A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.

Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules. However, many scoring functions make predictions based on data set biases rather than an understanding of the physics of binding. These scoring functions perform well when tested on similar targets to those in the training set but fail to generalize to dissimilar targets. To test what a machine learning-based scoring function has learned, input attribution, a technique for learning which features are important to a model when making a prediction on a particular data point, can be applied. If a model successfully learns something beyond data set biases, attribution should give insight into the important binding interactions that are taking place. We built a machine learning-based scoring function that aimed to avoid the influence of bias via thorough train and test data set filtering and show that it achieves comparable performance on the Comparative Assessment of Scoring Functions, 2016 (CASF-2016) benchmark to other leading methods. We then use the CASF-2016 test set to perform attribution and find that the bonds identified as important by PointVS, unlike those extracted from other scoring functions, have a high correlation with those found by a distance-based interaction profiler. We then show that attribution can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. We use this information to perform fragment elaboration and see improvements in docking scores compared to using structural information from a traditional, data-based approach. This not only provides definitive proof that the scoring function has learned to identify some important binding interactions but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design.

Technoeconomic and life-cycle analysis of single-step catalytic conversion of wet ethanol into fungible fuel blendstocks.

Technoeconomic and life-cycle analyses are presented for catalytic conversion of ethanol to fungible hydrocarbon fuel blendstocks, informed by advances in catalyst and process development. Whereas prior work toward this end focused on 3-step processes featuring dehydration, oligomerization, and hydrogenation, the consolidated alcohol dehydration and oligomerization (CADO) approach described here results in 1-step conversion of wet ethanol vapor (40 wt% in water) to hydrocarbons and water over a metal-modified zeolite catalyst. A development project increased liquid hydrocarbon yields from 36% of theoretical to >80%, reduced catalyst cost by an order of magnitude, scaled up the process by 300-fold, and reduced projected costs of ethanol conversion 12-fold. Current CADO products conform most closely to gasoline blendstocks, but can be blended with jet fuel at low levels today, and could potentially be blended at higher levels in the future. Operating plus annualized capital costs for conversion of wet ethanol to fungible blendstocks are estimated at $2.00/GJ for CADO today and $1.44/GJ in the future, similar to the unit energy cost of producing anhydrous ethanol from wet ethanol ($1.46/GJ). Including the cost of ethanol from either corn or future cellulosic biomass but not production incentives, projected minimum selling prices for fungible blendstocks produced via CADO are competitive with conventional jet fuel when oil is $100 per barrel but not at $60 per barrel. However, with existing production incentives, the projected minimum blendstock selling price is competitive with oil at $60 per barrel. Life-cycle greenhouse gas emission reductions for CADO-derived hydrocarbon blendstocks closely follow those for the ethanol feedstock.

A critical overview of computational approaches employed for COVID-19 drug discovery.

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.

Femur morphology in healthy infants and young children.

The objective of this study was to characterize femur morphology in healthy infants and young children. Anterior-posterior (AP) radiographs of the femur from children age 0-3 years with no history of bone disease were obtained from two children's hospitals and one medical examiner's office. Femur morphological measures (bone length, minimum diaphysis diameter, growth plate width, and femur radius of curvature) and sectional structural measures were determined. Measures were described and compared based on subject age and mass. Relationships between measures and age and mass were evaluated. The 169 AP femur radiographs were obtained from 99 children (59.6% males, median age = 12.0 months, IQR = 0-27.5 months, median body weight = 10.0 kg, IQR = 4.4-15.6 kg). Femur length (rs  = 0.97, p < 0.001; rs  = 0.89, p < 0.001), trochanter width (rs  = 0.86, p < 0.001; rs  = 0.85, p < 0.001), minimum diaphysis diameter (rs  = 0.91, p < 0.001; rs  = 0.87, p < 0.001), and growth plate width (rs  = 0.91, p < 0.001; rs  = 0.84, p < 0.001) increased with age and weight, respectively. Cross-sectional area (rs  = 0.87; rs  = 0.86; p < 0.01), polar moment of inertia (rs  = 0.91; rs  = 0.87; p < 0.001), moment of inertia (rs  = 0.91; rs  = 0.87; p < 0.001), polar modulus (rs  = 0.91; rs  = 0.87; p < 0.001) and medullary canal diameter (rs  = 0.83, p < 0.001; rs  = 0.73, p < 0.001) at the minimum diaphysis also increased with age and weight, respectively. Changes during rapid bone growth are important to understanding fracture risk in infants and young children as they transition to independent walking. Femur length, trochanter width, minimum diaphysis diameter and growth plate width increased with age and weight. Structural properties associated with fracture resistance also increased with age and weight.

