Evaluating point-prediction uncertainties in neural networks for protein-ligand binding prediction.

Neural Network (NN) models provide potential to speed up the drug discovery process and reduce its failure rates. The success of NN models requires uncertainty quantification (UQ) as drug discovery explores chemical space beyond the training data distribution. Standard NN models do not provide uncertainty information. Some methods require changing the NN architecture or training procedure, limiting the selection of NN models. Moreover, predictive uncertainty can come from different sources. It is important to have the ability to separately model different types of predictive uncertainty, as the model can take assorted actions depending on the source of uncertainty. In this paper, we examine UQ methods that estimate different sources of predictive uncertainty for NN models aiming at protein-ligand binding prediction. We use our prior knowledge on chemical compounds to design the experiments. By utilizing a visualization method we create non-overlapping and chemically diverse partitions from a collection of chemical compounds. These partitions are used as training and test set splits to explore NN model uncertainty. We demonstrate how the uncertainties estimated by the selected methods describe different sources of uncertainty under different partitions and featurization schemes and the relationship to prediction error.

Discovery of Small-Molecule Inhibitors of SARS-CoV-2 Proteins Using a Computational and Experimental Pipeline.

A rapid response is necessary to contain emergent biological outbreaks before they can become pandemics. The novel coronavirus (SARS-CoV-2) that causes COVID-19 was first reported in December of 2019 in Wuhan, China and reached most corners of the globe in less than two months. In just over a year since the initial infections, COVID-19 infected almost 100 million people worldwide. Although similar to SARS-CoV and MERS-CoV, SARS-CoV-2 has resisted treatments that are effective against other coronaviruses. Crystal structures of two SARS-CoV-2 proteins, spike protein and main protease, have been reported and can serve as targets for studies in neutralizing this threat. We have employed molecular docking, molecular dynamics simulations, and machine learning to identify from a library of 26 million molecules possible candidate compounds that may attenuate or neutralize the effects of this virus. The viability of selected candidate compounds against SARS-CoV-2 was determined experimentally by biolayer interferometry and FRET-based activity protein assays along with virus-based assays. In the pseudovirus assay, imatinib and lapatinib had IC50 values below 10 µM, while candesartan cilexetil had an IC50 value of approximately 67 µM against Mpro in a FRET-based activity assay. Comparatively, candesartan cilexetil had the highest selectivity index of all compounds tested as its half-maximal cytotoxicity concentration 50 (CC50) value was the only one greater than the limit of the assay (>100 µM).

Gut microbiome associations with outcome following co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2) in pigs immunized with a PRRS modified live virus vaccine.

Porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2) are two of the most significant pathogens affecting swine. Co-infections are common and result in respiratory disease and reduced weight gain in growing pigs. Although PRRS modified live virus (MLV) vaccines are widely used to decrease PRRS-associated losses, they are generally considered inadequate for disease control. The gut microbiome provides an alternative strategy to enhance vaccine efficacy and improve PRRS control. The objective of this study was to identify gut microbiome characteristics associated with improved outcome in pigs immunized with a PRRS MLV and co-challenged with PRRSV and PCV2b. Twenty-eight days after vaccination and prior to co-challenge, fecal samples were collected from an experimental population of 50 nursery pigs. At 42 days post-challenge, 20 pigs were retrospectively identified as having high or low growth outcomes during the post-challenge period. Gut microbiomes of the two outcome groups were compared using the Lawrence Livermore Microbial Detection Array (LLMDA) and 16S rDNA sequencing. High growth outcomes were associated with several gut microbiome characteristics, such as increased bacterial diversity, increased Bacteroides pectinophilus, decreased Mycoplasmataceae species diversity, higher Firmicutes:Bacteroidetes ratios, increased relative abundance of the phylum Spirochaetes, reduced relative abundance of the family Lachnospiraceae, and increased Lachnospiraceae species C6A11 and P6B14. Overall, this study identifies gut microbiomes associated with improved outcomes in PRRS vaccinated pigs following a polymicrobial respiratory challenge and provides evidence towards the gut microbiome playing a role in PRRS vaccine efficacy.

Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump.

Cholestatic liver injury is frequently associated with drug inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable in silico models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. We report our development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models. We assessed the performance effects of different methods of chemical featurization, data set partitioning, and class labeling and identified the methods producing models that generalized best to novel chemical entities.

AMPL: A Data-Driven Modeling Pipeline for Drug Discovery.

One of the key requirements for incorporating machine learning (ML) into the drug discovery process is complete traceability and reproducibility of the model building and evaluation process. With this in mind, we have developed an end-to-end modular and extensible software pipeline for building and sharing ML models that predict key pharma-relevant parameters. The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of ML and molecular featurization tools. We have benchmarked AMPL on a large collection of pharmaceutical data sets covering a wide range of parameters. Our key findings indicate that traditional molecular fingerprints underperform other feature representation methods. We also find that data set size correlates directly with prediction performance, which points to the need to expand public data sets. Uncertainty quantification can help predict model error, but correlation with error varies considerably between data sets and model types. Our findings point to the need for an extensible pipeline that can be shared to make model building more widely accessible and reproducible. This software is open source and available at: https://github.com/ATOMconsortium/AMPL.

Axiom Microbiome Array, the next generation microarray for high-throughput pathogen and microbiome analysis.

Microarrays have proven to be useful in rapid detection of many viruses and bacteria. Pathogen detection microarrays have been used to diagnose viral and bacterial infections in clinical samples and to evaluate the safety of biological drug materials. In this study, the Axiom Microbiome Array was evaluated to determine its sensitivity, specificity and utility in microbiome analysis of veterinary clinical samples. The array contains probes designed to detect more than 12,000 species of viruses, bacteria, fungi, protozoa and archaea, yielding the most comprehensive microbial detection platform built to date. The array was able to detect Shigella and Aspergillus at 100 genome copies, and vaccinia virus DNA at 1,000 genome copies. The Axiom Microbiome Array made correct species-level calls in mock microbial community samples. When tested against serum, tissue, and fecal samples from pigs experimentally co-infected with porcine reproductive and respiratory syndrome virus and porcine circovirus type 2, the microarray correctly detected these two viruses and other common viral and bacterial microbiome species. This cost-effective and high-throughput microarray is an efficient tool to rapidly analyze large numbers of clinical and environmental samples for the presence of multiple viral and bacterial pathogens.

Identification of Genome-Wide Mutations in Ciprofloxacin-Resistant F. tularensis LVS Using Whole Genome Tiling Arrays and Next Generation Sequencing.

Francisella tularensis is classified as a Class A bioterrorism agent by the U.S. government due to its high virulence and the ease with which it can be spread as an aerosol. It is a facultative intracellular pathogen and the causative agent of tularemia. Ciprofloxacin (Cipro) is a broad spectrum antibiotic effective against Gram-positive and Gram-negative bacteria. Increased Cipro resistance in pathogenic microbes is of serious concern when considering options for medical treatment of bacterial infections. Identification of genes and loci that are associated with Ciprofloxacin resistance will help advance the understanding of resistance mechanisms and may, in the future, provide better treatment options for patients. It may also provide information for development of assays that can rapidly identify Cipro-resistant isolates of this pathogen. In this study, we selected a large number of F. tularensis live vaccine strain (LVS) isolates that survived in progressively higher Ciprofloxacin concentrations, screened the isolates using a whole genome F. tularensis LVS tiling microarray and Illumina sequencing, and identified both known and novel mutations associated with resistance. Genes containing mutations encode DNA gyrase subunit A, a hypothetical protein, an asparagine synthase, a sugar transamine/perosamine synthetase and others. Structural modeling performed on these proteins provides insights into the potential function of these proteins and how they might contribute to Cipro resistance mechanisms.

