Minimalist representations and the importance of nearest neighbor effects in protein folding simulations

In order to investigate the level of representation required to simulate folding and predict structure, we test the ability of a variety of reduced representations to identify native states in decoy libraries and to recover the native structure given the advanced knowledge of the very broad native Ramachandran basin assignments. Simplifications include the removal of the entire side-chain or the retention of only the Cbeta atoms. Scoring functions are derived from an all-atom statistical potential that distinguishes between atoms and different residue types. Structures are obtained by minimizing the scoring function with a computationally rapid simulated annealing algorithm. Results are compared for simulations in which backbone conformations are sampled from a Protein Data Bank-based backbone rotamer library generated by either ignoring or including a dependence on the identity and conformation of the neighboring residues. Only when the Cbeta atoms and nearest neighbor effects are included do the lowest energy structures generally fall within 4 A of the native backbone root-mean square deviation (RMSD), despite the initial configuration being highly expanded with an average RMSD > or = 10 A. The side-chains are reinserted into the Cbeta models with minimal steric clash. Therefore, the detailed, all-atom information lost in descending to a Cbeta-level representation is recaptured to a large measure using backbone dihedral angle sampling that includes nearest neighbor effects and an appropriate scoring function.     

Protein design based on folding models

The basic rules governing the folding of small, single-domain proteins are being discovered. New algorithms that can predict the major features of the folding process give the opportunity to design and optimise protein folding in a rational way. Recent experimental works suggest that sequence-specific features should be integrated in folding models to improve their performance.     

Understanding protein folding through peptide models

From the time it was recognized that proteins are made up primarily of secondary structures, theories of protein folding have used secondary structural elements as important building blocks. Peptides have played a central role in elucidating the factors that stabilize individual elements of secondary structure and are now being employed to study higher levels of organization. The control of conformation in peptides has taken on new relevance with the realization that protein folding plays a central role in many disease states.     

Scoring functions in protein folding and design

We present an analysis of the assumptions behind some of the most commonly used methods for evaluating the goodness of the fit between a sequence and a structure. Our studies on a lattice model show that methods based on statistical considerations are easy to use and can capture some of the features of protein-like sequences and their corresponding native states, but unfortunately are incapable of recognizing, with certainty, the native-like conformation of a sequence among a set of decoys. Meanwhile, an optimization method, entailing the determination of the parameters of an effective free energy of interaction, is much more reliable in recognizing the native state of a sequence. However, the statistical method is shown to perform quite well in tests of protein design.     

Low-throughput model design of protein folding inhibitors

The stabilization energy of proteins in their native conformation is not distributed uniformly among all the amino acids, but is concentrated in few (short) fragments, fragments which play a key role in the folding process and in the stability of the protein. Peptides displaying the same sequence as these key fragments can compete with the formation of the most important native contacts, destabilizing the protein and thus inhibiting its biological activity. We present an essentially automatic method to individuate such peptidic inhibitors based on a low-throughput screening of the fragments which build the target protein. The efficiency and generality of the method is tested on proteins Src-SH3, G, CI2, and HIV-1-PR with the help of a simplified computational model. In each of the cases studied, we find few peptides displaying strong inhibitory properties, properties which are quite robust with respect to point mutations. The possibility of implementing the method through low-throughput experimental screening of the target protein is discussed.     

A framework for describing topological frustration in models of protein folding

In a natively folded protein of moderate or larger size, the protein backbone may weave through itself in complex ways, raising questions about what sequence of events might have to occur in order for the protein to reach its native configuration from the unfolded state. A mathematical framework is presented here for describing the notion of a topological folding barrier, which occurs when a protein chain must pass through a hole or opening, formed by other regions of the protein structure. Different folding pathways encounter different numbers of such barriers and therefore different degrees of frustration. A dynamic programming algorithm finds the optimal theoretical folding path and minimal degree of frustration for a protein based on its natively folded configuration. Calculations over a database of protein structures provide insights into questions such as whether the path of minimal frustration might tend to favor folding from one or from many sites of folding nucleation, or whether proteins favor folding around the N terminus, thereby providing support for the hypothesis that proteins fold co-translationally. The computational methods are applied to a multi-disulfide bonded protein, with computational findings that are consistent with the experimentally observed folding pathway. Attention is drawn to certain complex protein folds for which the computational method suggests there may be a preferred site of nucleation or where folding is likely to proceed through a relatively well-defined pathway or intermediate. The computational analyses lead to testable models for protein folding.     

