Development Of Computational Approaches For Structural Classification Analysis And Prediction Of Molecular Recognition Regions In Proteins

A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.

Volume One of this two-volume sequence focuses on the basic characterization of known protein structures, and structure prediction from protein sequence information. Eleven chapters survey of the field, covering key topics in modeling, force fields, classification, computational methods, and structure prediction. Each chapter is a self contained review covering definition of the problem and historical perspective; mathematical formulation; computational methods and algorithms; performance results; existing software; strengths, pitfalls, challenges, and future research.

The growing flood of new experimental data generated by genome sequencing has provided an impetus for the development of automated methods for predicting the functions of proteins that have been deduced by sequence analysis and lack experimental characterization. Prediction of Protein Structures, Functions and Interactions presents a comprehensive overview of methods for prediction of protein structure or function, with the emphasis on their availability and possibilities for their combined use. Methods of modeling of individual proteins, prediction of their interactions, and docking of complexes are put in the context of predicting gene ontology (biological process, molecular function, and cellular component) and discussed in the light of their contribution to the emerging field of systems biology. Topics covered include: first steps of protein sequence analysis and structure prediction automated prediction of protein function from sequence template-based prediction of three-dimensional protein structures: fold-recognition and comparative modelling template-free prediction of three-dimensional protein structures quality assessment of protein models prediction of molecular interactions: from small ligands to large protein complexes macromolecular docking integrating prediction of structure, function, and interactions Prediction of Protein Structures, Functions and Interactions focuses on the methods that have performed well in CASPs, and which are constantly developed and maintained, and are freely available to academic researchers either as web servers or programs for local installation. It is an essential guide to the newest, best methods for prediction of protein structure and functions, for researchers and advanced students working in structural bioinformatics, protein chemistry, structural biology and drug discovery.

This book is a collection of selected high-quality research papers presented at International Conference on Paradigms of Communication, Computing and Data Analytics (PCCDA 2023), held at South Asian University, New Delhi, India, during 22–23 April 2023. It discusses cutting-edge research in the areas of advanced computing, communications and data science techniques. The book is a collection of latest research articles in computation algorithm, communication and data sciences, intertwined with each other for efficiency.

The number of protein sequences grows each year, yet the number of structures deposited in the Protein Data Bank remains relatively small. The importance of protein structure prediction cannot be overemphasized, and this volume is a timely addition to the literature in this field. Protein Structure Prediction: Methods and Protocols is a departure from the normal Methods in Molecular Biology series format. By its very nature, protein structure prediction demands that there be a greater mix of theoretical and practical aspects than is normally seen in this series. This book is aimed at both the novice and the experienced researcher who wish for detailed inf- mation in the field of protein structure prediction; a major intention here is to include important information that is needed in the day-to-day work of a research scientist, important information that is not always decipherable in scientific literature. Protein Structure Prediction: Methods and Protocols covers the topic of protein structure prediction in an eclectic fashion, detailing aspects of pred- tion that range from sequence analysis (a starting point for many algorithms) to secondary and tertiary methods, on into the prediction of docked complexes (an essential point in order to fully understand biological function). As this volume progresses, the authors contribute their expert knowledge of protein structure prediction to many disciplines, such as the identification of motifs and domains, the comparative modeling of proteins, and ab initio approaches to protein loop, side chain, and protein prediction.