Emergency Department Assessment of Suspected Acute Coronary Syndrome Using the IMPACT Pathway in Aboriginal and Torres Strait Islander People.

OBJECTIVES: The Improved Assessment of Chest pain Trial (IMPACT) pathway is an accelerated strategy for the assessment of emergency patients presenting with suspected acute coronary syndrome (ACS). The objective of this study was to report outcomes for Aboriginal and Torres Strait Islander patients deemed low-, intermediate-, or high-risk according to this pathway. DESIGN: This was a prospective observational trial conducted between November 2017 and December 2019. SETTING: Regional hospital in Far North Queensland. PARTICIPANTS: Aboriginal and Torres Strait Islander people presenting to the Emergency Department with suspected ACS were asked to participate. Participants were stratified as low-, intermediate- or high-risk of ACS according to the IMPACT pathway. High-and intermediate risk patients were managed according to the IMPACT pathway. Management of low-risk patients included additional inpatient cardiac testing, which was not part of the original IMPACT pathway. MAIN OUTCOME MEASURES: The primary outcome was acute coronary syndrome within 30-days. Secondary outcomes included length of stay and prevalence of objective testing. RESULTS: A total of 155 participants were classified as either at low-risk (n=18 11.6%), intermediate-risk (n=87 56.1%), or high-risk (n=50 32.3%) of ACS. Thirty-day (30-day) ACS occurred in 29 (18.6%) patients, which included 26 (52.0%) high-risk patients and three (3.4%) intermediate-risk patients. No patients in the low-risk group were diagnosed with ACS during their index presentation or by 30-days. Median hospital length-of-stay was 11.9 hours (interquartile range [IQR] 5.3-20.2 hrs) for low- and 15.5 hours (IQR 5.9-29.2 hrs) for intermediate-risk patients. CONCLUSION: The IMPACT pathway, which has been associated with reduced LOS in other settings, could be safely implemented for patients of Aboriginal and Torres Strait Islander origin, classifying two-thirds as low- or intermediate risk. However, a clinically significant proportion of Aboriginal and Torres Strait Islander patients experience cardiac events, which supports the need to provide early objective testing for coronary artery disease.

Bayesian framework for THz-TDS plasma diagnostics.

Terahertz time-domain spectroscopy (THz-TDS) is an optical diagnostic used to noninvasively measure plasma electron density and collision frequency. Conventional methods for analyzing THz-TDS plasma diagnostic data often do not account for measurement artifacts and do not quantify parameter uncertainties. We introduce a novel Bayesian framework that overcomes these deficiencies. The framework enables computation of both the density and collision frequency, compensates for artifacts produced by refraction and delay line errors, and quantifies parameter uncertainties caused by noise and imprecise knowledge of unmeasured plasma properties. We demonstrate the framework with sample measurements of a radio frequency inductively-coupled plasma discharge.

Design of a Single Layer Metamaterial for Pressure Offloading of Transtibial Amputees.

