Protein folding mechanisms and energy landscape of src SH3 domain studied by a structure prediction toolbox

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The folding energy landscape of proteins has been suggested to be funnel-like with some degree of ruggedness on the slope. How complex the landscape, however, is still rather unclear. Many experiments for globular proteins suggested relative simplicity, whereas molecular simulations of shorter peptides implied more complexity. Here, by using complete conformational sampling of 2 globular proteins, protein G and src SH3 domain and 2 related random peptides, we investigated their energy landscapes, topological properties of folding networks, and folding dynamics. The projected energy surfaces of globular proteins were funneled in the vicinity of the native but also have other quite deep, accessible minima, whereas the randomized peptides have many local basins, including some leading to seriously misfolded forms. Dynamics in the denatured part of the network exhibited basin-hopping itinerancy among many conformations, whereas the protein reached relatively well-defined final stages that led to their native states. We also found that the folding network has the hierarchic nature characterized by the scale-free and the small-world properties.contact maps ͉ folding pathways ͉ multiple pathways ͉ principal coordinates P roteins fold on large-dimensional energy landscapes through myriads of conformations. One energy-landscape theory suggests that the global shape of the landscape is primarily funnel-like with some degree of ruggedness on the slope of the funnel (1, 2). How complex/rugged the energy landscape is and how diverse the folding-pathway ensemble is are still rather controversial. Experimentally, many small fast-folding proteins exhibit single-exponential behavior, suggesting simplicity (3). For such proteins, a perfect funnel model, Go model, has been used, as an extreme of simplicity, to model folding routes, often showing modestly good agreement with experiments (4). Conversely, there exist several clear evidences of complexity in folding. Under some conditions, proteins show strange and glassy kinetics, suggesting ruggedness of the landscape (5). Some ␤-sheet proteins, such as ␤-lactoglobulin, form nonnative ␣-helices at early stages of folding (6, 7).The computational approach has been the most direct to elucidate the complexity of folding energy landscapes. Methods developed in other areas, such as atomic clusters, have been applied to peptides and proteins, illustrating the multiple minima on the landscape (8-11). Recently, with background (10, 11), Krivov and Karplus developed the transition disconnectivity graph to visualize quantitatively the free-energy landscape and applied it for peptides finding a highly rugged non-funnel-like landscape with competing minima (12, 13). Caflisch and coworkers (14, 15) constructed a folding network for a designed peptide and uncovered a highly heterogeneous denatured ensemble. They both used network analyses without the data reduction to lower dimension and warned that the projection to low dimension, as is often done in conventional folding studies, can hide the co...

supporting

confidence: 81%

Section: Multicanonical-ensemble Fragment Assembly Simulationmentioning

confidence: 99%

Section: Multicanonical-ensemble Fragment Assembly Simulationmentioning

confidence: 99%

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Folding energy landscape and network dynamics of small globular proteins

Hori

Chikenji

Berry

et al. 2009

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“…in experimental (13)(14)(15) as well as computational studies using simplified approaches (16,17). Our results are obtained using first-principle all-atom Monte Carlo simulations.…”

