Control of catalytic efficiency by a coevolving network of catalytic and noncatalytic residues

Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis

Though statistical normalizations are often used in differential abundance or differential expression analysis to address sample-to-sample variation in sequencing depth, we offer a better alternative. These normalizations often make strong, implicit assumptions about the scale of biological systems (e.g., microbial load). Thus, analyses are susceptible to even slight errors in these assumptions, leading to elevated rates of false positives and false negatives. We introduce scale models as a generalization of normalizations so researchers can model potential errors in assumptions about scale. By incorporating scale models into the popular ALDEx2 software, we enhance the reproducibility of analyses while often drastically decreasing false positive and false negative rates. We design scale models that are guaranteed to reduce false positives compared to equivalent normalizations. At least in the context of ALDEx2, we recommend using scale models over normalizations in all practical situations.

Intein-based thermoregulated meganucleases for containment of genetic material

Limiting the spread of synthetic genetic information outside of the intended use is essential for applications where biocontainment is critical. In particular, biocontainment of engineered probiotics and plasmids that are excreted from the mammalian gastrointestinal tract is needed to prevent escape and acquisition of genetic material that could confer a selective advantage to microbial communities. Here, we built a simple and lightweight biocontainment system that post-translationally activates a site-specific DNA endonuclease to degrade DNA at 18°C and not at higher temperatures. We constructed an orthogonal set of temperature-sensitive meganucleases (TSMs) by inserting the yeast VMA1 L212P temperature-sensitive intein into the coding regions of LAGLIDADG homing endonucleases. We showed that the TSMs eliminated plasmids carrying the cognate TSM target site from laboratory strains of Escherichia coli at the permissive 18°C but not at higher restrictive temperatures. Plasmid elimination is dependent on both TSM endonuclease activity and intein splicing. TSMs eliminated plasmids from E. coli Nissle 1917 after passage through the mouse gut when fecal resuspensions were incubated at 18°C but not at 37°C. Collectively, our data demonstrates the potential of thermoregulated meganucleases as a means of restricting engineered plasmids and probiotics to the mammalian gut.

Customized multiple sequence alignment as an effective strategy to improve performance of Taq DNA polymerase

Engineering Taq DNA polymerase (TaqPol) for improved activity, stability and sensitivity was critical for its wide applications. Multiple sequence alignment (MSA) has been widely used in engineering enzymes for improved properties. Here, we first designed TaqPol mutations based on MSA of 2756 sequences from both thermophilic and non-thermophilic organisms. Two double mutations were generated including a variant H676F/R677G showing a decrease in both activity and stability, and a variant Y686R/E687K showing an improved activity, but a decreased stability. Mutations targeted on coevolutionary residues of Arg677 and Tyr686 were then applied to rescue stability or activity loss of the double mutants, which achieved a partial success. Sequence analysis revealed that the two mutations are abundant in non-thermophilic sequences but not in thermophilic homologues. Then, a small-scale MSA containing sequences from only thermophilic organisms was applied to predict 13 single variants and two of them, E507Q and E734N showed a simultaneous increase in both stability and activity, even in sensitivity. A customized MSA was hence more effective in engineering a thermophilic enzyme and could be used in engineering other enzymes. Molecular dynamics simulations revealed the impact of mutations on the protein dynamics and interactions between TaqPol and substrates. KEY POINTS: • The pool of sequence for alignment is critical to engineering Taq DNA polymerase. • The variants with low properties can be rescued by mutations in coevolving network. • Improving binding with DNA can improve DNA polymerase stability and activity.

A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets

The CRISPR/Cas9 nuclease from Streptococcus pyogenes (SpCas9) can be used with single guide RNAs (sgRNAs) as a sequence-specific antimicrobial agent and as a genome-engineering tool. However, current bacterial sgRNA activity models struggle with accurate predictions and do not generalize well, possibly because the underlying datasets used to train the models do not accurately measure SpCas9/sgRNA activity and cannot distinguish on-target cleavage from toxicity. Here, we solve this problem by using a two-plasmid positive selection system to generate high-quality data that more accurately reports on SpCas9/sgRNA cleavage and that separates activity from toxicity. We develop a machine learning architecture (crisprHAL) that can be trained on existing datasets, that shows marked improvements in sgRNA activity prediction accuracy when transfer learning is used with small amounts of high-quality data, and that can generalize predictions to different bacteria. The crisprHAL model recapitulates known SpCas9/sgRNA-target DNA interactions and provides a pathway to a generalizable sgRNA bacterial activity prediction tool that will enable accurate antimicrobial and genome engineering applications.