On the design and analysis of protein folding potentials

Pairwise interaction models to recognize native folds are designed and analyzed. Different sets of parameters are considered but the focus was on 20 x 20 contact matrices. Simultaneous solution of inequalities and minimization of the variance of the energy find matrices that recognize exactly the native folds of 572 sequences and structures from the protein data bank (PDB). The set includes many homologous pairs, which present a difficult recognition problem. Significant recognition ability is recovered with a small number of parameters (e.g., the H/P model). However, full recognition requires a complete set of amino acids. In addition to structures from the PDB, a folding program (MONSSTER) was used to generate decoy structures for 75 proteins. It is impossible to recognize all the native structures of the extended set by contact potentials. We therefore searched for a new functional form. An energy function U, which is based on a sum of general pairwise interactions limited to a resolution of 1 angstrom, is considered. This set was infeasible too. We therefore conjecture that it is not possible to find a folding potential, resolved to 1 angstrom, which is a sum of pair interactions.     

Simulated folding in polypeptides of diversified molecular tacticity: implications for protein folding and de novo design

Stereochemistry could be a powerful variable for conformational tune up of polypeptides for de novo design. It may be also useful probe of possible role of interamide energetics in selection and stabilization of conformation. The homopolypeptides Ac-Xxx30-NHMe, with Xxx = Ala, Val, and Leu, of diversified stereochemical structure are generated by simulated racemization with a modified GROMOS-96 force field. The polypeptides, and other systematic stereochemical variants, are folded by simulated annealing with another modified GROMOS-96 force field under the dielectric constant values 1, 4, and 10. The resultant 15,000 molecular folds of isotactic (poly-L-chiral), syndiotactic (alternating L,D-chiral), and heterotactic (random-L,D-chiral) stereochemical structure, belonging to three polypeptide series, achieved under three different folding conditions, are assessed statistically for structure-to-energy-to-conformation relationship. The results suggest that interamide electrostatics could be a major factor in secondary-structure selection in polypeptides while main-chain stereochemistry could dictate molecular packing and therefore the relative magnitude of hydrogen-bond and Lennard-Jones (LJ) contributions in conformational energy. A method for computational design of heterotactic molecular folds in polypeptide structure has been developed, and the first road map for a chiral tune up of polypeptide structure based on stereochemical engineering has been laid down. Broad implications for protein structure, folding, and de novo design are briefly discussed.     

Database-derived potentials dependent on protein size for in silico folding and design

Knowledge-based potentials are widely used in simulations of protein folding, structure prediction, and protein design. Their advantages include limited computational requirements and the ability to deal with low-resolution protein models compatible with long-scale simulations. Their drawbacks comprehend their dependence on specific features of the dataset from which they are derived, such as the size of the proteins it contains, and their physical meaning is still a subject of debate. We address these issues by probing the theoretical validity of these potentials as mean-force potentials that take the solvent implicitly into account and involve entropic contributions due to atomic degrees of freedom and solvation. The dependence on the size of the system is checked on distance-dependent amino acid pair potentials, derived from six protein structure sets containing proteins of increasing length N. For large inter-residue distances, they are found to display the theoretically predicted 1/N behavior weighted by a factor depending on the boundaries and the compressibility of the system. For short distances, different trends are observed according to the nature of the residue pairs and their ability to form, for example, electrostatic, cation-pi or pi-pi interactions, or hydrophobic packing. The results of this analysis are used to devise a novel protein size-dependent distance potential, which displays an improved performance in discriminating native sequence-structure matches among decoy models.     

Polymer models of protein stability, folding, and interactions

The unfolded state and flexible linkers in the folded structure play essential roles in protein stability and folding and protein-protein interactions. Intrinsic to these roles is the fact that unfolded proteins and flexible linkers sample many different conformations. Polymer models may capture this and complement experiments in elucidating the contributions of the unfolded state and flexible linkers. Here I review what can be predicted from these models and how well these predictions match experiments. For example, Gaussian chain models give quantitatively reasonable predictions of the effects of residual charge-charge interactions in the unfolded state and qualitatively reasonable results for the effects of spatial confinement and macromolecular crowding on protein stability. A wormlike chain model has met with success in quantifying the effects of flexible linkers in binding affinity enhancement and in regulatory switches. In future developments, more realistic models may emerge from molecular dynamics simulations, and these models will guide experiments to advance our understanding of the unfolded state and flexible linkers.     

