Presaging critical residues in protein interfaces-web server (PCRPi-W):a web server to chart hot spots in protein interfaces

BACKGROUND: It is well established that only a portion of residues that mediate protein-protein interactions (PPIs), the so-called hot spot, contributes the most to the total binding energy, and thus its identification is an important and relevant question that has clear applications in drug discovery and protein design. The experimental identification of hot spots is however a lengthy and costly process, and thus there is an interest in computational tools that can complement and guide experimental efforts. PRINCIPAL FINDINGS: Here, we present Presaging Critical Residues in Protein interfaces-Web server (http://www.bioinsilico.org/PCRPi), a web server that implements a recently described and highly accurate computational tool designed to predict critical residues in protein interfaces: PCRPi. PRCPi depends on the integration of structural, energetic, and evolutionary-based measures by using Bayesian Networks (BNs). CONCLUSIONS: PCRPi-W has been designed to provide an easy and convenient access to the broad scientific community. Predictions are readily available for download or presented in a web page that includes among other information links to relevant files, sequence information, and a Jmol applet to visualize and analyze the predictions in the context of the protein structure

PCRPi, Presaging Critical Residues in Protein interfaces, a new computational tool to chart hot spots in protein interfaces

Protein–protein interactions (PPIs) are ubiquitous in Biology, and thus offer an enormous potential for the discovery of novel therapeutics. Although protein interfaces are large and lack defining physiochemical traits, is well established that only a small portion of interface residues, the so-called hot spot residues, contribute the most to the binding energy of the protein complex. Moreover, recent successes in development of novel drugs aimed at disrupting PPIs rely on targeting such residues. Experimental methods for describing critical residues are lengthy and costly; therefore, there is a need for computational tools that can complement experimental efforts. Here, we describe a new computational approach to predict hot spot residues in protein interfaces. The method, called Presaging Critical Residues in Protein interfaces (PCRPi), depends on the integration of diverse metrics into a unique probabilistic measure by using Bayesian Networks. We have benchmarked our method using a large set of experimentally verified hot spot residues and on a blind prediction on the protein complex formed by HRAS protein and a single domain antibody. Under both scenarios, PCRPi delivered consistent and accurate predictions. Finally, PCRPi is able to handle cases where some of the input data is either missing or not reliable (e.g. evolutionary information)

A FOXO1-induced oncogenic network defines the AML1-ETO preleukemic program

Key Points Increased FOXO1 is oncogenic in human CD34+ cells and promotes preleukemia transition. FOXO1 is required by AE preleukemia cells for the activation of a stem cell molecular program.</jats:p

RUNX1 Reshapes the Epigenetic Landscape at the Onset of Haematopoiesis

Cell fate decisions during haematopoiesis are governed by lineage-specific transcription factors, such as RUNX1, SCL/TAL1, FLI1 and C/EBP family members. To gain insight into how these transcription factors regulate the activation of haematopoietic genes during embryonic development, we measured the genome-wide dynamics of transcription factor assembly on their target genes during the RUNX1-dependent transition from haemogenic endothelium (HE) to haematopoietic progenitors. Using a $Runx1^{−/−}$ embryonic stem cell differentiation model expressing an inducible Runx1 gene, we show that in the absence of RUNX1, haematopoietic genes bind SCL/TAL1, FLI1 and C/EBPβ and that this early priming is required for correct temporal expression of the myeloid master regulator PU.1 and its downstream targets. After induction, RUNX1 binds to numerous de novo sites, initiating a local increase in histone acetylation and rapid global alterations in the binding patterns of SCL/TAL1 and FLI1. The acquisition of haematopoietic fate controlled by Runx1 therefore does not represent the establishment of a new regulatory layer on top of a pre-existing HE program but instead entails global reorganization of lineage-specific transcription factor assemblies

Identification of gene specific cis-regulatory elements during differentiation of mouse embryonic stem cells: An integrative approach using high-throughput datasets.

Gene expression governs cell fate, and is regulated via a complex interplay of transcription factors and molecules that change chromatin structure. Advances in sequencing-based assays have enabled investigation of these processes genome-wide, leading to large datasets that combine information on the dynamics of gene expression, transcription factor binding and chromatin structure as cells differentiate. While numerous studies focus on the effects of these features on broader gene regulation, less work has been done on the mechanisms of gene-specific transcriptional control. In this study, we have focussed on the latter by integrating gene expression data for the in vitro differentiation of murine ES cells to macrophages and cardiomyocytes, with dynamic data on chromatin structure, epigenetics and transcription factor binding. Combining a novel strategy to identify communities of related control elements with a penalized regression approach, we developed individual models to identify the potential control elements predictive of the expression of each gene. Our models were compared to an existing method and evaluated using the existing literature and new experimental data from embryonic stem cell differentiation reporter assays. Our method is able to identify transcriptional control elements in a gene specific manner that reflect known regulatory relationships and to generate useful hypotheses for further testing.Wellcome Trust, BBSRC, CRU

Gene regulatory network analysis predicts cooperating transcription factor regulons required for FLT3-ITD+ AML growth

Acute myeloid leukemia (AML) is a heterogeneous disease caused by different mutations. Previously, we showed that each mutational subtype develops its specific gene regulatory network (GRN) with transcription factors interacting within multiple gene modules, many of which are transcription factor genes themselves. Here, we hypothesize that highly connected nodes within such networks comprise crucial regulators of AML maintenance. We test this hypothesis using FLT3-ITD-mutated AML as a model and conduct an shRNA drop-out screen informed by this analysis. We show that AML-specific GRNs predict crucial regulatory modules required for AML growth. Furthermore, our work shows that all modules are highly connected and regulate each other. The careful multi-omic analysis of the role of one (RUNX1) module by shRNA and chemical inhibition shows that this transcription factor and its target genes stabilize the GRN of FLT3-ITD+ AML and that its removal leads to GRN collapse and cell death.</p