Oligomerization of FVFLM peptides and their ability to inhibit beta amyloid peptides aggregation: consideration as a possible model.

Preeclampsia, a pregnancy-specific disorder, shares typical pathophysiological features with protein misfolding disorders including Alzheimer's disease. Characteristic for preeclampsia is the involvement of multiple proteins of which fragments of SERPINA1 and ß-amyloid co-aggregate in urine and placenta of preeclamptic women. To explore the biophysical basis of this interaction, we investigated the multidimensional efficacy of the FVFLM sequence in SERPINA1, as a model inhibitory agent of ß-amyloid aggregation. After studying the oligomerization of FVFLM peptides using all-atom molecular dynamics simulations with the GROMOS43a1 force field and explicit water, we report that FVFLM can aggregate and its aggregation is spontaneous with a remarkably faster rate than that recorded for KLVFF (aggregation "hot-spot" from ß-amyloid). The fast kinetics of FVFLM aggregation was found to be driven primarily by core-like aromatic interactions originating from the anti-parallel orientation of complementarily uncharged strands. The conspicuously stable aggregation mechanism observed for FVFLM peptides is found not to conform to the popular 'dock-lock' scheme. We also found high propensity of FVFLM for KLVFF binding. When present, FVFLM disrupts the ß-amyloid aggregation pathway and we propose that FVFLM-like peptides might be used to prevent the assembly of full-length Aß or other pro-amyloidogenic peptides into amyloid fibrils.

Simulations of the folding of a globular protein.

Dynamic Monte Carlo simulations of the folding of a globular protein, apoplastocyanin, have been undertaken in the context of a new lattice model of proteins that includes both side chains and a-carbon backbone atoms and that can approximate native conformations at the level of 2 angstroms (root mean square) or better. Starting from random-coil unfolded states, the model apoplastocyanin was folded to a native conformation that is topologically similar to the real protein. The present simulations used a marginal propensity for local secondary structure consistent with but by no means enforcing the native conformation and a full hydrophobicity scale in which any nonbonded pair of side chains could interact. These molecules folded through a punctuated on-site mechanism of assembly where folding initiated at or near one of the turns ultimately found in the native conformation. Thus these simulations represent a partial solution to the globular-protein folding problem.

A method for predicting protein structure from sequence.

BACKGROUND: The ability to predict the native conformation of a globular protein from its amino-acid sequence is an important unsolved problem of molecular biology. We have previously reported a method in which reduced representations of proteins are folded on a lattice by Monte Carlo simulation, using statistically-derived potentials. When applied to sequences designed to fold into four-helix bundles, this method generated predicted conformations closely resembling the real ones. RESULTS: We now report a hierarchical approach to protein-structure prediction, in which two cycles of the above-mentioned lattice method (the second on a finer lattice) are followed by a full-atom molecular dynamics simulation. The end product of the simulations is thus a full-atom representation of the predicted structure. The application of this procedure to the 60 residue, B domain of staphylococcal protein A predicts a three-helix bundle with a backbone root mean square (rms) deviation of 2.25-3 A from the experimentally determined structure. Further application to a designed, 120 residue monomeric protein, mROP, based on the dimeric ROP protein of Escherichia coli, predicts a left turning, four-helix bundle native state. Although the ultimate assessment of the quality of this prediction awaits the experimental determination of the mROP structure, a comparison of this structure with the set of equivalent residues in the ROP dime- crystal structure indicates that they have a rms deviation of approximately 3.6-4.2 A. CONCLUSION: Thus, for a set of helical proteins that have simple native topologies, the native folds of the proteins can be predicted with reasonable accuracy from their sequences alone. Our approach suggest a direction for future work addressing the protein-folding problem.

Structural genomics and its importance for gene function analysis.

Structural genomics projects aim to solve the experimental structures of all possible protein folds. Such projects entail a conceptual shift from traditional structural biology in which structural information is obtained on known proteins to one in which the structure of a protein is determined first and the function assigned only later. Whereas the goal of converting protein structure into function can be accomplished by traditional sequence motif-based approaches, recent studies have shown that assignment of a protein's biochemical function can also be achieved by scanning its structure for a match to the geometry and chemical identity of a known active site. Importantly, this approach can use low-resolution structures provided by contemporary structure prediction methods. When applied to genomes, structural information (either experimental or predicted) is likely to play an important role in high-throughput function assignment.

Dynamic Monte Carlo simulations of a new lattice model of globular protein folding, structure and dynamics.

