Fast de novo discovery of low‐energy protein loop conformations

In the prediction of protein structure from amino acid sequence, loops are challenging regions for computational methods. Since loops are often located on the protein surface, they can have significant roles in determining protein functions and binding properties. Loop prediction without the aid of a structural template requires extensive conformational sampling and energy minimization, which are computationally difficult. In this article we present a new de novo loop sampling method, the Parallely filtered Energy Targeted All‐atom Loop Sampler (PETALS) to rapidly locate low energy conformations. PETALS explores both backbone and side‐chain positions of the loop region simultaneously according to the energy function selected by the user, and constructs a nonredundant ensemble of low energy loop conformations using filtering criteria. The method is illustrated with the DFIRE potential and DiSGro energy function for loops, and shown to be highly effective at discovering conformations with near‐native (or better) energy. Using the same energy function as the DiSGro algorithm, PETALS samples conformations with both lower RMSDs and lower energies. PETALS is also useful for assessing the accuracy of different energy functions. PETALS runs rapidly, requiring an average time cost of 10 minutes for a length 12 loop on a single 3.2 GHz processor core, comparable to the fastest existing de novo methods for generating an ensemble of conformations. Proteins 2017; 85:1402–1412. © 2017 Wiley Periodicals, Inc.     

Modeling of loops in protein structures

Comparative protein structure prediction is limited mostly by the errors in alignment and loop modeling. We describe here a new automated modeling technique that significantly improves the accuracy of loop predictions in protein structures. The positions of all nonhydrogen atoms of the loop are optimized in a fixed environment with respect to a pseudo energy function. The energy is a sum of many spatial restraints that include the bond length, bond angle, and improper dihedral angle terms from the CHARMM-22 force field, statistical preferences for the main-chain and side-chain dihedral angles, and statistical preferences for nonbonded atomic contacts that depend on the two atom types, their distance through space, and separation in sequence. The energy function is optimized with the method of conjugate gradients combined with molecular dynamics and simulated annealing. Typically, the predicted loop conformation corresponds to the lowest energy conformation among 500 independent optimizations. Predictions were made for 40 loops of known structure at each length from 1 to 14 residues. The accuracy of loop predictions is evaluated as a function of thoroughness of conformational sampling, loop length, and structural properties of native loops. When accuracy is measured by local superposition of the model on the native loop, 100, 90, and 30% of 4-, 8-, and 12-residue loop predictions, respectively, had <2 A RMSD error for the mainchain N, C(alpha), C, and O atoms; the average accuracies were 0.59 +/- 0.05, 1.16 +/- 0.10, and 2.61 +/- 0.16 A, respectively. To simulate real comparative modeling problems, the method was also evaluated by predicting loops of known structure in only approximately correct environments with errors typical of comparative modeling without misalignment. When the RMSD distortion of the main-chain stem atoms is 2.5 A, the average loop prediction error increased by 180, 25, and 3% for 4-, 8-, and 12-residue loops, respectively. The accuracy of the lowest energy prediction for a given loop can be estimated from the structural variability among a number of low energy predictions. The relative value of the present method is gauged by (1) comparing it with one of the most successful previously described methods, and (2) describing its accuracy in recent blind predictions of protein structure. Finally, it is shown that the average accuracy of prediction is limited primarily by the accuracy of the energy function rather than by the extent of conformational sampling.     

Ab initio construction of polypeptide fragments: Accuracy of loop decoy discrimination by an all-atom statistical potential and the AMBER force field with the Generalized Born solvation model