While using a prosthesis, transtibial amputees can experience pain and discomfort brought on by large pressure gradients at the interface between the residual limb and the prosthetic socket. Current prosthetic interface solutions attempt to alleviate these pressure gradients using soft homogenous liners to reduce and distribute pressures. This research investigates an additively manufactured metamaterial inlay with a tailored mechanical response to reduce peak pressure gradients around the limb. The inlay uses a hyperelastic behaving metamaterial (US10244818) comprised of triangular pattern unit cells, 3D printed with walls of various thicknesses controlled by draft angles. The hyperelastic material properties are modeled using a Yeoh third-order model. The third-order coefficients can be adjusted and optimized, which corresponds to a change in the unit cell wall thickness to create an inlay that can meet the unique offloading needs of an amputee. Finite element analysis simulations evaluated the pressure gradient reduction from (1) a standard homogenous silicone liner, (2) a prosthetist's inlay prescription that utilizes three variations of the metamaterial, and (3) a metamaterial solution with optimized Yeoh third-order coefficients. Compared to a traditional homogenous silicone liner for two unique limb loading scenarios, the prosthetist prescribed inlay and the optimized material inlay can achieve equal or greater pressure gradient reduction capabilities. These preliminary results show the potential feasibility of implementing this metamaterial as a method of personalized medicine for transtibial amputees by creating a customizable interface solution to meet the unique performance needs of an individual patient.

Understanding the Impact of Walkability, Population Density, and Population Size on COVID-19 Spread: A Pilot Study of the Early Contagion in the United States.

The novel coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global event that has been challenging governments, health systems, and communities worldwide. Available data from the first months indicated varying patterns of the spread of COVID-19 within American cities, when the spread was faster in high-density and walkable cities such as New York than in low-density and car-oriented cities such as Los Angeles. Subsequent containment efforts, underlying population characteristics, variants, and other factors likely affected the spread significantly. However, this work investigates the hypothesis that urban configuration and associated spatial use patterns directly impact how the disease spreads and infects a population. It follows work that has shown how the spatial configuration of urban spaces impacts the social behavior of people moving through those spaces. It addresses the first 60 days of contagion (before containment measures were widely adopted and had time to affect spread) in 93 urban counties in the United States, considering population size, population density, walkability, here evaluated through walkscore, an indicator that measures the density of amenities, and, therefore, opportunities for population mixing, and the number of confirmed cases and deaths. Our findings indicate correlations between walkability, population density, and COVID-19 spreading patterns but no clear correlation between population size and the number of cases or deaths per 100 k habitants. Although virus spread beyond these initial cases may provide additional data for analysis, this study is an initial step in understanding the relationship between COVID-19 and urban configuration.

The volatile anesthetic sevoflurane reduces neutrophil apoptosis via Fas death domain-Fas-associated death domain interaction.

Sepsis remains a significant health care burden, with high morbidities and mortalities. Patients with sepsis often require general anesthesia for procedures and imaging studies. Knowing that anesthetic drugs can pose immunomodulatory effects, it would be critical to understand the impact of anesthetics on sepsis pathophysiology. The volatile anesthetic sevoflurane is a common general anesthetic derived from ether as a prototype. Using a murine sepsis model induced by cecal ligation and puncture surgery, we examined the impact of sevoflurane on sepsis outcome. Different from volatile anesthetic isoflurane, sevoflurane exposure significantly improved the outcome of septic mice. This was associated with less apoptosis in the spleen. Because splenic apoptosis was largely attributed to the apoptosis of neutrophils, we examined the effect of sevoflurane on FasL-induced neutrophil apoptosis. Sevoflurane exposure significantly attenuated apoptosis. Sevoflurane did not affect the binding of FasL to the extracellular domain of Fas receptor. Instead, in silico analysis suggested that sevoflurane would bind to the interphase between Fas death domain (DD) and Fas-associated DD (FADD). The effect of sevoflurane on Fas DD-FADD interaction was examined using fluorescence resonance energy transfer (FRET). Sevoflurane attenuated FRET efficiency, indicating that sevoflurane hindered the interaction between Fas DD and FADD. The predicted sevoflurane binding site is known to play a significant role in Fas DD-FADD interaction, supporting our in vitro and in vivo apoptosis results.-Koutsogiannaki, S., Hou, L., Babazada, H., Okuno, T., Blazon-Brown, N., Soriano, S. G., Yokomizo, T., Yuki, K. The volatile anesthetic sevoflurane reduces neutrophil apoptosis via Fas death domain-Fas-associated death domain interaction.

Data Set Augmentation Allows Deep Learning-Based Virtual Screening to Better Generalize to Unseen Target Classes and Highlight Important Binding Interactions.

