Antibodies are the fundamental agents of the immune system. The CDRs, or Complementarity Determining Regions act as the functional surfaces in binding antibodies to their targets. These CDR structures, which are peptide loops, are diverse in both amino acid sequence and structure. In 2011, we surveyed a database of CDR loop structures using the affinity propagation clustering algorithm of Frey and Dueck. With the growth of the number of structures deposited in the Protein Data Bank, the number of antibody CDRs has approximately tripled. In addition, although the affinity clustering in 2011 was successful in many ways, the methods used left too much noise in the data, and the affinity clustering algorithm tended to clump diverse structures together. This work revisits the antibody CDR clustering problem and uses five different clustering algorithms to categorize the data. Three of the clustering algorithms use DBSCAN but differ in the data comparison functions used. One uses the sum of the dihedral distances, while another uses the supremum of the dihedral distances, and the third uses the Jarvis-Patrick shared nearest neighbor similarity, where the nearest neighbor lists are compiled using the sum of the dihedral distances. The other two clustering methods use the k-medoids algorithm, one of which has been modified to include the use of pairwise constraints. Overall, the DBSCAN using the sum of dihedral distances and the supremum of the dihedral distances produced the best clustering results as measured by the average silhouette coefficient, while the constrained k-medoids clustering algorithm had the worst clustering results overall. / Computer and Information Science

Nuclear export of messenger ribonucleic acid (mRNA) is a complex and essential process for a correct gene expression in all eukaryotic cells. The export of mRNA through the nuclear pore complex depends mostly on the crosstalk and coordination of several proteins forming what is known as mRNPs (messenger ribonucleoproteins) that play dynamic, interconnecting roles in the different mRNA biogenesis steps such as pre-mRNA processing, stability, and export. One key protein in this process is Mex67, conserved from yeast to humans, is the major messenger RNA exporter also involved in ribosomal RNA export. Mex67 interacts with Mtr2 to form an evolutionary conserved heterodimer essential for proper mRNA export and subsequently the survival of the cell. Mex67 have been studied for many years, however due to the complexity and interconnectivity of the different processes in mRNA biogenesis, there is yet to uncover many details on the dynamics of the process and the crosstalk between Mex67 and its many partners. In this study, using a combination of biochemical, biophysical, and structural analysis, we characterize the interaction between Mex67 and a novel partner protein called Mip6 (Mex67 interacting protein 6). We were able to reconstitute a stable complex in vitro, and extensively study the mechanism in which the two proteins interact. We also solved the crystal structure of the C-terminal region of Mex67 that interacts with Mip6 and identified the UBA domain of Mex67, known to bind FG nucleoporins and Hpr1 protein as also the site where Mip6 binds. However, little was known about the structure or function of Mip6 and its paralogue Pes4. Here we proved that Mip6 is an RNA binding protein with four RNA recognition motifs that binds RNA in vitro with high affinity. Additionally, its fourth RNA recognition motif was also the site of binding of Mex67. Furthermore, we showed that the Mex67 complex formation with Mip6 RRM4 compromises its ability to bind RNA or vice versa. We also designed a point mutation on Mip6 RRM4 that disrupts its interaction with Mex67 but not with RNA. Subsequent in vivo yeast assays led us to hypothesize a role of Mip6 as an adaptor protein for Mex67 in nuclear export especially upon stress. Additional function of Mip6 was the localization of its bound mRNA to cytoplasmic stress granules in cellular stress conditions. Moreover, the crystal structures of Mip6 RRM3, Pes4 RRM3, Pes4 RRM4, and Pes4 RRM3/4 were also solved. All RRMs adopted a canonical RRM