Microbiome associations in pigs with the best and worst clinical outcomes following co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2).

On a world-wide basis, co-infections involving porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2) are common and contribute to a range of polymicrobial disease syndromes in swine. Both viruses compromise host defenses, resulting in increased susceptibility to infections by primary and secondary pathogens that can affect growth performance as well as increased morbidity and mortality. An experimental population of 95 pigs was co-infected with PRRSV and PCV2. At 70days post-infection (dpi), 20 representative pigs were selected as having the best or worst clinical outcome based on average daily gain (ADG) and the presence of clinical disease. Worst clinical outcome pigs had prolonged and greater levels of viremia as measured by qPCR. Serum, lung and fecal samples collected at 70 dpi were analyzed using a comprehensive DNA microarray technology, the Lawrence Livermore Microbial Detection Array, to detect over 8000 microbes. Bacterial species, such as Bacillus cereus, were detected at a higher rate in the serum of worst performing pigs. At the level of the fecal microbiome, the overall microbial diversity was lower in the worst clinical outcome group. The results reinforce the importance of pathogen load in determining clinical outcome and suggest an important role of microbial diversity as a contributing factor in disease.

Characterization of Genetic Variability of Venezuelan Equine Encephalitis Viruses.

Venezuelan equine encephalitis virus (VEEV) is a mosquito-borne alphavirus that has caused large outbreaks of severe illness in both horses and humans. New approaches are needed to rapidly infer the origin of a newly discovered VEEV strain, estimate its equine amplification and resultant epidemic potential, and predict human virulence phenotype. We performed whole genome single nucleotide polymorphism (SNP) analysis of all available VEE antigenic complex genomes, verified that a SNP-based phylogeny accurately captured the features of a phylogenetic tree based on multiple sequence alignment, and developed a high resolution genome-wide SNP microarray. We used the microarray to analyze a broad panel of VEEV isolates, found excellent concordance between array- and sequence-based SNP calls, genotyped unsequenced isolates, and placed them on a phylogeny with sequenced genomes. The microarray successfully genotyped VEEV directly from tissue samples of an infected mouse, bypassing the need for viral isolation, culture and genomic sequencing. Finally, we identified genomic variants associated with serotypes and host species, revealing a complex relationship between genotype and phenotype.

Pathogen surveillance in wild bottlenose dolphins Tursiops truncatus.

The number and prevalence of diseases is rapidly increasing in the marine ecosystem. Although there is an increase in the number of marine diseases observed world-wide, current understanding of the pathogens associated with marine mammals is limited. An important need exists to develop and apply platforms for rapid detection and characterization of pathogenic agents to assess, prevent and respond to disease outbreaks. In this study, a broad-spectrum molecular detection technology capable of detecting all sequenced microbial organisms, the Lawrence Livermore Microbial Detection Array, was used to assess the microbial agents that could be associated with wild Atlantic dolphins. Blowhole, gastric, and fecal samples from 8 bottlenose dolphins were collected in Charleston, SC, as part of the dolphin assessment effort. The array detected various microbial agents from the dolphin samples. Clostridium perfringens was most prevalent in the samples surveyed using the microarray. This pathogen was also detected using microbiological culture techniques. Additionally, Campylobacter sp., Staphylococcus sp., Erwinia amylovora, Helicobacter pylori, and Frankia sp. were also detected in more than one dolphin using the microarray, but not in culture. This study provides the first survey of pathogens associated with 3 tissue types in dolphins using a broad-spectrum microbial detection microarray and expands insight on the microbial community profile in dolphins.

Application of a pathogen microarray for the analysis of viruses and bacteria in clinical diagnostic samples from pigs.

