Chapter 5 - Homology modeling: Developing 3D structures of target proteins missing in databases

Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.

A drug takes many years to develop and reach the market using the conventional drug discovery procedure. Computer-aided drug design (CADD) is an emerging technology that accelerates the process of drug discovery and minimizes the total expenditure associated with labour and resources. In the current scenario, the computational aided drug design (CADD) techniques play a significant role in the design and development of lead molecules for the treatment of various lethal pathological conditions. The prediction of the tertiary structure of a protein is a big concern in drug design and discovery. A typical drug discovery procedure starts with the tertiary structure of a protein. At present, a total of 184,407 protein structures are available in the protein data bank, which are determinedusing experimental methods. However, the procedures are difficult and time-consuming. A more advanced technique has been developed for the prediction of the 3D structure of a protein using a computational method. This technique has played a vital role in drug discovery. It has not only facilitated but also hastened the process of drug discovery. The method is named homology modeling since it involves the building of a protein model based on its homology to similar evolutionary proteins. The method is based on the fact that evolutionary related proteins have similar structures. In homology modelling, the 3D structure of a protein is derived from its primary sequence based on its similarity to the existing protein templates. There are many computational tools for homology modelling such as Modeller, Swiss model, Composer, 3D-JIGSAW, etc. The proposed book chapter will cover the introduction to homology modelling, step-by-step guide to building a protein model, various challenges and how to refine and validate the model, different algorithms related to sequence alignment, similarity search, and the applications of homology modelling in drug design and discovery. The chapter would be very fruitful to the readers to get insights into protein modelling, which will facilitate their research activities. It will be of great application in various disciplines,such as bioinformatics, physics, structural biology, and molecular biology. The content of the chapter will cover various research papers, review papers, and corresponding reference books.

Protein-Peptide Complex Prediction through Fragment Interaction Patterns

2 - The Physiological Structure and Function of Proteins

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Modeller: Generation and Refinement of Homology-Based Protein Structure Models

Knowledge regarding the 3D structure of a protein provides useful information about the protein's functional properties. Particularly, structural similarity between proteins can be used as a good predictor of functional similarity. One method that uses the 3D geometrical structure of proteins in order to compare them is the similarity value (SV). In this paper, we introduce a new definition of the SV measure for comparing two proteins. To this end, we consider the mass of the protein's atoms and concentrate on the number of protein's atoms to be compared. This defines a new measure, called the weighted similarity value (WSV), adding physical properties to geometrical properties. We also show that our results are in good agreement with the results obtained by TM-SCORE and DALILITE. WSV can be of use in protein classification and in drug discovery.

Chikungunya is viral disease is transmitted to human beings by mosquito bite. Chikungunya can cause fever, chills, nausea, vomiting, joint pain, head ache, and swollen in the joints. A cause of Chikungunya is mainly due to the replication of Chikungunya virus in the human body. The nsP2 (non structural Protein 2) protein is responsible for Chikungunya virus replication. CHIKV replication causes general host shut-off. CHIKV replication is resistant to inhibition by interferon once RNA replication has been established and that CHIKV actively suppress the antiviral IFN response by preventing IFN-induced gene expression. Chikungunya virus replication and propagation is dependent on the protease activity of the viral nsP2 protein, which cleaves the nsP1234 polyprotein replication complex into functional unit This study investigates about the nsP2 protease protein of Chikungunya 37997 and computational methods have been used to find the best interaction site in the protease protein and identify the best ligand compounds for inhibiting the nsP2 protein. The Homology Modelling was used to predict the 3-Dimensional structure of nsP2 Protease protein. The 3D structure was validated using Structure Validation Server (SAVS) in which 85.2% of residues present in the favored regions of the Ramachandran Plot. Docking studies were carried out with various inhibitors and it was found that LIGAND_4 ( N-butyl-9-[3,4-dipropoxy-5-(propoxymethyl)oxolan-2-yl] purin-6-amine ) had the most stable interaction with the nsP2 CHIKV Protease. Indicate that LIGAND_4 could be a promising inhibitor for nsP2 Protease of CHIKV virus as the drug target.

Protein is one of the giant biomolecules that act as the main component of the organism. Protein is formed from building blocks namely amino acids. Hierarchically, the structure of protein is divided into four levels: primary, secondary, tertiary, and quaternary structure. Protein secondary structure is formed by amino acid sequences that would form three-dimensional structures and have information about the tertiary structure and function of proteins. This study used 277,389 protein residue data from enzyme categories. Position-specific scoring matrix (PSSM) profile and physicochemical are used for features. This study developed support vector machine models to predict the protein secondary structure by recognizing patterns of amino acid sequences. The Q3 results showed that the best scores obtained are 93.16% from the dataset that has 260 features with the radial kernel. Combining PSSM and physicochemical feature additions can be used for prediction.

