Exploring conformational space using a mean field technique with MOLS sampling

The computational identification of all the low energy structures of a peptide given only its sequence is not an easy task even for small peptides,due to the multiple-minima problem and combinatorial explosion. We have developed an algorithm, called the MOLS technique,that addresses this problem, and have applied it to a number of different aspects of the study of peptide and protein structure. Conformational studies of oligopeptides, including loop sequences in proteins have been carried out using this technique. In general the calculations identified all the folds determined by previous studies,and in addition picked up other energetically favorable structures. The method was also used to map the energy surface of the peptides. In another application, we have combined the MOLS technique, using it to generate a library of low energy structures of an oligopeptide, with a genetic algorithm to predict protein structures. The method has also been applied to explore the conformational space of loops in protein structures.Further, it has been applied to the problem of docking a ligand in its receptor site, with encouraging results.     

MOLS sampling and its applications in structural biophysics

This review describes the MOLS method and its applications. This computational method has been developed in our laboratory primarily to explore the conformational space of small peptides and identify features of interest, particularly the minima, i.e., the low energy conformations. A systematic “brute-force” search through the vast conformational space for such features faces the insurmountable problem of combinatorial explosion, whilst other techniques, e.g., Monte Carlo searches, are somewhat limited in their region of exploration and may be considered inexhaustive. The MOLS method, on the other hand, uses a sampling technique commonly employed in experimental design theory to identify a small sample of the conformational space that nevertheless retains information about the entire space. The information is extracted using a technique that is a variant of the self-consistent mean field technique, which has been used to identify, for example, the optimal set of side-chain conformations in a protein. Applications of the MOLS method to understand peptide structure, predict the structures of loops in proteins, predict three-dimensional structures of small proteins, and arrive at the best conformation, orientation, and positions of a small molecule ligand in a protein receptor site have all yielded satisfactory results.     

Conformational studies on enkephalins using the MOLS technique

Conformational studies of two linear enkephalin molecules, Met-enkephalin and Leu-enkephalin, have been carried using the mutually orthogonal Latin squares (MOLS) technique with the ECEPP/3 force field. This technique was developed recently in our laboratory to perform an unbiased search of the conformational space of peptides and to locate low energy conformations. The present study identified all the folds predicted by other studies, and in addition picked up other energetically favorable structures. The results suggest that the peptide backbone exists as a mixture of folded and unfolded forms (approximately 50% each). The study also provides information on the distribution of the low energy conformations that we have classified on the basis of structural motifs, backbone hydrogen-bonding patterns, and root mean square deviations in atomic positions.     

Conformational space exploration of Met- and Leu-enkephalin using the MOLS method, molecular dynamics, and Monte Carlo simulation--a comparative study

We report here a comparative study of the molecular conformational energy landscape generated using the mutually orthogonal Latin squares (MOLS) method, molecular dynamics (MD), and Monte Carlo (MC) simulation. The MOLS method, as described earlier from our laboratory, uses an experimental design technique to rapidly and exhaustively sample the low energy conformations of a molecule. MD and MC simulations have been used to perform similar tasks. In the comparison reported here, the three methods were applied to a pair of neuropeptides, namely Met- and Leu-enkephalin. A set of 1500 conformations of these enkephalins were generated using these methods with CHARMM22 force field, and the resulting samples were analyzed to determine the extent and nature of coverage of the conformational space. The results indicate that the MOLS method samples a larger number of possible conformations and identifies conformations closer to the experimental structures than the MD and MC simulations.     

Enhanced sampling of the molecular potential energy surface using mutually orthogonal latin squares: application to peptide structures

The computational identification of the optimal three-dimensional fold of even a small peptide chain from its sequence, without reference to other known structures, is a complex problem. There have been several attempts at solving this by sampling the potential energy surface of the molecule in a systematic manner. Here we present a new method to carry out the sampling, and to identify low energy conformers of the molecule. The method uses mutually orthogonal Latin squares to select (of the order of) n(2) points from the multidimensional conformation space of size m(n), where n is the number of dimensions (i.e., the number of conformational variables), and m specifies the fineness of the search grid. The sampling is accomplished by first calculating the value of the potential energy function at each one of the selected points. This is followed by analysis of these values of the potential energy to obtain the optimal value for each of the n-variables separately. We show that the set of the n-optimal values obtained in this manner specifies a low energy conformation of the molecule. Repeated application of the method identifies other low energy structures. The computational complexity of this algorithm scales as the fourth power of the size of the molecule. We applied this method to several small peptides, such as the neuropeptide enkephalin, and could identify a set of low energy conformations for each. Many of the structures identified by this method have also been previously identified and characterized by experiment and theory. We also compared the best structures obtained for the tripeptide (Ala)(3) by the present method, with those obtained by an exhaustive grid search, and showed that the algorithm is successful in identifying all the low energy conformers of this molecule.     

