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

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively. Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction. Finally, in recent blind CAMEO benchmark our method successfully folded 5 test proteins with a novel fold

Protein Structure Prediction: The Next Generation

Over the last 10-15 years a general understanding of the chemical reaction of protein folding has emerged from statistical mechanics. The lessons learned from protein folding kinetics based on energy landscape ideas have benefited protein structure prediction, in particular the development of coarse grained models. We survey results from blind structure prediction. We explore how second generation prediction energy functions can be developed by introducing information from an ensemble of previously simulated structures. This procedure relies on the assumption of a funnelled energy landscape keeping with the principle of minimal frustration. First generation simulated structures provide an improved input for associative memory energy functions in comparison to the experimental protein structures chosen on the basis of sequence alignment

Cleavage of DFNA5 by caspase-3 during apoptosis mediates progression to secondary necrotic/pyroptotic cell death.

Apoptosis is a genetically regulated cell suicide programme mediated by activation of the effector caspases 3, 6 and 7. If apoptotic cells are not scavenged, they progress to a lytic and inflammatory phase called secondary necrosis. The mechanism by which this occurs is unknown. Here we show that caspase-3 cleaves the GSDMD-related protein DFNA5 after Asp270 to generate a necrotic DFNA5-N fragment that targets the plasma membrane to induce secondary necrosis/pyroptosis. Cells that express DFNA5 progress to secondary necrosis, when stimulated with apoptotic triggers such as etoposide or vesicular stomatitis virus infection, but disassemble into small apoptotic bodies when DFNA5 is deleted. Our findings identify DFNA5 as a central molecule that regulates apoptotic cell disassembly and progression to secondary necrosis, and provide a molecular mechanism for secondary necrosis. Because DFNA5-induced secondary necrosis and GSDMD-induced pyroptosis are dependent on caspase activation, we propose that they are forms of programmed necrosis

Gasdermin pores permeabilize mitochondria to augment caspase-3 activation during apoptosis and inflammasome activation.

Gasdermin E (GSDME/DFNA5) cleavage by caspase-3 liberates the GSDME-N domain, which mediates pyroptosis by forming pores in the plasma membrane. Here we show that GSDME-N also permeabilizes the mitochondrial membrane, releasing cytochrome c and activating the apoptosome. Cytochrome c release and caspase-3 activation in response to intrinsic and extrinsic apoptotic stimuli are significantly reduced in GSDME-deficient cells comparing with wild type cells. GSDME deficiency also accelerates cell growth in culture and in a mouse model of melanoma. Phosphomimetic mutation of the highly conserved phosphorylatable Thr6 residue of GSDME, inhibits its pore-forming activity, thus uncovering a potential mechanism by which GSDME might be regulated. Like GSDME-N, inflammasome-generated gasdermin D-N (GSDMD-N), can also permeabilize the mitochondria linking inflammasome activation to downstream activation of the apoptosome. Collectively, our results point to a role of gasdermin proteins in targeting the mitochondria to promote cytochrome c release to augment the mitochondrial apoptotic pathway

93m^{93m}Mo isomer depletion via beam-based nuclear excitation by electron capture

A recent nuclear physics experiment [C. J. Chiara {\it et al.}, Nature (London) {\bf 554}, 216 (2018)] reports the first direct observation of nuclear excitation by electron capture (NEEC) in the depletion of the $^{93m}$Mo isomer. The experiment used a beam-based setup in which Mo highly charged ions with nuclei in the isomeric state $^{93m}$Mo at 2.4 MeV excitation energy were slowed down in a solid-state target. In this process, nuclear excitation to a higher triggering level led to isomer depletion. The reported excitation probability $P_{\rm{exc}} = 0.01$ was solely attributed to the so-far unobserved process of NEEC in lack of a different known channel of comparable efficiency. In this work, we investigate theoretically the beam-based setup and calculate excitation rates via NEEC using state-of-the-art atomic structure and ion stopping power models. For all scenarios, our results disagree with the experimental data by approximately nine orders of magnitude. This stands in conflict with the conclusion that NEEC was the excitation mechanism behind the observed depletion rate.Comment: 6 pages, 3 figures; minor modifications made; accepted for publication in Physical Review Letter

Modeling and minimizing CAPRI round 30 symmetrical protein complexes from CASP-11 structural models

International audienceMany of the modeling targets in the blind CASP-11/CAPRI-30 experiment were protein homo-dimers and homo-tetramers. Here, we perform a retrospective docking-based analysis of the perfectly symmetrical CAPRI Round 30 targets whose crystal structures have been published. Starting from the CASP “stage-2” fold prediction models, we show that using our recently developed “SAM” polar Fourier symmetry docking algorithm combined with NAMD energy minimization often gives acceptable or better 3D models of the target complexes. We also use SAM to analyze the overall quality of all CASP structural models for the selected targets from a docking-based perspective. We demonstrate that docking only CASP “center” structures for the selected targets provides a fruitful and economical docking strategy. Furthermore, our results show that many of the CASP models are dockable in the sense that they can lead to acceptable or better models of symmetrical complexes. Even though SAM is very fast, using docking and NAMD energy minimization to pull out acceptable docking models from a large ensemble of docked CASP models is computationally expensive. Nonetheless, thanks to our SAM docking algorithm, we expect that applying our docking protocol on a modern computer cluster will give us the ability to routinely model 3D structures of symmetrical protein complexes from CASP-quality models