From Protein Structure to Function with Bioinformatics.pdf

42 L.A. Kelleyperformance at CASP4 of several groups who outperformed previously successfulthreading groups using PSI-BLAST or some variant.The key to the success of the PSI-BLAST approach lies in a realisation thatevery position in a protein sequence will be under different evolutionary pressures.For example, a glycine in one position may be highly conserved as it isrequired for a particularly tight turn of the protein chain to maintain its topology.Any mutation in this glycine may be lethal as the protein would fail to foldcorrectly. A different glycine elsewhere in the sequence may be in a highly variableloop region under minimal selection pressure. Thus when aligning a querysequence against this structure, the first glycine must be present, but the secondone may vary. It is this position-specific mutational propensity that permits farmore sensitive remote homology detection.A typical use for PSI-BLAST-generated profiles is where the profile for a querysequence is scanned against a database of sequences from the PDB, or conversely,a query sequence is scanned against a library of template profiles. The profilesthemselves need not come from PSI-BLAST. Profile Hidden Markov Models(HMMs) are built from multiple sequence alignments but include more informationthan a standard profile, including the positions of common insertions anddeletions, and transition probabilities to and from match states to each position.Once again this was often coupled to the use of predicted structural features suchas secondary structure. The alternative sequence-profile and profile-sequenceapproaches are shown schematically in Figs. 2.4c and d.Improved profiles and HMMs can be built by using structure alignments ofremote homologues and by adding sequences of unknown structure that can be easilyaligned with each structure (Kelley et al. 2000; Tang et al. 2003). However, usingstructure alignments to build better profiles has often resulted in modest improvementsin remote homologue detection or alignment accuracy. This is probably dueto the non-uniqueness of sequence alignments generated from structure alignments,especially in the vicinity of large insertions or deletions or significant structuralchanges. These may result in misalignments between sets of sequences related toeach structure. The solution of Zhou and Zhou (2005) was to generate fragments ofproteins and use these to build profiles in their successful SP3 method.Hidden Markov Models have been used extensively by a variety of groups to goodeffect in recent years. As mentioned above, one of their key advantages over the relativelysimpler profiles generated by PSI-BLAST is the presence of extra informationregarding gaps and neighbouring residues. However, for both profiles and HMMs,the multiple sequence alignment from which they are derived is of key importance.The sequences used and the quality of the alignment are probably more important tothe power of the profile than subtleties regarding the statistical handling of the alignmentin generating the profile. As a result many groups have found it useful to gatherhomologous sequences using PSI-BLAST, but use a more powerful multiple sequencealignment program to generate a more accurate multiple alignment.As a generalisation of sequence-profile alignments or sequence-HMM comparisons,profile-profile and HMM-HMM alignments have recently demonstrated significantlysuperior performance. Thus instead of using profiles (or HMMs) for only the