４種類の免疫ペプチド分類問題を解決する機械学習アプローチ

Peptides play an important role in all aspects of the immunological reactions to invading cancer and pathogen cells. It has been known for over 40-years that peptides are critical influences in assembling the immune system against foreign invaders. Since then, new knowledge about the generation and function of peptides in immunology has supported efforts to harness the immune system to treat disease. Yet, with little immunological insight, most of the highly productive treatments, including vaccines, have been developed empirically. Nonetheless, increased knowledge of the biology of antigen processing as well as chemistry and pharmacological properties of antigenic and antimicrobial peptides has now permitted to development of drugs and vaccines. Due to advanced technologies, it is vitally important to develop automatic computational methods for rapidly and accurately predicting immune-peptides. In this thesis, the author focuses on the machine learning approaches for addressing classification problems of four types of immune-peptides (anti-inflammatory, proinflammatory, anti-tuberculosis, and linear B-cell peptides).Numerous inflammatory diseases and autoimmune disorders by therapeutic peptides have received substantial consideration; however, the exploration of anti-inflammatory peptides via biological experiments is often a time consuming and expensive task. The development of novel in silico predictors is desired to classify potential anti-inflammatory peptides prior to in vitro investigation. Herein, an accurate predictor, called PreAIP (Predictor of Anti-Inflammatory Peptides) was developed by integrating multiple complementary features. We systematically investigated different types of features including primary sequence, evolutionary and structural information through a random forest classifier. The final PreAIP model achieved an AUC value of 0.833 in the training dataset via 10-fold cross-validation test, which was better than that of existing models. Moreover, we assessed the performance of the PreAIP with an AUC value of 0.840 on a test dataset to demonstrate that the proposed method outperformed the two existing methods. These results indicated that the PreAIP is an accurate predictor for identifying anti-inflammatory peptides and contributes to the development of anti-inflammatory peptides therapeutics and biomedical research. The curated datasets and the PreAIP are freely available at http://kurata14.bio.kyutech.ac.jp/PreAIP/. A proinflammatory peptide (PIP) is a type of signaling molecules that are secreted from immune cells, which contributes to the first line of defense against invading pathogens. Numerous experiments have shown that PIPs play an important role in human physiology such as vaccines and immunotherapeutic drugs. Considering high-throughput laboratory methods that are time consuming and costly, effective computational methods are great demand to timely and accurately identify PIPs. Thus, in this study, we proposed a computational model in conjunction with a multiple feature representation, called ProIn-Fuse, to improve the performance of PIPs identification. Specifically, a feature representation learning model was utilized to generate a set of informative probabilistic features by making the use of random forest models with eight sequence encoding schemes. Finally, the ProIn-Fuse was constructed by the linearly combined models of the informative probabilistic features. The generalization capability of our proposed method evaluated through independent test showed that ProIn-Fuse yielded an accuracy of 0.746, which was over 10% higher than those obtained by the state-of-the-art PIP predictors. Cross-validation and independent results consistently demonstrated that ProIn-Fuse is more precise and promising in the identification of PIPs than existing PIP predictors. The web server, datasets and online instruction are freely accessible at http://kurata14.bio.kyutech.ac.jp/ProIn-Fuse/. We believe that the proposed ProIn-Fuse can facilitate faster and broader applications of PIPs in drug design and development. Tuberculosis (TB) is a leading killer caused by Mycobacterium tuberculosis. Recently anti-TB peptides have provided an alternative approach to combat antibiotic tolerance. Herein, we have developed an effective computational predictor iAntiTB (identification of anti-tubercular peptides) that integrates multiple feature vectors deriving from the amino acid sequences via Random Forest (RF) and Support Vector Machine (SVM) classifiers. The iAntiTB combined the RF and SVM scores via linear regression to enhance the prediction accuracy. To make a robust and accurate predictor we prepared the two datasets with different types of negative samples. The iAntiTB achieved AUC values of 0.896 