A long-standing problem of molecular biology is the prediction of globular protein tertiary structure from the primary sequence. In the context of a new, 24-nearest-neighbor lattice model of proteins that includes both alpha and beta-carbon atoms, the requirements for folding to a unique four-member beta-barrel, four-helix bundles and a model alpha/beta-bundle have been explored. A number of distinct situations are examined, but the common requirements for the formation of a unique native conformation are tertiary interactions plus the presence of relatively small (but not irrelevant) intrinsic turn preferences that select out the native conformer from a manifold of compact states. When side-chains are explicitly included, there are many conformations having the same or a slightly greater number of side-chain contacts as in the native conformation, and it is the local intrinsic turn preferences that produce the conformational selectivity on collapse. The local preference for helix or beta-sheet secondary structure may be at odds with the secondary structure ultimately found in the native conformation. The requisite intrinsic turn populations are about 0.3% for beta-proteins, 2% for mixed alpha/beta-proteins and 6% for helix bundles. In addition, an idealized model of an allosteric conformational transition has been examined. Folding occurs predominantly by a sequential on-site assembly mechanism with folding initiating either at a turn or from an isolated helix or beta-strand (where appropriate). For helical and beta-protein models, similar folding pathways were obtained in diamond lattice simulations, using an entirely different set of local Monte Carlo moves. This argues strongly that the results are universal; that is, they are independent of lattice, protein model or the particular realization of Monte Carlo dynamics. Overall, these simulations demonstrate that the folding of all known protein motifs can be achieved in the context of a single class of lattice models that includes realistic backbone structures and idealized side-chains.

Dynamic Monte Carlo simulations of globular protein folding/unfolding pathways. I. Six-member, Greek key beta-barrel proteins.

In the context of a simplified diamond lattice model of a six-member, Greek key beta-barrel protein that is closely related in topology to plastocyanin, the nature of the folding and unfolding pathways have been investigated using dynamic Monte Carlo techniques. The mechanism of Greek key assembly is best described as punctuated "on site construction". Folding typically starts at or near a beta-turn, and then the beta-strands sequentially form by using existing folded structure as a scaffold onto which subsequent tertiary structure assembles. On average, beta-strands tend to zip up from one tight bend to the next. After the four-member, beta-barrel assembles, there is a long pause as the random coil portion of the chain containing the long loop thrahes about trying to find the native state. Thus, there is an entropic barrier that must be surmounted. However, while a given piece of the protein may be folding, another section may be unfolding. A competition therefore exists to assemble a fairly stable intermediate before it dissolves. Folding may initiate at any of the tight turns, but the turn closer to the N terminus seems to be preferred due to well-known excluded volume effects. When the protein first starts to fold, there are a multiplicity of folding pathways, but the number of options is reduced as the system gets closer to the native state. In the early stages, the excluded volume effect exerted by the already assembled protein helps subsequent assembly. Then, near the native conformation, the folded parts reduce the accessible conformational space available to the remaining unfolded sections. Unfolding essentially occurs in reverse. Employing a simple statistical mechanical theory, the configurational free energy along the reaction co-ordinate for this model has been constructed. The free energy surface, in agreement with the simulations, provides the following predictions. The transition state is quite near the native state, and consists of five of the six beta-strands being fully assembled, with the remaining long loop plus sixth beta-strand in place, but only partially assembled. It is separated from the beta-barrel intermediate by a free energy barrier of mainly entropic origin and from the native state by a barrier that is primarily energetic in origin. The latter feature is in agreement with the "Cardboard Box" model described by Goldenberg and Creighton but, unlike their model, the transition state is not a high-energy distorted form of the native state.(ABSTRACT TRUNCATED AT 250 WORDS)

Topology fingerprint approach to the inverse protein folding problem.

We describe the most general solution to date of the problem of matching globular protein sequences to the appropriate three-dimensional structures. The screening template, against which sequences are tested, is provided by a protein "structural fingerprint" library based on the contact map and the buried/exposed pattern of residues. Then, a lattice Monte Carlo algorithm validates or dismisses the stability of the proposed fold. Examples of known structural similarities between proteins having weakly or unrelated sequences such as the globins and phycocyanins, the eight-member alpha/beta fold of triose phosphate isomerase and even a close structural equivalence between azurin and immunoglobulins are found.

MONSSTER: a method for folding globular proteins with a small number of distance restraints.

