147,233 research outputs found
Energetics of Protein-DNA Interactions
Protein-DNA interactions are vital for many processes in living cells,
especially transcriptional regulation and DNA modification. To further our
understanding of these important processes on the microscopic level, it is
necessary that theoretical models describe the macromolecular interaction
energetics accurately. While several methods have been proposed, there has not
been a careful comparison of how well the different methods are able to predict
biologically important quantities such as the correct DNA binding sequence,
total binding free energy, and free energy changes caused by DNA mutation. In
addition to carrying out the comparison, we present two important theoretical
models developed initially in protein folding that have not yet been tried on
protein-DNA interactions. In the process, we find that the results of these
knowledge-based potentials show a strong dependence on the interaction distance
and the derivation method. Finally, we present a knowledge-based potential that
gives comparable or superior results to the best of the other methods,
including the molecular mechanics force field AMBER99
Buried and accessible surface area control intrinsic protein flexibility
Proteins experience a wide variety of conformational dynamics that can be
crucial for facilitating their diverse functions. How is the intrinsic
flexibility required for these motions encoded in their three-dimensional
structures? Here, the overall flexibility of a protein is demonstrated to be
tightly coupled to the total amount of surface area buried within its fold. A
simple proxy for this, the relative solvent accessible surface area (Arel),
therefore shows excellent agreement with independent measures of global protein
flexibility derived from various experimental and computational methods.
Application of Arel on a large scale demonstrates its utility by revealing
unique sequence and structural properties associated with intrinsic
flexibility. In particular, flexibility as measured by Arel shows little
correspondence with intrinsic disorder, but instead tends to be associated with
multiple domains and increased {\alpha}- helical structure. Furthermore, the
apparent flexibility of monomeric proteins is found to be useful for
identifying quaternary structure errors in published crystal structures. There
is also a strong tendency for the crystal structures of more flexible proteins
to be solved to lower resolutions. Finally, local solvent accessibility is
shown to be a primary determinant of local residue flexibility. Overall this
work provides both fundamental mechanistic insight into the origin of protein
flexibility and a simple, practical method for predicting flexibility from
protein structures.Comment: 36 pages, 11 figures, author's manuscript, accepted for publication
in Journal of Molecular Biolog
Prediction of Novel High Pressure H2O-NaCl and Carbon Oxide Compounds with Symmetry-Driven Structure Search Algorithm
Crystal structure prediction with theoretical methods is particularly
challenging when unit cells with many atoms need to be considered. Here we
employ a symmetry-driven structure search (SYDSS) method and combine it with
density functional theory (DFT) to predict novel crystal structures at high
pressure. We sample randomly from all 1,506 Wyckoff positions of the 230 space
groups to generate a set of initial structures. During the subsequent
structural relaxation with DFT, existing symmetries are preserved, but the
symmetries and the space group may change as atoms move to more symmetric
positions. By construction, our algorithm generates symmetric structures with
high probability without excluding any configurations. This improves the search
efficiency, especially for large cells with 20 atoms or more. We apply our
SYDSS algorithm to identify stoichiometric (H2O)_n-(NaCl)_m and C_nO_m
compounds at high pressure. We predict a novel H2O-NaCl structure with Pnma
symmetry to form at 3.4 Mbar, which is within the range of diamond anvil
experiments. In addition, we predict a novel C2O structure at 19.8 Mbar and C4O
structure at 44.0 Mbar with Pbca and C2/m symmetry respectively.Comment: 8 pages,8 figures, 3 table, Physical Review B, 201
Computational predictions of energy materials using density functional theory
In the search for new functional materials, quantum mechanics is an exciting starting point. The fundamental laws that govern the behaviour of electrons have the possibility, at the other end of the scale, to predict the performance of a material for a targeted application. In some cases, this is achievable using density functional theory (DFT). In this Review, we highlight DFT studies predicting energy-related materials that were subsequently confirmed experimentally. The attributes and limitations of DFT for the computational design of materials for lithium-ion batteries, hydrogen production and storage materials, superconductors, photovoltaics and thermoelectric materials are discussed. In the future, we expect that the accuracy of DFT-based methods will continue to improve and that growth in computing power will enable millions of materials to be virtually screened for specific applications. Thus, these examples represent a first glimpse of what may become a routine and integral step in materials discovery
Experimental maps of DNA structure at nucleotide resolution distinguish intrinsic from protein-induced DNA deformations
Recognition of DNA by proteins depends on DNA sequence and structure. Often unanswered is whether the structure of naked DNA persists in a proteināDNA complex, or whether protein binding changes DNA shape. While X-ray structures of proteināDNA complexes are numerous, the structure of naked cognate DNA is seldom available experimentally. We present here an experimental and computational analysis pipeline that uses hydroxyl radical cleavage to map, at single-nucleotide resolution, DNA minor groove width, a recognition feature widely exploited by proteins. For 11 proteināDNA complexes, we compared experimental maps of naked DNA minor groove width with minor groove width measured from X-ray co-crystal structures. Seven sites had similar minor groove widths as naked DNA and when bound to protein. For four sites, part of the DNA in the complex had the same structure as naked DNA, and part changed structure upon protein binding. We compared the experimental map with minor groove patterns of DNA predicted by two computational approaches, DNAshape and ORChID2, and found good but not perfect concordance with both. This experimental approach will be useful in mapping structures of DNA sequences for which high-resolution structural data are unavailable. This approach allows probing of protein family-dependent readout mechanisms.National Institutes of Health [R01GM106056 to R.R., T.D.T.; U54CA121852 in part to T.D.T.]; Boston University Undergraduate Research Opportunities Program [Faculty Matching Grants to D.O. and Y.J.]; USC Graduate School [Research Enhancement Fellowship and Manning Endowed Fellowship to T.P.C.]. R.R. is an Alfred P. Sloan Research Fellow. Funding for open access charge: Boston University. (R01GM106056 - National Institutes of Health; U54CA121852 - National Institutes of Health; Boston University Undergraduate Research Opportunities Program; USC Graduate School; Boston University)https://academic.oup.com/nar/article/46/5/2636/4829691?searchresult=1https://academic.oup.com/nar/article/46/5/2636/4829691?searchresult=1Published versio
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
The use of machine learning methods for accelerating the design of
crystalline materials usually requires manually constructed feature vectors or
complex transformation of atom coordinates to input the crystal structure,
which either constrains the model to certain crystal types or makes it
difficult to provide chemical insights. Here, we develop a crystal graph
convolutional neural networks framework to directly learn material properties
from the connection of atoms in the crystal, providing a universal and
interpretable representation of crystalline materials. Our method provides a
highly accurate prediction of density functional theory calculated properties
for eight different properties of crystals with various structure types and
compositions after being trained with data points. Further, our
framework is interpretable because one can extract the contributions from local
chemical environments to global properties. Using an example of perovskites, we
show how this information can be utilized to discover empirical rules for
materials design.Comment: 6+9 pages, 3+6 figure
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