Ladies and gentlemen I am incredibly proud and excited to present the very first public product of , the . Peptone Database of Structural Propensities of Proteins is the world’s first interactive repository of structural and dynamic features of proteins with seamless integration for leading frameworks, Keras and Tensorflow. Database of Structural Propensities of Proteins ( ) dSPP Machine Learning dSPP is based on peer-reviewed research from leading academic institutions around the world involved in Nuclear Magnetic Resonance spectroscopy techniques for protein structure and disorder characterization. dSPP data are derived from solution and solid state Nuclear Magnetic Resonance spectroscopy experiments for 7200+ unrelated proteins studied under physiologically-relevant conditions. dSPP is a unique source of information for Intrinsically Disordered Proteins (IDPs), which are a challenging class of proteins to study. IDPs are implicated in numerous debilitating human pathologies, including Alzheimer’s, Parkinson’s, prion diseases, molecular basis of cancer, HIV, HSV, HVC, ZIKVR, and many others. Structural interpretation of propensity score for MOAG-4 protein, extracted from dSPP database https://peptone.io/dspp/entry/dSPP27058_0. MOAG-4 is known to enhance the process of protein aggregation in animal brain models, thus accelerating an early onset of Parkinson’s disease. dSPP data can be readily used by experimentalist to gain exclusive insight into structural stability of secondary structure motifs, as well as high throughput computational techniques, which aim to deliver realistic models of medically relevant proteins. As opposed to binary ( ) secondary structure assignments available in other protein datasets for experimentalists and the machine learning community, dSPP data report on protein structure and local dynamics at the residue level with atomic resolution, as gauged from continuous structural propensity assignment in a . logits range -1.0 to 1.0 dSPP user interface overview (https://peptone.io/dspp) dSPP experimental data were collected at physiologically-relevant conditions, rendering them absolutely unique for structure and disorder prediction methods that aim to tackle protein folding and stability in biologically and medically relevant contexts. dSPP is equipped with intuitive user interface which offers seamless access to relevant decision data, original literature citations, and uniform rendering of Machine Learning data belonging to protein of interest. Seamless dSPP integration with and machine learning frameworks is achieved via , available for download and setup in under 60 seconds time. Thus, virtually any person with basic understanding of machine learning can start experimenting with protein structure prediction methodology. Keras Tensorflow dspp-keras Python package dSPP is the first publicly available product by Peptone with , made specifically for c . automated 14-day updatecycle ontinuous learning AI applications Scientific reference: Structural Propensity Database Of Proteins. Kamil Tamiola, Matthew Michael Heberling, Jan Domanski. bioRxiv 144840; doi: https://doi.org/10.1101/144840 Availability and data rendering: Interactive search engine https://peptone.io/dspp Standalone and Python : JSON cPickle downloads https://peptone.io/dspp/download and : Keras Tensorflow integration https://github.com/PeptoneInc/dspp-keras from within Terminal: pip install dspp-keras Acknowledgements We want to acknowledge ( ), ( ) and ( ) for insightful comments and editorial suggestions concerning our . Dr. Wenwei Zheng NIDDK, US Dr. Ruud Scheek University of Groningen, NL Dr. Xavier Periole Aarhus University, DK dSPP paper of is greatly acknowledged for insightful feedback on database interface and straightforward suggestions concerning integration. François Chollet Keras / Google Keras We extend sincere thanks to , and , ( ) for facilitating collaboration and access to DGX-1 supercomputer. Alison Lowndes Carlo Ruiz Dr. Adam Grzywaczewski NVIDIA Corporation ( ) is greatly acknowledged for technical support with NMR resonance assignment retrieval from BMRB. Jon Wedell BMRB We thank ( ) and ( ) for providing structural ensemble models of MOAG-4. Dr. Frans A.A. Mulder Aarhus University, DK Dr. Predrag Kukic University of Cambridge, UK Lastly, we want to greatly acknowledge ( ) for overwhelming support throughout the execution of this project. Mark Berger NVIDIA Corporation Press release This press release along with the media assets can be downloaded from https://drive.google.com/open?id=0B0VsF9FO3J_OMXljcm1MS3NCRHc About Peptone Founded in 2016 (Amsterdam, The Netherlands), offers state of the art solutions for protein biotechnology via Machine Learning and AI. We transform from public and private repositories into powerful predictive models and intuitive tools for protein production, stability, disorder, engineering, and directed evolution experiments, providing our clients with transparent and complementary software that saves time and yields precise research answers. Peptone big data