The MONSSTER (MOdeling of New Structures from Secondary and TEritary Restraints) method for folding of proteins using a small number of long-distance restraints (which can be up to seven times less than the total number of residues) and some knowledge of the secondary structure of regular fragments is described. The method employs a high-coordination lattice representation of the protein chain that incorporates a variety of potentials designed to produce protein-like behaviour. These include statistical preferences for secondary structure, side-chain burial interactions, and a hydrogen-bond potential. Using this algorithm, several globular proteins (1ctf, 2gbl, 2trx, 3fxn, 1mba, 1pcy and 6pti) have been folded to moderate-resolution, native-like compact states. For example, the 68 residue 1ctf molecule having ten loosely defined, long-range restraints was reproducibly obtained with a C alpha-backbone root-mean-square deviation (RMSD) from native of about 4. A. Flavodoxin with 35 restraints has been folded to structures whose average RMSD is 4.28 A. Furthermore, using just 20 restraints, myoglobin, which is a 146 residue helical protein, has been folded to structures whose average RMSD from native is 5.65 A. Plastocyanin with 25 long-range restraints adopts conformations whose average RMSD is 5.44 A. Possible applications of the proposed approach to the refinement of structures from NMR data, homology model-building and the determination of tertiary structure when the secondary structure and a small number of restraints are predicted are briefly discussed.

Fold assembly of small proteins using monte carlo simulations driven by restraints derived from multiple sequence alignments.

The feasibility of predicting the global fold of small proteins by incorporating predicted secondary and tertiary restraints into ab initio folding simulations has been demonstrated on a test set comprised of 20 non-homologous proteins, of which one was a blind prediction of target 42 in the recent CASP2 contest. These proteins contain from 37 to 100 residues and represent all secondary structural classes and a representative variety of global topologies. Secondary structure restraints are provided by the PHD secondary structure prediction algorithm that incorporates multiple sequence information. Predicted tertiary restraints are derived from multiple sequence alignments via a two-step process. First, seed side-chain contacts are identified from correlated mutation analysis, and then a threading-based algorithm is used to expand the number of these seed contacts. A lattice-based reduced protein model and a folding algorithm designed to incorporate these predicted restraints is described. Depending upon fold complexity, it is possible to assemble native-like topologies whose coordinate root-mean-square deviation from native is between 3.0 A and 6.5 A. The requisite level of accuracy in side-chain contact map prediction can be roughly 25% on average, provided that about 60% of the contact predictions are correct within +/-1 residue and 95% of the predictions are correct within +/-4 residues. Precision in tertiary contact prediction is more critical than absolute accuracy. Furthermore, only a subset of the tertiary contacts, on the order of 25% of the total, is sufficient for successful topology assembly. Overall, this study suggests that the use of restraints derived from multiple sequence alignments combined with a fold assembly algorithm holds considerable promise for the prediction of the global topology of small proteins.

Prediction of quaternary structure of coiled coils. Application to mutants of the GCN4 leucine zipper.

Using a simplified protein model, the equilibrium between different oligomeric species of the wild-type GCN4 leucine zipper and seven of its mutants have been predicted. Over the entire experimental concentration range, agreement with experiment is found in five cases, while in two cases agreement is found over a portion of the concentration range. These studies demonstrate a methodology for predicting coiled coil quaternary structure and allow for the dissection of the interactions responsible for the global fold. In agreement with the conclusion of Harbury et al., the results of the simulations indicate that the pattern of hydrophobic and hydrophilic residues alone is insufficient to define a protein's three-dimensional structure. In addition, these simulations indicate that the degree of chain association is determined by the balance between specific side-chain packing preferences and the entropy reduction associated with side-chain burial in higher-order multimers.

Prediction of the folding pathways and structure of the GCN4 leucine zipper.

A hierarchical approach is described for the prediction of the three-dimensional structure and folding pathway of the GCN4 leucine zipper. Dimer assembly is simulated by Monte Carlo dynamics. The resulting lowest energy structures undergo cooperative rearrangement of their hydrophobic core leading to side-chain fixation. The coarse-grained structures are further refined using a molecular dynamics annealing protocol. This produces full atom models with a backbone root-mean-square deviation from the crystal structure of 0.81 A. Thus, we demonstrate the predictive ability of our approach to yield high resolution structures of small coiled coils from their sequence.

Are proteins ideal mixtures of amino acids? Analysis of energy parameter sets.

