Program protein structure

An advantage of Matras is its structure similarity score, which is defined as the log-odds of the probabilities, similar to Dayhoff's substitution model of amino acids. This score is designed to detect evolutionarily related homologous structural similarities.

Our web server has three main services. The most important output files are model-single. In this example, the second model TvLDH. All of these scores are not absolute measures, in the sense that they can only be used to rank models calculated from the same alignment. Once a final model is selected, there are many ways to further assess it. Links to other programs for model assessment can be found in Table 5. However, before any external evaluation of the model, one should check the log file from the modeling run for runtime errors model-single.

This is necessary for MODELLER to correctly calculate the energy, and additionally allows for the possibility of the PDB file having atoms in a nonstandard order, or having different subsets of atoms e. An energy profile is additionally requested, smoothed over a residue window, and normalized by the number of restraints acting on each residue.

This profile is written to a file TvLDH. A comparison of the two profiles is shown in Figure 5. It can be seen that the DOPE score profile shows clear differences between the two profiles for the long active-site loop between residues 90 and and the long helices at the C-terminal end of the target sequence. This long loop interacts with region to , which forms the other half of the active site. This latter region is well resolved in both the template and the target structure. However, probably due to the unfavorable nonbonded interactions with the 90 to region, it is reported to be of high energy by DOPE.

It is to be noted that a region of high energy indicated by DOPE may not always necessarily indicate actual error, especially when it highlights an active site or a protein-protein interface. However, in this case, the same active-site loops have a better profile in the template structure, which strengthens the argument that the model is probably incorrect in the active-site region. A comparison of the pseudo-energy profiles of the model red and the template green structures.

The procedures for other operating systems differ slightly. The key will be E-mailed to the address provided. Open a terminal or console and change to the directory containing the downloaded distribution. The distributed file is a compressed archive file called modeller The files needed for the installation can be found in a newly created directory called modeller Move into that directory and start the installation with the following commands:.

The installation script will prompt the user with several questions and suggest default answers. To accept the default answers, press the Enter key. The various prompts are briefly discussed below:.

For the prompt below, choose the appropriate combination of the machine architecture and operating system. For this example, choose the default answer by pressing the Enter key. The currently supported architectures are as follows:.

Alternative Linux x86 PC binary e. Select the type of your computer from the list above [1]:. The default choice will place it in the directory indicated, but any directory to which the user has write permissions may be specified.

The installer will now confirm the answers to the above prompts. Press Enter to begin the installation. The mod9. Users can also browse through or search the archived messages of the mailing list. As stated earlier, comparative modeling consists of four main steps: fold assignment, target-template alignment, model building and model evaluation Marti-Renom et al.

Although fold assignment and sequence-structure alignment are logically two distinct steps in the process of comparative modeling, in practice, almost all fold-assignment methods also provide sequence-structure alignments. In the past, fold-assignment methods were optimized for better sensitivity in detecting remotely related homologs, often at the cost of alignment accuracy.

However, recent methods simultaneously optimize both the sensitivity and alignment accuracy. Therefore, in the following discussion, fold assignment and sequence-structure alignment will be treated as a single procedure, explaining the differences as needed. The primary requirement for comparative modeling is the identification of one or more known template structures with detectable similarity to the target sequence. The identification of suitable templates is achieved by scanning structure databases, such as PDB Berman et al.

The detected similarity is usually quantified in terms of sequence identity or statistical measures such as E -value or z -score, depending on the method used. Brenner et al. The sensitivity of the search and accuracy of the alignment become progressively difficult as the relationships move into the twilight zone Saqi et al. A significant improvement in this area was the introduction of profile methods by Gribskov et al.

The profile of a sequence is derived from a multiple sequence alignment and specifies residue-type occurrences for each alignment position. The information in a multiple sequence alignment is most often encoded as either a position-specific scoring matrix PSSM; Henikoff and Henikoff, ; Altschul et al.

In order to identify suitable templates for comparative modeling, the profile of the target sequence is used to search against a database of template sequences. As a natural extension, the profile-sequence alignment methods have led to profile-profile alignment methods that search for suitable template structures by scanning the profile of the target sequence against a database of template profiles as opposed to a database of template sequences.

