Teaching computers to fold proteins

A new general algorithm for optimization of potential functions for protein folding is introduced. It is based upon gradient optimization of the thermodynamic stability of native folds of a training set of proteins with known structure. The iterative update rule contains two thermodynamic averages which are estimated by (generalized ensemble) Monte Carlo. We test the learning algorithm on a Lennard-Jones (LJ) force field with a torsional angle degrees-of-freedom and a single-atom side-chain. In a test with 24 peptides of known structure, none folded correctly with the initial potential functions, but two-thirds came within 3{\AA} to their native fold after optimizing the potential functions.Comment: 4 pages, 3 figure

Autoencoding beyond pixels using a learned similarity metric

We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic

Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Bounds

This paper introduces novel results for the score function gradient estimator of the importance weighted variational bound (IWAE). We prove that in the limit of large $K$ (number of importance samples) one can choose the control variate such that the Signal-to-Noise ratio (SNR) of the estimator grows as $\sqrt{K}$. This is in contrast to the standard pathwise gradient estimator where the SNR decreases as $1/\sqrt{K}$. Based on our theoretical findings we develop a novel control variate that extends on VIMCO. Empirically, for the training of both continuous and discrete generative models, the proposed method yields superior variance reduction, resulting in an SNR for IWAE that increases with $K$ without relying on the reparameterization trick. The novel estimator is competitive with state-of-the-art reparameterization-free gradient estimators such as Reweighted Wake-Sleep (RWS) and the thermodynamic variational objective (TVO) when training generative models

How replacing fossil fuels with electrofuels could influence the demand for renewable energy and land area

During recent years, electrofuels (fuels from electricity, water, and carbon) have gained increased interest as substitute for fossil fuels in all energy and chemical sectors. The feasibility of electrofuels has been assessed from a range of aspects but no study has assessed the land area needed if scaling up the production based on renewables. The amount of land on Earth is limited and the competition for land, in a long-term perspective, imposes a risk of, e.g., increased food prices and biodiversity losses. The aim of this paper is to assess how much land area it would require if all fossil fuels were substituted by electrofuels (‘All electrofuel’-scenario) and compare this with the area needed if all fossil fuels were substituted by bioenergy (‘All biomass’-scenario) or by electricity (‘All electric’-scenario). Each scenario represents extreme cases towards fully renewable energy systems to outline the theoretical area needed. Main conclusions are (1) the electricity demand, if substituting all fossil fuels with electrofuels, is huge (1540 EJ) but technically obtainable, demanding 1.1% of the Earth\u27s surface, for solar panels, in the most optimistic case, and (2) the sustainable technical potential for biomass cannot alone substitute all fossil fuels, unless radical energy demand reductions