9,239 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
Distance-based Protein Folding Powered by Deep Learning
Contact-assisted protein folding has made very good progress, but two
challenges remain. One is accurate contact prediction for proteins lack of many
sequence homologs and the other is that time-consuming folding simulation is
often needed to predict good 3D models from predicted contacts. We show that
protein distance matrix can be predicted well by deep learning and then
directly used to construct 3D models without folding simulation at all. Using
distance geometry to construct 3D models from our predicted distance matrices,
we successfully folded 21 of the 37 CASP12 hard targets with a median family
size of 58 effective sequence homologs within 4 hours on a Linux computer of 20
CPUs. In contrast, contacts predicted by direct coupling analysis (DCA) cannot
fold any of them in the absence of folding simulation and the best CASP12 group
folded 11 of them by integrating predicted contacts into complex,
fragment-based folding simulation. The rigorous experimental validation on 15
CASP13 targets show that among the 3 hardest targets of new fold our
distance-based folding servers successfully folded 2 large ones with <150
sequence homologs while the other servers failed on all three, and that our ab
initio folding server also predicted the best, high-quality 3D model for a
large homology modeling target. Further experimental validation in CAMEO shows
that our ab initio folding server predicted correct fold for a membrane protein
of new fold with 200 residues and 229 sequence homologs while all the other
servers failed. These results imply that deep learning offers an efficient and
accurate solution for ab initio folding on a personal computer
REinforcement learning based Adaptive samPling: REAPing Rewards by Exploring Protein Conformational Landscapes
One of the key limitations of Molecular Dynamics simulations is the
computational intractability of sampling protein conformational landscapes
associated with either large system size or long timescales. To overcome this
bottleneck, we present the REinforcement learning based Adaptive samPling
(REAP) algorithm that aims to efficiently sample conformational space by
learning the relative importance of each reaction coordinate as it samples the
landscape. To achieve this, the algorithm uses concepts from the field of
reinforcement learning, a subset of machine learning, which rewards sampling
along important degrees of freedom and disregards others that do not facilitate
exploration or exploitation. We demonstrate the effectiveness of REAP by
comparing the sampling to long continuous MD simulations and least-counts
adaptive sampling on two model landscapes (L-shaped and circular), and
realistic systems such as alanine dipeptide and Src kinase. In all four
systems, the REAP algorithm consistently demonstrates its ability to explore
conformational space faster than the other two methods when comparing the
expected values of the landscape discovered for a given amount of time. The key
advantage of REAP is on-the-fly estimation of the importance of collective
variables, which makes it particularly useful for systems with limited
structural information
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