One approach to 3D structure prediction, sometimes termed

*ab initio* prediction, is based on the thermodynamic hypothesis formulated by Anfinsen, according to which the native structure of a protein corresponds to the global minimum of the free energy of the system comprising the macromolecule [

13]. Accordingly, physics-based methods model the process of folding by simulating the conformational changes of a macromolecule while it searches for the state of minimal free energy (review: [

14]). The “score” of each conformation is calculated as the true physical energy based on the interactions within the macromolecule and between the macromolecule and the solvent [

15]. While in physics the term

*ab initio* is often used to refer to find a set of wave functions and energies by solving the Schrödinger equation without external parameters, the physics-based methods described here offer a simplified approach to calculate the energy. The functional form and parameter sets used to describe the potential energy of a system is called a force field. There exist a number of software packages for simulation of protein folding in atomic detail, they typically implement various versions of molecular dynamics (MD) and Monte Carlo (MC) protocols for searching the conformational space, and force fields such as AMBER [

16], CHARMM [

17] or GROMOS [

18] to calculate the energy.

In order to facilitate the identification of the native state as the one of the lowest energy, the energy landscape that describes the relationship between the distance from the native-like conformation and the energy should have a funnel-like shape (review: [

19]). More explicitly, when plotting the energy of models versus a structural difference between the models and the native structure (e.g., expressed as the root-mean-square deviation (RMSD) between pairs of equivalent atoms in optimally superimposed structures), there should be a funnel-shaped tip at the bottom left corner of the plot. In particular, the native structure should exhibit the lowest energy, and the farther a given conformation is from the native structure, the higher its energy should be. The prediction of the native structure is easier if this relationship between the value and variability of energies and deviation from the native structure holds across the entire range of possible conformations.

Figure presents diagrams comparing an ‘ideal’ (from a practical point of view of macromolecular structure prediction) relationship between the energy of models and their distance from the native structure and a ‘real life’ example of such a distribution obtained from a folding simulation of an immunoglobulin light chain-binding domain of protein L (2ptl in the Protein Data Bank), carried out using the REFINER method [

20]. One conceptual difference between the energy function in a folding simulation and the real physical energy becomes apparent in this and similar plots: In reality, the energy differences between the folded and unfolded state are very small, while in practice the effective discrimination of native-like models from non-native-like ones requires maximization of the energy difference.

The

*ab initio* approach is plagued by serious problems. In particular, a full-atom structural model of a macromolecule has a large number of degrees of freedom (3*N

_{atoms}-5), which makes the search space enormous, and the function with which to calculate the energy of the system is very complex. As a result, both the sampling and energy calculations are very costly in terms of computational power required. Typically, the free energy landscape is extremely rugged, i.e., it possesses multiple local minima, and it is essentially impossible to perform an exhaustive evaluation of all these minima to identify the one with the globally lowest value. Further, some of the components of the free energy function (e.g., the entropy) are very difficult to calculate, and may not be inferred accurately for large molecules. For these reasons the use of

*ab initio* methods is limited to very small molecules. Thus far, most of the reported successful all-atom folding simulations have been for very small proteins, such as the 20-residue “Trp-cage” miniprotein [

21], and they rarely exceed the threshold of one microsecond. Further, even extended simulations, such as a ten microsecond simulation performed for a fast-folding WW domain [

22] may not sample a native-like conformation. Hence, folding simulations have not yet matured to the state of a reliable method for protein 3D structure prediction.

One important problem for algorithms that deal with macromolecular structures is the representation of coordinates. Cartesian coordinates (three numbers representing distances for each atom) are used to represent the final model in a common PDB file format, but they may be impractical at some stages of modeling, as they utilize 3*N

_{atoms} degrees of freedom. To increase the efficiency of computations, the system may be transformed into internal coordinate systems and/or bond lengths and angles may be restricted to idealized values. For instance in torsion angle dynamics (e.g., as implemented in the program DYANA [

23]), torsion angles are used instead of Cartesian coordinates as degrees of freedom, and the only degrees of freedom are rotations about single bonds. A biopolymer can be represented as a tree structure consisting of n

+

1 atoms connected by n rotatable bonds of fixed length. The tree structure starts from a base, typically at the N-terminus of the polypeptide chain, and terminates with “leaves” at the ends of the side-chains and at the C-terminus. The conformation of the molecule is uniquely specified by the values of all torsion angles and torsion angles may be allowed to assume only discrete values. The conversion of a model from internal coordinates to a Cartesian representation can be achieved, e.g., with the Nerf algorithm [

24], which requires three coordinates per atom: a bond length, a flat angle, and a torsion angle.

Another approach to reduce the number of degrees of freedom is to use coarse-grained models, which treat groups of atoms as single interaction centers, so that a smaller number of elements and interactions need to be considered (review: [

25]). Actually, the first simulation of protein folding reported in the literature used a simplified chain and time-averaged forces to fold bovine pancreatic trypsin inhibitor from an open-chain conformation into a folded conformation close to that of the native molecule [

26]. Another advantage of coarse-grained modeling is that the force field derived for the united interaction centers yields a much smoother energy surface than that for the all-atom energy function. As a result, many local energy minima are removed, in which the system could become trapped during the simulation. However, it must be emphasized that simplification of the model and the energy function usually leads to reduced accuracy. As of today, it is not practical to expect that a folding simulation for a macromolecule comprising more than 100 residues would confidently predict a native-like structure with a correctly estimated energy. Contemporary methods for coarse-grained protein structure prediction can be exemplified by UNRES [

27], which represents side chains by ellipsoids, and the peptide bonds by united atoms located in the middle of two consecutive Cα atoms. The only degrees of freedom in the continuous space are the bond and torsion pseudoangles defined between the Cα atoms. The free energy function includes terms for interactions between the side chain centers, steric repulsion between side chains and peptide group centers, and electrostatic interactions between peptide groups. Local conformational propensities of a polypeptide are described by torsional and angle-bending potentials. Multibody interactions, which are the most important for reproducing regular secondary structure elements, are described by higher order terms.

