Faculty Bibliography
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139
A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin
BACKGROUND:Understanding the interactions between antibodies and the linear epitopes that they recognize is an important task in the study of immunological diseases. We present a novel computational method for the design of linear epitopes of specified binding affinity to Intravenous Immunoglobulin (IVIg). RESULTS:We show that the method, called Pythia-design can accurately design peptides with both high-binding affinity and low binding affinity to IVIg. To show this, we experimentally constructed and tested the computationally constructed designs. We further show experimentally that these designed peptides are more accurate that those produced by a recent method for the same task. Pythia-design is based on combining random walks with an ensemble of probabilistic support vector machines (SVM) classifiers, and we show that it produces a diverse set of designed peptides, an important property to develop robust sets of candidates for construction. We show that by combining Pythia-design and the method of (PloS ONE 6(8):23616, 2011), we are able to produce an even more accurate collection of designed peptides. Analysis of the experimental validation of Pythia-design peptides indicates that binding of IVIg is favored by epitopes that contain trypthophan and cysteine. CONCLUSIONS:Our method, Pythia-design, is able to generate a diverse set of binding and non-binding peptides, and its designs have been experimentally shown to be accurate.
PMCID:4826543
PMID: 27059896
ISSN: 1471-2105
CID: 5822472
Inferring causal molecular networks: empirical assessment through a community-based effort
It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.
PMCID:4854847
PMID: 26901648
ISSN: 1548-7105
CID: 5822462
A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis
Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response.
PMCID:4924788
PMID: 27351836
ISSN: 1553-7358
CID: 5822492
Nanoscale lateral displacement arrays for the separation of exosomes and colloids down to 20 nm
Deterministic lateral displacement (DLD) pillar arrays are an efficient technology to sort, separate and enrich micrometre-scale particles, which include parasites, bacteria, blood cells and circulating tumour cells in blood. However, this technology has not been translated to the true nanoscale, where it could function on biocolloids, such as exosomes. Exosomes, a key target of 'liquid biopsies', are secreted by cells and contain nucleic acid and protein information about their originating tissue. One challenge in the study of exosome biology is to sort exosomes by size and surface markers. We use manufacturable silicon processes to produce nanoscale DLD (nano-DLD) arrays of uniform gap sizes ranging from 25 to 235 nm. We show that at low Péclet (Pe) numbers, at which diffusion and deterministic displacement compete, nano-DLD arrays separate particles between 20 to 110 nm based on size with sharp resolution. Further, we demonstrate the size-based displacement of exosomes, and so open up the potential for on-chip sorting and quantification of these important biocolloids.
PMID: 27479757
ISSN: 1748-3395
CID: 5822512
Crowdsourcing biomedical research: leveraging communities as innovation engines
The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.
PMCID:5918684
PMID: 27418159
ISSN: 1471-0064
CID: 5822502
Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.
PMID: 27549343
ISSN: 2041-1723
CID: 4178352
Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease
Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.
PMCID:5474755
PMID: 27079753
ISSN: 1552-5279
CID: 2143082
The Prostate Cancer DREAM Challenge: A Community-Wide Effort to Use Open Clinical Trial Data for the Quantitative Prediction of Outcomes in Metastatic Prostate Cancer [Editorial]
Project Data Share and Sage Bionetworks/DREAM are launching the Prostate Cancer DREAM Challenge to improve a predictive model of disease progression and treatment toxicity in prostate cancer using historical trial data. Predictions identified through this challenge have the potential to translate into reduced trial redundancy, better clinical decision tools, and improved patient outcomes. The challenge launches on March 16, 2015.
PMCID:4425397
PMID: 25777346
ISSN: 1549-490x
CID: 5822382
Hydrodynamics of diamond-shaped gradient nanopillar arrays for effective DNA translocation into nanochannels
Effective DNA translocation into nanochannels is critical for advancing genome mapping and future single-molecule DNA sequencing technologies. We present the design and hydrodynamic study of a diamond-shaped gradient pillar array connected to nanochannels for enhancing the success of DNA translocation events. Single-molecule fluorescence imaging is utilized to interrogate the hydrodynamic interactions of the DNA with this unique structure, evaluate key DNA translocation parameters, including speed, extension, and translocation time, and provide a detailed mapping of the translocation events in nanopillar arrays coupled with 10 and 50 μm long channels. Our analysis reveals the important roles of diamond-shaped nanopillars in guiding DNA into as small as 30 nm channels with minimized clogging, stretching DNA to nearly 100% of their dyed contour length, inducing location-specific straddling of DNA at nanopillar interfaces, and modulating DNA speeds by pillar geometries. Importantly, all critical features down to 30 nm wide nanochannels are defined using standard photolithography and fabrication processes, a feat aligned with the requirement of high-volume, low-cost production.
PMID: 25626162
ISSN: 1936-086x
CID: 5822372
Preface: RECOMB/ISCB systems biology, regulatory genomics, and DREAM 2014 Special Issue [Editorial]
PMID: 25844665
ISSN: 1557-8666
CID: 5822392
