A Well-Mixed E. coli Genome: Widespread Contacts Revealed by Tracking Mu Transposition.

The three-dimensional structures of chromosomes are increasingly being recognized as playing a major role in cellular regulatory states. The efficiency and promiscuity of phage Mu transposition was exploited to directly measure in vivo interactions between genomic loci in E. coli. Two global organizing principles have emerged: first, the chromosome is well-mixed and uncompartmentalized, with transpositions occurring freely between all measured loci; second, several gene families/regions show "clustering": strong three-dimensional co-localization regardless of linear genomic distance. The activities of the SMC/condensin protein MukB and nucleoid-compacting protein subunit HU-α are essential for the well-mixed state; HU-α is also needed for clustering of 6/7 ribosomal RNA-encoding loci. The data are explained by a model in which the chromosomal structure is driven by dynamic competition between DNA replication and chromosomal relaxation, providing a foundation for determining how region-specific properties contribute to both chromosomal structure and gene regulation.

The dawn of virtual cell biology.

To fulfill systems biology's promise of providing fundamental new insights will require the development of quantitative and predictive models of whole cells. In this issue, Karr et al. present the first integrated and dynamic computational model of a bacterium that accounts for all of its components and their interactions.

Introducing a Spectrum of Long-Range Genomic Deletions in Human Embryonic Stem Cells Using Type I CRISPR-Cas.

CRISPR-Cas systems enable microbial adaptive immunity and provide eukaryotic genome editing tools. These tools employ a single effector enzyme of type II or V CRISPR to generate RNA-guided, precise genome breaks. Here we demonstrate the feasibility of using type I CRISPR-Cas to effectively introduce a spectrum of long-range chromosomal deletions with a single RNA guide in human embryonic stem cells and HAP1 cells. Type I CRISPR systems rely on the multi-subunit ribonucleoprotein (RNP) complex Cascade to identify DNA targets and on the helicase-nuclease enzyme Cas3 to degrade DNA processively. With RNP delivery of T. fusca Cascade and Cas3, we obtained 13%-60% editing efficiency. Long-range PCR-based and high-throughput-sequencing-based lesion analyses reveal that a variety of deletions, ranging from a few hundred base pairs to 100 kilobases, are created upstream of the target site. These results highlight the potential utility of type I CRISPR-Cas for long-range genome manipulations and deletion screens in eukaryotes.

Distinct heterochromatin-like domains promote transcriptional memory and silence parasitic genetic elements in bacteria.

There is increasing evidence that prokaryotes maintain chromosome structure, which in turn impacts gene expression. We recently characterized densely occupied, multi-kilobase regions in the E. coli genome that are transcriptionally silent, similar to eukaryotic heterochromatin. These extended protein occupancy domains (EPODs) span genomic regions containing genes encoding metabolic pathways as well as parasitic elements such as prophages. Here, we investigate the contributions of nucleoid-associated proteins (NAPs) to the structuring of these domains, by examining the impacts of deleting NAPs on EPODs genome-wide in E. coli and B. subtilis. We identify key NAPs contributing to the silencing of specific EPODs, whose deletion opens a chromosomal region for RNA polymerase binding at genes contained within that region. We show that changes in E. coli EPODs facilitate an extra layer of transcriptional regulation, which prepares cells for exposure to exotic carbon sources. Furthermore, we distinguish novel xenogeneic silencing roles for the NAPs Fis and Hfq, with the presence of at least one being essential for cell viability in the presence of domesticated prophages. Our findings reveal previously unrecognized mechanisms through which genomic architecture primes bacteria for changing metabolic environments and silences harmful genomic elements.

Beyond homeostasis: a predictive-dynamic framework for understanding cellular behavior.

Microbial regulatory strategies have long been understood in terms of the homeostatic framework, in which a response is interpreted as a restoring force counteracting the immediate intracellular consequences of a change in the environment. In this review, we summarize the breadth of recent discoveries of cellular behavior extending beyond the homeostatic framework. We argue that the nonrandom structure of native habitats makes environmental fluctuations inherently multidimensional. Beyond its utility for accurate perception of immediate events, the temporal regularity of this multidimensional correlation structure allows microbes to make predictions about the trajectory of their sensory environment. We describe recently discovered examples of such predictive behavior, their physiological benefits, and the underlying evolutionary forces shaping them. These observations compel us to go beyond homeostasis and consider a predictive-dynamic framework in which cellular behavior is orchestrated in response to the meaning of an environmental perturbation, not only its direct and immediate fitness consequences.