In silico identification and design of potent peptide inhibitors against PDZ-3 domain of Postsynaptic Density Protein (PSD-95)

Unique intrinsic properties of peptides like low toxicity, high biological activity, and specificity make them attractive therapeutic agents. PDZ-binding peptide inhibitors have been demonstrated for curing of Alzheimer, Parkinson, Dementia, and other central nervous system ailments. In this article, we report the successful use of an integrated computational protocol to analyze the structural basis of how peptides bind to the shallow groove of the third PDZ domain (PDZ-3) from the postsynaptic density (PSD-95) protein. This protocol employs careful and precise computational techniques for design of new strategy for predicting novel and potent peptides against PDZ protein. We attempted to generate a pharmacophore model using crystal structure of peptide inhibitor bound to the PDZ-3. A highly specific and sensitive generated pharmacophore model was used for screening virtual database generated using different combination of amino acid substitutions as well as decoy peptide database for its sensitivity and specificity. Identified hit peptides were further analyzed by docking studies, and their stability analyzed using solvated molecular dynamics. Quantum Mechanics/Molecular Mechanics (QM/MM) interaction energy and GMX-PBSA scoring schemes were used for ranking of stable peptides. Computational approach applied here generated encouraging results for identifying peptides against PDZ interaction model. The workflow can be further exercised as a virtual screening technique for reducing the search space for candidate target peptides against PDZ domains.     

Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes

Since determining the crystallographic structure of all peptide-MHC complexes is infeasible, an accurate prediction of the conformation is a critical computational problem. These models can be useful for determining binding energetics, predicting the structures of specific ternary complexes with T-cell receptors, and designing new molecules interacting with these complexes. The main difficulties are (1) adequate sampling of the large number of conformational degrees of freedom for the flexible peptide, (2) predicting subtle changes in the MHC interface geometry upon binding, and (3) building models for numerous MHC allotypes without known structures. Whereas previous studies have approached the sampling problem by dividing the conformational variables into different sets and predicting them separately, we have refined the Biased-Probability Monte Carlo docking protocol in internal coordinates to optimize a physical energy function for all peptide variables simultaneously. We also imitated the induced fit by docking into a more permissive smooth grid representation of the MHC followed by refinement and reranking using an all-atom MHC model. Our method was tested by a comparison of the results of cross-docking 14 peptides into HLA-A*0201 and 9 peptides into H-2K(b) as well as docking peptides into homology models for five different HLA allotypes with a comprehensive set of experimental structures. The surprisingly accurate prediction (0.75 A backbone RMSD) for cross-docking of a highly flexible decapeptide, dissimilar to the original bound peptide, as well as docking predictions using homology models for two allotypes with low average backbone RMSDs of less than 1.0 A illustrate the method's effectiveness. Finally, energy terms calculated using the predicted structures were combined with supervised learning on a large data set to classify peptides as either HLA-A*0201 binders or nonbinders. In contrast with sequence-based prediction methods, this model was also able to predict the binding affinity for peptides to a different MHC allotype (H-2K(b)), not used for training, with comparable prediction accuracy.     

Peptide arrays with designed secondary structures for protein characterization using fluorescent fingerprint patterns

To realize a practical high-throughput protein-detection system, novel peptide arrays have been constructed using designed peptide libraries with loop, alpha-helix, or beta-strand structures. Here, we describe the overview of the reported designed peptide arrays with loop and alpha-helix structures and the new results of those with beta-strand structures. Initially, several model peptides known to interact with model structured proteins were selected to establish the present strategy for high-throughput detection of proteins. The fluorescent probes and suitable scaffolds of peptides were examined for the effective detection of proteins. The detection methods were established in solution and in an immobilized manner using the model systems. In the case of alpha-helix peptide, the response of a peptide with fluorescent resonance energy transfer between two probes at both termini was several times higher than that of a peptide with a single probe. In the cases of peptides with other structures, however, proteins were effectively detectable even by the fluorescent change of one probe. Furthermore, structurally focused libraries consisting of a total of ca. 250 different peptides based on the model peptides with secondary and/or tertiary structures were constructed with systematic replacement of residues. Using these libraries, various proteins were characterized effectively to give their own fluorescent "protein fingerprint" patterns. The resulting protein fingerprints correlated with the recognition properties of the proteins. These studies demonstrate that arrays with peptide libraries based on designed structures can be promising tools for detecting the target proteins. Designed synthetic peptides play roles as the capturing agents to be developed for practical protein chips.     