Current deep learning methods for structure-based virtual screening take the structures of both the protein and the ligand as input but make little or no use of the protein structure when predicting ligand binding. Here, we show how a relatively simple method of data set augmentation forces such deep learning methods to take into account information from the protein. Models trained in this way are more generalizable (make better predictions on protein/ligand complexes from a different distribution to the training data). They also assign more meaningful importance to the protein and ligand atoms involved in binding. Overall, our results show that data set augmentation can help deep learning-based virtual screening to learn physical interactions rather than data set biases.

The relationship between time to analgesic administration and emergency department length of stay: A retrospective review.

AIM: To determine the association between time to first analgesic medication and emergency department length of stay (ED LOS). DESIGN: Retrospective cohort study. METHOD: We conducted this study in a large, inner-city emergency department and included adult patients who presented with pain as a symptom and received analgesic medication(s). Study participants were identified from a random selection of 2,000 adult patients who presented between August-October 2018. The relationship between ED LOS and time to first analgesic medication was described using bivariate and multivariate linear regression. RESULTS: Of the 2,000 randomly selected patients presenting between August and October 2018, 727 (36.35%) had pain as a symptom on arrival, 423 (21.15%) had analgesic medication administered. The median time to first analgesic medication was in 62 (interquartile range: 36-105) minutes and median ED LOS was 218 (interquartile range: 160-317.5) minutes. After adjusting for the effects of sex, urgency of the presentation, emergency department location first seen by clinician, departure destination and workload metrics (average daily time to be seen and daily access block). Time to first analgesic medication was independently associated with ED LOS, contributing to 6.96% of the variance in the multivariate model. CONCLUSION: Providing analgesic medication faster to patients presenting in pain, in addition to previously demonstrated positive patient outcomes, may decrease their ED LOS. IMPACT: Reducing ED LOS through faster pain care, benefits the patient through faster pain relief and can benefit the department by reducing the total amount of care delivered in the emergency department. Reducing total care delivery frees up resources to improve the care to all emergency department patients.

The effects of therapeutic activity kits in emergency department patients with dementia: Study protocol for a pragmatic randomized control trial.

AIM: To determine the effectiveness of therapeutic activity kits on health service use and treatment delivered in the emergency department (ED) in patients with pre-morbid dementia. DESIGN: Pragmatic randomized control trial with equal parallel groups. METHODS: Participants with dementia will be randomly assigned to the control group (N = 56) or the intervention group (N = 56). The intervention group will be given access to a therapeutic activity kit containing several different activities and sensory stimuli to engage the person with dementia during their ED stay in addition to usual care, and the control group will be given usual care only. A research nurse will observe participants at 30-60-min intervals throughout their ED stay for responsive behaviours, one-on-one nursing, and the use of chemical and physical restraint. This study has received Research Ethics Committee approval from the institutional review board and funding from the Rosemary Bryant Foundation (May 2019). DISCUSSION: Emergency departments are busy and noisy environments and can be intimidating and disorientating for patients with dementia, which can result in responsive behaviours. Responsive behaviours are often managed with restrictive interventions, such as chemical or physical restraint, or with constant bedside nursing (one-on-one nursing) to ensure patient safety. Alternatively, non-restrictive and non-pharmacological interventions that divert or occupy the attention of patients such as those contained in the therapeutic activity kit can be considered as a more person-centred strategy. Therapeutic activity kits have been reported as feasible for the use in ED; however, there is limited quality evidence at present to support the implementation of such interventions in the ED. IMPACT: If this study is successful, it will demonstrate that a therapeutic activity kit containing activities (puzzles, colouring, music, and tactile activities) is inexpensive, easily implemented intervention that can prevent this patient group from demonstrating unsafe behaviours and requiring one-on-one nursing and restraints.

Increasing the Rate of Documentation of Security Interventions in the Emergency Department: Big Orange Security Sticker (BOSS) Project.

