Annotation of proteins of unknown function: initial enzyme results

Evolution and enrichment of CYP5035 in Polyporales: functionality of an understudied P450 family

Abstract

Bioprospecting for innovative basidiomycete cytochrome P450 enzymes (P450s) is highly desirable due to the fungi’s enormous enzymatic repertoire and outstanding ability to degrade lignin and detoxify various xenobiotics. While fungal metagenomics is progressing rapidly, the biocatalytic potential of the majority of these annotated P450 sequences usually remains concealed, although functional profiling identified several P450 families with versatile substrate scopes towards various natural products. Functional knowledge about the CYP5035 family, for example, is largely insufficient. In this study, the families of the putative P450 sequences of the four white-rot fungi Polyporus arcularius, Polyporus brumalis, Polyporus squamosus and Lentinus tigrinus were assigned, and the CYPomes revealed an unusual enrichment of CYP5035, CYP5136 and CYP5150. By computational analysis of the phylogeny of the former two P450 families, the evolution of their enrichment could be traced back to the Ganoderma macrofungus, indicating their evolutionary benefit. In order to address the knowledge gap on CYP5035 functionality, a representative subgroup of this P450 family of P. arcularius was expressed and screened against a test set of substrates. Thereby, the multifunctional enzyme CYP5035S7 converting several plant natural product classes was discovered. Aligning CYP5035S7 to 102,000 putative P450 sequences of 36 fungal species from Joint Genome Institute-provided genomes located hundreds of further CYP5035 family members, which subfamilies were classified if possible. Exemplified by these specific enzyme analyses, this study gives valuable hints for future bioprospecting of such xenobiotic-detoxifying P450s and for the identification of their biocatalytic potential.

Graphical abstract

Key points

• The P450 families CYP5035 and CYP5136 are unusually enriched in P. arcularius.

• Functional screening shows CYP5035 assisting in the fungal detoxification mechanism.

• Some Polyporales encompass an unusually large repertoire of detoxification P450s.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-021-11444-2.

Identification and characterization of proteins of unknown function (PUFs) in Clostridium thermocellum DSM 1313 strains as potential genetic engineering targets

BACKGROUND

Mass spectrometry-based proteomics can identify and quantify thousands of proteins from individual microbial species, but a significant percentage of these proteins are unannotated and hence classified as proteins of unknown function (PUFs). Due to the difficulty in extracting meaningful metabolic information, PUFs are often overlooked or discarded during data analysis, even though they might be critically important in functional activities, in particular for metabolic engineering research.

RESULTS

We optimized and employed a pipeline integrating various "guilt-by-association" (GBA) metrics, including differential expression and co-expression analyses of high-throughput mass spectrometry proteome data and phylogenetic coevolution analysis, and sequence homology-based approaches to determine putative functions for PUFs in Clostridium thermocellum. Our various analyses provided putative functional information for over 95% of the PUFs detected by mass spectrometry in a wild-type and/or an engineered strain of C. thermocellum. In particular, we validated a predicted acyltransferase PUF (WP_003519433.1) with functional activity towards 2-phenylethyl alcohol, consistent with our GBA and sequence homology-based predictions.

CONCLUSIONS

This work demonstrates the value of leveraging sequence homology-based annotations with empirical evidence based on the concept of GBA to broadly predict putative functions for PUFs, opening avenues to further interrogation via targeted experiments.

The IDIP framework for assessing protein function and its application to the prion protein

The quest to determine the function of a protein can represent a profound challenge. Although this task is the mandate of countless research groups, a general framework for how it can be approached is conspicuously lacking. Moreover, even expectations for when the function of a protein can be considered to be 'known' are not well defined. In this review, we begin by introducing concepts pertinent to the challenge of protein function assignments. We then propose a framework for inferring a protein's function from four data categories: 'inheritance', 'distribution', 'interactions' and 'phenotypes' (IDIP). We document that the functions of proteins emerge at the intersection of inferences drawn from these data categories and emphasise the benefit of considering them in an evolutionary context. We then apply this approach to the cellular prion protein (PrP^C ), well known for its central role in prion diseases, whose function continues to be considered elusive by many investigators. We document that available data converge on the conclusion that the function of the prion protein is to control a critical post-translational modification of the neural cell adhesion molecule in the context of epithelial-to-mesenchymal transition and related plasticity programmes. Finally, we argue that this proposed function of PrP^C has already passed the test of time and is concordant with the IDIP framework in a way that other functions considered for this protein fail to achieve. We anticipate that the IDIP framework and the concepts analysed herein will aid the investigation of other proteins whose primary functional assignments have thus far been intractable.

OMPdb: A Global Hub of Beta-Barrel Outer Membrane Proteins

OMPdb (www.ompdb.org) was introduced as a database for β-barrel outer membrane proteins from Gram-negative bacteria in 2011 and then included 69,354 entries classified into 85 families. The database has been updated continuously using a collection of characteristic profile Hidden Markov Models able to discriminate between the different families of prokaryotic transmembrane β-barrels. The number of families has increased ultimately to a total of 129 families in the current, second major version of OMPdb. New additions have been made in parallel with efforts to update existing families and add novel families. Here, we present the upgrade of OMPdb, which from now on aims to become a global repository for all transmembrane β-barrel proteins, both eukaryotic and bacterial.

