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 Table of Contents  
REVIEW ARTICLE
Year : 2018  |  Volume : 2  |  Issue : 1  |  Page : 20-25

Advances in protein tertiary structure prediction


Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran

Date of Web Publication5-Mar-2018

Correspondence Address:
Dr. Tayebeh Farhadi
Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, Shiraz University of Medical Sciences, Shiraz
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/bbrj.bbrj_94_17

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  Abstract 


Proteins are composed of linear chains of amino acids that form a unique three-dimensional structure in their native environment. Such native structure favors the proteins to perform their biochemical activity. Protein is formed of some levels of structure. The primary structure of a protein is specified by the particular amino acid sequence. In an amino acid sequence, patterns of local bonding can be identified as secondary structure. The final level that forms a tertiary protein structure is composed of the mentioned elements and form after the protein folds into its native state. To find the native structure of proteins, the physicochemical principles as well as identifying the lowest free-energy states are considered as the best properties and to predict target proteins with unknown structures, the bioinformatics-based methods have earned considerable success. Protein structure prediction methods have been mainly classified into three types: ab Initio folding, comparative (homology) modeling and threading. Each mentioned method may be applied for a protein structure, depending on the existence of related experimental structures that are deposited in the PDB. Once an initial model is generated, refinement simulations are conducted to reassemble the global topology and the local structures of the protein chains. Since significant features of a model may be in regions that are structurally distinct from the template, refining of a primary model is influential. A trustful strategy is included a stereo-chemical check and discovering how the model deviates from the basic disciplines of known experimental structures.

Keywords: Model evaluation, model refinement, protein modeling, protein tertiary structure


How to cite this article:
Farhadi T. Advances in protein tertiary structure prediction. Biomed Biotechnol Res J 2018;2:20-5

How to cite this URL:
Farhadi T. Advances in protein tertiary structure prediction. Biomed Biotechnol Res J [serial online] 2018 [cited 2018 May 27];2:20-5. Available from: http://www.bmbtrj.org/text.asp?2018/2/1/20/226584




  Introduction Top


Proteins are composed of linear chains of amino acids that form a unique three-dimensional (3D) structure in their native environment. Such native structure favors the proteins to perform their biochemical activity.[1]

Protein is formed of some levels of structure. The primary structure of a protein is specified by the particular amino acid sequence. In addition, in an amino acid sequence, patterns of local bonding can be identified as secondary structure.[2] Two most prevalent types of secondary structure are “α-helices” and “β-sheets” and regions that are named “loop regions” connect these elements of secondary structure.[3] The final level that forms a tertiary (or 3D) protein structure is composed of the mentioned elements and form after the protein folds into its native state.[4] As an example, [Figure 1] represents the 3D structure of CRISPR-associated 9 (Cas9) protein (PDB ID: 5FQ5). The structure of Cas9 was visualized using PyMOL software. In the figure, the α-helices, β-sheets, and loop regions are displayed in red, yellow, and green colors, respectively.
Figure 1: The three-dimensional structure of CRISPR-associated 9 protein (Protein Data Bank ID: 5FQ5). The protein structure was visualized using PyMOL molecular visualization tool. The α-helices, α-sheets and loop regions are shown in red, yellow and green colors, respectively

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  Protein Structure Prediction Top


For many years, a challenge about the prediction of proteins tertiary structure from their amino acid sequence has attracted researchers in the different field of study. There is sufficient evidence about the importance of three-dimensional structure information in the recent years, and consequently, the potential impact of advances in proteins structure prediction is huge. As an example, one cannot gain considerable evidence about the structure-function relationships among the members of a protein family based on a small number of available structures of family members. However, models that are generated from protein family members derived by using experimentally determined structures make it possible to deduce such structure-function relationships.[5],[6] Models can also be utilized as a basis for analyzing the function of individual proteins, much in the way that is performed with experimentally resolved structures. However, in spite of the enormous potential impact of the protein structure prediction, the degree of confidence in which generated models can be utilized in various scientific applications is ambiguous.[7]

To find the native structure of proteins, the physicochemical principles as well as identifying the lowest free-energy states are considered to be the best properties.[8] Aimed to predict target proteins with unknown structures, the bioinformatics-based methods have earned considerable success. Such approaches gather information from solved structures of other related proteins that are deposited in the Protein Data Bank (PDB).[9] The critical steps of bioinformatics-based methods involve target (query)-template sequence alignments, fold-recognition, fragment-based structural assembly and multiple template-based structural refinements.[6],[10] In [Table 1], a summary of the publicly available software and web-servers for automated protein structure predictions is listed.
Table 1: A list of publicly available tools for protein structure modeling

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  Review of Protein Structure Prediction Approaches Top


Protein structure prediction methods have been mainly classified into three types: ab Initio folding, comparative (homology) modeling, and threading.[8] Each mentioned method may be applied for a protein structure, depending on the existence of related experimental structures that are deposited in the PDB.