Identification and engineering on the nonconserved residues of metallo-β-lactamase-type thioesterase to improve the enzymatic activity

The standalone metallo-β-lactamase-type thioesterase (MβL-TE), belongs to the group V nonreducing polyketide synthase agene cluster, catalyzes the rate-limiting step of product releasing. Our work first investigated on the orthologous MβL-TEs from different origins to determine which nonconserved amino acid residues are important to the hydrolysis efficiency. A series of chimeric MβL-TEs were constructed by fragment swapping and site-directed mutagenesis, in vivo enzymatic assay showed that two nonconserved residues A19 and E75 (numbering in HyTE) were critical to the catalytic performance. Protein structure modeling suggested that these two residues are located in different areas of HyTE. A19 is on the entrance to the active sites, whereas E75 resides in the linker between the two β strands which hold the metal-binding sites. Combining with computational simulations and comparative enzymatic assay, different screening criteria were set up for selecting the variants on the two noncatalytic and nonconserved key residues to improve the catalytic activity. The rational design on A19 and E75 gave five candidates in total, two (A19F and E75Q) of which were thus found significantly improved the enzymatic performance of HyTE. The double-point mutant was constructed to further improve the activity, which was increased by 28.4-fold on product accumulation comparing to the wild-type HyTE. This study provides a novel approach for engineering on nonconserved residues to optimize enzymatic performance.

How a Scorpion Toxin Selectively Captures a Prey Sodium Channel: The Molecular and Evolutionary Basis Uncovered

The growing resistance of insects to chemical pesticides is reducing the effectiveness of conventional methods for pest control and thus, the development of novel insecticidal agents is imperative. Scorpion toxins specific for insect voltage-gated sodium channels (Navs) have been considered as one of the most promising insecticide alternatives due to their host specificity, rapidly evoked toxicity, biodegradability, and the lack of resistance. However, they have not been developed for uses in agriculture and public health, mainly because of a limited understanding of their molecular and evolutionary basis controlling their phylogenetic selectivity. Here, we show that the traditionally defined insect-selective scorpion toxin LqhIT2 specifically captures a prey Nav through a conserved trapping apparatus comprising a three-residue-formed cavity and a structurally adjacent leucine. The former serves as a detector to recognize and bind a highly exposed channel residue conserved in insects and spiders, two major prey items for scorpions; and the latter subsequently seizes the "moving" voltage sensor via hydrophobic interactions to reduce activation energy for channel opening, demonstrating its action in an enzyme-like manner. Based on the established toxin-channel interaction model in combination with toxicity assay, we enlarged the toxic spectrum of LqhIT2 to spiders and certain other arthropods. Furthermore, we found that genetic background-dependent cavity shapes determine the species selectivity of LqhIT2-related toxins. We expect that the discovery of the trapping apparatus will improve our understanding of the evolution and design principle of Nav-targeted toxins from a diversity of arthropod predators and accelerate their uses in pest control.

Naught all zeros in sequence count data are the same

Genomic studies feature multivariate count data from high-throughput DNA sequencing experiments, which often contain many zero values. These zeros can cause artifacts for statistical analyses and multiple modeling approaches have been developed in response. Here, we apply different zero-handling models to gene-expression and microbiome datasets and show models can disagree substantially in terms of identifying the most differentially expressed sequences. Next, to rationally examine how different zero handling models behave, we developed a conceptual framework outlining four types of processes that may give rise to zero values in sequence count data. Last, we performed simulations to test how zero handling models behave in the presence of these different zero generating processes. Our simulations showed that simple count models are sufficient across multiple processes, even when the true underlying process is unknown. On the other hand, a common zero handling technique known as "zero-inflation" was only suitable under a zero generating process associated with an unlikely set of biological and experimental conditions. In concert, our work here suggests several specific guidelines for developing and choosing state-of-the-art models for analyzing sparse sequence count data.

