Blind protein structure prediction using accelerated free-energy simulations.

We report a key proof of principle of a new acceleration method [Modeling Employing Limited Data (MELD)] for predicting protein structures by molecular dynamics simulation. It shows that such Boltzmann-satisfying techniques are now sufficiently fast and accurate to predict native protein structures in a limited test within the Critical Assessment of Structure Prediction (CASP) community-wide blind competition

How Water's Properties Are Encoded in Its Molecular Structure and Energies.

How are water's material properties encoded within the structure of the water molecule? This is pertinent to understanding Earth's living systems, its materials, its geochemistry and geophysics, and a broad spectrum of its industrial chemistry. Water has distinctive liquid and solid properties: It is highly cohesive. It has volumetric anomalies-water's solid (ice) floats on its liquid; pressure can melt the solid rather than freezing the liquid; heating can shrink the liquid. It has more solid phases than other materials. Its supercooled liquid has divergent thermodynamic response functions. Its glassy state is neither fragile nor strong. Its component ions-hydroxide and protons-diffuse much faster than other ions. Aqueous solvation of ions or oils entails large entropies and heat capacities. We review how these properties are encoded within water's molecular structure and energies, as understood from theories, simulations, and experiments. Like simpler liquids, water molecules are nearly spherical and interact with each other through van der Waals forces. Unlike simpler liquids, water's orientation-dependent hydrogen bonding leads to open tetrahedral cage-like structuring that contributes to its remarkable volumetric and thermal properties

Conditional Reversible Work method: a novel approach to obtain pair potentials for coarse-grained simulation of soft matter

Molecular dynamic simulations provide valuable tools to study soft matter systems, since they can in principle shed light on many mechanisms happening at a size-scale hardly reachable experimentally. Unfortunately, despite the continuous development in computer technology and simulation algorithms, many phenomena characteriz- ing soft matter still happen at a size and time scale that is not easily reachable by simulation carried with atomistic resolution. Coarse graining (CG) the system by rep- resenting groups of atoms as single interaction sites (beads) enables the possibility of studying such phenomena. The quality of a coarse-grained model is related with the quality of the description of the interaction between the beads, which should prop- erly account for the chemical nature of the groups of atoms that they represent. In the recent years several methods have been developed to obtain coarse-grained in- teraction potentials. In between the available approaches to CG of particular interest is the class of methods that undergoes under the name of systematic coarse graining. Common characteristic of the methods belonging to this class is to develop models using informations obtained at a more detailed level of description of the system (e.g. atomistic level). The quality of a CG model is normally evaluated discussing its ability to predict property at the state point considered during its development (representability) and also at different state points (transferability). This thesis reviews the current state of the art of available systematic coarse grain- ing methodologies and proposes the conditional reversible work (CRW) method as a new approach to develop interaction potentials. CRW is based on the calculation of the interaction free energy between the groups of atoms that will be represented by the beads, under the condition that they are embedded in their respective molecules. Unlike many other methods CRW delivers pair interaction potentials that are free from many-body contributions and that have a clear physical meaning. In princi- ple different routes can be used to calculate the interaction free energy between the beads. In this thesis two different approaches are presented: the first one is based on a thermodynamic cycle that makes use of reversible work calculations and the second one is based on thermodynamic perturbation theory. The representability and the transferability of coarse grained models obtained employing CRW methods are discussed. In particular first a comparison is carried between the performances of two toluene CG model developed employing CRW and a well-established CG method, namely Iterative Boltzmann Inversion (IBI), showing a remarkable transferability of the CRW model. Then a model of hexane where two neighboring carbons and their belonging hydrogens are lumped together in a single interaction site is developed using CRW methods. A comparison between the property of the parent atomistic model and of the CG model proves a good representability and transferability of the model. Considering the fact that the interaction potentials are representative of the conditional free energy of the group of atoms, also the chemical transferability is in- vestigated. This expresses the possibility of employing the developed CG models for small molecules as building blocks for bigger molecules. The interaction potentials developed for the hexane molecules have been employed to simulate linear alkanes up to dodecane finding a good agreement between the properties calculated using the CG model and the parent atomistic model. Also the possibility of using CRW potentials for mixture has been tested investigating the behaviors of n-alkane mix- ture and the thermodynamic of solvating additives molecule in a polymeric matrix. Again a comparison between atomistic and CG results agree over a wide range of temperatures even for a sensitive quantities like the solvation free energy. In or- der to understand the limit of applicability of CRW pair potential approach also a study has been carried on the applicability of this method on systems that are gov- erned by multi body interactions. In particular a series of increasingly polar liquid has been CG as single bead. It has been found that when directional interactions are CG away the model rapidly loses the ability of describing property of the sys- tem. It is important to notice that in principle it is possible to repair at least part of this failure by employing a lower level of CG (i.e. using more bead to describe the molecules), since this allows maintaining part of the directionality of the interactions. This thesis proves the quality of CRW method to develop interaction potentials for soft matter systems. In particular it has to be remembered that the potentials devel- oped using this method are pair potentials, free of indirect contributions. Therefore this method is straightforwardly applicable to develop coarse grained models for sys- tems where pair additivity of the interaction can be considered like van der Waals dominated systems. A more careful implementation is necessary in system where multibody interactions are important. Since the CRW potentials are representative of a conditional free energy it is possible to think future application of this methods also to develop models for systems where this kind of interaction are important, like hydrophobic interaction between amino acids in water

Predicting Protein Dimer Structures Using MELD × MD.

It is challenging to predict the docked conformations of two proteins. Current methods are susceptible to errors from treating proteins as rigid bodies and from an inability to compute relative Boltzmann populations of different docked conformations. Here, we show that by using the ClusPro server as a front end to generate possible protein-protein contacts, and using Modeling Employing Limited Data (MELD) accelerated molecular dynamics (MELD × MD) as a back end for atomistic simulations, we can find 16/20 native dimer structures of small proteins as those having the lowest free energy, starting from good-bound-backbone structures. We show that atomistic MD free energies can be used to identify native protein dimer structures

Computing Ligands Bound to Proteins Using MELD-Accelerated MD.

Predicting the poses of small-molecule ligands in protein binding sites is often done by virtual screening algorithms such as DOCK. In principle, molecular dynamics (MD) using atomistic force fields could give better free-energy-based pose selection, but MD is computationally expensive. Here, we ask if modeling employing limited data (MELD)-accelerated MD (MELD × MD) can pick out the best DOCK poses taken as input. We study 30 different ligand-protein pairs. MELD × MD finds native poses, based on best free energies, in 23 out of the 30 cases, 20 of which were previously known DOCK failures. We conclude that MELD × MD can add value for predicting accurate poses of small molecules bound to proteins