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1. Bioinformatics Identification of Anti-CRISPR Loci by Using Homology, Guilt-by-Association, and CRISPR Self-Targeting Spacer Approaches

   https://doi.org/10.1128/mSystems.00455-19  

   Yin, Yanbin ; Yang, Bowen ; Entwistle, Sarah ; Claesson, Marcus J. ( October 2019 , mSystems)

   ABSTRACT Anti-CRISPR (Acr) loci/operons encode Acr proteins and Acr-associated (Aca) proteins. Forty-five Acr families have been experimentally characterized inhibiting seven subtypes of CRISPR-Cas systems. We have developed a bioinformatics pipeline to identify genomic loci containing Acr homologs and/or Aca homologs by combining three computational approaches: homology, guilt-by-association, and self-targeting spacers. Homology search found thousands of Acr homologs in bacterial and viral genomes, but most are homologous to AcrIIA7 and AcrIIA9. Investigating the gene neighborhood of these Acr homologs revealed that only a small percentage (23.0% in bacteria and 8.2% in viruses) of them have neighboring Aca homologs and thus form Acr-Aca operons. Surprisingly, although a self-targeting spacer is a strong indicator of the presence of Acr genes in a genome, a large percentage of Acr-Aca loci are found in bacterial genomes without self-targeting spacers or even without complete CRISPR-Cas systems. Additionally, for Acr homologs from genomes with self-targeting spacers, homology-based Acr family assignments do not always agree with the self-targeting CRISPR-Cas subtypes. Last, by investigating Acr genomic loci coexisting with self-targeting spacers in the same genomes, five known subtypes (I-C, I-E, I-F, II-A, and II-C) and five new subtypes (I-B, III-A, III-B, IV-A, and V-U4) of Acrs were inferred. Based on these findings, we conclude that the discovery of new anti-CRISPRs should not be restricted to genomes with self-targeting spacers and loci with Acr homologs. The evolutionary arms race of CRISPR-Cas systems and anti-CRISPR systems may have driven the adaptive and rapid gain and loss of these elements in closely related genomes. IMPORTANCE As a naturally occurring adaptive immune system, CRISPR-Cas (clustered regularly interspersed short palindromic repeats–CRISPR-associated genes) systems are widely found in bacteria and archaea to defend against viruses. Since 2013, the application of various bacterial CRISPR-Cas systems has become very popular due to their development into targeted and programmable genome engineering tools with the ability to edit almost any genome. As the natural off-switch of CRISPR-Cas systems, anti-CRISPRs have a great potential to serve as regulators of CRISPR-Cas tools and enable safer and more controllable genome editing. This study will help understand the relative usefulness of the three bioinformatics approaches for new Acr discovery, as well as guide the future development of new bioinformatics tools to facilitate anti-CRISPR research. The thousands of Acr homologs and hundreds of new anti-CRISPR loci identified in this study will be a valuable data resource for genome engineers to search for new CRISPR-Cas regulators. 

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2. AcrDB: a database of anti-CRISPR operons in prokaryotes and viruses

   https://doi.org/10.1093/nar/gkaa857  

   Huang, Le ; Yang, Bowen ; Yi, Haidong ; Asif, Amina ; Wang, Jiawei ; Lithgow, Trevor ; Zhang, Han ; Minhas, Fayyaz ul Amir Afsar ; Yin, Yanbin ( October 2020 , Nucleic Acids Research)

   null (Ed.)