Ab Initio (also named de novo) modeling class is originally defined as the methods that are based on the first principle laws of chemistry and physics that declare the native state of a protein places at the minimum of global free-energy.[11],[12] Hence, Ab Initio procedure tries to fold a given protein from the query sequence employing different force fields and broad conformational search algorithms. However, limited success has been illustrated by applying such physicochemical principle-based techniques. The most appropriate methods in this class still use the evolutionary and knowledge-based information to gather short structural fragments and spatial restraints to aid structural assembly process.[13],[14] This class is now named “free modeling” in the CASP experiments because many of the techniques do not perfectly trust in the first principles.[15]

In comparative modeling (CM), protein structure is predicted by comparing the sequence of a query (also named target) protein to an evolutionarily associate protein with a known structure (also named template) in the PDB.[8] Therefore, a necessity for CM method is the existence of a homologous protein in the PDB database.[16] The CM models routinely have a strong bias and are closer to the template structure rather than the native structure of the target protein. In this context, the CM methods produce models by copying the aligned structures of the templates or satisfying contact/distance restraints from the templates.[17] It is considered as an essential limit of the approach. Consequently, one of the significant questions to CM (and to other template-based approaches) is how to refine the generated models closer to the native structure than the used templates.

Threading (also named fold recognition) is a bioinformatics strategy that search in the PDB library to find protein templates that have a similar fold or structural motif to the query protein. It is comparable to CM in the sense that both strategies attempt to generate a structural model by applying the experimentally solved structures as a template.[8] It is demonstrated that many proteins with low sequence identity can have similar folds. Therefore, threading procedure focuses to detect the target-template alignments regardless of the evolutionary relationship.

When the sequence identity is low, recognition of exact target-template alignments is a critically significant issue. Thus, the design of exact alignment scoring function is significant to the effectiveness of the methods. The frequently employed alignment scores contain sequence-structural profile match,[18] secondary structure match, sequence profile–profile alignments,[19] and residue–residue contacts [20] with the best scoring alignments commonly discovered by Hidden Markov modeling [21] or dynamic simulation.[22] In the recent years, the approaches of composite scoring functions containing multiple structural properties such as torsion angles and solvent accessibility can produce additional advantages in the protein template identifications.[23]

In the field of protein structure prediction, a common trend that borders between the conventional types of modeling approaches has become blurred. Many Ab Initio techniques apply spatial restraints or structural fragments that are identified by threading method.[24] Besides, both comparative and threading modeling techniques depend on multiple sequence alignments. However, in the field of protein structure prediction, no single technique can outperform others for all protein targets, therefore meta-server approaches have been introduced as the second trend.[25] A common meta-server approach is to generate a number of models by multiple programs which are developed by different laboratories, then selection the final models from the best ranking ones.[26] In spite of availability of different approaches that can be tried in protein model and template selections, the most effective model selection strategy seems to be the consensus selection. By definition, consensus selection is the most efficient model selection approach and selects the models that are most often build by various methods and generally the one that is the closest to the native.[27]

Another efficient meta-server approach for ranking, selection and reconstructing protein models is based on multiple templates information. To direct the physics-based structural assembly simulations, this approach can exploit the spatial restraints and structural fragments elicited from the numerous templates. Therefore, the mentioned approach can generate models that have a refined quality compared to the models based on information of the individual templates. Considering to community-wide benchmark results of the recent CASP experiments, this approach represents the most effective and successful method.[28]


  Application of Modeling Top


Structure-based strategies are widely used in the rational development of drugs to discover the potent, selective, and low molecular-weight molecules. Homology models are considered as useful models in structure-based virtual screening processes, as demonstrated by various retrospective investigations on a large variety of different targets.[29] In 2017, we investigated interactions between CTX-M-15 protein of Klebsiella pneumoniae and 2000 drug-like compounds as potential competitive inhibitors to carry out virtual screening and detect novel drug-like compounds as potential competitive inhibitors.[29] [Figure 2], that is retrieved from our previous published article,[29] displays molecular complex of a drug-like compound (ID: ZINC21811621) with CTX-M-15 visualized through PyMOL.[29]
Figure 2: Molecular complex of a drug-like compound (ID: ZINC21811621) with CTX-M-15 visualized via PyMOL[29]

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Moreover, in different studies, predicting the possible effects of amino acid sequence variations within the spatial locations of functionally important residues (such as active/binding sites and sites of disease-associated mutations) is reported as an important issue.[8],[30] Such prediction can be done using the structural modeling.