Modifying a covarying protein-DNA interaction changes substrate preference of a site-specific endonuclease

Identifying and validating intermolecular covariation between proteins and their DNA-binding sites can provide insights into mechanisms that regulate selectivity and starting points for engineering new specificity. LAGLIDADG homing endonucleases (meganucleases) can be engineered to bind non-native target sites for gene-editing applications, but not all redesigns successfully reprogram specificity. To gain a global overview of residues that influence meganuclease specificity, we used information theory to identify protein-DNA covariation. Directed evolution experiments of one predicted pair, 227/+3, revealed variants with surprising shifts in I-OnuI substrate preference at the central 4 bases where cleavage occurs. Structural studies showed significant remodeling distant from the covarying position, including restructuring of an inter-hairpin loop, DNA distortions near the scissile phosphates, and new base-specific contacts. Our findings are consistent with a model whereby the functional impacts of covariation can be indirectly propagated to neighboring residues outside of direct contact range, allowing meganucleases to adapt to target site variation and indirectly expand the sequence space accessible for cleavage. We suggest that some engineered meganucleases may have unexpected cleavage profiles that were not rationally incorporated during the design process.

omicplotR: visualizing omic datasets as compositions

BACKGROUND

Differential abundance analysis is widely used with high-throughput sequencing data to compare gene abundance or expression between groups of samples. Many software packages exist for this purpose, but each uses a unique set of statistical assumptions to solve problems on a case-by-case basis. These software packages are typically difficult to use for researchers without command-line skills, and software that does offer a graphical user interface do not use a compositionally valid method.

RESULTS

omicplotR facilitates visual exploration of omic datasets for researchers with and without prior scripting knowledge. Reproducible visualizations include principal component analysis, hierarchical clustering, MA plots and effect plots. We demonstrate the functionality of omicplotR using a publicly available metatranscriptome dataset.

CONCLUSIONS

omicplotR provides a graphical user interface to explore sequence count data using generalizable compositional methods, facilitating visualization for investigators without command-line experience.

MISTIC2: comprehensive server to study coevolution in protein families

Correlated mutations between residue pairs in evolutionarily related proteins arise from constraints needed to maintain a functional and stable protein. Identifying these inter-related positions narrows down the search for structurally or functionally important sites. MISTIC is a server designed to assist users to calculate covariation in protein families and provide them with an interactive tool to visualize the results. Here, we present MISTIC2, an update to the previous server, that allows to calculate four covariation methods (MIp, mfDCA, plmDCA and gaussianDCA). The results visualization framework has been reworked for improved performance, compatibility and user experience. It includes a circos representation of the information contained in the alignment, an interactive covariation network, a 3D structure viewer and a sequence logo. Others components provide additional information such as residue annotations, a roc curve for assessing contact prediction, data tables and different ways of filtering the data and exporting figures. Comparison of different methods is easily done and scores combination is also possible. A newly implemented web service allows users to access MISTIC2 programmatically using an API to calculate covariation and retrieve results. MISTIC2 is available at: https://mistic2.leloir.org.ar.

Active site residue identity regulates cleavage preference of LAGLIDADG homing endonucleases

LAGLIDADG homing endonucleases (meganucleases) are site-specific mobile endonucleases that can be adapted for genome-editing applications. However, one problem when reprogramming meganucleases on non-native substrates is indirect readout of DNA shape and flexibility at the central 4 bases where cleavage occurs. To understand how the meganuclease active site regulates DNA cleavage, we used functional selections and deep sequencing to profile the fitness landscape of 1600 I-LtrI and I-OnuI active site variants individually challenged with 67 substrates with central 4 base substitutions. The wild-type active site was not optimal for cleavage on many substrates, including the native I-LtrI and I-OnuI targets. Novel combinations of active site residues not observed in known meganucleases supported activity on substrates poorly cleaved by the wild-type enzymes. Strikingly, combinations of E or D substitutions in the two metal-binding residues greatly influenced cleavage activity, and E184D variants had a broadened cleavage profile. Analyses of I-LtrI E184D and the wild-type proteins co-crystallized with the non-cognate AACC central 4 sequence revealed structural differences that correlated with kinetic constants for cleavage of individual DNA strands. Optimizing meganuclease active sites to enhance cleavage of non-native central 4 target sites is a straightforward addition to engineering workflows that will expand genome-editing applications.