The accuracy of model selection from decoy ensembles of protein loop conformations was explored by comparing the performance of the Samudrala-Moult all-atom statistical potential (RAPDF) and the AMBER molecular mechanics force field, including the Generalized Born/surface area solvation model. Large ensembles of consistent loop conformations, represented at atomic detail with idealized geometry, were generated for a large test set of protein loops of 2 to 12 residues long by a novel ab initio method called RAPPER that relies on fine-grained residue-specific phi/psi propensity tables for conformational sampling. Ranking the conformers on the basis of RAPDF scores resulted in selected conformers that had an average global, non-superimposed RMSD for all heavy mainchain atoms ranging from 1.2 A for 4-mers to 2.9 A for 8-mers to 6.2 A for 12-mers. After filtering on the basis of anchor geometry and RAPDF scores, ranking by energy minimization of the AMBER/GBSA potential energy function selected conformers that had global RMSD values of 0.5 A for 4-mers, 2.3 A for 8-mers, and 5.0 A for 12-mers. Minimized fragments had, on average, consistently lower RMSD values (by 0.1 A) than their initial conformations. The importance of the Generalized Born solvation energy term is reflected by the observation that the average RMSD accuracy for all loop lengths was worse when this term is omitted. There are, however, still many cases where the AMBER gas-phase minimization selected conformers of lower RMSD than the AMBER/GBSA minimization. The AMBER/GBSA energy function had better correlation with RMSD to native than the RAPDF. When the ensembles were supplemented with conformations extracted from experimental structures, a dramatic improvement in selection accuracy was observed at longer lengths (average RMSD of 1.3 A for 8-mers) when scoring with the AMBER/GBSA force field. This work provides the basis for a promising hybrid approach of ab initio and knowledge-based methods for loop modeling.     

Toward better refinement of comparative models: predicting loops in inexact environments

Achieving atomic-level accuracy in comparative protein models is limited by our ability to refine the initial, homolog-derived model closer to the native state. Despite considerable effort, progress in developing a generalized refinement method has been limited. In contrast, methods have been described that can accurately reconstruct loop conformations in native protein structures. We hypothesize that loop refinement in homology models is much more difficult than loop reconstruction in crystal structures, in part, because side-chain, backbone, and other structural inaccuracies surrounding the loop create a challenging sampling problem; the loop cannot be refined without simultaneously refining adjacent portions. In this work, we single out one sampling issue in an artificial but useful test set and examine how loop refinement accuracy is affected by errors in surrounding side-chains. In 80 high-resolution crystal structures, we first perturbed 6-12 residue loops away from the crystal conformation, and placed all protein side chains in non-native but low energy conformations. Even these relatively small perturbations in the surroundings made the loop prediction problem much more challenging. Using a previously published loop prediction method, median backbone (N-Calpha-C-O) RMSD's for groups of 6, 8, 10, and 12 residue loops are 0.3/0.6/0.4/0.6 A, respectively, on native structures and increase to 1.1/2.2/1.5/2.3 A on the perturbed cases. We then augmented our previous loop prediction method to simultaneously optimize the rotamer states of side chains surrounding the loop. Our results show that this augmented loop prediction method can recover the native state in many perturbed structures where the previous method failed; the median RMSD's for the 6, 8, 10, and 12 residue perturbed loops improve to 0.4/0.8/1.1/1.2 A. Finally, we highlight three comparative models from blind tests, in which our new method predicted loops closer to the native conformation than first modeled using the homolog template, a task generally understood to be difficult. Although many challenges remain in refining full comparative models to high accuracy, this work offers a methodical step toward that goal.     

Protein side chain conformation predictions with an MMGBSA energy function

The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an “MMGBSA” energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803–819. © 2016 Wiley Periodicals, Inc.     

Assignment of side-chain conformation using adiabatic energy mapping, free energy perturbation, and molecular dynamic simulations

NMR spectroscopic analysis of the C-terminal Kunitz domain fragment (alpha3(VI)) from the human alpha3-chain of type VI collagen has revealed that the side chain of Trp21 exists in two unequally populated conformations. The major conformation (M) is identical to the conformation observed in the X-ray crystallographic structure, while the minor conformation (m) cannot structurally be resolved in detail by NMR due to insufficient NOE data. In the present study, we have applied: (1) rigid and adiabatic mapping, (2) free energy simulations, and (3) molecular dynamic simulations to elucidate the structure of the m conformer and to provide a possible pathway of the Trp21 side chain between the two conformers. Adiabatic energy mapping of conformations of the Trp21 side chain obtained by energy minimization identified two energy minima: One corresponding to the conformation of Trp21 observed in the X-ray crystallographic structure and solution structure of alpha3(VI) (the M conformation) and the second corresponding to the m conformation predicted by NMR spectroscopy. A transition pathway between the M and m conformation is suggested. The free-energy difference between the two conformers obtained by the thermodynamic integration method is calculated to 1.77+/-0.7 kcal/mol in favor of the M form, which is in good agreement with NMR results. Structural and dynamic properties of the major and minor conformers of the alpha3(VI) molecule were investigated by molecular dynamic. Essential dynamics analysis of the two resulting 800 ps trajectories reveals that when going from the M to the m conformation only small, localized changes in the protein structure are induced. However, notable differences are observed in the mobility of the binding loop (residues Thr13-Ile18), which is more flexible in the m conformation than in the M conformation. This suggests that the reorientation of Trp2 might influence the inhibitory activity against trypsin, despite the relative large distance between the binding loop and Trp21.     