and 0.946 on the training datasets of the first and second datasets, respectively. The iAntiTB outperformed the other existing predictors. Thus, the iAntiTB is a robust and accurate predictor that is helpful for researchers working on peptide therapeutics and immunotherapy. All the employed datasets and software application are accessible at http://kurata14.bio.kyutech.ac.jp/iAntiTB/. Linear B-cell peptides are critically important for immunological applications such as vaccine design, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. The accurate identification of linear B-cell peptides remains challenging despite several decades of research. In this work, we have developed a novel predictor, iLBE (Identification of B-Cell Epitope), by integrating evolutionary and sequence-based features. The successive feature vectors were optimized by a Wilcoxon rank-sum test. Then the random forest (RF) algorithm used the optimal consecutive feature vectors to predict linear B-cell epitopes. We combined the RF scores by the logistic regression to enhance the prediction accuracy. The performance of the final iLBE yielded an AUC score of 0.809 on the training dataset. It outperformed other existing prediction models on a comprehensive independent dataset. The iLBE is suggested to be a powerful computational tool to identify the linear B-cell peptides and development of penetrating diagnostic tests. A web application with curated datasets is freely accessible of iLBE at http://kurata14.bio.kyutech.ac.jp/iLBE/. Taken together, the above results suggest that our proposed predictors (PreAIP, ProIn-Fuse, iAntiTB, and iLBE) would be helpful computational resources for the prediction of anti-inflammatory, pro-inflammatory, tuberculosis, and linear B-cell peptides. / ペプチドは、癌や病原体細胞に対する免疫反応のあらゆる側面で重要な役割を果たす。ペプチドが外来の侵入物に対する免疫系を起動する上で決定的な影響を与えることは40年以上前から知られている。それ以来、免疫学におけるペプチドの生成と機能に関する新しい知見は、病気を治療するために免疫系を利用する研究を支えてきた。依然として、免疫学的洞察がほとんどないため、ワクチンを含む効率的治療法のほとんどは、経験的に開発されている。それでもなお、抗原プロセシングの生物学、ならびに抗原性および抗菌性ペプチドの化学・薬理学に関する知見の増加により、現在、薬物およびワクチンの開発が可能になっている。高度な技術により、免疫ペプチドを迅速かつ正確に予測するためのコンピュータ技術を開発することが非常に重要である。この論文では、著者は4種類の免疫ペプチド（抗炎症、炎症誘発性、抗結核、および線形B細胞エピトープ）の分類問題に対処するための機械学習アプローチに焦点を当てる。炎症性疾患および自己免疫疾患に対する治療用ペプチドは、多くの検討がなされてきた。しかし、生物学的実験による抗炎症ペプチドの探索は、多くの場合、時間と費用のかかる作業である。新しいin siloco予測器の開発は、in vitro実験に先立って、潜在的な抗炎症ペプチドを同定するために望まれている。ここでは、PreAIP（抗炎症ペプチドの予測器）と呼ばれる予測器が、複数の補完的機能を統合することによって開発された。一次配列、進化的および構造的情報を含むさまざまなタイプの特徴量を、ランダムフォレスト分類器を介して抽出した。最終的なPreAIPモデルは、10分割交差検定によるトレーニングデータセットで0.833のAUC値を達成した。これは、既存のモデルよりも優れた値である。さらに、独立の検証用データセットでAUC値0.840を達成し、提案された方法が2つの既存の予測器よりも優れていることを示した。これらの結果は、PreAIPが抗炎症ペプチドを同定するための正確な予測器であり、抗炎症ペプチド治療および生物医学研究の開発に貢献した。用いたデータセットとPreAIPは、http：//kurata14.bio.kyutech.ac.jp/PreAIP/から自由に利用できる。炎症誘発性ペプチド（PIP）は、免疫細胞から分泌されるシグナル伝達分子の一種であり、侵入する病原体に対する防御の第一線を担当する。多くの実験により、PIPはワクチンや免疫療法薬などにおいて重要な役割を果たすことが示されている。ハイスループットな生物実験に時間と費用が掛かることを考えると、効率的なコンピュータ予測は、PIPを短時間にかつ正確に特定するために大きな需要がある。したがって、この研究では、PIP識別性能を向上させるために、ProIn-Fuseと呼ばれる複数の特徴表現を組み合わせた計算モデルを提案した。具体的には、特徴表現学習モデルを利用して、8つのシーケンスエンコーディングスキームを備えたランダムフォレストモデルを利用することにより、確率的予測スコアを計算した。ProIn-Fuseは、確率的予測スコアの線形結合モデルによって構築された。提案手法の汎化性能を独立したテストデータで評価した結果、ProIn-Fuseの精度は0.746であり、これは最新のPIP予測器によって得られた精度よりも10％以上高かった。テストデータによる検証結果は、ProIn-Fuseが既存のPIP予測器よりも正確にPIP識別できることを示した。Webサーバー、データセット、および説明書は、http：//kurata14.bio.kyutech.ac.jp/ProIn-Fuse/から自由にアクセスできる。ProIn-Fuseは、ドラッグデザイン含む幅広いアプリケーションに応用できる。結核（TB）は、結核菌によって引き起こされる疾患である。最近、抗結核ペプチドは抗生物質耐性に対抗するための代替アプローチを提供している。ここでは、ランダムフォレスト（RF）およびサポートベクターマシン（SVM）分類器を用いてアミノ酸配列に由来する複数の特徴ベクトルを統合する効果的な予測器iAntiTB（抗結核ペプチドの識別）を開発した。iAntiTBは、線形回帰を介してRFスコアとSVMスコアを組み合わせて、予測精度を向上させた。ロバストで正確な予測器を作成するために、異なるタイプのネガティブサンプルを使用して2つのデータセットを準備した。iAntiTBは、1番目と2番目のデータセットのトレーニングデータセットでそれぞれ0.896と0.946のAUC値を達成した。iAntiTBは、他の既存の予測器の性能を上回った。このように、iAntiTBは、ペプチド治療および免疫療法に取り組んでいる研究者に役立つロバストで正確な予測器である。利用されたすべてのデータセットとソフトウェアアプリケーションは、http：//kurata14.bio.kyutech.ac.jp/iAntiTB/から自由にアクセスできる。線形B細胞エピトープは、ワクチンの設計、免疫診断テスト、抗体産生、疾患の診断や治療などの免疫学的応用に非常に重要である。線形B細胞エピトープの正確な同定は、数十年の研究にもかかわらず、依然として挑戦的課題のままである。本研究では、配列の進化的特徴や物理化学的特徴等を統合することにより、新規な線形B細胞エピトープ予測モデル（iLBE）を開発した。Wilcoxon順位和検定によって最適化した特徴ベクトル群をランダムフォレスト（RF）アルゴリズムを用いて学習して、線形B細胞エピトープの予測スコアを計算した。ロジスティック回帰を用いてRFスコアを組合せて、予測精度を高めた。iLBEは、トレーニングデータセットで0.809のAUCを達成し、独立のテストデータセットを用いた検定では、既存の予測モデルの性能を超えた。線形B細胞エピトープを同定する強力な計算ツールであるiLBEは、診断テストの開発に有用である。注釈付きデータセットを備えたiLBEモデルのウエブアプリケーションは自由にアクセスできるhttp://kurata14.bio.kyutech.ac.jp/iLBE/。九州工業大学博士学位論文 学位記番号：情工博甲第358号 学位授与年月日：令和3年3月25日1 Introduction|2 Prediction of Anti-Inflammatory Peptides by Integrating Mulptle Complementary Features|3 Prediction of Proinflammatory Peptides by Fusing of Multiple Feature Representations|4 Prediction of Anti-Tubercular Peptides by Exploiting Amino Acid Pattern and Properties|5 Prediction of Linear B-Cell Epitopes by Integrating Sequence and Evolutionary Features|6 Conclusions and Perspectives九州工業大学令和2年