Functional trade-offs and environmental variation shaped ancient trajectories in the evolution of dim-light vision

Trade-offs between protein stability and activity can restrict access to evolutionary trajectories, but widespread epistasis may facilitate indirect routes to adaptation. This may be enhanced by natural environmental variation, but in multicellular organisms this process is poorly understood. We investigated a paradoxical trajectory taken during the evolution of tetrapod dim-light vision, where in the rod visual pigment rhodopsin, E122 was fixed 350 million years ago, a residue associated with increased active-state (MII) stability but greatly diminished rod photosensitivity. Here, we demonstrate that high MII stability could have likely evolved without E122, but instead, selection appears to have entrenched E122 in tetrapods via epistatic interactions with nearby coevolving sites. In fishes by contrast, selection may have exploited these epistatic effects to explore alternative trajectories, but via indirect routes with low MII stability. Our results suggest that within tetrapods, E122 and high MII stability cannot be sacrificed-not even for improvements to rod photosensitivity.

Engineering the conserved and noncatalytic residues of a thermostable β-1,4-endoglucanase to improve specific activity and thermostability

Endoglucanases are increasingly applied in agricultural and industrial applications as a key biocatalyst for cellulose biodegradation. However, the low performance in extreme conditions seriously challenges the enzyme’s commercial utilization. To obtain endoglucanases with substantially improved activity and thermostability, structure-based rational design was carried out based on the Chaetomium thermophilum β-1,4-endoglucanase CTendo45. In this study, five mutant enzymes were constructed by substitution of conserved and noncatalytic residues using site-directed mutagenesis. Mutants were constitutively expressed in Pichia pastoris, purified, and ultimately tested for enzymatic characteristics. Two single mutants, Y30F and Y173F, increased the enzyme’s specific activity 1.35- and 1.87-fold using carboxymethylcellulose sodium (CMC-Na) as a substrate, respectively. Furthermore, CTendo45 and mutants exhibited higher activity towards β-D-glucan than that of CMC-Na, and activities of Y173F and Y30F were also increased obviously against β-D-glucan. In addition, Y173F significantly improved the enzyme’s heat resistance at 80 °C and 90 °C. More interestingly, the double mutant Y30F/Y173F obtained considerably higher stability at elevated temperatures but failed to inherit the increased catalytic efficiency of its single mutant counterparts. This work gives an initial insight into the biological function of conserved and noncatalytic residues of thermostable endoglucanases and proposes a feasible path for the improvement of enzyme redesign proposals.

Statistical Coupling Analysis-Guided Library Design for the Discovery of Mutant Luciferases

Directed evolution has proven to be an invaluable tool for protein engineering; however, there is still a need for developing new approaches to continue to improve the efficiency and efficacy of these methods. Here, we demonstrate a new method for library design that applies a previously developed bioinformatic method, Statistical Coupling Analysis (SCA). SCA uses homologous enzymes to identify amino acid positions that are mutable and functionally important and engage in synergistic interactions between amino acids. We use SCA to guide a library of the protein luciferase and demonstrate that, in a single round of selection, we can identify luciferase mutants with several valuable properties. Specifically, we identify luciferase mutants that possess both red-shifted emission spectra and improved stability relative to those of the wild-type enzyme. We also identify luciferase mutants that possess a >50-fold change in specificity for modified luciferins. To understand the mutational origin of these improved mutants, we demonstrate the role of mutations at N229, S239, and G246 in altered function. These studies show that SCA can be used to guide library design and rapidly identify synergistic amino acid mutations from a small library.

From RNA-seq to Biological Inference: Using Compositional Data Analysis in Meta-Transcriptomics

The proper analysis of high-throughput sequencing datasets of mixed microbial communities (meta-transcriptomics) is substantially more complex than for datasets composed of single organisms. Adapting commonly used RNA-seq methods to the analysis of meta-transcriptome datasets can be misleading and not use all the available information in a consistent manner. However, meta-transcriptomic experiments can be investigated in a principled manner using Bayesian probabilistic modeling of the data at a functional level coupled with analysis under a compositional data analysis paradigm. We present a worked example for the differential functional evaluation of mixed-species microbial communities obtained from human clinical samples that were sequenced on an Illumina platform. We demonstrate methods to functionally map reads directly, conduct a compositionally appropriate exploratory data analysis, evaluate differential relative abundance, and finally identify compositionally associated (constant ratio) functions. Using these approaches we have found that meta-transcriptomic functional analyses are highly reproducible and convey significant information regarding the ecosystem.

