I22-Small angle scattering & Diffraction
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Minghui
Sun
,
Zheng
Dong
,
Liyuan
Wu
,
Haodong
Yao
,
Wenchao
Niu
,
Deting
Xu
,
Ping
Chen
,
Himadri S.
Gupta
,
Yi
Zhang
,
Yuhui
Dong
,
Chunying
Chen
,
Lina
Zhao
Open Access
Abstract: Structural disclosure of biological materials can help our understanding of design disciplines in nature and inspire research for artificial materials. Synchrotron microfocus X-ray diffraction is one of the main techniques for characterizing hierarchically structured biological materials, especially the 3D orientation distribution of their interpenetrating nanofiber networks. However, extraction of 3D fiber orientation from X-ray patterns is still carried out by iterative parametric fitting, with disadvantages of time consumption and demand for expertise and initial parameter estimates. When faced with high-throughput experiments, existing analysis methods cannot meet the real time analysis challenges. In this work, using the assumption that the X-ray illuminated volume is dominated by two groups of nanofibers in a gradient biological composite, a machine-learning based method is proposed for fast and automatic fiber orientation metrics prediction from synchrotron X-ray micro-focused diffraction data. The simulated data were corrupted in the training procedure to guarantee the prediction ability of the trained machine-learning algorithm in real-world experimental data predictions. Label transformation was used to resolve the jump discontinuity problem when predicting angle parameters. The proposed method shows promise for application in the automatic data-processing pipeline for fast analysis of the vast data generated from multiscale diffraction-based tomography characterization of textured biomaterials.
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May 2023
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I06-Nanoscience
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O. J.
Amin
,
S. F.
Poole
,
S.
Reimers
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L. X.
Barton
,
A.
Dal Din
,
F.
Maccherozzi
,
S. S.
Dhesi
,
V.
Novák
,
F.
Krizek
,
J. S.
Chauhan
,
R. P.
Campion
,
A. W.
Rushforth
,
T.
Jungwirth
,
O. A.
Tretiakov
,
K. W.
Edmonds
,
P.
Wadley
Diamond Proposal Number(s):
[26255, 27845]
Open Access
Abstract: Topologically protected magnetic textures are promising candidates for information carriers in future memory devices, as they can be efficiently propelled at very high velocities using current-induced spin torques. These textures—nanoscale whirls in the magnetic order—include skyrmions, half-skyrmions (merons) and their antiparticles. Antiferromagnets have been shown to host versions of these textures that have high potential for terahertz dynamics, deflection-free motion and improved size scaling due to the absence of stray field. Here we show that topological spin textures, merons and antimerons, can be generated at room temperature and reversibly moved using electrical pulses in thin-film CuMnAs, a semimetallic antiferromagnet that is a testbed system for spintronic applications. The merons and antimerons are localized on 180° domain walls, and move in the direction of the current pulses. The electrical generation and manipulation of antiferromagnetic merons is a crucial step towards realizing the full potential of antiferromagnetic thin films as active components in high-density, high-speed magnetic memory devices.
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May 2023
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Jack
Scantlebury
,
Lucy
Vost
,
Anna
Carbery
,
Thomas E.
Hadfield
,
Oliver M.
Turnbull
,
Nathan
Brown
,
Vijil
Chenthamarakshan
,
Payel
Das
,
Harold
Grosjean
,
Frank
Von Delft
,
Charlotte M.
Deane
Open Access
Abstract: Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules. However, many scoring functions make predictions based on data set biases rather than an understanding of the physics of binding. These scoring functions perform well when tested on similar targets to those in the training set but fail to generalize to dissimilar targets. To test what a machine learning-based scoring function has learned, input attribution, a technique for learning which features are important to a model when making a prediction on a particular data point, can be applied. If a model successfully learns something beyond data set biases, attribution should give insight into the important binding interactions that are taking place. We built a machine learning-based scoring function that aimed to avoid the influence of bias via thorough train and test data set filtering and show that it achieves comparable performance on the Comparative Assessment of Scoring Functions, 2016 (CASF-2016) benchmark to other leading methods. We then use the CASF-2016 test set to perform attribution and find that the bonds identified as important by PointVS, unlike those extracted from other scoring functions, have a high correlation with those found by a distance-based interaction profiler. We then show that attribution can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. We use this information to perform fragment elaboration and see improvements in docking scores compared to using structural information from a traditional, data-based approach. This not only provides definitive proof that the scoring function has learned to identify some important binding interactions but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design.
