Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these ...mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.
ELASPIC is a novel ensemble machine-learning approach that predicts the effects of mutations on protein folding and protein-protein interactions. Here, we present the ELASPIC webserver, which makes ...the ELASPIC pipeline available through a fast and intuitive interface. The webserver can be used to evaluate the effect of mutations on any protein in the Uniprot database, and allows all predicted results, including modeled wild-type and mutated structures, to be managed and viewed online and downloaded if needed. It is backed by a database which contains improved structural domain definitions, and a list of curated domain-domain interactions for all known proteins, as well as homology models of domains and domain-domain interactions for the human proteome. Homology models for proteins of other organisms are calculated on the fly, and mutations are evaluated within minutes once the homology model is available.
The ELASPIC webserver is available online at http://elaspic.kimlab.org
pm.kim@utoronto.ca or pi@kimlab.orgSupplementary data: Supplementary data are available at Bioinformatics online.
Intrinsically disordered regions have been associated with various cellular processes and are implicated in several human diseases, but their exact roles remain unclear. We previously defined two ...classes of conserved disordered regions in budding yeast, referred to as "flexible" and "constrained" conserved disorder. In flexible disorder, the property of disorder has been positionally conserved during evolution, whereas in constrained disorder, both the amino acid sequence and the property of disorder have been conserved. Here, we show that flexible and constrained disorder are widespread in the human proteome, and are particularly common in proteins with regulatory functions. Both classes of disordered sequences are highly enriched in regions of proteins that undergo tissue-specific (TS) alternative splicing (AS), but not in regions of proteins that undergo general (i.e., not tissue-regulated) AS. Flexible disorder is more highly enriched in TS alternative exons, whereas constrained disorder is more highly enriched in exons that flank TS alternative exons. These latter regions are also significantly more enriched in potential phosphosites and other short linear motifs associated with cell signaling. We further show that cancer driver mutations are significantly enriched in regions of proteins associated with TS and general AS. Collectively, our results point to distinct roles for TS alternative exons and flanking exons in the dynamic regulation of protein interaction networks in response to signaling activity, and they further suggest that alternatively spliced regions of proteins are often functionally altered by mutations responsible for cancer.
Bekçilerin görev yaptıkları çarşı veya mahallelerde topluma sundukları hizmetin, görevlerini yerine getirirken gerçekleştirdikleri sözlü veya sözsüz iletişim faaliyetlerinin ve davranışlarının toplum ...tarafından nasıl algılandığı bekçi imajını belirlemektedir. Bu araştırmada toplumun bekçi imajını ölçmeye yönelik ölçme aracının geliştirilmesi amaçlanmıştır. Veriler, açımlayıcı (AFA) ve doğrulayıcı faktör analizi (DFA) uygulamak üzere toplam 664 katılımcıdan oluşan iki araştırma grubundan elde edilmiştir. AFA ölçme aracının, toplam varyansın %65.37’sini açıklayan ve 35 maddeden oluşan beş faktörlü bir yapıdan oluştuğunu göstermiştir. Bu beş faktör “Tatmin”, “Tutum”, “İletişim”, “Mesleki Yeterlilik” ve “Saygınlık” olarak adlandırılmıştır. DFA sonucunda 34 madde ve beş faktörden oluşan yapıya ait uyum indekslerinin yeterli olduğu görülmüştür. Güvenirlik analizi kapsamında hesaplanan Cronbach alfa iç tutarlık katsayısı AFA uygulanan grup için .97, DFA uygulanan grup için .96 olarak bulunmuştur. Madde analizi sonuçları, ölçekte yer alan maddelerin ayırt edicilik düzeylerinin ve toplam puanı yordama gücünün yeterli düzeyde olduğunu göstermiştir. Yapılan analizler sonucunda Bekçi İmajı Ölçeği’nin geçerliği ve güvenirliği kanıtlanmış bir ölçme aracı olduğu sonucuna ulaşılmıştır.
