A PATH PLANNING-BASED STUDY OF PROTEIN FOLDING WITH A CASE STUDY OF HAIRPIN FORMATION IN PROTEIN <font>G</font> AND <font>L</font>

Song, Guang; Thomas, Shawna; Dill, Ken A.; Scholtz, J. Martin; Amato, Nancy M.

doi:10.1142/9789812776303_0023

Cited by 21 publications

(45 citation statements)

References 7 publications

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“…previously reported by Song et al [26]. It should also be noted that although we compute other configurations of the sequence that are energetically favorable (faded states), they are not predicted to form because they are unreachable from the unfolded state.…”

Section: Predicting the Folding Pathways Of The B1 Domain Of Protein Gmentioning

confidence: 59%

Efficient Traversal of Beta-Sheet Protein Folding Pathways Using Ensemble Models

Shenker

O'Donnell²,

Devadas³

et al. 2011

Lecture Notes in Computer Science

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Abstract. Molecular Dynamics (MD) simulations can now predict mstimescale 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 slowerfolding 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.Corresponding authors.

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Section: Predicting the Folding Pathways Of The B1 Domain Of Protein Gmentioning

confidence: 59%

Efficient Traversal of Beta-Sheet Protein Folding Pathways Using Ensemble Models

Shenker

O'Donnell²,

Devadas³

et al. 2011

Lecture Notes in Computer Science

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show abstract

“…We obtained promising results for several small proteins (∼60 amino acids) and validated our pathways by comparing the secondary structure formation order with known experimental results [28] using timed contact analysis. We also demonstrated that this technique is sensitive enough to identify the different folding behaviors of structurally similar Proteins G and L [34].…”

Section: Protein Folding Using Prmsmentioning

confidence: 83%

“…For example, if the roadmap maps the potential energy landscape well, then the percentage of pathways in the roadmap that contain a particular formation order should reflect the probability of that order occurring. Table 2 shows secondary structure formation order results for Proteins G and L [34]. Our results were able to identify the different folding behaviors of the two structurally similar proteins.…”

Section: Protein Folding Using Prmsmentioning

confidence: 91%

“…In previous work, we successfully applied the PRM framework to study protein folding pathways [4,3,34]. The protein is modeled as an articulated linkage.…”

Section: Protein Folding Using Prmsmentioning

confidence: 99%

“…In previous work [4,3,34], we developed a new computational technique for studying protein folding. It is based on a class of motion planning techniques, called probabilistic roadmap methods (PRMs) [21], that have proven effective on a wide range of applications from robotics, to computer animation, to molecules.…”

Section: Introductionmentioning

confidence: 99%

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Parallel protein folding with STAPL

Thomas

Amato

18th International Parallel and Distributed Processing Symposium, 2004. Proceedings.

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Control of Lipase Enantioselectivity by Engineering the Substrate Binding Site and Access Channel

et al. 2009

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Lipase from Burkholderia cepacia (BCL) has proven to be a very useful biocatalyst for the resolution of 2-substituted racemic acid derivatives, which are important chiral building blocks. Our previous work showed that enantioselectivity of the wild-type BCL could be improved by chemical engineering of the substrate's molecular structure. From this earlier study, three amino acids (L17, V266 and L287) were proposed as targets for mutagenesis aimed at tailoring enzyme enantioselectivity. In the present work, a small library of 57 BCL single mutants targeted on these three residues was constructed and screened for enantioselectivity towards (R,S)-2-chloro ethyl 2-bromophenylacetate. This led to the fast isolation of three single mutants with a remarkable tenfold enhanced or reversed enantioselectivity. Analysis of substrate docking and access trajectories in the active site was then performed. From this analysis, the construction of 13 double mutants was proposed. Among them, an outstanding improved mutant of BCL was isolated that showed an E value of 178 and a 15-fold enhanced specific activity compared to the parental enzyme; thus, this study demonstrates the efficiency of the semirational engineering strategy.

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A PATH PLANNING-BASED STUDY OF PROTEIN FOLDING WITH A CASE STUDY OF HAIRPIN FORMATION IN PROTEIN G AND L

Cited by 21 publications

References 7 publications

Efficient Traversal of Beta-Sheet Protein Folding Pathways Using Ensemble Models

Efficient Traversal of Beta-Sheet Protein Folding Pathways Using Ensemble Models

Parallel protein folding with STAPL

Control of Lipase Enantioselectivity by Engineering the Substrate Binding Site and Access Channel

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