4,577 research outputs found
An Alternative Model of Amino Acid Replacement
The observed correlations between pairs of homologous protein sequences are
typically explained in terms of a Markovian dynamic of amino acid substitution.
This model assumes that every location on the protein sequence has the same
background distribution of amino acids, an assumption that is incompatible with
the observed heterogeneity of protein amino acid profiles and with the success
of profile multiple sequence alignment. We propose an alternative model of
amino acid replacement during protein evolution based upon the assumption that
the variation of the amino acid background distribution from one residue to the
next is sufficient to explain the observed sequence correlations of homologs.
The resulting dynamical model of independent replacements drawn from
heterogeneous backgrounds is simple and consistent, and provides a unified
homology match score for sequence-sequence, sequence-profile and
profile-profile alignment.Comment: Minor improvements. Added figure and reference
Improving the Alignment Quality of Consistency Based Aligners with an Evaluation Function Using Synonymous Protein Words
Most sequence alignment tools can successfully align protein sequences with higher levels of sequence identity. The accuracy of corresponding structure alignment, however, decreases rapidly when considering distantly related sequences (<20% identity). In this range of identity, alignments optimized so as to maximize sequence similarity are often inaccurate from a structural point of view. Over the last two decades, most multiple protein aligners have been optimized for their capacity to reproduce structure-based alignments while using sequence information. Methods currently available differ essentially in the similarity measurement between aligned residues using substitution matrices, Fourier transform, sophisticated profile-profile functions, or consistency-based approaches, more recently
MUSCLE: a multiple sequence alignment method with reduced time and space complexity
BACKGROUND: In a previous paper, we introduced MUSCLE, a new program for creating multiple alignments of protein sequences, giving a brief summary of the algorithm and showing MUSCLE to achieve the highest scores reported to date on four alignment accuracy benchmarks. Here we present a more complete discussion of the algorithm, describing several previously unpublished techniques that improve biological accuracy and / or computational complexity. We introduce a new option, MUSCLE-fast, designed for high-throughput applications. We also describe a new protocol for evaluating objective functions that align two profiles. RESULTS: We compare the speed and accuracy of MUSCLE with CLUSTALW, Progressive POA and the MAFFT script FFTNS1, the fastest previously published program known to the author. Accuracy is measured using four benchmarks: BAliBASE, PREFAB, SABmark and SMART. We test three variants that offer highest accuracy (MUSCLE with default settings), highest speed (MUSCLE-fast), and a carefully chosen compromise between the two (MUSCLE-prog). We find MUSCLE-fast to be the fastest algorithm on all test sets, achieving average alignment accuracy similar to CLUSTALW in times that are typically two to three orders of magnitude less. MUSCLE-fast is able to align 1,000 sequences of average length 282 in 21 seconds on a current desktop computer. CONCLUSIONS: MUSCLE offers a range of options that provide improved speed and / or alignment accuracy compared with currently available programs. MUSCLE is freely available at
Homology-extended sequence alignment
We present a profile–profile multiple alignment strategy that uses database searching to collect homologues for each sequence in a given set, in order to enrich their available evolutionary information for the alignment. For each of the alignment sequences, the putative homologous sequences that score above a pre-defined threshold are incorporated into a position-specific pre-alignment profile. The enriched position-specific profile is used for standard progressive alignment, thereby more accurately describing the characteristic features of the given sequence set. We show that owing to the incorporation of the pre-alignment information into a standard progressive multiple alignment routine, the alignment quality between distant sequences increases significantly and outperforms state-of-the-art methods, such as T-COFFEE and MUSCLE. We also show that although entirely sequence-based, our novel strategy is better at aligning distant sequences when compared with a recent contact-based alignment method. Therefore, our pre-alignment profile strategy should be advantageous for applications that rely on high alignment accuracy such as local structure prediction, comparative modelling and threading
Exploration of the Disambiguation of Amino Acid Types to Chi-1 Rotamer Types in Protein Structure Prediction and Design
A protein’s global fold provide insight into function; however, function specificity is often detailed in sidechain orientation. Thus, determining the rotamer conformations is often crucial in the contexts of protein structure/function prediction and design. For all non-glycine and non-alanine types, chi-1 rotamers occupy a small number of discrete number of states. Herein, we explore the possibility of describing evolution from the perspective of the sidechains’ structure versus the traditional twenty amino acid types. To validate our hypothesis that this perspective is more crucial to our understanding of evolutionary relationships, we investigate its uses as evolutionary, substitution matrices for sequence alignments for fold recognition purposes and computational protein design with specific focus in designing beta sheet environments, where previous studies have been done on amino acid-types alone. Throughout this study, we also propose the concept of the “chi-1 rotamer sequence” that describes the chi-1 rotamer composition of a protein. We also present attempts to predict these sequences and real-value torsion angles from amino acid sequence information.
First, we describe our developments of log-odds scoring matrices for sequence alignments. Log-odds substitution matrices are widely used in sequence alignments for their ability to determine evolutionary relationship between proteins. Traditionally, databases of sequence information guide the construction of these matrices which illustrates its power in discovering distant or weak homologs. Weak homologs, typically those that share low sequence identity (< 30%), are often difficult to identify when only using basic amino acid sequence alignment. While protein threading approaches have addressed this issue, many of these approaches include sequenced-based information or profiles guided by amino acid-based substitution matrices, namely BLOSUM62. Here, we generated a structural-based substitution matrix born by TM-align structural alignments that captures both the sequence mutation rate within same protein family folds and the chi-1 rotamer that represents each amino acid. These rotamer substitution matrices (ROTSUMs) discover new homologs and improved alignments in the PDB that traditional substitution matrices, based solely on sequence information, cannot identify.
