Finding the low-energy forms of avian pancreatic polypeptide with the diffusion-process-controlled Monte Carlo method

Developing Hybrid All-Atom and Ultra-Coarse-Grained Models to Investigate Taxol-Binding and Dynein Interactions on Microtubules

Simulating the conformations and functions of biological macromolecules by using all-atom (AA) models is a challenging task due to expensive computational costs. One possible strategy to solve this problem is to develop hybrid all-atom and ultra-coarse-grained (AA/UCG) models of the biological macromolecules. In the AA/UCG scheme, the interest regions are described by AA models, while the other regions are described in the UCG representation. In this study, we develop the hybrid AA/UCG models and apply them to investigate the conformational changes of microtubule-bound tubulins. The simulation results of the hybrid models elucidated the mechanism of why the taxol molecules selectively bound microtubules but not tubulin dimers. In addition, we also explore the interactions of the microtubules and dyneins. Our study shows that the hybrid AA/UCG model has great application potential in studying the function of complex biological systems.

The OPEP protein model: from single molecules, amyloid formation, crowding and hydrodynamics to DNA/RNA systems

The OPEP coarse-grained protein model has been applied to a wide range of applications since its first release 15 years ago. The model, which combines energetic and structural accuracy and chemical specificity, allows the study of single protein properties, DNA-RNA complexes, amyloid fibril formation and protein suspensions in a crowded environment. Here we first review the current state of the model and the most exciting applications using advanced conformational sampling methods. We then present the current limitations and a perspective on the ongoing developments.

The complex folding pathways of protein A suggest a multiple-funnelled energy landscape

Folding proteins into their native states requires the formation of both secondary and tertiary structures. Many questions remain, however, as to whether these form into a precise order, and various pictures have been proposed that place the emphasis on the first or the second level of structure in describing folding. One of the favorite test models for studying this question is the B domain of protein A, which has been characterized by numerous experiments and simulations. Using the activation-relaxation technique coupled with a generic energy model (optimized potential for efficient peptide structure prediction), we generate more than 50 folding trajectories for this 60-residue protein. While the folding pathways to the native state are fully consistent with the funnel-like description of the free energy landscape, we find a wide range of mechanisms in which secondary and tertiary structures form in various orders. Our nonbiased simulations also reveal the presence of a significant number of non-native beta and alpha conformations both on and off pathway, including the visit, for a non-negligible fraction of trajectories, of fully ordered structures resembling the native state of nonhomologous proteins.

The Role of Aromatic Residues in Stabilizing the Secondary and Tertiary Structure of Avian Pancreatic Polypeptide

Avian Pancreatic Polypeptide is a 36 residue protein that exhibits a tertiary fold. Results of previous experimental and computational studies indicate that the structure of aPP is stabilized more by non-bonded interactions than by the hydrophobic effect. Aromatic residues are known to participate in a variety of long range non-bonded interactions, with both backbone atoms and the atoms of other side-chains, which could be responsible, in part, for the stability of both the local secondary structure and the tertiary fold. The effect of these aromatic interactions on the stability of aPP was calculated using BHandHLYP/cc-pVTZ. Aromatic residues were shown to participate in multiple hydrogen bonded and weakly polar interactions in the secondary structure. The energies of the weakly polar interactions are comparable with those of hydrogen bonds. Aromatic residues were also shown to participate in multiple weakly polar interactions across the tertiary fold, again with energies similar to those of hydrogen bonds.