Many of the disease syndromes challenging the commercial swine industry involve the analysis of complex problems caused by polymicrobial, emerging or reemerging, and transboundary pathogens. This study investigated the utility of the Lawrence Livermore Microbial Detection Array (Lawrence Livermore National Laboratory, Livermore, California), designed to detect 8,101 species of microbes, in the evaluation of known and unknown microbes in serum, oral fluid, and tonsil from pigs experimentally coinfected with Porcine reproductive and respiratory syndrome virus (PRRSV) and Porcine circovirus-2 (PCV-2). The array easily identified PRRSV and PCV-2, but at decreased sensitivities compared to standard polymerase chain reaction detection methods. The oral fluid sample was the most informative, possessing additional signatures for several swine-associated bacteria, including Streptococcus sp., Clostridium sp., and Staphylococcus sp.

The microbial detection array for detection of emerging viruses in clinical samples--a useful panmicrobial diagnostic tool.

Emerging viruses are usually endemic to tropical and sub-tropical regions of the world, but increased global travel, climate change and changes in lifestyle are believed to contribute to the spread of these viruses into new regions. Many of these viruses cause similar disease symptoms as other emerging viruses or common infections, making these unexpected pathogens difficult to diagnose. Broad-spectrum pathogen detection microarrays containing probes for all sequenced viruses and bacteria can provide rapid identification of viruses, guiding decisions about treatment and appropriate case management. We report a modified Whole Transcriptome Amplification (WTA) method that increases unbiased amplification, particular of RNA viruses. Using this modified WTA method, we tested the specificity and sensitivity of the Lawrence Livermore Microbial Detection Array (LLMDA) against a wide range of emerging viruses present in both non-clinical and clinical samples using two different microarray data analysis methods.

Microbial profiling of combat wound infection through detection microarray and next-generation sequencing.

Combat wound healing and resolution are highly affected by the resident microbial flora. We therefore sought to achieve comprehensive detection of microbial populations in wounds using novel genomic technologies and bioinformatics analyses. We employed a microarray capable of detecting all sequenced pathogens for interrogation of 124 wound samples from extremity injuries in combat-injured U.S. service members. A subset of samples was also processed via next-generation sequencing and metagenomic analysis. Array analysis detected microbial targets in 51% of all wound samples, with Acinetobacter baumannii being the most frequently detected species. Multiple Pseudomonas species were also detected in tissue biopsy specimens. Detection of the Acinetobacter plasmid pRAY correlated significantly with wound failure, while detection of enteric-associated bacteria was associated significantly with successful healing. Whole-genome sequencing revealed broad microbial biodiversity between samples. The total wound bioburden did not associate significantly with wound outcome, although temporal shifts were observed over the course of treatment. Given that standard microbiological methods do not detect the full range of microbes in each wound, these data emphasize the importance of supplementation with molecular techniques for thorough characterization of wound-associated microbes. Future application of genomic protocols for assessing microbial content could allow application of specialized care through early and rapid identification and management of critical patterns in wound bioburden.

Screening of viral pathogens from pediatric ileal tissue samples after vaccination.

In 2010, researchers reported that the two US-licensed rotavirus vaccines contained DNA or DNA fragments from porcine circovirus (PCV). Although PCV, a common virus among pigs, is not thought to cause illness in humans, these findings raised several safety concerns. In this study, we sought to determine whether viruses, including PCV, could be detected in ileal tissue samples of children vaccinated with one of the two rotavirus vaccines. A broad spectrum, novel DNA detection technology, the Lawrence Livermore Microbial Detection Array (LLMDA), was utilized, and confirmation of viral pathogens using the polymerase chain reaction (PCR) was conducted. The LLMDA technology was recently used to identify PCV from one rotavirus vaccine. Ileal tissue samples were analyzed from 21 subjects, aged 15-62 months. PCV was not detected in any ileal tissue samples by the LLMDA or PCR. LLMDA identified a human rotavirus A from one of the vaccinated subjects, which is likely due to a recent infection from a wild type rotavirus. LLMDA also identified human parechovirus, a common gastroenteritis viral infection, from two subjects. Additionally, LLMDA detected common gastrointestinal bacterial organisms from the Enterobacteriaceae, Bacteroidaceae, and Streptococcaceae families from several subjects. This study provides a survey of viral and bacterial pathogens from pediatric ileal samples, and may shed light on future studies to identify pathogen associations with pediatric vaccinations.

