Protein stability is fundamental in biology. When mutations in proteins occur resulting in a less stable form the consequences are lethal. Several diseases ranging from cancer to numerous neurological disorders are all results of protein instability. There are two broad ways protein stability can alter healthy function of a cell. One possibility is less stable proteins can have lower propensity to be in the desirable form of the protein. This affects the original function of the protein for which proteins were designed, examples are low enzymatic activity due to certain mutations. These mechanisms can be operative for diseases such as ALS or ADSL deficiency where altered stability of essential proteins can hamper proper function. However, there is another possible mechanism when proteins misfold into different conformations resulting in the formation of lethal toxic species. Example problems are several neurodegenerative diseases such as Alzheimer's, Parkinson, Huntington to name a few. These are results of abnormal accumulation of toxic protein aggregates. These could also be an operative mechanism for ADSL deficiency and other diseases as well.
Our goal is to build theoretical models to understand both of these different mechanisms of disease propagation either by "loss of function" due to low stability of proteins or by gain in "toxic function" due to protein aggregation. We focus on building a combination of macroscopic and microscopic theoretical models to investigate protein stability and protein aggregation. Our novel theoretical methods are significantly faster than other elaborate detailed all-atom models because of the macroscopic aspects and provide us insights into these fundamental problems of protein physical chemistry. It is possible to gain knowledge and predict behavior under different conditions such as acidity, salinity, confinement, crowding that are present in cellular environment using our model where detailed simulation or experimental approaches are restricted due to time and cost limitations. Furthermore, we try to use ideas of statistical physics to build such macroscopic models that are transferrable from one protein to another. We benchmark our initial model against available experimental data to learn more about model parameters which is then utilized to predict behaviors of other proteins causing a wide variety of diseases from ASL, ADSL, Alzheimer's to Huntington and other neural disorders.
We are also interested in building up models at the proteome level addressing questions such as: What is the stability distribution, size distribution of the entire proteome in E. coli? Thus proteome models can be coupled to better understand proteostasis network (PN), a network of different pathways and proteins. Regulating PN offers potential new therapeutic strategy that can target many diseases at once that are related to the onset of aging and thus can improve over all standard of living. This is a revolutionary concept as opposed to targeting one disease at a time and new pharmaceutical efforts are already under way based on this novel concept.
Finally, our group is building a new theoretical framework to model noise in Biology. These are particularly important in gene expression, cell differentiation and evolution. Example problems range from biological switches, clocks to cellular fate decision in stem cells to name a few problems.