Various existing derivations of the effective potentials of mean force for the two-body interactions between amino acid side chains in proteins are reviewed and compared to each other. The differences between different parameter sets can be traced to the reference state used to define the zero of energy. Depending on the reference state, the transfer free energy or other pseudo-one-body contributions can be present to various extents in two-body parameter sets. It is, however, possible to compare various derivations directly by concentrating on the "excess" energy-a term that describes the difference between a real protein and an ideal solution of amino acids. Furthermore, the number of protein structures available for analysis allows one to check the consistency of the derivation and the errors by comparing parameters derived from various subsets of the whole database. It is shown that pair interaction preferences are very consistent throughout the database. Independently derived parameter sets have correlation coefficients on the order of 0.8, with the mean difference between equivalent entries of 0.1 kT. Also, the low-quality (low resolution, little or no refinement) structures show similar regularities. There are, however, large differences between interaction parameters derived on the basis of crystallographic structures and structures obtained by the NMR refinement. The origin of the latter difference is not yet understood.

Derivation and testing of pair potentials for protein folding. When is the quasichemical approximation correct?

Many existing derivations of knowledge-based statistical pair potentials invoke the quasichemical approximation to estimate the expected side-chain contact frequency if there were no amino acid pair-specific interactions. At first glance, the quasichemical approximation that treats the residues in a protein as being disconnected and expresses the side-chain contact probability as being proportional to the product of the mole fractions of the pair of residues would appear to be rather severe. To investigate the validity of this approximation, we introduce two new reference states in which no specific pair interactions between amino acids are allowed, but in which the connectivity of the protein chain is retained. The first estimates the expected number of side-chain contracts by treating the protein as a Gaussian random coil polymer. The second, more realistic reference state includes the effects of chain connectivity, secondary structure, and chain compactness by estimating the expected side-chain contrast probability by placing the sequence of interest in each member of a library of structures of comparable compactness to the native conformation. The side-chain contact maps are not allowed to readjust to the sequence of interest, i.e., the side chains cannot repack. This situation would hold rigorously if all amino acids were the same size. Both reference states effectively permit the factorization of the side-chain contact probability into sequence-dependent and structure-dependent terms. Then, because the sequence distribution of amino acids in proteins is random, the quasichemical approximation to each of these reference states is shown to be excellent. Thus, the range of validity of the quasichemical approximation is determined by the magnitude of the side-chain repacking term, which is, at present, unknown. Finally, the performance of these two sets of pair interaction potentials as well as side-chain contact fraction-based interaction scales is assessed by inverse folding tests both without and with allowing for gaps.

The protein folding problem: a biophysical enigma.

Protein folding, the problem of how an amino acid sequence folds into a unique three-dimensional shape, has been a long-standing problem in biology. The success of genome-wide sequencing efforts has increased the interest in understanding the protein folding enigma, because realizing the value of the genomic sequences rests on the accuracy with which the encoded gene products are understood. Although a complete understanding of the kinetics and thermodynamics of protein folding has remained elusive, there has been considerable progress in techniques to predict protein structure from amino acid sequences. The prediction techniques fall into three general classes: comparative modeling, threading and ab initio folding. The current state of research in each of these three areas is reviewed here in detail. Efforts to apply each method to proteome-wide analysis are reviewed, and some of the key technical hurdles that remain are presented. Protein folding technologies, while not yet providing a full understanding of the protein folding process, have clearly progressed to the point of being useful in enabling structure-based annotation of genomic sequences.

Prediction of protein secondary structure by an enhanced neural network.

Computational model of neural network is used for prediction of secondary structure of globular proteins of known sequence. In contrast to earlier works some information about expected tertiary interactions were built in into the neural network. As a result the prediction accuracy was improved by 3% to 5%. Possible applications of this new approach are briefly discussed.

High coordination lattice models of protein structure, dynamics and thermodynamics.

A high coordination lattice discretization of protein conformational space is described. The model allows discrete representation of polypeptide chains of globular proteins and small macromolecular assemblies with an accuracy comparable to the accuracy of crystallographic structures. Knowledge based force field, that consists of sequence specific short range interactions, cooperative model of hydrogen bond network and tertiary one body, two body and multibody interactions, is outlined and discussed. A model of stochastic dynamics for these protein models is also described. The proposed method enables moderate resolution tertiary structure prediction of simple and small globular proteins. Its applicability in structure prediction increases significantly when evolutionary information is exploited or/and when sparse experimental data are available. The model responds correctly to sequence mutations and could be used at early stages of a computer aided protein design and protein redesign. Computational speed, associated with the discrete structure of the model, enables studies of the long time dynamics of polypeptides and proteins and quite detailed theoretical studies of thermodynamics of nontrivial protein models.