Section: Resultsmentioning

confidence: 99%

Simulation of Top7-CFr: A transient helix extension guides folding

Mohanty

Meinke

Zimmermann

et al. 2008

Protein structures often feature β-sheets in which adjacent β-strands have large sequence separation. How the folding process orchestrates the formation and correct arrangement of these strands is not comprehensively understood. Particularly challenging are proteins in which β-strands at the N and C termini are neighbors in a β-sheet. The N-terminal β-strand is synthesized early on, but it can not bind to the C terminus before the chain is fully synthesized. During this time, there is a danger that the β-strand at the N terminus interacts with nearby molecules, leading to potentially harmful aggregates of incompletely folded proteins. Simulations of the C-terminal fragment of Top7 show that this risk of misfolding and aggregation can be avoided by a "caching" mechanism that relies on the "chameleon" behavior of certain segments.protein folding | all-atom simulation | folding mechanism | chameleon segment | nonnative intermediates S tructure and function of proteins are determined by their amino acid sequence. How proteins find their functional native form is a long-standing question (1-3). Protein synthesis is directional from the N to the C terminus. In proteins with end-toend β-sheets, there is a danger that the N-terminal strand binds to nearby molecules or other parts of the chain, as the strand cannot bind to the C-terminal strand until the molecule is fully synthesized. Misfolding and aggregation may be the consequence. In our simulations, the N terminus of fragment Glu-2-Leu-50 of the 59-residue CFr (Protein Data Bank ID code 2GJH) (4) avoids the risk of misfolding by growing first into a non-native extension of an existing α-helix. Only after the other structural elements have formed and correctly assembled, does the N terminus unfold and attach to the C-terminal β-sheet as its last closing strand. We speculate that such a temporary caching of β-strands is a common mechanism that eases folding and hinders aggregation.The C-terminal fragment (CFr) (5) of the designed protein Top7 (6) forms a stable homodimer, whose secondary structure remains nearly unchanged up to 98 • C and high concentrations of denaturant (4). It is a model for small fast-folding proteins with complex topology and diverse secondary structure elements (see Fig. 1). Such proteins often have long-distance (in sequence) contacts between β-strands. Unlike helix contacts, these depend on the conformation of a large segment between the strands. It is unlikely that these contacts form before the intermediate segment has folded, as this would lead to a large entropic cost, or even interfere with the folding of the connecting segment. For slowfolding proteins, one can conjecture a "backtracking" mechanism (7) where folding succeeds only after breaking of prematurely formed β-contacts. In this study, we explore in silico the behavior of fast-folding proteins, as computational approaches (8, 9) can resolve details of folding that are beyond the reach of experiments. Results and DiscussionWe find that the CFr monomer folds to a native-like conform...

“…SIMFOLD employs a coarse-grained protein representation and has its own energy function derived from physicochemical considerations (16,17). Conformational sampling is performed by the FA method (18,19). In the recent CASP6, our group Rokko (as well as the server Rokky) used the SIMFOLD software as a primary prediction tool, demonstrating one of the top-level prediction performances in the new fold category (elite club membership by the assessor, B.K.…”

mentioning

confidence: 99%

Shaping up the protein folding funnel by local interaction: Lesson from a structure prediction study

Chikenji

Fujitsuka

Takada

2006

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Predicting protein tertiary structure by folding-like simulations is one of the most stringent tests of how much we understand the principle of protein folding. Currently, the most successful method for folding-based structure prediction is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson for the folding problem. To do so, using the FA method, we designed a structure prediction test of ''chimera proteins.'' In the chimera proteins, local structural preference is specific to the target sequences, whereas nonlocal interactions are only sequence-independent compaction forces. We find that these chimera proteins can find the native folds of the intact sequences with high probability indicating dominant roles of the local interactions. We further explore roles of local structural preference by exact calculation of the HP lattice model of proteins. From these results, we suggest principles of protein folding: For small proteins, compact structures that are fully compatible with local structural preference are few, one of which is the native fold. These local biases shape up the funnel-like energy landscape.computational protein design ͉ energy landscape ͉ fragment assembly ͉ Go model ͉ SIMFOLD N atural proteins have the algorithm of finding the global minimum of their free energy surface within a biologically relevant timescale (1). One of the most stringent tests of how much we understand the algorithm may be to predict protein tertiary structures by simulating processes that are analogous to folding, which is often called de novo structure prediction. Recently, significant progress has been made in de novo structure prediction, in which the most successful method is the fragment assembly (FA) method developed by Baker and coworkers (2) and others (3-6). The FA method shows considerable promise for new fold targets of recent Critical Assessments of Techniques for Protein Structure Prediction (CASPs), the community-wide blind tests of structure prediction (7-11). In the FA method, the protocol is separated into two stages: First, we collect structural candidates for every short segment of the target sequence, retrieving them from the structural database. The second stage is to assemble͞fold these fragments for constructing tertiary structures that have low energies.Simple questions arose as to why the FA method is so successful and what we can learn about protein folding from the success of the FA method. According to Baker and coworkers (12,13), the FA method is based on the experimental observation that local sequence of a protein biases but does not uniquely decide its local structure. To what extent does the modest local bias influence tertiary structures generated? How is the FA method related to recently developed protein folding theory (14, 15)? In this work, we address these questions.For this purpose, we need structure prediction software that uses the FA method. Here, we use the in-house-developed software, SIMFOLD. SIMFOLD employs a coarse-grained protei...