Shaping up the protein folding funnel by local interaction: Lesson from a structure prediction study George Chikenji*, Yoshimi Fujitsuka, and Shoji Takada* PNAS | February 28, 2006 | vol. 103 | no. 9 | Journal club 3/20/2006 by Peng-Liang Wang

Introduction How much do we understand protein folding? Do a test with de novo structure prediction Most successful structure prediction method -- Fragment Assembly (FA) The author’s group participated in the CASP6 and developed structure prediction software, SIMFOLD CASPs (Critical Assessments of Structure Prediction) Questions: –Why FA method is so successful? –What we can learn about protein folding from the success of FA method? –To what extent does the modest local bias influence tertiary structure generated? –How is the FA method related to protein folding theory?

Experimental design Experiment 1 –Comparison of prediction (FA method) (1) wild-type proteins (local + non-local interaction) (2) Chimera (local + altered non-local interaction) Experiment 2 –Comparison of prediction (HP lattice model) (1) without constraint (2) with local constraints

The first experiment -- Comparison of prediction with FA method Protocol of FA method (two stages) First, collect structural candidates for every short segment of the target sequence, retrieving them from the structural database. (20 candidates/8-residue segment) Second, to assemble/fold these fragments for constructing tertiary structures that have low energies. Clustering analysis followed

FA method has better performance than that of MD, Why? Fig. 1. Comparison of structures generated by MD (a) and FA (b) with the same energy function and corresponding schematic energy landscapes. (a) MD simulations generated four misfold structures, suggesting a highly rugged energy landscape. (b) The FA method generated the native fold with high probability, suggesting a funnel-like landscape. Structures without red X were obtained by the corresponding simulation, whereas those with red X were not observed. The cartoons of protein structures were generated by MOLSCRIPT (59). Molecular Dynamics Fragment Assemble Observation 1

The prediction of the FA methods is robust against difference in scoring functions Fig. 2. Examples of structure predictions from the new fold category in CASP6. (a) The native structure of target T0201 (PDB ID code 1s12). (b) Model1s12 1 from group Rokko. (c) Model 6 from group Baker_Robetta. (d) Model 1 from group Jones_UCL. Observation 2:

Design of chimera experiment Protein G ( 56 residues  +  protein ) as an example 1 Prepared fragment candidates (8-residue) as wild-type (local interaction) 2. convert the sequence into ployValine sequence (56-Val) and assemble the segments (non-local interaction) Note: (1) local structure preference is for Protein G (2) non-local interaction is for ploy-Val. (3) Choice of Val is not essential, also tried Leu and Ile.

Results of control and chimera experiments Fig. 3. Results of control and chimera experiments of protein G (a–e), 434 C1 repressor (f–j), and Hex1 (k–o). (a) rmsd–energy plots of protein G for [WT/WT]. (b) rmsd–energy plots for [polyVal/WT]. (c) The native structure of protein G (PDB ID code 1pgb). (d) The structure of1pgb the largest cluster center of protein G for [WT/WT]. (e) The structure of the largest cluster center for [polyVal/WT]. (f) rmsd–energy plots of 434 C1 repressor for [WT/WT]. (g) rmsd–energy plots for [polyVal/WT]. (h) The native structure of 434 C1 repressor (PDB ID code 1r69). (i) The structure of the largest cluster center of 434 C1 repressor for1r69 [WT/WT]. (j) The structure of the largest cluster center for [polyVal/WT]. (k) rmsd–energy plots of Hex1 for [WT/WT]. (l) rmsd–energy plots for [polyVal/WT]. (m) The native structure of Hex1 (PDB ID code 1khi). (n) The structure of the second1khi largest cluster center of Hex1 for [WT/WT]. (o) The structure of the largest cluster center for [polyVal/WT].  +  protein Protein G (PDB:1pgb)  protein 434 C1 repressor (PDB:1r69)  protein Hex1 (PDB:1khi)

Conclusion from chimera experiments Given the fragment structure ensemble that reflects local structure preferences of the target sequence, compact structures that are fully compatible with these fragments are few, one of which is the native fold (Limited to small protein)

Nature of short segments taken from protein First, a class of peptides excised from proteins can fold to the native structure by themselves, independently from other parts of the proteins Another class of short sequences, often called chameleon sequences, is known to fold to entirely different conformations when they are embedded in different sequence contexts, demonstrating possible multiplicity of local structure bias Some other peptides, once cleaved, do not possess any well defined structure

The second experiment 2D-Lattice HP model The assumption: the native conformation is the one with lowest energy Protein sequence is represented by 2 types of residues, H (hydrophobic) and P (polar) e.g. HHHPHHPHHHHPHHP Protein conformation – self-avoid chain on a 2D square lattice Energy E= -  h h – number of hydrophobic contacts With this simplicity, it is possible to enumerate completely the possible conformations for a short sequence of protein

Example of HP lattice model (with/without local constraint) Fig. 4. The HP model studied here. (a) Native structure of the HP model. (b) Example of a maximally compact structure of the HP model. (c) All four-residue segments of the HP sequence and all possible structures. Structures surrounded by green rectangles are prohibited by the local structure constraint we impose here. (d) All structures of the first excited states (E = –8) of the HP model. Structures are classified by the number of native contacts Q. Only two gray shaded structures are not prohibited by imposing the local conformational constraints. Local structure constraint: Eliminate impossible structure The sequence (16-residues) HHHPHHPHHHHPHHP

Comparison of energy landscape of HP model (with/without local structure constraints) Fig. 5. Schematic energy landscapes of the pure HP model (a) and the HP model with local structure constraints (b).

The effect of the local structure constraint on the conformational space of lattice protein models Fig. 6. The effect of the local structure constraint on the conformational space of lattice protein models. (a) The number of conformations (in logarithmic scale) as a function of total number of contacts C. The white bar indicates log (C) of the pure HP model. The dotted bar indicates log (C) of the HP model with local constraints. (b) Temperature dependence of the number of nonnative contacts. Dotted, solid, and broken lines correspond to the pure HP model, the locally constrained HP model, and G model, respectively.

Discussion (1/3) What Makes the Protein Energy Landscape Funnel-Like? Go-like model Basic assumption: only native contacts are favorable Perfect funnel transition state ensemble of folding pathways can be described by Go-like model  Energy landscape of protein is funnel-like

Discussion (2/3) Implication to Computational Protein Design The goal of protein design is to seek sequence fold into target structure Folding theory Optimize energy gap E = E target – E denatureEnsemble –target energy is optimized in protein design From this work, –Destabilize alternative conformation may not be necessary

Discussion (3/3) Denatured States of Proteins Protein folding –Local structural preference –Compaction force Characteristics of denatured states of protein –Example: Under highly denatured condition,  -hairpin still posses partially native-like order –Explanation May be explained by local structure preference

Conclusions Principles of protein folding (i) There are only limited numbers of compact conformations that are fully compatible with local structural preferences, and the native fold is one of them (ii) local structure preferences strongly shape up the protein folding funnel.