Prediction of the native conformation of a polypeptide by a statistical-mechanical procedure. III. Probable and average conformations of enkephalin

The program SMAPPS (Statistical-Mechanical Algorithm for Predicting Protein Structure) was originally designed to determine the probable and average backbone (?, ψ) conformations of a polypeptide by the application of equilibrium statistical mechanics in conjunction with an adaptive importance sampling Monte Carlo procedure. In the present paper, the algorithm has been extended to include the variation of all side-chain (χ) and peptide-bond (ω) dihedral angles of a polypeptide during the Monte Carlo search of the conformational space. To test the effectiveness of the generalized algorithm, SMAPPS was used to calculate the probable and average conformations of Met-enkephalin for which all dihedral angles of the pentapeptide were allowed to vary. The total conformational energy for each randomly generated structure of Met-enkephalin was obtained by summing over the interaction energies of all pairs of nonbonded atoms of the whole molecule. The interaction energies were computed by the program ECEPP /2 (Empirical Conformational Energy Program for Peptides). Solvent effects were not included in the computation. The results of the Monte Carlo calculation of the structure of Met-enkephalin indicate that the thermodynamically preferred conformation of the pentapeptide contains a γ-turn involving the three residues Gly²-Gly³-Phe⁴. The γ-turn conformation, however, does not correspond to the structure of lowest conformational energy. Rather, the global minimum-energy conformation, recently determined by a new optimization technique developed in this laboratory, contains a type II′ β-bend that is formed by the interaction of the four residues Gly²-Gly³-Phe⁴-Met⁵. A similar minimum-energy conformation is found by the SMAPPS procedure. The thermodynamically preferred γ-turn structure has a conformational energy of 4.93 kcal/mole higher than the β-bend structure of lowest energy but, because of the inclusion of entropy in the SMAPPS procedure, it is estimated to be ～ 9 kcal/mole lower in free energy. The calculation of the average conformation of Met-enkephalin was repeated until a total of ten independent average conformations were established. As far as the phenylalanine residue of the pentapeptide is concerned, the results of the ten independent average conformations were all found to lie in the region of conformational space corresponding to the γ-turn. These results further support the conclusion that the γturn conformation is thermodynamically favored.     

Prediction of the native conformation of a polypeptide by a statistical-mechanical procedure. II. Average backbone structure of enkephalin

The average conformation of Met-enkephalin was determined by using an adaptive, importance-sampling Monte Carlo algorithm (SMAPPS—Statistical Mechanical Algorithm for Predicting Protein Structure). In the calculation, only the backbone dihedral angles (? and ψ) were allowed to vary; i.e., all side-chain (χ) and peptide-bond (ω) dihedral angles were kept fixed at the values corresponding to a low-energy structure of the pentapeptide. The total conformational energy for each randomly generated structure of the polypeptide was obtained by summing over the interaction energies of all pairs of nonbonded atoms of the whole molecule. The interaction energies were computed by the program ECEPP/2 (Empirical Conformational Energy Program for Peptides). Solvent effects were not included in the computation. The calculation was repeated until a total of 10 independent average conformations were established. The regions of conformational space occupied by the average structures were compared with the regions of low conditional free energy obtained by SMAPPS in the first paper of this series. Such a comparison provides an analysis of the capacity of SMAPPS to adjust the Monte Carlo search to regions of highest probability. The results demonstrate that the ability of SMAPPS to focus the Monte Carlo search is excellent. Finally, the 10 independent average conformations and the mean of the 10 average structures were utilized as the initial conformations for a direct energy minimization of the pentapeptide. Of the 11 final energy-minimized structures, three of the conformations were found to be equivalent to the conformation of lowest energy determined previously. In addition, all but two of the remaining energy-minimized structures were found to correspond to one of the two other conformations of high probability obtained in the first paper of this series. These results indicate that a set of independent average conformations can provide a rational, unbiased choice for the initial conformation, to be used in a direct energy minimization of a polypeptide. The final energy-minimized structures consequently constitute a set of low-energy conformations, which include the global energy minimum.     