These methods have proven to include the most sensitive and accurate fold assignment and alignment protocols to date Edgar and Sjolander, ; Marti-Renom et al.

There are a number of variants of profile-profile alignment methods that differ in the scoring functions they use Pietrokovski, ; Rychlewski et al. However, several analyses have shown that the overall performances of these methods are comparable Edgar and Sjolander, ; Marti-Renom et al. As the sequence identity drops below the threshold of the twilight zone, there is usually insufficient signal in the sequences or their profiles for the sequence-based methods discussed above to detect true relationships Lindahl and Elofsson, Sequence-structure threading methods are most useful in this regime, as they can sometimes recognize common folds even in the absence of any statistically significant sequence similarity Godzik, These methods achieve higher sensitivity by using structural information derived from the templates.

The accuracy of a sequence-structure match is assessed by the score of a corresponding coarse model and not by sequence similarity, as in sequence-comparison methods Godzik, The scoring scheme used to evaluate the accuracy is either based on residue substitution tables dependent on structural features such as solvent exposure, secondary structure type, and hydrogen-bonding properties Shi et al.

The use of structural data does not have to be restricted to the structure side of the aligned sequence-structure pair. For example, SAM-T08 makes use of the predicted local structure for the target sequence to enhance homolog detection and alignment accuracy Karplus et al. Yet another strategy is to optimize the alignment by iterating over the process of calculating alignments, building models, and evaluating models.

Such a protocol can sample alignments that are not statistically significant and identify the alignment that yields the best model. Although this procedure can be time consuming, it can significantly improve the accuracy of the resulting comparative models in difficult cases John and Sali, Regardless of the method used, searching in the twilight and midnight zones of the sequence-structure relationship often results in false negatives, false positives, or alignments that contain an increasingly large number of gaps and alignment errors.

Improving the performance and accuracy of methods in this regime remains one of the main tasks of comparative modeling today Moult, It is imperative to calculate an accurate alignment between the target-template pair, as comparative modeling can almost never recover from an alignment error Sanchez and Sali, a. After a list of all related protein structures and their alignments with the target sequence have been obtained, template structures are prioritized depending on the purpose of the comparative model.

Template structures may be chosen based purely on the target-template sequence identity, or on a combination of several other criteria, such as experimental accuracy of the structures resolution of X-ray structures, number of restraints per residue for NMR structures , conservation of active-site residues, holo-structures that have bound ligands of interest, and prior biological information that pertains to the solvent, pH, and quaternary contacts.

It is not necessary to select only one template. In fact, the use of several templates approximately equidistant from the target sequence generally increases the model accuracy Srinivasan and Blundell, ; Sanchez and Sali, b.

The first and still widely used approach in comparative modeling is to assemble a model from a small number of rigid bodies obtained from the aligned protein structures Browne et al. The approach is based on the natural dissection of the protein structures into conserved core regions, variable loops that connect them, and side chains that decorate the backbone. First, the template structures are selected and superposed.

Third, the main-chain atoms of each core region in the target model are obtained by superposing the core segment, from the template whose sequence is closest to the target, on the framework. Fourth, the loops are generated by scanning a database of all known protein structures to identify the structurally variable regions that fit the anchor core regions and have a compatible sequence Topham et al.

Fifth, the side chains are modeled based on their intrinsic conformational preferences and on the conformation of the equivalent side chains in the template structures Sutcliffe et al.

Finally, the stereochemistry of the model is improved either by a restrained energy minimization or a molecular dynamics refinement. The accuracy of a model can be somewhat increased when more than one template structure is used to construct the framework and when the templates are averaged into the framework using weights corresponding to their sequence similarities to the target sequence Srinivasan and Blundell, The basis of modeling by coordinate reconstruction is the finding that most hexapeptide segments of protein structure can be clustered into only structurally different classes Jones and Thirup, ; Claessens et al.

Thus, comparative models can be constructed by using a subset of atomic positions from template structures as guiding positions to identify and assemble short, all-atom segments that fit these guiding positions. The all-atom segments that fit the guiding positions can be obtained either by scanning all known protein structures, including those that are not related to the sequence being modeled Claessens et al.

