Systematic Assessment of Deep Learning-Based Predictors of Fragmentation Intensity Profiles.

In recent years, several deep learning-based methods have been proposed for predicting peptide fragment intensities. This study aims to provide a comprehensive assessment of six such methods, namely Prosit, DeepMass:Prism, pDeep3, AlphaPeptDeep, Prosit Transformer, and the method proposed by Guan et al. To this end, we evaluated the accuracy of the predicted intensity profiles for close to 1.7 million precursors (including both tryptic and HLA peptides) corresponding to more than 18 million experimental spectra procured from 40 independent submissions to the PRIDE repository that were acquired for different species using a variety of instruments and different dissociation types/energies. Specifically, for each method, distributions of similarity (measured by Pearson's correlation and normalized angle) between the predicted and the corresponding experimental b and y fragment intensities were generated. These distributions were used to ascertain the prediction accuracy and rank the prediction methods for particular types of experimental conditions. The effect of variables like precursor charge, length, and collision energy on the prediction accuracy was also investigated. In addition to prediction accuracy, the methods were evaluated in terms of prediction speed. The systematic assessment of these six methods may help in choosing the right method for MS/MS spectra prediction for particular needs.

Exploring induced pluripotency in human fibroblasts via construction, validation, and application of a gene regulatory network.

Reprogramming of somatic cells to induced pluripotent stem cells, by overexpressing certain factors referred to as the reprogramming factors, can revolutionize regenerative medicine. To provide a coherent description of induced pluripotency from the gene regulation perspective, we use 35 microarray datasets to construct a reprogramming gene regulatory network. Comprising 276 nodes and 4471 links, the resulting network is, to the best of our knowledge, the largest gene regulatory network constructed for human fibroblast reprogramming and it is the only one built using a large number of experimental datasets. To build the network, a model that relates the expression profiles of the initial (fibroblast) and final (induced pluripotent stem cell) states is proposed and the model parameters (link strengths) are fitted using the experimental data. Twenty nine additional experimental datasets are collectively used to test the model/network, and good agreement between experimental and predicted gene expression profiles is found. We show that the model in conjunction with the constructed network can make useful predictions. For example, we demonstrate that our approach can incorporate the effect of reprogramming factor stoichiometry and that its predictions are consistent with the experimentally observed trends in reprogramming efficiency when the stoichiometric ratios vary. Using our model/network, we also suggest new (not used in training of the model) candidate sets of reprogramming factors, many of which have already been experimentally verified. These results suggest our model/network can potentially be used in devising new recipes for induced pluripotency with higher efficiencies. Additionally, we classify the links of the network into three classes of different importance, prioritizing them for experimental verification. We show that many of the links in the top ranked class are experimentally known to be important in reprogramming. Finally, comparing with other methods, we show that using our model is advantageous.

Ultra-High-Frequency Reprogramming of Individual Long-Term Hematopoietic Stem Cells Yields Low Somatic Variant Induced Pluripotent Stem Cells.

Efficiency of reprogramming of human cells into induced pluripotent stem cells (iPSCs) has remained low. We report that individual adult human CD49f+ long-term hematopoietic stem cells (LT-HSCs) can be reprogrammed into iPSCs at close to 50% efficiency using Sendai virus transduction. This exquisite sensitivity to reprogramming is specific to LT-HSCs, since it progressively decreases in committed progenitors. LT-HSC reprogramming can follow multiple paths and is most efficient when transduction is performed after the cells have exited G0. Sequencing of 75 paired skin fibroblasts/LT-HSC samples collected from nine individuals revealed that LT-HSCs contain a lower load of somatic single-nucleotide variants (SNVs) and indels than skin fibroblasts and accumulate about 12 SNVs/year. Mutation analysis revealed that LT-HSCs and fibroblasts have very different somatic mutation signatures and that somatic mutations in iPSCs generally exist prior to reprogramming. LT-HSCs may become the preferred cell source for the production of clinical-grade iPSCs.

Mechanism-based disease similarity.

In recent years several methods have been proposed to assign pairwise mechanism- based similarity scores to human diseases. Despite their differences in approach and performance, these methods work in a somewhat similar manner: first a set of biomolecules (genes, proteins, chemicals, etc.) is associated with each disease, and then a measure is defined to calculate the similarity between the sets assigned to a pair of diseases. Since the similarity score between two diseases is defined based on the underlying molecular processes, a high score may hint at a shared cause, and therefore a similar treatment, for both diseases. This is of great practical importance especially when a rare or newly-discovered disease, for which limited information is available, is found to be related to a disease with a known treatment. Thus, in this mini-review we briefly discuss the recently developed methods for computing mechanism-based disease- disease similarities.

DeCoaD: determining correlations among diseases using protein interaction networks.

