62,734 research outputs found
Uncertainty quantification of receptor ligand binding sites prediction
Recent advancements in protein docking site prediction have highlighted the
limitations of traditional rigid docking algorithms, like PIPER, which often
neglect critical stochastic elements such as solvent-induced fluctuations.
These oversights can lead to inaccuracies in identifying viable docking sites
due to the complexity of high-dimensional, stochastic energy manifolds with low
regularity. To address this issue, our research introduces a novel model where
the molecular shapes of ligands and receptors are represented using
multi-variate Karhunen-Lo `eve (KL) expansions. This method effectively
captures the stochastic nature of energy manifolds, allowing for a more
accurate representation of molecular interactions.Developed as a plugin for
PIPER, our scientific computing software enhances the platform, delivering
robust uncertainty measures for the energy manifolds of ranked binding sites.
Our results demonstrate that top-ranked binding sites, characterized by lower
uncertainty in the stochastic energy manifold, align closely with actual
docking sites. Conversely, sites with higher uncertainty correlate with less
optimal docking positions. This distinction not only validates our approach but
also sets a new standard in protein docking predictions, offering substantial
implications for future molecular interaction research and drug development
Analyzing Machupo virus-receptor binding by molecular dynamics simulations
In many biological applications, we would like to be able to computationally
predict mutational effects on affinity in protein-protein interactions.
However, many commonly used methods to predict these effects perform poorly in
important test cases. In particular, the effects of multiple mutations,
non-alanine substitutions, and flexible loops are difficult to predict with
available tools and protocols. We present here an existing method applied in a
novel way to a new test case; we interrogate affinity differences resulting
from mutations in a host-virus protein-protein interface. We use steered
molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike
glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then
approximate affinity using the maximum applied force of separation and the area
under the force-versus-distance curve. We find, even without the rigor and
planning required for free energy calculations, that these quantities can
provide novel biophysical insight into the GP1/hTfR1 interaction. First, with
no prior knowledge of the system we can differentiate among wild type and
mutant complexes. Moreover, we show that this simple SMD scheme correlates well
with relative free energy differences computed via free energy perturbation.
Second, although the static co-crystal structure shows two large
hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate
that one of them may not be important for tight binding. Third, one viral site
known to be critical for infection may mark an important evolutionary
suppressor site for infection-resistant hTfR1 mutants. Finally, our approach
provides a framework to compare the effects of multiple mutations, individually
and jointly, on protein-protein interactions.Comment: 33 pages, 8 figures, 5 table
BLSSpeller : exhaustive comparative discovery of conserved cis-regulatory elements
Motivation: The accurate discovery and annotation of regulatory elements remains a challenging problem. The growing number of sequenced genomes creates new opportunities for comparative approaches to motif discovery. Putative binding sites are then considered to be functional if they are conserved in orthologous promoter sequences of multiple related species. Existing methods for comparative motif discovery usually rely on pregenerated multiple sequence alignments, which are difficult to obtain for more diverged species such as plants. As a consequence, misaligned regulatory elements often remain undetected.
Results: We present a novel algorithm that supports both alignment-free and alignment-based motif discovery in the promoter sequences of related species. Putative motifs are exhaustively enumerated as words over the IUPAC alphabet and screened for conservation using the branch length score. Additionally, a confidence score is established in a genome-wide fashion. In order to take advantage of a cloud computing infrastructure, the MapReduce programming model is adopted. The method is applied to four monocotyledon plant species and it is shown that high-scoring motifs are significantly enriched for open chromatin regions in Oryza sativa and for transcription factor binding sites inferred through protein-binding microarrays in O. sativa and Zea mays. Furthermore, the method is shown to recover experimentally profiled ga2ox1-like KN1 binding sites in Z. mays
A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment
Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to conventional systematic parallel virtual screening methods in two ways. Firstly, it automates the process of creating and launching multiple independent dockings on a high performance computing cluster. Secondly, it uses a NË™ aive Bayes scoring function to calculate binding energy of un-docked ligands to identify and preferentially dock (Autodock predicted) better binders. The application was tested on four proteins using a library of 10,573 ligands. In all the experiments, (i). 200 of the 1000 best binders are identified after docking only 14% of the chemical library, (ii). 9 or 10 best-binders are identified after docking only 19% of the chemical library, and (iii). no significant enrichment is observed after docking 70% of the chemical library. The results show significant increase in enrichment of potential drug leads in early rounds of virtual screening
Stochastic approach to molecular interactions and computational theory of metabolic and genetic regulations
Binding and unbinding of ligands to specific sites of a macromolecule are one
of the most elementary molecular interactions inside the cell that embody the
computational processes of biological regulations. The interaction between
transcription factors and the operators of genes and that between ligands and
binding sites of allosteric enzymes are typical examples of such molecular
interactions. In order to obtain the general mathematical framework of
biological regulations, we formulate these interactions as finite Markov
processes and establish a computational theory of regulatory activities of
macromolecules based mainly on graphical analysis of their state transition
diagrams. The contribution is summarized as follows: (1) Stochastic
interpretation of Michaelis-Menten equation is given. (2) Notion of
\textit{probability flow} is introduced in relation to detailed balance. (3) A
stochastic analogy of \textit{Wegscheider condition} is given in relation to
loops in the state transition diagram. (4) A simple graphical method of
computing the regulatory activity in terms of ligands' concentrations is
obtained for Wegscheider cases.Comment: 20 pages, 13 figure
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