Computational design and experimental study of tighter binding peptides to an inactivated mutant of HIV-1 protease

Drug resistance in HIV-1 protease, a barrier to effective treatment, is generally caused by mutations in the enzyme that disrupt inhibitor binding but still allow for substrate processing. Structural studies with mutant, inactive enzyme, have provided detailed information regarding how the substrates bind to the protease yet avoid resistance mutations; insights obtained inform the development of next generation therapeutics. Although structures have been obtained of complexes between substrate peptide and inactivated (D25N) protease, thermodynamic studies of peptide binding have been challenging due to low affinity. Peptides that bind tighter to the inactivated protease than the natural substrates would be valuable for thermodynamic studies as well as to explore whether the structural envelope observed for substrate peptides is a function of weak binding. Here, two computational methods-namely, charge optimization and protein design-were applied to identify peptide sequences predicted to have higher binding affinity to the inactivated protease, starting from an RT-RH derived substrate peptide. Of the candidate designed peptides, three were tested for binding with isothermal titration calorimetry, with one, containing a single threonine to valine substitution, measured to have more than a 10-fold improvement over the tightest binding natural substrate. Crystal structures were also obtained for the same three designed peptide complexes; they show good agreement with computational prediction. Thermodynamic studies show that binding is entropically driven, more so for designed affinity enhanced variants than for the starting substrate. Structural studies show strong similarities between natural and tighter-binding designed peptide complexes, which may have implications in understanding the molecular mechanisms of drug resistance in HIV-1 protease.     

Structures of cyclic, antimicrobial peptides in a membrane-mimicking environment define requirements for activity.

New antimicrobial compounds are of major importance because of the growing problem of bacterial resistance. In this context, antimicrobial peptides have received a lot of attention. Their mechanism of action, however, is often obscure. Here, the structures of two cyclic, antimicrobial peptides from the family of arginine- and tryptophan-rich peptides determined in a membrane-mimicking environment are described. The sequence of the peptides has been obtained from a cyclic parent peptide by scrambling the amino acids. While the activity of the peptides is similar to that of the parent peptide, the structures are not. The peptides do, however, all adopt an amphiphilic structure. A comparison between the structures helps to define the requirements for the activity of these peptides.     

Cellular uptake [correction of utake] of the Tat peptide: an endocytosis mechanism following ionic interactions

The cellular delivery of various biological compounds has recently been improved by conjugating them to short peptides known as protein transduction domains or cell penetrating peptides. These peptides include Tat, Antennapedia and arginine-rich peptides. The common feature of these peptides is their highly cationic nature. Up to now, the cellular uptake of about 50 different peptides and proteins coupled to Tat or Antennapedia peptides has been reported. The ability to deliver molecules into cells is not limited to peptide moieties, since oligonucleotides, peptide nucleic acids or other low molecular weight entities have been successfully internalized. Moreover, most of these examples have been accompanied by the expected biological response. More surprisingly, the uptake of large structures such as liposomes, phages, nanoparticles or adenoviruses has also been documented. Indeed the mechanism by which these very different entities could enter cells following a putative common pathway appeared more and more intriguing after each new reported example of cellular uptake mediated by these peptides. After a long period of uncertainty regarding the mechanism of entry, data from several groups now argue for an energy-dependent process of entry. The entry of most of these molecules is likely to be inhibited by low temperature incubation or in the presence of various drugs applied to inhibit the energy-dependent pathway of cell entry. Moreover, the binding of the highly cationic Tat peptide to various anionic membrane components probably initiates the first step of the cell internalization process.     

