Proteins It is safe to say are and the which defines . In the last 70 years tremendous progress has been made in their isolation, production, characterization, and finally engineering. Although great advancements in laboratory and industrial-scale protein production have been made, protein engineering and all the associated steps remain , and truly . proteins building blocks machinery living matter laborious expensive complicated If you prefer to skip further reading, and start hacking straight-away simply visit our 👉🏼 Github repository at: https://github.com/PeptoneInc/dspp-keras Proteins are polymers Proteins are complex biomolecules made of , , which are connected sequentially into ; commonly known as . 20 building blocks amino acids long non-branching chains polypeptide chains Unique of polypeptide chains yields structures, which define protein function and interactions with other biomolecules. spatial arrangement 3D molecular Although the very basic that govern protein 3D structure formation are and , the exact nature of polypeptide remains and has been studied extensively for the past 50 years. forces known understood folding elusive Protein engineering is complex We want to engineer proteins to enhance their properties. Typically, under different , or . Frequently, researchers are aiming at improving of protein enzymes, or adding completely new types of to known proteins. stability temperatures pH salinity catalytic performance chemical activities The most common and established way to a protein is to create its with substituted amino acids, also known as . Subsequently, newly produced mutants are characterized using various experimental techniques to measure the degree of enhancement; e.g. scanning calorimetry, isoelectric point determination, simple solubility studies or advanced enzymatic activity assays. However, since there are protein amino acids, a would yield engineer variants mutants 20 standard complete mutagenesis of 100-residue long polypeptide 20¹⁰⁰ mutant combinations, should you decide to explore all possible combinations of typical protein amino acids. Exploring all possible mutants of a 100 amino acid protein (polypeptide) requires more time than the age of known universe, given you could express, purify and characterize each variant in 1 second time. 100,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 Quite likely, only a marginal fraction of the mutants would have desired properties, as usually ( ). the more you change the protein the further you step away from its original function this is absolutely not a rule, as it is protein specific. However, a logical consequence of replacing a major part of protein with completely new amino acid sequence will likely be new fold, hence new functionality. Moreover, I have intentionally left out a fundamentally important fact — mutations may significantly affect protein dynamics, and thus its function Protein biotechnology is to a large extent hampered by scale and of mutational analysis. complexity How can Machine Learning accelerate progress in protein science? The most and (Bayesian) variants of heavily on the . This argument is especially for and techniques in , where the levels of model are perplexing or simply . advanced probabilistic Machine Learning depend size and quality of input data important inference prediction life sciences complexity unknown Protein structure, function and dynamics predictions through Machine Learning methodology are not an exception. However, even with the (compared to a number of possible combinations of all protein amino acids in lengthy polypeptide chains) protein Machine Learning can help to , non-linear between and their and . These relationships are either very to model or simply . The predictions are coarse. relatively sparse databases, unravel complex relationships protein sequences structural variability dynamics difficult not fully understood The biggest of Machine Learning methods in of biophysical properties of proteins is their ability to “ ” loosely protein to measurable . Thus predictions using complex numerical models that underlie Machine Learning methodology, can be further tweaked and refined by providing independent experimental proxies of protein structure and dynamics. value prediction equate related features experimental data A simplified connectivity diagram of methodology developed at . Hybrid Machine Learning Peptone — The Protein Intelligence Company Proteins are dynamic and exhibit variable degrees of disorder Just like every other molecule present in our natural environment, undergo at time scales ranging from to . polypeptide chains molecular motions nanoseconds minutes It is accepted that complete understanding of protein functions and activity requires knowledge of structures and dynamics. is a very peculiar of many known and characterised proteins. It has been attributed to , and it has an immediate consequence for protein , to enzymatic digestion inside living cells, protein-protein and in turn a decisive role in many . Structural disorder property specific patterns in protein sequence stability susceptibility interactions debilitating human pathologies From an industrial biotechnology point of view, the ability to accurately of amino acid mutations in engineered proteins can . An accurate disorder prediction for an arbitrary protein mutant can immediately report on problematic combinations of amino acid sequences, thus excluding the residues from further mutational analysis and vastly reducing the mutation search space. discern disordering effects save vast amounts of time and resources Singular protein structure model is not enough Under of living organisms ( ) in an aqueous environment, the can be thought of as an , which at any given moment in time have slightly different conformations, as a consequence of protein and “ . conditions aka native conditions state of an arbitrary polypeptide ensemble of structures dynamics intrinsic flexibility” or simply disorder MOAG-4 ensemble model has been kindly provided by Dr. Frans A.A. Mulder (Aarhus University, DK) and Dr. Predrag Kukic (University of Cambridge, UK). Please read “MOAG-4 Promotes the Aggregation of α-Synuclein by Competing with Self-Protective Electrostatic Interactions” to learn more about this protein and its medical relevance. The