23 research outputs found
NOEnet–Use of NOE networks for NMR resonance assignment of proteins with known 3D structure
Motivation: A prerequisite for any protein study by NMR is the assignment of the resonances from the 15N−1H HSQC spectrum to their corresponding atoms of the protein backbone. Usually, this assignment is obtained by analyzing triple resonance NMR experiments. An alternative assignment strategy exploits the information given by an already available 3D structure of the same or a homologous protein. Up to now, the algorithms that have been developed around the structure-based assignment strategy have the important drawbacks that they cannot guarantee a high assignment accuracy near to 100%
On the Power of Threshold-Based Algorithms for Detecting Cycles in the CONGEST Model
It is known that, for every , -freeness can be decided by a
generic Monte-Carlo algorithm running in rounds in the
CONGEST model. For , faster Monte-Carlo algorithms do exist,
running in rounds, based on upper bounding the number of
messages to be forwarded, and aborting search sub-routines for which this
number exceeds certain thresholds. We investigate the possible extension of
these threshold-based algorithms, for the detection of larger cycles. We first
show that, for every , there exists an infinite family of graphs
containing a -cycle for which any threshold-based algorithm fails to detect
that cycle. Hence, in particular, neither -freeness nor
-freeness can be decided by threshold-based algorithms. Nevertheless,
we show that -freeness can still be decided by a
threshold-based algorithm, running in rounds,
which is faster than using the generic algorithm, which would run in
rounds. Moreover, we exhibit an
infinite collection of families of cycles such that threshold-based algorithms
can decide -freeness for every in this collection.Comment: to be published in SIROCCO 202
The Communication Complexity of Set Intersection and Multiple Equality Testing
In this paper we explore fundamental problems in randomized communication
complexity such as computing Set Intersection on sets of size and Equality
Testing between vectors of length . Sa\u{g}lam and Tardos and Brody et al.
showed that for these types of problems, one can achieve optimal communication
volume of bits, with a randomized protocol that takes
rounds. Aside from rounds and communication volume, there is a \emph{third}
parameter of interest, namely the \emph{error probability} .
It is straightforward to show that protocols for Set Intersection or Equality
Testing need to send bits. Is it
possible to simultaneously achieve optimality in all three parameters, namely
communication and rounds? In
this paper we prove that there is no universally optimal algorithm, and
complement the existing round-communication tradeoffs with a new tradeoff
between rounds, communication, and probability of error. In particular:
1. Any protocol for solving Multiple Equality Testing in rounds with
failure probability has communication volume .
2. There exists a protocol for solving Multiple Equality Testing in rounds with communication, thereby essentially
matching our lower bound and that of Sa\u{g}lam and Tardos.
Our original motivation for considering as an independent
parameter came from the problem of enumerating triangles in distributed
() networks having maximum degree . We prove that
this problem can be solved in time with
high probability .Comment: 44 page
Robust structure-based resonance assignment for functional protein studies by NMR
High-throughput functional protein NMR studies, like protein interactions or dynamics, require an automated approach for the assignment of the protein backbone. With the availability of a growing number of protein 3D structures, a new class of automated approaches, called structure-based assignment, has been developed quite recently. Structure-based approaches use primarily NMR input data that are not based on J-coupling and for which connections between residues are not limited by through bonds magnetization transfer efficiency. We present here a robust structure-based assignment approach using mainly HN–HN NOEs networks, as well as 1H–15N residual dipolar couplings and chemical shifts. The NOEnet complete search algorithm is robust against assignment errors, even for sparse input data. Instead of a unique and partly erroneous assignment solution, an optimal assignment ensemble with an accuracy equal or near to 100% is given by NOEnet. We show that even low precision assignment ensembles give enough information for functional studies, like modeling of protein-complexes. Finally, the combination of NOEnet with a low number of ambiguous J-coupling sequential connectivities yields a high precision assignment ensemble. NOEnet will be available under: http://www.icsn.cnrs-gif.fr/download/nmr