BioLiP2: an updated structure database for biologically relevant ligand-protein interactions.

With the progress of structural biology, the Protein Data Bank (PDB) has witnessed rapid accumulation of experimentally solved protein structures. Since many structures are determined with purification and crystallization additives that are unrelated to a protein's in vivo function, it is nontrivial to identify the subset of protein-ligand interactions that are biologically relevant. We developed the BioLiP2 database (https://zhanggroup.org/BioLiP) to extract biologically relevant protein-ligand interactions from the PDB database. BioLiP2 assesses the functional relevance of the ligands by geometric rules and experimental literature validations. The ligand binding information is further enriched with other function annotations, including Enzyme Commission numbers, Gene Ontology terms, catalytic sites, and binding affinities collected from other databases and a manual literature survey. Compared to its predecessor BioLiP, BioLiP2 offers significantly greater coverage of nucleic acid-protein interactions, and interactions involving large complexes that are unavailable in PDB format. BioLiP2 also integrates cutting-edge structural alignment algorithms with state-of-the-art structure prediction techniques, which for the first time enables composite protein structure and sequence-based searching and significantly enhances the usefulness of the database in structure-based function annotations. With these new developments, BioLiP2 will continue to be an important and comprehensive database for docking, virtual screening, and structure-based protein function analyses.

CR-I-TASSER: assemble protein structures from cryo-EM density maps using deep convolutional neural networks.

Cryo-electron microscopy (cryo-EM) has become a leading approach for protein structure determination, but it remains challenging to accurately model atomic structures with cryo-EM density maps. We propose a hybrid method, CR-I-TASSER (cryo-EM iterative threading assembly refinement), which integrates deep neural-network learning with I-TASSER assembly simulations for automated cryo-EM structure determination. The method is benchmarked on 778 proteins with simulated and experimental density maps, where CR-I-TASSER constructs models with a correct fold (template modeling (TM) score >0.5) for 643 targets that is 64% higher than the best of some other de novo and refinement-based approaches on high-resolution data samples. Detailed data analyses showed that the main advantage of CR-I-TASSER lies in the deep learning-based Cα position prediction, which significantly improves the threading template quality and therefore boosts the accuracy of final models through optimized fragment assembly simulations. These results demonstrate a new avenue to determine cryo-EM protein structures with high accuracy and robustness covering various target types and density map resolutions.

Correction: Dynamic landscape of protein occupancy across the Escherichia coli chromosome.

[This corrects the article DOI: 10.1371/journal.pbio.3001306.].

Intracellular acidification is a hallmark of thymineless death in E. coli.

Thymidine starvation causes rapid cell death. This enigmatic process known as thymineless death (TLD) is the underlying killing mechanism of diverse antimicrobial and antineoplastic drugs. Despite decades of investigation, we still lack a mechanistic understanding of the causal sequence of events that culminate in TLD. Here, we used a diverse set of unbiased approaches to systematically determine the genetic and regulatory underpinnings of TLD in Escherichia coli. In addition to discovering novel genes in previously implicated pathways, our studies revealed a critical and previously unknown role for intracellular acidification in TLD. We observed that a decrease in cytoplasmic pH is a robust early event in TLD across different genetic backgrounds. Furthermore, we show that acidification is a causal event in the death process, as chemical and genetic perturbations that increase intracellular pH substantially reduce killing. We also observe a decrease in intracellular pH in response to exposure to the antibiotic gentamicin, suggesting that intracellular acidification may be a common mechanistic step in the bactericidal effects of other antibiotics.

Dynamic landscape of protein occupancy across the Escherichia coli chromosome.

