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Article ; Online: Effusion: prediction of protein function from sequence similarity networks.

Yunes, Jeffrey M / Babbitt, Patricia C

2018 Volume 35, Issue 3, Page(s) 442–451

Abstract: Motivation: Critical evaluation of methods for protein function prediction shows that data integration improves the performance of methods that predict protein function, but a basic BLAST-based method is still a top contender. We sought to engineer a ... ...

Abstract	Motivation: Critical evaluation of methods for protein function prediction shows that data integration improves the performance of methods that predict protein function, but a basic BLAST-based method is still a top contender. We sought to engineer a method that modernizes the classical approach while avoiding pitfalls common to state-of-the-art methods. Results: We present a method for predicting protein function, Effusion, which uses a sequence similarity network to add context for homology transfer, a probabilistic model to account for the uncertainty in labels and function propagation, and the structure of the Gene Ontology (GO) to best utilize sparse input labels and make consistent output predictions. Effusion's model makes it practical to integrate rare experimental data and abundant primary sequence and sequence similarity. We demonstrate Effusion's performance using a critical evaluation method and provide an in-depth analysis. We also dissect the design decisions we used to address challenges for predicting protein function. Finally, we propose directions in which the framework of the method can be modified for additional predictive power. Availability and implementation: The source code for an implementation of Effusion is freely available at https://github.com/babbittlab/effusion. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Computational Biology ; Gene Ontology ; Proteins/chemistry ; Software
Chemical Substances	Proteins
Language	English
Publishing date	2018-08-05
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bty672
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Article ; Online: New computational approaches to understanding molecular protein function.

Fetrow, Jacquelyn S / Babbitt, Patricia C

PLoS computational biology

2018 Volume 14, Issue 4, Page(s) e1005756

MeSH term(s)	Amino Acid Sequence ; Catalytic Domain/genetics ; Cluster Analysis ; Computational Biology ; Databases, Protein ; Enzymes/chemistry ; Enzymes/genetics ; Enzymes/physiology ; Gene Ontology ; Models, Molecular ; Proteins/chemistry ; Proteins/genetics ; Proteins/physiology ; Sequence Alignment
Chemical Substances	Enzymes ; Proteins
Language	English
Publishing date	2018
Publishing country	United States
Document type	Editorial
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1005756
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Article ; Online: Effusion: prediction of protein function from sequence similarity networks

Yunes, Jeffrey M / Babbitt, Patricia C.

Bioinformatics. 2019 Feb. 01, v. 35, no. 3, p. 442-451

2019 , Page(s) 442–451

Abstract: Critical evaluation of methods for protein function prediction shows that data integration improves the performance of methods that predict protein function, but a basic BLAST-based method is still a top contender. We sought to engineer a method that ... ...

Abstract	Critical evaluation of methods for protein function prediction shows that data integration improves the performance of methods that predict protein function, but a basic BLAST-based method is still a top contender. We sought to engineer a method that modernizes the classical approach while avoiding pitfalls common to state-of-the-art methods. We present a method for predicting protein function, Effusion, which uses a sequence similarity network to add context for homology transfer, a probabilistic model to account for the uncertainty in labels and function propagation, and the structure of the Gene Ontology (GO) to best utilize sparse input labels and make consistent output predictions. Effusion’s model makes it practical to integrate rare experimental data and abundant primary sequence and sequence similarity. We demonstrate Effusion’s performance using a critical evaluation method and provide an in-depth analysis. We also dissect the design decisions we used to address challenges for predicting protein function. Finally, we propose directions in which the framework of the method can be modified for additional predictive power. The source code for an implementation of Effusion is freely available at https://github.com/babbittlab/effusion. Supplementary data are available at Bioinformatics online.
Keywords	bioinformatics ; gene ontology ; prediction ; probabilistic models ; sequence homology ; uncertainty
Language	English
Dates of publication	2019-0201
Size	p. 442-451
Publishing place	Oxford University Press
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	1468345-3
ISSN	1367-4811 ; 1460-2059
ISSN	1367-4811 ; 1460-2059
DOI	10.1093/bioinformatics/bty672
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Evolutionary reprograming of protein-protein interaction specificity.

Akiva, Eyal / Babbitt, Patricia C

Cell

2015 Volume 163, Issue 3, Page(s) 535–537

Abstract: Using mutation libraries and deep sequencing, Aakre et al. study the evolution of protein-protein interactions using a toxin-antitoxin model. The results indicate probable trajectories via "intermediate" proteins that are promiscuous, thus avoiding ... ...

Abstract	Using mutation libraries and deep sequencing, Aakre et al. study the evolution of protein-protein interactions using a toxin-antitoxin model. The results indicate probable trajectories via "intermediate" proteins that are promiscuous, thus avoiding transitions via non-interactions. These results extend observations about other biological interactions and enzyme evolution, suggesting broadly general principles.
MeSH term(s)	Evolution, Molecular ; Mesorhizobium/metabolism ; Protein Interaction Maps
Language	English
Publishing date	2015-10-22
Publishing country	United States
Document type	Comment ; Journal Article
ZDB-ID	187009-9
ISSN	1097-4172 ; 0092-8674
ISSN (online)	1097-4172
ISSN	0092-8674
DOI	10.1016/j.cell.2015.10.010
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Article ; Online: New insights about enzyme evolution from large scale studies of sequence and structure relationships.

