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Figure 5 Comparison of energies found by REMC and...

Figure 2 Positional weighting profiles associated...

Figure 3. Projection of sampled states (in referen...

Figure 2 Equilibration and calibration of REMS si...

Figure 3 REMS calculation of approximately canoni...

Figure 3 Schedule of visits.

Figure 3 Summary of trial visits.

Figure 1 Study visit flowchart.

Figure 3 Visits following enrollment

Figure 4. Free energy landscape for the Trp-cage c...

Figure 6: Example of distributions of energies visited by different replicas in REMC and PHAT. Distributions of energies visited by different replicas in a representative run of REMC (a) and of PHAT (b) for the homopolymer of length 64. The time cut-off used was 28 CPU min on our 2.4 GHz reference machine.

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Other Images from "An adaptive bin framework search method for a beta-sheet protein homopolymer model":


Figure 4 Examples of the lowest energy conformati...

Figure 7 Example of distributions of energies vis...

Figure 6 Example of distributions of energies vis...

Figure 5 Distribution of run-times for all algori...

Figure 1 High-level outline of the main body of t...

Figure 3 The lowest energy conformation found by ...

Figure 2 Relationship between the bin framework a...

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Abstract

BackgroundThe problem of protein structure prediction consists of predicting the functional or native structure of a protein given its linear sequence of amino acids. This problem has played a prominent role in the fields of biomolecular physics and algorithm design for over 50 years. Additionally, its importance increases continually as a result of an exponential growth over time in the number of known protein sequences in contrast to a linear increase in the number of determined structures. Our work focuses on the problem of searching an exponentially large space of possible conformations as efficiently as possible, with the goal of finding a global optimum with respect to a given energy function. This problem plays an important role in the analysis of systems with complex search landscapes, and particularly in the context of ab initio protein structure prediction.ResultsIn this work, we introduce a novel approach for solving this conformation search problem based on the use of a bin framework for adaptively storing and retrieving promising locally optimal solutions. Our approach provides a rich and general framework within which a broad range of adaptive or reactive search strategies can be realized. Here, we introduce adaptive mechanisms for choosing which conformations should be stored, based on the set of conformations already stored in memory, and for biasing choices when retrieving conformations from memory in order to overcome search stagnation.ConclusionWe show that our bin framework combined with a widely used optimization method, Monte Carlo search, achieves significantly better performance than state-of-the-art generalized ensemble methods for a well-known protein-like homopolymer model on the face-centered cubic lattice.


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