This method can construct both main-chain and side-chain atoms, and can also model unaligned regions gaps. It is implemented in the program SegMod Levitt, Even some side-chain modeling methods Chinea et al. The methods in this class begin by generating many constraints or restraints on the structure of the target sequence, using its alignment to related protein structures as a guide.

The procedure is conceptually similar to that used in determination of protein structures from NMR-derived restraints. The restraints are generally obtained by assuming that the corresponding distances between aligned residues in the template and the target structures are similar.

These homology-derived restraints are usually supplemented by stereochemical restraints on bond lengths, bond angles, dihedral angles, and nonbonded atom-atom contacts that are obtained from a molecular mechanics force field.

The model is then derived by minimizing the violations of all the restraints. This optimization can be achieved either by distance geometry or real-space optimization.

For example, an elegant distance geometry approach constructs all-atom models from lower and upper bounds on distances and dihedral angles Havel and Snow, The program was designed to use as many different types of information about the target sequence as possible. In the first step of model building, distance and dihedral angle restraints on the target sequence are derived from its alignment with template 3-D structures.

The form of these restraints was obtained from a statistical analysis of the relationships between similar protein structures. The analysis relied on a database of family alignments that included proteins of known 3-D structure Sali and Overington, These relationships are expressed as conditional probability density functions pdf's , and can be used directly as spatial restraints.

For example, probabilities for different values of the main-chain dihedral angles are calculated from the type of residue considered, from main-chain conformation of an equivalent residue, and from sequence similarity between the two proteins. An important feature of the method is that the form of spatial restraints was obtained empirically, from a database of protein structure alignments. For a 10,atom system, there can be on the order of , restraints.

The functional form of each term is simple; it includes a quadratic function, harmonic lower and upper bounds, cosine, a weighted sum of a few Gaussian functions, Coulomb law, Lennard-Jones potential, and cubic splines. The geometric features presently include a distance, an angle, a dihedral angle, a pair of dihedral angles between two, three, four, and eight atoms, respectively, the shortest distance in the set of distances, solvent accessibility, and atom density that is expressed as the number of atoms around the central atom.

Some restraints can be used to restrain pseudo-atoms, e. Finally, the model is obtained by optimizing the objective function in Cartesian space. The optimization is carried out by the use of the variable target function method Braun and Go, , employing methods of conjugate gradients and molecular dynamics with simulated annealing Clore et al. Several slightly different models can be calculated by varying the initial structure, and the variability among these models can be used to estimate the lower bound on the errors in the corresponding regions of the fold.

Because the modeling by satisfaction of spatial restraints can use many different types of information about the target sequence, it is perhaps the most promising of all comparative modeling techniques.

One of the strengths of modeling by satisfaction of spatial restraints is that restraints derived from a number of different sources can easily be added to the homology-derived restraints. For example, restraints could be provided by rules for secondary-structure packing Cohen et al. Accuracies of the various model-building methods are relatively similar when used optimally Marti-Renom et al. Other factors such as template selection and alignment accuracy usually have a larger impact on the model accuracy, especially for models based on low sequence identity to the templates.

However, it is important that a modeling method allow a degree of flexibility and automation to obtain better models more easily and rapidly. For example, a method should allow for an easy recalculation of a model when a change is made in the alignment. It should also be straightforward enough to calculate models based on several templates, and should provide tools for incorporation of prior knowledge about the target e.

In this range of overall similarity, loops among the homologs vary while the core regions are still relatively conserved and aligned accurately. Loops often play an important role in defining the functional specificity of a given protein, forming the active and binding sites. Loop modeling can be seen as a mini protein folding problem, because the correct conformation of a given segment of a polypeptide chain has to be calculated mainly from the sequence of the segment itself.

However, loops are generally too short to provide sufficient information about their local fold. Even identical decapeptides in different proteins do not always have the same conformation Kabsch and Sander, ; Mezei, Some additional restraints are provided by the core anchor regions that span the loop and by the structure of the rest of the protein that cradles the loop.

