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Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions.

We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference.

Find the full codebase at https//github.com/gitter-lab/ssps.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Conceptually, epitope-based vaccine design poses two distinct problems (i) selecting the best epitopes to elicit the strongest possible immune response and (ii) arranging and linking them through short spacer sequences to string-of-beads vaccines, so that their recovery likelihood during antigen processing is maximized. Current state-of-the-art approaches solve this design problem sequentially. Consequently, such approaches are unable to capture the inter-dependencies between the two design steps, usually emphasizing theoretical immunogenicity over correct vaccine processing, thus resulting in vaccines with less effective immunogenicity in vivo.

In this work, we present a computational approach based on linear programming, called JessEV, that solves both design steps simultaneously, allowing to weigh the selection of a set of epitopes that have great immunogenic potential against their assembly into a string-of-beads construct that provides a high chance of recovery. We conducted Monte Carlo cleavage simulations to show that a fixed set of epitopes often cannot be assembled adequately, whereas selecting epitopes to accommodate proper cleavage requirements substantially improves their recovery probability and thus the effective immunogenicity, pathogen and population coverage of the resulting vaccines by at least 2-fold.

The software and the data analyzed are available at https//github.com/SchubertLab/JessEV.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Disordered flexible linkers (DFLs) are abundant and functionally important intrinsically disordered regions that connect protein domains and structural elements within domains and which facilitate disorder-based allosteric regulation. Although computational estimates suggest that thousands of proteins have DFLs, they were annotated experimentally in <200 proteins. This substantial annotation gap can be reduced with the help of accurate computational predictors. The sole predictor of DFLs, DFLpred, trade-off accuracy for shorter runtime by excluding relevant but computationally costly predictive inputs. Moreover, it relies on the local/window-based information while lacking to consider useful protein-level characteristics.

We conceptualize, design and test APOD (Accurate Predictor Of DFLs), the first highly accurate predictor that utilizes both local- and protein-level inputs that quantify propensity for disorder, sequence composition, sequence conservation and selected putative structural properties. Consequently, APOD offers significantly more accurate predictions when compared with its faster predecessor, DFLpred, and several other alternative ways to predict DFLs. click here These improvements stem from the use of a more comprehensive set of inputs that cover the protein-level information and the application of a more sophisticated predictive model, a well-parametrized support vector machine. APOD achieves area under the curve = 0.82 (28% improvement over DFLpred) and Matthews correlation coefficient = 0.42 (180% increase over DFLpred) when tested on an independent/low-similarity test dataset. Consequently, APOD is a suitable choice for accurate and small-scale prediction of DFLs.

https//yanglab.nankai.edu.cn/APOD/.

https//yanglab.nankai.edu.cn/APOD/.

When phase III clinical drug trials fail their endpoint, enormous resources are wasted. Moreover, even if a clinical trial demonstrates a significant benefit, the observed effects are often small and may not outweigh the side effects of the drug. Therefore, there is a great clinical need for methods to identify genetic markers that can identify subgroups of patients which are likely to benefit from treatment as this may (i) rescue failed clinical trials and/or (ii) identify subgroups of patients which benefit more than the population as a whole. When single genetic biomarkers cannot be found, machine learning approaches that find multivariate signatures are required. For single nucleotide polymorphism (SNP) profiles, this is extremely challenging owing to the high dimensionality of the data. Here, we introduce RAINFOREST (tReAtment benefIt prediction using raNdom FOREST), which can predict treatment benefit from patient SNP profiles obtained in a clinical trial setting.

We demonstrate the performance of RAINFOREST on the CAIRO2 dataset, a phase III clinical trial which tested the addition of cetuximab treatment for metastatic colorectal cancer and concluded there was no benefit.