fold with conserved RNP1 and RNP2 sequences normally involved in RNA binding, except Mip6 RRM3 that was missing the aromatic ring in RNP2. In the structure of RNA-free Pes4 RRM3/4, the tandem RRM domains were connected with a flexible disordered linker and no inter-domain contact between them. Finally, although Pes4 RRM4 was binding RNA in vitro, it did not have the ability to interact with Mex67 thus suggesting a separate evolutionary function for Mip6 and Pes4. / La exportación nuclear de ácido ribonucleico mensajero (ARNm) es un proceso complejo y esencial para una expresión correcta de los genes en todas las células eucariotas. La exportación de ARNm a través del complejo del poro nuclear depende principalmente de la interacción y coordinación de varias proteínas, que forman lo que se conoce como mRNPs (ribonucleoproteínas mensajeras), que tienen un papel dinámico e interconectado en las diferentes etapas de la biogénesis de ARNm, tales como el procesamiento del pre-ARNm, estabilidad, y exportación. Una proteína clave en este proceso es Mex67, conservada de levaduras a humanos, que es la principal exportadora de ARN mensajero y también está implicada en la exportación de ARN ribosomal. Mex67 interacciona con Mtr2 para formar un heterodímero conservado evolutivamente esencial para una exportación adecuada de ARNm y la consiguiente supervivencia de la célula. Se ha estudiado Mex67 durante muchos años, sin embargo, debido a la complejidad e interconectividad de los diferentes procesos de biogénesis de ARNm, todavía quedan por descubrir muchos detalles de la dinámica del proceso y las interacciones entre Mex67 y sus muchas proteínas asociadas. En este estudio, combinando un análisis bioquímico, biofísico y estructural, hemos caracterizado la interacción entre Mex67 y una nueva proteína asociada denominada Mip6 (proteína 6 que interacciona con Mex67). Hemos podido reconstituir un complejo estable in vitro y estudiar extensivamente el mecanismo por el cual interaccionan estas dos proteínas. También hemos resuelto la estructura cristalográfica de la región C-terminal de Mex67 que interacciona con Mip6 e identificado el dominio UBA de Mex67, conocido por unirse a nucleoporinas FG y a la proteína Hpr1, así como el sitio por el que se une Mip6. No obstante, se sabía muy poco sobre la estructura o la función de Mip6 y su parálogo Pes4. Hemos probado que Mip6 es una proteína de unión a ARN con cuatro motivos de reconocimiento de ARN que se unen a ARN in vitro con una afinidad alta. Además, su cuarto motivo de reconocimiento de ARN es también el sitio de unión a Mex67. Posteriormente, demostramos que la formación del complejo de Mex67 con el dominio RRM4 de Mip6 compromete su capacidad para unir ARN o viceversa. También diseñamos una mutación puntual en el RRM4 de Mip6 que rompe la interacción con Mex67 pero no con el ARN. Los ensayos posteriores in vivo en levaduras nos permitieron establecer una hipótesis sobre el papel de Mip6 como proteína adaptadora para Mex67 en la exportación nuclear, especialmente en condiciones de estrés. Una función adicional de Mip6 era la localización del ARNm que se unía a ella en gránulos de estrés en condiciones de estrés celular. Además, hemos resuelto las estructuras cristalográficas del RRM3 de Mip6, RRM3 de Pes4, RRM4 de Pes4 y los RRM3 y 4 de Pes4. Todos los RRMs adoptaron una conformación canónica RRM con secuencias RNP1 y RNP2 conservadas generalmente implicadas en la unión a ARN, excepto el RRM3 de Mip6 que carecía del anillo aromático en RNP2. En la estructura sin ARN de los RRM3 y 4 de Pes4, los dominios RRM tándem estaban conectados por una región flexible desordenada y no había un contacto inter-dominio entre ellos. Finalmente, aunque el RRM4 de Pes4 se unía a ARN in vitro, no presentaba la capacidad de interaccionar con Mex67 lo cual sugiere una divergencia evolutiva de la función de Mip6 y Pes4. / L¿exportació nuclear d¿àcid ribonucleic missatger (mRNA) es un procés complex i essencial per a una correcta expresió gènica en totes cèl¿lules eucariotes. L¿exportació del mRNA a través del complex del porus nuclear depén principalment de la interacció i coordinació de diverses proteïnes, que formen