Free-living bacteria adapt to environmental change by reprogramming gene expression through precise interactions of hundreds of DNA-binding proteins. A predictive understanding of bacterial physiology requires us to globally monitor all such protein-DNA interactions across a range of environmental and genetic perturbations. Here, we show that such global observations are possible using an optimized version of in vivo protein occupancy display technology (in vivo protein occupancy display-high resolution, IPOD-HR) and present a pilot application to Escherichia coli. We observe that the E. coli protein-DNA interactome organizes into 2 distinct prototypic features: (1) highly dynamic condition-dependent transcription factor (TF) occupancy; and (2) robust kilobase scale occupancy by nucleoid factors, forming silencing domains analogous to eukaryotic heterochromatin. We show that occupancy dynamics across a range of conditions can rapidly reveal the global transcriptional regulatory organization of a bacterium. Beyond discovery of previously hidden regulatory logic, we show that these observations can be utilized to computationally determine sequence specificity models for the majority of active TFs. Our study demonstrates that global observations of protein occupancy combined with statistical inference can rapidly and systematically reveal the transcriptional regulatory and structural features of a bacterial genome. This capacity is particularly crucial for non-model bacteria that are not amenable to routine genetic manipulation.

Genetic context effects can override canonical cis regulatory elements in Escherichia coli.

Recent experiments have shown that in addition to control by cis regulatory elements, the local chromosomal context of a gene also has a profound impact on its transcription. Although this chromosome-position dependent expression variation has been empirically mapped at high-resolution, the underlying causes of the variation have not been elucidated. Here, we demonstrate that 1 kb of flanking, non-coding synthetic sequences with a low frequency of guanosine and cytosine (GC) can dramatically reduce reporter expression compared to neutral and high GC-content flanks in Escherichia coli. Natural and artificial genetic context can have a similarly strong effect on reporter expression, regardless of cell growth phase or medium. Despite the strong reduction in the maximal expression level from the fully-induced reporter, low GC synthetic flanks do not affect the time required to reach the maximal expression level after induction. Overall, we demonstrate key determinants of transcriptional propensity that appear to act as tunable modulators of transcription, independent of regulatory sequences such as the promoter. These findings provide insight into the regulation of naturally occurring genes and an independent control for optimizing expression of synthetic biology constructs.

LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation.

Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function annotation, which integrates newly developed deep learning threading methods. For the first time, we have extended LOMETS3 to handle multi-domain proteins and to construct full-length models with gradient-based optimizations. Starting from a FASTA-formatted sequence, LOMETS3 performs four steps of domain boundary prediction, domain-level template identification, full-length template/model assembly and structure-based function prediction. The output of LOMETS3 contains (i) top-ranked templates from LOMETS3 and its component threading programs, (ii) up to 5 full-length structure models constructed by L-BFGS (limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm) optimization, (iii) the 10 closest Protein Data Bank (PDB) structures to the target, (iv) structure-based functional predictions, (v) domain partition and assembly results, and (vi) the domain-level threading results, including items (i)-(iii) for each identified domain. LOMETS3 was tested in large-scale benchmarks and the blind CASP14 (14th Critical Assessment of Structure Prediction) experiment, where the overall template recognition and function prediction accuracy is significantly beyond its predecessors and other state-of-the-art threading approaches, especially for hard targets without homologous templates in the PDB. Based on the improved developments, LOMETS3 should help significantly advance the capability of broader biomedical community for template-based protein structure and function modelling.

The leucine-responsive regulatory proteins/feast-famine regulatory proteins: an ancient and complex class of transcriptional regulators in bacteria and archaea.

Since the discovery of the Escherichia coli leucine-responsive regulatory protein (Lrp) almost 50 years ago, hundreds of Lrp homologs have been discovered, occurring in 45% of sequenced bacteria and almost all sequenced archaea. Lrp-like proteins are often referred to as the feast/famine regulatory proteins (FFRPs), reflecting their common regulatory roles. Acting as either global or local transcriptional regulators, FFRPs detect the environmental nutritional status by sensing small effector molecules (usually amino acids) and regulate the expression of genes involved in metabolism, virulence, motility, nutrient transport, stress tolerance, and antibiotic resistance to implement appropriate behaviors for the specific ecological niche of each organism. Despite FFRPs' complexity, a significant role in gene regulation, and prevalence throughout prokaryotes, the last comprehensive review on this family of proteins was published about a decade ago. In this review, we integrate recent notable findings regarding E. coli Lrp and other FFRPs across bacteria and archaea with previous observations to synthesize a more complete view on the mechanistic details and biological roles of this ancient class of transcription factors.

