Within the twilight zone: a sensitive profile-profile comparison tool based on information theory

Yona, Golan; Levitt, Michael

doi:10.1006/jmbi.2001.5293

Cited by 259 publications

(222 citation statements)

References 52 publications

Supporting

Mentioning

220

Contrasting

Order By: Relevance

“…Hence, the information from locally similar, analogous sequences that are contained in the context profiles is at least partly orthogonal to the evolutionary information in the homologous sequences that contribute to the sequence profiles. Consequently, we can expect improvements when applying the new paradigm to the pairwise comparison of sequence profiles (34)(35)(36) and profile HMMs (10, 37), or to hierarchical multiple sequence alignment programs (8,42).…”

Section: Discussionmentioning

confidence: 99%

Sequence context-specific profiles for homology searching

Biegert

Söding

2009

Proc. Natl. Acad. Sci. U.S.A.

156

138

View full text Add to dashboard Cite

Sequence alignment and database searching are essential tools in biology because a protein's function can often be inferred from homologous proteins. Standard sequence comparison methods use substitution matrices to find the alignment with the best sum of similarity scores between aligned residues. These similarity scores do not take the local sequence context into account. Here, we present an approach that derives context-specific amino acid similarities from short windows centered on each query sequence residue. Our results demonstrate that the sequence context contains much more information about the expected mutations than just the residue itself. By employing our context-specific similarities (CS-BLAST) in combination with NCBI BLAST, we increase the sensitivity more than 2-fold on a difficult benchmark set, without loss of speed. Alignment quality is likewise improved significantly. Furthermore, we demonstrate considerable improvements when applying this paradigm to sequence profiles: Two iterations of CSI-BLAST, our context-specific version of PSI-BLAST, are more sensitive than 5 iterations of PSI-BLAST. The paradigm for biological sequence comparison presented here is very general. It can replace substitution matrices in sequence-and profile-based alignment and search methods for both protein and nucleotide sequences.alignment ͉ pseudocounts ͉ substitution matrix ͉ context-sensitive

show abstract

Section: Discussionmentioning

confidence: 99%

Sequence context-specific profiles for homology searching

Biegert

Söding

2009

Proc. Natl. Acad. Sci. U.S.A.

156

138

View full text Add to dashboard Cite

show abstract

“…This work differs from traditional homology modeling methods (6,(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), which are based on empirical sequence equivalencies and evolutionary assumptions, in that the present approach is based on the axiom that the physical properties written into a protein sequence are the key determinants Fig. 4.…”

Section: Methodsmentioning

confidence: 99%

Alternative approach to protein structure prediction based on sequential similarity of physical properties

Rackovsky

Yin

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

The relationship between protein sequence and structure arises entirely from amino acid physical properties. An alternative method is therefore proposed to identify homologs in which residue equivalence is based exclusively on the pairwise physical property similarities of sequences. This approach, the property factor method (PFM), is entirely different from those in current use. A comparison is made between our method and PSI BLAST. We demonstrate that traditionally defined sequence similarity can be very low for pairs of sequences (which therefore cannot be identified using PSI BLAST), but similarity of physical property distributions results in almost identical 3D structures. The performance of PFM is shown to be better than that of PSI BLAST when sequence matching is comparable, based on a comparison using targets from CASP10 (89 targets) and CASP11 (51 targets). It is also shown that PFM outperforms PSI BLAST in informatically challenging targets.homology modeling | amino acid physical properties | protein structure prediction T he prediction of protein structure from sequence is a central problem in molecular biology, with important practical applications. The most reliable approach to this problem is homology modeling (1-3), in which the structure of a target sequence is modeled on the known structures of candidate proteins whose sequences are judged to be similar to that of the target. The standard procedure for calculating the degree of similarity between two sequences involves alignment techniques. Sequence alignment, in its usual incarnation, involves two computational features: i) a matrix that establishes a quantitative degree of similarity between any two amino acids; and ii) a procedure for locating equivalent subsequence fragments in the two sequences of interest and assessing numerical penalties for the presence of insertions or deletions in one sequence relative to the other.In previous work (4), we discussed the problems associated with this approach. In standard alignment, equivalence matrices are self-referential, in the sense that they are generally constructed from alignments. Biases and preconceptions inherent in the initial alignment will therefore be incorporated in a nonlinear fashion into the final result. In the present work, we use a residue-equivalence measure based entirely on amino acid physical properties (5) [the property factor-based method (PFM), details of which are given in Methods]. As a result, no preconceptions as to correct matches color our results. Furthermore, a matrix-matching algorithm adapted from image processing is used to provide an extremely efficient sequence search/matching procedure. It is shown that, together, these lead to results that are not attainable by current methods and to the accurate prediction of structures that were poorly predicted in the two most recent CASP (critical assessment of protein structure prediction) exercises. This approach recalibrates what is possible using sequence alignment, and in the process eliminates much of the computatio...

show abstract

“…the query family), and compare it with profiles pre-calculated for a large number of protein families. Examples include PROF SIM, 89 COMPASS, 90 and HHsearch. 91 Profile-profile search methods have been also adapted to assist in template-based protein structure prediction (described in more detail in chapter by Kosinski et al).…”

Section: Database Searches and Pairwise Alignments 13mentioning

confidence: 99%