Analysis of sensitivity and rapid hybridization of a multiplexed Microbial Detection Microarray.

Microarrays have proven to be useful in rapid detection of many viruses and bacteria. Pathogen detection microarrays have been used to diagnose viral and bacterial infections in clinical samples and to evaluate the safety of biological drug materials. A multiplexed version of the Lawrence Livermore Microbial Detection Array (LLMDA) was developed and evaluated with minimum detectable concentrations for pure unamplified DNA viruses, along with mixtures of viral and bacterial DNA subjected to different whole genome amplification protocols. In addition the performance of the array was tested when hybridization time was reduced from 17 h to 1h. The LLMDA was able to detect unamplified vaccinia virus DNA at a concentration of 14 fM, or 100,000 genome copies in 12 µL of sample. With amplification, positive identification was made with only 100 genome copies of input material. When tested against human stool samples from patients with acute gastroenteritis, the microarray detected common gastroenteritis viral and bacterial infections such as rotavirus and E. coli. Accurate detection was found but with a 4-fold drop in sensitivity for a 1h compared to a 17 h hybridization. The array detected 2 ng (equivalent concentration of 15.6 fM) of labeled DNA from a virus with 1h hybridization without any amplification, and was able to identify the components of a mixture of viruses and bacteria at species and in some cases strain level resolution. Sensitivity improved by three orders of magnitude with random whole genome amplification prior to hybridization; for instance, the array detected a DNA virus with only 20 fg or 100 genome copies as input. This multiplexed microarray is an efficient tool to analyze clinical and environmental samples for the presence of multiple viral and bacterial pathogens rapidly.

Ancient pathogen DNA in archaeological samples detected with a Microbial Detection Array.

Ancient human remains of paleopathological interest typically contain highly degraded DNA in which pathogenic taxa are often minority components, making sequence-based metagenomic characterization costly. Microarrays may hold a potential solution to these challenges, offering a rapid, affordable, and highly informative snapshot of microbial diversity in complex samples without the lengthy analysis and/or high cost associated with high-throughput sequencing. Their versatility is well established for modern clinical specimens, but they have yet to be applied to ancient remains. Here we report bacterial profiles of archaeological and historical human remains using the Lawrence Livermore Microbial Detection Array (LLMDA). The array successfully identified previously-verified bacterial human pathogens, including Vibrio cholerae (cholera) in a 19th century intestinal specimen and Yersinia pestis ("Black Death" plague) in a medieval tooth, which represented only minute fractions (0.03% and 0.08% alignable high-throughput shotgun sequencing reads) of their respective DNA content. This demonstrates that the LLMDA can identify primary and/or co-infecting bacterial pathogens in ancient samples, thereby serving as a rapid and inexpensive paleopathological screening tool to study health across both space and time.

Association of Kaposi's sarcoma-associated herpesvirus (KSHV) with bladder cancer in Croatian patients.

As the seventh most common human malignancy, bladder cancer represents a global health problem. In addition to well-recognized risk factors such as smoking and exposure to chemicals, various infectious agents have been implicated as cofactors in the pathogenesis of urothelial malignancies. The aim of the present study was to assess the possible association of viral infection and bladder cancer in Croatian patients. Biopsy specimens were collected from a total of 55 patients diagnosed with different stages of bladder cancer. Initial screening of DNA extracts for the presence of viruses on Lawrence Livermore Microbial Detection Array revealed Kaposi's sarcoma-associated herpesvirus (KSHV) in each of three randomly chosen biopsy specimens. The prevalence of infection with KSHV among study population was then examined by KSHV-specific polymerase chain reaction (PCR) and immunoblotting. By nested PCR, KSHV DNA was detected in 55% of patients. KSHV, also known as human herpesvirus 8, is an infectious agent known to cause cancer. Its oncogenic potential is primarily recognized from its role in Kaposi's sarcoma, but it has also been involved in pathogenesis of two lymphoproliferative disorders. A high prevalence of KSHV infection in our study indicates that KSHV may play a role in tumorigenesis of bladder cancer and warrants further studies.