Microbiome Datasets Are Compositional: And This Is Not Optional

Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume specific properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We briefly introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and finally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis.

Microbiome Datasets Are Compositional: And This Is Not Optional

Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume specific properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We briefly introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and finally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis.

Correlated positions in protein evolution and engineering

Statistical analysis of a protein multiple sequence alignment can reveal groups of positions that undergo interdependent mutations throughout evolution. At these so-called correlated positions, only certain combinations of amino acids appear to be viable for maintaining proper folding, stability, catalytic activity or specificity. Therefore, it is often speculated that they could be interesting guides for semi-rational protein engineering purposes. Because they are a fingerprint from protein evolution, their analysis may provide valuable insight into a protein's structure or function and furthermore, they may also be suitable target positions for mutagenesis. Unfortunately, little is currently known about the properties of these correlation networks and how they should be used in practice. This review summarises the recent findings, opportunities and pitfalls of the concept.

Connecting Active-Site Loop Conformations and Catalysis in Triosephosphate Isomerase: Insights from a Rare Variation at Residue 96 in the Plasmodial Enzyme

Despite extensive research into triosephosphate isomerases (TIMs), there exists a gap in understanding of the remarkable conjunction between catalytic loop-6 (residues 166-176) movement and the conformational flip of Glu165 (catalytic base) upon substrate binding that primes the active site for efficient catalysis. The overwhelming occurrence of serine at position 96 (98% of the 6277 unique TIM sequences), spatially proximal to E165 and the loop-6 residues, raises questions about its role in catalysis. Notably, Plasmodium falciparum TIM has an extremely rare residue--phenylalanine--at this position whereas, curiously, the mutant F96S was catalytically defective. We have obtained insights into the influence of residue 96 on the loop-6 conformational flip and E165 positioning by combining kinetic and structural studies on the PfTIM F96 mutants F96Y, F96A, F96S/S73A, and F96S/L167V with sequence conservation analysis and comparative analysis of the available apo and holo structures of the enzyme from diverse organisms.

Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib) and promiscuous (Bosutinib, Dasatinib) kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations of key mediating residues. This study has outlined mechanisms by which inhibitor binding could modulate resilience and efficiency of allosteric interactions in the kinase structures, while preserving structural topology required for catalytic activity and regulation.

Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis

BACKGROUND

Experimental designs that take advantage of high-throughput sequencing to generate datasets include RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), sequencing of 16S rRNA gene fragments, metagenomic analysis and selective growth experiments. In each case the underlying data are similar and are composed of counts of sequencing reads mapped to a large number of features in each sample. Despite this underlying similarity, the data analysis methods used for these experimental designs are all different, and do not translate across experiments. Alternative methods have been developed in the physical and geological sciences that treat similar data as compositions. Compositional data analysis methods transform the data to relative abundances with the result that the analyses are more robust and reproducible.

RESULTS

Data from an in vitro selective growth experiment, an RNA-seq experiment and the Human Microbiome Project 16S rRNA gene abundance dataset were examined by ALDEx2, a compositional data analysis tool that uses Bayesian methods to infer technical and statistical error. The ALDEx2 approach is shown to be suitable for all three types of data: it correctly identifies both the direction and differential abundance of features in the differential growth experiment, it identifies a substantially similar set of differentially expressed genes in the RNA-seq dataset as the leading tools and it identifies as differential the taxa that distinguish the tongue dorsum and buccal mucosa in the Human Microbiome Project dataset. The design of ALDEx2 reduces the number of false positive identifications that result from datasets composed of many features in few samples.

CONCLUSION

Statistical analysis of high-throughput sequencing datasets composed of per feature counts showed that the ALDEx2 R package is a simple and robust tool, which can be applied to RNA-seq, 16S rRNA gene sequencing and differential growth datasets, and by extension to other techniques that use a similar approach.