image above demonstrates the superposition of models belonging to a structural ensemble of MOAG-4 protein, . You can infer from this model that MOAG-4 has a relatively stable (well-defined) alpha-helical structure colored in grey, and a highly disordered tail, depicted by floating polypeptide chains of individual ensemble members. which in turn controls aggregation of proteins implicated in Parkinson’s disease Structural disorder is a medically relevant example of a protein that exhibits a high degree of intrinsic structural disorder. MOAG-4 (dSPP27058_0 in our database) The Alpha-synuclein ensemble has been adopted from “Structural Ensembles of Membrane-bound α-Synuclein Reveal the Molecular Determinants of Synaptic Vesicle Affinity” . Alpha-synuclein, pictured above, is a seminal example of a completely disordered protein. Although the ensemble of Alpha-synuclein is heterogenous, this protein plays an important role in neurotransmitter mediation in human brain, and . has been implicated as the key player in Parkinson’s disease development Putting protein order and disorder together Among the multitude of advanced experimental protein techniques, offers exquisite and at a . We have used NMR resonance assignment data from 7200+ proteins stored in public repositories and computed sequence-specific . NMR spectroscopy sensitivity to structural detail dynamics single residue level propensity scores The ensemble behavior of partially disordered MOAG-4 can be characterized and “ (with few critical assumptions ) to a structural propensity vector. compressed” discussed in our paper Importantly, our method excels at capturing , as seen in the example of intrinsically disordered Alpha-synuclein. residual intrinsic disorder Getting started with the data for Machine Learning In order to facilitate open source development of Machine Learning methodologies for disorder and order predictions we have prepared our dSPP data in a Keras, Tensorflow and Edward friendly forms . Simply check our . Github repository To install the integration, just type the following in your Terminal, dspp-keras pip install dspp-keras How to use to train models? dspp-keras To load the dataset in your Python models, use the module. dsppkeras.datasets from dsppkeras.datasets import dsppX, Y = dspp.load_data() How to encode protein sequence for Machine Learning? One of the most important questions when designing a Machine Learning inference and training procedure are the data input and output. Amino acid sequence of an arbitrary protein can be represented as a one-hot encoded vector, with every amino acid assigned a unique binary representation. As an example, let’s consider the amino acid sequence of , NS2B polypeptide from Zika Virus MGSSHHHHHHSSGLVPRGSHMTGKSVDMYIERAGDITWEKDAEVTGNSPRLDVALDESGDFSLVEEDGPPMRE The one-hot vector, which represents the NS2B Zika Virus sequence can be written as, np.array([0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], dtype=np.uint8) The output. Structural propensity data. Individual, residue-specific scores are bound between and . A propensity of implicates the sampling of beta-sheet conformations. A score of indicates behaviour found in disordered proteins, whereas is an indicator of properly folded alpha-helix. A score of should be understood as a situation when 50% of ensemble members form a helix and the remaining part samples different conformations. 1.0 3.0 1.0 2.0 3.0 2.5 The propensity score vector for is given by, NS2B polypeptide from Zika Virus np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.82, 1.70, 1.56, 1.43, 1.28, 1.59, 1.58, 1.77, 1.75, 2.06, 1.70, 1.95, 1.84, 2.17, 2.10, 2.16, 2.14, 2.21, 2.06, 2.06, 2.09, 2.07, 0.0, 0.0, 0.0, 2.02, 1.95, 1.96, 1.94, 1.97, 2.01, 2.06, 2.10, 2.14, 2.17, 2.13, 2.12, 2.05, 2.04, 2.03, 0.00, 2.09, 2.14, 2.16, 0.0, 2.16, 2.16, 2.16, 2.17], dtype=np.float32) Note: _0.0_ denotes missing experimental assignment data. Making it all interactive For the biology-oriented audience and curious computational scientists, we’ve created a web service at . https://peptone.io/dspp Use it to find proteins, explore their propensities and preview all the data in a machine learning-ready form. What’s next? We have made a model for development for you. boilerplate https://github.com/PeptoneInc/dspp-keras/tree/master/examples It is just a single Dense layer network. However, you should be able to add more layers and experiment with the best network architecture on your own. Call to action Feel free to ask questions about at dspp-keras support@peptone.io If you have found our useful, give us a Github repo STAR ✭. If you have further questions about structural propensity, feel free to leave comments under this article. References is based on ongoing scientific research into protein stability and intrinsic disorder. Therefore we suggest: dspp-keras Download and read the original research paper at http://biorxiv.org/content/early/2017/06/01/144840 Search, browse and download complete dSPP database at https://peptone.io/dspp Please cite us as, @article {Tamiola144840,	author = {Tamiola, Kamil and Heberling, Matthew Michael and Domanski, Jan},	title = {Structural Propensity Database Of Proteins},	year = {2017},	doi = {10.1101/144840},	publisher = {Cold Spring Harbor Labs Journals},	URL = {http://biorxiv.org/content/early/2017/06/01/144840},	eprint = {http://biorxiv.org/content/early/2017/06/01/144840.full.pdf},	journal = {bioRxiv}} Importantly, if you have decided to keep on reading, I strongly recommend the recent articles by which elegantly describe the issues with current protein biotechnology protocols. Matthew Heberling _Protein Science Deserves More from Big Data_blog.peptone.io Fame and Fundamentals Complicate Protein Biotech _Ever wonder whether protein structural variability could be packaged into a single score at the amino acid level? We…_blog.peptone.io Transforming big data to understand protein disorder: Insight into Zika Virus drug design 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

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Hacking in silico protein engineering.

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Hacking in silico protein engineering.

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Kamil Tamiola

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