The service that the night watchmen provide to the society in the bazaars or neighborhoods where they work, how the verbal or non-verbal communication activities and behaviors they perform while performing their duties are perceived by a society determines the night watchman image. This research, it is aimed to develop a measurement tool to measure the night watchman image in society. The research was carried out considering the scale development stages. The data were obtained from two research groups consisting of a total of 664 participants to apply exploratory (EFA) and confirmatory factor analysis (CFA). EFA showed that the measurement tool consisted of a five-factor structure consisting of 35 items that explained 65.37% of the total variance. These five factors were named “Satisfaction”, “Attitude”, “Communication”, “Professional Qualification” and “Prestige”. As a result of the CFA, it was seen that the fit indexes of the structure consisting of 34 items and five factors were sufficient. The Cronbach's alpha internal consistency coefficient calculated within the scope of reliability analysis was found to be .97 for the group in which EFA was applied, and .96 for the group in which CFA was applied. The results of the item analysis showed that the discrimination levels of the items in the scale and their predictive power of the total score were sufficient. As a result of the analyzes made, it was concluded that the Night Watchman Image Scale is a measurement tool with proven validity and reliability.
Rapid advances in genotyping and genome-wide association studies have enabled the discovery of many new genotype-phenotype associations at the resolution of individual markers. However, these ...associations explain only a small proportion of theoretically estimated heritability of most diseases. In this work, we propose an integrative mixture model called JBASE: joint Bayesian analysis of subphenotypes and epistasis. JBASE explores two major reasons of missing heritability: interactions between genetic variants, a phenomenon known as epistasis and phenotypic heterogeneity, addressed via subphenotyping.
Our extensive simulations in a wide range of scenarios repeatedly demonstrate that JBASE can identify true underlying subphenotypes, including their associated variants and their interactions, with high precision. In the presence of phenotypic heterogeneity, JBASE has higher Power and lower Type 1 Error than five state-of-the-art approaches. We applied our method to a sample of individuals from Mexico with Type 2 diabetes and discovered two novel epistatic modules, including two loci each, that define two subphenotypes characterized by differences in body mass index and waist-to-hip ratio. We successfully replicated these subphenotypes and epistatic modules in an independent dataset from Mexico genotyped with a different platform.
JBASE is implemented in C++, supported on Linux and is available at http://www.cs.toronto.edu/∼goldenberg/JBASE/jbase.tar.gz. The genotype data underlying this study are available upon approval by the ethics review board of the Medical Centre Siglo XXI. Please contact Dr Miguel Cruz at mcruzl@yahoo.com for assistance with the application.
anna.goldenberg@utoronto.ca
Supplementary data are available at Bioinformatics online.
How species with similar repertoires of protein-coding genes differ so markedly at the phenotypic level is poorly understood. By comparing organ transcriptomes from vertebrate species spanning ∼350 ...million years of evolution, we observed significant differences in alternative splicing complexity between vertebrate lineages, with the highest complexity in primates. Within 6 million years, the splicing profiles of physiologically equivalent organs diverged such that they are more strongly related to the identity of a species than they are to organ type. Most vertebrate species-specific splicing patterns are cis-directed. However, a subset of pronounced splicing changes are predicted to remodel protein interactions involving trans-acting regulators. These events likely further contributed to the diversification of splicing and other transcriptomic changes that underlie phenotypic differences among vertebrate species.
Alternative splicing acts on transcripts from almost all human multi-exon genes. Notwithstanding its ubiquity, fundamental ramifications of splicing on protein expression remain unresolved. The ...number and identity of spliced transcripts that form stably folded proteins remain the sources of considerable debate, due largely to low coverage of experimental methods and the resulting absence of negative data. We circumvent this issue by developing a semi-supervised learning algorithm, positive unlabeled learning for splicing elucidation (PULSE; http://www.kimlab.org/software/pulse), which uses 48 features spanning various categories. We validated its accuracy on sets of bona fide protein isoforms and directly on mass spectrometry (MS) spectra for an overall AU-ROC of 0.85. We predict that around 32% of “exon skipping” alternative splicing events produce stable proteins, suggesting that the process engenders a significant number of previously uncharacterized proteins. We also provide insights into the distribution of positive isoforms in various functional classes and into the structural effects of alternative splicing.
