Binding Free Energy Landscape of Domain-Peptide Interactions

Peptide recognition domains (PRDs) are ubiquitous protein domains which mediate large numbers of protein interactions in the cell. How these PRDs are able to recognize peptide sequences in a rapid and specific manner is incompletely understood. We explore the peptide binding process of PDZ domains, a large PRD family, from an equilibrium perspective using an all-atom Monte Carlo (MC) approach. Our focus is two different PDZ domains representing two major PDZ classes, I and II. For both domains, a binding free energy surface with a strong bias toward the native bound state is found. Moreover, both domains exhibit a binding process in which the peptides are mostly either bound at the PDZ binding pocket or else interact little with the domain surface. Consistent with this, various binding observables show a temperature dependence well described by a simple two-state model. We also find important differences in the details between the two domains. While both domains exhibit well-defined binding free energy barriers, the class I barrier is significantly weaker than the one for class II. To probe this issue further, we apply our method to a PDZ domain with dual specificity for class I and II peptides, and find an analogous difference in their binding free energy barriers. Lastly, we perform a large number of fixed-temperature MC kinetics trajectories under binding conditions. These trajectories reveal significantly slower binding dynamics for the class II domain relative to class I. Our combined results are consistent with a binding mechanism in which the peptide C terminal residue binds in an initial, rate-limiting step

Monte Carlo approach to binding and aggregation of disordered peptides

In this thesis, theoretical models are applied to study some aspects of intrinsically disordered proteins, i.e. proteins that partly or entirely lack a stable native structure. In particular, focus is on peptide binding and aggregation. Knowledge about these mechanisms is important for understanding how proteins interact with each other and how mutations can affect the function and sometimes give rise to disease. The methods used are based on all-atom Monte Carlo simulations with implicit solvent. In Papers I and II a model is developed for analysis of peptide binding. It is tested on PDZ-domains, structural units that are often found in signaling proteins, which bind to the C-termini of other proteins. They especially recognize specific patterns of short amino-acid sequences. Paper III likewise deals with peptide binding, but the system of interest is the S100B protein, which in the same manner is able to bind various sequences. In this case, the two peptides studied are disordered when they are free, but assume helical structures as they become part of the complex. Since the peptides differ in their final bound configurations, the main question is whether there still are any similarities between their binding processes. Papers IV and V are devoted to questions related to peptide aggregation. They describe the analysis of Abeta42, a peptide that forms miscellaneous aggregates, and which is believed to be involved in the development of Alzheimer's disease. Monomers and dimers of the wild type, as well as three mutants, are examined in order to observe the initial interactions and features of peptide aggregation, and comparisons of the four variants are made aiming to identify structural propensities that could explain their different aggregation properties

All-Atom Monte Carlo Approach to Protein-Peptide Binding.

We develop a procedure for exploring the free energy landscape of protein-peptide binding at atomic detail and apply it to PDZ domain-peptide interactions. The procedure involves soft constraints on receptor proteins providing limited chain flexibility, including backbone motions. Peptide chains are left fully flexible and kept in spatial proximity of the protein through periodic boundary conditions. By extensive Monte Carlo simulations, full representative conformational ensembles at temperatures where bound and unbound states coexist are obtained. To make this approach computationally feasible, we develop an effective all-atom energy function centering on hydrophobicity, hydrogen bonding, and electrostatic interactions. Our initial focus is a set of 11 PDZ domain-peptide pairs with experimentally determined complex structures. Minimum-energy conformations are found to be highly similar to the respective native structures in eight of the cases (all-atom peptide RMSDs <6 A). Having achieved that, we turn to a more complete characterization of the bound peptide state through a clustering scheme applied on the full ensembles of peptide structures. We find a significant diversity among bound peptide conformations for several PDZ domains, in particular involving the N terminal side of the peptide chains. Our computational model is then tested further on a set of nine PDZ domain-peptide pairs where the peptides are not originally present in the experimentally determined structures. We find a similar success rate in terms of the nativeness of minimum-energy conformations. Finally, we investigate the ability of our approach to capture variations in binding affinities for different peptide sequences. This is done in particular for a set of related sequences binding to the third PDZ domain of PSD-95 with encouraging results

Monte Carlo Study of the Formation and Conformational Properties of Dimers of Aβ42 Variants.

