Modeling large regions in proteins: Applications to loops, termini, and folding

Adhikari, Aashish N.; Peng, Jian; Wilde, Michael; Xu, Jinbo; Freed, Karl F.; Sosnick, Tobin R.

doi:10.1002/pro.767

Cited by 18 publications

(16 citation statements)

References 45 publications

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“…A hybrid version of TerItFix utilizing sequence but not structural homology (3) has been validated in CASP8 and 9 and ranks as one of the best groups in the CASP9 refinement category that involves improving template-based models to solve the crystallographic phase problem (10). Nonetheless, these methods still primarily focus on one aspect, either structure prediction or the folding mechanism.…”

Section: Discussionmentioning

confidence: 99%

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De novo prediction of protein folding pathways and structure using the principle of sequential stabilization

Adhikari

Freed

Sosnick

2012

Proc. Natl. Acad. Sci. U.S.A.

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Motivated by the relationship between the folding mechanism and the native structure, we develop a unified approach for predicting folding pathways and tertiary structure using only the primary sequence as input. Simulations begin from a realistic unfolded state devoid of secondary structure and use a chain representation lacking explicit side chains, rendering the simulations many orders of magnitude faster than molecular dynamics simulations. The multiple round nature of the algorithm mimics the authentic folding process and tests the effectiveness of sequential stabilization (SS) as a search strategy wherein 2°structural elements add onto existing structures in a process of progressive learning and stabilization of structure found in prior rounds of folding. Because no a priori knowledge is used, we can identify kinetically significant nonnative interactions and intermediates, sometimes generated by only two mutations, while the evolution of contact matrices is often consistent with experiments. Moreover, structure prediction improves substantially by incorporating information from prior rounds. The success of our simple, homology-free approach affirms the validity of our description of the primary determinants of folding pathways and structure, and the effectiveness of SS as a search strategy.TerItFix | foldons | kinetic traps | Monte Carlo simulation D espite numerous advances since the original sequenceto-structure folding paradigm was proposed over 50 years ago (1), we still lack a general framework that enables simultaneous prediction of the folding mechanism and structure using only the amino acid (aa) sequence [notwithstanding recent successes of all-atom simulations to fold small, fast-folding proteins (2)]. An obvious obstacle is the astronomical number of conformations available to a polypeptide. Proteins overcome this obstacle by sampling a limited set of conformations, guided by the folding process itself. However, most successful structure prediction methods do not consider the folding mechanism when sampling conformations. Conversely, many methods for predicting folding mechanism rely on knowledge of the final structure (e.g., Gō models).Another obstacle emerges because many non-native and nearnative conformations often differ by only a few RT, which is at or beyond the ability of current energy functions to reliably distinguish. A related difficulty arises because the native state is the global free energy minimum even if three competing propertieslocal backbone torsional angle preferences, hydrogen bonded 2°structure, and 3°packing-are not individually optimized. For example, 3°context can overcome local biases in determining the final 2°structure (3). Hence, a successful framework should couple 3°context to 2°structure formation, rather than relying on a strict hierarchical approach.

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Section: Discussionmentioning

confidence: 99%

“…S2). These angles are used for pivot moves, where only a single residue's ϕ, ψ angles are changed, as well as for double crankshaft local moves, where two consecutive peptide groups are rotated (9,10) (Fig. S3).…”

Section: Modelmentioning

confidence: 99%

De novo prediction of protein folding pathways and structure using the principle of sequential stabilization

Adhikari

Freed

Sosnick

2012

Proc. Natl. Acad. Sci. U.S.A.

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“…We conducted folding simulations using TerItFix (17,23,25,26,29), our homology-free, C β -level folding program that uses realistic sampling of the Ramachandran dihedral angles and authentic backbone H-bonding. Its Monte Carlo (MC) search strategy uses the principle of sequential stabilization to iteratively promote the formation of tertiary contacts and H-bonds across multiple rounds of folding.…”

Section: Significancementioning

confidence: 99%

Even with nonnative interactions, the updated folding transition states of the homologs Proteins G & L are extensive and similar

Baxa

Adhikari

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Experimental and computational folding studies of Proteins L & G and NuG2 typically find that sequence differences determine which of the two hairpins is formed in the transition state ensemble (TSE). However, our recent work on Protein L finds that its TSE contains both hairpins, compelling a reassessment of the influence of sequence on the folding behavior of the other two homologs. We characterize the TSEs for Protein G and NuG2b, a triple mutant of NuG2, using ψ analysis, a method for identifying contacts in the TSE. All three homologs are found to share a common and near-native TSE topology with interactions between all four strands. However, the helical content varies in the TSE, being largely absent in Proteins G & L but partially present in NuG2b. The variability likely arises from competing propensities for the formation of nonnative β turns in the naturally occurring proteins, as observed in our TerItFix folding algorithm. All-atom folding simulations of NuG2b recapitulate the observed TSEs with four strands for 5 of 27 transition paths [Lindorff-Larsen K, Piana S, Dror RO, Shaw DE (2011) Science 334(6055):517–520]. Our data support the view that homologous proteins have similar folding mechanisms, even when nonnative interactions are present in the transition state. These findings emphasize the ongoing challenge of accurately characterizing and predicting TSEs, even for relatively simple proteins.

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“…As secondary structure information is deduced from prior rounds, angle selection is correspondingly biased. Three energy functions capture the chemical properties of the different amino acids 16, 17, 19, 20 . The first function includes a pairwise additive, distance, orientation, and secondary structure dependent statistical potential designed to promote the formation of chain topologies with hydrophobic cores.…”

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confidence: 99%

Simplified Protein Models: Predicting Folding Pathways and Structure Using Amino Acid Sequences

2013

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We demonstrate the ability of simultaneously determining a protein’s folding pathway and structure using a properly formulated model without prior knowledge of the native structure. Our model employs a natural coordinate system for describing proteins and a search strategy inspired by the observation that real proteins fold in a sequential fashion by incrementally stabilizing native-like substructures or "foldons". Comparable folding pathways and structures are obtained for the twelve proteins recently studied using atomistic molecular dynamics simulations [K. Lindorff-Larsen, S. Piana, R.O. Dror, D. E. Shaw, Science 334, 517 (2011)], with our calculations running several orders of magnitude faster. We find that native-like propensities in the unfolded state do not necessarily determine the order of structure formation, a departure from a major conclusion of the MD study. Instead, our results support a more expansive view wherein intrinsic local structural propensities may be enhanced or overridden in the folding process by environmental context. The success of our search strategy validates it as an expedient mechanism for folding both in silico and in vivo.

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Modeling large regions in proteins: Applications to loops, termini, and folding

Cited by 18 publications

References 45 publications

De novo prediction of protein folding pathways and structure using the principle of sequential stabilization

De novo prediction of protein folding pathways and structure using the principle of sequential stabilization

Even with nonnative interactions, the updated folding transition states of the homologs Proteins G & L are extensive and similar

Simplified Protein Models: Predicting Folding Pathways and Structure Using Amino Acid Sequences

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