63,567 research outputs found
Identification of functionally related enzymes by learning-to-rank methods
Enzyme sequences and structures are routinely used in the biological sciences
as queries to search for functionally related enzymes in online databases. To
this end, one usually departs from some notion of similarity, comparing two
enzymes by looking for correspondences in their sequences, structures or
surfaces. For a given query, the search operation results in a ranking of the
enzymes in the database, from very similar to dissimilar enzymes, while
information about the biological function of annotated database enzymes is
ignored.
In this work we show that rankings of that kind can be substantially improved
by applying kernel-based learning algorithms. This approach enables the
detection of statistical dependencies between similarities of the active cleft
and the biological function of annotated enzymes. This is in contrast to
search-based approaches, which do not take annotated training data into
account. Similarity measures based on the active cleft are known to outperform
sequence-based or structure-based measures under certain conditions. We
consider the Enzyme Commission (EC) classification hierarchy for obtaining
annotated enzymes during the training phase. The results of a set of sizeable
experiments indicate a consistent and significant improvement for a set of
similarity measures that exploit information about small cavities in the
surface of enzymes
Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.
Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
In domains like bioinformatics, information retrieval and social network
analysis, one can find learning tasks where the goal consists of inferring a
ranking of objects, conditioned on a particular target object. We present a
general kernel framework for learning conditional rankings from various types
of relational data, where rankings can be conditioned on unseen data objects.
We propose efficient algorithms for conditional ranking by optimizing squared
regression and ranking loss functions. We show theoretically, that learning
with the ranking loss is likely to generalize better than with the regression
loss. Further, we prove that symmetry or reciprocity properties of relations
can be efficiently enforced in the learned models. Experiments on synthetic and
real-world data illustrate that the proposed methods deliver state-of-the-art
performance in terms of predictive power and computational efficiency.
Moreover, we also show empirically that incorporating symmetry or reciprocity
properties can improve the generalization performance
Beyond element-wise interactions: identifying complex interactions in biological processes
Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations.
Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction.
Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem
Analysis of the impact degree distribution in metabolic networks using branching process approximation
Theoretical frameworks to estimate the tolerance of metabolic networks to
various failures are important to evaluate the robustness of biological complex
systems in systems biology. In this paper, we focus on a measure for robustness
in metabolic networks, namely, the impact degree, and propose an approximation
method to predict the probability distribution of impact degrees from metabolic
network structures using the theory of branching process. We demonstrate the
relevance of this method by testing it on real-world metabolic networks.
Although the approximation method possesses a few limitations, it may be a
powerful tool for evaluating metabolic robustness.Comment: 17 pages, 4 figures, 4 table
The protein cost of metabolic fluxes: prediction from enzymatic rate laws and cost minimization
Bacterial growth depends crucially on metabolic fluxes, which are limited by
the cell's capacity to maintain metabolic enzymes. The necessary enzyme amount
per unit flux is a major determinant of metabolic strategies both in evolution
and bioengineering. It depends on enzyme parameters (such as kcat and KM
constants), but also on metabolite concentrations. Moreover, similar amounts of
different enzymes might incur different costs for the cell, depending on
enzyme-specific properties such as protein size and half-life. Here, we
developed enzyme cost minimization (ECM), a scalable method for computing
enzyme amounts that support a given metabolic flux at a minimal protein cost.
The complex interplay of enzyme and metabolite concentrations, e.g. through
thermodynamic driving forces and enzyme saturation, would make it hard to solve
this optimization problem directly. By treating enzyme cost as a function of
metabolite levels, we formulated ECM as a numerically tractable, convex
optimization problem. Its tiered approach allows for building models at
different levels of detail, depending on the amount of available data.
Validating our method with measured metabolite and protein levels in E. coli
central metabolism, we found typical prediction fold errors of 3.8 and 2.7,
respectively, for the two kinds of data. ECM can be used to predict enzyme
levels and protein cost in natural and engineered pathways, establishes a
direct connection between protein cost and thermodynamics, and provides a
physically plausible and computationally tractable way to include enzyme
kinetics into constraint-based metabolic models, where kinetics have usually
been ignored or oversimplified
A simple yet effective baseline for non-attributed graph classification
Graphs are complex objects that do not lend themselves easily to typical
learning tasks. Recently, a range of approaches based on graph kernels or graph
neural networks have been developed for graph classification and for
representation learning on graphs in general. As the developed methodologies
become more sophisticated, it is important to understand which components of
the increasingly complex methods are necessary or most effective.
As a first step, we develop a simple yet meaningful graph representation, and
explore its effectiveness in graph classification. We test our baseline
representation for the graph classification task on a range of graph datasets.
Interestingly, this simple representation achieves similar performance as the
state-of-the-art graph kernels and graph neural networks for non-attributed
graph classification. Its performance on classifying attributed graphs is
slightly weaker as it does not incorporate attributes. However, given its
simplicity and efficiency, we believe that it still serves as an effective
baseline for attributed graph classification. Our graph representation is
efficient (linear-time) to compute. We also provide a simple connection with
the graph neural networks.
Note that these observations are only for the task of graph classification
while existing methods are often designed for a broader scope including node
embedding and link prediction. The results are also likely biased due to the
limited amount of benchmark datasets available. Nevertheless, the good
performance of our simple baseline calls for the development of new, more
comprehensive benchmark datasets so as to better evaluate and analyze different
graph learning methods. Furthermore, given the computational efficiency of our
graph summary, we believe that it is a good candidate as a baseline method for
future graph classification (or even other graph learning) studies.Comment: 13 pages. Shorter version appears at 2019 ICLR Workshop:
Representation Learning on Graphs and Manifolds. arXiv admin note: text
overlap with arXiv:1810.00826 by other author
Composite structural motifs of binding sites for delineating biological functions of proteins
Most biological processes are described as a series of interactions between
proteins and other molecules, and interactions are in turn described in terms
of atomic structures. To annotate protein functions as sets of interaction
states at atomic resolution, and thereby to better understand the relation
between protein interactions and biological functions, we conducted exhaustive
all-against-all atomic structure comparisons of all known binding sites for
ligands including small molecules, proteins and nucleic acids, and identified
recurring elementary motifs. By integrating the elementary motifs associated
with each subunit, we defined composite motifs which represent
context-dependent combinations of elementary motifs. It is demonstrated that
function similarity can be better inferred from composite motif similarity
compared to the similarity of protein sequences or of individual binding sites.
By integrating the composite motifs associated with each protein function, we
define meta-composite motifs each of which is regarded as a time-independent
diagrammatic representation of a biological process. It is shown that
meta-composite motifs provide richer annotations of biological processes than
sequence clusters. The present results serve as a basis for bridging atomic
structures to higher-order biological phenomena by classification and
integration of binding site structures.Comment: 34 pages, 7 figure
- …