Bioinformatics is a rapidly expanding field of research due to multiple recent advancements: 1) the advent of machine intelligence, 2) the increase of computing power, 3) our better understanding of the underlying biomolecular mechanisms, and 4) the drastic reduction of biosequencing cost and time. Since wet laboratory approaches to analysing the protein sequencing is still labour intensive and time consuming, more cost-effective computational approaches for analyzing protein sequences and their biochemical interactions are crucial. This is especially true when we encounter a large collection of protein sequences. Aligned Pattern CLustering (APCL), an algorithm which combines machine intelligence methodologies such as pattern recognition, pattern discovery, pattern clustering and alignment, formulated by my research group and myself, is one such technique. APCL discovers, prunes, and clusters aligned statistically significant patterns to assemble a related, or specifically, a homologous group of patterns in the form of an Aligned Pattern Cluster (APC). The APC obtained is found to correspond to statistically and functionally significant association patterns, which corresponds as conserved regions, such as binding segments within and between protein sequences as well as between Protein Transcription Factor (TF) and DNA Transcription Factor Binding Sites (TFBS) in many of our empirical experiments. While several known algorithms also exist to find functionally conserved segments in biosequences, they are less flexible and require more parameters than what APCL requires. Hence, APCL is a powerful tool to analyze biosequences. Because of its effectiveness, the usefulness of APCL is further expanded from the assist of discovering and analyzing functional regions of protein sequences to the exploration of co-occurrence of patterns on the same sequences or on interacting patterns between sequences from the discovered APCs. Two new algorithms are introduced and reported in this thesis in the exploration of 1) APCs containing patterns residing within the same biosequences and 2) APCs containing patterns residing between interacting biosequences. The first algorithm attempts to cluster APCs from APCs that share patterns on the same biosequences. It uses a co-occurrence score between APCs in a co-occurrence APC pair (two APCs containing co-occurrence patterns) to account for the proportion of biosequences of co-occurrence patterns they share against the total number of sequences containing them. Using this score as a similarity measure (or more precisely, as a co-occurring measure), we devise a Co-occurrence APC Clustering Algorithm to cluster APCs obtained from a collection of related biosequences into a Co-Occurrence Cluster of APCs abbreviated by cAPC. It is then analyzed and verified to see whether or not there are essential biological functions associating with the APCs within that cluster. Cytochrome c and ubiquitin families were analyzed in depth, and it was validated that members in the same cAPC do cover the functional regions that have essential cooperative biological functions. The second algorithm takes advantage of the effectiveness of APCL to create a protein-protein interaction (PPI) identification and prediction algorithm. PPI prediction is a hot research problem in bioinformatics and proteomic. A good number of algorithms exist. The state of the art algorithm is one which could achieve high success rate in prediction performance, but provides results that are difficult to interpret. The research in this thesis tries to overcome this hurdle. This second algorithm uses an APC-PPI score between two APCs to account for the proportion of patterns residing on two different protein sequences. This score measures how often patterns in both APCs co-occur in the sequence data of two known interacting proteins. The scores are then used to construct feature vectors to first train a learning model from the known PPI data and later used to predict the possible PPI between a protein pair. The algorithm performance was comparable to the state of the art algorithms, but provided results that are interpretable. The results from both algorithms built upon the extension of APCL in finding co-occurring patterns via co-occurrence of APCs are proved to be effective and useful since its performance in finding APCs is fast and effective. The first algorithm discovered biological insights, supported by biological literature, which are typically unable to be discovered solely through the analysis of biosequences. The second algorithm succeeded in providing accurate and descriptive PPI predictions. Hence, these two algorithms are useful in the analysis and prediction of proteins. In addition, through continued research and development to the second algorithm, it will be a powerful tool for the drug industry, as it can help find new PPI, an important step in developing new drugs for different drug targets.

The papers collected in this volume reproduce contributions by leading sch- arstoaninternationalschoolandworkshopwhichwasorganizedandheldwith thegoaloftakinga snapshotofadiscipline undertumultuous growth. Indeed, the area of protein folding, docking and alignment is developing in response to needs for a mix of heterogeneous expertise spanning biology, chemistry, mathematics, computer science, and statistics, among others. Some of the problems encountered in this area are not only important for the scienti?c challenges they pose, but also for the opportunities they disclose intermsofmedicalandindustrialexploitation. Atypicalexampleiso?eredby protein-drug interaction (docking), a problem posing daunting computational problems at the crossroads of geometry, physics and chemistry, and, at the same time, a problem with unimaginable implications for the pharmacopoeia of the future. The schoolfocused on problems posed by the study of the mechanisms - hind protein folding, and explored di?erent ways of attacking these problems under objective evaluations of the methods. Together with a relatively small core of consolidated knowledge and tools, important re?ections were brought to this e?ort by studies in a multitude of directions and approaches. It is obviously impossible to predict which, if any, among these techniques will prove completely successful, but it is precisely the implicit dialectic among them that best conveys the current ?avor of the ?eld. Such unique diversity and richness inspired the format of the meeting, and also explains the slight departure of the present volume from the typical format in this series: the exposition of the current sediment is complemented here by a selection of quali?ed specialized contributions.

Proteins lie at the heart of almost all biological processes and have an incredibly wide range of activities. Central to the function of all proteins is their ability to adopt, stably or sometimes transiently, structures that allow for interaction with other molecules. An understanding of the structure of a protein can therefore lead us to a much improved picture of its molecular function. This realisation has been a prime motivation of recent Structural Genomics projects, involving large-scale experimental determination of protein structures, often those of proteins about which little is known of function. These initiatives have, in turn, stimulated the massive development of novel methods for prediction of protein function from structure. Since model structures may also take advantage of new function prediction algorithms, the first part of the book deals with the various ways in which protein structures may be predicted or inferred, including specific treatment of membrane and intrinsically disordered proteins. A detailed consideration of current structure-based function prediction methodologies forms the second part of this book, which concludes with two chapters, focusing specifically on case studies, designed to illustrate the real-world application of these methods. With bang up-to-date texts from world experts, and abundant links to publicly available resources, this book will be invaluable to anyone who studies proteins and the endlessly fascinating relationship between their structure and function.

Volume Two of this two-volume sequence presents a comprehensive overview of protein structure prediction methods and includes protein threading, De novo methods, applications to membrane proteins and protein complexes, structure-based drug design, as well as structure prediction as a systems problem. A series of appendices review the biological and chemical basics related to protein structure, computer science for structural informatics, and prerequisite mathematics and statistics.