Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics

Allosteric drugs have been attracting increasing interest over the past few years. In this context, it is common practice to use high-throughput screening for the discovery of non-natural allosteric drugs. While the discovery stage is supported by a growing amount of biological information and increasing computing power, major challenges still remain in selecting allosteric ligands and predicting their effect on the target protein's function. Indeed, allosteric compounds can act both as inhibitors and activators of biological responses. Computational approaches to the problem have focused on variations on the theme of molecular docking coupled to molecular dynamics with the aim of recovering information on the (long-range) modulation typical of allosteric proteins.

/pdf/machine-learning-prediction-of-allosteric-drug-activity-from-200skdsbku.pdf

Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics.

Spurred into action by the COVID-19 pandemic, the global scientific community has, in a short of period of time, made astonishing progress in understanding and combating COVID-19. Given the known human protein machinery for (a) SARS-CoV-2 entry, (b) the host innate immune response, and (c) virus-host interactions (protein-protein and RNA-protein), the potential effects of human genetic variation in this machinery, which may contribute to clinical differences in SARS-CoV-2 pathogenesis and help determine individual risk for COVID-19 infection, are explored. The Genome Aggregation Database (gnomAD) was used to show that several rare germline exome variants of proteins in these pathways occur in the human population, suggesting that carriers of these rare variants (especially for proteins of innate immunity pathways) are at risk for severe symptoms (like the severe symptoms in patients who are known to be rare variant carriers), whereas carriers of other variants could have a protective advantage against infection. The occurrence of genetic variation is thus expected to motivate the experimental probing of natural variants to understand the mechanistic differences in SARS-CoV-2 pathogenesis from one individual to another.

COVID-19: The Effect of Host Genetic Variations on Host-Virus Interactions.

While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the SARS-CoV-2 protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD.

/pdf/insights-from-incorporating-quantum-computing-into-drug-2y986srj.pdf

Bo Wang

Papers

Building a Hybrid Physical-Statistical Classifier for Predicting the Effect of Variants Related to Protein-Drug Interactions.

Intensification: A Resource for Amplifying Population-Genetic Signals with Protein Repeats