Assessment of Protein Distance Measures and Tree-Building Methods for Phylogenetic Tree Reconstruction

Hollich, Volker; Milchert, Lena; Arvestad, Lars; Sonnhammer, Erik L. L.

doi:10.1093/molbev/msi224

Cited by 20 publications

(23 citation statements)

References 26 publications

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“…This is consistent with a previous simulation study of realistic topologies and protein alignments (Hollich et al 2005). Because using uncorrected distances leads to relatively few errors, FastTree can correct these errors by doing a few rounds of NNIs.…”

Section: Resultssupporting

confidence: 93%

FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix

Price¹,

Dehal²,

Arkin³

2009

Molecular Biology and Evolution

4,228

3,288

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Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N2) space and O(N2L) time, but FastTree requires just O(NLa + N) memory and O(Nlog (N)La) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 h and 2.4 GB of memory. Just computing pairwise Jukes–Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 h and 50 GB of memory. In simulations, FastTree was slightly more accurate than Neighbor-Joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

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Section: Resultssupporting

confidence: 93%

FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix

Price¹,

Dehal²,

Arkin³

2009

Molecular Biology and Evolution

4,228

3,288

View full text Add to dashboard Cite

show abstract

“…The algorithm starts from an initial tree constructed by the BIONJ method,41 a fast distance‐based algorithm, and improves it by branch‐length optimization and tree swapping procedures. BIONJ has been shown to outperform other distance‐based tree inference methods 42. To compute the likelihood of a surface patch as PBI/NPBI, we feed PHYML with the MSA of the patch and select the PBI/NPBI substitution model and the amino acid frequency distribution of PBI/NPBI, which is used as the equilibrium frequencies.…”

Section: Methodsmentioning

confidence: 99%

A novel method for protein–protein interaction site prediction using phylogenetic substitution models

David

2011

Proteins

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Protein-protein binding events mediate many critical biological functions in the cell. Typically, functionally important sites in proteins can be well identified by considering sequence conservation. However, protein-protein interaction sites exhibit higher sequence variation than other functional regions, such as catalytic sites of enzymes. Consequently, the mutational behavior leading to weak sequence conservation poses significant challenges to the protein-protein interaction site prediction. Here, we present a phylogenetic framework to capture critical sequence variations that favor the selection of residues essential for protein-protein binding. Through the comprehensive analysis of diverse protein families, we show that protein binding interfaces exhibit distinct amino acid substitution as compared with other surface residues. Based on this analysis, we have developed a novel method, BindML, which utilizes the substitution models to predict protein-protein binding sites of protein with unknown interacting partners. BindML estimates the likelihood that a phylogenetic tree of a local surface region in a query protein structure follows the substitution patterns of protein binding interface and non-binding surfaces. BindML is shown to perform well compared to alternative methods for protein binding interface prediction. The methodology developed in this study is very versatile in the sense that it can be generally applied for predicting other types of functional sites, such as DNA, RNA, and membrane binding sites in proteins.

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“…BioNJ has been shown to be the best NJ method (23). In total, we have 10 different combinations of tree reconstruction algorithms and distance metrics.…”

Section: Methodsmentioning

confidence: 99%

Performance comparison between k-tuple distance and four model-based distances in phylogenetic tree reconstruction

Yang

Zhang

2008

Nucleic Acids Research

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Phylogenetic tree reconstruction requires construction of a multiple sequence alignment (MSA) from sequences. Computationally, it is difficult to achieve an optimal MSA for many sequences. Moreover, even if an optimal MSA is obtained, it may not be the true MSA that reflects the evolutionary history of the underlying sequences. Therefore, errors can be introduced during MSA construction which in turn affects the subsequent phylogenetic tree construction. In order to circumvent this issue, we extend the application of the k-tuple distance to phylogenetic tree reconstruction. The k-tuple distance between two sequences is the sum of the differences in frequency, over all possible tuples of length k, between the sequences and can be estimated without MSAs. It has been traditionally used to build a fast ‘guide tree’ to assist the construction of MSAs. Using the 1470 simulated sets of sequences generated under different evolutionary scenarios, the neighbor-joining trees and BioNJ trees, we compared the performance of the k-tuple distance with four commonly used distance estimators including Jukes–Cantor, Kimura, F84 and Tamura–Nei. These four distance estimators fall into the category of model-based distance estimators, as each of them takes account of a specific substitution model in order to compute the distance between a pair of already aligned sequences. Results show that trees constructed from the k-tuple distance are more accurate than those from other distances most time; when the divergence between underlying sequences is high, the tree accuracy could be twice or higher using the k-tuple distance than other estimators. Furthermore, as the k-tuple distance voids the need for constructing an MSA, it can save tremendous amount of time for phylogenetic tree reconstructions when the data include a large number of sequences.

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Assessment of Protein Distance Measures and Tree-Building Methods for Phylogenetic Tree Reconstruction

Cited by 20 publications

References 26 publications

FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix

FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix

A novel method for protein–protein interaction site prediction using phylogenetic substitution models

Performance comparison between k-tuple distance and four model-based distances in phylogenetic tree reconstruction

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