The multiple-minima problem in the conformational analysis of polypeptides. III. An Electrostatically Driven Monte Carlo Method: Tests on enkephalin

The three-dimensional conformation of Met-enkephalin, corresponding to the lowest minimum of the empirical potential energy function ECEPP/2 (empirical conformational energy program for peptides), has been determined using a new algorithm, viz. the Electrostatically Driven Monte Carlo Method. This methodology assumes that a polypeptide or protein molecule is driven toward the native structure by the combined action of electrostatic interactions and stochastic conformational changes associated with thermal movements. These features are included in the algorithm that produces a Monte Carlo search in the conformational hyperspace of the polypeptide, using electrostatic predictions and a random sampling technique to locate low-energy conformations. In addition, we have incorporated an alternative mechanism that allows the structure to escape from some conformational regions representing metastable local energy minima and even from regions of the conformational space with great stability. In 33 test calculations on Met-enkephalin, starting from arbitrary or completely random conformations, the structure corresponding to the global energy minimum was found inall the cases analyzed, with a relatively small search of the conformational space. Some of these starting conformations wereright orleft-handed -helices, characterized by good electrostatic interactions involving their backbone peptide dipoles; nevertheless, the procedure was able to convert such locally stable structures to the global-minimum conformation.On leave from the National University of San Luis, Faculty of Sciences and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Matemática Aplicada, San Luis, Ejército de los Andes 950, 5700 San Luis, Argentina.     

An approach to the multiple-minima problem in protein folding by relaxing dimensionality. Tests on enkephalin

An algorithm for locating the region in conformational space containing the global energy minimum of a polypeptide is described. Distances are used as the primary variables in the minimization of an objective function that incorporates both energetic and distance-geometric terms. The latter are obtained from geometry and energy functions, rather than nuclear magnetic resonance experiments, although the algorithm can incorporate distances from nuclear magnetic resonance data if desired. The polypeptide is generated originally in a space of high dimensionality. This has two important consequences. First, all interatomic distances are initially at their energetically most favorable values; i.e. the polypeptide is initially at a global minimum-energy conformation, albeit a high-dimensional one. Second, the relaxation of dimensionality constraints in the early stages of the minimization removes many potential energy barriers that exist in three dimensions, thereby allowing a means of escaping from three-dimensional local minima. These features are used in an algorithm that produces short trajectories of three-dimensional minimum-energy conformations. A conformation in the trajectory is generated by allowing the previous conformation in the trajectory to evolve in a high-dimensional space before returning to three dimensions. The resulting three-dimensional structure is taken to be the next conformation in the trajectory, and the process is iterated. This sequence of conformations results in a limited but efficient sampling of conformational space. Results for test calculations on Met-enkephalin, a pentapeptide with the amino acid sequence H-Tyr-Gly-Gly-Phe-Met-OH, are presented. A tight cluster of conformations (in three-dimensional space) is found with ECEPP energies (Empirical Conformational Energy Program for Peptides) lower than any previously reported. This cluster of conformations defines a region in conformational space in which the global-minimum-energy conformation of enkephalin appears to lie.     

Monte Carlo minimization with thermalization for global optimization of polypeptide conformations in cartesian coordinate space.

A new minimization procedure for the global optimization in cartesian coordinate space of the conformational energy of a polypeptide chain is presented. The Metropolis Monte Carlo minimization is thereby supplemented by a thermalization process, which is initiated whenever a structure becomes trapped in an area containing closely located local minima in the conformational space. The method has been applied to the endogenous opioid pentapeptide methionine enkephalin. Five among 13 different starting conformations led to the same apparent global minimum of an in-house developed energy function, a type II' reverse turn, the central residues of which are Gly-3-Phe-4. A comparison between the ECEPP/2 global minimum conformation of methionine enkephalin and the apparent one achieved by the present method shows that minimum-energy conformations having a certain similarity can be generated by relatively different force fields.     

Use of buildup and energy-minimization procedures to compute low-energy structures of the backbone of enkephalin

A method for the conformational analysis of a polypeptide, using buildup and energy-minimization procedures, is presented. It has been tested by computing the conformation of the backbone of Met-enkephalin, a pentapeptide, and comparing the results with those obtained by a Monte Carlo procedure. The agreement between the results of the two different methods indicates that the multiple-minima problem has been overcome for the enkephalin molecule (with fixed side-chain conformations). The buildup procedure uses objective criteria, which should be applicable to the conformational analysis of polypeptides much larger than enkephalin. With anticipated advances in supercomputers, the methodology should be applicable to proteins.     

