Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins

Abstract

Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.

A Global Review on Short Peptides: Frontiers and Perspectives

“…When the number of rotatable bonds exceeds 5, the entire conformational space of a molecule can become extremely large. In recent years, the methods of molecular dynamics were increasingly used to gain insight into the structure/function relationships in short peptides and proteins [ 183 , 184 , 185 , 186 , 187 ]. One of the key questions to be answered when checking the applicability of molecular dynamic simulations for peptides and/or proteins is the extent to which the simulations appropriately sample the conformational space of these molecules.…”

“…If a given property is poorly sampled over the molecular dynamics (MD) simulations, the results obtained have limited usefulness [ 184 ]. To improve the sampling efficiency, new techniques were developed [ 184 , 185 , 187 ]. All-atom molecular dynamic simulations can predict structures of peptides and other peptide foldamers with accuracy of experiments [ 187 , 188 ].…”

“…To improve the sampling efficiency, new techniques were developed [ 184 , 185 , 187 ]. All-atom molecular dynamic simulations can predict structures of peptides and other peptide foldamers with accuracy of experiments [ 187 , 188 ]. Thus, MD simulations are a useful tool for prediction and/or reproduction of experimental 3D structures of small proteins.…”

In silico assessment of the inhibitory effect of four flavonoids (Chrysin, Naringin, Quercetin, Kaempferol) on tyrosinase activity using the MD simulation approach

“…Today, studying the three-dimensional structures of proteins and protein-protein interactions is an important part of research on biology and drug design. Molecular dynamics (MD) simulation is an advanced method in evaluating macromolecular complexes such as proteins, ribosomes, and nucleosomes, and it could be applied in various reactions such as determination of drug molecule binding sites and their mechanisms, the mechanism of functional proteins, protein folding evaluation, and identification of different molecular processes (Aminpour et al, 2019;Geng et al, 2019;Hospital et al, 2015). The MD simulation process has a major role in the recognition of protein-ligand interactions and the protein conformational modifications at the atomic level.…”

How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs

“…Synthesis and the subsequent biological tests are the heart of the anticancer drug development process, however two other research fields, apparently far from each other, such as computational chemistry and drug delivery, can significantly contribute to the success of new therapeutic strategies. Computational methods, for example consolidated simulation techniques, such as molecular dynamics (MD), docking, free energy calculations, chemoinformatic and machine learning algorithms are mainly used to: i) design new chemical entities able to bind to a given target and ii) to improve the selectivity of known hit or lead compounds [5][6][7][8][9][10][11][12][13]. Combinations of the same computational approaches can be also employed to improve the ability of the molecules to penetrate the cells or to resist to metabolism [14][15][16][17][18][19].…”