This book covers elements of both the data-driven comparative modeling approach to structure prediction and also recent attempts to simulate folding using explicit or simplified models. Despite the unsolved mystery of how a protein folds, advances are being made in predicting the interactions of proteins with other molecules. Also rapidly advancing are the methods for solving the inverse folding problem, the problem of finding a sequence to fit a structure. This book focuses on the various computational methods for prediction, their successes and their limitations, from the perspective of their most well known practitioners.

This book constitutes the proceedings of the 9th International Conference on Exploring Services Science, IESS 2018, held in Karlsruhe, Germany, in September 2018. The 30 papers presented in this volume were carefully reviewed and selected from 67 submissions. The book is structured in six parts, each featuring contributions describing current research in a particular domain of service science: Service Design and Innovation; Smart Service Processes; Big Data in Services; Service Topics Open Exploration; Design Science Research in Services. The book offers an extended, ICT-focused vision on services and addresses multiple relevant aspects, including underlying business models, the necessary processes and technological capabilities like big data and machine learning. The academic work showcased at the conference should help to advance service science and its application in practice.

This book covers elements of both the data-driven comparative modeling approach to structure prediction and also recent attempts to simulate folding using explicit or simplified models. Despite the unsolved mystery of how a protein folds, advances are being made in predicting the interactions of proteins with other molecules. Also rapidly advancing are the methods for solving the inverse folding problem, the problem of finding a sequence to fit a structure.

This book focuses on the various computational methods for prediction, their successes and their limitations, from the perspective of their most well known practitioners.