Prediction of a 12-residue loop in bovine pancreatic trypsin inhibitor: effects of buried water

The x-ray conformations of 5-, 7-, 9-, and 12-residue loops in bovine pancreatic trypsin inhibitor (BPTI) were predicted by the use of multiple independent Monte Carlo simulating annealing (MCSA) runs starting from random conformations. Four buried water molecules interacted with a 12-residue loop that started at residue 8 and ended at residue 19, and that included the binding region. The final conformation at the end of an MCSA run was characterized. Solvation free energy based on the solvent accessible surface area was included in the energy function at low simulated annealing temperatures. Conformational states were interactively separated by a recently developed algorithm. Computed loops were characterized in terms of total energy, and backbone and side chain root mean square deviations (RMSDs) between computed native loop conformations and the x-ray conformation. The 12-residue loop was computed with and without buried water [called WL12(8-19) and L12(8-19), respectively]. The backbone was reliably and reproducibly computed to within 1.1 A in L12(8-19) and 0.9 A in WL12(8-19). L12(8-19) required significantly more MCSA runs to achieve the same level of reproducibility as WL12(8-19). Based on the size of the cluster of low energy native loop conformations, and the computational effort, WL12(8-19) had greater entropy. In calculations of 7-, 9-, and 12-residue loops without buried water, the effects of buried water became obvious in the 12-residue loop calculation, which interacted with all four buried water molecules. Nearly all conformations of the native loop conformer had a hydrogen bond between the Lys 15 side chain and the backbone of Gly 12, Pro 13, and Cys 14, which may have implications in the rate of exchange of buried water with bulk solvent and in protein folding. The present version of MCSA program was more efficient than earlier versions.     

Accurate and efficient loop selections by the DFIRE-based all-atom statistical potential

The conformations of loops are determined by the water-mediated interactions between amino acid residues. Energy functions that describe the interactions can be derived either from physical principles (physical-based energy function) or statistical analysis of known protein structures (knowledge-based statistical potentials). It is commonly believed that statistical potentials are appropriate for coarse-grained representation of proteins but are not as accurate as physical-based potentials when atomic resolution is required. Several recent applications of physical-based energy functions to loop selections appear to support this view. In this article, we apply a recently developed DFIRE-based statistical potential to three different loop decoy sets (RAPPER, Jacobson, and Forrest-Woolf sets). Together with a rotamer library for side-chain optimization, the performance of DFIRE-based potential in the RAPPER decoy set (385 loop targets) is comparable to that of AMBER/GBSA for short loops (two to eight residues). The DFIRE is more accurate for longer loops (9 to 12 residues). Similar trend is observed when comparing DFIRE with another physical-based OPLS/SGB-NP energy function in the large Jacobson decoy set (788 loop targets). In the Forrest-Woolf decoy set for the loops of membrane proteins, the DFIRE potential performs substantially better than the combination of the CHARMM force field with several solvation models. The results suggest that a single-term DFIRE-statistical energy function can provide an accurate loop prediction at a fraction of computing cost required for more complicate physical-based energy functions. A Web server for academic users is established for loop selection at the softwares/services section of the Web site http://theory.med.buffalo.edu/.     

