<p>In this work, I develop computational and visual tools for analyzing and manipulating the backbone of macromolecules, and I demonstrate that these tools support building better structures than currently done. These visualization and analysis tools belong to an "Intelligence Amplification" (IA) tradition (rather than complete Artificial Intelligence (AI) automation), empowering users to improve structures.</p><p>Proteins and nucleic acids are among the most important molecules in biology, mediating the majority of biochemical processes that comprise a living organism. Therefore, these macromolecules are important targets, both for basic research to improve understanding of how life works, and for medical research as possible drug targets. </p><p>The function of these macromolecules is largely determined by their 3D structure. Although these macromolecules are chemically fairly simple, made up of linear sequences of a few possible subunits, they physically fold into complex, compact structures. Overall, structural biology aims to determine the general relationship between sequence and structure of these macromolecules, through determination of the positions of the atoms within individual macromolecules. </p><p>Because it is currently impossible to directly see the position of atoms in a molecule, all structural determination techniques, including X-ray crystallography, NMR, and homology modeling, result in an interpreted <italic>model</italic> of a structure. Nearly all of these models contain mistakes, in which atoms are fit in incorrect or impossible positions. These mistakes, especially at a functionally-important location in a structure, can mislead both basic and medical research, making it critical for structural biologists to build the highest quality models possible. </p><p>This document details how my dissertation work enables the building of better macromolecular structure models. This work follows an iterative development cycle, where visual analysis of models spurs development of better tools, which in turn improves the analysis. First, I describe how my analysis of protein loops from X-ray crystal structures reveals that the traditional definition of loop endpoints is too restrictive. Second, I create a protein backbone analysis and modeling tool, using a new peptide-centric division system. I show how this tool makes it easier to study protein loops, and also how it improves an algorithm for calculating core protein models from NMR residual dipolar coupling (RDC) data. Third, I describe how 3D visualization of RDCs in their structural context improves understanding of RDCs and validates NMR models in a novel way. Fourth, I describe how local quality analysis can diagnose problems in homology models. Fifth, I demonstrate that local quality analysis can be successfully used in conjunction with model rebuilding software to correct errors in low resolution structures. The various tools and software packages I created during the course of my work are freely available and have already made a positive impact on structures being generated by the community.</p><p>Archive versions of several of these software packages (JiffiLoop, RDCvis, and KiNG) should be included with this document; current versions can be downloaded from http://kinemage.biochem.duke.edu.</p> / Dissertation

Proteins are indispensable players in virtually all biological events. The functions of proteins are determined by their three dimensional (3D) structure and coordinated through intricate networks of protein-protein interactions (PPIs). Hence, a deep comprehension of such networks turns out to be crucial for understanding the cellular biology. Computational approaches have become critical tools for analysing PPI networks. In silico methods take advantage of the existing PPI knowledge to both predict new interactions and predict the function of proteins. Regarding the task of predicting PPIs, several methods have been already developed. However, recent findings demonstrate that such methods could take advantage of the knowledge on non-interacting protein pairs (NIPs). On the task of predicting the function of proteins,the Guilt-by-Association (GBA) principle can be exploited to extend the functional annotation of proteins over PPI networks. In this thesis, a new algorithm for PPI prediction and a protocol to complete cell signalling networks are presented. iLoops is a method that uses NIP data and structural information of proteins to predict the binding fate of protein pairs. A novel protocol for completing signalling networks –a task related to predicting the function of a protein, has also been developed. The protocol is based on the application of GBA principle in PPI networks. / Les proteïnes tenen un paper indispensable en virtualment qualsevol procés biològic. Les funcions de les proteïnes estan determinades per la seva estructura tridimensional (3D) i són coordinades per mitjà d’una complexa xarxa d’interaccions protiques (en anglès, protein-protein interactions, PPIs). Axí doncs, una comprensió en profunditat d’aquestes xarxes és fonamental per entendre la biologia cel•lular. Per a l’anàlisi de les xarxes d’interacció de proteïnes, l’ús de tècniques computacionals ha esdevingut fonamental als darrers temps. Els mètodes in silico aprofiten el coneixement actual sobre les interaccions proteiques per fer prediccions de noves interaccions o de les funcions de les proteïnes. Actualment existeixen diferents mètodes per a la predicció de noves interaccions de proteines. De tota manera, resultats recents demostren que aquests mètodes poden beneficiar-se del coneixement sobre parelles de proteïnes no interaccionants (en anglès, non-interacting pairs, NIPs). Per a la tasca de predir la funció de les proteïnes, el principi de “culpable per associació” (en anglès, guilt by association, GBA) és usat per extendre l’anotació de proteïnes de funció coneguda a través de xarxes d’interacció de proteïnes. En aquesta tesi es presenta un nou mètode pre a la predicció d’interaccions proteiques i un nou protocol basat per a completar xarxes de senyalització cel•lular. iLoops és un mètode que utilitza dades de parells no interaccionants i coneixement de l’estructura 3D de les proteïnes per a predir interaccions de proteïnes. També s’ha desenvolupat un nou protocol per a completar xarxes de senyalització cel•lular, una tasca relacionada amb la predicció de les funcions de les proteïnes. Aquest protocol es basa en aplicar el principi GBA a xarxes d’interaccions proteiques.