BACKGROUND: Security interventions in aggressive and violent patients in the emergency department (ED) are not always documented in the clinical record, which can compromise the effectiveness of communication, and increase clinical risks. LOCAL PROBLEM: Fewer than half of all security interventions are documented in the clinical record. METHODS: The study had a pre- and posttest design including a retrospective audit of patient medical records and a staff survey. INTERVENTION: A dedicated sticker, to be completed by nursing and security staff, was placed into the clinical notes as a record of the security intervention. RESULTS: From 1 month before to 1 month after implementation, the rate of documentation of security interventions in clinical notes increased from 43.3% to 68.8% (P = .01), and was maintained for 3 months after implementation. CONCLUSIONS: The rate of documentation of ED security interventions in clinical notes can be increased by encouraging clinicians and security staff to collaborate and share documentation responsibilities.

Helical side chain chemistry of a peptoid-based SP-C analogue: Balancing structural rigidity and biomimicry.

Surfactant protein C (SP-C) is an important constituent of lung surfactant (LS) and, along with SP-B, is included in exogenous surfactant replacement therapies for treating respiratory distress syndrome (RDS). SP-C's biophysical activity depends upon the presence of a rigid C-terminal helix, of which the secondary structure is more crucial to functionality than precise side-chain chemistry. SP-C is highly sequence-conserved, suggesting that the ß-branched, aliphatic side chains of the helix are also important. Nonnatural mimics of SP-C were created using a poly-N-substituted glycine, or "peptoid," backbone. The mimics included varying amounts of α-chiral, aliphatic side chains and α-chiral, aromatic side chains in the helical region, imparting either biomimicry or structural rigidity. Biophysical studies confirmed that the peptoids mimicked SP-C's secondary structure and replicated many of its surface-active characteristics. Surface activity was optimized by incorporating both structurally rigid and biomimetic side chain chemistries in the helical region indicating that both characteristics are important for activity. By balancing these features in one mimic, a novel analogue was created that emulates SP-C's in vitro surface activity while overcoming many of the challenges related to natural SP-C. Peptoid-based analogues hold great potential for use in a synthetic, biomimetic LS formulation for treating RDS.

GuacaMol: Benchmarking Models for de Novo Molecular Design.

De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural networks appeared recently and show promising results. However, the new models have not been profiled on consistent tasks, and comparative studies to well-established algorithms have only seldom been performed. To standardize the assessment of both classical and neural models for de novo molecular design, we propose an evaluation framework, GuacaMol, based on a suite of standardized benchmarks. The benchmark tasks encompass measuring the fidelity of the models to reproduce the property distribution of the training sets, the ability to generate novel molecules, the exploration and exploitation of chemical space, and a variety of single and multiobjective optimization tasks. The benchmarking open-source Python code and a leaderboard can be found on https://benevolent.ai/guacamol .

Factors influencing variation in investigations after a negative CT brain scan in suspected subarachnoid haemorrhage: a qualitative study.

INTRODUCTION: Variation in the approach to the patient with a possible subarachnoid haemorrhage (SAH) has been previously documented. The purpose of this study was to identify factors that influence emergency physicians' decisions about diagnostic testing after a normal CT brain scan for ED patients with a headache suspicious of a SAH. METHODS: We conducted an interview-based qualitative study informed by social constructionist theory. Fifteen emergency physicians from six EDs across Queensland, Australia, underwent individual face-to-face or telephone interviews. Content analysis was performed whereby transcripts were examined and coded independently by two co-investigators, who then jointly agreed on the influencing factors. RESULTS: Six categories of influencing factors were identified. Patient interaction was at the forefront of the identified factors. This shared decision-making process incorporated 'what the patient wants' but may be biased by how the clinician communicates the benefits and harms of the diagnostic options to the patient. Patient risk profile, practice evidence and guidelines were also important. Other influencing factors included experiential factors of the clinician, consultation with colleagues and external influences where practice location and work processes impose constraints on test ordering external to the preferences of the clinician or patient. The six categories were organised within a conceptual framework comprising four components: the context, the evidence, the experience and the decision. CONCLUSIONS: When clinicians are faced with a diagnostic challenge, such as the workup of a patient with suspected SAH, there are a number of influencing factors that can result in a variation in approach. These need to be considered in approaches to improve the appropriateness and consistency of medical care.