The synthesis and study of side-chain lactam-bridged peptides

Side-chain lactam bridges linking amino acid residues that are spaced several residues apart in the linear sequence offer a convenient and flexible method for introducing conformational constraints into a peptide structure. The availability of a variety of selectively cleavable protecting groups for amines and carboxylic acids allows for several approaches to the synthesis of monocyclic, dicyclic, and bicyclic lactam-bridged peptides by solid-phase methods. Multicyclic structures are also accessible, but segment-condensation syntheses with solution-phase cyclizations are most likely to provide the best synthetic approach to these more complex constrained peptides. Lactam bridges linking (i, i + 3)-, (i, i + 4), and (i, i + 7)-spaced residue pairs have all proven useful for stabilization of alpha helices, and (i, i + 3)-linked residues have also been demonstrated to stabilize beta-turns. These structures are finding an increasing number of applications in protein biology, including studies of protein folding, protein aggregation, peptide ligand-receptor recognition, and the development of more potent peptide therapeutics. Defining the functional roles of the amphiphilic alpha-helices in medium-sized peptide hormones, and studying helix propagation from rigid, alpha-helix initiating bicyclic peptides are among the most exciting developments currently underway in this field.     

Conformational analysis of invariant peptide sequences in bacterial genomes

The functional significance of evolutionarily conserved motifs/patterns of short regions in proteins is well documented. Although a large number of sequences are conserved, only a small fraction of these are invariant across several organisms. Here, we have examined the structural features of the functionally important peptide sequences, which have been found invariant across diverse bacterial genera. Ramachandran angles (phi,psi) have been used to analyze the conformation, folding patterns and geometrical location (buried/exposed) of these invariant peptides in different crystal structures harboring these sequences. The analysis indicates that the peptides preferred a single conformation in different protein structures, with the exception of only a few longer peptides that exhibited some conformational variability. In addition, it is noticed that the variability of conformation occurs mainly due to flipping of peptide units about the virtual C(alpha)...C(alpha) bond. However, for a given invariant peptide, the folding patterns are found to be similar in almost all the cases. Over and above, such peptides are found to be buried in the protein core. Thus, we can safely conclude that these invariant peptides are structurally important for the proteins, since they acquire unique structures across different proteins and can act as structural determinants (SD) of the proteins. The location of these SD peptides on the protein chain indicated that most of them are clustered towards the N-terminal and middle region of the protein with the C-terminal region exhibiting low preference. Another feature that emerges out of this study is that some of these SD peptides can also play the roles of "fold boundaries" or "hinge nucleus" in the protein structure. The study indicates that these SD peptides may act as chain-reversal signatures, guiding the proteins to adopt appropriate folds. In some cases the invariant signature peptides may also act as folding nuclei (FN) of the proteins.     

Epitope mapping using combinatorial phage-display libraries: a graph-based algorithm

A phage-display library of random peptides is a combinatorial experimental technique that can be harnessed for studying antibody–antigen interactions. In this technique, a phage peptide library is scanned against an antibody molecule to obtain a set of peptides that are bound by the antibody with high affinity. This set of peptides is regarded as mimicking the genuine epitope of the antibody's interacting antigen and can be used to define it. Here we present PepSurf, an algorithm for mapping a set of affinity-selected peptides onto the solved structure of the antigen. The problem of epitope mapping is converted into the task of aligning a set of query peptides to a graph representing the surface of the antigen. The best match of each peptide is found by aligning it against virtually all possible paths in the graph. Following a clustering step, which combines the most significant matches, a predicted epitope is inferred. We show that PepSurf accurately predicts the epitope in four cases for which the epitope is known from a solved antibody–antigen co-crystal complex. We further examine the capabilities of PepSurf for predicting other types of protein–protein interfaces. The performance of PepSurf is compared to other available epitope mapping programs.     

Development and testing of protocols for computer-aided design of peptide drugs, using oxytocin

Considerable clinical interest in neuropeptides and peptide hormones has stimulated recent research and development of peptide-based drugs. This process differs from most classical drug discovery procedures because peptide molecules have considerable inherent flexibility. In the present paper, to identify lowest energy and metastable conformers for drug design, and to develop protocols for such studies, conformational search algorithms, incorporating empirical energy calculations, have been applied in the analysis of the peptide oxytocin. Energy minimization in torsion angle space was carried out from a variety of starting conformations, including published structures, in all-atom mode and all with distance constraints for disulphide bond formation. The energy-minimized conformations have been further optimized by a mapping method. Complementary simulations have been performed in united-atom mode and a model representing the effects of water using dummy sites has been developed and tested for this representation. Several of the preferred conformers together with de novo conformations have been used as starting points in molecular dynamics simulations; 28 low potential energy conformations were located at a temperature of 4 K. Conformations are analysed to identify hydrogen bonds, phi-psi angle distributions and the RMS values relative to the X-ray structure of deamino-oxytocin. The modelled structure of lowest energy in the molecular mechanics calculations was also that of least RMS deviation from the crystal structure; whilst structures of lower energy but larger deviation were identified by molecular dynamics techniques. A metastable structure has been identified which satisfies existing criteria for the "active form", and this model is tested by a theoretical residue-substitution technique, to provide clues on the agonist/antagonist relationship at the atomic level.     