Flexible Implementation of the BASIL CURE

Course-based Undergraduate Research Experiences (CUREs) can be a very effective means to introduce a large number of students to research. CUREs are often an extension of the instructor's research, which may make them difficult to replicate in other settings because of differences in expertise or facilities. The BASIL (Biochemistry Authentic Scientific Inquiry Lab) CURE has evolved over the past 4 years as faculty members with different backgrounds, facilities, and campus cultures have all contributed to a robust curriculum focusing on enzyme function prediction that is suitable for implementation in a wide variety of academic settings. © 2019 International Union of Biochemistry and Molecular Biology, 47(5):498-505, 2019.

Launching the C-HPP neXt-CP50 Pilot Project for Functional Characterization of Identified Proteins with No Known Function

An important goal of the Human Proteome Organization (HUPO) Chromosome-centric Human Proteome Project (C-HPP) is to correctly define the number of canonical proteins encoded by their cognate open reading frames on each chromosome in the human genome. When identified with high confidence of protein evidence (PE), such proteins are termed PE1 proteins in the online database resource, neXtProt. However, proteins that have not been identified unequivocally at the protein level but that have other evidence suggestive of their existence (PE2-4) are termed missing proteins (MPs). The number of MPs has been reduced from 5511 in 2012 to 2186 in 2018 (neXtProt 2018-01-17 release). Although the annotation of the human proteome has made significant progress, the "parts list" alone does not inform function. Indeed, 1937 proteins representing ∼10% of the human proteome have no function either annotated from experimental characterization or predicted by homology to other proteins. Specifically, these 1937 "dark proteins" of the so-called dark proteome are composed of 1260 functionally uncharacterized but identified PE1 proteins, designated as uPE1, plus 677 MPs from categories PE2-PE4, which also have no known or predicted function and are termed uMPs. At the HUPO-2017 Annual Meeting, the C-HPP officially adopted the uPE1 pilot initiative, with 14 participating international teams later committing to demonstrate the feasibility of the functional characterization of large numbers of dark proteins (CP), starting first with 50 uPE1 proteins, in a stepwise chromosome-centric organizational manner. The second aim of the feasibility phase to characterize protein (CP) functions of 50 uPE1 proteins, termed the neXt-CP50 initiative, is to utilize a variety of approaches and workflows according to individual team expertise, interest, and resources so as to enable the C-HPP to recommend experimentally proven workflows to the proteome community within 3 years. The results from this pilot will not only be the cornerstone of a larger characterization initiative but also enhance understanding of the human proteome and integrated cellular networks for the discovery of new mechanisms of pathology, mechanistically informative biomarkers, and rational drug targets.

Anticipated learning outcomes for a biochemistry course-based undergraduate research experience aimed at predicting protein function from structure: Implications for assessment design

Several course-based undergraduate research experiences (CUREs) have been published in the literature. However, only limited attempts have been made to rigorously identify the discovery-type research abilities that students actually develop during such experiences. Instead, there has been a greater focus on technical or procedural-type knowledge or general CURE skills that are too comprehensive to effectively assess. Before the extent of discovery-type learning outcomes can be established in students (termed verified learning outcomes or VLOs), it is important to rigorously identify the anticipated learning outcomes (ALOs) and to then develop student assessments that target each ALO to reveal the nature of such student learning. In this article we present a matrix of 43 ALOs, or course-based undergraduate research abilities (CURAs), that instructors anticipate students will develop during a recently-developed biochemistry CURE focusing on the prediction of protein function from structure. The CURAs were identified using the process for identifying course-based undergraduate research abilities (PICURA) and classified into seven distinct themes that enabled the characterization of the CURE and a comparison to other published inventories of research competencies and CURE aspects. These themes and the CURE protocols aligning to the CURAs were used to form the ALO matrix that was, in turn, used to inform the design of an assessment that revealed evidence that a student had developed some of the targeted CURAs. Future research will focus on further assessment development that targets other identified CURAs. This approach has potential applications to other CUREs both in biochemistry and other science disciplines. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):478-492, 2018.

Revealing Unexplored Sequence-Function Space Using Sequence Similarity Networks

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.

Lessons from my undergraduate research students

From very early on, my personal/professional life has been shaped by teachers in many different settings. Teaching and learning form a two-way street. In the process of teaching undergraduate students, particularly in the research lab, I have learned some profound lessons about the importance of listening to them, challenging them, giving them autonomy, and allowing them to enjoy success and to risk failure. I am now working with a team of faculty members to implement these lessons in a course-based undergraduate research experience in the biochemistry teaching laboratory. Our goal is to seek answers to the question "How do students become scientists?" and to implement those answers with our future students.