Graph theory-based sequence descriptors as remote homology predictors

Indexación: Scopus.Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies. The most popular alignment-free methodologies, as well as their applications to classification problems, have been described in previous reviews. Despite a new set of graph theory-derived sequence/structural descriptors that have been gaining relevance in the detection of remote homology, they have been omitted as AF predictors when the topic is addressed. Here, we first go over the most popular AF approaches used for detecting homology signals within the twilight zone and then bring out the state-of-the-art tools encoding graph theory-derived sequence/structure descriptors and their success for identifying remote homologs. We also highlight the tendency of integrating AF features/measures with the AB ones, either into the same prediction model or by assembling the predictions from different algorithms using voting/weighting strategies, for improving the detection of remote signals. Lastly, we briefly discuss the efforts made to scale up AB and AF features/measures for the comparison of multiple genomes and proteomes. Alongside the achieved experiences in remote homology detection by both the most popular AF tools and other less known ones, we provide our own using the graphical–numerical methodologies, MARCH-INSIDE, TI2BioP, and ProtDCal. We also present a new Python-based tool (SeqDivA) with a friendly graphical user interface (GUI) for delimiting the twilight zone by using several similar criteria.https://www.mdpi.com/2218-273X/10/1/2

Structural and functional investigation of the cytoplasmic domain of the Fas death receptor

