Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables.

The typically rugged nature of molecular free-energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free-energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for the data-driven discovery of CVs parametrizing the important large-scale motions of the system.

Data-driven prediction of αβ integrin activation paths using manifold learning and deep generative modeling.

The integrin heterodimer is a transmembrane protein critical for driving cellular process and is a therapeutic target in the treatment of multiple diseases linked to its malfunction. Activation of integrin involves conformational transitions between bent and extended states. Some of the conformations that are intermediate between bent and extended states of the heterodimer have been experimentally characterized, but the full activation pathways remain unresolved both experimentally due to their transient nature and computationally due to the challenges in simulating rare barrier crossing events in these large molecular systems.

Advancing Rational Control of Peptide-Surface Complexes.

Understanding peptide-surface interactions is crucial for programming self-assembly of peptides at surfaces and in realizing their applications, such as biosensors and biomimetic materials. In this study, we developed insights into the dependence of a residue's interaction with a surface on its neighboring residue in a tripeptide using molecular dynamics simulations. This knowledge is integral for designing rational mutations to control peptide-surface complexes.

Adsorption of amino acids on graphene: assessment of current force fields.

We compare the free energies of adsorption (ΔAads) and the structural preferences of amino acids on graphene obtained using the non-polarizable force fields-Amberff99SB-ILDN/TIP3P, CHARMM36/modified-TIP3P, OPLS-AA/M/TIP3P, and Amber03w/TIP4P/2005. The amino acid-graphene interactions are favorable irrespective of the force field. While the magnitudes of ΔAads differ between the force fields, the relative free energy of adsorption across amino acids is similar for the studied force fields.

Influence of carbon nanomaterial defects on the formation of protein corona.

In any physiological media, carbon nanomaterials (CNM) strongly interact with biomolecules leading to the formation of biocorona, which subsequently dictate the physiological response and the fate of CNMs. Defects in CNMs play an important role not only in material properties but also in the determination of how materials interact at the nano-bio interface. In this article, we probed the influence of defect-induced hydrophilicity on the biocorona formation using micro-Raman, photoluminescence, infrared spectroscopy, electrochemistry, and molecular dynamics simulations.