On the multiple-minima problem in the conformational analysis of polypeptides. IV. Application of the electrostatically driven Monte Carlo method to the 20-residue membrane-bound portion of melittin

The conformational space of the membrane-bound portion of melittin has been searched using the electrostatically driven Monte Carlo (EDMC) method with the ECEPP/2 (empirical conformational energy program for peptides) algorithm. The former methodology assumes that a polypeptide or protein molecule is driven toward the native structure by the combined action of electrostatic interactions and stochastic conformational changes associated with thermal movements. The algorithm produces a Monte Carlo search in the conformational hyperspace of the polypeptide using electrostatic predictions and a random sampling technique, combined with local minimization of the energy function, to locate low-energy conformations. As a result of 8 test calculations on the 20-residue membrane-bound portion of melittin, starting from six arbitrary and two completely random conformations, the method was able to locate a very low-energy region of the potential with a well-defined structure for the backbone. In all of the cases under study, the method found a cluster of similar low-energy conformations that agree well with the structure deduced from x-ray diffraction experiments and with one computed earlier by the build-up procedure.     

Equilibrium folding and unfolding pathways for a model protein

The folding–unfolding process of reduced bovine pancreatic trypsin inhibitor was investigated with an idealized model employing approximate free energies. The protein is regarded to consist of only C^α and C^β atoms. The backbone dihedral angles are the only conformational variables and are permitted to take discrete values at every 10°. Intraresidue energies consist of two terms: an empirical part taken from the observed frequency distributions of (?,ψ) and an additional favorable energy assigned to the native conformation of each residue. Interresidue interactions are simplified by assuming that there is an attractive energy operative only between residue pairs in close contact in the native structure. A total of 230,000 molecular conformations, with no atomic overlaps, ranging from the native state to the denatured state, are randomly generated by changing the sampling bias. Each conformation is classified according to its conformational energy, F; a conformational entropy, S(F) is estimated for each value of F from the number of samples. The dependence of S(F) on energy reveals that the folding–unfolding transition for this idealized model is an “all-or-none” type; this is attributable to the specific long-range interactions. Interresidue contact probabilities, averaged over samples representing various stages of folding, serve to characterize folding intermediates. Most probable equilibrium pathways for the folding–unfolding transition are constructed by connecting conformationally similar intermediates. The specific details obtained for bovine pancreatic trypsin inhibitor are as follows: (1) Folding begins with the appearance of nativelike medium-range contacts at a β-turn and at the α-helix. (2) These grow to include the native pair of interacting β-strands. This state includes intact regular secondary conformations, as well as the interstrand sheet contacts, and corresponds to an activated state with the highest free energy on the pathway. (3) Additional native long-range contacts are completely formed either toward the amino terminus or toward the carboxyl terminus. (4) In a final step, the missing contacts appear. Although these folding pathways for this model are not consistent with experimental reports, it does indicate multiple folding pathways. The method is general and can be applied to any set of calculated conformational energies and furthermore permits investigation of gross folding features.     

Monte Carlo calculations on the conformations of models for the glycopeptide linkage of glycoproteins

In order to search for probable conformations of the peptide, the amino acid side chain, and the carbohydrate linkage in glycoproteins, conformational energy surfaces of glycopeptide model compounds were studied by Monte Carlo methods using the Metropolis algorithm. The potential energies were composed of empirical energy functions which include nonbonded interactions, electrostatics, hydrogen bonding, and torsional energies specified by parameters which have been used for peptides. Calculations were performed on 1-N-acetyl-2-acetamido-beta-D-glucopyranosyl amine and the glycosylated dipeptide N-acetyl-delta-N-(2-acetamido-beta-D-glucopyranosyl)-L-asparaginyl-N'-methyl amide as models for N-glycosylated peptides and on methyl-2-acetamido-alpha-D-galactopyranoside as well as the glycosylated dipeptides N-acetyl-gamma-O-(2-acetamido-alpha-D-galactopyranosyl)-L-threonyl-N'-methyl amide and its seryl analog as models for O-glycosylated glycoproteins. The probable conformations of these compounds were analyzed by single-angle probability tables and by two-dimensional conformation density maps projected from the Markov chains which contained up to six independently varied conformational dihedral angles. The presence of high barriers to rotation required the use of search strategies which resulted in a rather low acceptance rate for new conformations in the Metropolis algorithm in order to avoid trapping of the Markov chain in local energy minima. This problem contributed to the failure of these calculations to attain complete convergence to the thermodynamic limit for the glycosylated dipeptide models in which six dihedral angles were independently varied. Analysis of the results shows that the conformational space available to the highly crowded axial glycosides of the alpha-O-GalNAc type is much more restricted than that for the N-asparaginyl glycopeptides. The most probable conformation for the O-glycosylated peptides is is a beta-turn while N-glycosylated peptides may be either in a beta-turn or an extended conformation.     

