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.

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.

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.

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.