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•Predicting which isoforms produce stably folded proteins has been an open problem•Recent advancements in theoretical machine learning enable us solve this problem•PULSE, our proposed algorithm, predicts ∼32% of isoforms as stably folded•Probability of stably folding varies significantly across functional gene categories
Here, Hao et al. present PULSE, a novel machine-learning method that predicts which alternative splicing isoforms generate stably folded, viable proteins. They predict roughly one-third of isoforms as functional, many of which have considerable variation within their folded domains.
Alternative splicing plays a key role in the expansion of proteomic and regulatory complexity, yet the functions of the vast majority of differentially spliced exons are not known. In this study, we ...observe that brain and other tissue-regulated exons are significantly enriched in flexible regions of proteins that likely form conserved interaction surfaces. These proteins participate in significantly more interactions in protein-protein interaction (PPI) networks than other proteins. Using LUMIER, an automated PPI assay, we observe that approximately one-third of analyzed neural-regulated exons affect PPIs. Inclusion of these exons stimulated and repressed different partner interactions at comparable frequencies. This assay further revealed functions of individual exons, including a role for a neural-specific exon in promoting an interaction between Bridging Integrator 1 (Bin1)/Amphiphysin II and Dynamin 2 (Dnm2) that facilitates endocytosis. Collectively, our results provide evidence that regulated alternative exons frequently remodel interactions to establish tissue-dependent PPI networks.
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▸ Proteins containing tissue-regulated exons have high centrality in PPI networks ▸ Tissue-regulated exons are highly enriched in protein regions that mediate PPIs ▸ LUMIER reveals that one-third of analyzed neural-specific exons modulate PPIs ▸ Exon-resolution functional mapping reveals specific roles for neural AS events
Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction ...networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense) regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented.
We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB), by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples.
We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely available large-scale datasets.
Software and data sets are available at http://www.sfu.ca/~ester/software/DECOB.zip.
The article focuses on the importance of sea bass, which is preferred by consumers in Turkey and worldwide. However, seafood can deteriorate rapidly under unfavorable conditions during storage due to ...their nutrient content, water content, and weakness in connective tissues. Temperature changes, inappropriate processing methods during transportation, and temperature changes during storage in markets are reported to cause losses in seafood quality. The deterioration of seafood, especially in seafood stored under inappropriate conditions because of temperature, causes changes contrary to consumer preferences because of the rapid growth of microorganisms, especially odor changes in seafood. This study examines the models related to the discipline of predictive microbiology, which are stated to provide an accurate shelf life prediction of the rate of microbiological spoilage and emphasize the importance of mathematical predictions of these models for seafood. Furthermore, the paper observes that machine learning algorithms such as Random Forest, Decision Tree, k-Nearest Neighbors, AdaBoost, Gradient Tree Boosting, Random Forest, Decision Tree, k-Nearest Neighbors, AdaBoost, and Gradient Tree Boosting have been used to predict the shelf life of seafood products. Finally, how to augment the limited data in a laboratory study to evaluate the shelf life of sea bass stored at different temperatures, how to prove the consistency of the augmented data with the original data, and how to optimize successful machine learning methods for robust problem-solving processes between different engineering fields are explained in detail. The results show that the optimized Extra Tree algorithm is the most successful for
Pseudomonas
quantity estimation with an
R
2
metric value of 0.9940 and TVC quantity estimation with an
R
2
metric value of 0.9910, while the other algorithms are less successful than this algorithm. These results show that machine learning methods can be a rapid, powerful, and effective tool for shelf life prediction of sea bass. Additionally, it should be emphasized that the number of input parameters (temperature, number of the bacteria) are of utmost significant for augmentation of the data for development and application of the machine learning algorithms.
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