Sampling Realistic Protein Conformations Using Local Structural Bias

Hamelryck, Thomas; Kent, John T.; Krogh, Anders

doi:10.1371/journal.pcbi.0020131

Cited by 91 publications

(75 citation statements)

References 68 publications

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“…The success of these methods suggests the advantage of continuous torsion angle distributions over discrete structural motifs: Using the torsion angle distribution technique, it is possible to generate conformations with local structures not occurring in the structural fragment library. In addition, experimental results demonstrate that the derived local biases can help to generate native-like conformations and support the view that relatively few conformations are compatible with the local structural biases (Hamelryck et al 2006).…”

supporting

confidence: 59%

“…This model has advantages over existing works as follows: Our Fragment-HMM model combines the very successful fragment assembly method (Simons et al 1997) and the elegant FB5-HMM idea (Hamelryck et al 2006). Rather than using the fragments as building blocks, we use them to produce local bias information.…”

Section: The New Paradigmmentioning

confidence: 99%

“…We used the six proteins that were used in previous studies (Simons et al 1997;Kolodny et al 2002;Hamelryck et al 2006). They are displayed in Table 1: Protein A (code 1FC2), Homeodomain (code 1ENH), protein G (code 2GB1), Cro repressor (code 2CRO), protein L7/L12 (code 1CTF), and calbidin (code 4ICB).…”

Section: Data Setmentioning

confidence: 99%

“…As the third precursor to our work, Hamelryck et al (2006) proposed to apply FB5, a directional distribution, to parameterize the local structural bias. Using this tool, they investigated the local bias in (u, t) space rather than (f, c) space.…”

mentioning

confidence: 99%

“…Third, FB5-HMM is incapable of capturing the relationships among the residues of a local segment. As a consequence, though equipped with an elegant statistical model, FB5-HMM has a much lower prediction accuracy compared to ROSETTA (Hamelryck et al 2006;Zhao et al 2008). …”

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confidence: 99%

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Fragment‐HMM: A new approach to protein structure prediction

et al. 2008

Protein Science

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We designed a simple position-specific hidden Markov model to predict protein structure. Our new framework naturally repeats itself to converge to a final target, conglomerating fragment assembly, clustering, target selection, refinement, and consensus, all in one process. Our initial implementation of this theory converges to within 6 Å of the native structures for 100% of decoys on all six standard benchmark proteins used in ROSETTA (discussed by Simons and colleagues in a recent paper), which achieved only 14%-94% for the same data. The qualities of the best decoys and the final decoys our theory converges to are also notably better.Keywords: protein structure prediction; hidden Markov model; fragment assemblyWe wished to find a unified and the simplest model for protein structure prediction, one of the major open problems in science. We were not interested in trying PSI-BLAST for easy targets, threading by RAPTOR (Xu and Li 2003) for harder targets, fragment assembly by ROSETTA (Simons et al. 1997) for ab initio targets, or consensus for everything. We were also not interested in using different methods for different steps, such as Monte Carlo fragment assembly, clustering, selecting, and refinement. Nature does not do this. It does not fit with Occam's razor principle (Li and Vitanyi 1997;Baker 2000).Nature prefers simplicity. We wished to find one theory, one model, as simple as possible that goes from an input sequence to the final structure. This theory should embody homology modeling, threading, fragment assembly (all stages of it), loop modeling, refinement, side-chain packing, and consensus. The theory must be simple, robust, and effective.This work presents our initial efforts in building a theory toward this goal and our preliminary implementation of this theory, FALCON, together with clear-cut experimental results. Some ideas of our work come from three lines of research: fragment assembly, hidden Markov model sampling, and Ramachandran basins.The most successful approach for ab initio structure prediction is to use short structural fragments to model local interactions among the amino acids of a segment and utilize the nonlocal interactions to arrange these short structural fragments to form native-like structures (Simons et al. 1997). Despite the importance of nonlocal interactions in directing the search to discover the nativelike protein structures, the relationship between local structures and the interactions among amino acids within a local structure remain active issues of research. An accurate prediction of the local structural bias for a sequence segment is critically important to protein structure prediction. 4 These authors contributed equally to this work.

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supporting

confidence: 59%

Section: The New Paradigmmentioning

confidence: 99%

Section: Data Setmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Fragment‐HMM: A new approach to protein structure prediction

et al. 2008

Protein Science

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Directional Distributions

Mardia¹,

Ley²

2018

Wiley StatsRef: Statistics Reference Online

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Various practical situations give rise to observations that are directions, and this has led to the field of directional statistics. Especially in recent years, new applications of this area have emerged, such as in structural bioinformatics, machine learning and cosmology. Consequently, various new directional distributions have appeared in the literature including on the circle, sphere, cylinder, torus, among others. These distributions take into account the underlying manifold. In this article, we provide a thorough review of the most important directional distributions with their main properties. Wherever relevant, we also give some indication of the particular application area which has led to a specific distribution.

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