Big Data in Drug Discovery.

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

Integrating regulatory surveys and citizen science to map outbreaks of forest diseases: acute oak decline in England and Wales.

The number of emerging tree diseases has increased rapidly in recent times, with severe environmental and economic consequences. Systematic regulatory surveys to detect and establish the distribution of pests are crucial for successful management efforts, but resource-intensive and costly. Volunteers who identify potential invasive species can form an important early warning network in tree health; however, what these data can tell us and how they can be best used to inform and direct official survey effort is not clear. Here, we use an extensive dataset on acute oak decline (AOD) as an opportunity to ask how verified data received from the public can be used. Information on the distribution of AOD was available as (i) systematic regulatory surveys conducted throughout England and Wales, and (ii) ad hoc sightings reported by landowners, land managers and members of the public (i.e. 'self-reported' cases). By using the available self-reported cases at the design stage, the systematic survey could focus on defining the boundaries of the affected area. This maximized the use of available resources and highlights the benefits to be gained by developing strategies to enhance volunteer efforts in future programmes.

Applications of Systematic Molecular Scaffold Enumeration to Enrich Structure-Activity Relationship Information.

Establishing structure-activity relationships (SARs) in hit identification during early stage drug discovery is important in accelerating hit confirmation and expansion. We describe the development of EnCore, a systematic molecular scaffold enumeration protocol using single atom mutations, to enhance the application of objective scaffold definitions and to enrich SAR information from analysis of high-throughput screening output. A list of 43 literature medicinal chemistry compound series, each containing a minimum of 100 compounds, published in the Journal of Medicinal Chemistry was collated to validate the protocol. Analysis using the top representative Level 1 scaffolds this list of literature compound series demonstrated that EnCore could mimic the scaffold exploration conducted when establishing SAR. When EnCore was applied to analyze an HTS library containing over 200 000 compounds, we observed that over 70% of the molecular scaffolds matched extant scaffolds within the library after enumeration. In particular, over 60% of the singleton scaffolds with only one representative compound were found to have structurally related compounds after enumeration. These results illustrate the potential of EnCore to enrich SAR information. A case study using literature cyclooxygenase-2 inhibitors further demonstrates the advantage of EnCore application in establishing SAR from structurally related scaffolds. EnCore complements literature enumeration methods in enabling changes to the physicochemical properties of molecular scaffolds and structural modifications to aliphatic rings and linkers. The enumerated scaffold clusters generated would constitute a comprehensive collection of scaffolds for scaffold morphing and hopping.

The Kil peptide of bacteriophage λ blocks Escherichia coli cytokinesis via ZipA-dependent inhibition of FtsZ assembly.

Assembly of the essential, tubulin-like FtsZ protein into a ring-shaped structure at the nascent division site determines the timing and position of cytokinesis in most bacteria and serves as a scaffold for recruitment of the cell division machinery. Here we report that expression of bacteriophage λ kil, either from a resident phage or from a plasmid, induces filamentation of Escherichia coli cells by rapid inhibition of FtsZ ring formation. Mutant alleles of ftsZ resistant to the Kil protein map to the FtsZ polymer subunit interface, stabilize FtsZ ring assembly, and confer increased resistance to endogenous FtsZ inhibitors, consistent with Kil inhibiting FtsZ assembly. Cells with the normally essential cell division gene zipA deleted (in a modified background) display normal FtsZ rings after kil expression, suggesting that ZipA is required for Kil-mediated inhibition of FtsZ rings in vivo. In support of this model, point mutations in the C-terminal FtsZ-interaction domain of ZipA abrogate Kil activity without discernibly altering FtsZ-ZipA interactions. An affinity-tagged-Kil derivative interacts with both FtsZ and ZipA, and inhibits sedimentation of FtsZ filament bundles in vitro. Together, these data inspire a model in which Kil interacts with FtsZ and ZipA in the cell to prevent FtsZ assembly into a coherent, division-competent ring structure. Phage growth assays show that kil+ phage lyse ∼30% later than kil mutant phage, suggesting that Kil delays lysis, perhaps via its interaction with FtsZ and ZipA.