Brown, Shoshana D / Babbitt, Patricia C

The Journal of biological chemistry

2014 Volume 289, Issue 44, Page(s) 30221–30228

Abstract: Understanding how enzymes have evolved offers clues about their structure-function relationships and mechanisms. Here, we describe evolution of functionally diverse enzyme superfamilies, each representing a large set of sequences that evolved from a ... ...

Abstract	Understanding how enzymes have evolved offers clues about their structure-function relationships and mechanisms. Here, we describe evolution of functionally diverse enzyme superfamilies, each representing a large set of sequences that evolved from a common ancestor and that retain conserved features of their structures and active sites. Using several examples, we describe the different structural strategies nature has used to evolve new reaction and substrate specificities in each unique superfamily. The results provide insight about enzyme evolution that is not easily obtained from studies of one or only a few enzymes.
MeSH term(s)	Biocatalysis ; Catalytic Domain ; Enzymes/chemistry ; Enzymes/genetics ; Evolution, Molecular ; Humans ; Oxidation-Reduction ; Phylogeny ; Sequence Analysis, Protein
Chemical Substances	Enzymes
Language	English
Publishing date	2014-09-10
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	2997-x
ISSN	1083-351X ; 0021-9258
ISSN (online)	1083-351X
ISSN	0021-9258
DOI	10.1074/jbc.R114.569350
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Article ; Online: A strategy for large-scale comparison of evolutionary- and reaction-based classifications of enzyme function.

Holliday, Gemma L / Brown, Shoshana D / Mischel, David / Polacco, Benjamin J / Babbitt, Patricia C

Database : the journal of biological databases and curation

2020 Volume 2020

Abstract: Determining the molecular function of enzymes discovered by genome sequencing represents a primary foundation for understanding many aspects of biology. Historically, classification of enzyme reactions has used the enzyme nomenclature system developed to ...

Abstract	Determining the molecular function of enzymes discovered by genome sequencing represents a primary foundation for understanding many aspects of biology. Historically, classification of enzyme reactions has used the enzyme nomenclature system developed to describe the overall reactions performed by biochemically characterized enzymes, irrespective of their associated sequences. In contrast, functional classification and assignment for the millions of protein sequences of unknown function now available is largely done in two computational steps, first by similarity-based assignment of newly obtained sequences to homologous groups, followed by transferring to them the known functions of similar biochemically characterized homologs. Due to the fundamental differences in their etiologies and practice, `how' these chemistry- and evolution-centric functional classification systems relate to each other has been difficult to explore on a large scale. To investigate this issue in a new way, we integrated two published ontologies that had previously described each of these classification systems independently. The resulting infrastructure was then used to compare the functional assignments obtained from each classification system for the well-studied and functionally diverse enolase superfamily. Mapping these function assignments to protein structure and reaction similarity networks shows a profound and complex disconnect between the homology- and chemistry-based classification systems. This conclusion mirrors previous observations suggesting that except for closely related sequences, facile annotation transfer from small numbers of characterized enzymes to the huge number uncharacterized homologs to which they are related is problematic. Our extension of these comparisons to large enzyme superfamilies in a computationally intelligent manner provides a foundation for new directions in protein function prediction for the huge proportion of sequences of unknown function represented in major databases. Interactive sequence, reaction, substrate and product similarity networks computed for this work for the enolase and two other superfamilies are freely available for download from the Structure Function Linkage Database Archive (http://sfld.rbvi.ucsf.edu).
MeSH term(s)	Computational Biology/methods ; Databases, Protein ; Enzymes/chemistry ; Enzymes/classification ; Enzymes/physiology ; Molecular Sequence Annotation ; Structure-Activity Relationship
Chemical Substances	Enzymes
Language	English
Publishing date	2020-05-25
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2496706-3
ISSN	1758-0463 ; 1758-0463
ISSN (online)	1758-0463
ISSN	1758-0463
DOI	10.1093/database/baaa034
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Article ; Online: Revealing Unexplored Sequence-Function Space Using Sequence Similarity Networks.

Copp, Janine N / Akiva, Eyal / Babbitt, Patricia C / Tokuriki, Nobuhiko

Biochemistry

2018 Volume 57, Issue 31, Page(s) 4651–4662

Abstract: The rapidly expanding number of protein sequences found in public databases can improve our understanding of how protein functions evolve. However, our current knowledge of protein function likely represents a small fraction of the diverse repertoire ... ...

