Introduction and Methods:

Today I prsented the paper which introduces HHsearch. HHSearch is mainly used for template detection and still is one of the best known methods. The early methods for template detection simply performed sequence-sequence comparison between a query protein and a database of targets: such as BLAST or FASTA. But then more advanced methods such as PSI-BLAST were introduced which perform sequence-profile comparisons. Profiles are build using multiple sequence alignments (MSA) of protein families which describes the probability of the occurrence of an amino acid in a column of a MSA. In other words, profiles describe the conservation of amino acids among families. A remarkable advance in template detection was introduced by methods performing profile-profile comparisons such as COMPASS, PROF-SIM and LAMA.

Hidden Markov Model (HMM) introduced a new way of building profiles which resulted in methods performing sequence-HMM comparisons to detect templates. A HMM is similar to a state machine and is build using a MSA where each column is given a ‘M’ (match) state. A match state emits amino acids based on probability calculated from the MSA. In addition of a match state, all columns of the MSA will have an ‘I’ (insert) state and ‘D’ (delete/gap) state (See below figure for an example). There is a transition between states (shown by arrows) where all transitions also have probabilities. Having a sequence and a HMM, the sequence can be alignment on the HMM. In other words there is path in the HMM which associates to emitting the sequence and a log-odd score associated with this path. Dynamic programming (Viterbi algorithm) is used to detect this path (similar to needleman and wunsch algorithm for sequence-sequence alignments). More detail can be found here.

Example of an HMM. Taken from Bioinformatics Sequence and Genome Analysis, David W. Mount (http://compbio.pbworks.com/w/page/16252909/Multiple%20Sequence%20Alignment)

Example of an HMM. Taken from Bioinformatics Sequence and Genome Analysis, David W. Mount

The novel idea of HHsearch was that instead of performing sequence-HMM alignment, HMM-HMM alignments can be used for template detection. Therefore, they first discuss the formula used to calculate the log-odd scores which are required by the Viterbi algorithm to find the best aligned path. The score of aligning two columns in two HMMs (query profile q and template profile t) are calculated as:


Using this score, the best alignment between two profile HMMs is generated by maximizing the log-sum-odds score as in the formula below:


Now that the scoring function is defined, Viterbi algorithm is used to maximize this function. For simplicity HHsearch has described five pair states and the allowed transition between them (see figure 1.C of the paper). Therefore five dynamic program matrices are needed to align the two HMMs.

There are two other main parameters that can contribute to the final score of the HHsearch functions: 1) Correlation score: this score is based on the idea that if two proteins are homologs then once they are aligned high score columns (conserved columns) should cluster together. Which means the higher this score is the more homolog the sequences are. This score can be simply added to the ‘final best alignment score’ from the Viterbi algorithm. 2) While aligning two columns of the two HMMs, Secondary Structure (SS) elemnts are scored using statistical scores generated by the authors, which take into account the confidence values of the SS predictions. HHsearch provides two set of scores for SS comparison: 1- predicted vs predicted 2- predicted vs known. The second one is mainly used when performing 3D structure prediction. These acores are added to the Viterbi algorithm scoring functions in the formula 5,6 and 7 of the paper.



HHsearch performance was compared to BLAST(sequence-sequence), PSI-BLAST(profile-sequence) , HMMER(HMM-sequence) and COMPASS and PROF-SIM(profile-profile) methods. five version of HHsearch was benchmarked:

HHserach 0 -> basic profile-profile comparison with fix gap penalties
HHserach 1 -> basic HMM-HMM comparison
HHsearch 2 -> HMM-HMM comparison including correlation score
HHsearch 3 -> HMM-HMM comparison including correlation score + predicted vs predicted SS
HHsearch 4 -> HMM-HMM comparison including correlation score + predicted vs known SS

The dataset used in the comparison study consists of 3691 sequences taken from the SCOP database filtered at 20% sequence identity (SCOP-20). For each sequence an alignment is prepared using PSI-BLAST. These alignments are used to compare methods.

