The goal of our research is to develop new computational models and methods in order to obtain molecular-level knowledge about surfactant protein systems found at chemical interfaces in the human body. Currently, our group is focused on the pulmonary surfactant system. Through the use of computer simulations, we aim to gain a better understanding of the functions of this vital biological system, explore the effects of various compounds on the lung, identify potential applications for drug delivery, and aid in engineering a novel synthetic pulmonary surfactant.
In spite of the vast number of studies on the pulmonary surfactant system, we still lack a complete understanding of its functions. Additionally, many of the proteins, known as surfactant proteins A, B, C, and D, found in the pulmonary surfactant system are also found in essential systems throughout the body. Currently, patients with a deficiency or defect in pulmonary surfactant are treated with a pure or synthetic mix containing bovine or porcine pulmonary surfactant. These treatment methods are expensive and still lack many of the vital components of the pulmonary surfactant. Additional improvements are needed to improve patient outcomes and to increase accessibility by lowering costs.
Our goal is to utilize simulations and models to better understand the individual functions of the various lipids and proteins that make up this complex and vital biological system. We also aim to elucidate on the functions and roles of the surfactant proteins throughout the human body. This fundamental knowledge will also enable us to help engineer an improved synthetic pulmonary surfactant to improve lung health.
The respiratory system as the target of drugs has increased with modern technology and medicine. The high permeability, large adsorptive surface area, and good blood supply makes it an ideal site for delivery of drugs, but drugs are slowly cleared through the lungs, resulting in poor absorption compared to other methods. Exploiting the basic knowledge obtained through our simulations, we will study the moieties of pulmonary surfactant as potential drug carriers for inhalation therapy.
The recent outbreak of e-cigarette-related lung injuries and deaths has identified a significant lack of regulation and safety information. Using both Monte Carlo and molecular dynamics simulations, we are investigating the effect of e-cigarette compounds on the lipid and protein components of the pulmonary surfactant system to identify compounds that may be detrimental to lung health. These results can help prioritize compounds for experimental studies and pinpoint substances for preventative measures, such as FDA regulation.
To effectively study large biological systems, like the proteins and lipids found in lung surfactant, there is a need for more efficient computational methods. Historically, force fields have been atomistic, describing each atom separately, sacrificing efficiency for accuracy; or coarse-grained, describing beads that are composed of multiple atoms, sacrificing accuracy for efficiency. Atomistic modeling is computationally expensive, and course-grained modeling leads to inaccuracies. To overcome these limitations, we are developing a hybrid force field that combines both atomistic and coarse-grained approaches. This force field will enable the study of surfactant protein systems with computational efficiency and extreme detail at the important functional groups and interaction sites.
Deficiencies and defects in the pulmonary surfactant are the cause of some respiratory diseases such as neonatal respiratory distress syndrome (RDS), acute respiratory distress syndrome (ARDS), and pulmonary alveolar proteinosis (PAP). Pulmonary surfactant is also linked to other diseases such as idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD), pneumonia, chronic bronchitis, asthma, tuberculosis, and sarcoidosis. Using computer simulations, we aim to identify the role of the pulmonary surfactant in various respiratory diseases, which could illuminate new potential treatment methods.