Biology subjects

Trudeau, S. J.

Publications and source records attributed to Trudeau, S. J..

3 recordsLinked to original sources

bioRxiv2023

PrePPI: A structure informed proteome-wide database of protein-protein interactions

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural clues within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) clue is derived from templatebased modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ~1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of- the-art resource that offers an unprecedented structure-informed view of the human interactome. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/530276v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@b66c15org.highwire.dtl.DTLVardef@71d817org.highwire.dtl.DTLVardef@21d984org.highwire.dtl.DTLVardef@4f6f55_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions

We describe the Predicting Protein Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between nearly 7 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence and structural similarity-based metrics are established between template proteins in the Protein Data Bank, T, that bind small molecules, C, and proteins in the models database, Q. When these metrics pass a sequence threshold value, it is assumed that C also binds to Q with a probability derived from machine learning. If the relationship is based on structure, this probability is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto Coefficient identifies other small molecules that may bind to Q. A likelihood ratio for the binding of C to Q is obtained from naive Bayesian statistics. The PrePCI algorithm performs well under different validations. It can be queried by entering a UniProt ID for a protein and obtaining a list of compounds predicted to bind to it along with associated probabilities. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database are described and a strategy is introduced to use PrePCI as a first step in a docking screen.

systems biology↗

bioRxiv2020

Histone loaders CAF1 and HIRA restrict Epstein-Barr virus B-cell lytic reactivation

Epstein-Barr virus (EBV) infects 95% of adults worldwide and causes infectious mononucleosis. EBV is associated with endemic Burkitt lymphoma, Hodgkin lymphoma, post-transplant lymphomas, nasopharyngeal and gastric carcinomas. In these cancers and in most infected B-cells, EBV maintains a state of latency, where nearly 80 lytic cycle antigens are epigenetically suppressed. To gain insights into host epigenetic factors necessary for EBV latency, we recently performed a human genome-wide CRISPR screen that identified the chromatin assembly factor CAF1 as a putative Burkitt latency maintenance factor. CAF1 loads histones H3 and H4 onto newly synthesized host DNA, though its roles in EBV genome chromatin assembly are uncharacterized. Here, we identified that CAF1 depletion triggered lytic reactivation and transforming virion secretion from Burkitt cells, despite strongly also inducing interferon stimulated genes. CAF1 perturbation diminished occupancy of histones 3.1, 3.3 and repressive H3K9me3 and H3K27me3 marks at multiple viral genome lytic cycle regulatory elements. Suggestive of an early role in establishment of latency, EBV strongly upregulated CAF1 expression in newly infected primary human B-cells prior to the first mitosis, and histone 3.1 and 3.3 were loaded on the EBV genome by this timepoint. Knockout of CAF1 subunit CHAF1B impaired establishment of latency in newly EBV-infected Burkitt cells. A non-redundant latency maintenance role was also identified for the DNA synthesis-independent histone 3.3 loader HIRA. Since EBV latency also requires histone chaperones ATRX and DAXX, EBV coopts multiple host histone pathways to maintain latency, and these are potential targets for lytic induction therapeutic approaches. IMPORTANCEEpstein-Barr virus (EBV) was discovered as the first human tumor virus in endemic Burkitt lymphoma, the most common childhood cancer in sub-Saharan Africa. In Burkitt lymphoma and in 200,000 EBV-associated cancers per year, epigenetic mechanisms maintain viral latency, where lytic cycle factors are silenced. This property complicated EBVs discovery and facilitates tumor immunoevasion. DNA methylation and chromatin-based mechanisms contribute to lytic gene silencing. Here, we identify histone chaperones CAF1 and HIRA, which have key roles in host DNA replication-dependent and replication independent pathways, respectively, are each important for EBV latency. EBV strongly upregulates CAF1 in newly infected B-cells, where viral genomes acquire histone 3.1 and 3.3 variants prior to the first mitosis. Since histone chaperones ATRX and DAXX also function in maintenance of EBV latency, our results suggest that EBV coopts multiple histone pathways to reprogram viral genomes and highlights targets for lytic induction therapeutic strategies.

microbiology↗