Detection of homolog:

The ability of methods in detecting homologs are compared against each other. Homolog and non-homolog definitions are: If two domains of SCOP-20 are in the same SCOP superfamily then they are considered as homologs. If the two domains are from different classes they are considered as non-homologs. All other pairs are classified as unknown.
The performance are compared by drawing TP (number ofhomolog pairs) vs FP (number of non-homolog pairs) curves, for all-against-all comparison of data in SCOP-20. The highest curves represent the best performing method. This curve is shown in Figure 2 of the paper. In this dataset the total number of TP are 41505 and the total number of FP are 1.08 x 10^7. The worse method is BLAST which at a error rate of 10% will only find 22% of the homologs. carrying on from BLAST the detection ability grows as:

BLAST < PSI_BLAST < HMMER < PROF-SIM < COMPASS < HHsearch0 < HHsearch1 < HHsearch2 < HHsearch3 < HHsearch4 Studying the results from HHsearch in detail the authors realised that in some cases HHSearch (with high confidence) groups two domains from different superfamily or even different folds as homologs. Looking at the structures of these proteins the authors noticed that the structure of these proteins are either locally or globally similar. Therefore, proteins defined by SCOP to be in different superfamilies or fold might actually be homologs. Therefore, they repeated the same test but changing the definition of TP and F: -homologs (TPs) are domains from the same superfamily OR if their sequence-based alignment resulted in an structure alignment with MaxSub score of 0.1. (MaxSub ranges from 0-1, where 0 is insignificant and 1 very significant.) -non-homologs (FPs) are those domains from different classes AND zero MaxSub score. Domains not in these two categories are grouped as unknow. Figure 3 of the paper displays the new results. Although the figures 2 and 3 look similar there are few main points concluded from these diagrams: 1- At the same error rate all methods except BLAST detect more homologs compared to Figure 2 of the paper. 2- With the new definitions, sensitive tools improve more than others in homolog detection such as HHsearch 3 and 4. Since the new set of homologs are harder to detect and therefore only sensitive tools can detect them 3- COMPASS and PRO-SIM perform better than HHsearch 0 which means they are better at remote homolog detection. To check this hypothesis, they draw TP vs FP (Figure 4 of paper) only for TPs from different families. Not only they confirm the hypothesis, they notice that using SS (HHsearch 3 and 4) they detect more TPs. Interesting enough as the pairs under study evolutionary diverge, the power of SS in detection becomes bolder. Alignment quality:

The quality of a homology model really depends on how well the query protein is aligned with the homolog. Therefore, in this paper all of the methods are compared on their ability on building accurate alignments. To do so, using an aligned pair of sequences, the 3D structures of two proteins
are superposed and the spatial distances are evaluated (similar to MaxSub method). The authors also introduced the Sbalance score which is similar to MaxSub but considers over and under predictions that MaxSub fails to consider.

Comparing alignment qualities using MaxSub score (Fig 5 of paper) we can roughly conclude the performance as:

BLAST < PSI_BLAST < PROF-SIM < COMPASS < HHsearch0 < HMMER < HHsearch1 < HHsearch2 < HHsearch3 < HHsearch4 again more sensitive methods are able to build better alignments for distant homologs. Comparing alignment qualities using Sbalance score (Fig 6 of paper) we can roughly conclude the performance as: BLAST < PSI_BLAST < HMMER < PROF-SIM < COMPASS < HHsearch0 < HHsearch1 < HHsearch2 < HHsearch4 < HHsearch3 HMMER now is inferior to profile-profile alignment methods. In addition HHsearch 3 is the winner. In general HMM-HMM methods are superior to all other methods for homolog detection and building alignments. HHsearch 4 has shown to be able to detect related structures (more than other methods) in the the same superfamily and folds. Using the same idea more accurate tools have been developed since this paper such as HHblits from the same group. Also, recently a method has introduced Markov Random Fields to detect homologs, with better performance than HHsearch and HHblits.

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