About

Gaurav Moghe is an Associate Professor in the School of Integrative Plant Science at Cornell University, working within the Plant Biology Section. His research focuses on investigating the origins of biological complexity through plant specialized metabolism, utilizing both experimental and computational approaches. His lab employs a variety of model and non-model plant species to explore these biological questions, with applications that extend to agriculture, nutrition, and medicine.

Research topics

• Biology
• Computational biology
• Artificial Intelligence
• Genetics
• Computer Science
• Bioinformatics
• Machine Learning
• Biochemistry
• Data science
• Botany
• Chemistry
• Stereochemistry
• Ecology

Selected publications

• BAHD Company: The Ever-Expanding Roles of the BAHD Acyltransferase Gene Family in Plants

  Annual Review of Plant Biology · 2022 · 76 citations

  1st authorCorresponding
  • Biology
  • Computational biology
  • Biochemistry

  Plants' ability to chemically modify core structures of specialized metabolites is the main reason why the plant kingdom contains such a wide and rich array of diverse compounds. One of the most important types of chemical modifications of small molecules is the addition of an acyl moiety to produce esters and amides. Large-scale phylogenomics analyses have shown that the enzymes that perform acyl transfer reactions on the myriad small molecules synthesized by plants belong to only a few gene families. This review is focused on describing the biochemistry, evolutionary origins, and chemical ecology implications of one of these families-the BAHD acyltransferases. The growth of advanced metabolomic studies coupled with next-generation sequencing of diverse plant species has confirmed that the BAHD family plays critical roles in modifying nearly all known classes of specialized metabolites. The current and future outlook for research on BAHDs includes expanding their roles in synthetic biology and metabolic engineering.

  DOI

• Orthology‐based analysis helps map evolutionary diversification and predict substrate class use of <scp>BAHD</scp> acyltransferases

  The Plant Journal · 2022 · 39 citations

  Senior authorCorresponding
  • Biology
  • Computational biology
  • Genetics

  Large enzyme families catalyze metabolic diversification by virtue of their ability to use diverse chemical scaffolds. How enzyme families attain such functional diversity is not clear. Furthermore, duplication and promiscuity in such enzyme families limits their functional prediction, which has produced a burgeoning set of incompletely annotated genes in plant genomes. Here, we address these challenges using BAHD acyltransferases as a model. This fast-evolving family expanded drastically in land plants, increasing from one to five copies in algae to approximately 100 copies in diploid angiosperm genomes. Compilation of >160 published activities helped visualize the chemical space occupied by this family and define eight different classes based on structural similarities between acceptor substrates. Using orthologous groups (OGs) across 52 sequenced plant genomes, we developed a method to predict BAHD acceptor substrate class utilization as well as origins of individual BAHD OGs in plant evolution. This method was validated using six novel and 28 previously characterized enzymes and helped improve putative substrate class predictions for BAHDs in the tomato genome. Our results also revealed that while cuticular wax and lignin biosynthetic activities were more ancient, anthocyanin acylation activity was fixed in BAHDs later near the origin of angiosperms. The OG-based analysis enabled identification of signature motifs in anthocyanin-acylating BAHDs, whose importance was validated via molecular dynamic simulations, site-directed mutagenesis and kinetic assays. Our results not only describe how BAHDs contributed to evolution of multiple chemical phenotypes in the plant world but also propose a biocuration-enabled approach for improved functional annotation of plant enzyme families.

  DOI

• A roadmap for the functional annotation of protein families: a community perspective

  Database · 2022 · 61 citations

  • Computer Science
  • Data science
  • Artificial Intelligence

  Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3-4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

  DOI

• Acylsugars protect Nicotiana benthamiana against insect herbivory and desiccation

  Plant Molecular Biology · 2021 · 49 citations

  • Biology
  • Botany
  • Genetics
  DOI

• Machine learning: A powerful tool for gene function prediction in plants

  Applications in Plant Sciences · 2020 · 148 citations

  Senior authorCorresponding
  • Machine Learning
  • Artificial Intelligence
  • Computer Science

  Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule-based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning-based approaches in plants.

  DOI

Frequent coauthors

• Lars Kruse

  Kiel University

  35 shared

• Robert L. Last

  Michigan State University

  24 shared

• Alexandra Bennett

  University of Florida

  22 shared

• Bryan J. Leong

  University of Florida

  18 shared

• A. Daniel Jones

  Michigan State University

  17 shared

• Shin‐Han Shiu

  Michigan State University

  14 shared

• Seung Ho Chung

  Bennett Aerospace (United States)

  11 shared

• Jing Ning

  The University of Texas MD Anderson Cancer Center

  10 shared

Education

• PhD in Genetics and Quantitative Biology, Plant Biology

  Michigan State University

  2013

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