Knowledge of the Free Energy Landscape topology is the essential key to understanding many biochemical processes. The determination of the conformers of a protein and their basins of attraction takes a central role for studying molecular isomerization reactions. In this work, we present a novel framework to unveil the features of a Free Energy Landscape answering questions such as how many meta-stable conformers there are, what the hierarchical relationship among them is, or what the structure and kinetics of the transition paths are. Exploring the landscape by molecular dynamics simulations, the microscopic data of the trajectory are encoded into a Conformational Markov Network. The structure of this graph reveals the regions of the conformational space corresponding to the basins of attraction. In addition, handling the Conformational Markov Network, relevant kinetic magnitudes as dwell times and rate constants, or hierarchical relationships among basins, completes the global picture of the landscape. We show the power of the analysis studying a toy model of a funnel-like potential and computing efficiently the conformers of a short peptide, dialanine, paving the way to a systematic study of the Free Energy Landscape in large peptides. A complete description of complex polymers, such as proteins, includes information about their structure and their dynamics. In particular it is of utmost importance to answer the following questions: What are the structural conformations possible? Is there any relevant hierarchy among these conformers? What are the transition paths between them? These and other questions can be addressed by analyzing in an efficient way the Free Energy Landscape of the system. With this knowledge, several problems about biomolecular reactions (such as enzymatic activity, protein folding, protein deposition diseases, etc.) can be tackled. In this article we show how to efficiently describe the Free Energy Landscape for small and large peptides. By mapping the trajectories of molecular dynamics simulations into a graph (the Conformational Markov Network) and unveiling its structural organization, we obtain a coarse grained description of the protein dynamics across the Free Energy Landscape in terms of the relevant kinetic magnitudes of the system. Therefore, we show the way to bridge the gap between the microscopic dynamics and the macroscopic kinetics by means of a mesoscopic description of the associated Conformational Markov Network. Along this path the compromise between the physical nature of the process and the magnitudes that characterize the network is carefully kept to assure the reliability of the results shown.

A new “on the fly” method to perform Born-Oppenheimer ab initio molecular dynamics (AIMD) simulations is presented. Inspired by Ehrenfest dynamics in time-dependent density functional theory, the electronic orbitals are evolved by a Schrödinger-like equation, where the orbital time derivative is multiplied by a parameter. This parameter controls the time scale of the fictitious electronic motion and speeds up the calculations with respect to standard Ehrenfest dynamics. In contrast with other methods, wave function orthogonality needs not be imposed as it is automatically preserved, which is of paramount relevance for large-scale AIMD simulations.

In the last decades several hydrogen-bond definitions were proposed by classical computer simulations. Aiming at validating their self-consistency on a wide range of conditions, here we present a comparative study of six among the most common hydrogen-bond definitions for temperatures ranging from 220 K to 400 K and six classical water models. Our results show that, in the interval of temperatures investigated, a generally weak agreement among definitions is present. Moreover, cutoff choice for geometrically based definitions depends on both temperature and water model. As such, analysis of the same water model at different temperatures as well as different water models at the same temperature would require the development of specific cutoff values. Interestingly, large discrepancies were found between two hydrogen-bond definitions which were recently introduced to improve on more conventional methods. Our results reinforce the idea that a more universal way to characterize hydrogen bonds in classical molecular systems is needed.

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

The lipid composition of cell membranes has increasingly been recognized as playing an important role in the function of various membrane proteins, including G Protein-Coupled Receptors (GPCRs). For instance, experimental and computational evidence has pointed to lipids influencing receptor oligomerization directly, by physically interacting with the receptor, and/or indirectly, by altering the bulk properties of the membrane. While the exact role of oligomerization in the function of class A GPCRs such as the μ-opioid receptor (MOR) is still unclear, insight as to how these receptors oligomerize and the relevance of the lipid environment to this phenomenon is crucial to our understanding of receptor function. To examine the effect of lipids and different MOR conformations on receptor oligomerization we carried out extensive coarse-grained molecular dynamics simulations of crystal structures of inactive and/or activated MOR embedded in an idealized mammalian plasma membrane composed of 63 lipid types asymmetrically distributed across the two leaflets. The results of these simulations point, for the first time, to specific direct and indirect effects of the lipids, as well as the receptor conformation, on the spatio-temporal organization of MOR in the plasma membrane. While sphingomyelin-rich, high-order lipid regions near certain transmembrane (TM) helices of MOR induce an effective long-range attractive force on individual protomers, both long-range lipid order and interface formation are found to be conformation dependent, with a larger number of different interfaces formed by inactive MOR compared to active MOR. The μ-opioid receptor (MOR) is an important pharmaceutical target in the treatment of pain. In order to develop novel pain therapies, devoid of the serious side-effects of present opioid analgesics, we need to understand the fundamentals of how MOR works on the molecular level. While some studies suggest that oligomers of MOR could play a role in signaling, how MOR forms dimers, which interfaces form, and the exact role of oligomers in MOR function remain unclear. While research has shown that the membrane environment can affect membrane protein function, most previous computational work to study oligomerization has been performed in a very simple membrane. Here, we use molecular dynamics simulations of MOR in a heterogeneous plasma membrane model (comprising 63 lipid types) to investigate how the presence of the protein modulates its lipid environment, affecting species distribution and sculpting characteristic order and thickness profiles around the receptors. Such modulations, in turn, induce long-range interactions between the proteins and favor the formation of specific dimeric conformations.

Various experimental and computational techniques have been employed over the past decade to provide structural and thermodynamic insights into G Protein-Coupled Receptor (GPCR) dimerization. Here, we use multiple microsecond-long, coarse-grained, biased and unbiased molecular dynamics simulations (a total of ~4 milliseconds) combined with multi-ensemble Markov state models to elucidate the kinetics of homodimerization of a prototypic GPCR, the µ-opioid receptor (MOR), embedded in a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC)/cholesterol lipid bilayer. Analysis of these computations identifies kinetically distinct macrostates comprising several different short-lived dimeric configurations of either inactive or activated MOR. Calculated kinetic rates and fractions of dimers at different MOR concentrations suggest a negligible population of MOR homodimers at physiological concentrations, which is supported by acceptor photobleaching fluorescence resonance energy transfer (FRET) experiments. This study provides a rigorous, quantitative explanation for some conflicting experimental data on GPCR oligomerization.