Loop prediction for a GPCR homology model: Algorithms and results

We present loop structure prediction results of the intracellular and extracellular loops of four G‐protein‐coupled receptors (GPCRs): bovine rhodopsin (bRh), the turkey β1‐adrenergic (β1Ar), the human β2‐adrenergic (β2Ar) and the human A2a adenosine receptor (A2Ar) in perturbed environments. We used the protein local optimization program, which builds thousands of loop candidates by sampling rotamer states of the loops' constituent amino acids. The candidate loops are discriminated between with our physics‐based, all‐atom energy function, which is based on the OPLS force field with implicit solvent and several correction terms. For relevant cases, explicit membrane molecules are included to simulate the effect of the membrane on loop structure. We also discuss a new sampling algorithm that divides phase space into different regions, allowing more thorough sampling of long loops that greatly improves results. In the first half of the paper, loop prediction is done with the GPCRs' transmembrane domains fixed in their crystallographic positions, while the loops are built one‐by‐one. Side chains near the loops are also in non‐native conformations. The second half describes a full homology model of β2Ar using β1Ar as a template. No information about the crystal structure of β2Ar was used to build this homology model. We are able to capture the architecture of short loops and the very long second extracellular loop, which is key for ligand binding. We believe this the first successful example of an RMSD validated, physics‐based loop prediction in the context of a GPCR homology model. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

New computational method for prediction of interacting protein loop regions

Flexible loop regions of proteins play a crucial role in many biological functions such as protein–ligand recognition, enzymatic catalysis, and protein–protein association. To date, most computational methods that predict the conformational states of loops only focus on individual loop regions. However, loop regions are often spatially in close proximity to one another and their mutual interactions stabilize their conformations. We have developed a new method, titled CorLps, capable of simultaneously predicting such interacting loop regions. First, an ensemble of individual loop conformations is generated for each loop region. The members of the individual ensembles are combined and are accepted or rejected based on a steric clash filter. After a subsequent side‐chain optimization step, the resulting conformations of the interacting loops are ranked by the statistical scoring function DFIRE that originated from protein structure prediction. Our results show that predicting interacting loops with CorLps is superior to sequential prediction of the two interacting loop regions, and our method is comparable in accuracy to single loop predictions. Furthermore, improved predictive accuracy of the top‐ranked solution is achieved for 12‐residue length loop regions by diversifying the initial pool of individual loop conformations using a quality threshold clustering algorithm. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Structural basis of hierarchical multiple substates of a protein. III: Side chain and main chain local conformations

An analysis is carried out of differences in the minimum energy conformations obtained in the previous paper by energy minimization starting from conformations sampled by a Monte Carlo simulation of conformational fluctuations in the native state of a globular protein, bovine pancreatic trypsin inhibitor. Main conformational differences in each pair of energy minima are found usually localized in several side chains and in a few local main chain segments. Such side chains and local main chain segments are found to take a few distinct local conformations in the minimum energy conformations. Energy minimum conformations can thus be described in terms of combinations of these multiple local conformations.     

Force field based conformational analysis of RNA structural motifs: GNRA tetraloops and their pyrimidine relatives

The protocol of conformational analysis applied here to ribonucleotide oligomers combines conformational search in the space of torsion angles and energy minimization using the AMBER4.1 force field with a continuum treatment of electrostatic solute-solvent interactions. RNA fragments with 5′-GGGCGNNAGCCU-3′ sequences commonly fold into hairpins with four-membered loops. The combinatorial search for acceptable conformations using the MC-SYM program was restricted to loop nucleotides and yielded roughly 1500 structures being compatible with a double-stranded stem. After energy minimization by the JUMNA program (without applying any experimental constraints), these structures converged into an ensemble of 74 different conformers including 26 structures which contained the sheared G-A base pair observed in experimental studies of GNRA tetraloops. Energetic analysis shows that inclusion of solvent electrostatic effects is critically important for the selection of conformers that agree with experimentally determined structures. The continuum model accounts for solvent polarization by means of the electrostatic reaction field. In the case of GNRA loop sequences, the contributions of the reaction field shift relative stabilities towards conformations showing most of the structural features derived from NMR studies. The agreement of computed conformations with the experimental structures of GAAA, GCAA, and GAGA tetraloops suggests that the continuum treatment of the solvent represents a definitive improvement over methods using simple damping models in electrostatic energy calculations. Application of the procedure described here to the evaluation of the relative stabilities of conformers resulting from searching the conformational space of RNA structural motifs provides some progress in (non-homology based) RNA 3D-structure prediction. Received: 20 January 1999 / Revised version: 4 June 1999 / Accepted: 10 June 1999     

