Small Libraries of Protein Fragments Model Native Protein Structures Accurately

Kolodny, Rachel; Koehl, Patrice; Guibas, Leonidas J.; Levitt, Michael

doi:10.1016/s0022-2836(02)00942-7

Cited by 177 publications

(181 citation statements)

References 19 publications

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“…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%

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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Section: Data Setmentioning

confidence: 99%

Fragment‐HMM: A new approach to protein structure prediction

et al. 2008

Protein Science

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“…The structural alphabet represents advantageous local structures and has been used to 1) compare/analyze 3D structures [14]- [16], 2) predict protein 3D structures from amino acid sequences [6], [9], 3) reconstruct protein backbones [11], and 4) model loops [17]. In addition, given that local structures are generally more evolutionary conserved than amino acid sequences, a series of research has been developed to explore protein structures [18].…”

Section: Introductionmentioning

confidence: 99%

A Complex Network Approach for the Analysis of Protein Units Similarity Using Structural Alphabet

Tung¹,

Nacher²

2013

IJBBB

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Abstract-In this paper we present a network approach based on the recent developed 3D-BLAST method of rapid protein structure search. We defined new local segments that represent structural feature of proteins named units of structural alphabet (USA). Each USA is composed of two protein secondary structures, and one loop located between these two secondary structures. We performed all-against-all structural comparison of USA and recognized the USA-based similarity network. The analytical result shows that the network with a power degree distribution is called scale free. These results not only suggest the existence of organizing principles in the local protein structure but also allow us to identify potential key fragments that could be useful for future new drug development and design.Index Terms-Local structure similarity network, network biology, protein modularity. I. INTRODUCTIONIn the past few decades, genomics (DNA sequences), structural genomics (protein structures), and proteomics (protein expression and interactions) have rapidly enhanced knowledge on biological functions and systems. With structural models developed using genome-wide investigative strategies [1]-[3], the number of protein structures in the Protein Data Bank (PDB) has rapidly increased. By Dec. 25, 2012, there were already more than 87,090 known protein structures [4]. The increasing number of known protein structures with unknown/unassigned functions emphasizes the demand for effective bioinformatics methods for annotating the structural homology or evolutionary family of proteins and inferring their cellular functions.The comparison and analysis of the relationship between new protein structures with unclear functions and well-known structures seeks to bridge the protein structure-function research gap. Given a query protein structure, we may search through the database and report similar protein structures. However, unlike one-dimensional sequence comparison, structural alignment for determining similarities is much more complex and computationally expensive. Some methods can be used for efficient pair-wise Manuscript received January 15, 2013; revised March 15, 2013. This work was supported in part by the National Science Council (NSC), Taiwan, under Contract of NSC 101-2311-B-216-001 and 101-2221-E-216-041.Chi-Hua Tung is with the Department of Bioinformatics, Chung-Hua University, Hsinchu 300, Taiwan (e-mail: chihua.tung@chu.edu.tw).Jose C. Nacher is with Department of Information Science, Faculty of Science, Toho University, Miyama 2-2-1, Funabashi, Chiba 274-8510, Japan (e-mail: nacher@is.sci.toho-u.ac.jp). structural comparison [5], but these methods entail an exhaustive search to compare the query structure against all protein structures in the database.To bridge the current protein structure-function research gap and address anterior questions, many approaches have been proposed for encoding 3D local structural fragments based on Cartesian coordinates into a one-dimensional representation using several letters called the str...

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“…The local sequence-structure correlation of residues capture the propensity of small sequences adopting specific local spatial structures. The existence of such propensity has been well recognized and it has been used in protein structure prediction [16,17], in remote homology detection [18], and in discriminating native structures from decoys [19][20][21][22]. The non-overlapping nature of these two types of descriptors indicates that they contain different information.…”

Section: Introductionmentioning

confidence: 99%

Empirical potential function for simplified protein models: Combining contact and local sequence–structure descriptors

2006

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An effective potential function is critical for protein structure prediction and folding simulation. Simplified protein models such as those requiring only C α or backbone atoms are attractive because they enable efficient search of the conformational space. We show residue specific reduced discrete state models can represent the backbone conformations of proteins with small RMSD values. However, no potential functions exist that are designed for such simplified protein models. In this study, we develop optimal potential functions by combining contact interaction descriptors and local sequence-structure descriptors. The form of the potential function is a weighted linear sum of all descriptors, and the optimal weight coefficients are obtained through optimization using both native and decoy structures. The performance of the potential function in test of discriminating native protein structures from decoys is evaluated using several benchmark decoy sets. Our potential function requiring only backbone atoms or C α atoms have comparable or better performance than several residue-based potential functions that require additional coordinates of side chain centers or coordinates of all side chain atoms. By reducing the residue alphabets down to size 5 for local structure-sequence relationship, the performance of the potential function can be further improved. Our results also suggest that local sequence-structure correlation may play important role in reducing the entropic cost of protein folding.

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Small Libraries of Protein Fragments Model Native Protein Structures Accurately

Cited by 177 publications

References 19 publications

Fragment‐HMM: A new approach to protein structure prediction

Fragment‐HMM: A new approach to protein structure prediction

A Complex Network Approach for the Analysis of Protein Units Similarity Using Structural Alphabet

Empirical potential function for simplified protein models: Combining contact and local sequence–structure descriptors

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