Small soluble oligomers, as well as dimers in particular, of the amyloid β-peptide (Aβ) are believed to play an important pathological role in Alzheimer's disease. Here, we investigate the spontaneous dimerization of Aβ42, with 42 residues, by implicit solvent all-atom Monte Carlo simulations, for the wild-type peptide and the mutants F20E, E22G and E22G/I31E. The observed dimers of these variants share many overall conformational characteristics but differ in several aspects at a detailed level. In all four cases, the most common type of secondary structure is intramolecular antiparallel β-sheets. Parallel, in-register β-sheet structure, as in models for Aβ fibrils, is rare. The primary force driving the formation of dimers is hydrophobic attraction. The conformational differences that we do see involve turns centered in the 20-30 region. The probability of finding turns centered in the 25-30 region, where there is a loop in Aβ fibrils, is found to increase upon dimerization and to correlate with experimentally measured rates of fibril formation for the different Aβ42 variants. Our findings hint at reorganization of this part of the molecule as a potentially critical step in Aβ aggregation

Comparing the folding free-energy landscapes of Abeta42 variants with different aggregation properties.

The properties of the amyloid-beta peptide that lead to aggregation associated with Alzheimer's disease are not fully understood. This study aims at identifying conformational differences among four variants of full-length Abeta42 that are known to display very different aggregation properties. By extensive all-atom Monte Carlo simulations, we find that a variety of beta-sheet structures with distinct turns are readily accessible for full-length Abeta42. In the simulations, wild type (WT) Abeta42 preferentially populates two major classes of conformations, either extended with high beta-sheet content or more compact with lower beta-sheet content. The three mutations studied alter the balance between these classes. Strong mutational effects are observed in a region centered at residues 23-26, where WT Abeta42 tends to form a turn. The aggregation-accelerating E22G mutation associated with early onset of Alzheimer's disease makes this turn region conformationally more diverse, whereas the aggregation-decelerating F20E mutation has the reverse effect, and the E22G/I31E mutation reduces the turn population. Comparing results for the four Abeta42 variants, we identify specific conformational properties of residues 23-26 that might play a key role in aggregation. Proteins 2010. (c) 2010 Wiley-Liss, Inc

Binding of two intrinsically disordered peptides to a multi-specific protein: a combined Monte Carlo and molecular dynamics study

The unique ability of intrinsically disordered proteins (IDPs) to fold upon binding to partner molecules makes them functionally well-suited for cellular communication networks. For example, the folding-binding of different IDP sequences onto the same surface of an ordered protein provides a mechanism for signaling in a many-to-one manner. Here, we study the molecular details of this signaling mechanism by applying both Molecular Dynamics and Monte Carlo methods to S100B, a calcium-modulated homodimeric protein, and two of its IDP targets, p53 and TRTK-12. Despite adopting somewhat different conformations in complex with S100B and showing no apparent sequence similarity, the two IDP targets associate in virtually the same manner. As free chains, both target sequences remain flexible and sample their respective bound, natively alpha-helical states to a small extent. Association occurs through an intermediate state in the periphery of the S100B binding pocket, stabilized by nonnative interactions which are either hydrophobic or electrostatic in nature. Our results highlight the importance of overall physical properties of IDP segments, such as net charge or presence of strongly hydrophobic amino acids, for molecular recognition via coupled folding-binding

Monte Carlo Studies of Protein Aggregation

The disease-linked amyloid β (Aβ) and α-synuclein (αS) proteins are both fibril-forming and natively unfolded in free monomeric form. Here, we discuss two recent studies, where we used extensive implicit solvent all-atom Monte Carlo (MC) simulations to elucidate the conformational ensembles sampled by these proteins. For αS, we somewhat unexpectedly observed two distinct phases, separated by a clear free-energy barrier. The presence of the barrier makes αS, with 140 residues, a challenge to simulate. By using a two-step simulation procedure based on flat-histogram techniques, it was possible to alleviate this problem. The barrier may in part explain why fibril formation is much slower for αS than it is for Aβ

Involvement in binding for different amino acid positions of the S100B binding pocket.

<p>For each S100B position, the number of contacted amino acids on the peptide is counted. Shown are the average numbers obtained separately for the bound (BS) and intermediate (IS) states. Statistical errors are estimated using the jackknife method <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002682#pcbi.1002682-Miller1" target="_blank">[58]</a>.</p