Certain tools and algorithms to estimate rotamer torsions angles have been developed but typically require either knowledge of backbone coordinates and/or experimental data to help guide the prediction. Herein, we developed a fragment-based algorithm, Rot1Pred, to determine the chi-1 states in each position of a given amino acid sequence, yielding a chi-1 rotamer sequence. This approach employs fragment matching of the query sequence to sequence-structure fragment pairs in the PDB to predict the query’s sidechain structure information. Real-value torsion angles were also predicted and compared against SCWRL4. Results show that overall and for most amino-acid types, Rot1Pred can calculate chi-1 torsion angles significantly closer to native angles compared to SCWRL4 when evaluated on I-TASSER generated model backbones.
Finally, we’ve developed and explored chi-1-rotamer-based statistical potentials and evolutionary profiles constructed for de novo computational protein design. Previous analyses which aim to energetically describe the preference of amino acid types in beta sheet environments (parallel vs antiparallel packing or n- and c-terminal beta strand capping) have been performed with amino acid types although no explicit rotamer representation is given in their scoring functions. In our study, we construct statistical functions which describes chi-1 rotamer preferences in these environments and illustrate their improvement over previous methods. These specialized knowledge-based energy functions have generated sequences whose I-TASSER predicted models are structurally-alike to their input structures yet consist of low sequence identity.PHDChemical BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145951/1/jarrettj_1.pd
Progressive Mauve: Multiple alignment of genomes with gene flux and rearrangement
Multiple genome alignment remains a challenging problem. Effects of
recombination including rearrangement, segmental duplication, gain, and loss
can create a mosaic pattern of homology even among closely related organisms.
We describe a method to align two or more genomes that have undergone
large-scale recombination, particularly genomes that have undergone substantial
amounts of gene gain and loss (gene flux). The method utilizes a novel
alignment objective score, referred to as a sum-of-pairs breakpoint score. We
also apply a probabilistic alignment filtering method to remove erroneous
alignments of unrelated sequences, which are commonly observed in other genome
alignment methods. We describe new metrics for quantifying genome alignment
accuracy which measure the quality of rearrangement breakpoint predictions and
indel predictions. The progressive genome alignment algorithm demonstrates
markedly improved accuracy over previous approaches in situations where genomes
have undergone realistic amounts of genome rearrangement, gene gain, loss, and
duplication. We apply the progressive genome alignment algorithm to a set of 23
completely sequenced genomes from the genera Escherichia, Shigella, and
Salmonella. The 23 enterobacteria have an estimated 2.46Mbp of genomic content
conserved among all taxa and total unique content of 15.2Mbp. We document
substantial population-level variability among these organisms driven by
homologous recombination, gene gain, and gene loss. Free, open-source software
implementing the described genome alignment approach is available from
http://gel.ahabs.wisc.edu/mauve .Comment: Revision dated June 19, 200
Probabilistic protein homology modeling
Searching sequence databases and building 3D models for proteins are important tasks
for biologists. When the structure of a query protein is given, its function can be inferred. However, experimental methods for structure prediction are both expensive and
time consuming. Fully automatic homology modeling refers to building a 3D model for
a query sequence from an alignment to related homologous proteins with known structure (templates) by a computer. Current prediction servers can provide accurate models
within a few hours to days. Our group has developed HHpred, which is one of the top
performing structure prediction servers in the field.
In general, homology based structure modeling consists of four steps: (1) finding homologous templates in a database, (2) selecting and (3) aligning templates to the query, (4)
building a 3D model based on the alignment.
In part one of this thesis, we will present improvements of step (2) and (4). Specifically,
homology modeling has been shown to work best when multiple templates are selected
instead of only a single one. Yet, current servers are using rather ad-hoc approaches to
combine information from multiple templates. We provide a rigorous statistical framework for multi-template homology modeling. Given an alignment, we employ Modeller to calculate the most probable structure for a query. The 3D model is obtained
by optimally satisfying spatial restraints derived from the alignment and expressed as
probability density functions. We find that the query’s atomic distance restraints can
be accurately described by two-component Gaussian mixtures. Moreover, we derive statistical weights to quantify the redundancy among related templates. This allows us to
apply the standard rules of probability theory to combine restraints from several templates. Together with a heuristic template selection strategy, we have implemented this
approach within HHpred and could significantly improve model quality. Furthermore,
we took part in CASP, a community wide competition for structure prediction, where
we were ranked first in template based modeling and, at the same time, were more than
450 times faster than all other top servers.
Homology modeling heavily relies on detecting and correctly aligning templates to the
query sequence (step (1) and (3) from above). But remote homologies are difficult to
detect and hard to align on a pure sequence level. Hence, modern tools are based on
profiles instead of sequences. A profile summarizes the evolutionary history of a given
sequence and consists of position specific amino acid probabilities for each residue. In
addition to the similarity score between profile columns, most methods use extra terms
that compare 1D structural properties such as secondary structure or solvent accessibility. These can be predicted from local profile windows.
In the second part of this thesis, we develop a new score that is independent of any predefined structural property. For this purpose, we learn a library of 32 profile patterns that
are most conserved in alignments of remotely homologous, structurally aligned proteins.
Each so called “context state” in the library consists of a 13-residue sequence profile.
We integrate the new context score into our Hmm-Hmm alignment tool HHsearch and
improve especially the sensitivity and precision of difficult pairwise alignments significantly.
Taken together, we introduced probabilistic methods to improve all four main steps in
homology based structure prediction
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