FTMAP: extended protein mapping with user-selected probe molecules.

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Protein binding hot spots are crucial for molecular interactions and drug design.
• Experimental methods like X-ray crystallography and NMR identify hot spots by screening small molecules.
• Fragment-based drug design relies on identifying these high-affinity binding sites.

Purpose of the Study:

• To computationally identify protein binding hot spots using a method analogous to experimental screening.
• To enhance the FTMAP computational tool to predict binding poses of user-selected molecules.
• To provide a computational approach for guiding ligand design and assessing compound binding likelihood.

Main Methods:

• FTMAP computationally samples protein surfaces with small organic molecule probes.
• Favorable binding positions are identified, clustered, and ranked by average energy.
• The enhanced FTMAP incorporates user-selected molecules as additional probes.
• The updated tool identifies hot spots and predicts representative low-energy cluster structures for new probes.

Main Results:

• FTMAP successfully predicts protein binding hot spots, showing good agreement with experimental data.
• The enhanced FTMAP accurately identifies hot spots using a standard probe set.
• For user-selected probes, FTMAP provides representative bound poses and assesses binding likelihood within hot spots.

Conclusions:

• FTMAP serves as a computational analog to experimental hot spot identification methods.
• The enhanced FTMAP tool facilitates the prediction of binding poses for arbitrary small molecules.
• This computational approach aids in drug discovery by identifying potential binding sites and informing ligand design.