Abstract	The rapidly expanding number of protein sequences found in public databases can improve our understanding of how protein functions evolve. However, our current knowledge of protein function likely represents a small fraction of the diverse repertoire that exists in nature. Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions through the observation and investigation of the complex sequence-structure-function relationships within protein superfamilies. Here, we highlight the use of sequence similarity networks (SSNs) to identify previously unexplored sequence and function space. We exemplify this approach using the nitroreductase (NTR) superfamily. We demonstrate that SSN investigations can provide a rapid and effective means to classify groups of proteins, therefore exposing experimentally unexplored sequences that may exhibit novel functionality. Integration of such approaches with systematic experimental characterization will expand our understanding of the functional diversity of enzymes and their associated physiological roles.
MeSH term(s)	Amino Acid Sequence ; Computational Biology/methods ; Databases, Protein ; Evolution, Molecular ; Nitroreductases/chemistry ; Nitroreductases/metabolism ; Proteins/chemistry ; Proteins/metabolism ; Structure-Activity Relationship
Chemical Substances	Proteins ; Nitroreductases (EC 1.7.-)
Language	English
Publishing date	2018-07-27
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1108-3
ISSN	1520-4995 ; 0006-2960
ISSN (online)	1520-4995
ISSN	0006-2960
DOI	10.1021/acs.biochem.8b00473
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Article ; Online: Exploring the sequence, function, and evolutionary space of protein superfamilies using sequence similarity networks and phylogenetic reconstructions.

Copp, Janine N / Anderson, Dave W / Akiva, Eyal / Babbitt, Patricia C / Tokuriki, Nobuhiko

Methods in enzymology

2019 Volume 620, Page(s) 315–347

Abstract: Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions by enabling the observation and investigation of complex sequence-structure-function and evolutionary relationships within protein ... ...

Abstract	Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions by enabling the observation and investigation of complex sequence-structure-function and evolutionary relationships within protein superfamilies. Here, we highlight the use of sequence similarity networks (SSNs) and phylogenetic reconstructions to map the functional divergence and evolutionary history of protein superfamilies. We exemplify this approach using the nitroreductase (NTR) flavoenzyme superfamily, demonstrating that SSN investigations can provide a rapid and effective means to classify groups of proteins, expose sequence similarity relationships across the global scale of a protein superfamily, and efficiently support detailed phylogenetic analyses. Integration of such approaches with systematic experimental characterization will expand our understanding of the functional diversity of enzymes, their evolution, and their associated physiological roles.
MeSH term(s)	Computational Biology/methods ; Databases, Protein ; Evolution, Molecular ; Models, Molecular ; Nitroreductases/chemistry ; Nitroreductases/genetics ; Nitroreductases/metabolism ; Phylogeny ; Sequence Analysis, Protein
Chemical Substances	Nitroreductases (EC 1.7.-)
Language	English
Publishing date	2019-04-17
Publishing country	United States
Document type	Journal Article
ISSN	1557-7988 ; 0076-6879
ISSN (online)	1557-7988
ISSN	0076-6879
DOI	10.1016/bs.mie.2019.03.015
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Pythoscape: a framework for generation of large protein similarity networks.

Barber, Alan E / Babbitt, Patricia C

Bioinformatics (Oxford, England)

2012 Volume 28, Issue 21, Page(s) 2845–2846

Abstract: Pythoscape is a framework implemented in Python for processing large protein similarity networks for visualization in other software packages. Protein similarity networks are graphical representations of sequence, structural and other similarities among ... ...

Abstract	Pythoscape is a framework implemented in Python for processing large protein similarity networks for visualization in other software packages. Protein similarity networks are graphical representations of sequence, structural and other similarities among proteins for which pairwise all-by-all similarity connections have been calculated. Mapping of biological and other information to network nodes or edges enables hypothesis creation about sequence-structure-function relationships across sets of related proteins. Pythoscape provides several options to calculate pairwise similarities for input sequences or structures, applies filters to network edges and defines sets of similar nodes and their associated data as single nodes (termed representative nodes) for compression of network information and output data or formatted files for visualization.
MeSH term(s)	Base Sequence ; Data Collection ; Data Display ; Glutathione Transferase/chemistry ; Glutathione Transferase/classification ; Multigene Family ; Proteins/chemistry ; Proteins/classification ; Software ; Structure-Activity Relationship ; Substrate Specificity
Chemical Substances	Proteins ; Glutathione Transferase (EC 2.5.1.18)
Language	English
Publishing date	2012-09-08
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bts532
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Article ; Online: Evolutionary and molecular foundations of multiple contemporary functions of the nitroreductase superfamily.

Akiva, Eyal / Copp, Janine N / Tokuriki, Nobuhiko / Babbitt, Patricia C

Proceedings of the National Academy of Sciences of the United States of America

2017 Volume 114, Issue 45, Page(s) E9549–E9558

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 ... ...

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.
MeSH term(s)	Computational Biology/methods ; Evolution, Molecular ; Flavin Mononucleotide/genetics ; Models, Molecular ; Nitroreductases/genetics ; Phylogeny
Chemical Substances	Flavin Mononucleotide (7N464URE7E) ; Nitroreductases (EC 1.7.-)
Language	English
Publishing date	2017--07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.1706849114
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