Visualizing and Clustering Protein Similarity Networks: Sequences, Structures, and Functions

The application of machine learning in clinical microbiology and infectious diseases

With the development of artificial intelligence(AI) in computer science and statistics, it has been further applied to the medical field. These applications include the management of infectious diseases, in which machine learning has created inroads in clinical microbiology, radiology, genomics, and the analysis of electronic health record data. Especially, the role of machine learning in microbiology has gradually become prominent, and it is used in etiological diagnosis, prediction of antibiotic resistance, association between human microbiome characteristics and complex host diseases, prognosis judgment, and prevention and control of infectious diseases. Machine learning in the field of microbiology mainly adopts supervised learning and unsupervised learning, involving algorithms from classification and regression to clustering and dimensionality reduction. This Review explains crucial concepts in machine learning for unfamiliar readers, describes machine learning's current applications in clinical microbiology and infectious diseases, and summarizes important approaches clinicians must be aware of when evaluating research using machine learning.

Mechanisms of efficient polyacrylamide degradation: From multi-omics analysis to structural characterization of two amidohydrolases

Polyacrylamide (PAM) is a high molecular weight polymer with extensive applications. However, inefficient natural degradation of PAM results in its environmental accumulation. Here, using multi-omics analysis, we constructed the PAM biodegradation pathway in Klebsiella sp. PCX, an efficient PAM-degrading bacterium. Subsequently, two unclassified amidohydrolases (PCX00451 and PCX04581) were identified as key factors for rapid PAM biodegradation, both of which possessed much higher hydrolysis efficiency for PAM than for small molecule amide compounds. Besides, crystal structures of PCX00451 and PCX04581 were solved. Both two amidohydrolases were consisted with a twisted triosephosphateisomerase (TIM)-barrel and a smaller β-sandwich domain. And their binding pockets were in the conserved metal center of TIM-barrel domain. Moreover, Asp267 of PCX00451 and Asp282 of PCX04581 were examined as active sites for acid/base catalysis. Our research characterized the molecular mechanisms of two efficient amidohydrolases, providing theoretical basis and valuable tools for PAM bioremediation.

Structural and Mechanistic Insights into a Novel Monooxygenase for Poly(acrylic acid) Biodegradation

Polyacrylamide (PAM) is a high-molecular-weight polymer with extensive applications. However, the inefficient natural degradation of PAM results in environmental accumulation of the polymer. Biodegradation is an environmentally friendly approach in the field of PAM treatment. The first phase of PAM biodegradation is the deamination of PAM, forming the product poly(acrylic acid) (PAA). The second phase of PAM biodegradation involves the cleavage of PAA into small molecules, which is a crucial step in the degradation pathway of PAM. However, the enzyme that catalyzes the degradation of PAA and the molecular mechanism remain unclear. Here, a novel monooxygenase PCX02514 is identified as the key enzyme for PAA degradation. Through biochemical experiments, the monooxygenase PCX02514 oxidizes PAA with the participation of NADPH, causing the cleavage of carbon chains and a decrease in the molecular weight of PAA. In addition, the crystal structure of the monooxygenase PCX02514 is solved at a resolution of 1.97 Å. The active pocket is in a long cavity that extends from the C-terminus of the TIM barrel to the protein surface and exhibits positive electrostatic potential, thereby causing the migration of oxygen-negative ions into the active pocket and facilitating the reaction between the substrates and monooxygenase PCX02514. Moreover, Arg10-Arg125-Ser186-Arg187-His253 are proposed as potential active sites in monooxygenase PCX02514. Our research characterizes the molecular mechanism of this monooxygenase, providing a theoretical basis and valuable tools for PAM bioremediation.

CBP60-DB: An AlphaFold-predicted plant kingdom-wide database of the CALMODULIN-BINDING PROTEIN 60 protein family with a novel structural clustering algorithm

Molecular genetic analyses in the model species Arabidopsis thaliana have demonstrated the major roles of different CALMODULIN-BINDING PROTEIN 60 (CBP60) proteins in growth, stress signaling, and immune responses. Prominently, CBP60g and SARD1 are paralogous CBP60 transcription factors that regulate numerous components of the immune system, such as cell surface and intracellular immune receptors, MAP kinases, WRKY transcription factors, and biosynthetic enzymes for immunity-activating metabolites salicylic acid (SA) and N-hydroxypipecolic acid (NHP). However, their function, regulation, and diversification in most species remain unclear. Here, we have created CBP60-DB (https://cbp60db.wlu.ca/), a structural and bioinformatic database that comprehensively characterized 1052 CBP60 gene homologs (encoding 2376 unique transcripts and 1996 unique proteins) across 62 phylogenetically diverse genomes in the plant kingdom. We have employed deep learning-predicted structural analyses using AlphaFold2 and then generated dedicated web pages for all plant CBP60 proteins. Importantly, we have generated a novel clustering visualization algorithm to interrogate kingdom-wide structural similarities for more efficient inference of conserved functions across various plant taxa. Because well-characterized CBP60 proteins in Arabidopsis are known to be transcription factors with putative calmodulin-binding domains, we have integrated external bioinformatic resources to analyze protein domains and motifs. Collectively, we present a plant kingdom-wide identification of this important protein family in a user-friendly AlphaFold-anchored database, representing a novel and significant resource for the broader plant biology community.