Prediction of protein secondary structure by neural networks: encoding short and long range patterns of amino acid packing.

A complex, cascaded neural network designed to predict the secondary structure of globular proteins has been developed. Information about the local buried-unburied pattern and the average tendency of the particular types of amino acids to be buried inside the globule were used. Nonspecific information about long distance contact maps was also employed. These modifications result in a noticeable improvement (3-9%) of prediction accuracy. The best result for the average success ratio for the testing set of nonhomologous proteins was 68.3% (with corresponding Matthews' coefficients, C alpha,beta,coil equal to 0.60, 0.47, 0.43, respectively).

Reduced protein models and their application to the protein folding problem.

One of the most important unsolved problems of computational biology is prediction of the three-dimensional structure of a protein from its amino acid sequence. In practice, the solution to the protein folding problem demands that two interrelated problems be simultaneously addressed. Potentials that recognize the native state from the myriad of misfolded conformations are required, and the multiple minima conformational search problem must be solved. A means of partly surmounting both problems is to use reduced protein models and knowledge-based potentials. Such models have been employed to elucidate a number of general features of protein folding, including the nature of the energy landscape, the factors responsible for the uniqueness of the native state and the origin of the two-state thermodynamic behavior of globular proteins. Reduced models have also been used to predict protein tertiary and quaternary structure. When combined with a limited amount of experimental information about secondary and tertiary structure, molecules of substantial complexity can be assembled. If predicted secondary structure and tertiary restraints are employed, low resolution models of single domain proteins can be successfully predicted. Thus, simplified protein models have played an important role in furthering the understanding of the physical properties of proteins.

Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure.

Exponential growth in the number of available protein sequences is unmatched by the slower growth in the number of structures. As a result, the development of efficient and fast protein secondary structure prediction methods is essential for the broad comprehension of protein structures. Computational methods that can efficiently determine secondary structure can in turn facilitate protein tertiary structure prediction, since most methods rely initially on secondary structure predictions. Recently, we have developed a fast learning optimized prediction methodology (FLOPRED) for predicting protein secondary structure (Saraswathi et al. in JMM 18:4275, 2012). Data are generated by using knowledge-based potentials combined with structure information from the CATH database. A neural network-based extreme learning machine (ELM) and advanced particle swarm optimization (PSO) are used with this data to obtain better and faster convergence to more accurate secondary structure predicted results. A five-fold cross-validated testing accuracy of 83.8 % and a segment overlap (SOV) score of 78.3 % are obtained in this study. Secondary structure predictions and their accuracy are usually presented for three secondary structure elements: α-helix, ß-strand and coil but rarely have the results been analyzed with respect to their constituent amino acids. In this paper, we use the results obtained with FLOPRED to provide detailed behaviors for different amino acid types in the secondary structure prediction. We investigate the influence of the composition, physico-chemical properties and position specific occurrence preferences of amino acids within secondary structure elements. In addition, we identify the correlation between these properties and prediction accuracy. The present detailed results suggest several important ways that secondary structure predictions can be improved in the future that might lead to improved protein design and engineering.

Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction.

Computational methods are rapidly gaining importance in the field of structural biology, mostly due to the explosive progress in genome sequencing projects and the large disparity between the number of sequences and the number of structures. There has been an exponential growth in the number of available protein sequences and a slower growth in the number of structures. There is therefore an urgent need to develop computational methods to predict structures and identify their functions from the sequence. Developing methods that will satisfy these needs both efficiently and accurately is of paramount importance for advances in many biomedical fields, including drug development and discovery of biomarkers. A novel method called fast learning optimized prediction methodology (FLOPRED) is proposed for predicting protein secondary structure, using knowledge-based potentials combined with structure information from the CATH database. A neural network-based extreme learning machine (ELM) and advanced particle swarm optimization (PSO) are used with this data that yield better and faster convergence to produce more accurate results. Protein secondary structures are predicted reliably, more efficiently and more accurately using FLOPRED. These techniques yield superior classification of secondary structure elements, with a training accuracy ranging between 83 % and 87 % over a widerange of hidden neurons and a cross-validated testing accuracy ranging between 81 % and 84 % and a segment overlap (SOV) score of 78 % that are obtained with different sets of proteins. These results are comparable to other recently published studies, but are obtained with greater efficiencies, in terms of time and cost.