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May 2023
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I06-Nanoscience
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S.
Reimers
,
Y.
Lytvynenko
,
Y. R.
Niu
,
E.
Golias
,
B.
Sarpi
,
L. S. I.
Veiga
,
T.
Denneulin
,
A.
Kovács
,
R. E.
Dunin-Borkowski
,
J.
Bläßer
,
M.
Klaui
,
M.
Jourdan
Diamond Proposal Number(s):
[30141]
Open Access
Abstract: Current pulse driven Néel vector rotation in metallic antiferromagnets is one of the most promising concepts in antiferromagnetic spintronics. We show microscopically that the Néel vector of epitaxial thin films of the prototypical compound Mn2Au can be reoriented reversibly in the complete area of cross shaped device structures using single current pulses. The resulting domain pattern with aligned staggered magnetization is long term stable enabling memory applications. We achieve this switching with low heating of ≈20 K, which is promising regarding fast and efficient devices without the need for thermal activation. Current polarity dependent reversible domain wall motion demonstrates a Néel spin-orbit torque acting on the domain walls.
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Apr 2023
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Luiz Carlos
Saramago
,
Marcos V.
Santana
,
Bárbara Figueira
Gomes
,
Rafael Ferreira
Dantas
,
Mario R.
Senger
,
Pedro Henrique
Oliveira Borges
,
Vivian Neuza
Dos Santos Ferreira
,
Alice
Dos Santos Rosa
,
Amanda Resende
Tucci
,
Milene
Dias Miranda
,
Petra
Lukacik
,
Claire
Strain-Damerell
,
C. David
Owen
,
Martin A.
Walsh
,
Sabrina
Baptista Ferreira
,
Floriano Paes
Silva-Junior
Abstract: SARS-CoV-2 is the causative agent of COVID-19 and is responsible for the current global pandemic. The viral genome contains 5 major open reading frames of which the largest ORF1ab codes for two polyproteins, pp1ab and pp1a, which are subsequently cleaved into 16 nonstructural proteins (nsp) by two viral cysteine proteases encoded within the polyproteins. The main protease (Mpro, nsp5) cleaves the majority of the nsp’s, making it essential for viral replication and has been successfully targeted for the development of antivirals. The first oral Mpro inhibitor, nirmatrelvir, was approved for treatment of COVID-19 in late December 2021 in combination with ritonavir as Paxlovid. Increasing the arsenal of antivirals and development of protease inhibitors and other antivirals with a varied mode of action remains a priority to reduce the likelihood for resistance emerging. Here, we report results from an artificial intelligence-driven approach followed by in vitro validation, allowing the identification of five fragment-like Mpro inhibitors with IC50 values ranging from 1.5 to 241 μM. The three most potent molecules (compounds 818, 737, and 183) were tested against SARS-CoV-2 by in vitro replication in Vero E6 and Calu-3 cells. Compound 818 was active in both cell models with an EC50 value comparable to its measured IC50 value. On the other hand, compounds 737 and 183 were only active in Calu-3, a preclinical model of respiratory cells, showing selective indexes twice as high as those for compound 818. We also show that our in silico methodology was successful in identifying both reversible and covalent inhibitors. For instance, compound 818 is a reversible chloromethylamide analogue of 8-methyl-γ-carboline, while compound 737 is an N-pyridyl-isatin that covalently inhibits Mpro. Given the small molecular weights of these fragments, their high binding efficiency in vitro and efficacy in blocking viral replication, these compounds represent good starting points for the development of potent lead molecules targeting the Mpro of SARS-CoV-2.