Since the same basic laws of physics apply to all types of molecules, one can postulate that analogous methods should work for RNA as well. As mentioned earlier, RNA folding relies on the modulation of electrostatic repulsion by counterions, while protein folding relies on the formation of a hydrophobic core, and the secondary structure formation requires hydrogen-bonding either via protein side chain or RNA main chain functional groups, respectively. Nonetheless, computational methods developed to study protein folding have been successfully used to simulate RNA folding (review: [

28]). Examples of all-atom simulations with general-purpose software packages such as AMBER or CHARMM include the folding of small RNA hairpins [

29,

30], the analysis of H-bond stability in the anticodon loop of tRNA(Asp) [

31] or modeling the interaction of “kissing loops” in the dimerization initiation site (DIS) of HIV [

32]. Molecular dynamics simulations restrained by experimental data have also been used to model the conformational transitions of large macromolecular complexes involving both RNAs and proteins, such as the ribosome (review: [

33]).

The modeling of nucleic acid structures can also take advantage of the use of local coordinate systems and/or coarse-graining to reduce the number of variables in the system. For instance the 3DNA program [

34] constructs reference coordinate frames around bases and base pairs, while using idealized values for bond lengths and bond angles. The treatment of nucleobase moieties as rigid bodies allows one to drastically reduce the number of degrees of freedom. Further, a total of three angle and three translation variables are enough to describe the relative orientation of two bases (with parameters called propeller, buckle, opening, shear, stretch, and stagger) or two base pairs (twist, tilt, roll, shift, slide, and rise). Because these parameters are independent of the Cartesian system, they allow to directly compare two structures without the spatial superposition of coordinates. The miniCarlo program for energy minimizations and Monte Carlo simulations of nucleic acid structures applies a very similar scheme. It uses helical parameters that determine the relative position of bases in a pair and relative position of base pairs, pseudorotation parameters of sugars that determine internal geometry of sugar moieties, glycosidic angles that determine orientation of sugars relative to the bases, and torsion angles that determine the orientation of methyl groups in thymines and hydroxyl groups in riboses [

35]. The commercially distributed program junction minimization of nucleic acids (JUMNA) uses a reduced coordinate approach to gain roughly an order of magnitude in the number of variables necessary to model a nucleic acid fragment [

36].

One of the first applications of the coarse-grained approach for RNA 3D structure modeling involved the refinement of low-resolution structures of ribosomal RNAs with restraints from experimental data and a representation with pseudoatoms at different levels of detail - from a single pseudoatom per helix to a single pseudoatom for each nucleotide [

37]. More recently, a number of new methods have been developed that allow for coarse-grained folding simulations with or without experimental data. YUP [

38] and NAST [

39] represent RNA by just one pseudoatom per nucleotide residue: phosphate and C3′, respectively. Vfold [

40] and DMD [

41] represent RNA by three pseudoatoms per residue, while HiRE-RNA [

42] uses six or seven pseudoatoms for purine or pyrimidine residues, respectively. For bonded interactions (bonds, angles, and dihedrals) all these methods use parameters derived from a database of known RNA structures. For non-bonded interactions, the energy terms differ. NAST actually does not use a full energy function, it generates plausible 3D structures based on restraints supplied by the user (e.g., on secondary structure or tertiary contacts). Vfold and DMD use experimentally tabulated energy values [

43] to parameterize (in different ways) base-pairing and base-stacking, as well as to estimate loop entropy. DMD also uses an explicit representation of hydrogen bonding to enforce base pairs formation and an additional term for phosphate-phosphate repulsion. With the simplifications introduced, both methods are capable of folding RNAs up to 100 residues long. Vfold has been recently used to successfully model pseudoknotted RNA structures and to estimate the conformational entropy for stem-loop tertiary contacts [

44].

HiRE-RNA is an excellent example of a protein-like coarse-grained method for RNA modeling. It uses an implicit solvent force field similar to the protein OPEP force field [

42]. It is expressed as a sum of local (bonded), nonbonded, and hydrogen-bond terms. All bonded interactions are described by harmonic terms. For non-bonded interactions, a Lennard-Jones potential is used, modified to mimic some of the excluded volume and screening effect that gets lost in coarse-graining. In particular, it implements a repulsive power law at short distances, an exponential tail at large distances to account for an extra screening given by the atoms that are missing from the coarse-grained representation, and a narrower well varying with the equilibrium distance. Hi-RNA energy model does not take into account electrostatic interactions explicitly, except for the repulsion between the phosphates. The base pairing is modeled by hydrogen bonding interactions consisting of 2-, 3-, and 4-body terms. The interactions taken into account include canonical A-U and G-C Watson-Crick pairs, A-U Hoogsteen pairs, G-U wobble pairs, as well as the relatively rare A-C, A-G, and U-C pairs. All the HiRE-RNA parameters were derived from a statistical study of 220 structures in the Nucleic Acids Database (NDB) and subsequently refined through the analysis of long molecular dynamics simulations for a poly-A molecule of 15 nucleotides. Tests of HiRE-RNA on two structures (22 and 36 nucleotide long) solved by NMR, have demonstrated that the method is capable of sampling the native state, however the selection of the most native-like conformation remains a challenge.