Critical assessment of side-chain conformational space sampling procedures designed for quantifying the effect of side-chain environment

We introduce a family of procedures designed to sample side-chain conformational space at particular locations in protein structures. These procedures (CRSP) use intensive cycles of random assignment of side-chain conformations followed by minimization to determine all the conformations that a group of side-chains can adopt simultaneously. First, we consider a procedure evolving in the dihedral space (dCRSP). Our results suggest that it can accurately map low-energy conformations adopted by clusters of side-chains of a protein. dCRSP is relatively insensitive to various important parameters, and it is sufficiently accurate to capture efficiently the constraint induced by the environment on the conformations a particular side-chain can adopt. Our results show that dCRSP, compared with molecular dynamics (MD), can overcome the problem of the limited set of conformations reached in a reasonable amount of simulations. Next, we introduce procedures (vCRSP) in which valence angles are relaxed, and we assess how efficiently they quantify the conformational entropy of side-chains in the protein native state. For simple peptides, entropies obtained with vCRSP are fully compatible with those obtained with a Monte Carlo procedure. For side-chains in a protein environment, however, vCRSP appears of limited use. Finally, we consider a two-step procedure that combines dCRSP and vCRSP. Our tests suggest that it is able to overcome the limitations of vCRSP. We also note that dCRSP provides a reasonable initial approximation. This family of procedures offers promise in quantifying the contribution of conformational entropy to the energetics of protein structures.

Computer simulations aimed at structure prediction of supersecondary motifs in proteins

It is well established that protein structures are more conserved than protein sequences. One-third of all known protein structures can be classified into ten protein folds, which themselves are composed mainly of alpha-helical hairpin, beta hairpin, and betaalphabeta supersecondary structural elements. In this study, we explore the ability of a recent Monte Carlo-based procedure to generate the 3D structures of eight polypeptides that correspond to units of supersecondary structure and three-stranded antiparallel beta sheet. Starting from extended or misfolded compact conformations, all Monte Carlo simulations show significant success in predicting the native topology using a simplified chain representation and an energy model optimized on other structures. Preliminary results on model peptides from nucleotide binding proteins suggest that this simple protein folding model can help clarify the relation between sequence and topology.

Ab initio protein structure prediction using physicochemical potentials and a simplified off-lattice model

This study describes a computational method for ab inito protein structure prediction. Protein conformation has been modeled by using six optimized backbone torsion angles and fixed side chains approximating rotationally averaged real side chains. The approximations aim to keep complexity of the structure description to a minimum without seriously compromising the accuracy of the structural representation. An evolutionary Monte Carlo algorithm has been developed to search through this restricted conformational space to locate low-energy protein structures. A simple physicochemical force field has been developed to assess the energies of different conformations within this structural description. The corresponding residue interaction energies are based on hydrophobic, hydrophilic, steric, and hydrogen-bonding potentials. The search procedure has been used to locate native energy minima from primary sequence alone. The 3-D structures of polypeptides up to 38 residues with both beta and alpha secondary structural elements have been accurately predicted. The search procedure has been found to be highly efficient and follows an energetically and structurally plausible pathway to locate native populations. The simple force field described in the study has been compared with a more complex all-atom model and been found to be similarly effective in predicting the structures of proposed independent folding units. Proteins 2001;43:186-202.

Generating ensemble averages for small proteins from extended conformations by Monte Carlo simulations

Since it is not feasible to determine the structure of every protein by experiment, algorithms delivering the folded conformation of a protein solely from its amino acid sequence are desirable. Here the diffusion-process controlled-Monte Carlo approach has been applied to generating ensemble averages for three small proteins with 31, 36, and 46 residues. Starting from extended conformations and using an energy model that was developed on other protein models, the simulations find nativelike structures deviating by 3 A rms from the experimental structures for the main chain atoms. The balance between long-range and short-range interactions is discussed briefly in the context of stability and folding.

New Monte Carlo algorithms for protein folding

Over the past three decades, a number of powerful simulation algorithms have been introduced to the protein folding problem. For many years, the emphasis has been placed on how to both overcome the multiple minima problem and find the conformation with the global minimum potential energy. Since the new view of the protein folding mechanism (based on the free energy landscape of the protein system) arose in the past few years, however, it is now of interest to obtain a global knowledge of the phase space, including the intermediate and denatured states of proteins. Monte Carlo methods have proved especially valuable for these purposes. As well as new, powerful optimization techniques, novel algorithms that can sample much a wider phase space than conventional methods have been established.