Article

Alignment of protein sequences by their profiles

Mission Bay Genentech Hall, University of California, San Francisco, San Francisco, CA 94143, USA.
Protein Science (Impact Factor: 2.85). 05/2004; 13(4):1071-87. DOI: 10.1110/ps.03379804
Source: PubMed

ABSTRACT The accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. A test set of 200 pairwise, structure-based alignments with sequence identities below 40% is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43% for the other methods, respectively. The new method is currently applied to large-scale comparative protein structure modeling of all known sequences.

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Available from: Marc Marti-Renom, Aug 02, 2015
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    • "We compare our CNF threading method, CNFpred, with the topnotch profile-based and threading methods such as HHpred (Söding et al., 2005), MUSTER (Wu and Zhang, 2008), SPARKS/SP3/SP5 (Zhou and Zhou, 2005), SALIGN (Marti Renom et al., 2004), RAPTOR (Xu et al., 2003) and BThreader (Peng and Xu, 2009). We use the published results for SPARKS/SP3/SP5 since they have their own template file formats and we cannot correctly run them locally. "
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    ABSTRACT: Motivation: Alignment errors are still the main bottleneck for current template-based protein modeling (TM) methods, including protein threading and homology modeling, especially when the sequence identity between two proteins under consideration is low (<30%). Results: We present a novel protein threading method, CNFpred, which achieves much more accurate sequence–template alignment by employing a probabilistic graphical model called a Conditional Neural Field (CNF), which aligns one protein sequence to its remote template using a non-linear scoring function. This scoring function accounts for correlation among a variety of protein sequence and structure features, makes use of information in the neighborhood of two residues to be aligned, and is thus much more sensitive than the widely used linear or profile-based scoring function. To train this CNF threading model, we employ a novel quality-sensitive method, instead of the standard maximum-likelihood method, to maximize directly the expected quality of the training set. Experimental results show that CNFpred generates significantly better alignments than the best profile-based and threading methods on several public (but small) benchmarks as well as our own large dataset. CNFpred outperforms others regardless of the lengths or classes of proteins, and works particularly well for proteins with sparse sequence profiles due to the effective utilization of structure information. Our methodology can also be adapted to protein sequence alignment. Contact: j3xu@ttic.edu Supplementary information: Supplementary data are available at Bioinformatics online.
    Bioinformatics 06/2012; 28(12):i59-66. DOI:10.1093/bioinformatics/bts213 · 4.62 Impact Factor
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    • "SALIGN's default settings suffice for many applications. It has been fine tuned and extensively tested for alignment accuracy (Davis et al., 2006; Madhusudhan et al., 2006; 2009; Marti-Renom et al., 2004; 2007; Pieper et al., 2011). Nevertheless, the interface allows the user to manipulate many options if so desired. "
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    Bioinformatics 05/2012; 28(15):2072-3. DOI:10.1093/bioinformatics/bts302 · 4.62 Impact Factor
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    • "PDB files were manually modified to include only amino acids of the defined 100- residue region of the DBD. Then, a multiple-structure alignment of the DBD was constructed with the SALIGN module from the MODELLER version 9.9 software package [16] [25]. The SALIGN module reports a table with the number of equivalent C α positions (the alignment length; 3.5 ˚ A cut-off), the root mean squared (RMS) distance of equivalent positions, and the sequence identity of equivalent residues for all pairs of proteins, as well as the multiplesequence alignment (MSA) derived from the multiple optimal superposition of protein structures. "
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