Integrating deep learning, threading alignments, and a multi-MSA strategy for high-quality protein monomer and complex structure prediction in CASP15.

We report the results of the "UM-TBM" and "Zheng" groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were obtained using the D-I-TASSER and DMFold-Multimer algorithms, respectively. For monomer structure prediction, D-I-TASSER introduced four new features during CASP15: (i) a multiple sequence alignment (MSA) generation protocol that combines multi-source MSA searching and a structural modeling-based MSA ranker; (ii) attention-network based spatial restraints; (iii) a multi-domain module containing domain partition and arrangement for domain-level templates and spatial restraints; (iv) an optimized I-TASSER-based folding simulation system for full-length model creation guided by a combination of deep learning restraints, threading alignments, and knowledge-based potentials. For 47 free modeling targets in CASP15, the final models predicted by D-I-TASSER showed average TM-score 19% higher than the standard AlphaFold2 program. We thus showed that traditional Monte Carlo-based folding simulations, when appropriately coupled with deep learning algorithms, can generate models with improved accuracy over end-to-end deep learning methods alone. For protein complex structure prediction, DMFold-Multimer generated models by integrating a new MSA generation algorithm (DeepMSA2) with the end-to-end modeling module from AlphaFold2-Multimer. For the 38 complex targets, DMFold-Multimer generated models with an average TM-score of 0.83 and Interface Contact Score of 0.60, both significantly higher than those of competing complex prediction tools. Our analyses on complexes highlighted the critical role played by MSA generating, ranking, and pairing in protein complex structure prediction. We also discuss future room for improvement in the areas of viral protein modeling and complex model ranking.

Protein target highlights in CASP15: Analysis of models by structure providers.

We present an in-depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the submitted predictions. While the overall ability to predict three-dimensional protein structures continues to impress, reproducing uncommon features not previously observed in experimental structures is still a challenge. Furthermore, instances with conformational flexibility and large multimeric complexes highlight the need for novel scoring strategies to better emphasize biologically relevant structural regions. Looking ahead, closer integration of computational and experimental techniques will play a key role in determining the next challenges to be unraveled in the field of structural molecular biology.

VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations.

Modeling and simulation of small molecules such as drugs and biological cofactors have been both a major focus of computational chemistry for decades and a growing need among computational biophysicists who seek to investigate the interaction of different types of ligands with biomolecules. Of particular interest in this regard are quantum mechanical (QM) calculations that are used to more accurately describe such small molecules, which can be of heterogeneous structures and chemistry, either in purely QM calculations or in hybrid QM/molecular mechanics (MM) simulations. QM programs are also used to develop MM force field parameters for small molecules to be used along with established force fields for biomolecules in classical simulations. With this growing need in mind, here we report a set of software tools developed and closely integrated within the broadly used molecular visualization/analysis program, VMD, that allow the user to construct, modify, and parametrize small molecules and prepare them for QM, hybrid QM/MM, or classical simulations. The tools also provide interactive analysis and visualization capabilities in an easy-to-use and integrated environment. In this paper, we briefly report on these tools and their major features and capabilities, along with examples of how they can facilitate molecular research in computational biophysics that might be otherwise prohibitively complex.

Escherichia coli YigI is a Conserved Gammaproteobacterial Acyl-CoA Thioesterase Permitting Metabolism of Unusual Fatty Acid Substrates.

Thioesterases play a critical role in metabolism, membrane biosynthesis, and overall homeostasis for all domains of life. In this present study, we characterize a putative thioesterase from Escherichia coli MG1655 and define its role as a cytosolic enzyme. Building on structure-guided functional predictions, we show that YigI is a medium- to long-chain acyl-CoA thioesterase that is involved in the degradation of conjugated linoleic acid (CLA) in vivo, showing overlapping specificity with two previously defined E. coli thioesterases TesB and FadM. We then bioinformatically identify the regulatory relationships that induce YigI expression, which include: an acidic environment, high oxygen availability, and exposure to aminoglycosides. Our findings define a role for YigI and shed light on why the E. coli genome harbors numerous thioesterases with closely related functions. IMPORTANCE Previous research has shown that long chain acyl-CoA thioesterases are needed for E. coli to grow in the presence of carbon sources such as conjugated linoleic acid, but that E. coli must possess at least one such enzyme that had not previously been characterized. Building off structure-guided function predictions, we showed that the poorly annotated protein YigI is indeed the previously unidentified third acyl CoA thioesterase. We found that the three potentially overlapping acyl-CoA thioesterases appear to be induced by nonoverlapping conditions and use that information as a starting point for identifying the precise reactions catalyzed by each such thioesterase, which is an important prerequisite for their industrial application and for more accurate metabolic modeling of E. coli.

