Ohmic and step noise from a single trapping center hybridized with a Fermi sea

We show that single electron tunneling devices such as the Cooper-pair box or double quantum dot can be sensitive to the zero-point fluctuation of a single trapping center hybridized with a Fermi sea. If the trap energy level is close to the Fermi sea and has line-width \gamma > k_B T, its noise spectrum has an Ohmic Johnson-Nyquist form, whereas for \gamma < k_B T the noise has a Lorentzian form expected from the semiclassical limit. Trap levels above the Fermi level are shown to lead to steps in the noise spectrum that can be used to probe their energetics, allowing the identification of individual trapping centers coupled to the device.Comment: Revised version to appear in Phys. Rev. Let

Structure of the CaMKIIδ/Calmodulin Complex Reveals the Molecular Mechanism of CaMKII Kinase Activation

Structural and biophysical studies reveal how CaMKII kinases, which are important for cellular learning and memory, are switched on by binding of Ca2+/calmodulin

Microscopic model of critical current noise in Josephson-junction qubits: Subgap resonances and Andreev bound states

We propose a microscopic model of critical current noise in Josephson-junctions based on individual trapping-centers in the tunnel barrier hybridized with electrons in the superconducting leads. We calculate the noise exactly in the limit of no on-site Coulomb repulsion. Our result reveals a noise spectrum that is dramatically different from the usual Lorentzian assumed in simple models. We show that the noise is dominated by sharp subgap resonances associated to the formation of pairs of Andreev bound states, thus providing a possible explanation for the spurious two-level systems (microresonators) observed in Josephson junction qubits [R.W. Simmonds et al., Phys. Rev. Lett. 93, 077003 (2004)]. Another implication of our model is that each trapping-center will contribute a sharp dielectric resonance only in the superconducting phase, providing an effective way to validate our results experimentally. We derive an effective Hamiltonian for a qubit interacting with Andreev bound states, establishing a direct connection between phenomenological models and the microscopic parameters of a Fermionic bath.Comment: 11 pages, 8 figure

Structure and inhibitor specificity of the PCTAIRE-family kinase CDK16

CDK16 (also known as PCTAIRE1 or PCTK1) is an atypical member of the cyclin-dependent protein kinase (CDK) family that has emerged as a key regulator of neurite outgrowth, vesicle trafficking and cancer cell proliferation. CDK16 is activated through binding to cyclin Y via a phosphorylation-dependent 14-3-3 interaction and has an unique consensus substrate phosphorylation motif compared to conventional CDKs. To elucidate the structure and inhibitor binding properties of this atypical CDK we screened the CDK16 kinase domain against different inhibitor libraries and determined the co-structures of identified hits. We discovered that the ATP-binding pocket of CDK16 can accommodate both type I and type II kinase inhibitors. The most potent CDK16 inhibitors revealed by cell-free and cell-based assays were the multi-targeted cancer drugs dabrafenib and rebastinib. An inactive DFG-out binding conformation was confirmed by the first crystal structures of CDK16 in separate complexes with the inhibitors indirubin E804 and rebastinib, respectively. The structures revealed considerable conformational plasticity suggesting that the isolated CDK16 kinase domain was relatively unstable in the absence of a cyclin partner. The unusual structural features and chemical scaffolds identified here hold promise for the development of more selective CDK16 inhibitors and provide opportunity to better characterise the role of CDK16 and its related CDK family members in various physiological and pathological contexts

Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins.

Funder: ExscientiaFunder: Diamond Light SourceFunder: Kungliga Tekniska HoegskolanFunder: Chinese Center for Disease Control and PreventionFunder: European Federation of Pharmaceutical Industries and AssociationsFunder: European CommissionFunder: Kennedy Trust for Rheumatology ResearchFunder: Ontario Institute for Cancer ResearchFunder: Royal Institution for the Advancement of Learning McGill UniversityFunder: UCBSelectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family

Discovery and development strategies for SARS-CoV-2 NSP3 macrodomain inhibitors

The worldwide public health and socioeconomic consequences caused by the COVID-19 pandemic highlight the importance of increasing preparedness for viral disease outbreaks by providing rapid disease prevention and treatment strategies. The NSP3 macrodomain of coronaviruses including SARS-CoV-2 is among the viral protein repertoire that was identified as a potential target for the development of antiviral agents, due to its critical role in viral replication and consequent pathogenicity in the host. By combining virtual and biophysical screening efforts, we discovered several experimental small molecules and FDA-approved drugs as inhibitors of the NSP3 macrodomain. Analogue characterisation of the hit matter and crystallographic studies confirming binding modes, including that of the antibiotic compound aztreonam, to the active site of the macrodomain provide valuable structure–activity relationship information that support current approaches and open up new avenues for NSP3 macrodomain inhibitor development

Fragment merging using a graph database samples different catalogue space than similarity search

Fragment merging is a promising approach to progressing fragments directly to on-scale potency: each designed compound incorporates the structural motifs of overlapping fragments in a way that ensures compounds recapitulate multiple high-quality interactions. Searching commercial catalogues provides one useful way to quickly and cheaply identify such merges and circumvents the challenge of synthetic accessibility, provided they can be readily identified. Here, we demonstrate that the Fragment Network, a graph database that provides a novel way to explore the chemical space surrounding fragment hits, is well-suited to this challenge. We use an iteration of the database containing >120 million catalogue compounds to find fragment merges for four crystallographic screening campaigns and contrast the results with a traditional fingerprint-based similarity search. The two approaches identify complementary sets of merges that recapitulate the observed fragment–protein interactions but lie in different regions of chemical space. We further show our methodology is an effective route to achieving on-scale potency by retrospective analyses for two different targets; in analyses of public COVID Moonshot and Mycobacterium tuberculosis EthR inhibitors, potential inhibitors with micromolar IC50 values were identified. This work demonstrates the use of the Fragment Network to increase the yield of fragment merges beyond that of a classical catalogue search

In crystallo-screening for discovery of human norovirus 3C-like protease inhibitors.

Outbreaks of human epidemic nonbacterial gastroenteritis are mainly caused by noroviruses. Viral replication requires a 3C-like cysteine protease (3CLpro) which processes the 200 kDa viral polyprotein into six functional proteins. The 3CLpro has attracted much interest due to its potential as a target for antiviral drugs. A system for growing high-quality crystals of native Southampton norovirus 3CLpro (SV3CP) has been established, allowing the ligand-free crystal structure to be determined to 1.3 Å in a tetrameric state. This also allowed crystal-based fragment screening to be performed with various compound libraries, ultimately to guide drug discovery for SV3CP. A total of 19 fragments were found to bind to the protease out of the 844 which were screened. Two of the hits were located at the active site of SV3CP and showed good inhibitory activity in kinetic assays. Another 5 were found at the enzyme's putative RNA-binding site and a further 11 were located in the symmetric central cavity of the tetramer

A small step toward generalizability: training a machine learning scoring function for structure-based virtual screening

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