A review of visualisations of protein fold networks and their relationship with sequence and function

Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

Misregulation of ELK1, AP1, and E12 Transcription Factor Networks Is Associated with Melanoma Progression

Melanoma is among the most malignant cutaneous cancers and when metastasized results in dramatically high mortality. Despite advances in high-throughput gene expression profiling in cancer transcriptomic studies, our understanding of mechanisms driving melanoma progression is still limited. We present here an in-depth bioinformatic analysis of the melanoma RNAseq, chromatin immunoprecipitation (ChIP)seq, and single-cell (sc)RNA seq data to understand cancer progression. Specifically, we have performed a consensus network analysis of RNA-seq data from clinically re-grouped melanoma samples to identify gene co-expression networks that are conserved in early (stage 1) and late (stage 4/invasive) stage melanoma. Overlaying the fold-change information on co-expression networks revealed several coordinately up or down-regulated subnetworks that may play a critical role in melanoma progression. Furthermore, by incorporating histone lysine-27 acetylation information and highly expressed genes identified from the single-cell RNA data from melanoma patient samples, we present a comprehensive list of pathways, putative protein-protein interactions (PPIs) and transcription factor (TF) networks that are driving cancer progression. From this analysis, we have identified Elk1, AP1 and E12 TF networks that coordinately change expression in late melanoma when compared to early melanoma, implicating these TFs in melanoma progression. Additionally, the sumoylation-associated interactome is upregulated in invasive melanoma. Together, this bioinformatic analysis potentially implicates a combination of TF networks and PPIs in melanoma progression, which if confirmed in the experimental systems, could be used as targets for drug intervention in melanoma.

SeQuery: an interactive graph database for visualizing the GPCR superfamily

The rate at which new protein and gene sequences are being discovered has grown explosively in the omics era, which has increasingly complicated the efficient characterization and analysis of their biological properties. In this study, we propose a web-based graphical database tool, SeQuery, for intuitively visualizing proteome/genome networks by integrating the sequential, structural and functional information of sequences. As a demonstration of our tool’s effectiveness, we constructed a graph database of G protein-coupled receptor (GPCR) sequences by integrating data from the UniProt, GPCRdb and RCSB PDB databases. Our tool attempts to achieve two goals: (i) given the sequence of a query protein, correctly and efficiently identify whether the protein is a GPCR, and, if so, define its sequential and functional roles in the GPCR superfamily; and (ii) present a panoramic view of the GPCR superfamily and its network centralities that allows users to explore the superfamily at various resolutions. Such a bottom-up-to-top-down view can provide the users with a comprehensive understanding of the GPCR superfamily through interactive navigation of the graph database. A test of SeQuery with the GPCR2841 dataset shows that it correctly identifies 99 out of 100 queried protein sequences. The developed tool is readily applicable to other biological networks, and we aim to expand SeQuery by including additional biological databases in the near future.

The Applications of Clustering Methods in Predicting Protein Functions

Background:

The understanding of protein function is essential to the study of biological processes. However, the prediction of protein function has been a difficult task for bioinformatics to overcome. This has resulted in many scholars focusing on the development of computational methods to address this problem.

Objective:

In this review, we introduce the recently developed computational methods of protein function prediction and assess the validity of these methods. We then introduce the applications of clustering methods in predicting protein functions.

Scaffold Diversity Synthesis Delivers Complex, Structurally, and Functionally Distinct Tetracyclic Benzopyrones

Complexity-generating chemical transformations that afford novel molecular scaffolds enriched in sp3 character are highly desired. Here, we present a highly stereoselective scaffold diversity synthesis approach that utilizes cascade double-annulation reactions of diverse pairs of zwitterionic and non-zwitterionic partners with 3-formylchromones to generate highly complex tetracyclic benzopyrones. Each pair of annulation partners adds to the common chroman-4-one scaffold to build two new rings, supporting up to four contiguous chiral centers that include an all-carbon quaternary center. Differently ring-fused benzopyrones display different biological activities, thus demonstrating their immense potential in medicinal chemistry and chemical biology research.

Nucleobase deaminases: a potential enzyme system for new therapies

This review presents an overview of the structure, function and mechanism of CDA deaminases and their potential as enzyme systems for development of new antimicrobial therapies.

Biocuration in the structure-function linkage database: the anatomy of a superfamily

With ever-increasing amounts of sequence data available in both the primary literature and sequence repositories, there is a bottleneck in annotating molecular function to a sequence. This article describes the biocuration process and methods used in the structure-function linkage database (SFLD) to help address some of the challenges. We discuss how the hierarchy within the SFLD allows us to infer detailed functional properties for functionally diverse enzyme superfamilies in which all members are homologous, conserve an aspect of their chemical function and have associated conserved structural features that enable the chemistry. Also presented is the Enzyme Structure-Function Ontology (ESFO), which has been designed to capture the relationships between enzyme sequence, structure and function that underlie the SFLD and is used to guide the biocuration processes within the SFLD.

Database URL:http://sfld.rbvi.ucsf.edu/

Visualizing the GPCR Network: Classification and Evolution

In this study, we delineate an unsupervised clustering algorithm, minimum span clustering (MSC), and apply it to detect G-protein coupled receptor (GPCR) sequences and to study the GPCR network using a base dataset of 2770 GPCR and 652 non-GPCR sequences. High detection accuracy can be achieved with a proper dataset. The clustering results of GPCRs derived from MSC show a strong correlation between their sequences and functions. By comparing our level 1 MSC results with the GPCRdb classification, the consistency is 87.9% for the fourth level of GPCRdb, 89.2% for the third level, 98.4% for the second level, and 100% for the top level (the lowest resolution level of GPCRdb). The MSC results of GPCRs can be well explained by estimating the selective pressure of GPCRs, as exemplified by investigating the largest two subfamilies, peptide receptors (PRs) and olfactory receptors (ORs), in class A GPCRs. PRs are decomposed into three groups due to a positive selective pressure, whilst ORs remain as a single group due to a negative selective pressure. Finally, we construct and compare phylogenetic trees using distance-based and character-based methods, a combination of which could convey more comprehensive information about the evolution of GPCRs.