Principles of mRNA control by human PUM proteins elucidated from multimodal experiments and integrative data analysis.

The human PUF-family proteins, PUM1 and PUM2, posttranscriptionally regulate gene expression by binding to a PUM recognition element (PRE) in the 3'-UTR of target mRNAs. Hundreds of PUM1/2 targets have been identified from changes in steady-state RNA levels; however, prior studies could not differentiate between the contributions of changes in transcription and RNA decay rates. We applied metabolic labeling to measure changes in RNA turnover in response to depletion of PUM1/2, showing that human PUM proteins regulate expression almost exclusively by changing RNA stability. We also applied an in vitro selection workflow to precisely identify the binding preferences of PUM1 and PUM2. By integrating our results with prior knowledge, we developed a "rulebook" of key contextual features that differentiate functional versus nonfunctional PREs, allowing us to train machine learning models that accurately predict the functional regulation of RNA targets by the human PUM proteins.

Unique repression domains of Pumilio utilize deadenylation and decapping factors to accelerate destruction of target mRNAs.

Pumilio is an RNA-binding protein that represses a network of mRNAs to control embryogenesis, stem cell fate, fertility and neurological functions in Drosophila. We sought to identify the mechanism of Pumilio-mediated repression and find that it accelerates degradation of target mRNAs, mediated by three N-terminal Repression Domains (RDs), which are unique to Pumilio orthologs. We show that the repressive activities of the Pumilio RDs depend on specific subunits of the Ccr4-Not (CNOT) deadenylase complex. Depletion of Pop2, Not1, Not2, or Not3 subunits alleviates Pumilio RD-mediated repression of protein expression and mRNA decay, whereas depletion of other CNOT components had little or no effect. Moreover, the catalytic activity of Pop2 deadenylase is important for Pumilio RD activity. Further, we show that the Pumilio RDs directly bind to the CNOT complex. We also report that the decapping enzyme, Dcp2, participates in repression by the N-terminus of Pumilio. These results support a model wherein Pumilio utilizes CNOT deadenylase and decapping complexes to accelerate destruction of target mRNAs. Because the N-terminal RDs are conserved in mammalian Pumilio orthologs, the results of this work broadly enhance our understanding of Pumilio function and roles in diseases including cancer, neurodegeneration and epilepsy.

Functions of Essential Genes and a Scale-Free Protein Interaction Network Revealed by Structure-Based Function and Interaction Prediction for a Minimal Genome.

When the JCVI-syn3.0 genome was designed and implemented in 2016 as the minimal genome of a free-living organism, approximately one-third of the 438 protein-coding genes had no known function. Subsequent refinement into JCVI-syn3A led to inclusion of 16 additional protein-coding genes, including several unknown functions, resulting in an improved growth phenotype. Here, we seek to unveil the biological roles and protein-protein interaction (PPI) networks for these poorly characterized proteins using state-of-the-art deep learning contact-assisted structure prediction, followed by structure-based annotation of functions and PPI predictions. Our pipeline is able to confidently assign functions for many previously unannotated proteins such as putative vitamin transporters, which suggest the importance of nutrient uptake even in a minimized genome. Remarkably, despite the artificial selection of genes in the minimal syn3 genome, our reconstructed PPI network still shows a power law distribution of node degrees typical of naturally evolved bacterial PPI networks. Making use of our framework for combined structure/function/interaction modeling, we are able to identify both fundamental aspects of network biology that are retained in a minimal proteome and additional essential functions not yet recognized among the poorly annotated components of the syn3.0 and syn3A proteomes.