   Abstract CRISPR–Cas is an anti-viral mechanism of prokaryotes that has been widely adopted for genome editing. To make CRISPR–Cas genome editing more controllable and safer to use, anti-CRISPR proteins have been recently exploited to prevent excessive/prolonged Cas nuclease cleavage. Anti-CRISPR (Acr) proteins are encoded by (pro)phages/(pro)viruses, and have the ability to inhibit their host's CRISPR–Cas systems. We have built an online database AcrDB (http://bcb.unl.edu/AcrDB) by scanning ∼19 000 genomes of prokaryotes and viruses with AcrFinder, a recently developed Acr-Aca (Acr-associated regulator) operon prediction program. Proteins in Acr-Aca operons were further processed by two machine learning-based programs (AcRanker and PaCRISPR) to obtain numerical scores/ranks. Compared to other anti-CRISPR databases, AcrDB has the following unique features: (i) It is a genome-scale database with the largest collection of data (39 799 Acr-Aca operons containing Aca or Acr homologs); (ii) It offers a user-friendly web interface with various functions for browsing, graphically viewing, searching, and batch downloading Acr-Aca operons; (iii) It focuses on the genomic context of Acr and Aca candidates instead of individual Acr protein family and (iv) It collects data with three independent programs each having a unique data mining algorithm for cross validation. AcrDB will be a valuable resource to the anti-CRISPR research community. 

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3. Efficient and modular CRISPR‐Cas9 vector system for Physcomitrella patens

   https://doi.org/10.1002/pld3.168  

   Mallett, Darren R. ; Chang, Mingqin ; Cheng, Xiaohang ; Bezanilla, Magdalena ( September 2019 , Plant Direct)

   CRISPR‐Cas9 has been shown to be a valuable tool in recent years, allowing researchers to precisely edit the genome using an RNA‐guided nuclease to initiate double‐strand breaks. Until recently, classical RAD51‐mediated homologous recombination has been a powerful tool for gene targeting in the mossPhyscomitrella patens. However, CRISPR‐Cas9‐mediated genome editing inP. patenswas shown to be more efficient than traditional homologous recombination (Plant Biotechnology Journal, 15, 2017, 122). CRISPR‐Cas9 provides the opportunity to efficiently edit the genome at multiple loci as well as integrate sequences at precise locations in the genome using a simple transient transformation. To fully take advantage of CRISPR‐Cas9 genome editing inP. patens, here we describe the generation and use of a flexible and modular CRISPR‐Cas9 vector system. Without the need for gene synthesis, this vector system enables editing of up to 12 loci simultaneously. Using this system, we generated multiple lines that had null alleles at four distant loci. We also found that targeting multiple sites within a single locus can produce larger deletions, but the success of this depends on individual protospacers. To take advantage of homology‐directed repair, we developed modular vectors to rapidly generate DNA donor plasmids to efficiently introduce DNA sequences encoding for fluorescent proteins at the 5′ and 3′ ends of gene coding regions. With regard to homology‐directed repair experiments, we found that if the protospacer sequence remains on the DNA donor plasmid, then Cas9 cleaves the plasmid target as well as the genomic target. This can reduce the efficiency of introducing sequences into the genome. Furthermore, to ensure the generation of a null allele near the Cas9 cleavage site, we generated a homology plasmid harboring a “stop codon cassette” with downstream near‐effortless genotyping.

    

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4. Resistance-Guided Mining of Bacterial Genotoxins Defines a Family of DNA Glycosylases

   https://doi.org/10.1128/mbio.03297-21  

   Bradley, Noah P. ; Wahl, Katherine L. ; Steenwyk, Jacob L. ; Rokas, Antonis ; Eichman, Brandt F. ( April 2022 , mBio)

   Simmons, Lyle A. ; Bush, Karen (Ed.)

