Machine learning in the analysis of biomolecular simulations

Machine learning has rapidly become a key method for the analysis and organization of large-scale data in all scientific disciplines. In life sciences, the use of machine learning techniques is a particularly appealing idea since the enormous capacity of computational infrastructures generates terabytes of data through millisecond simulations of atomistic and molecular-scale biomolecular systems. Due to this explosion of data, the automation, reproducibility, and objectivity provided by machine learning methods are highly desirable features in the analysis of complex systems. In this review, we focus on the use of machine learning in biomolecular simulations. We discuss the main categories of machine learning tasks, such as dimensionality reduction, clustering, regression, and classification used in the analysis of simulation data. We then introduce the most popular classes of techniques involved in these tasks for the purpose of enhanced sampling, coordinate discovery, and structure prediction. Whenever possible, we explain the scope and limitations of machine learning approaches, and we discuss examples of applications of these techniques.Peer reviewe

Cationic DMPC/DMTAP Lipid Bilayers: Molecular Dynamics Study

Cationic lipid membranes are known to form compact complexes with DNA and to be effective as gene delivery agents both in vitro and in vivo. Here we employ molecular dynamics simulations for a detailed atomistic study of lipid bilayers consisting of a mixture of cationic dimyristoyltrimethylammonium propane (DMTAP) and zwitterionic dimyristoylphosphatidylcholine (DMPC). Our main objective is to examine how the composition of the bilayers affects their structural and electrostatic properties in the liquid-crystalline phase. By varying the mole fraction of DMTAP, we have found that the area per lipid has a pronounced non-monotonic dependence on the DMTAP concentration, with a minimum around the point of equimolar mixture. We show that this behavior has an electrostatic origin and is driven by the interplay between positively charged TAP headgroups and the zwitterionic PC heads. This interplay leads to considerable re-orientation of PC headgroups for an increasing DMTAP concentration, and gives rise to major changes in the electrostatic properties of the lipid bilayer, including a significant increase of total dipole potential across the bilayer and prominent changes in the ordering of water in the vicinity of the membrane. Moreover, chloride counter-ions are bound mostly to PC nitrogens implying stronger screening of PC heads by Cl ions compared to TAP head groups. The implications of these findings are briefly discussed

Lipid membranes : Theory and simulations bridged to experiments

Peer reviewe

Understanding the role of lipids in signaling through atomistic and multiscale simulations of cell membranes

Peer reviewe

Microscopic mechanism for cold denaturation

We elucidate the mechanism of cold denaturation through constant-pressure simulations for a model of hydrophobic molecules in an explicit solvent. We find that the temperature dependence of the hydrophobic effect is the driving force/induces/facilitates cold denaturation. The physical mechanism underlying this phenomenon is identified as the destabilization of hydrophobic contact in favor of solvent separated configurations, the same mechanism seen in pressure induced denaturation. A phenomenological explanation proposed for the mechanism is suggested as being responsible for cold denaturation in real proteins

Nanoscale Membrane Domain Formation Driven by Cholesterol

Biological membranes generate specific functions through compartmentalized regions such as cholesterol-enriched membrane nanodomains that host selected proteins. Despite the biological significance of nanodomains, details on their structure remain elusive. They cannot be observed via microscopic experimental techniques due to their small size, yet there is also a lack of atomistic simulation models able to describe spontaneous nanodomain formation in sufficiently simple but biologically relevant complex membranes. Here we use atomistic simulations to consider a binary mixture of saturated dipalmitoylphosphatidylcholine and cholesterol - the "minimal standard" for nanodomain formation. The simulations reveal how cholesterol drives the formation of fluid cholesterol-rich nanodomains hosting hexagonally packed cholesterol-poor lipid nanoclusters, both of which show registration between the membrane leaflets. The complex nanodomain substructure forms when cholesterol positions itself in the domain boundary region. Here cholesterol can also readily flip-flop across the membrane. Most importantly, replacing cholesterol with a sterol characterized by a less asymmetric ring region impairs the emergence of nanodomains. The model considered explains a plethora of controversial experimental results and provides an excellent basis for further computational studies on nanodomains. Furthermore, the results highlight the role of cholesterol as a key player in the modulation of nanodomains for membrane protein function.Peer reviewe

Atomistic fingerprint of hyaluronan-CD44 binding

Hyaluronan is a polyanionic, megadalton-scale polysaccharide, which initiates cell signaling by interacting with several receptor proteins including CD44 involved in cell-cell interactions and cell adhesion. Previous studies of the CD44 hyaluronan binding domain have identified multiple widespread residues to be responsible for its recognition capacity. In contrast, the X-ray structural characterization of CD44 has revealed a single binding mode associated with interactions that involve just a fraction of these residues. In this study, we show through atomistic molecular dynamics simulations that hyaluronan can bind CD44 with three topographically different binding modes that in unison define an interaction fingerprint, thus providing a plausible explanation for the disagreement between the earlier studies. Our results confirm that the known crystallographic mode is the strongest of the three binding modes. The other two modes represent metastable configurations that are readily available in the initial stages of the binding, and they are also the most frequently observed modes in our unbiased simulations. We further discuss how CD44, fostered by the weaker binding modes, diffuses along HA when attached. This 1D diffusion combined with the constrained relative orientation of the diffusing proteins is likely to influence the aggregation kinetics of CD44. Importantly, CD44 aggregation has been suggested to be a possible mechanism in CD44-mediated signaling.Peer reviewe

Excessive aggregation of membrane proteins in the Martini model

The coarse-grained Martini model is employed extensively to study membrane protein oligomerization. While this approach is exceptionally promising given its computational efficiency, it is alarming that a significant fraction of these studies demonstrate unrealistic protein clusters, whose formation is essentially an irreversible process. This suggests that the protein-protein interactions are exaggerated in the Martini model. If this held true, then it would limit the applicability of Martini to study multi-protein complexes, as the rapidly clustering proteins would not be able to properly sample the correct dimerization conformations. In this work we first demonstrate the excessive protein aggregation by comparing the dimerization free energies of helical transmembrane peptides obtained with the Martini model to those determined from FRET experiments. Second, we show that the predictions provided by the Martini model for the structures of transmembrane domain dimers are in poor agreement with the corresponding structures resolved using NMR. Next, we demonstrate that the first issue can be overcome by slightly scaling down the Martini protein-protein interactions in a manner, which does not interfere with the other Martini interaction parameters. By preventing excessive, irreversible, and non-selective aggregation of membrane proteins, this approach renders the consideration of lateral dynamics and protein-lipid interactions in crowded membranes by the Martini model more realistic. However, this adjusted model does not lead to an improvement in the predicted dimer structures. This implicates that the poor agreement between the Martini model and NMR structures cannot be cured by simply uniformly reducing the interactions between all protein beads. Instead, a careful amino-acid specific adjustment of the protein-protein interactions is likely required.Peer reviewe