Conformational states and folding pathways of peptides revealed by principal-independent component analyses

Modeling the Binding of Anticancer Peptides and Mcl-1

Mcl-1 (myeloid cell leukemia 1), a member of the Bcl-2 family, is upregulated in various types of cancer. Peptides representing the BH3 (Bcl-2 homology 3) region of pro-apoptotic proteins have been demonstrated to bind the hydrophobic groove of anti-apoptotic Mcl-1, and this interaction is responsible for regulating apoptosis. Structural studies have shown that, while there is high overall structural conservation among the anti-apoptotic Bcl-2 (B-cell lymphoma 2) proteins, differences in the surface groove of these proteins facilitates binding specificity. This binding specificity is crucial for the mechanism of action of the Bcl-2 family in regulating apoptosis. Bim-based peptides bind specifically to the hydrophobic groove of Mcl-1, emphasizing the importance of these interactions in the regulation of cell death. Molecular docking was performed with BH3-like peptides derived from Bim to identify high affinity peptides that bind to Mcl-1 and to understand the molecular mechanism of their interactions. The interactions of three identified peptides, E2gY, E2gI, and XXA1_F3dI, were further evaluated using 250 ns molecular dynamics simulations. Conserved hydrophobic residues of the peptides play an important role in their binding and the structural stability of the complexes. Understanding the molecular basis of interaction of these peptides will assist in the development of more effective Mcl-1 specific inhibitors.

Principal Component Analysis and Related Methods for Investigating the Dynamics of Biological Macromolecules

Principal component analysis (PCA) is used to reduce the dimensionalities of high-dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt intricate structures and exhibit complex functions by undergoing large conformational changes. Therefore, molecular simulations of and experiments on proteins generate a large number of structure variations in high-dimensional space. PCA and many PCA-related methods have been developed to extract key features from such structural data, and these approaches have been widely applied for over 30 years to elucidate macromolecular dynamics. This review mainly focuses on the methodological aspects of PCA and related methods and their applications for investigating protein dynamics.

Macromolecular Entropy Can Be Accurately Computed from Force

A method is presented to evaluate a molecule's entropy from the atomic forces calculated in a molecular dynamics simulation. Specifically, diagonalization of the mass-weighted force covariance matrix produces eigenvalues which in the harmonic approximation can be related to vibrational frequencies. The harmonic oscillator entropies of each vibrational mode may be summed to give the total entropy. The results for a series of hydrocarbons, dialanine and a β hairpin are found to agree much better with values derived from thermodynamic integration than results calculated using quasiharmonic analysis. Forces are found to follow a harmonic distribution more closely than coordinate displacements and better capture the underlying potential energy surface. The method's accuracy, simplicity, and computational similarity to quasiharmonic analysis, requiring as input force trajectories instead of coordinate trajectories, makes it readily applicable to a wide range of problems.