Molecular Dynamics Simulation of Peptide Folding

Dynamics at a Peptide-TiO2 Anatase (101) Interface

The interface between biological matter and inorganic materials is a widely investigated research topic due to possible applications in biomedicine and nanotechnology. In this context, the molecular level adsorption mechanism that drives specific recognition between small peptide sequences and inorganic surfaces represents an important topic likely to provide much information useful for designing bioderived materials. Here, we investigate the dynamics at the interface between a Ti-binding peptide sequence (AMRKLPDAPGMHC) and a TiO₂ anatase surface by using molecular dynamics (MD) simulations. In the simulations the adsorption mechanism is characterized by diffusion of the peptide from the bulk water phase toward the TiO₂ surface, followed by the anchoring of the peptide to the surface. The anchoring is mediated by the interfacial water layers by means of the charged groups of the side chains of the peptide. The peptide samples anchored and dissociated states from the surface and its conformation is not affected by the surface when anchored.

Assessment of the Sampling Performance of Multiple-Copy Dynamics versus a Unique Trajectory

The goal of the present study was to ascertain the differential performance of a long molecular dynamics trajectory versus several shorter ones starting from different points in the phase space and covering the same sampling time. For this purpose, we selected the 16-mer peptide Bak₁₆BH3 as a model for study and carried out several samplings in explicit solvent. These samplings included an 8 μs trajectory (sampling S1); two 4 μs trajectories (sampling S2); four 2 μs trajectories (sampling S3); eight 1 μs trajectories (sampling S4); 16 0.5 μs trajectories (sampling S5), and 80 0.1 μs trajectories (sampling S6). Moreover, the 8 μs trajectory was further extended to 16 μs to have reference values of the diverse properties measured. The diverse samplings were compared qualitatively and quantitatively. Among the former, we carried out a comparison of the conformational profiles of the peptide using cluster analysis. Moreover, we also gained insight into the interchange among these structures along the sampling process. Among the latter, we computed the number of new conformational patterns sampled with time using strings defined from the conformations attained by each of the residues in the peptide. We also compared the locations and depths of the obtained minima on the free energy surface using principal component analysis. Finally, we also compared the helical profiles per residue at the end of the sampling process. The results suggest that a few short molecular dynamics trajectories may provide better sampling than one unique trajectory. Moreover, this procedure can also be advantageous to avoid getting trapped in a local minimum. However, caution should be exercised since short trajectories need to be long enough to overcome local barriers surrounding the starting point and the required sampling time depends on the number of degrees of freedom of the system under study. An effective way to gain insight into the minimum MD trajectory length is to monitor the convergence of different structural features, as shown in the present work.

Molecular simulations of protein disorder

Protein disorder is abundant in proteomes throughout all kingdoms of life and serves many biologically important roles. Disordered states of proteins are challenging to study experimentally due to their structural heterogeneity and tendency to aggregate. Computer simulations, which are not impeded by these properties, have recently emerged as a useful tool to characterize the conformational ensembles of intrinsically disordered proteins. In this review, we provide a survey of computational studies of protein disorder with an emphasis on the interdisciplinary nature of these studies. The application of simulation techniques to the study of disordered states is described in the context of experimental and bioinformatics approaches. Experimental data can be incorporated into simulations, and simulations can provide predictions for experiment. In this way, simulations have been integrated into the existing methodologies for the study of disordered state ensembles. We provide recent examples of simulations of disordered states from the literature and our own work. Throughout the review, we emphasize important predictions and biophysical understanding made possible through the use of simulations. This review is intended as both an overview and a guide for structural biologists and theoretical biophysicists seeking accurate, atomic-level descriptions of disordered state ensembles.

Kinetic and binding effects in peptide substrate selectivity of matrix metalloproteinase-2: Molecular dynamics and QM/MM calculations

Herein, we examine computationally the binding and hydrolysis reaction of the MMP-2 enzyme with two peptide substrates selected by the enzyme from a phage peptide library. Molecular dynamics simulations of the Michaelis complexes (25 ns) allow us to characterize the main enzyme/substrate contacts. Subsequently MM-PBSA calculations using independent trajectories for the complexes and the free substrates provide relative binding energies in good agreement with the experimental K(M) results. Computational alanine scanning analyses of the enzyme/substrate interaction energies confirm the relevance of the P(3), P(2), and P(1)' side chains for ligand binding. Finally, the hydrolysis of both peptides taking place at the MMP-2 active site is explored by means of hybrid quantum mechanical/molecular mechanics calculations. The computed reaction mechanisms result in rate-determining energy barriers being in consonance with the experimental k(cat) values. Overall, the computational protocol seems to capture the subtle differences in binding and catalysis experimentally observed for the two peptide substrates. Some implications of our results for the future design of novel and more specific MMP-2 inhibitors are also discussed.

Computational approaches for the design of peptides with anti-breast cancer properties

Background: Breast cancer is the most common cancer among women. Tamoxifen is the preferred drug for estrogen receptor-positive breast cancer treatment, yet many of these cancers are intrinsically resistant to tamoxifen or acquire resistance during treatment. Therefore, scientists are searching for breast cancer drugs that have different molecular targets. Methodology: Recently, a computational approach was used to successfully design peptides that are new lead compounds against breast cancer. We used replica exchange molecular dynamics to predict the structure and dynamics of active peptides, leading to the discovery of smaller bioactive peptides. Conclusions: These analogs inhibit estrogen-dependent cell growth in a mouse uterine growth assay, a test showing reliable correlation with human breast cancer inhibition. We outline the computational methods that were tried and used along with the experimental information that led to the successful completion of this research.

Computational design and experimental discovery of an antiestrogenic peptide derived from alpha-fetoprotein

Breast cancer is the most common cancer among women, and tamoxifen is the preferred drug for estrogen receptor-positive breast cancer treatment. Many of these cancers are intrinsically resistant to tamoxifen or acquire resistance during treatment. Consequently, there is an ongoing need for breast cancer drugs that have different molecular targets. Previous work has shown that 8-mer and cyclic 9-mer peptides inhibit breast cancer in mouse and rat models, interacting with an unsolved receptor, while peptides smaller than eight amino acids did not. We show that the use of replica exchange molecular dynamics predicts the structure and dynamics of active peptides, leading to the discovery of smaller peptides with full biological activity. Simulations identified smaller peptide analogues with the same conserved reverse turn demonstrated in the larger peptides. These analogues were synthesized and shown to inhibit estrogen-dependent cell growth in a mouse uterine growth assay, a test showing reliable correlation with human breast cancer inhibition.