25,980 research outputs found
PyMod: sequence similarity searches, multiple sequence-structure alignments, and homology modeling within PyMOL
Background: In recent years, an exponential growing number of tools for protein sequence analysis, editing and modeling tasks have been put at the disposal of the scientific community. Despite the vast majority of these tools have been released as open source software, their deep learning curves often discourages even the most experienced users. Results: A simple and intuitive interface, PyMod, between the popular molecular graphics system PyMOL and several other tools (i.e., [PSI-] BLAST, ClustalW, MUSCLE, CEalign and MODELLER) has been developed, to show how the integration of the individual steps required for homology modeling and sequence/structure analysis within the PyMOL framework can hugely simplify these tasks. Sequence similarity searches, multiple sequence and structural alignments generation and editing, and even the possibility to merge sequence and structure alignments have been implemented in PyMod, with the aim of creating a simple, yet powerful tool for sequence and structure analysis and building of homology models. Conclusions: PyMod represents a new tool for the analysis and the manipulation of protein sequences and structures. The ease of use, integration with many sequence retrieving and alignment tools and PyMOL, one of the most used molecular visualization system, are the key features of this tool. Source code, installation instructions, video tutorials and a user's guide are freely available at the URL http://schubert.bio.uniroma1.it/pymod/index.htm
Algorithm engineering for optimal alignment of protein structure distance matrices
Protein structural alignment is an important problem in computational
biology. In this paper, we present first successes on provably optimal pairwise
alignment of protein inter-residue distance matrices, using the popular Dali
scoring function. We introduce the structural alignment problem formally, which
enables us to express a variety of scoring functions used in previous work as
special cases in a unified framework. Further, we propose the first
mathematical model for computing optimal structural alignments based on dense
inter-residue distance matrices. We therefore reformulate the problem as a
special graph problem and give a tight integer linear programming model. We
then present algorithm engineering techniques to handle the huge integer linear
programs of real-life distance matrix alignment problems. Applying these
techniques, we can compute provably optimal Dali alignments for the very first
time
Protein sectors: statistical coupling analysis versus conservation
Statistical coupling analysis (SCA) is a method for analyzing multiple
sequence alignments that was used to identify groups of coevolving residues
termed "sectors". The method applies spectral analysis to a matrix obtained by
combining correlation information with sequence conservation. It has been
asserted that the protein sectors identified by SCA are functionally
significant, with different sectors controlling different biochemical
properties of the protein. Here we reconsider the available experimental data
and note that it involves almost exclusively proteins with a single sector. We
show that in this case sequence conservation is the dominating factor in SCA,
and can alone be used to make statistically equivalent functional predictions.
Therefore, we suggest shifting the experimental focus to proteins for which SCA
identifies several sectors. Correlations in protein alignments, which have been
shown to be informative in a number of independent studies, would then be less
dominated by sequence conservation.Comment: 36 pages, 17 figure
Who Watches the Watchmen? An Appraisal of Benchmarks for Multiple Sequence Alignment
Multiple sequence alignment (MSA) is a fundamental and ubiquitous technique
in bioinformatics used to infer related residues among biological sequences.
Thus alignment accuracy is crucial to a vast range of analyses, often in ways
difficult to assess in those analyses. To compare the performance of different
aligners and help detect systematic errors in alignments, a number of
benchmarking strategies have been pursued. Here we present an overview of the
main strategies--based on simulation, consistency, protein structure, and
phylogeny--and discuss their different advantages and associated risks. We
outline a set of desirable characteristics for effective benchmarking, and
evaluate each strategy in light of them. We conclude that there is currently no
universally applicable means of benchmarking MSA, and that developers and users
of alignment tools should base their choice of benchmark depending on the
context of application--with a keen awareness of the assumptions underlying
each benchmarking strategy.Comment: Revie
Convergent dynamics in the protease enzymatic superfamily
Proteases regulate various aspects of the life cycle in all organisms by
cleaving specific peptide bonds. Their action is so central for biochemical
processes that at least 2% of any known genome encodes for proteolytic enzymes.
Here we show that selected proteases pairs, despite differences in oligomeric
state, catalytic residues and fold, share a common structural organization of
functionally relevant regions which are further shown to undergo similar
concerted movements. The structural and dynamical similarities found
pervasively across evolutionarily distant clans point to common mechanisms for
peptide hydrolysis.Comment: 13 pages, 6 figure
Towards Reliable Automatic Protein Structure Alignment
A variety of methods have been proposed for structure similarity calculation,
which are called structure alignment or superposition. One major shortcoming in
current structure alignment algorithms is in their inherent design, which is
based on local structure similarity. In this work, we propose a method to
incorporate global information in obtaining optimal alignments and
superpositions. Our method, when applied to optimizing the TM-score and the GDT
score, produces significantly better results than current state-of-the-art
protein structure alignment tools. Specifically, if the highest TM-score found
by TMalign is lower than (0.6) and the highest TM-score found by one of the
tested methods is higher than (0.5), there is a probability of (42%) that
TMalign failed to find TM-scores higher than (0.5), while the same probability
is reduced to (2%) if our method is used. This could significantly improve the
accuracy of fold detection if the cutoff TM-score of (0.5) is used.
In addition, existing structure alignment algorithms focus on structure
similarity alone and simply ignore other important similarities, such as
sequence similarity. Our approach has the capacity to incorporate multiple
similarities into the scoring function. Results show that sequence similarity
aids in finding high quality protein structure alignments that are more
consistent with eye-examined alignments in HOMSTRAD. Even when structure
similarity itself fails to find alignments with any consistency with
eye-examined alignments, our method remains capable of finding alignments
highly similar to, or even identical to, eye-examined alignments.Comment: Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013
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