Detection of Bacillus anthracis DNA in complex soil and air samples using next-generation sequencing.

Bacillus anthracis is the potentially lethal etiologic agent of anthrax disease, and is a significant concern in the realm of biodefense. One of the cornerstones of an effective biodefense strategy is the ability to detect infectious agents with a high degree of sensitivity and specificity in the context of a complex sample background. The nature of the B. anthracis genome, however, renders specific detection difficult, due to close homology with B. cereus and B. thuringiensis. We therefore elected to determine the efficacy of next-generation sequencing analysis and microarrays for detection of B. anthracis in an environmental background. We applied next-generation sequencing to titrated genome copy numbers of B. anthracis in the presence of background nucleic acid extracted from aerosol and soil samples. We found next-generation sequencing to be capable of detecting as few as 10 genomic equivalents of B. anthracis DNA per nanogram of background nucleic acid. Detection was accomplished by mapping reads to either a defined subset of reference genomes or to the full GenBank database. Moreover, sequence data obtained from B. anthracis could be reliably distinguished from sequence data mapping to either B. cereus or B. thuringiensis. We also demonstrated the efficacy of a microbial census microarray in detecting B. anthracis in the same samples, representing a cost-effective and high-throughput approach, complementary to next-generation sequencing. Our results, in combination with the capacity of sequencing for providing insights into the genomic characteristics of complex and novel organisms, suggest that these platforms should be considered important components of a biosurveillance strategy.

Optimizing SNP microarray probe design for high accuracy microbial genotyping.

Microarrays to characterize single nucleotide polymorphisms (SNPs) provide a cost-effective and rapid method (under 24h) to genotype microbes as an alternative to sequencing. We developed a pipeline for SNP discovery and microarray design that scales to 100's of microbial genomes. Here we tested various SNP probe design strategies against 8 sequenced isolates of Bacillus anthracis to compare sequence and microarray data. The best strategy allowed probe length to vary within 32-40 bp to equalize hybridization free energy. This strategy resulted in a call rate of 99.52% and concordance rate of 99.86% for finished genomes. Other probe design strategies averaged substantially lower call rates (94.65-96.41%) and slightly lower concordance rates (99.64-99.80%). These rates were lower for draft than finished genomes, consistent with higher incidence of sequencing errors and gaps. Highly accurate SNP calls were possible in complex soil and blood backgrounds down to 1000 copies, and moderately accurate SNP calls down to 100 spiked copies. The closest genome to the spiked strain was correctly identified at only 10 spiked copies. Discrepancies between sequence and array data did not alter the SNP-based phylogeny, regardless of the probe design strategy, indicating that SNP arrays can accurately place unsequenced isolates on a phylogeny.

Microarrays for pathogen detection and analysis.

DNA microarrays have emerged as a viable platform for detection of pathogenic organisms in clinical and environmental samples. These microbial detection arrays occupy a middle ground between low cost, narrowly focused assays such as multiplex PCR and more expensive, broad-spectrum technologies like high-throughput sequencing. While pathogen detection arrays have been used primarily in a research context, several groups are aggressively working to develop arrays for clinical diagnostics, food safety testing, environmental monitoring and biodefense. Statistical algorithms that can analyze data from microbial detection arrays and provide easily interpretable results are absolutely required in order for these efforts to succeed. In this article, we will review the most promising array designs and analysis algorithms that have been developed to date, comparing their strengths and weaknesses for pathogen detection and discovery.