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Apr 2023
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I22-Small angle scattering & Diffraction
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Zhongzheng
Zhou
,
Chun
Li
,
Xiaoxue
Bi
,
Chenglong
Zhang
,
Yingke
Huang
,
Jian
Zhuang
,
Wenqiang
Hua
,
Zheng
Dong
,
Lina
Zhao
,
Yi
Zhang
,
Yuhui
Dong
Open Access
Abstract: With the advancements in instrumentations of next-generation synchrotron light sources, methodologies for small-angle X-ray scattering (SAXS)/wide-angle X-ray diffraction (WAXD) experiments have dramatically evolved. Such experiments have developed into dynamic and multiscale in situ characterizations, leaving prolonged exposure time as well as radiation-induced damage a serious concern. However, reduction on exposure time or dose may result in noisier images with a lower signal-to-noise ratio, requiring powerful denoising mechanisms for physical information retrieval. Here, we tackle the problem from an algorithmic perspective by proposing a small yet effective machine-learning model for experimental SAXS/WAXD image denoising, allowing more redundancy for exposure time or dose reduction. Compared with classic models developed for natural image scenarios, our model provides a bespoke denoising solution, demonstrating superior performance on highly textured SAXS/WAXD images. The model is versatile and can be applied to denoising in other synchrotron imaging experiments when data volume and image complexity is concerned.
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Apr 2023
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I10-Beamline for Advanced Dichroism
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Diamond Proposal Number(s):
[30613]
Open Access
Abstract: Magnetic chiral soliton lattices (CSLs) emerge from the helical phase in chiral magnets when magnetic fields are applied perpendicular to the helical propagation vector, and they show great promise for next-generation magnetic memory applications. These one-dimensional structures are previously observed at low temperatures in samples with uniaxial symmetry. Here, it is found that in-plane fields are the key to stabilizing the CSL in cubic Co8Zn10Mn2 over the entire temperature range from 15 K to below the Curie temperature (365 K). Using small-angle resonant elastic X-ray scattering, it is observed that the CSL is stabilized with an arbitrary in-plane propagation vector, while its thin plate geometry plays a deciding role in the soliton wavelength as a function of applied field. This work paves the way for high temperature, real world applications of soliton physics in future magnetic memory devices.
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Apr 2023
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David M.
Rogers
,
Rupesh
Agarwal
,
Josh V.
Vermaas
,
Micholas Dean
Smith
,
Rajitha T.
Rajeshwar
,
Connor
Cooper
,
Ada
Sedova
,
Swen
Boehm
,
Matthew
Baker
,
Jens
Glaser
,
Jeremy C.
Smith
Open Access
Abstract: This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens.
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Mar 2023
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William
Mccorkindale
,
Kadi L.
Saar
,
Daren
Fearon
,
Melissa
Boby
,
Haim
Barr
,
Amir
Ben-Shmuel
,
Nir
London
,
Frank
Von Delft
,
John D.
Chodera
,
Alpha. A.
Lee
,
The
Covid Moonshot Consortium
Open Access
Abstract: A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilized advance is the increase in structural biology throughput, which has progressed from an artisanal endeavor to a monthly throughput of hundreds of different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high-throughput crystallography data into predictive models for ligand design. Here, we designed a simple machine learning approach that predicts protein–ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein–ligand complexes and a learning-to-rank approach that infers the relevant differences between binding modes. We ran a high-throughput crystallography campaign against the SARS-CoV-2 main protease (MPro), obtaining parallel measurements of over 200 protein–ligand complexes and their binding activities. This allows us to design one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a noncovalent and nonpeptidomimetic inhibitor with 120 nM antiviral efficacy. Crucially, our approach successfully extends ligands to unexplored regions of the binding pocket, executing large and fruitful moves in chemical space with simple chemistry.
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Mar 2023
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I12-JEEP: Joint Engineering, Environmental and Processing
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Diamond Proposal Number(s):
[20096]
Open Access
Abstract: Laser additive manufacturing is transforming several industrial sectors, especially the directed energy deposition process. A key challenge in the widespread uptake of this emerging technology is the formation of undesirable microstructural features such as pores, cracks, and large epitaxial grains. The trial and error approach to establish the relationship between process parameters and material properties is problematic due to the transient nature of the process and the number of parameters involved. In this work, the relationship between process parameters, melt pool geometry and quality of build measures, using directed energy deposition additive manufacturing for IN718, is quantified using neural networks as generalised regressors in a statistically robust manner. The data was acquired using in-situ synchrotron x-ray imaging providing unique and accurate measurements for our analysis. An analysis of the variations across repeated measurements show heteroscedastic error characteristics that are accounted for using a principled nonlinear data transformation method. The results of the analysis show that surface roughness correlates with melt pool geometry while the track height directly correlates with process parameters indicating a potential to directly control efficiency and layer thickness while independently minimising surface roughness.
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Mar 2023
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