Simple physical models connect theory and experiment in protein folding kinetics

Our understanding of the principles underlying the protein-folding problem can be tested by developing and characterizing simple models that make predictions which can be compared to experimental data. Here we extend our earlier model of folding free energy landscapes, in which each residue is considered to be either folded as in the native state or completely disordered, by investigating the role of additional factors representing hydrogen bonding and backbone torsion strain, and by using a hybrid between the master equation approach and the simple transition state theory to evaluate kinetics near the free energy barrier in greater detail. Model calculations of folding phi-values are compared to experimental data for 19 proteins, and for more than half of these, experimental data are reproduced with correlation coefficients between r=0.41 and 0.88; calculations of transition state free energy barriers correlate with rates measured for 37 single domain proteins (r=0.69). The model provides insight into the contribution of alternative-folding pathways, the validity of quasi-equilibrium treatments of the folding landscape, and the magnitude of the Arrhenius prefactor for protein folding. Finally, we discuss the limitations of simple native-state-based models, and as a more general test of such models, provide predictions of folding rates and mechanisms for a comprehensive set of over 400 small protein domains of known structure.     

Conformational strain in the hydrophobic core and its implications for protein folding and design

We have designed de novo 13 divergent spectrin SH3 core sequences to determine their folding properties. Kinetic analysis of the variants with stability similar to that of the wild type protein shows accelerated unfolding and refolding rates compatible with a preferential stabilization of the transition state. This is most likely caused by conformational strain in the native state, as deletion of a methyl group (Ile-->Val) leads to deceleration in unfolding and increased stability (up to 2 kcal x mol(-1)). Several of these Ile-->Val mutants have negative phi(-U) values, indicating that some noncanonical phi(-U) values might result from conformational strain. Thus, producing a stable protein does not necessarily mean that the design process has been entirely successful. Strained interactions could have been introduced, and a reduction in the buried volume could result in a large increase in stability and a reduction in unfolding rates.     

Exploring folding free energy landscapes using computational protein design

Recent advances in computational protein design have allowed exciting new insights into the sequence dependence of protein folding free energy landscapes. Whereas most previous studies have examined the sequence dependence of protein stability and folding kinetics by characterizing naturally occurring proteins and variants of these proteins that contain a small number of mutations, it is now possible to generate and characterize computationally designed proteins that differ significantly from naturally occurring proteins in sequence and/or structure. These computer-generated proteins provide insights into the determinants of protein structure, stability and folding, and make it possible to disentangle the properties of proteins that are the consequence of natural selection from those that reflect the fundamental physical chemistry of polypeptide chains.     

Efficient traversal of beta-sheet protein folding pathways using ensemble models

Molecular dynamics (MD) simulations can now predict ms-timescale folding processes of small proteins; however, this presently requires hundreds of thousands of CPU hours and is primarily applicable to short peptides with few long-range interactions. Larger and slower-folding proteins, such as many with extended β-sheet structure, would require orders of magnitude more time and computing resources. Furthermore, when the objective is to determine only which folding events are necessary and limiting, atomistic detail MD simulations can prove unnecessary. Here, we introduce the program tFolder as an efficient method for modelling the folding process of large β-sheet proteins using sequence data alone. To do so, we extend existing ensemble β-sheet prediction techniques, which permitted only a fixed anti-parallel β-barrel shape, with a method that predicts arbitrary β-strand/β-strand orientations and strand-order permutations. By accounting for all partial and final structural states, we can then model the transition from random coil to native state as a Markov process, using a master equation to simulate population dynamics of folding over time. Thus, all putative folding pathways can be energetically scored, including which transitions present the greatest barriers. Since correct folding pathway prediction is likely determined by the accuracy of contact prediction, we demonstrate the accuracy of tFolder to be comparable with state-of-the-art methods designed specifically for the contact prediction problem alone. We validate our method for dynamics prediction by applying it to the folding pathway of the well-studied Protein G. With relatively very little computation time, tFolder is able to reveal critical features of the folding pathways which were only previously observed through time-consuming MD simulations and experimental studies. Such a result greatly expands the number of proteins whose folding pathways can be studied, while the algorithmic integration of ensemble prediction with Markovian dynamics can be applied to many other problems.     

Rattling the cage: computational models of chaperonin-mediated protein folding

Chaperonins are known to maintain the stability of the proteome by facilitating the productive folding of numerous misfolded or aggregation-prone proteins and are thus essential for cell viability. Despite their established importance, the mechanism by which chaperonins facilitate protein folding remains unknown. Computer simulation techniques are now being employed to complement experimental ones in order to shed light on this mystery. Here we review previous computational models of chaperonin-mediated protein folding in the context of the two main hypotheses for chaperonin function: iterative annealing and landscape modulation. We then discuss new results pointing to the importance of solvent (a previously neglected factor) in chaperonin activity. We conclude with our views on the future role of simulation in studying chaperonin activity as well as protein folding in other biologically relevant confined contexts.