**3.3 Application examples of MD simulations**

MD simulations have immensely contributed to solve and hypothesize many biological research problems. The significance of the computational microscope can be well understood by observing the increase in the vast repertoire of literature in the recent decade. The technique of simulation along with other computational tools plays a significant role in the field of protein structure prediction. Using a set of seven small proteins Kato et al. have validated the application of MD simulations to predict the 3D structure of proteins. The set of small proteins were in the range of 10 to 46 residues. They have considered two properties; root mean squared deviation (RMSD) and occurrence of secondary structure to validate the predicted structures from simulation with that of the available experimental ones. AMBER12 simulation package with AMBER ff12SB have been utilized to carry out their simulations. With the help of MD simulations, they have shown the possibility of reproducing the secondary structures of small proteins [56]. Our group has also utilized the indispensable technique of simulation recently to investigate the dynamics and stability of *ab-initio* predicted structure of bacterial effector protein, HopS2. The importance of the

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**4. Molecular docking**

*Role of Force Fields in Protein Function Prediction DOI: http://dx.doi.org/10.5772/intechopen.93901*

effector proteins lies with them conferring pathogenicity to bacteria. As the sequence similarity of the effector proteins lies in the twilight zone along with the few partially solved structures of effector proteins at disposal, it is a perplexing task to study the sequence-structure-function relationship of these proteins. With the assistance of MD simulations, our group was able to show the stability of local secondary structural elements of HopS2 which are vital to its overall structure and interaction. These

Another interesting aspect of human proteome is the intrinsically disordered proteins (IDP). There are many examples of proteins with folded domains but they feature disordered regions while some are entirely unstructured. Some IDPs fold upon interacting with their binding partners while other persists in unfolded state even in a bound complex. The IDPs plays a critical role in cell signaling and regulation. Pietrek et al. have carried out a recent work in this direction. They have considered a hierarchical algorithm to generate large ensembles of full length IDP structures and these structures can be further used as starting points for atomistic simulations. The IDP structures generated by their hierarchical approach implemented with all atom MD simulations were able to capture both local conformations compared with NMR experiments and also the gross dimension described by small angle X-ray experiments. Gromacs simulation package along with Amber03ws and Amber99SB\*- ILDN-q FF were utilized by them to carry out the investigation [58]. The powerful computational microscope was also applied to investigate structure and dynamics of plasma membrane proteins. Mattedi et al. recently utilized MD simulations to study glucagon receptor, a class B GPCR. The glucagon-induced release of glucose from the liver into the bloodstream is facilitated by the glucagon receptor. There is scarce information about the mechanism of this receptor. They utilized extensive MD simulations and free energy landscape computation to elucidate the activation mechanism of the receptor. Through their simulation work, they identified an intermediate state of the glucagon receptor and decipher the mechanism of allosteric antagonists of the glucagon which locks transmemebrane helix 6. They have employed AMBER14SB FF and LipidBook parameters for lipids with Gromacs package in their work [59].

The plethora of diseases discovered ever since and being investigated tirelessly by scientists all over the world ultimately culminates to the sole objective of finding effective solutions. The therapeutic targets in most of the cases are proteins. After knowing their mechanism of actions, how the proteins works and what goes wrong during the diseased state, the next notion is to challenge their functionality with designing some inhibitors. It comes under the domain of drug discovery. And one of the most challenging fields of study is the drug design and development. The complete clinical trials take about 10–15 years of time with billions of dollars expenses for a single drug to reach market. With the completion of human genome project which leads to identification of ever-increasing number of new drug targets (mainly proteins); the efforts were strengthened to find solution to the diseases. Additionally, the availability of 3D structures of protein and protein-ligand complexes made it feasible to carry out research in this area. However, to experimentally screen millions of compounds and their conformers for a single therapeutic target requires enormous amount of time and resources which makes it quite challenging. With the application of computational techniques, the pre-clinical period can be reduced to save valuable assets. The *in-silico* approaches will significantly curtail the time needed for hit identification and also improve the chances of finding the anticipated drug molecules. To facilitate drug design and discovery, several modeling

investigation have been performed using Gromacs along with OPLS FF [57].

#### *Role of Force Fields in Protein Function Prediction DOI: http://dx.doi.org/10.5772/intechopen.93901*

*Homology Molecular Modeling - Perspectives and Applications*

CHARMM36, and CHARMM36m [55].

