RESEARCH: CANCER
FOLDING PROJECT #17603 PROFILE

This project tries to improve how we study protein shapes using computer simulations. It focuses on finding important shapes of a protein called MET kinase, which is linked to lung cancer. By choosing the most interesting shapes to simulate, it hopes to learn more efficiently than traditional methods.

This project is an attempt at implementing adaptive sampling in Folding@home.

Adaptive sampling is a way of enhancing sampling of protein conformational space by selectively launching simulations from the most "valuable" work units. Identifying druggable states or exploring conformational state space relevant to disease is an existing challenge.

The embarassingly parallel nature of Folding@home allows us to massively scale up our exploration.

However, the underlying methods still rely on luck to a large extent – we must discover the states in work units as the dataset grows in size and more work units are run.

This can be an incredibly inefficient process, wasting work units on regions of state space that are irrelevant or uninteresting to the question at hand.

Adaptive Sampling is a way to tackle this inefficiency.

Using iterative rounds, where we collect the work units so far and select the "best/most valuable" conformational state worth exploring.

New simulations and work units are launched from these most valuable work units, hopefully more efficiently exploring state space.

This project is identical in calculation to 16497, exploring conformations of MET kinase, involved in non-small-cell lung carcinoma, but acting as a test bed.

Adaptive Sampling

A technique to enhance protein structure exploration in simulations.

Adaptive sampling is a method used in computer simulations to study the shapes and movements of proteins. It focuses on exploring the most promising regions of the protein's shape space by selectively running simulations from the most valuable starting points. This helps researchers efficiently identify important protein conformations for drug development and understanding disease mechanisms.

Folding@home

A distributed computing project for protein folding research.

Folding@home is a massive scientific project that utilizes donated computer processing power to simulate the folding of proteins. This helps scientists understand how proteins fold into their functional shapes, which is crucial for understanding diseases and developing new drugs.

Protein Conformational Space

The set of all possible shapes a protein can take.

Proteins are complex molecules that fold into specific 3D shapes. The 'protein conformational space' refers to the vast number of different shapes a protein can adopt. Understanding how proteins move between these shapes is essential for understanding their function and developing drugs that target them.

Druggable States

Protein conformations that are potential targets for drug binding.

Druggable states are specific shapes of proteins that can be targeted by drugs. These states are often associated with the protein's function and may be involved in disease processes. Identifying druggable states is a crucial step in drug discovery.

MET Kinase

A type of enzyme involved in cell growth and signaling.

MET kinase is a protein that plays a role in regulating cell growth and division. Mutations or overactivation of MET kinase can contribute to the development of certain cancers, such as non-small-cell lung carcinoma.

Non-Small-Cell Lung Carcinoma

A type of lung cancer.

Non-small-cell lung carcinoma (NSCLC) is the most common type of lung cancer. It arises from abnormal cells in the tissues lining the lungs and can spread to other parts of the body.