el que es coneix com mRNPs (ribonucleoproteïnes missatgeres), que tenen un paper dinàmic i interconnectat en les diferents etapes de la biogènesi d¿ARNm, com el processament del pre-ARNm, estabilitat, localització i exportació. Una proteïna clau en aquest procés és MEX67, conservada de llevats fins a humans, que és la principal exportadora de ARN missatger i també està implicada en l¿exportació de ARN ribosomal. Mex67 interacciona amb Mtr2 per a formar un heterodímer conservat evolutivament essencial per a una exportació adequada d¿ARNm i la consegüent supervivència de la cèl¿lula. S¿ha estudiat Mex67 durant molts anys, però degut a la complexitat i interconectivitat dels diferents processos de biogènesi d¿ARNm, encara queden per descobrir molts detalls de la dinàmica del procés i les interaccions entre Mex67 i les seues moltes proteïnes associades. En aquest estudi, combinant l¿anàlisi bioquímic, biofísic i estructural, hem caracteritzat la interacció entre Mex67 i una nova proteïna associada anomenada Mip6 (proteïna 6 que interacciona amb Mex67). Hem pogut reconstituir un complex estable in vitro i estudiar extensivament el mecanisme pel qual interaccionen estes dos proteïnes. També hem resolt l¿estructura cristal¿logràfica de la regió C-terminal de Mex67 que interacciona amb Mip6 i identificat el domini UBA de Mex67, conegut per unir-se a nucleoporines FG i a la proteïna Hpr1, així com ser el lloc pel que s¿uneix Mip6. No obstant, se sabia molt poc sobre l¿estructura o la funció de Mip6 i el seu paràleg Pes4. Hem comprobat que Mip6 es una proteïna d¿unió a ARN amb quatre motius de reconeixement d¿ARN que s¿uneixen a ARN in vitro amb una afinitat alta. A més, el seu quart motiu de reconeixement d¿ARN és també el lloc d¿unió a Mex67. Posteriorment, demostràrem que la formació del complex de Mex67 amb el domini RRM4 de Mip6 compromet la seua capacitat per a unir ARN o viceversa. També vam dissenyar una mutació puntual en el RRM4 de Mip6 que trenca la interacció amb Mex67 però no amb l¿ARN. Els assajos posteriors in vivo en llevats ens van permetre establir una hipòtesi sobre el paper de Mip6 com a proteïna adaptadora per a Mex67 en l¿exportació nuclear, especialment en condicions d¿estrès. Una funció adicional de Mip6 era la localització de l¿ARNm que s¿unia a ella en grànuls d¿estrès en condicions d¿estrès cel¿lular. A més, hem resolt les estructures cristal¿logràfiques del RRM3 de Mip6, RRM3 de Pes4, RRM4 de Pes4 i els RRM3 i 4 de Pes4. Tots els RRMs adoptaren una conformació canònica RRM amb seqüències RNP1 i RNP2 conservades generalment implicades en la unió a ARN, excepte el RRM3 de Mip6 que mancava del anell aromàtic en RNP2. En la estructura sense ARN dels RRM3 i 4 de Pes4, els dominis RRM tàndem estàven conectats per una regió flexible desordenada i no hi havia un contacte interdomini entre ells. Finalment, encara que el RRM4 de Pes4 es unia a ARN in vitro, no presentava la capacitat d¿interaccionar amb Mex67, la cual cosa sugerix una divergencia evolutiva de la funció de Mip6 y Pes4. / Mohamad, N. (2017). The RNA binding protein Mip6, a novel cellular partner of Mex67 export factor with implications in mRNA export [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90397

Doctor of Philosophy / In my research, I developed protein model quality estimation methods aimed at evaluating the reliability of computationally predicted protein models in the absence of experimentally solved ground truth structures. These methods specifically focus on estimating errors within the protein models to quantify their structural accuracy. Recognizing that even the most advanced protein structure prediction techniques may produce models with errors, I also developed a complementary protein model refinement method. This refinement method iteratively optimizes the weakly modeled regions, guided by the error estimation module of my quality estimation approach. The development of these model quality estimation methods, therefore, not only offers valuable insights into the structural reliability of protein models but also contributes to optimizing the overall reliability of protein models generated by state-of-the-art computational methods.

