34 research outputs found
Blind protein structure prediction using accelerated free-energy simulations.
We report a key proof of principle of a new acceleration method [Modeling Employing Limited Data (MELD)] for predicting protein structures by molecular dynamics simulation. It shows that such Boltzmann-satisfying techniques are now sufficiently fast and accurate to predict native protein structures in a limited test within the Critical Assessment of Structure Prediction (CASP) community-wide blind competition
Single Molecule Conformational Memory Extraction: P5ab RNA Hairpin
Extracting kinetic models from single
molecule data is an important
route to mechanistic insight in biophysics, chemistry, and biology.
Data collected from force spectroscopy can probe discrete hops of
a single molecule between different conformational states. Model extraction
from such data is a challenging inverse problem because single molecule
data are noisy and rich in structure. Standard modeling methods normally
assume (i) a prespecified number of discrete states and (ii) that
transitions between states are Markovian. The data set is then fit
to this predetermined model to find a handful of rates describing
the transitions between states. We show that it is unnecessary to
assume either (i) or (ii) and focus our analysis on the zipping/unzipping
transitions of an RNA hairpin. The key is in starting with a very
broad class of non-Markov models in order to let the data guide us
toward the best model from this very broad class. Our method suggests
that there exists a folding intermediate for the P5ab RNA hairpin
whose zipping/unzipping is monitored by force spectroscopy experiments.
This intermediate would not have been resolved if a Markov model had
been assumed from the onset. We compare the merits of our method with
those of others
Bacterial Cyclic Diguanylate Signaling Networks Sense Temperature
Many bacteria use the second messenger cyclic diguanylate (c-di-GMP) to control motility, biofilm production and virulence. Here, we identify a thermosensory diguanylate cyclase (TdcA) that modulates temperature-dependent motility, biofilm development and virulence in the opportunistic pathogen Pseudomonas aeruginosa. TdcA synthesizes c-di-GMP with catalytic rates that increase more than a hundred-fold over a ten-degree Celsius change. Analyses using protein chimeras indicate that heat-sensing is mediated by a thermosensitive Per-Arnt-SIM (PAS) domain. TdcA homologs are widespread in sequence databases, and a distantly related, heterologously expressed homolog from the Betaproteobacteria order Gallionellales also displayed thermosensitive diguanylate cyclase activity. We propose, therefore, that thermotransduction is a conserved function of c-di-GMP signaling networks, and that thermosensitive catalysis of a second messenger constitutes a mechanism for thermal sensing in bacteria
Socio-sexuality and episodic memory function in women: further evidence of an adaptive “mating mode”
Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference
More than 100,000 protein structures are now known at atomic detail. However, far more are not yet known, particularly among large or complex proteins. Often, experimental information is only semireliable because it is uncertain, limited, or confusing in important ways. Some experiments give sparse information, some give ambiguous or nonspecific information, and others give uncertain information-where some is right, some is wrong, but we don't know which. We describe a method called Modeling Employing Limited Data (MELD) that can harness such problematic information in a physics-based, Bayesian framework for improved structure determination. We apply MELD to eight proteins of known structure for which such problematic structural data are available, including a sparse NMR dataset, two ambiguous EPR datasets, and four uncertain datasets taken from sequence evolution data. MELD gives excellent structures, indicating its promise for experimental biomolecule structure determination where only semireliable data are available
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Accelerating molecular simulations of proteins using Bayesian inference on weak information
Atomistic molecular dynamics (MD) simulations of protein molecules are too computationally expensive to predict most native structures from amino acid sequences. Here, we integrate "weak" external knowledge into folding simulations to predict protein structures, given their sequence. For example, we instruct the computer "to form a hydrophobic core," "to form good secondary structures," or "to seek a compact state." This kind of information has been too combinatoric, nonspecific, and vague to help guide MD simulations before. Within atomistic replica-exchange molecular dynamics (REMD), we develop a statistical mechanical framework, modeling using limited data with coarse physical insight(s) (MELD + CPI), for harnessing weak information. As a test, we apply MELD + CPI to predict the native structures of 20 small proteins. MELD + CPI samples to within less than 3.2 Å from native for all 20 and correctly chooses the native structures (<4 Å) for 15 of them, including ubiquitin, a millisecond folder. MELD + CPI is up to five orders of magnitude faster than brute-force MD, satisfies detailed balance, and should scale well to larger proteins. MELD + CPI may be useful where physics-based simulations are needed to study protein mechanisms and populations and where we have some heuristic or coarse physical knowledge about states of interest