Integrating AlphaFold pLDDT Scores into CABS-flex for enhanced protein flexibility simulations

Abstract

CABS-flex is a well-established method for fast protein flexibility simulations, offering an effective balance between computational efficiency and accuracy in modeling protein dynamics. To further enhance its predictive capabilities, we propose incorporating AlphaFold's predicted Local Distance Difference Test (pLDDT) scores into CABS-flex simulations. The pLDDT scores, which reflect the confidence of AlphaFold's structural predictions, were integrated with secondary structure information to refine the restraint schemes used in the simulations. We tested this approach on the ATLAS database, which includes molecular dynamics (MD) simulations of nearly 1400 proteins. The results showed improved alignment of flexibility predictions with the MD data compared to previous restraint schemes. The integration of pLDDT scores also offers a new perspective on protein flexibility by incorporating structural confidence into the analysis. This development enhances the utility of CABS-flex for investigating protein dynamics and motion.