Due to the well-known fact that amino acid sequence homology at a given level leads to similar 3D structure of proteins, several databases are interrelating the databases of sequences and structures. However, the term homology, a fundamental concept in bioinformatics, is often used incorrectly . Sequences are homologous if they are related by divergence from a common ancestor (as a first consequence, the search for homology in the sequence database is used to determine indications for function of proteins). Conversely, analogy relates to the acquisition of common structural or functional features via convergent evolution from unrelated ancestors . Homology is not a measure of similarity, but rather an absolute statement that sequences have a divergent rather than a convergent relationship. Among homologous sequences we can distinguish orthologs (proteins having the same function in different species) and paralogs (proteins performing different but related functions within one organism).
The model building of a target structure based on the comparison with the data extracted from homologous sequences with known structures (parents or templates) is named comparative modelling. Besides, this can be extended to homologs with low percentage of identity. All current comparative modelling methods consist of four sequential steps :1) fold assignment and template selection; 2) template-target alignment; 3) model building; and 4) model evaluation.
STEP 1: Fold assignment
To start the modeling process, we have to identify the template and define an alignment (residue-by-residue equivalences between the target and the template sequences. In homology modelling the stretches to be built are chosen according to their sequence alignment, consequently this is the most crucial step in a modeling process. Any errors at this stage are usually impossible to correct later . The sequences of the fold having the larger similarity with the target sequence will be taken as parents or templates. Currently, around 40% of all protein sequences can have at least one domain modelled on a related known protein structure . In particular, some proteins can have very low sequence identity and yet all share the same fold and a closely related function . The current theory of evolution would hold that such structures, having diverged from a common ancestor, often retain some functional and sequence similarity . In addition, divergent evolution has been recently reported on the basis of a biochemical pathway evolution for some proteins with a common (ba)8 barrel fold for which sequence similarity was not detected .
Originally, searches of homologous sequences to the target were done with local alignement programs as for example: FASTA ; SSEARCH or BLAST that are able to find identities shared between pairs of related sequences. With the high rate at which new sequences become available from genomic initiatives the importance of the sensitive methods of recognizing distant homologies has increased. Such methods are the main source of annotation, hence in the last decade very sensitive approaches have been developed to recognise fold. They have succeeded in different degrees of identification of relationships between remote homologues. These methods include:
2) Advanced sequence comparison procedures that take into account multiple sequence alignments with a position specific scoring system , either provided by a coherent theory for profile methods using machine learning probabilistic models (Hidden Markov Models) ; by a position specific iterative BLAST (PSI-BLAST) ; or by searching in sequence space using intermediate sequences (ISS) . These methods were shown to get better results than simple threading .
3) Finally, new approaches incorporating sequence profiles and knowledge-based threading potential have been used, improving the recognition of remote homologues
2) It exploits the transitivity of homology like the intermediate sequence search , by which a query sequence is aligned to a database (i.e. SWISS-PROT) . Then, all aligned sequences with high significance similarity (E-values<0.001) are used as new seeds and this is iterated until no new sequences are found. This procedure implies a larger search than the obtained by a single sequence search.
STEP 2: Template selection and alignment
For the template selection, one or more templates can be used. The use of multiple templates is not justified when the sequence spread between parents, relative to the target, is not appropriate for the level of expected model error. If both the average level of sequence identity between target and parents is larger than 40% and the sequence spread is too small between parents, then a single parent is used . The search on the database produces several local alignments according to the best score that correlates both target and template sequences. However, this is not necessarily the best alignment to identify residue correspondences and construct the target protein conformation, because the procedure was tuned to find remote homolgues and not the best alignment. Therefore, although target and templates are likely to be correctly aligned if sharing more than 40% identity, they need to be realigned if they are in the "twilight zone" sharing less than 30% identity.
The optimal alignment between homologous proteins, one of them with known 3D structure (template), is further used for constructing a model of the spatial structure of the target. However, after superposition of protein cores, amino acids from loop regions can be significantly displaced . At least 2/3 of the comparative protein modelling cases are based on less than 40% sequence identity between target and templates. To obtain a reasonable level of accuracy, the models must be based on alignments with few errors. Such alignments can usually be obtained when the sequence identity between the modelled sequence and at least one known structure is larger than 30% . A remarkable improvement is obtained by using multiple alignments of global sequence plus additional structural informations instead of the pair sequence local alignments used on the search of likely relatives. Several alignment programs ( MULTIALIGN ; MULTAL ; CLUSTALW ) have been tested against a database of correctly aligned multiple sequences ( BaliBase) . After all, the recent approaches that include local and pre-processed alignments, like those already found by using PSI-BLAST (i.e. DbClustal ); or those recalculating the local ( i.e. using Lalign ) and pre-processed alignments for segemnt pairs (i.e. using Dialign2 ) as for example the program T-Coffee ; or by iterative refinement of the multiple alignement like the program Prrp have obtained extraordinary good results.
