Biology subjects

Benny, B.

Publications and source records attributed to Benny, B..

3 recordsLinked to original sources

bioRxiv2026

From Predicted Ki to Surrogate IC50: Similarity-Guided Empirical Calibration of Drug Target Affinity Predictions

Drug target affinity models return the endpoint on which they are trained, whereas medicinal chemistry decisions are often made with a different assay readout. Here, we trained a DeepPurpose model to estimate inhibition constants (Ki) from molecular graphs and protein sequences and asked whether those predictions could be aligned empirically with measured IC50 values without treating Ki and IC50 as interchangeable. A BindingDB-trained checkpoint retained useful cross-target ranking on the Davis kinase benchmark without Davis training data (concordance index 0.860). We then rebuilt the Ki training set from ChEMBL 37 records coded as Binding assays (assay_type = 'B'), after removing censored records and targets with poor replicate reproducibility. This reduced median fold error from 16.35x to 9.23x on a leakage-cleaned kinase panel and from 15.30x to 7.90x on a 14-target non-kinase panel before any IC50 calibration. Similarity-guided leave-one-out calibration further reduced the non-kinase panel median error to 3.12x for the original checkpoint and 3.22x for the ChEMBL checkpoint at Tanimoto T = 0.6. Because retraining removed a substantial part of the apparent correction, we interpret the calibration as a target- and chemistry-dependent empirical offset between model output and IC50 assay space, not as a mechanistic Ki-to-IC50 conversion. In a separate project-level stratification of public records, median replicate variability was 2.41x for the subset classified as biochemical Ki and 3.30x for biochemical IC50; a small-sample correction placed the Ki variability nearer 2.8x. These values provide an empirical scale for the remaining calibration error rather than a theoretical performance limit. Similarity, rather than the number of calibrators, governed the main accuracy coverage trade-off. The resulting values are surrogate IC50 estimates for cross-target triage; within-target ranking remains a limitation of the present architecture.

biochemistry↗

bioRxiv2025

EnviroAmpDesigner, a tool for designing high specificity multiplex PCR primer panels for detecting and subtyping a target organism in environmental surveillance samples

BackgroundAmplicon sequencing is a popular method for understanding the diversity of bacterial communities in environmental or similar samples as exemplified by 16S rRNA sequencing. This approach has been extended into multiplex amplicon sequencing in which multiple targets are amplified in the same PCR reaction such as virus sequencing using tiled amplicons. Multiple tools exist to design PCR primers, and some support design of multiplex panels. However, despite increasing interest in the use of environmental or wastewater sampling for detecting and typing specific antimicrobial resistant (AMR) bacteria such as typhoid or cholera agents, we were unable to find a tool for designing a multiplex PCR panel for environmental samples that not only focused on detection of a specific organism, but also on amplifying lineage-specific or AMR-associated alleles of the target organism while minimising amplification of off-target genomes present in the sample. We found that existing tools either depend on the target organism being very distinct from the rest of the organisms in the sample, or focus on detection rather than genotyping of the organism of interest. We have developed EnviroAmpDesigner (v0.1.3, DOI: 10.5281/zenodo.14967337) to fill this gap, which we used to design a multiplex amplicon panel for detection, genotyping and AMR profiling of the typhoidal pathogens Salmonella enterica serovars Typhi and Paratyphi A. EnviroAmpDesigner can design amplicons for both short and long-read sequencing. The software first identifies single nucleotide polymorsphisms (SNPs) that distinguish individual genotypes of the target pathogen (in our case, S. Typhi). It then identifies SNPs that distinguish the target organism from others. Finally, the software designs primers that simultaneously target genotyping SNPs and have at least one primer with a 3 end at each SNP that distinguishes the target organism. The purpose of the latter condition is to increase the specificity of the primers, to minimise amplification of homologous sequences in non-target organisms. While our use case focuses on S. Typhi and Paratyphi A, the tool is organism agnostic and should work for any haploid organism. The tool is freely available via Bioconda and GitHub (https://github.com/AntonS-bio/EnviroAmpDesigner). Impact statementWe have developed EnviroAmpDesigner (v 0.1.3) software to design a multiplexed panel of long-read amplicons for detection and lineage typing of bacteria in environmental samples. We apply this tool to Salmonella Typhi and Paratyphi A, the bacterial agents of typhoid fever, but the tool is organism agnostic. The tool is computationally efficient and can be used on mid-range laptop. It has potential to support more widespread deployment of sequencing-based surveillance for bacterial pathogens in environmental and wastewater samples. Data SummaryFor the purposes of designing primers for S. Typhi and Paratyphi A we used 13,134 samples from the Global Typhoid Genomics Consortium data (target organisms) (1) and 1,810 publicly available Enterobacterales genomes from NCBIs RefSeq database (representing off-target related organisms) (2). To validate the primers, we applied an amplicon sequencing protocol (3) to DNA extracted from two S. Typhi isolates (NCBI accessions SRR5949979 and SRR7165748) provided by Satheesh Nair (UKHSA) (4), and a pooled DNA sample of American Type Culture Collection (ATCC) isolates S. Paratyphi A (ATCC 9150D), S. Paratyphi B (ATCC-BAA-1250D), S. Paratyphi C (ATCC-BAA-1715D), Aeromonas hydrophila (ATCC-7965D), Klebsiella pneumoniae (ATCC-BAA-1706D), and Citrobacter freundii (ATCC-8090D) chosen for their close relationship to S. Typhi. The resulting amplicon sequence data was deposited in European Nucleotide Archive (project accession: PRJEB81565).

bioinformatics↗

bioRxiv2020

Rapid and sensitive direct detection and identification of poliovirus from stool and environmental surveillance samples using nanopore sequencing

Global poliovirus surveillance involves virus isolation from stool and environmental samples, intratypic differential (ITD) by PCR and sequencing of the VP1 region to distinguish vaccine (Sabin), vaccine-derived and wild-type polioviruses and ensure an appropriate response. This cell-culture algorithm takes 2-3 weeks on average between sample receipt and sequencing. Direct detection of viral RNA using PCR allows faster detection but has traditionally faced challenges related to poor sensitivity and difficulties in sequencing common samples containing poliovirus and enterovirus mixtures. We present a nested PCR and nanopore sequencing protocol that allows rapid (<3 days) and sensitive direct detection and sequencing of polioviruses in stool and environmental samples. We developed barcoded primers and a real-time analysis platform that generate accurate VP1 consensus sequences from multiplexed samples. The sensitivity and specificity compared with cell-culture were 90.9% (95% Confidence Interval: 75.7-98.1%) and 99.2% (95.5-100.0%) for wild-type 1 poliovirus, 92.5% (79.6-98.4%) and 98.7% (95.4-99.8%) for vaccine and vaccine-derived serotype 2 poliovirus, and 88.3% (81.2-93.5%) and 93.2% (88.6-96.3%) for Sabin 1 and 3 poliovirus alone or in mixtures when tested on 155 stool samples in Pakistan. Variant analysis of sequencing reads also allowed identification of polioviruses and enteroviruses in artificial mixtures and was able to distinguish complex mixtures of polioviruses in environmental samples. The median identity of consensus nanopore sequences with Sanger or Illumina sequences from the same samples was >99.9%. This novel method shows promise as a faster and safer alternative to cell-culture for the detection and real-time sequencing of polioviruses in stool and environmental samples.

microbiology↗