The global spread of antibiotic resistance warrants concerted surveillance in the clinic and in the environment. The widespread use of metagenomics for various studies has led to the generation of a large amount of sequencing data. Next-generation sequencing of microbial communities provides an opportunity for proactive detection of emerging antibiotic resistance genes (ARGs) from such data, but there are a limited number of pipelines that enable the identification of novel ARGs belonging to diverse antibiotic classes at present. Therefore, there is a need for the development of computational pipelines that can identify these putative novel ARGs. Such pipelines should be scalable, accessible and have good performance. To address this problem we develop a new method for predicting novel ARGs from genomic or metagenomic sequences, leveraging known ARGs of different resistance categories. Our method takes into account the physio-chemical properties that are intrinsic to different ARG families. Traditionally, new ARGs are predicted by making sequence alignment and calculating sequence similarity to existing ARG reference databases, which can be very time consuming. Here we introduce an alignment free and deep learning prediction method that incorporates both the primary protein sequences of ARGs and their physio-chemical properties. We compare our method with existing pipelines including hidden Markov model based Resfams and fARGene, sequence alignment and machine learning-based DeepARG-LS, and homology modelling based Pairwise Comparative Modelling. We also use our model to detect novel ARGs from various environments including human-gut, soil, activated sludge and the influent samples collected from a waste water treatment plant. Results show that our method achieves greater accuracy compared to existing models for the prediction of ARGs and enables the detection of putative novel ARGs, providing promising targets for experimental characterization to the scientific community. / Master of Science / Various bacteria contain genes that allow them to survive and grow even after the application of antibiotics. Such genes are called antibiotic resistance genes (ARGs). Each ARG has properties that make it resistant to a particular class of antibiotics. This class is called the resistance class/category of the gene. Antimicrobial resistance (AMR) is one of the biggest challenges to public health in recent times. It has been projected that a large number of deaths might occur due to AMR in the future. Therefore, there is a need for monitoring AMR in various environments. Currently, developed methods use the sequence's similarity with the existing database as a feature for ARG prediction. Some tools also use the 3D structure of proteins as a feature for ARG prediction. In this thesis, we develop a tool that incorporates both the sequence similarity and the structural information of proteins for ARG prediction. The structural information is encoded with physio-chemical properties (such as hydrophobicity, molecular weight etc.) of the amino acids. Our results show the efficacy of the pipeline in various environments. Results also show that our method achieves accuracy greater than existing models for the prediction of ARGs from metagenomic data. It also enables the detection of putative novel ARGs, providing promising targets for experimental characterization to the scientific community.

Membrane proteins are critical to many biological processes, including molecular transport, signal transduction, and cellular interactions. Through the use of molecular dynamics (MD) simulations, we are able to model this environment at an atomistic scale. However, traditionally used nonpolarizable force fields (FF) are thought to model the unique dielectric gradient posed by the lipid environment with a limited accuracy due to the mean field approximation of charge. Advancements in polarizable FFs and computing efficiency has enabled the explicit modeling of polarization responses and charge distribution, enabling a deeper understanding of the electrostatics driving these processes. Through the use of the Drude FF, we study three specific model systems to understand where explicit polarization is important in describing membranes and membrane proteins. These studies sought to answer the questions: (1) How does explicit electronic polarization impact small molecule permeation and localization preference?, (2) What electrostatic interactions underlie membrane protein secondary structure?, and (3) How do conformational changes propagate between microswitches in G-Protein Coupled Receptors? In this work, we show small molecule dipole moments changing as a function of localization in the bilayer. Additionally, we show differences in the free energy surfaces of permeation for aromatic, polar, and negatively charged species reliant upon force field used. For secondary structure, we showed key interactions which aided to stabilize model helices in bilayers. Finally, we showed potential inductive effects of key microswitch residues underlying prototypical G-Protein coupled receptor activation. This dissertation has helped to show the importance of including explicit polarization in membrane protein systems, especially when considering interactions at the interface and modeling species with charge. This work enables a refined view of the electrostatics occurring in membranes and membrane protein systems, and in the future, can be used as a basis for methodologies in computer aided drug design efforts. / Doctor of Philosophy / Deepening our understandings of membranes and membrane proteins enable better informed and more efficient drug design. In order to do this, biological processes can be simulated through molecular dynamics (MD) simulations. MD simulations use mathematical models known as force fields (FF) to represent the physics of biological systems at an atomistic scale. This enables the study of key interactions which can be leveraged for drug discovery efforts. However, traditional FF neglect electronic structural changes which are crucial for accurately describing the membrane environment and the influence it has on surrounding and embedded molecules. Using enhanced FFs, known as polarizable FFs, we can model this response and gain an entirely new perspective on membranes and membrane proteins. This work helps to define when these FFs are most important to be used when studying membranes and membrane proteins, and in the future, serve as a basis for further simulations in drug discovery efforts.

No description available.

