The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.

Protein structure prediction aims to determine the three-dimensional shape of a protein from its amino acid sequence 1. This problem is of fundamental importance to biology as the structure of a protein largely determines its function 2 but can be hard to determine experimentally. In recent years, considerable progress has been made by leveraging genetic information: analysing the co-variation of homologous sequences can allow one to infer which amino acid residues are in contact, which in turn can aid structure prediction 3. In this work, we show that we can train a neural network to accurately predict the distances between pairs of residues in a protein which convey more about structure than contact predictions. With this information we construct a potential of mean force 4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimised by a simple gradient descent algorithm, to realise structures without the need for complex sampling procedures. The resulting system, named AlphaFold, has been shown to achieve high accuracy, even for sequences with relatively few homologous sequences. In the most recent Critical Assessment of Protein Structure Prediction 5 (CASP13), a blind assessment of the state of the field of protein structure prediction, AlphaFold created high-accuracy structures (with TM-scores † of 0.7 or higher) for 24 out of 43 free modelling domains whereas the next best method, using sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a significant advance in protein structure prediction. We expect the increased accuracy of structure predictions for proteins to enable insights in understanding the function and malfunction of these proteins, especially in cases where no homologous proteins have been experimentally determined 7. Proteins are at the core of most biological processes. Since the function of a protein is dependent on its structure, understanding protein structure has been a grand challenge in biology for decades. While several experimental structure determination techniques have been developed † Template Modelling score 6 , between 0 and 1, measures the degree of match of the overall (backbone) shape of a proposed structure to a native structure.

The analysis of the small-signal stability of conventional power systems is well established, but for inverter based microgrids there is a need to establish how circuit and control features give rise to particular oscillatory modes and which of these have poor damping. This paper develops the modeling and analysis of autonomous operation of inverter-based microgrids. Each sub-module is modeled in state-space form and all are combined together on a common reference frame. The model captures the detail of the control loops of the inverter but not the switching action. Some inverter modes are found at relatively high frequency and so a full dynamic model of the network (rather than an algebraic impedance model) is used. The complete model is linearized around an operating point and the resulting system matrix is used to derive the eigenvalues. The eigenvalues (termed "modes") indicate the frequency and damping of oscillatory components in the transient response. A sensitivity analysis is also presented which helps identifying the origin of each of the modes and identify possible feedback signals for design of controllers to improve the system stability. With experience it is possible to simplify the model (reduce the order) if particular modes are not of interest as is the case with synchronous machine models. Experimental results from a microgrid of three 10-kW inverters are used to verify the results obtained from the model. Index Terms-Inverter, inverter model, microgrid, power control, small-signal stability.

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Abstract-Several forms of vibration-driven MEMS microgenerator are possible and are reported in the literature, with potential application areas including distributed sensing and ubiquitous computing. This paper sets out an analytical basis for their design and comparison, verified against full time-domain simulations. Most reported microgenerators are classified as either velocity-damped resonant generators (VDRGs) or Coulomb-damped resonant generators (CDRGs) and a unified analytical structure is provided for these generator types. Reported generators are shown to have operated at well below achievable power densities and design guides are given for optimising future devices. The paper also describes a new class-the Coulomb-force parametric generator (CFPG)-which does not operate in a resonant manner. For all three generators, expressions and graphs are provided showing the dependence of output power on key operating parameters. The optimization also considers physical generator constraints such as voltage limitation or maximum or minimum damping ratios. The sensitivity of each generator architecture to the source vibration frequency is analyzed and this shows that the CFPG can be better suited than the resonant generators to applications where the source frequency is likely to vary. It is demonstrated that mechanical resonance is particularly useful when the vibration source amplitude is small compared to the allowable mass-to-frame displacement. The CDRG and the VDRG generate the same power at resonance but give better performance below and above resonance respectively. Both resonant generator types are unable to operate when the allowable mass frame displacement is small compared to the vibration source amplitude, as is likely to be the case in some MEMS applications. The CFPG is, therefore, required for such applications.[944]