**3.3 Application examples of MD simulations**

MD simulations have immensely contributed to solve and hypothesize many biological research problems. The significance of the computational microscope can be well understood by observing the increase in the vast repertoire of literature in the recent decade. The technique of simulation along with other computational tools plays a significant role in the field of protein structure prediction. Using a set of seven small proteins Kato et al. have validated the application of MD simulations to predict the 3D structure of proteins. The set of small proteins were in the range of 10 to 46 residues. They have considered two properties; root mean squared deviation (RMSD) and occurrence of secondary structure to validate the predicted structures from simulation with that of the available experimental ones. AMBER12 simulation package with AMBER ff12SB have been utilized to carry out their simulations. With the help of MD simulations, they have shown the possibility of reproducing the secondary structures of small proteins [56]. Our group has also utilized the indispensable technique of simulation recently to investigate the dynamics and stability of *ab-initio* predicted structure of bacterial effector protein, HopS2. The importance of the

The FF is a group of equations and associated parameters designed to imitate molecular geometry and selected properties of some tested molecules. FF comprises primarily of two components; bonded and non-bonded terms. Any molecular feature can be basically represented with them. The bonded terms can be represented by springs for bond length and angles along with torsional angles; the non-bonded terms comprise of Lennard-Jones potentials for van der Waals (vdW) interactions and Coulomb's law for electrostatic interactions. They were primarily developed to reproduce structural properties and applied to predict other properties such as thermodynamic parameters. Further the energy functions utilized in molecular mechanics commonly comprise topological parameters which are obtained from experiments or quantum mechanical calculations. An important feature of FF is transferability of the parameters and the functional form. It means to model a series of related molecules; the same set of parameters can be utilized rather than defining a new set of parameters for each individual molecule. Even though most of the FF are additive, a number of them having higher order terms are called class II FF. Some of widely utilized FF for bio-molecular simulations are AMBER, CHARMM, GROMOS and OPLS [52]. Additionally it is noteworthy to mention the application of FF in predicting structures of proteins/RNA. FFs were developed and benchmarked against experimentally solved structures and these FF were later incorporated to predict the structure for the ones lacking experimental information. Another important aspect of the FF is to discriminate the near-native protein conformation among the generated 3D models [53]. FFs are subject to rigorous scrutinizing and they were refined to improve their accuracy over time. One such example is the improvement of the residue side-chain torsion potentials of the Amber ff99SB FF which is also validated with available NMR experimental datasets [54]. A number of benchmark studies were conducted time to time, to compare different FFs. One difference arises among the available variety of FF is the bias/overestimate towards particular secondary structure of proteins. Man et al. recently concluded from their comparative simulation study that FFs (AMBER94, AMBER99 & AMBER12SB) were not able to predict β-sheet formation whereas FFs (AMBER96, GROMOS45a3, GROMOS53a5, GROMOS53a6, GROMOS43a1, GROMOS43a2, and GROMOS54a7) were able to form β-sheets swiftly. Further they have showed that the best FFs for investigating amyloid peptide assembly based on their structure and kinetics were AMBER99-ILDN, AMBER14SB, CHARMM22\*,

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effector proteins lies with them conferring pathogenicity to bacteria. As the sequence similarity of the effector proteins lies in the twilight zone along with the few partially solved structures of effector proteins at disposal, it is a perplexing task to study the sequence-structure-function relationship of these proteins. With the assistance of MD simulations, our group was able to show the stability of local secondary structural elements of HopS2 which are vital to its overall structure and interaction. These investigation have been performed using Gromacs along with OPLS FF [57].

Another interesting aspect of human proteome is the intrinsically disordered proteins (IDP). There are many examples of proteins with folded domains but they feature disordered regions while some are entirely unstructured. Some IDPs fold upon interacting with their binding partners while other persists in unfolded state even in a bound complex. The IDPs plays a critical role in cell signaling and regulation. Pietrek et al. have carried out a recent work in this direction. They have considered a hierarchical algorithm to generate large ensembles of full length IDP structures and these structures can be further used as starting points for atomistic simulations. The IDP structures generated by their hierarchical approach implemented with all atom MD simulations were able to capture both local conformations compared with NMR experiments and also the gross dimension described by small angle X-ray experiments. Gromacs simulation package along with Amber03ws and Amber99SB\*- ILDN-q FF were utilized by them to carry out the investigation [58]. The powerful computational microscope was also applied to investigate structure and dynamics of plasma membrane proteins. Mattedi et al. recently utilized MD simulations to study glucagon receptor, a class B GPCR. The glucagon-induced release of glucose from the liver into the bloodstream is facilitated by the glucagon receptor. There is scarce information about the mechanism of this receptor. They utilized extensive MD simulations and free energy landscape computation to elucidate the activation mechanism of the receptor. Through their simulation work, they identified an intermediate state of the glucagon receptor and decipher the mechanism of allosteric antagonists of the glucagon which locks transmemebrane helix 6. They have employed AMBER14SB FF and LipidBook parameters for lipids with Gromacs package in their work [59].
