamics, and activity. Extensive correlation analysis of the TIM database has revealed some clues about factors leading to the unusual behavior of the previously constructed consensus proteins. Among other things, we have found that the more ill-behaved consensus mutant had more broken correlations than the better-behaved consensus variant. Moreover, we report three correlation and phylogeny-based consensus variants of TIM. These variants were more native-like than the previous consensus mutants and considerably more stable than a wild-type TIM from a mesophilic organism. This study highlights the importance of choosing the appropriate diversity of MSA for consensus analysis and provides information that can be used to engineer stable enzymes.

Because a vast majority (99%) of microbes in a given community is likely to be non-cultivable, metagenomics has gradually entered the mainstream of microbial research methods. With the development of high-throughput sequencing techniques, an increasing number of sequencing read data sets of metagenomes from various microbial communities have become available. For these data sets, metagenomic analysis based on mapping reads to microbial genomes has been hampered by the limited number of microbial genomes that are available. Further, this type of analysis is computationally intensive. Thus alignment-free methods, which characterize the sequencing reads with a genomic signature instead of with genomic alignments, can be applied. However, the main requirement of these alignment-free methods is a stable genomic signature that performs reliably. Here, we propose a novel genomic signature of microbial genomes called the intrinsic correlation of oligonucleotides (ICOs). This signature represents the quantification of an intrinsic relationship between any two oligonucleotides. We analyzed microbial genomes at different taxonomic levels using ICO profiles and confirmed the wide availability of useful ICOs. We used intra-genomic and inter-genomic distances and relational grades to evaluate the performance of ICOs as a genomic signature. The results of these experiments showed that ICOs can characterize microbial genomes well, and ICOs were better at distinguishing species than tetranucleotide composition, not only in terms of whole genomes but also in terms of sequence fragments. In addition, we evaluated the performance of a hybrid feature that combined ICOs and tetranucleotide composition. The experimental results showed that the hybrid feature performed better than ICOs or tetranucleotide composition alone. ICOs can characterize microbial genomes successfully and are capable of distinguishing organisms at different taxonomic levels. ICOs perform better than tetranucleotide composition in characterizing microbial genomes. The hybrid feature that used a combination of the two kinds of sequence features had advantages over a single sequence feature.

Consensus engineering, which is replacing amino acids by the most frequently occurring one at their positions in a multiple sequence alignment (MSA), is a known strategy to increase the stability of a protein. The application of this concept to the entire sequence of an enzyme, however, has been tried only a few times mainly because of the problems determining the consensus in highly variable regions. We show that this problem can be solved by replacing such problematic regions by the corresponding sequence of the natural homologue closest to the consensus. When one or a few sub-families are overrepresented in the MSA the consensus sequence is a biased representation of the sequence space. We examine the influence of this bias by constructing three consensus sequences using different MSAs of sucrose phosphorylase (SP). Each consensus enzyme contained about 70 mutations compared to its closest natural homologue and folded correctly and displayed activity on sucrose. Correlation analysis revealed that the family\'s co-evolution network was kept intact, which is one of the main advantages of full-length consensus design. The consensus enzymes displayed an \"average\" thermostability, that is, one that is higher than some but not all known representatives. We cautiously present practical rules for the design of consensus sequences, but warn that the measure of success depends on which natural enzyme is used as point of comparison.