MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download. This database will be updated every three months. The MusiteDeep server is available at https://www.musite.net. The stand-alone tools for locally using MusiteDeep are available at https://github.com/duolinwang/MusiteDeep_web.
The focused drug repurposing of known approved drugs (such as lopinavir/ritonavir) has been reported failed for curing SARS-CoV-2 infected patients. It is urgent to generate new chemical entities against this virus. As a key enzyme in the life-cycle of coronavirus, the 3C-like main protease (3CL pro or M pro ) is the most attractive for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CL pro . We obtained a series of derivatives from those lead compounds by our structure-based optimization policy (SBOP). All the 47 lead compounds directly from our AI-model and related derivatives based on SBOP are accessible in our molecular library at https://github.com/tbwxmu/2019-nCov. These compounds can be used as potential candidates for researchers in their development of drugs against SARS-CoV-2.author/funder. All rights reserved. No reuse allowed without permission. : bioRxiv preprint disease (COVID-19) worldwide. 1 As of March 2, 2020, more than 90,000 people have been infected by SARS-CoV-2 and more than 3000 people have been reported dead according to Johns Hopkins Coronavirus map tracker. 2 The numbers of infection and death are still increasing. To face the considerable threat of SARS-CoV-2, it is urgent to develop new inhibitors or drugs against this deadly virus. Unfortunately, since the outbreak of severe acute respiratory syndrome (SARS) eighteen years ago, there has been no approved treatment against the SARS coronavirus (SARS-CoV), 3 which is similar to SARS-CoV-2. Repurposing potential drugs such as lopinavir and ritonavir also failed to SARS-CoV-2 injected patients. 4 Structure-based antiviral drug design with a new artificial intelligence algorithm may represent a more helpful approach to get the SARS-CoV-2 targeted inhibitors or drugs. Thanks to the prompt efforts of many researchers, we have several pieces of important information about this vital virus genome and protein structures. We now know that the non-structural protein 5 (Nsp5) is the main protease (M pro ) of SARS-CoV-2 and it is a cysteine protease, which also been called "3C-like protease" (3CL pro ). Moreover, we know that the 3D structure of 3CL pro is very similar to SARS-CoV with a sequence identity of >96% and 3D structure superposition RMSDCα of 0.44 Å as shown in Figures S1 and S2. 3CL pro has been reported as an attractive target for developing anti-coronaviral drugs: 1) this protease is highly conserved in both sequences and 3D structures; 5 2) 3CL pro is a key enzyme for related virus (including SARS and SARS-CoV-2) replication; 3) it only exists in the virus, not in humans. Developing specific antiviral drugs targeting 3CL pro of the specific virus has shown significant success; for example, both approved drugs lopinavir and ritonavir can completely occupy the substrate-binding site of 3CL pro to break down the replication of human immunodeficien...
The rate constant for gas-phase reactions of OH radicals with 1H-heptafluorocyclopentene (cyc-CFCFCFCF═CH-) was measured using a relative rate method at 298 K: (5.20 ± 0.09) × 10 cm molecule s. The quoted uncertainty includes two standard deviations from the least-squares regression, the systematic error from the GC analysis, and the uncertainties of the rate constants of the reference compounds. The OH-radical-initiated oxidation of cyc-CFCFCFCF═CH- gives the main products COF, CO, and CO, leading to negligible environmental impact. For consumptions of cyc-CFCFCFCF═CH- of less than 54%, the yield of the formation of ([COF] + [CO] + [CO])/5 (based on the conservation of carbon) was 0.99 ± 0.02, which is very close to 100%. A possible degradation mechanism was proposed. The radiative efficiency (RE) of cyc-CFCFCFCF═CH- measured at room temperature was 0.215 W m ppb. The atmospheric lifetime of cyc-CFCFCFCF═CH- was calculated as 0.61 year, and the photochemical ozone creation potential (POCP) was negligible. The 20-, 100-, and 500-year time horizon global warming potentials (GWPs) were estimated as 153, 42, and 12, respectively.
Three main surveillance systems (laboratory-confirmed, influenza-like illness (ILI) and nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS)) have been used for influenza surveillance in China. However, it is unclear which surveillance system is more reliable in developing influenza early warning system based on surveillance data. This study aims to evaluate the similarity and difference of the three surveillance systems and provide practical knowledge for improving the effectiveness of influenza surveillance. Weekly influenza data for the three systems were obtained from March 2010 to February 2015. Spearman correlation and time series seasonal decomposition were used to assess the relationship between the three surveillance systems and to explore seasonal patterns and characteristics of influenza epidemics in Gansu, China. Our results showed influenza epidemics appeared a single-peak around January in all three surveillance systems. Time series seasonal decomposition analysis demonstrated a similar seasonal pattern in the three systems, while long-term trends were observed to be different. Our research suggested that a combination of the NIDRIS together with ILI and laboratory-confirmed surveillance is an informative, comprehensive way to monitor influenza transmission in Gansu, China. These results will provide a useful information for developing influenza early warning systems based on influenza surveillance data.
The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive target for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with a fragment-based drug design (ADQN–FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from the lead compounds based on our structure-based optimization policy (SBOP). All of the 47 lead compounds obtained directly with our AI model and related derivatives based on the SBOP are accessible in our molecular library. These compounds can be used as potential candidates by researchers to develop drugs against SARS-CoV-2.
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