There are two main classes of loop-modeling methods: i database search approaches that scan a database of all known protein structures to find segments fitting the anchor core regions Jones and Thirup, ; Chothia and Lesk, ; ii conformational search approaches that rely on optimizing a scoring function Moult and James, ; Bruccoleri and Karplus, ; Shenkin et al.

There are also methods that combine these two approaches van Vlijmen and Karplus, ; Deane and Blundell, There are attempts to classify loop conformations into more general categories, thus extending the applicability of the database search approach Ring et al. However, the database methods are limited because the number of possible conformations increases exponentially with the length of a loop, and until the late s only loops up to 7 residues long could be modeled using the database of known protein structures Fidelis et al.

There are many such methods, exploiting different protein representations, objective functions, and optimization or enumeration algorithms. The search algorithms include the minimum perturbation method Fine et al. The accuracy of loop predictions can be further improved by clustering the sampled loop conformations and partially accounting for the entropic contribution to the free energy Xiang et al.

Another way to improve the accuracy of loop predictions is to consider the solvent effects. Improvements in implicit solvation models, such as the Generalized Born solvation model, motivated their use in loop modeling. The solvent contribution to the free energy can be added to the scoring function for optimization, or it can be used to rank the sampled loop conformations after they are generated with a scoring function that does not include the solvent terms Fiser et al.

The main reasons for choosing this implementation are the generality and conceptual simplicity of scoring function minimization. Loop prediction by optimization is applicable to simultaneous modeling of several loops and loops interacting with ligands, which is not straightforward with the database-search approaches. The method was tested on a large number of loops of known structure, both in the native and near-native environments Fiser et al. Comparative or homology protein structure modeling is severely limited by errors in the alignment of a modeled sequence with related proteins of known three-dimensional structure.

To ameliorate this problem, one can use an iterative method that optimizes both the alignment and the model implied by it Sanchez and Sali, a ; Miwa et al. This task can be achieved by a genetic algorithm protocol that starts with a set of initial alignments and then iterates through realignment, model building, and model assessment to optimize a model assessment score John and Sali, During this iterative process: 1 new alignments are constructed by the application of a number of genetic algorithm operators, such as alignment mutations and crossovers; 2 comparative models corresponding to these alignments are built by satisfaction of spatial restraints, as implemented in the program MODELLER; and 3 the models are assessed by a composite score, partly depending on an atomic statistical potential Melo et al.

As the similarity between the target and the templates decreases, the errors in the model increase. Errors in comparative models can be divided into five categories Sanchez and Sali, a , b ; Fig. Typical errors in comparative modeling.

A Errors in side chain packing. The Trp residue in the crystal structure of mouse cellular retinoic acid binding protein I red is compared with its model green.

B Distortions and shifts in correctly aligned regions. A region in the crystal structure of mouse cellular retinoic acid binding protein I red is compared with its model green and with the template fatty acid binding protein blue. C Errors in regions without a template. D Errors due to misalignments. The N-terminal region in the crystal structure of human eosinophil neurotoxin red is compared with its model green. The corresponding region of the alignment with the template ribonuclease A is shown.

E Errors due to an incorrect template. As the sequences diverge, the packing of side chains in the protein core changes. Sometimes even the conformation of identical side chains is not conserved, a pitfall for many comparative modeling methods. Side-chain errors are critical if they occur in regions that are involved in protein function, such as active sites and ligand-binding sites.

As a consequence of sequence divergence, the main-chain conformation changes, even if the overall fold remains the same. The structural differences are sometimes not due to differences in sequence, but are a consequence of artifacts in structure determination or structure determination in different environments e.

The simultaneous use of several templates can minimize this kind of error Srinivasan and Blundell, ; Sanchez and Sali, a , b. Segments of the target sequence that have no equivalent region in the template structure i. Conditions for successful prediction are the correct alignment and an accurately modeled environment surrounding the insertion.

However, alignment errors can be minimized in two ways. First, it is usually possible to use a large number of sequences to construct a multiple alignment, even if most of these sequences do not have known structures. Multiple alignments are generally more reliable than pairwise alignments Barton and Sternberg, ; Taylor et al.