   ABSTRACT Unique DNA repair enzymes that provide self-resistance against therapeutically important, genotoxic natural products have been discovered in bacterial biosynthetic gene clusters (BGCs). Among these, the DNA glycosylase AlkZ is essential for azinomycin B production and belongs to the HTH_42 superfamily of uncharacterized proteins. Despite their widespread existence in antibiotic producers and pathogens, the roles of these proteins in production of other natural products are unknown. Here, we determine the evolutionary relationship and genomic distribution of all HTH_42 proteins from Streptomyces and use a resistance-based genome mining approach to identify homologs associated with known and uncharacterized BGCs. We find that AlkZ-like (AZL) proteins constitute one distinct HTH_42 subfamily and are highly enriched in BGCs and variable in sequence, suggesting each has evolved to protect against a specific secondary metabolite. As a validation of the approach, we show that the AZL protein, HedH4, associated with biosynthesis of the alkylating agent hedamycin, excises hedamycin-DNA adducts with exquisite specificity and provides resistance to the natural product in cells. We also identify a second, phylogenetically and functionally distinct subfamily whose proteins are never associated with BGCs, are highly conserved with respect to sequence and genomic neighborhood, and repair DNA lesions not associated with a particular natural product. This work delineates two related families of DNA repair enzymes—one specific for complex alkyl-DNA lesions and involved in self-resistance to antimicrobials and the other likely involved in protection against an array of genotoxins—and provides a framework for targeted discovery of new genotoxic compounds with therapeutic potential. IMPORTANCE Bacteria are rich sources of secondary metabolites that include DNA-damaging genotoxins with antitumor/antibiotic properties. Although Streptomyces produce a diverse number of therapeutic genotoxins, efforts toward targeted discovery of biosynthetic gene clusters (BGCs) producing DNA-damaging agents is lacking. Moreover, work on toxin-resistance genes has lagged behind our understanding of those involved in natural product synthesis. Here, we identified over 70 uncharacterized BGCs producing potentially novel genotoxins through resistance-based genome mining using the azinomycin B-resistance DNA glycosylase AlkZ. We validate our analysis by characterizing the enzymatic activity and cellular resistance of one AlkZ ortholog in the BGC of hedamycin, a potent DNA alkylating agent. Moreover, we uncover a second, phylogenetically distinct family of proteins related to Escherichia coli YcaQ, a DNA glycosylase capable of unhooking interstrand DNA cross-links, which differs from the AlkZ-like family in sequence, genomic location, proximity to BGCs, and substrate specificity. This work defines two families of DNA glycosylase for specialized repair of complex genotoxic natural products and generalized repair of a broad range of alkyl-DNA adducts and provides a framework for targeted discovery of new compounds with therapeutic potential. 

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5. SSIPe: accurately estimating protein–protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function

   https://doi.org/10.1093/bioinformatics/btz926  

   Huang, Xiaoqiang ; Zheng, Wei ; Pearce, Robin ; Zhang, Yang ; Valencia, ed., Alfonso ( December 2019 , Bioinformatics)

   Most proteins perform their biological functions through interactions with other proteins in cells. Amino acid mutations, especially those occurring at protein interfaces, can change the stability of protein–protein interactions (PPIs) and impact their functions, which may cause various human diseases. Quantitative estimation of the binding affinity changes (ΔΔGbind) caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses.

   We present SSIPe, which combines protein interface profiles, collected from structural and sequence homology searches, with a physics-based energy function for accurate ΔΔGbind estimation. To offset the statistical limits of the PPI structure and sequence databases, amino acid-specific pseudocounts were introduced to enhance the profile accuracy. SSIPe was evaluated on large-scale experimental data containing 2204 mutations from 177 proteins, where training and test datasets were stringently separated with the sequence identity between proteins from the two datasets below 30%. The Pearson correlation coefficient between estimated and experimental ΔΔGbind was 0.61 with a root-mean-square-error of 1.93 kcal/mol, which was significantly better than the other methods. Detailed data analyses revealed that the major advantage of SSIPe over other traditional approaches lies in the novel combination of the physical energy function with the new knowledge-based interface profile. SSIPe also considerably outperformed a former profile-based method (BindProfX) due to the newly introduced sequence profiles and optimized pseudocount technique that allows for consideration of amino acid-specific prior mutation probabilities.

   Web-server/standalone program, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/SSIPe and https://github.com/tommyhuangthu/SSIPe.

   Supplementary data are available at Bioinformatics online.

    

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