Nevertheless, all these alignements loose the structural information given by those templates for which the conformation is known. On superimposing very similar structures upon one another, one is immediately able to distinguish regions of higher conservation; these are commonly referred to as structurally conserved regions (SCRs), whilst those regions that present the largest differences in conformation are referred as structurally variable regions (SVRs). In order to avoid the lost of structural information we suggest the following re-alignement between the target a sequence and the template:
2) With the sequence alignment obtained previously for these templates proceed to calculate a hidden markov profile to align the target sequence to the HMM profile. or alternatively, use some of the following steps instead:
3') To align the related sequences (target , templates and extra related sequences) with Dbclustal, T-coffee, Prrp, etc.; check for the closest result to the structural alignment and refine manually the alignment of the target sequence.
3'') Use all the different alignments obtained by step 3 and/or 3' to model built several models and evaluate the final model by other means (see "evaluation of the model") to choose the best model.
3''') To align all the related sequences as in 3'; obtain hidden
markov profiles with these alignements and align both hidden markov profiles
obtained structuraly (from step 2) and sequencially (as in 3'). Several
alignements of the target sequence with the templates with known 3D structure
are extracted from the final alignments. These alignements will be used
to model built several conformations of the target sequence as in 3'' and
the resulting models will also be evaluated by other methods in order to
choose the best model.
Methods of model building
Several algorithms have been developed in order to obtain a rigid body superimposition between sequences no directly related (JIG-SAW , COMPOSER , among others). SCR construction follows the original approach of Greer using sequentially similar SCR from homologous proteins to define the new core from a multiple alignment: 1) superimposing the known structures of homologous proteins (parents) using the SCRs to construct a framework; 2) superimposing the closest template sequence to the target sequence in the averaged main chain of framework; 3) building the SVRs main chain conformations by fitting compatible structures in the anchored stumps of the framework (see section on SVRs modelling for identification of the stretches to use); and 4) completing the target structure by modelling the side-chains of the target sequence.
The methods based on the satisfaction of spatial restraints (like
MODELLER ) are based on generating as many constraints (or restraints)
as possible from the structural alignments of the parents and building
the target structure like in the NMR methods (using additional energetic
restraints according to the correct stereochemistry of the protein polymer).
It is clear that regions where the structure of the homologous templates
can not be structurally aligned, or where an alignment between the target
and the multiple alignment of the templates is not given, will have to
be built with an additional function. Most of the structural changes are
produced in the loop regions, but occasional secondary structures may also
be involved in variable regions . In the case of multiple superimposed
parents the coordinates are separated into conserved secondary structure
elements and conserved loops.
SVRs modelling can be seen as a mini protein folding problem, consequently the number of methods for predicting loop conformation are twofold: ab initio methods and adopting database searching techniques or knowledge-based approaches
1. The ab initio prediction is based on a conformational search guided by a scoring or energy function: (f,y) space sampling ; minimum perturbation random tweak method ; systematic conformational search ; global energy minimization , local energy minimization ; molecular dynamics simulations ; genetic algorithms ; Monte Carlo and molecular dynamics ; Monte Carlo sampling ; multiple copy sampling ; searching discrete conformations by dynamic programming ; self-consistent field optimization ; among others (for a review see )
2. The database approach to loop prediction consists of finding a segment of main chain that fits the two stem regions of a loop. The procedure has improved since the early works on modeling and in the last few years instead of a single conformation a number of loop conformations are selected for each gap that is as uniformely spread as possible . Hence, the remaining loops from the multiple parent modelling and all loops in the single parent modelling are modelled from database searches in three different databases: 1) homologous structures ; 2) cluster database of loops ; and 3) nonredundant database of proteins with less than 25% homology and accuracy higher than 2.5 A.
The requirements of the chosen loop cluster of conformations
are twofold: 1) the fitting between the two bracing secondary structures,
and 2) a sequence pattern presented in the target loop to model. This procedure
is based on the successful work on canonical loop structures of immunoglobulin
complementary determining regions (CDR) by Chothia et al.. Nevertheless,
the database search is valid only for short and medium sized loops or for
special cases where homologous proteins share some structural commonalities
on the loops although still being considered variable regions (as is the
case for immunoglobulins ). Up to date classifications of long loops have
failed, and it has been demonstrated that a correlation between the geometric
variables describing the loop stems is needed in order to obtain such classification.
This was only asserted for short and medium sized loops .
The source of errors in comparative modelling is mainly due to the lack of templates and the decrease in sequence identity between the target and the templates. These errors are split in five categories:
Finally, the recent work of Lazaridis and Karplus , shows the improvement on the classical molecular mechanics calculation of the energy by including solvation (environmental) terms to detect wrongly modelled regions. Consequently, the criticism on the potential of mean force can not be applied to this approach that did perform as well as statistical functions in discriminating correct and misfolded models .
The experimental evaluation of the model can only be done by site directed mutagenesis or additional information which is not commonly obtained. One way to escape the experiment is by using the knowledge obtained from a highly spread multiple alignments of related sequences introducing the following conditions:
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