Protein evolution is a complex and diverse process, yielding an incredible assortment of biological functions and pathways occurring in the cells of living organisms. The way in which a protein's structure is constrained by its functional role and its notable conservation across even distant evolutionary relationships highlight structure as an important unit when considering the evolutionary dynamics of proteins. This thesis attempts to place the structural landscape of the protein universe within an evolutionary framework. We investigate potential evolutionary histories of protein superfamilies by introducing an age, which estimates when the ancestor of that superfamily first evolved. The range of ages of known protein superfamilies goes right back to those which evolved before the diversification of life into three major superkingdoms. The structures of these proteins are varied but those which have evolved more recently tend to be shorter and have a less elaborate globular packing. Protein structures sit within a complex global landscape of three-dimensional folds and we attempt to model the dynamics of this space using networks of folds. These networks consist of a structurally diverse core of folds with older ages, and neighbouring folds tend to be of similar ages. Moreover, there are a few pivotal folds which appear repeatedly as central in the landscapes, connecting together otherwise disparate portions of the space. Sequence profiles which capture patterns of conservation and variation amongst naturally occurring proteins within a superfamily can be compared to identify distant evolutionary relationships. The power of these profiles to detect such relationships is improved by seeding them with structural alignments. A landscape of evolutionary links crossing between different protein folds is presented.

[pt] O problema da predição das estruturas de proteínas (Protein Structure Prediction (PSP)) é um dos desafios mais importantes na biologia molecular. Pelo fato deste problema ser muito difícil, têm sido propostos diferentes modelos simplificados para resolvê-lo. Um dos mais estudados é o modelo, Hidrofóbico-Polar (HP), o modelo HP fornece uma estimativa da energia da proteína com base na soma de interações entre pares de aminoácidos hidrofóbicos (contatos H-H). Entretanto, apesar das simplificações feitas no modelo HP, o problema permanece complexo, pertencendo à classe NP-Difícil. Muitas técnicas têm sido propostas para resolver este problema entre elas, técnicas baseadas em algoritmos genéticos. Em muitos casos, as técnicas baseadas em AG foram usadas com sucesso, mas, no entanto, abordagens utilizando AG muitas vezes não tratam adequadamente as soluções geradas, prejudicando o desempenho da busca. Além disso, mesmo que eles, em alguns casos, consigam atingir o mínimo de energia conhecido para uma conformação, estes modelos não levam em conta a forma da proteína um fator muito importante na hora de obter proteínas mais compactas. Foi desenvolvido um algoritmo genético multiobjetivo para PSP no modelo HP, de modo de avaliar de forma mais eficiente, as conformações produzidas. O modelo utiliza como avaliação uma combinação baseada no número de colisões, número de contatos hidrofóbicos, compactação dos aminoácidos hidrofóbicos e hidrofílicos, obtendo, desta forma estruturas mais naturais e de mínima energia. Os resultados obtidos demonstram a eficiência desse algoritmo na obtenção de estruturas proteicas compactas providenciando indicadores da compactação dos aminoácidos hidrofóbicos e hidrofílicos da proteína. / [en] The problem of protein structured prediction (PSP) is one of the most important challenges in molecular biology. Because this problem is very difficult, different simplified models have been proposed to solve it. One of the most studied is the Hydrophobic-Polar model HP this model provides an estimate of the protein energy based on the sum of hydrophobic contacts. However, despite the simplifications made in the HP model, the problem remains complex, belonging to the class of NP-Hard problems. Many techniques have been proposed to solve this problem as genetic algorithms. In many cases the GA techniques have been used successfully, but, however, with GA approaches often do not adequately address the generated solutions, impairing the performance of the search. Furthermore, in some cases would attain the minimum energy for a known conformation, these models do not take care the protein shape, a very important factor to obtain more compact proteins. This work developed a multiobjective genetic algorithm to PSP in HP model evaluating more efficiently, the conformations produced. This model is a combination of assessment based on the collisions numbers, hydrophobic contacts, hydrophobic and hydrophilic core compression, obtaining thus more natural structures with minimum energy. The results demonstrate the efficiency of this algorithm to obtain protein structures indicators providing compact compression of the hydrophobic and hydrophilic core protein.

