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      Evolutionary and molecular foundations of multiple contemporary functions of the nitroreductase superfamily

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          Significance

          Functionally diverse enzyme superfamilies are sets of homologs that conserve a structural fold and mechanistic details but perform various distinct chemical reactions. What are the evolutionary routes by which ancestral proteins diverge to produce extant enzymes? We present an approach that combines experimental data with computational tools to trace these sequence–structure–function transitions in a model system, the functionally diverse flavin mononucleotide-dependent nitroreductases (NTRs). Our results suggest an evolutionary model in which contemporary NTR classes have diverged in a radial manner from a minimal flavin-binding scaffold via insertions at key positions and fixation of functional residues, yielding the reaction versatility of contemporary enzymes. These principles will facilitate rational design of NTRs and advance general approaches for delineating the emergence of functional diversity in enzyme superfamilies.

          Abstract

          Insight regarding how diverse enzymatic functions and reactions have evolved from ancestral scaffolds is fundamental to understanding chemical and evolutionary biology, and for the exploitation of enzymes for biotechnology. We undertook an extensive computational analysis using a unique and comprehensive combination of tools that include large-scale phylogenetic reconstruction to determine the sequence, structural, and functional relationships of the functionally diverse flavin mononucleotide-dependent nitroreductase (NTR) superfamily (>24,000 sequences from all domains of life, 54 structures, and >10 enzymatic functions). Our results suggest an evolutionary model in which contemporary subgroups of the superfamily have diverged in a radial manner from a minimal flavin-binding scaffold. We identified the structural design principle for this divergence: Insertions at key positions in the minimal scaffold that, combined with the fixation of key residues, have led to functional specialization. These results will aid future efforts to delineate the emergence of functional diversity in enzyme superfamilies, provide clues for functional inference for superfamily members of unknown function, and facilitate rational redesign of the NTR scaffold.

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          Most cited references82

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          Basic Local Alignment Search Tool

          S Altschul (1990)
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            Maps of random walks on complex networks reveal community structure

            To comprehend the multipartite organization of large-scale biological and social systems, we introduce a new information theoretic approach that reveals community structure in weighted and directed networks. The method decomposes a network into modules by optimally compressing a description of information flows on the network. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of more than 6000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network -- including physics, chemistry, molecular biology, and medicine -- information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.
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              A new generation of homology search tools based on probabilistic inference.

              Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                7 November 2017
                24 October 2017
                24 October 2017
                : 114
                : 45
                : E9549-E9558
                Affiliations
                [1] aDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco , CA 94158;
                [2] bMichael Smith Laboratories, University of British Columbia , Vancouver, BC, Canada V6T 1Z4;
                [3] cCalifornia Institute for Quantitative Biosciences, University of California, San Francisco , CA 94158
                Author notes
                2To whom correspondence may be addressed. Email: tokuriki@ 123456msl.ubc.ca or babbitt@ 123456cgl.ucsf.edu .

                Edited by Jane S. Richardson, Duke University, Durham, NC, and approved September 28, 2017 (received for review April 25, 2017)

                Author contributions: E.A., J.N.C., N.T., and P.C.B. designed research; E.A. and J.N.C. performed research; E.A. and J.N.C. analyzed data; and E.A., J.N.C., N.T., and P.C.B. wrote the paper.

                1E.A. and J.N.C. contributed equally to this work.

                Article
                201706849
                10.1073/pnas.1706849114
                5692541
                29078300
                c9a80e2a-6fdd-4bc0-854b-f240429082f5
                Copyright © 2017 the Author(s). Published by PNAS.

                This is an open access article distributed under the PNAS license.

                History
                Page count
                Pages: 10
                Funding
                Funded by: HHS | National Institutes of Health (NIH) 100000002
                Award ID: GM60595
                Funded by: Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC) 501100000038
                Award ID: RGPIN 418262-12
                Funded by: Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC) 501100000038
                Award ID: RGPIN 2017-04909
                Funded by: HHS | National Institutes of Health (NIH) 100000002
                Award ID: NIGMS P41-GM103311
                Categories
                PNAS Plus
                Biological Sciences
                Biophysics and Computational Biology
                PNAS Plus

                enzyme superfamilies,evolution,flavoenzyme,sequence similarity network,nitroreductase

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