High-precision high-coverage functional inference from integrated data sources

<p>Abstract</p> <p>Background</p> <p>Information obtained from diverse data sources can be combined in a principled manner using various machine learning methods to increase the reliability and range of knowledge about protein function. The result is a weighted functional linkage network (FLN) in which linked neighbors share at least one function with high probability. Precision is, however, low. Aiming to provide precise functional annotation for as many proteins as possible, we explore and propose a two-step framework for functional annotation (1) construction of a high-coverage and reliable FLN via machine learning techniques (2) development of a decision rule for the constructed FLN to optimize functional annotation.</p> <p>Results</p> <p>We first apply this framework to <it>Saccharomyces cerevisiae</it>. In the first step, we demonstrate that four commonly used machine learning methods, Linear SVM, Linear Discriminant Analysis, Naïve Bayes, and Neural Network, all combine heterogeneous data to produce reliable and high-coverage FLNs, in which the linkage weight more accurately estimates functional coupling of linked proteins than use individual data sources alone. In the second step, empirical tuning of an adjustable decision rule on the constructed FLN reveals that basing annotation on maximum edge weight results in the most precise annotation at high coverages. In particular at low coverage all rules evaluated perform comparably. At coverage above approximately 50%, however, they diverge rapidly. At full coverage, the maximum weight decision rule still has a precision of approximately 70%, whereas for other methods, precision ranges from a high of slightly more than 30%, down to 3%. In addition, a scoring scheme to estimate the precisions of individual predictions is also provided. Finally, tests of the robustness of the framework indicate that our framework can be successfully applied to less studied organisms.</p> <p>Conclusion</p> <p>We provide a general two-step function-annotation framework, and show that high coverage, high precision annotations can be achieved by constructing a high-coverage and reliable FLN via data integration followed by applying a maximum weight decision rule.</p

An FPT Approach for Predicting Protein Localization from Yeast Genomic Data

Accurately predicting the localization of proteins is of paramount importance in the quest to determine their respective functions within the cellular compartment. Because of the continuous and rapid progress in the fields of genomics and proteomics, more data are available now than ever before. Coincidentally, data mining methods been developed and refined in order to handle this experimental windfall, thus allowing the scientific community to quantitatively address long-standing questions such as that of protein localization. Here, we develop a frequent pattern tree (FPT) approach to generate a minimum set of rules (mFPT) for predicting protein localization. We acquire a series of rules according to the features of yeast genomic data. The mFPT prediction accuracy is benchmarked against other commonly used methods such as Bayesian networks and logistic regression under various statistical measures. Our results show that mFPT gave better performance than other approaches in predicting protein localization. Meanwhile, setting 0.65 as the minimum hit-rate, we obtained 138 proteins that mFPT predicted differently than the simple naive bayesian method (SNB). In our analysis of these 138 proteins, we present novel predictions for the location for 17 proteins, which currently do not have any defined localization. These predictions can serve as putative annotations and should provide preliminary clues for experimentalists. We also compared our predictions against the eukaryotic subcellular localization database and related predictions by others on protein localization. Our method is quite generalized and can thus be applied to discover the underlying rules for protein-protein interactions, genomic interactions, and structure-function relationships, as well as those of other fields of research

Application of machine learning in systems biology

Biological systems are composed of a large number of molecular components. Understanding their behavior as a result of the interactions between the individual components is one of the aims of systems biology. Computational modelling is a powerful tool commonly used in systems biology, which relies on mathematical models that capture the properties and interactions between molecular components to simulate the behavior of the whole system. However, in many biological systems, it becomes challenging to build reliable mathematical models due to the complexity and the poor understanding of the underlying mechanisms. With the breakthrough in big data technologies in biology, data-driven machine learning (ML) approaches offer a promising complement to traditional theory-based models in systems biology. Firstly, ML can be used to model the systems in which the relationships between the components and the system are too complex to be modelled with theory-based models. Two such examples of using ML to resolve the genotype-phenotype relationships are presented in this thesis: (i) predicting yeast phenotypes using genomic features and (ii) predicting the thermal niche of microorganisms based on the proteome features. Secondly, ML naturally complements theory-based models. By applying ML, I improved the performance of the genome-scale metabolic model in describing yeast thermotolerance. In this application, ML was used to estimate the thermal parameters by using a Bayesian statistical learning approach that trains regression models and performs uncertainty quantification and reduction. The predicted bottleneck genes were further validated by experiments in improving yeast thermotolerance. In such applications, regression models are frequently used, and their performance relies on many factors, including but not limited to feature engineering and quality of response values. Manually engineering sufficient relevant features is particularly challenging in biology due to the lack of knowledge in certain areas. With the increasing volume of big data, deep-transfer learning enables us to learn a statistical summary of the samples from a big dataset which can be used as input to train other ML models. In the present thesis, I applied this approach to first learn a deep representation of enzyme thermal adaptation and then use it for the development of regression models for predicting enzyme optimal and protein melting temperatures. It was demonstrated that the transfer learning-based regression models outperform the classical ones trained on rationally engineered features in both cases. On the other hand, noisy response values are very common in biological datasets due to the variation in experimental measurements and they fundamentally restrict the performance attainable with regression models. I thereby addressed this challenge by deriving a theoretical upper bound for the coefficient of determination (R2) for regression models. This theoretical upper bound depends on the noise associated with the response variable and variance for a given dataset. It can thus be used to test whether the maximal performance has been reached on a particular dataset, or whether further model improvement is possible

Mapping the proteome with data-driven methods: A cycle of measurement, modeling, hypothesis generation, and engineering

The living cell exhibits emergence of complex behavior and its modeling requires a systemic, integrative approach if we are to thoroughly understand and harness it. The work in this thesis has had the more narrow aim of quantitatively characterizing and mapping the proteome using data-driven methods, as proteins perform most functional and structural roles within the cell. Covered are the different parts of the cycle from improving quantification methods, to deriving protein features relying on their primary structure, predicting the protein content solely from sequence data, and, finally, to developing theoretical protein engineering tools, leading back to experiment.\ua0\ua0\ua0\ua0 High-throughput mass spectrometry platforms provide detailed snapshots of a cell\u27s protein content, which can be mined towards understanding how the phenotype arises from genotype and the interplay between the various properties of the constituent proteins. However, these large and dense data present an increased analysis challenge and current methods capture only a small fraction of signal. The first part of my work has involved tackling these issues with the implementation of a GPU-accelerated and distributed signal decomposition pipeline, making factorization of large proteomics scans feasible and efficient. The pipeline yields individual analyte signals spanning the majority of acquired signal, enabling high precision quantification and further analytical tasks.\ua0\ua0\ua0 Having such detailed snapshots of the proteome enables a multitude of undertakings. One application has been to use a deep neural network model to learn the amino acid sequence determinants of temperature adaptation, in the form of reusable deep model features. More generally, systemic quantities may be predicted from the information encoded in sequence by evolutionary pressure. Two studies taking inspiration from natural language processing have sought to learn the grammars behind the languages of expression, in one case predicting mRNA levels from DNA sequence, and in the other protein abundance from amino acid sequence. These two models helped build a quantitative understanding of the central dogma and, furthermore, in combination yielded an improved predictor of protein amount. Finally, a mathematical framework relying on the embedded space of a deep model has been constructed to assist guided mutation of proteins towards optimizing their abundance

Rigidity and flexibility of biological networks

The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks.Comment: 21 pages, 4 figures, 1 tabl