How to Identify the Research Abilities That Instructors Anticipate Students Will Develop in a Biochemistry Course-Based Undergraduate Research Experience (CURE)

Course-based undergraduate research experiences (CUREs) have been described in a range of educational contexts. Although various anticipated learning outcomes (ALOs) have been proposed, processes for identifying them may not be rigorous or well documented, which can lead to inappropriate assessment and speculation about what students actually learn from CUREs. In this essay, we offer a user-friendly and rigorous approach based on evidence and an easy process to identify ALOs, namely, a five-step Process for Identifying Course-Based Undergraduate Research Abilities (PICURA), consisting of a content analysis, an open-ended survey, an interview, an alignment check, and a two-tiered Likert survey. The development of PICURA was guided by four criteria: 1) the process is iterative, 2) the overall process gives more insight than individual data sources, 3) the steps of the process allow for consensus across the data sources, and 4) the process allows for prioritization of the identified abilities. To address these criteria, we collected data from 10 participants in a multi-institutional biochemistry CURE. In this essay, we use two selected research abilities to illustrate how PICURA was used to identify and prioritize such abilities. PICURA could be applied to other CUREs in other contexts.

A survey on faculty perspectives on the transition to a biochemistry course-based undergraduate research experience laboratory

It will always remain a goal of an undergraduate biochemistry laboratory course to engage students hands-on in a wide range of biochemistry laboratory experiences. In 2006, our research group initiated a project for in silico prediction of enzyme function based only on the 3D coordinates of the more than 3800 proteins "of unknown function" in the Protein Data Bank, many of which resulted from the Protein Structure Initiative. Students have used the ProMOL plugin to the PyMOL molecular graphics environment along with BLAST, Pfam, and Dali to predict protein functions. As young scientists, these undergraduate research students wanted to see if their predictions were correct and so they developed an approach for in vitro testing of predicted enzyme function that included literature exploration, selection of a suitable assay and the search for commercially available substrates. Over the past two years, a team of faculty members from seven different campuses (California Polytechnic San Luis Obispo, Hope College, Oral Roberts University, Rochester Institute of Technology, St. Mary's University, Ursinus College, and Purdue University) have transferred this approach to the undergraduate biochemistry teaching laboratory as a Course-based Undergraduate Research Experience. A series of ten course modules and eight instructional videos have been created (www.promol.org/home/basil-modules-1) and the group is now expanding these resources, creating assessments and evaluating how this approach helps student to grow as scientists. The focus of this manuscript will be the logistical implications of this transition on campuses that have different cultures, expectations, schedules, and student populations. © 2017 by The International Union of Biochemistry and Molecular Biology, 45(5):426-436, 2017.

Nataraja K. An Approach to Function Annotation for Proteins of Unknown Function (PUFs) in the Transcriptome of Indian Mulberry

The modern sequencing technologies are generating large volumes of information at the transcriptome and genome level. Translation of this information into a biological meaning is far behind the race due to which a significant portion of proteins discovered remain as proteins of unknown function (PUFs). Attempts to uncover the functional significance of PUFs are limited due to lack of easy and high throughput functional annotation tools. Here, we report an approach to assign putative functions to PUFs, identified in the transcriptome of mulberry, a perennial tree commonly cultivated as host of silkworm. We utilized the mulberry PUFs generated from leaf tissues exposed to drought stress at whole plant level. A sequence and structure based computational analysis predicted the probable function of the PUFs. For rapid and easy annotation of PUFs, we developed an automated pipeline by integrating diverse bioinformatics tools, designated as PUFs Annotation Server (PUFAS), which also provides a web service API (Application Programming Interface) for a large-scale analysis up to a genome. The expression analysis of three selected PUFs annotated by the pipeline revealed abiotic stress responsiveness of the genes, and hence their potential role in stress acclimation pathways. The automated pipeline developed here could be extended to assign functions to PUFs from any organism in general. PUFAS web server is available at http://caps.ncbs.res.in/pufas/ and the web service is accessible at http://capservices.ncbs.res.in/help/pufas.

Automated protein motif generation in the structure-based protein function prediction tool ProMOL

ProMOL, a plugin for the PyMOL molecular graphics system, is a structure-based protein function prediction tool. ProMOL includes a set of routines for building motif templates that are used for screening query structures for enzyme active sites. Previously, each motif template was generated manually and required supervision in the optimization of parameters for sensitivity and selectivity. We developed an algorithm and workflow for the automation of motif building and testing routines in ProMOL. The algorithm uses a set of empirically derived parameters for optimization and requires little user intervention. The automated motif generation algorithm was first tested in a performance comparison with a set of manually generated motifs based on identical active sites from the same 112 PDB entries. The two sets of motifs were equally effective in identifying alignments with homologs and in rejecting alignments with unrelated structures. A second set of 296 active site motifs were generated automatically, based on Catalytic Site Atlas entries with literature citations, as an expansion of the library of existing manually generated motif templates. The new motif templates exhibited comparable performance to the existing ones in terms of hit rates against native structures, homologs with the same EC and Pfam designations, and randomly selected unrelated structures with a different EC designation at the first EC digit, as well as in terms of RMSD values obtained from local structural alignments of motifs and query structures. This research is supported by NIH grant GM078077.