The second way of improving the alignment is to iteratively modify those regions in the alignment that correspond to predicted errors in the model Sanchez and Sali, a , b ; John and Sali, This is a potential problem when distantly related proteins are used as templates i. Distinguishing between a model based on an incorrect template and a model based on an incorrect alignment with a correct template is difficult.

In both cases, the evaluation methods will predict an unreliable model. The conservation of the key functional or structural residues in the target sequence increases the confidence in a given fold assignment. The accuracy of the predicted model determines the information that can be extracted from it. Thus, estimating the accuracy of a model in the absence of the known structure is essential for interpreting it. However, when the sequence identity is lower, the first aspect of model evaluation is to confirm whether or not a correct template was used for modeling.

It is often the case, when operating in this regime, that the fold-assignment step produces only false positives. A further complication is that at such low similarities the alignment generally contains many errors, making it difficult to distinguish between an incorrect template on one hand and an incorrect alignment with a correct template on the other hand. There are several methods that use 3-D profiles and statistical potentials Sippl, ; Luthy et al.

These methods can be used to assess whether or not the correct template was used for the modeling. For instance, some calcium-binding proteins undergo large conformational changes when bound to calcium. If a calcium-free template is used to model the calcium-bound state of the target, it is likely that the model will be incorrect irrespective of the target-template similarity or accuracy of the template structure Pawlowski et al.

The model should also be subjected to evaluations of self-consistency to ensure that it satisfies the restraints used to calculate it. Additionally, the stereochemistry of the model e. Although errors in stereochemistry are rare and less informative than errors detected by statistical potentials, a cluster of stereochemical errors may indicate that there are larger errors e. Comparative modeling is often an efficient way to obtain useful information about the protein of interest.

For example, comparative models can be helpful in designing mutants to test hypotheses about the protein's function Wu et al. Fortunately, a 3-D model does not have to be absolutely perfect to be helpful in biology, as demonstrated by the applications listed above. The type of a question that can be addressed with a particular model does depend on its accuracy Fig.

Accuracy and application of protein structure models. The vertical axis indicates the different ranges of applicability of comparative protein structure modeling, the corresponding accuracy of protein structure models, and their sample applications.

A number of fatty acids were ranked for their affinity to brain lipid-binding protein consistently with site-directed mutagenesis and affinity chromatography experiments Xu et al. Typical overall accuracy of a comparative model in this range of sequence similarity is indicated by a comparison of a model for adipocyte fatty acid binding protein with its actual structure left. The prediction was confirmed by site-directed mutagenesis and heparin-affinity chromatography experiments Matsumoto et al.

Typical accuracy of a comparative model in this range of sequence similarity is indicated by a comparison of a trypsin model with the actual structure. Typical accuracy of a comparative model in this range of sequence similarity is indicated by a comparison of a model for a domain in L2 protein from B. However, such models still have the correct fold, and even knowing only the fold of a protein may sometimes be sufficient to predict its approximate biochemical function. Models in this low range of accuracy, combined with model evaluation, can be used for confirming or rejecting a match between remotely related proteins Sanchez and Sali, a ; Fortunately, the active and binding sites are frequently more conserved than the rest of the fold, and are thus modeled more accurately Sanchez and Sali, In general, medium-resolution models frequently allow a refinement of the functional prediction based on sequence alone, because ligand binding is most directly determined by the structure of the binding site rather than its sequence.

It is frequently possible to correctly predict important features of the target protein that do not occur in the template structure. For example, the location of a binding site can be predicted from clusters of charged residues Matsumoto et al. Medium-resolution models can also be used to construct site-directed mutants with altered or destroyed binding capacity, which in turn could test hypotheses about the sequence-structure-function relationships.

Other problems that can be addressed with medium-resolution comparative models include designing proteins that have compact structures, without long tails, loops, and exposed hydrophobic residues, for better crystallization, or designing proteins with added disulfide bonds for extra stability. Sequence features and user annotations in Repository are now easier to navigate.

This project has furthermore received funding from ELIXIR and the European Union's Horizon research and innovation programme under grant agreement number Nucleic Acids Res.

Bienert, S. Guex, N. Electrophoresis 30, SS Studer, G.