ALBERT

Description: Motivation: Evolutionarily conserved amino acids within proteins characterize functional or structural regions. Conversely, less conserved amino acids within these regions are generally areas of evolutionary divergence. A priori knowledge of biological function and species can help interpret the amino acid differences between sequences. However, this information is often erroneous or unavailable, hampering discovery with supervised algorithms. Also, most of the current unsupervised methods depend on full sequence similarity, which become inaccurate when proteins diverge (e.g. inversions, deletions, insertions). Due to these and other shortcomings, we developed a novel unsupervised algorithm which discovers highly conserved regions and uses two types of information measures: (i) data measures computed from input sequences; and (ii) class measures computed using a priori class groupings in order to reveal subgroups (i.e. classes) or functional characteristics. Results: Using known and putative sequences of two proteins belonging to a relatively uncharacterized protein family we were able to group evolutionarily related sequences and identify conserved regions, which are strong homologous association patterns called Aligned Pattern Clusters, within individual proteins and across the members of this family. An initial synthetic demonstration and in silico results reveal that (i) the data measures are unbiased and (ii) our class measures can accurately rank the quality of the evolutionarily relevant groupings. Furthermore, combining our data and class measures allowed us to interpret the results by inferring regions of biological importance within the binding domain of these proteins. Compared to popular supervised methods, our algorithm has a superior runtime and comparable accuracy. Availability and implementation: The dataset and results are available at www.pami.uwaterloo.ca/~ealee/files/classification2015 . Contact: akcwong@uwaterloo.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Metabolism and ageing are intimately linked. Compared with ad libitum feeding, dietary restriction consistently extends lifespan and delays age-related diseases in evolutionarily diverse organisms. Similar conditions of nutrient limitation and genetic or pharmacological perturbations of nutrient or energy metabolism also have longevity benefits. Recently, several metabolites have been identified that modulate ageing; however, the molecular mechanisms underlying this are largely undefined. Here we show that alpha-ketoglutarate (alpha-KG), a tricarboxylic acid cycle intermediate, extends the lifespan of adult Caenorhabditis elegans. ATP synthase subunit beta is identified as a novel binding protein of alpha-KG using a small-molecule target identification strategy termed drug affinity responsive target stability (DARTS). The ATP synthase, also known as complex V of the mitochondrial electron transport chain, is the main cellular energy-generating machinery and is highly conserved throughout evolution. Although complete loss of mitochondrial function is detrimental, partial suppression of the electron transport chain has been shown to extend C. elegans lifespan. We show that alpha-KG inhibits ATP synthase and, similar to ATP synthase knockdown, inhibition by alpha-KG leads to reduced ATP content, decreased oxygen consumption, and increased autophagy in both C. elegans and mammalian cells. We provide evidence that the lifespan increase by alpha-KG requires ATP synthase subunit beta and is dependent on target of rapamycin (TOR) downstream. Endogenous alpha-KG levels are increased on starvation and alpha-KG does not extend the lifespan of dietary-restricted animals, indicating that alpha-KG is a key metabolite that mediates longevity by dietary restriction. Our analyses uncover new molecular links between a common metabolite, a universal cellular energy generator and dietary restriction in the regulation of organismal lifespan, thus suggesting new strategies for the prevention and treatment of ageing and age-related diseases.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4263271/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4263271/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Chin, Randall M -- Fu, Xudong -- Pai, Melody Y -- Vergnes, Laurent -- Hwang, Heejun -- Deng, Gang -- Diep, Simon -- Lomenick, Brett -- Meli, Vijaykumar S -- Monsalve, Gabriela C -- Hu, Eileen -- Whelan, Stephen A -- Wang, Jennifer X -- Jung, Gwanghyun -- Solis, Gregory M -- Fazlollahi, Farbod -- Kaweeteerawat, Chitrada -- Quach, Austin -- Nili, Mahta -- Krall, Abby S -- Godwin, Hilary A -- Chang, Helena R -- Faull, Kym F -- Guo, Feng -- Jiang, Meisheng -- Trauger, Sunia A -- Saghatelian, Alan -- Braas, Daniel -- Christofk, Heather R -- Clarke, Catherine F -- Teitell, Michael A -- Petrascheck, Michael -- Reue, Karen -- Jung, Michael E -- Frand, Alison R -- Huang, Jing -- DP2 OD008398/OD/NIH HHS/ -- P01 HL028481/HL/NHLBI NIH HHS/ -- P40 OD010440/OD/NIH HHS/ -- T32 CA009120/CA/NCI NIH HHS/ -- T32 GM007104/GM/NIGMS NIH HHS/ -- T32 GM007185/GM/NIGMS NIH HHS/ -- T32 GM008496/GM/NIGMS NIH HHS/ -- England -- Nature. 