Computational Methods For Predicting Post Translational Modification Sites

This volume describes computational approaches to predict multitudes of PTM sites. Chapters describe in depth approaches on algorithms, state-of-the-art Deep Learning based approaches, hand-crafted features, physico-chemical based features, issues related to obtaining negative training, sequence-based features, and structure-based features. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Authoritative and cutting-edge, Computational Methods for Predicting Post-Translational Modification Sites aims to be a useful guide for researchers who are interested in the field of PTM site prediction.

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics An in-depth look at the latest research, methods, and applications in the field of protein bioinformatics This book presents the latest developments in protein bioinformatics, introducing for the first time cutting-edge research results alongside novel algorithmic and AI methods for the analysis of protein data. In one complete, self-contained volume, Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics addresses key challenges facing both computer scientists and biologists, arming readers with tools and techniques for analyzing and interpreting protein data and solving a variety of biological problems. Featuring a collection of authoritative articles by leaders in the field, this work focuses on the analysis of protein sequences, structures, and interaction networks using both traditional algorithms and AI methods. It also examines, in great detail, data preparation, simulation, experiments, evaluation methods, and applications. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics: Highlights protein analysis applications such as protein-related drug activity comparison Incorporates salient case studies illustrating how to apply the methods outlined in the book Tackles the complex relationship between proteins from a systems biology point of view Relates the topic to other emerging technologies such as data mining and visualization Includes many tables and illustrations demonstrating concepts and performance figures Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics is an essential reference for bioinformatics specialists in research and industry, and for anyone wishing to better understand the rich field of protein bioinformatics.

Biopharmaceuticals are emerging as frontline medicines to combat several life-threatening and chronic diseases. However, such medicines are expensive to develop and produce on a commercial scale, contributing to rising healthcare costs. Developability of Biotherapeutics: Computational Approaches describes applications of computational and molecular modeling techniques that improve the overall process of discovery and development by removing empiricism. The concept of developability involves making rational choices at the pre-clinical stages of biopharmaceutical drug development that could positively impact clinical outcomes. The book also addresses a general lack of awareness of the many different contributions that computation can make to biopharmaceutical drug development. This informative and practical reference is a valuable resource for professionals engaged in industrial research and development, scientists working with regulatory agencies, and pharmacy, medicine, and life science students and educators. It focuses primarily on the developability of monoclonal antibody candidates, but the principles described can also be extended to other modalities such as recombinant proteins, fusion proteins, antibody drug conjugates and vaccines. The book is organized into two sections. The first discusses principles and applications of computational approaches toward discovering and developing biopharmaceutical drugs. The second presents best practices in developability assessments of early-stage biopharmaceutical drug candidates. In addition to raising awareness of the promise of computational research, this book also discusses solutions required to improve the success rate of translating biologic drug candidates into products available in the clinic. As such, it is a rich source of information on current principles and practices as well as a starting point for finding innovative applications of computation towards biopharmaceutical drug development.

Most life science researchers will agree that biology is not a truly theoretical branch of science. The hype around computational biology and bioinformatics beginning in the nineties of the 20th century was to be short lived (1, 2). When almost no value of practical importance such as the optimal dose of a drug or the three-dimensional structure of an orphan protein can be computed from fundamental principles, it is still more straightforward to determine them experimentally. Thus, experiments and observationsdogeneratetheoverwhelmingpartofinsightsintobiologyandmedicine. The extrapolation depth and the prediction power of the theoretical argument in life sciences still have a long way to go. Yet, two trends have qualitatively changed the way how biological research is done today. The number of researchers has dramatically grown and they, armed with the same protocols, have produced lots of similarly structured data. Finally, high-throu- put technologies such as DNA sequencing or array-based expression profiling have been around for just a decade. Nevertheless, with their high level of uniform data generation, they reach the threshold of totally describing a living organism at the biomolecular level for the first time in human history. Whereas getting exact data about living systems and the sophistication of experimental procedures have primarily absorbed the minds of researchers previously, the weight increasingly shifts to the problem of interpreting accumulated data in terms of biological function and bio- lecular mechanisms.

The combination of faster, more advanced computers and more quantitatively oriented biomedical researchers has recently yielded new and more precise methods for the analysis of biomedical data. These better analyses have enhanced the conclusions that can be drawn from biomedical data, and they have changed the way that experiments are designed and performed. This volume, along with previous and forthcoming Computer Methods volumes for the Methods in Enzymology serial, aims to inform biomedical researchers about recent applications of modern data analysis and simulation methods as applied to biomedical research. Presents step-by-step computer methods and discusses the techniques in detail to enable their implementation in solving a wide range of problems Informs biomedical researchers of the modern data analysis methods that have developed alongside computer hardware Presents methods at the "nuts and bolts" level to identify and resolve a problem and analyze what the results mean

This book is about what happens to proteins once they have been synthesised within the plant cell.

This volume contains the proceedings of the European Conference on Machine Learning 1994, which continues the tradition of earlier meetings and which is a major forum for the presentation of the latest and most significant results in machine learning. Machine learning is one of the most important subfields of artificial intelligence and computer science, as it is concerned with the automation of learning processes. This volume contains two invited papers, 19 regular papers, and 25 short papers carefully reviewed and selected from in total 88 submissions. The papers describe techniques, algorithms, implementations, and experiments in the area of machine learning.