Protein loop selection using orientation-dependent force fields derived by parameter optimization

We used the orientation‐dependent Optimized Side Chain Atomic eneRgy (OSCAR‐o), derived in an early study, for protein loop selection. The prediction accuracy of OSCAR‐o was better than that of physics‐based force fields or statistical potential energy functions for both the RAPPER decoy set and the Jacobson decoy set. The native conformer was frequently ranked as lowest energy among the decoys. Furthermore, strong correlation was observed between the OSCAR‐o score and the root mean square deviation (RMSD) from the native structure for energy‐minimized decoys. In practical use, we applied OSCAR‐o to rescore decoys generated by a widely used loop‐modeling program, LOOPY. As a result, the mean RMSD values of top‐ranked decoys were reduced by 0.3 Å for loop targets of seven to nine residues. We expect similar performance for OSCAR‐o with other loop‐modeling algorithms in the context of decoy rescoring. A loop selection program (OSCAR‐ls) based on OSCAR‐o is available at http://sysimm.ifrec.osaka‐u.ac.jp/OSCAR/ . Proteins 2011; © 2011 Wiley‐Liss, Inc.     

Accuracy and reliability of the scaling-relaxation method for loop closure: An evaluation based on extensive and multiple copy conformational samplings

The accuracy and reliability of the recently proposed scaling-relaxation method for loop closure were examined by using extensive conformational sampling. For each of the eight heptapeptides chosen to represent a variety of protein conformations, 1,000–2,000 conformations were sampled. Each segment contained 14 rotatable backbone dihedral angles. The average root mean square deviations (RMSDs) between the predicted and the native conformations were 0.7 Å for the backbone and 1.2 Å for the side chain atoms. These predictions were substantially more accurate than the previous predictions (1.1 Å for the backbone and 2.2 Å for the side chain atoms) of the same eight protein segments based on limited conformational sampling (100 conformations for each segment). Large prediction errors mostly occurred at polar and surface side chains that are unlikely to have any meaningful conformation. Moreover, the reliability of seven of the eight predictions was demonstrated with their energy-RMSD and stability-RMSD correlations of the low-energy conformations, where the conformational stability was estimated by using the multiple copy simultaneous sampling method.     

Analysis of forces that determine helix formation in alpha-proteins

A model for prediction of alpha-helical regions in amino acid sequences has been tested on the mainly-alpha protein structure class. The modeling represents the construction of a continuous hypothetical alpha-helical conformation for the whole protein chain, and was performed using molecular mechanics tools. The positive prediction of alpha-helical and non-alpha-helical pentapeptide fragments of the proteins is 79%. The model considers only local interactions in the polypeptide chain without the influence of the tertiary structure. It was shown that the local interaction defines the alpha-helical conformation for 85% of the native alpha-helical regions. The relative energy contributions to the energy of the model were analyzed with the finding that the van der Waals component determines the formation of alpha-helices. Hydrogen bonds remain at constant energy independently whether alpha-helix or non-alpha-helix occurs in the native protein, and do not determine the location of helical regions. In contrast to existing methods, this approach additionally permits the prediction of conformations of side chains. The model suggests the correct values for ~60% of all chi-angles of alpha-helical residues.     

Large loop conformation sampling using the activation relaxation technique, ART-nouveau method

We present an adaptation of the ART-nouveau energy surface sampling method to the problem of loop structure prediction. This method, previously used to study protein folding pathways and peptide aggregation, is well suited to the problem of sampling the conformation space of large loops by targeting probable folding pathways instead of sampling exhaustively that space. The number of sampled conformations needed by ART nouveau to find the global energy minimum for a loop was found to scale linearly with the sequence length of the loop for loops between 8 and about 20 amino acids. Considering the linear scaling dependence of the computation cost on the loop sequence length for sampling new conformations, we estimate the total computational cost of sampling larger loops to scale quadratically compared to the exponential scaling of exhaustive search methods.     

