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Since the 16th century, assays and screens have been essential for scientific investigation. However, most methods could be significantly improved, especially in accuracy, scalability, and often lack adequate comparisons to negative controls. There is a lack of consistency in distinguishing assays, in which accuracy is the main goal, from screens, in which scalability is prioritized over accuracy. We dissected and modernized the original definitions of assays and screens based upon recent developments and the conceptual framework of the original definitions. All methods have three components: design/measurement, performance, and interpretation. We propose a model of method development in which reproducible observations become new methods, initially assessed by sensitivity. Further development can proceed along a path to either screens or assays. The screen path focuses on scalability first, but can later prioritize analysis of negatives. Alternatively, the assay path first compares results to negative controls, assessing specificity and accuracy, later adding scalability. Both pathways converge on a high-accuracy and throughput (HAT) assay, like next generation sequencing, which we suggest should be the ultimate goal of all testing methods. Our model will help scientists better select among available methods, as well as improve existing methods, expanding their impact on science.

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Tat is an essential gene for increasing the transcription of all HIV genes, and affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo and produces variants with diverse activities, contributing to HIV viral heterogeneity as well as drug-resistant clones. Thus, identifying the transcriptional activities of Tat variants will help to better understand AIDS pathology and treatment. We recently reported the missense mutation landscape of all single amino acid Tat variants. In these experiments, a fraction of double missense alleles exhibited intragenic epistasis. However, it is too time-consuming and costly to determine the effect of the variants for all double mutant alleles through experiments. Therefore, we propose a combined GigaAssay/deep learning approach. As a first step to determine activity landscapes for complex variants, we evaluated a deep learning framework using previously reported GigaAssay experiments to predict how transcription activity is affected by Tat variants with single missense substitutions. Our approach achieved a 0.94 Pearson correlation coefficient when comparing the predicted to experimental activities. This hybrid approach can be extensible to more complex Tat alleles for a better understanding of the genetic control of HIV genome transcription.

High-throughput assay systems have had a disproportionally large impact on uncovering how cells function, as well as how misregulation can lead to disease. However, no high-throughput assay systems have been developed to systematically address how mutations impact molecular functions or cell processes in human cells. This is arguably one of the most critical assays because human pathology and treatment are largely based upon molecular functions. To address this challenge, herein we engineered, developed, and tested the first modular high-throughput molecular function assay system. Note that this is not a selection lethality screen! This “GigaAssay” single cell / one-pot assay system was adapted to study how variants impact HIV Tat-driven transactivation of a green fluorescent protein (GFP) reporter. We assayed all 1,615 Tat single and 3,429 double amino acid substitutions with no single mutant dropout. Each mutant was assayed with replicate observations in LentiX293T and Jurkat cells with an average of 100s of separately barcoded cDNA molecules and cell groups for each mutant. Each mutant had ~2,000X-90,000X sequencing coverage to measure its transcriptional activity and had p value ranging as low as 10-271. Five independent assay performance assessments with benchmark data, individually tested clones, and replicate comparisons all indicate exceptional reproducibility, accuracy, and robustness. The shortcomings of alanine scanning mutagenesis and protein truncation studies are revealed by including exhaustive substitution tolerance and intragenic epistasis in the typical structure/function analysis(structure/function/tolerance/epistasis). This flexible and extensible technology enables a far more comprehensive holistic view of protein molecular function and yet with a highly simplified single-pot assay.

Tat is an essential gene for increasing the transcription of all HIV genes, and it affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo producing variants with diverse activities, contributing to HIV viral heterogeneity, as well as drug-resistant clones. Thus, identifying the transcriptional activities of Tat variants will help to better understand AIDS pathology and treatment. We recently reported the missense mutation landscape of all single amino acid Tat variants. In these experiments, a fraction of double missense alleles exhibited intragenic epistasis. It is too time-consuming and costly to determine a variants' effect for all double mutant alleles with experiments. Therefore, we propose a combined GigaAssay/Deep learning approach. As a first step for determining activity landscapes for complex variants, we evaluated a deep learning framework using previously reported GigaAssay experiments to predict how transcription activity is affected by Tat variants with single missense substitutions. Our approach achieves a 0.94 Pearson correlation coefficient when comparing experimental to predicted activities. This hybrid approach should be extensible to more complex Tat alleles for better understanding the genetic control of HIV genome transcription.

Synonymous variants, traditionally regarded as silent mutations due to their lack of impact on protein sequence, structure and function, have been the subject of increasing scrutiny. This commentary explores the emerging evidence challenging the notion of synonymous variants as functionally inert. Analysis of the activity of 70 synonymous variants in the HIV Tat transcription factor revealed that 50% of the variants exhibited significant deviations from wild-type activity. Our analysis supports previous work and raises important questions about the broader impact of non-silent synonymous variants in human genes. Considering the potential functional implications, the authors propose classifying such variants as “synonymous variants of uncertain silence” (sVUS), highlighting the need for cautious interpretation and further investigations in clinical and genetic testing settings.