Machine Learning for Peptide Structure, Function, and Design PDF Download
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Author: Ruiquan Ge
Publisher: Frontiers Media SA
ISBN: 2832503950
Category : Science
Languages : en
Pages : 146
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Book Description
Author: Ruiquan Ge
Publisher: Frontiers Media SA
ISBN: 2832503950
Category : Science
Languages : en
Pages : 146
Get Book Here
Book Description
Author: Jianhua Wang
Publisher: Frontiers Media SA
ISBN: 2832514979
Category : Science
Languages : en
Pages : 296
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Book Description
Author: Jianhua Wang
Publisher: Frontiers Media SA
ISBN: 2889760812
Category : Science
Languages : en
Pages : 294
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Book Description
Author: John F. Kolen
Publisher: John Wiley & Sons
ISBN: 9780780353695
Category : Technology & Engineering
Languages : en
Pages : 458
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Book Description
Acquire the tools for understanding new architectures and algorithms of dynamical recurrent networks (DRNs) from this valuable field guide, which documents recent forays into artificial intelligence, control theory, and connectionism. This unbiased introduction to DRNs and their application to time-series problems (such as classification and prediction) provides a comprehensive overview of the recent explosion of leading research in this prolific field. A Field Guide to Dynamical Recurrent Networks emphasizes the issues driving the development of this class of network structures. It provides a solid foundation in DRN systems theory and practice using consistent notation and terminology. Theoretical presentations are supplemented with applications ranging from cognitive modeling to financial forecasting. A Field Guide to Dynamical Recurrent Networks will enable engineers, research scientists, academics, and graduate students to apply DRNs to various real-world problems and learn about different areas of active research. It provides both state-of-the-art information and a road map to the future of cutting-edge dynamical recurrent networks.
Author: Tatiana V. Ovchinnikova
Publisher: MDPI
ISBN: 3039215329
Category : Medical
Languages : en
Pages : 442
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Book Description
This Special Issue Book, “Marine Bioactive Peptides: Structure, Function, and Therapeutic Potential" includes up-to-date information regarding bioactive peptides isolated from marine organisms. Marine peptides have been found in various phyla, and their numbers have grown in recent years. These peptides are diverse in structure and possess broad-spectrum activities that have great potential for medical applications. Various marine peptides are evolutionary ancient molecular factors of innate immunity that play a key role in host defense. A plethora of biological activities, including antibacterial, antifungal, antiviral, anticancer, anticoagulant, endotoxin-binding, immune-modulating, etc., make marine peptides an attractive molecular basis for drug design. This Special Issue Book presents new results in the isolation, structural elucidation, functional characterization, and therapeutic potential evaluation of peptides found in marine organisms. Chemical synthesis and biotechnological production of marine peptides and their mimetics is also a focus of this Special Issue Book.
Author: Naozumi Hiranuma
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Understanding the rules of protein structure folding has always been one of the central goals in computational biology. Deep learning is gaining popularity in protein machine learning due to its ability to learn complex functions on large amounts of protein geometry data. To help understand the rules of protein folding better, we developed neural networks (DeepAccNet and Pluto) that estimate the error in protein models. In other words, these networks estimate how much a computationally modeled protein structure deviates from its experimentally determined conformation. Approximately two million conformations from 21000 protein sequences located at different local energy minima with a large diversity of errors were sampled and used for training. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution. The network should be broadly helpful in assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. The DeepAccNet methods were selected as top-performing methods for the estimation of model accuracy (EMA) category in CASP14. We extended the accuracy prediction models for proteins to more general chemistry by training graph neural networks on a wide variety of protein and non-protein datasets. We showed that the resulting framework (GAAP) successfully estimates the accuracy of non-protein molecules, such as peptides and Protein-DNA complexes. Our results illustrate how deep learning can impact the efficiency and accuracy of large-scale simulations for both modeling and designing of molecules.
Author: Ilan Samish
Publisher: Humana
ISBN: 9781493966356
Category : Science
Languages : en
Pages : 0
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Book Description
The aim this volume is to present the methods, challenges, software, and applications of this widespread and yet still evolving and maturing field. Computational Protein Design, the first book with this title, guides readers through computational protein design approaches, software and tailored solutions to specific case-study targets. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Protein Design aims to ensure successful results in the further study of this vital field.
Author: Zhixiu Li
Publisher:
ISBN:
Category : Computational biology
Languages : en
Pages : 286
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Book Description
Computational protein design aims at designing amino acid sequences that can fold into a target structure and perform a desired function. Many computational design methods have been developed and their applications have been successful during past two decades. However, the success rate of protein design remains too low to be of a useful tool by biochemists whom are not an expert of computational biology. In this dissertation, we first developed novel computational assessment techniques to assess several state-of-the-art computational techniques. We found that significant progresses were made in several important measures by two new scoring functions from RosettaDesign and from OSCAR-design, respectively. We also developed the first machine-learning technique called SPIN that predicts a sequence profile compatible to a given structure with a novel nonlocal energy-based feature. The accuracy of predicted sequences is comparable to RosettaDesign in term of sequence identity to wild type sequences. In the last two application chapters, we have designed self-inhibitory peptides of Escherichia coli methionine aminopeptidase (EcMetAP) and de novo designed barstar. Several peptides were confirmed inhibition of EcMetAP at the micromole-range 50% inhibitory concentration. Meanwhile, the assessment of designed barstar sequences indicates the improvement of OSCAR-design over RosettaDesign.
Author: Timir Tripathi
Publisher: Academic Press
ISBN: 0323902650
Category : Science
Languages : en
Pages : 716
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Book Description
Advances in Protein Molecular and Structural Biology Methods offers a complete overview of the latest tools and methods applicable to the study of proteins at the molecular and structural level. The book begins with sections exploring tools to optimize recombinant protein expression and biophysical techniques such as fluorescence spectroscopy, NMR, mass spectrometry, cryo-electron microscopy, and X-ray crystallography. It then moves towards computational approaches, considering structural bioinformatics, molecular dynamics simulations, and deep machine learning technologies. The book also covers methods applied to intrinsically disordered proteins (IDPs)followed by chapters on protein interaction networks, protein function, and protein design and engineering. It provides researchers with an extensive toolkit of methods and techniques to draw from when conducting their own experimental work, taking them from foundational concepts to practical application. - Presents a thorough overview of the latest and emerging methods and technologies for protein study - Explores biophysical techniques, including nuclear magnetic resonance, X-ray crystallography, and cryo-electron microscopy - Includes computational and machine learning methods - Features a section dedicated to tools and techniques specific to studying intrinsically disordered proteins
Author: Laszlo Otvos
Publisher: Humana Press
ISBN: 9781617378690
Category : Medical
Languages : en
Pages : 0
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Book Description
Due to their high specificity and low toxicity profile, peptides have once again become central to the development of new drugs. In Peptide-Based Drug Design: Methods and Protocols, expert researchers provide a handbook which offers a selection of research and production tools suitable for transforming a promising protein fragment or stand-alone native peptide into a pharmaceutically acceptable composition. The volume delves into contemporary, cutting-edge subjects such as hit isolation and target validation, computer-aided design, sequence modifications to satisfy pharmacologists, in vivo stability and imaging, and the actual production of difficult sequences. Written in the highly successful Methods in Molecular BiologyTM series format, chapters include readily reproducible, step-by-step laboratory protocols, lists of materials, and the Notes section, which highlights tips on troubleshooting and avoiding known pitfalls. Comprehensive and up-to-date, Peptide-Based Drug Design: Methods and Protocols shows its subject to be an independent science on the rise, and provides scientists with a clear, concise guide for continuing this vital research.