Generalized pattern search algorithm for Peptide structure prediction

Finding the near-native structure of a protein is one of the most important open problems in structural biology and biological physics. The problem becomes dramatically more difficult when a given protein has no regular secondary structure or it does not show a fold similar to structures already known. This situation occurs frequently when we need to predict the tertiary structure of small molecules, called peptides. In this research work, we propose a new ab initio algorithm, the generalized pattern search algorithm, based on the well-known class of Search-and-Poll algorithms. We performed an extensive set of simulations over a well-known set of 44 peptides to investigate the robustness and reliability of the proposed algorithm, and we compared the peptide conformation with a state-of-the-art algorithm for peptide structure prediction known as PEPstr. In particular, we tested the algorithm on the instances proposed by the originators of PEPstr, to validate the proposed algorithm; the experimental results confirm that the generalized pattern search algorithm outperforms PEPstr by 21.17% in terms of average root mean-square deviation, RMSD C_α.     

MOLS--a program to explore the potential energy surface of a peptide and locate its low energy conformations

We describe a computer program that uses mutually orthogonal Latin squares (MOLS) to perform an efficient and exhaustive conformational search of the multi-dimensional potential energy hypersurface of an oligopeptide, and locate all its low energy conformations. The software package has been developed with a user-friendly graphical interface using the Fast Light Tool Kit (FLTK)--a cross platform C++ toolkit.     

A structure-based approach for prediction of MHC-binding peptides

Identification of immunodominant peptides is the first step in the rational design of peptide vaccines aimed at T-cell immunity. The advances in sequencing techniques and the accumulation of many protein sequences without the purified protein challenge the development of computer algorithms to identify dominant T-cell epitopes based on sequence data alone. Here, we focus on antigenic peptides recognized by cytotoxic T cells. The selection of T-cell epitopes along a protein sequence is influenced by the specificity of each of the processing stages that precede antigen presentation. The most selective of these processing stages is the binding of the peptides to the major histocompatibility complex molecules, and therefore many of the predictive algorithms focus on this stage. Most of these algorithms are based on known binding peptides whose sequences have been used for the characterization of binding motifs or profiles. Here, we describe a structure-based algorithm that does not rely on previous binding data. It is based on observations from crystal structures that many of the bound peptides adopt similar conformations and placements within the MHC groove. The algorithm uses a structural template of the peptide in the MHC groove upon which peptide candidates are threaded and their fit to the MHC groove is evaluated by statistical pairwise potentials. It can rank all possible peptides along a protein sequence or within a suspected group of peptides, directing the experimental efforts towards the most promising peptides. This approach is especially useful when no previous peptide binding data are available.     

Using peptide arrays to define nuclear carrier binding sites on nucleoporins

In the peptide SPOT array technique, an array of different peptides are synthesized on, and covalently linked to, cellulose membranes. In one usage of this technique, these peptides are screened in an overlay assay to determine which short sequence(s) contains a binding site for an interacting protein. By preparing overlapping peptides that cover the entire sequence of a protein, all of the binding domains on the protein for a second protein can be identified. We have utilized the peptide SPOT array technique to identify the short amino acid sequences within nuclear pore complex proteins (also known as nucleoporins or Nups) that bind the nuclear carrier importin-beta. Crystallization studies by others have indicated that nuclear carriers such as importin-beta bind to phenylalanine-glycine (FG) repeats present in numerous copies in the sequences of a family of nucleoporins. Consistent with this, we found that most (but not all) of the Nup binding sites for importin-beta identified by this technique contain Fx, FG, FxFG, FxFx, or GLFG sequences, although not all such sequences bound importin-beta. Peptide SPOT array substitution studies confirmed a crucial role for the phenylalanine in FG repeats and identified a lysine residue flanking some repeats that is crucial for importin-beta binding to those repeats. In addition to these expected binding sequences for importin-beta, we found multiple instances of a peptide lacking a canonical FG repeat that strongly bound importin-beta, indicating that additional Nup sequences may form binding sites for importin-beta.