  Model Refinement Strategies Top


Once an initial model is generated, refinement simulations are conducted to reassemble the global topology and the local structures of the protein chains. Since significant features of a model may be in regions that are structurally distinct from the template, refining of a primary model is influential. The mentioned regions are included side chains that are dissimilar in the template and its target and loops that are located between secondary structure elements and may have a quite distinct conformation in the target and template.[31] Side chain and loop modeling procedures are based on this assumption that the secondary structure elements of a target protein are alike to those in the template structure.[32]

For the computation of side chain conformations, the most frequently used approaches employ the detected relationship between backbone and side chain conformations and routinely utilize a “rotamer library” produced from a database of known structures.[33] Approaches vary in the manner in which rotamers are sampled. The energy function is exploited to assess the individual conformations. Currently, it is likely to predict the conformations of buried side chains with close to experimental precision.[33]

Loop modeling methods generally generate a starting model of the loop in ''open'' conformation in which one end of the loop is not linked to its subsequent residue. Then, the programs close the loop applying different algorithms.[34],[35] The procedure is repeated several times employing various starting conformations. Obtained conformations are then checked using several energy functions. In general, it is suggested that a combination of thorough sampling and a conformational energy calculation can generate very accurate results.[36],[37]


  Model Evaluation Top


A number of structural conformations (also named structural decoys) will be resulted from the structural assembly simulations. Among all the likely alternative conformations that are closest to the native structure, the high quality tertiary model with accurate fold must be selected. A trustful strategy is included a stereo-chemical check and discovering how the model deviates from the basic disciplines of known experimental structures.[8]

To determine whether a model satisfies standard steric and geometric criteria, a number of programs have been developed.[38] To building a model, all mentioned tools are involved in the template selection, alignment, model building, and refinement and have their own internal measures of quality. However, ultimately, the most significant criterion for the quality of a model is its conformational energy. Therefore, some scoring functions that reflect this energy should be applied to choose the best model by searching among the tens, hundreds, or even thousands of predicted potential models.[9],[32] Ramachandran plot in PROCHECK (http://swissmodel.expasy.org/workspace) is a usefulness plot to check the residue–residue stereochemical quality of a refined protein.[39] In one of the our previous published article, the modeled molecular chaperone GroEL from  Salmonella More Details typhi was modeled and evaluated through Ramachandran plot in PROCHECK.[40] Here, [Figure 3] that was retrieved from the mentioned published article shows the quality of the resulting stereochemistry of the model by using Ramachandran plot. Considering to the figure, most residues of the modeled GroEL are within allowed regions (98.8%).[40] This plot indicated that 91.9% of residues are located in most favored regions, 6.8% in additional allowed regions, 0.2% in generously allowed regions and 1.1% in disallowed regions of the plot. The most favored, additional and generously allowed regions are represented with red, yellow and pale yellow colors, respectively. The disallowed regions are in white color.[40]
Figure 3: Ramachandran plot of the GroEL predicted by PROCHECK[40]

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To deal with a large number of archived conformations, a hierarchical method to model valuation is usually employed. To rank all the original models, the method uses easy-to-evaluate and simplified scoring functions. With this strategy, a subset can be selected for more computationally detailed evaluation. A routinely used scoring function is Verify3D.[41],[42] Verify3D assesses segments of the model based on how well the environment of the residues in that segments correlate with their detected propensities for being in that environment.[43]

Statistics-based scoring functions, such as ProsaII,[44],[45] measure the stability of a polypeptide from the frequency that the interactions (atom-atom or residue–residue) identified in that conformation becomes clear in the database of known structures. Such functions are simple to assess since they depend only on the distance between pairs of atoms. In our previous article, the structure of the modeled GroEL was evaluated using ProSA-web [46] to see the energy distribution in the protein structure as a function of sequence position to determine the structure as native-like or fault.[40] Here, [Figure 4] (retrieved from the mentioned published article) shows that the modeled GroEL is within the range of scores typically found for native proteins of similar size. Considering to figure, the ProSAweb z-score of the structure is-11.0.[40]
Figure 4: Evaluation of the quality of GroEL tertiary structure via ProSa-web[40]

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There are several alternatives of statistics-based scoring functions.[47],[48] Detailed all-atom estimations of conformational stability can be employed using molecular mechanics force fields of the type applied in molecular dynamics simulations.[49]

These approaches have recorded impressive successes in their ability to fold protein fragments from unfolded conformations,[50],[51] their applications to the “decoy” problem and their capability to choose the experimentally determined X-ray structure among a large number of variant conformations of the same polypeptide chain.[52]

While predicting a native conformation from a set of decoys, there are major challenges including sampling and evaluating enough conformations. This is not a novel challenge, and it will not be simple to resolve.[49] Indeed, researchers believe that the molecular dynamics approaches can be employed to achieve this goal. Such methods can fold protein fragments from disordered states and give an inaccurate model that is relatively close to the native structure. Then, the model is refined to a conformation that is near the native conformation.[53] However, this goal has not yet been achieved. Another solution needs a combination of improved alignment methods, finding structural templates for each problematic region of a structure, and using the improved scoring functions and sampling procedures.[1],[52]

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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