The ability of mutations to change the fold of a protein provides evolutionary pathways to new structures. To study hypothetical pathways for protein fold evolution, we designed intermediate sequences between Xfaso1 and Pfl6, two homologous Cro proteins that have 40% sequence identity but adopt all–α and α+β folds, respectively. The designed hybrid sequences XPH1 and XPH2 have 70% sequence identity to each other. XPH1 is more similar in sequence to Xfaso1 (86% sequence identity) while XPH2 is more similar to Pfl6 (80% sequence identity). NMR solution ensembles show that XPH1 and XPH2 have structures intermediate between Xfaso1 and Pfl6. Specifically, XPH1 loses α-helices 5 and 6 of Xfaso1 and incorporates a small amount of β-sheet structure; XPH2 preserves most of the β-sheet of Pfl6 but gains a structure comparable to helix 6 of Xfaso1. These findings illustrate that the sequence space between two natural protein folds may encode a range of topologies, which may allow a protein to change its fold extensively through gradual, multistep mechanisms. Evolving a new fold may have consequences, such as a strained conformation. Here we show that Pfl6 represents an early, strained form of the α+β Cro fold resulting from an ancestral remnant of the all-α Cro proteins retained after the fold switch. This nascent fold can be stabilized through deletion mutations in evolution, which can relieve the strain but may also negatively affect DNA-binding function. Compensatory mutations that increase dimerization appear to offset these effects to maintain function. These findings suggest that new folds can undergo mutational editing through evolution, which may occur in parallel pathways with slightly different outcomes.

Cereals contain naturally occurring biopolymers (for example proteins and starches) that can be used as renewable raw materials in a variety of speciality chemical applications. The separation of protein and starch biopolymers from wheat is well established and relies on a group of proteins called glutens that have a unique network-forming functionality. Oat and other cereals do not naturally contain these gluten proteins and typically rely on chemical-based separation techniques which alter the chemical and physical structures and damage the inherent natural functionality of the biopolymers. This research study investigated the separation of the protein and starch fractions from cereals using the Al-Hakkak Process, a new aqueous process. This process involves adding water and wheat gluten protein to cereals that do not contain gluten. The wheat gluten interacts with the cereal proteins, facilitating the separation of the starch and protein fractions whilst retaining their inherent natural functionality. The aim of this research project was to investigate and optimise the pilot scale separation performance of the Al-Hakkak Process using oat flour. As very little prior research had been carried out, the focus was to characterise the oat starch and protein separation performance and gain an understanding of the mechanisms involved. A variety of techniques were employed. Large scale deformation rheology was used to gain an understanding of the oat-gluten dough rheology and establish the relationship between the rheology and the separation performance. Confocal scanning laser microscopy was used to investigate the structure of the oat-gluten protein network. The molecular interactions between the oat and gluten proteins were studied using gel electrophoresis. The network-forming functionality of the new oat-gluten protein was explored. The influence of various processing parameters on the pilot scale separation performance was investigated and the results compared with other data collected through the study to identify key processing parameters. This research programme has resulted in interesting, encouraging and some unexpected outcomes and these are discussed in detail in the thesis. It was concluded that an insoluble protein network formed in the oat-gluten dough and both kneading and extraction processes were found to contribute to the formation of this. A key conclusion was that the changes that took place in the oat-gluten dough were similar to, but not identical to, the changes that occur in wheat dough. It was proposed that the mechanism for the development of a protein network in oat-gluten dough differed from wheat dough for two main reasons: a) the presence of the oat flour disrupted the normal wheat gluten behaviour, and b) components in the oat flour altered the activity of the gluten proteins. The research identified key processing parameters for the Al-Hakkak Process including kneading time, gluten content, and sodium chloride content of the oat-gluten dough as well as sodium chloride concentration, pH, and temperature of the extract liquor. An important discovery was that the oat and gluten proteins interacted at a molecular level through reducible, covalent, bonding (most likely disulphide linkages) to form the insoluble protein network in the oat-gluten dough. It was concluded that these reducible bonds coupled the individual protein subunits to form new hybrid oat-gluten protein molecules (a combination of oat proteins and gluten proteins). Both insoluble and soluble proteins in the oat and gluten flour were involved in the formation of the insoluble protein network in the oat-gluten dough. This outcome has applications beyond the Al-Hakkak Process, as this new knowledge can be applied to the wider dough processing industry. It was concluded that the wheat gluten was the source of the protein network-forming functionality of the hybrid oat-gluten protein and that the oat proteins had a diluting effect. It was proposed that oat-gluten protein flour from the Al-Hakkak Process could be reused to replace the commercial wheat gluten flour in subsequent production batches. During spray drying of the starch stream, the soluble biopolymers in the extract liquor were found to act as an adhesive and glued individual starch granules together to form spherical agglomerates. Acidification of the extract liquor was found to enhance this agglomeration. It was proposed the acidified starch granules were sticker during spray drying due to the partial acid hydrolysis of the starch granule suface which enhanced the agglomeration.