Prediction of protein conformation on the basis of a search for compact structures: test on avian pancreatic polypeptide.

Based on the concept that hydrophobic interactions cause a polypeptide chain to adopt a compact structure, a method is proposed to predict the structure of a protein. The procedure is carried out in four stages: (1) use of a virtual-bond united-residue approximation with the side chains represented by spheres to search conformational space extensively using specially designed interactions to lead to a collapsed structure, (2) conversion of the lowest-energy virtual-bond united-residue chain to one with a real polypeptide backbone, with optimization of the hydrogen-bond network among the backbone groups, (3) perturbation of the latter structure by the electrostatically driven Monte Carlo (EDMC) procedure, and (4) conversion of the spherical representation of the side chains to real groups and perturbation of the whole molecule by the EDMC procedure using the empirical conformational energy program for peptides (ECEPP/2) energy function plus hydration. Application of this procedure to the 36-residue avian pancreatic polypeptide led to a structure that resembled the one determined by X-ray crystallography; it had an alpha-helix starting at residue 13, with the N-terminal portion of the chain in an extended conformation packed against the alpha-helix. Similar structures with slightly higher energies, but looser packing, were also obtained.     

Structural basis of hierarchical multiple substates of a protein. I: Introduction

A computer experiment of protein dynamics is carried out, which consists of two steps: (1) A Monte Carlo simulation of thermal fluctuations in the native state of a globular protein, bovine pancreatic trypsin inhibitor; and (2) a simulation of the quick freezing of fluctuating conformations into energy minima by minimization of the energy of a number of conformations sampled in the Monte Carlo simulation. From the analysis of results of the computer experiment is obtained the following picture of protein dynamics: multiple energy minima exist in the native state, and they are distributed in clusters in the conformational space. The dynamics has a hierarchical structure which has at least two levels. In the first level, dynamics is restricted within one of the clusters of minima. In the second, transitions occur among the clusters. Local parts of a protein molecule, side chains and local main chain segments, can take multiple locally stable conformations in the native state. Many minima result from combinations of these multiple local conformations. The hierarchical structure in the dynamics comes from interactions among the local parts. Protein molecules have two types of flexibility, each associated with elastic and plastic deformations, respectively.     

Local propensities and statistical potentials of backbone dihedral angles in proteins

The following three issues concerning the backbone dihedral angles of protein structures are presented. (1) How do the dihedral angles of the 20 amino acids depend on the identity and conformation of their nearest residues? (2) To what extent are the native dihedral angles determined by local (dihedral) potentials? (3) How to build a knowledge-based potential for a residue's dihedral angles, considering the identity and conformation of its nearest residues? We find that the dihedral angle distribution for a residue can significantly depend on the identity and conformation of its adjacent residues. These correlations are in sharp contrast to the Flory isolated-pair hypothesis. Statistical potentials are built for all combinations of residue triplets and depend on the dihedral angles between consecutive residues. First, a low-resolution potential is obtained, which only differentiates between the main populated basins in the dihedral angle density plots. Minimization of the dihedral potential for 125 test proteins reveals that most native alpha-helical residues (89%) and a large fraction of native beta-sheet residues (47%) adopt conformations close to their native one. For native loop residues, the percentage is 48%. It is also found that this fraction is higher for residues away from the ends of alpha or beta secondary structure elements. In addition, a higher resolution potential is built as a function of dihedral angles by a smoothing procedure and continuous functions interpolations. Monte Carlo energy minimization with this potential results in a lower fraction for native beta-sheet residues. Nevertheless, because of the higher flexibility and entropy of beta structures, they could be preferred under the influence of non-local interactions. In general, most alpha-helices and many beta-sheets are strongly determined by the local potential, while the conformations in loops and near the end of beta-sheets are more influenced by non-local interactions.     