BACKGROUND: Disease-disease similarities can be investigated from multiple perspectives. Identifying similar diseases based on the underlying biomolecular interactions can be especially useful, because it may shed light on the common causes of the diseases and therefore may provide clues for possible treatments. Here we introduce DeCoaD, a web-based program that uses a novel method to assign pair-wise similarity scores, called correlations, to genetic diseases. FINDINGS: DeCoaD uses a random walk to model the flow of information in a network within which nodes are either diseases or proteins and links signify either protein-protein interactions or disease-protein associations. For each protein node, the total number of visits by the random walker is called the weight of that node. Using a disease as both the starting and the terminating points of the random walks, a corresponding vector, whose elements are the weights associated with the proteins, can be constructed. The similarity between two diseases is defined as the cosine of the angle between their associated vectors. For a user-specified disease, DeCoaD outputs a list of similar diseases (with their corresponding correlations), and a graphical representation of the disease families that they belong to. Based on a probabilistic clustering algorithm, DeCoaD also outputs the clusters that the disease of interest is a member of, and the corresponding probabilities. The program also provides an interface to run enrichment analysis for the given disease or for any of the clusters that contains it. CONCLUSIONS: DeCoaD uses a novel algorithm to suggest non-trivial similarities between diseases with known gene associations, and also clusters the diseases based on their similarity scores. DeCoaD is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/mn/DeCoaD/.

A method for the inclusion of sphenoidal electrodes in realistic EEG source imaging.

SUMMARY: Although EEG source imaging (ESI) has become more popular over the last few years, sphenoidal electrodes (SPE) have never been incorporated in ESI using realistic head models. This is in part because of the true locations of these electrodes are not exactly known. In this study, we demonstrate the feasibility of determining the true locations of SPE and incorporating this information into realistic ESI. The impact of including these electrodes in ESI in mesial temporal lobe epilepsy is also discussed. Seventeen patients were retrospectively selected for this study. To determine the positions of SPE in each case, two orthogonal x-rays (sagittal and coronal) of the SPE needle stilette were taken in the presence of previously digitized scalp electrodes. An in-house computer program was then used to find the locations of the tip of the needle stilette relative to the surface electrodes. These locations were then incorporated in a realistic head model based on the finite element method. EEG source imaging was then performed using averaged spikes for included patients suspected of having mesial temporal lobe epilepsy. Including SPE significantly shifted the ESI result even in the presence of subtemporal electrodes, resulting in an inferior and mesial displacement.

Sphenoidal electrodes significantly change the results of source localization of interictal spikes for a large percentage of patients with temporal lobe epilepsy.

Although scalp EEG is a very useful tool for presurgical evaluation in epilepsy, the 10-20 system of electrodes in many cases fails to accurately localize the source of the epileptic seizures. One suggested solution to this problem is to use additional electrodes. Sphenoidal electrodes especially have been suggested to be helpful in identifying the irritative and seizure onset zones in patients with temporal lobe epilepsy. However, the value of these electrodes has been debated, and in many epilepsy centers they are not used. In this study, we investigate the impact of sphenoidal electrodes by comparing the results of EEG source localization with and without sphenoidal recordings. We retrospectively selected patients with temporal lobe epilepsy based on their clinical semiology and electrophysiologic data. For each patient, a prototype spike was used as a template for an automatic pattern search to find similar activities. The identified spikes were then averaged and analyzed by fitting a dipole to the data. The recordings from sphenoidal electrodes were then excluded and the analysis was repeated. It was found that in more than half of the patients inclusion of sphenoidal electrodes resulted in a shift of more than 2 cm in the location of the fitted dipole, and in some cases moved the dipole from the frontal lobe or the insula to the temporal lobe. Our results suggest that sphenoidal electrodes are helpful in the analysis of the EEG recordings of patients suspected of having temporal lobe epilepsy.

Structure and function of the intracellular region of the plexin-b1 transmembrane receptor.

Members of the plexin family are unique transmembrane receptors in that they interact directly with Rho family small GTPases; moreover, they contain a GTPase-activating protein (GAP) domain for R-Ras, which is crucial for plexin-mediated regulation of cell motility. However, the functional role and structural basis of the interactions between the different intracellular domains of plexins remained unclear. Here we present the 2.4 A crystal structure of the complete intracellular region of human plexin-B1. The structure is monomeric and reveals that the GAP domain is folded into one structure from two segments, separated by the Rho GTPase binding domain (RBD). The RBD is not dimerized, as observed previously. Instead, binding of a conserved loop region appears to compete with dimerization and anchors the RBD to the GAP domain. Cell-based assays on mutant proteins confirm the functional importance of this coupling loop. Molecular modeling based on structural homology to p120(GAP).H-Ras suggests that Ras GTPases can bind to the plexin GAP region. Experimentally, we show that the monomeric intracellular plexin-B1 binds R-Ras but not H-Ras. These findings suggest that the monomeric form of the intracellular region is primed for GAP activity and extend a model for plexin activation.