2014 Jun 19;510(7505):397-401. doi: 10.1038/nature13264. Epub 2014 May 14.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Molecular Biology Institute, University of California Los Angeles, Los Angeles, California 90095, USA. ; Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California 90095, USA. ; 1] Molecular Biology Institute, University of California Los Angeles, Los Angeles, California 90095, USA [2]. ; 1] Department of Human Genetics, University of California Los Angeles, Los Angeles, California 90095, USA [2]. ; 1] Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California 90095, USA [2]. ; Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, USA. ; Department of Biological Chemistry, University of California Los Angeles, Los Angeles, California 90095, USA. ; Department of Surgery, University of California Los Angeles, Los Angeles, California 90095, USA. ; Small Molecule Mass Spectrometry Facility, FAS Division of Science, Harvard University, Cambridge, Massachusetts 02138, USA. ; Department of Chemical Physiology, The Scripps Research Institute, La Jolla, California 92037, USA. ; Pasarow Mass Spectrometry Laboratory, Department of Psychiatry and Biobehavioral Sciences and Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California 90095, USA. ; Department of Environmental Health Sciences, University of California Los Angeles, Los Angeles, California 90095, USA. ; Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California 90095, USA. ; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA. ; 1] Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California 90095, USA [2] UCLA Metabolomics Center, University of California Los Angeles, Los Angeles, California 90095, USA. ; 1] Molecular Biology Institute, University of California Los Angeles, Los Angeles, California 90095, USA [2] Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, USA. ; 1] Molecular Biology Institute, University of California Los Angeles, Los Angeles, California 90095, USA [2] Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California 90095, USA. ; 1] Molecular Biology Institute, University of California Los Angeles, Los Angeles, California 90095, USA [2] Department of Human Genetics, University of California Los Angeles, Los Angeles, California 90095, USA. ; 1] Molecular Biology Institute, University of California Los Angeles, Los Angeles, California 90095, USA [2] Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California 90095, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24828042" target="_blank"〉PubMed〈/a〉

Description: Evolutionary studies usually use a two-step process to investigate sequence data. Step one estimates a multiple sequence alignment (MSA) and step two applies phylogenetic methods to ask evolutionary questions of that MSA. Modern phylogenetic methods infer evolutionary parameters using maximum likelihood or Bayesian inference, mediated by a probabilistic substitution model that describes sequence change over a tree. The statistical properties of these methods mean that more data directly translates to an increased confidence in downstream results, providing the substitution model is adequate and the MSA is correct. Many studies have investigated the robustness of phylogenetic methods in the presence of substitution model misspecification, but few have examined the statistical properties of those methods when the MSA is unknown. This simulation study examines the statistical properties of the complete two-step process when inferring sequence divergence and the phylogenetic tree topology. Both nucleotide and amino acid analyses are negatively affected by the alignment step, both through inaccurate guide tree estimates and through overfitting to that guide tree. For many alignment tools these effects become more pronounced when additional sequences are added to the analysis. Nucleotide sequences are particularly susceptible, with MSA errors leading to statistical support for long-branch attraction artifacts, which are usually associated with gross substitution model misspecification. Amino acid MSAs are more robust, but do tend to arbitrarily resolve multifurcations in favor of the guide tree. No inference strategies produce consistently accurate estimates of divergence between sequences, although amino acid MSAs are again more accurate than their nucleotide counterparts. We conclude with some practical suggestions about how to limit the effect of MSA uncertainty on evolutionary inference.