Monte Carlo simulations of a protein molecule with and without hydration energy calculated by the hydration-shell model

Monte Carlo simulations of a small protein, carmbin, were carried out with and without hydration energy. The methodology presented here is characterized, as compared with the other similar simulations of proteins in solution, by two points: (1) protein conformations are treated in fixed geometry so that dihedral angles are independent variables rather than cartesian coordinates of atoms; and (2) instead of treating water molecules explicitly in the calculation, hydration energy is incorporated in the conformational energy function in the form of g _i A _i, whereA _i is the accessible surface area of an atomic groupi in a given conformation, andg _i is the free energy of hydration per unit surface area of the atomic group (i.e., hydration-shell model). Reality of this model was tested by carrying out Monte Carlo simulations for the two kinds of starting conformations, native and unfolded ones, and in the two kinds of systems,in vacuo and solution. In the simulations starting from the native conformation, the differences between the mean propertiesin vacuo and solution simulations are not very large, but their fluctuations around the mean conformation during the simulation are relatively smaller in solution thanin vacuo. On the other hand, in the simulations starting from the unfolded conformation, the molecule fluctuates much more largely in solution thanin vacuo, and the effects of taking into account the hydration energy are pronounced very much. The results suggest that the method presented in this paper is useful for the simulations of proteins in solution.     

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.     

Protein-inhibitor flexible docking by a multicanonical sampling: native complex structure with the lowest free energy and a free-energy barrier distinguishing the native complex from the others

Flexible docking between a protein (lysozyme) and an inhibitor (tri-N-acetyl-D-glucosamine, tri-NAG) was carried out by an enhanced conformational sampling method, multicanonical molecular dynamics simulation. We used a flexible all-atom model to express lysozyme, tri-NAG, and water molecules surrounding the two bio-molecules. The advantages of this sampling method are as follows: the conformation of system is widely sampled without trapping at energy minima, a thermally equilibrated conformational ensemble at an arbitrary temperature can be reconstructed from the simulation trajectory, and the thermodynamic weight can be assigned to each sampled conformation. During the simulation, exchanges between the binding and free (i.e., unbinding) states of the protein and the inhibitor were repeatedly observed. The conformational ensemble reconstructed at 300 K involved various conformational clusters. The main outcome of the current study is that the most populated conformational cluster (i.e., the cluster of the lowest free energy) was assigned to the native complex structure (i.e., the X-ray complex structure). The simulation also produced non-native complex structures, where the protein and the inhibitor bound with different modes from that of the native complex structure, as well as the unbinding structures. A free-energy barrier (i.e., activation free energy) was clearly detected between the native complex structures and the other structures. The thermal fluctuations of tri-NAG in the lowest free-energy complex correlated well with the X-ray B-factors of tri-NAG in the X-ray complex structure. The existence of the free-energy barrier ensures that the lowest free-energy structure can be discriminated naturally from the other structures. In other words, the multicanonical molecular dynamics simulation can predict the native complex structure without any empirical objective function. The current study also manifested that the flexible all-atom model and the physico-chemically defined atomic-level force field can reproduce the native complex structure. A drawback of the current method is that it requires a time consuming computation due to the exhaustive conformational sampling. We discussed a possibility for combining the current method with conventional docking methods.     

Conformational Characteristics of Mixed Sugar Puckered Deoxydinucleoside Triphosphate Units d-pCpGp and d-pGpCp from Energy Minimization Studies

Abstract

The deoxydinucleoside triphosphate units d-pCpGp and d-pGpCp were subjected to a rigorous theoretical investigation with a view to describing their distinctive conformational characteristics. For each unit 216 probable three-dimensional forms defined by the backbone-base dihedral angles and sugar pucker modes were considered for conformational energy minimization process and scrutinized with reference to properties, such as base-stacking, hydrogen-bonding, internal flexibility and base sequence-phosphate influence. The P-O bond torsions and the phosphate groups were treated with special attention. The results reveal a number of preferred conformational states other than the known helical forms, such as, A-, B-, C-, Z-, and Watson-Crick conformation. Many interesting one-step (change in only one of the dihedral angles or sugar puckers) conformational transitions which involve just about a kcal/mol of energy came to light. The two base sequences CG and GC were noted to differ strikingly in many of their conformational characteristics.     