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Chemokines play a crucial role in the trafficking of leukocytes in the body through the binding to their related receptors. Chemokine Receptor 2 (CCR2) belongs to GProtein Coupled Receptor (GPCR) family and expressed in monocytes, immature dendritic cells, activated T lymphocytes, basophils, and endothelial and vascular smooth-muscle cells. CCR2 binds several chemokines; CCL2, CCL7, CCL8, and CCL13. CCR2 and its ligand have been implicated in the pathophysiology of a number of diseases, including rheumatoid arthritis, multiple sclerosis, atherosclerosis, organ transplant rejection, and insulin resistance. Knowledge of the structural basis on CCR2-ligand interaction could help facilitate the design of novel CCR2 antagonists. We modeled and predicted the binding sites of widely known CCR2 antagonists, TAK-779 and Teijin-lead (Fig. 1), using ligand supported homology modeling method. Homology modeling predicts the three-dimensional structure of a given protein sequence based on its alignment to reference proteins of known three-dimensional structure (socalled templates). It is known as the most successful technique for predicting the three-dimensional protein structure. In conventional homology modeling method, the ligand molecules are not considered in the process of protein model building. After building the protein model, the ligand molecules are docked into the protein model. These approaches often showed the consequence that residue side chains involved in ligand binding are inappropriately modeled. The time-consuming re-modeling of the binding site or docking is required to generate sensible side-chain and ligand orientations. In the case of GPCR, the conventional homology modeling approaches for obtaining the binding site models are more challenging because the number of the available templates is very limited and the sequence identity to the templates is very low. The available templates for GPCR are the solved three-dimensional structures of bovine rhodopsin, β2-adrenergic receptor (β2AR), turkey β1adrenergic receptor, and human adenosine A2A receptor. The homology modeling from those limited number of templates may result in uncertainty not only regarding backbone positioning but also about side chain conformations. One approach to solve this is to optimize the side chains through mechanics and/or dynamics in an empty pocket; however, this method may be inaccurate due to possible disruption of the binding site. Another approach is to use dynamics and ligand data as a restraint. This method also has a possibility of poor quality. Thus, the development of modified homology modeling methods for constructing a plausible ligand binding mode is an important theme in GPCR-ligand modeling research. The incorporation of ligand information from the early stage of homology modeling has been studied as an alternative strategy to the conventional homology modeling. This approach was first developed and termed as ligand supported homology modeling by Klebe and coworkers. Recently, the improved and diversified methods have been published as ligand-steered homology modeling or homology-modeling protein-ligand interactions. The results presented reasonable binding site models. We recently reported the protocol and evaluation for the proprietary CCR2 antagonist by ligand-supported homology modeling. We here employed the protocol to predict the binding mode for known CCR2 antagonists, TAK-779 and Teijin-lead. The method in this study for structure modeling of CCR2 and its antagonists is summarized in Figure 2. In the first place of homology modeling, we retained the bound ligand, carazolol, from the crystal structure of β2AR. Then, carazolol was replaced by the various conformations of the target

Intermediate structure of normal human haemoglobin: Methaemoglobin in the deoxy quaternary conformation

The Fenna-Matthews-Olson (FMO) antenna protein from the green bacterium Pelodictyon phaeum mediates the transfer of energy from the peripheral chlorosome antenna complex to the membrane-bound reaction center. The three-dimensional structure of this protein has been solved using protein crystallography to a resolution limit of 2.0 Å, with R(work) and R(free) values of 16.6 and 19.9%, respectively. The structure is a trimer of three identical subunits related by a threefold symmetry axis. Each subunit has two beta sheets that surround 8 bacteriochlorophylls. The bacteriochlorophylls are all five-coordinated, with the axial ligand being a histidine, serine, backbone carbonyl, or bound water molecule. The arrangement of the bacteriochlorophylls is generally well conserved in comparison to other FMO structures, but differences are apparent in the interactions with the surrounding protein. In this structure the position and orientation of the eighth bacteriochlorophyll is well defined and shows differences in its location and the coordination of the central Mg compared to previous models. The implications of this structure on the ability of the FMO protein to perform energy transfer are discussed in terms of the experimental optical measurements.

The 3D structure of proteins is the major factor that determines their biological activity. The synthesis of new proteins and the crystallographic analysis of their 3D structure is very slow and very expensive process. If we can predict the 3D structure of many proteins, than only proteins with expected properties have to be synthesized. The main idea, implemented in our research, is not to use lattice cube with constant size to make possible conformations in this space, but to use flexible constrain for spreading away from the center of the formed molecule, which constrain has a coefficient that can vary in the process of folding according the percentage of failing to make possible conformation, caused by lack of space. Less space allowed causes difficulties to make the conformations but the achieved forms are more compact and with lower energy. More space causes making many useless random conformations and more computational time is needed to find the best conformation and to make the same one more times in this random process in order to have bigger probability that it is the best one. This method may be used in every other model for protein folding prediction to improve the computational time and the probability of finding the accurate 3D structure – our results show that advantage.

Tandem repeats in proteins: From sequence to structure