Molecular dynamics (MD) simulations are widely used to study the behavior of proteins and other biological macromolecules. This review outlines the development of MD simulations for protein systems and their current applications. MD simulations use potential-energy functions to model molecular systems and solve Newton's equations of motion to observe structural fluctuations over time. These simulations provide detailed insights into the motions of individual particles and can quantify properties of a system with precision and on time scales otherwise inaccessible. They are valuable tools for understanding protein structure and function, and can be combined with other computational methods to investigate these aspects. MD simulations have evolved significantly since their inception in the 1950s, with early simulations using simple models like the hard-sphere model. Modern simulations can now run for tens of nanoseconds, with the first microsecond simulation reported in 1998. The number of atoms in simulations has increased dramatically, from around 500 in the first protein simulation to over 10^4-10^6 atoms today. Advances in computing power and theoretical methods have contributed to these improvements. MD simulations can study various dynamic processes in proteins, including rapid localized motions, slower whole-protein motions, and subunit associations. These simulations are used to investigate biomolecular phenomena such as macromolecular stability, conformational and allosteric properties, enzyme activity, molecular recognition, ion transport, and protein folding. They also provide insights into the thermodynamic and kinetic properties of proteins. The energy function in MD simulations is crucial for accurate results. Force fields, which are sets of parametrized functions, are used to describe the energy landscape of a system. The CHARMM22 force field is a widely used example. Energy minimization is a fundamental concept in MD simulations, used to refine molecular structures. Various algorithms, such as the steepest descent and Newton-Raphson methods, are used for this purpose. Adiabatic mapping is a method used to study specific motions in proteins by characterizing low-energy paths. It is computationally inexpensive but has limitations in capturing time scales of dynamic mechanisms. Molecular dynamics simulations involve the iterative calculation of forces and movements in a system, using Newtonian mechanics. The Verlet algorithm and leapfrog algorithm are commonly used for integrating the equations of motion. Simulations can be performed in various environments, including explicit and implicit solvents. Implicit solvent models, such as the generalized Born model, are used to reduce computational costs while still capturing solvent effects. Explicit solvent models, such as TIP3P and TIP4P, are used when detailed solvent interactions are necessary. Electrostatic interactions are a dominant factor in protein stability and are computationally expensive to evaluate. Techniques such as Ewald summation and the fast multipole method are used to efficiently calculate these interactions. Langevin dynamics incorporates stochastic terms to account for neglected degrees of freedom, providing a more realistic simulation of systems in contact with a solvent.Molecular dynamics (MD) simulations are widely used to study the behavior of proteins and other biological macromolecules. This review outlines the development of MD simulations for protein systems and their current applications. MD simulations use potential-energy functions to model molecular systems and solve Newton's equations of motion to observe structural fluctuations over time. These simulations provide detailed insights into the motions of individual particles and can quantify properties of a system with precision and on time scales otherwise inaccessible. They are valuable tools for understanding protein structure and function, and can be combined with other computational methods to investigate these aspects. MD simulations have evolved significantly since their inception in the 1950s, with early simulations using simple models like the hard-sphere model. Modern simulations can now run for tens of nanoseconds, with the first microsecond simulation reported in 1998. The number of atoms in simulations has increased dramatically, from around 500 in the first protein simulation to over 10^4-10^6 atoms today. Advances in computing power and theoretical methods have contributed to these improvements. MD simulations can study various dynamic processes in proteins, including rapid localized motions, slower whole-protein motions, and subunit associations. These simulations are used to investigate biomolecular phenomena such as macromolecular stability, conformational and allosteric properties, enzyme activity, molecular recognition, ion transport, and protein folding. They also provide insights into the thermodynamic and kinetic properties of proteins. The energy function in MD simulations is crucial for accurate results. Force fields, which are sets of parametrized functions, are used to describe the energy landscape of a system. The CHARMM22 force field is a widely used example. Energy minimization is a fundamental concept in MD simulations, used to refine molecular structures. Various algorithms, such as the steepest descent and Newton-Raphson methods, are used for this purpose. Adiabatic mapping is a method used to study specific motions in proteins by characterizing low-energy paths. It is computationally inexpensive but has limitations in capturing time scales of dynamic mechanisms. Molecular dynamics simulations involve the iterative calculation of forces and movements in a system, using Newtonian mechanics. The Verlet algorithm and leapfrog algorithm are commonly used for integrating the equations of motion. Simulations can be performed in various environments, including explicit and implicit solvents. Implicit solvent models, such as the generalized Born model, are used to reduce computational costs while still capturing solvent effects. Explicit solvent models, such as TIP3P and TIP4P, are used when detailed solvent interactions are necessary. Electrostatic interactions are a dominant factor in protein stability and are computationally expensive to evaluate. Techniques such as Ewald summation and the fast multipole method are used to efficiently calculate these interactions. Langevin dynamics incorporates stochastic terms to account for neglected degrees of freedom, providing a more realistic simulation of systems in contact with a solvent.