Conformational energy landscape of the acyl pocket loop in acetylcholinesterase: a Monte Carlo-generalized Born model study

The X-ray crystal structure of the reaction product of acetylcholinesterase (AChE) with the inhibitor diisopropylphosphorofluoridate (DFP) showed significant structural displacement in a loop segment of residues 287-290. To understand this conformational selection, a Monte Carlo (MC) simulation study was performed of the energy landscape for the loop segment. A computational strategy was applied by using a combined simulated annealing and room temperature Metropolis sampling approach with solvent polarization modeled by a generalized Born (GB) approximation. Results from thermal annealing reveal a landscape topology of broader basin opening and greater distribution of energies for the displaced loop conformation, while the ensemble average of conformations at 298 K favored a shift in populations toward the native by a free-energy difference in good agreement with the estimated experimental value. Residue motions along a reaction profile of loop conformational reorganization are proposed where Arg-289 is critical in determining electrostatic effects of solvent interaction versus Coulombic charging.     

The Genetic Algorithm and the Conformational Search of Polypeptides and Proteins

Abstract

The genetic algorithm is a technique of function optimization derived from the principles of evolutionary theory. We have adapted it to perform conformational search on polypeptides and proteins. The algorithm was first tested on several small polypeptides and the 46 amino acid protein crambin under the AMBER potential energy function. The probable global minimum conformations of the polypeptides were located 90% of the time and a non-native conformation of crambin was located that was 150kcal/mol lower in potential energy than the minimized crystal structure conformation. Next, we used a knowledge-based potential function to predict the structures of melittin, pancreatic polypeptide, and crambin. A 2.31 Å ΔRMS conformation of melittin and a 5.33 Å ΔRMS conformation of pancreatic polypeptide were located by genetic algorithm-based conformational search under the knowledge-based potential function. Although the ΔRMS of pancreatic polypeptide was somewhat high, most of the secondary structure was correct. The secondary structure of crambin was predicted correctly, but the potential failed to promote packing interactions. Finally, we tested the packing aspects of our potential function by attempting to predict the tertiary structure of cytochrome b ₅₆₂ given correct secondary structure as a constraint. The final predicted conformation of cytochrome b ₅₆₂ was an almost completely extended continuous helix which indicated that the knowledge-based potential was useless for tertiary structure prediction. This work serves as a warning against testing potential functions designed for tertiary structure prediction on small proteins.     

Optimal protocol and trajectory visualization for conformational searches of peptides and proteins.

Conformational searches by molecular dynamics and different types of Monte Carlo or build-up methods usually aim to find the lowest-energy conformation. However, this is often misleading, as the energy functions used in conformational calculations are imprecise. For instance, though positions of local minima defined by the repulsive part of the Lennard-Jones potential are usually altered only slightly by functional modification, the relative depths of the minima could change significantly. Thus, the purpose of conformational searches and, correspondingly, performance criteria should be reformulated and appropriate methods found to extract different local minima from the search trajectory and allow visualization in the search space. Attempts at convergence to the lowest-energy structure should be replaced with efforts to visit a maximum number of different local energy minima with energies within a certain range. We use this quantitative criterion consistently to evaluate performances of different search procedures. To utilize information generated in the course of simulation, a "stack" of low energy conformations is created and stored. It keeps track of variables and visit numbers for the best representatives of different conformational families. To visualize the search, projection of multidimensional walks onto a principal plane defined by a set of reference structures is used. With Met-enkephalin as a structural example and a Monte Carlo procedure combined with energy minimization (MCM) as a basic search method, we analyzed the influence on search efficiency of different characteristics as temperature schedules, the step size for variable modification, constrained random step and response mechanisms to search difficulties. Simulated annealing MCM had comparable efficiency with MCM at constant and elevated temperature (about 600 K). Constraining the randomized choice of side-chain chi angles to optimal values (rotamers) on every MCM step did not improve, but rather worsened, the search efficiency. Two low-energy Met-enkephalin conformations with parallel Tyr1 and Phe4 rings, a gamma-turn around the Gly2 residue, and Phe4 and Met5 side-chains forming together a compact hydrophobic cluster were found and are suggested as possible structural candidates for interaction with a receptor or a membrane.