88,861 research outputs found
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Although deep learning approaches have had tremendous success in image, video
and audio processing, computer vision, and speech recognition, their
applications to three-dimensional (3D) biomolecular structural data sets have
been hindered by the entangled geometric complexity and biological complexity.
We introduce topology, i.e., element specific persistent homology (ESPH), to
untangle geometric complexity and biological complexity. ESPH represents 3D
complex geometry by one-dimensional (1D) topological invariants and retains
crucial biological information via a multichannel image representation. It is
able to reveal hidden structure-function relationships in biomolecules. We
further integrate ESPH and convolutional neural networks to construct a
multichannel topological neural network (TopologyNet) for the predictions of
protein-ligand binding affinities and protein stability changes upon mutation.
To overcome the limitations to deep learning arising from small and noisy
training sets, we present a multitask topological convolutional neural network
(MT-TCNN). We demonstrate that the present TopologyNet architectures outperform
other state-of-the-art methods in the predictions of protein-ligand binding
affinities, globular protein mutation impacts, and membrane protein mutation
impacts.Comment: 20 pages, 8 figures, 5 table
High-speed scanless entire bandwidth mid-infrared chemical imaging
Mid-infrared spectroscopy probes molecular vibrations to identify chemical
species and functional groups. Therefore, mid-infrared hyperspectral imaging is
one of the most powerful and promising candidates for chemical imaging using
optical methods. Yet high-speed and entire bandwidth mid-infrared hyperspectral
imaging has not been realized. Here we report a mid-infrared hyperspectral
chemical imaging technique that uses chirped pulse upconversion of sub-cycle
pulses at the image plane. This technique offers a lateral resolution of 15
m, and the field of view is adjustable between 800 m 600
m to 12 mm 9 mm. The hyperspectral imaging produces a 640
480 pixel image in 8 s, which covers a spectral range of 640-3015
cm, comprising 1069 wavelength points and offering a wavenumber
resolution of 2.6-3.7 cm. For discrete frequency mid-infrared imaging,
the measurement speed reaches a frame rate of 5 kHz, the repetition rate of the
laser. As a demonstration, we effectively identified and mapped different
components in a microfluidic device, plant cell, and mouse embryo section. The
great capacity and latent force of this technique in chemical imaging promise
to be applied to many fields such as chemical analysis, biology, and medicine.Comment: 22 pages, 10 figure
Super-Resolution Microscopy: A Virus’ Eye View of the Cell
It is difficult to observe the molecular choreography between viruses and host cell components, as they exist on a spatial scale beyond the reach of conventional microscopy. However, novel super-resolution microscopy techniques have cast aside technical limitations to reveal a nanoscale view of virus replication and cell biology. This article provides an introduction to super-resolution imaging; in particular, localisation microscopy, and explores the application of such technologies to the study of viruses and tetraspanins, the topic of this special issue
Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening
This work introduces a number of algebraic topology approaches, such as
multicomponent persistent homology, multi-level persistent homology and
electrostatic persistence for the representation, characterization, and
description of small molecules and biomolecular complexes. Multicomponent
persistent homology retains critical chemical and biological information during
the topological simplification of biomolecular geometric complexity.
Multi-level persistent homology enables a tailored topological description of
inter- and/or intra-molecular interactions of interest. Electrostatic
persistence incorporates partial charge information into topological
invariants. These topological methods are paired with Wasserstein distance to
characterize similarities between molecules and are further integrated with a
variety of machine learning algorithms, including k-nearest neighbors, ensemble
of trees, and deep convolutional neural networks, to manifest their descriptive
and predictive powers for chemical and biological problems. Extensive numerical
experiments involving more than 4,000 protein-ligand complexes from the PDBBind
database and near 100,000 ligands and decoys in the DUD database are performed
to test respectively the scoring power and the virtual screening power of the
proposed topological approaches. It is demonstrated that the present approaches
outperform the modern machine learning based methods in protein-ligand binding
affinity predictions and ligand-decoy discrimination
- …