Loop propensity of the sequence YKGQP from staphylococcal nuclease: implications for the folding of nuclease.

Recently we performed molecular dynamics (MD) simulations on the folding of the hairpin peptide DTVKLMYKGQPMTFR from staphylococcal nuclease in explicit water. We found that the peptide folds into a hairpin conformation with native and nonnative hydrogen-bonding patterns. In all the folding events observed in the folding of the hairpin peptide, loop formation involving the region YKGQP was an important event. In order to trace the origins of the loop propensity of the sequence YKGQP, we performed MD simulations on the sequence starting from extended, polyproline II and native type I' turn conformations for a total simulation length of 300 ns, using the GROMOS96 force field under constant volume and temperature (NVT) conditions. The free-energy landscape of the peptide YKGQP shows minima corresponding to loop conformation with Tyr and Pro side-chain association, turn and extended conformational forms, with modest free-energy barriers separating the minima. To elucidate the role of Gly in facilitating loop formation, we also performed MD simulations of the mutated peptide YKAQP (Gly --> Ala mutation) under similar conditions starting from polyproline II conformation for 100 ns. Two minima corresponding to bend/turn and extended conformations were observed in the free-energy landscape for the peptide YKAQP. The free-energy barrier between the minima in the free-energy landscape of the peptide YKAQP was also modest. Loop conformation is largely sampled by the YKGQP peptide, while extended conformation is largely sampled by the YKAQP peptide. We also explain why the YKGQP sequence samples type II turn conformation in these simulations, whereas the sequence as part of the hairpin peptide DTVKLMYKGQPMTFR samples type I' turn conformation both in the X-ray crystal structure and in our earlier simulations on the folding of the hairpin peptide. We discuss the implications of our results to the folding of the staphylococcal nuclease.     

Constructing side chains on near-native main chains for ab initio protein structure prediction

Is there value in constructing side chains while searching protein conformational space during an ab initio simulation? If so, what is the most computationally efficient method for constructing these side chains? To answer these questions, four published approaches were used to construct side chain conformations on a range of near-native main chains generated by ab initio protein structure prediction methods. The accuracy of these approaches was compared with a naive approach that selects the most frequently observed rotamer for a given amino acid to construct side chains. An all-atom conditional probability discriminatory function is useful at selecting conformations with overall low all-atom root mean square deviation (r.m.s.d.) and the discrimination improves on sets that are closer to the native conformation. In addition, the naive approach performs as well as more sophisticated methods in terms of the percentage of chi(1) angles built accurately and the all-atom r. m.s.d., between the native and near-native conformations. The results suggest that the naive method would be extremely useful for fast and efficient side chain construction on vast numbers of conformations for ab initio prediction of protein structure.     

Ab initio construction of all-atom loop conformations

In this study, a new ab initio method named CLOOP has been developed to build all-atom loop conformations. In this method, a loop main-chain conformation is generated by sampling main-chain dihedral angles from a restrained varphi/psi set, and the side-chain conformations are built randomly. The CHARMM all-atom force field was used to evaluate the loop conformations. Soft core potentials were used to treat the non-bond interactions, and a designed energy-minimization technique was used to close and optimize the loop conformations. It is shown that the two strategies improve the computational efficiency and the loop-closure rate substantially compared to normal minimization methods. CLOOP was used to construct the conformations of 4-, 8-, and 12-residue loops in Fiser's test set. The average main-chain root-mean-square deviations obtained in 1,000 trials for the 10 different loops of each size are 0.33, 1.27, and 2.77 A, respectively. CLOOP can build all-atom loop conformations with a sampling accuracy comparable with previous loop main-chain construction algorithms. [Figure: see text].     

Inclusion of solvation free energy with molecular mechanics energy: alanyl dipeptide as a test case.

A combined force field of molecular mechanics and solvation free energy is tested by carrying out energy minimization and molecular dynamics on several conformations of the alanyl dipeptide. Our results are qualitatively consistent with previous experimental and computational studies, in that the addition of solvation energy stabilizes the C5 conformation of the alanyl dipeptide relative to the C7.