Activation of the transmembrane death receptor Fas (CD95/APO-1) by a membrane bound ligand (FasL/CD95L) activates the extrinsic pathway of apoptosis. Intracellular Fas death domains (DDs) are induced to oligomerise enabling binding to the adaptor protein FADD, thereby leading to the recruitment of procaspase 8 and other proteins to form the death inducing signalling complex (DISC).This thesis describes an investigation of the structure and function of the cytoplasmic Fas-DD. A model for the solution structure of the Fas-DD was published in 1996, it has since been reported that the death domain can form at least one other conformation when in complex with FADD. As a foundation to the work in this thesis, modern multidimensional NMR techniques have been used to solve the structure of the FasDD, to further probe the potential for alternative conformations. It has previously been reported that Fas can be phosphorylated at Tyr291, providing a platform for the recruitment of binding partners that can affect non-apoptotic signalling. The second part of this thesis details the development of an expressed protein ligation methodology to prepare a Tyr291 phosphorylated Fas DD to provide a basis for in vitro studies of the structural, dynamic and functional effects of phosphorylation. It is widely accepted that Fas is palmitoylated at Cys199 and recognised by the membrane cytoskeletal protein, ezrin. Fas palmitoylation is important for clathrinmediated internalisation of the DISC, and amplification of the caspase cascade. There are multiple reports detailing the binding of ezrin to Fas, but it is not clear whether this interaction occurs in a palmitoylation-dependent manner. Efforts to characterise an interaction between bacterially expressed intracellular Fas and ezrin proteins were carried out using a number of biophysical assays, described in the third part of this thesis. Building upon this, the fourth section explores the preparation of a palmitoylated Fas construct suitable for biophysical analysis by incubating recombinant Fas with palmitoyl-CoA

Systems Biology of Protein Secretion in Human Cells: Multi-omics Analysis and Modeling of the Protein Secretion Process in Human Cells and its Application.

Since the emergence of modern biotechnology, the production of recombinant pharmaceutical proteins has been an expanding field with high demand from industry. Pharmaceutical proteins have constituted the majority of top-selling drugs in the pharma industry during recent years. Many of these proteins require post-translational modifications and are therefore produced using mammalian cells such as Chinese Hamster Ovary cells. Despite frequent improvements in developing efficient cell factories for producing recombinant proteins, the natural complexity of the protein secretion process still poses serious challenges for the production of some proteins at the desired quantity and accepted quality. These challenges have been intensified by the growing demands of the pharma industry to produce novel products with greater structural complexity,\ua0\ua0as well as increasing expectations from regulatory authorities in the form of new quality control criteria to guarantee product safety.This thesis focuses on different aspects of the protein secretion process, including its engineering for cell factory development and analysis in diseases associated with its deregulation. A major part of this thesis involved the use of HEK293 cells as a human model cell-line for investigating the protein secretion process by generating different types of omics data and developing a computational model of the human protein secretion pathway. We compared the transcriptomic profile of cell lines producing erythropoietin (EPO; as a model secretory protein) at different rates to identify key genes that potentially contributed to higher rates of protein secretion. Moreover, by performing a transcriptomic comparison of cells producing green fluorescent protein (GFP; as a model non-secretory protein) with EPO producers, we captured differences that specifically relate to secretory protein production. We sought to further investigate the factors contributing to increased recombinant protein production by analyzing additional omic layers such as proteomics and metabolomics in cells that exhibited different rates of EPO production. Moreover, we developed a toolbox (HumanSec) to extend the reference human genome-scale metabolic model (Human1) to encompass protein-specific reactions for each secretory protein detected in our proteomics dataset. By generating cell-line specific protein secretion models and constraining the models using metabolomics data, we could predict the top host cell proteins (HCPs) that compete with EPO for metabolic and energetic resources.\ua0Finally,\ua0based on the detected patterns of changes in our multi-omics investigations combined with a protein secretion sensitivity analysis using the metabolic model, we identified a list of genes and pathways that potentially play a key role in recombinant protein production and could serve as promising candidates for targeted cell factory design.In another part of the thesis, we studied the link between the expression profiles of genes involved in the protein secretory pathway (PSP) and various hallmarks of cancer. By\ua0implementing a dual approach involving differential expression analysis and eight different machine learning algorithms, we investigated the expression changes in secretory pathway components across different cancer types to identify PSP genes whose expression was associated with tumor characteristics. We demonstrated that a combined machine learning and differential expression approach have a complementary nature and could highlight key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets

Biological Systems Workbook: Data modelling and simulations at molecular level

Nowadays, there are huge quantities of data surrounding the different fields of biology derived from experiments and theoretical simulations, where results are often stored in biological databases that are growing at a vertiginous rate every year. Therefore, there is an increasing research interest in the application of mathematical and physical models able to produce reliable predictions and explanations to understand and rationalize that information. All these investigations are helping to overcome biological questions pushing forward in the solution of problems faced by our society. In this Biological Systems Workbook, we aim to introduce the basic pieces allowing life to take place, from the 3D structural point of view. We will start learning how to look at the 3D structure of molecules from studying small organic molecules used as drugs. Meanwhile, we will learn some methods that help us to generate models of these structures. Then we will move to more complex natural organic molecules as lipid or carbohydrates, learning how to estimate and reproduce their dynamics. Later, we will revise the structure of more complex macromolecules as proteins or DNA. Along this process, we will refer to different computational tools and databases that will help us to search, analyze and model the different molecular systems studied in this course

Modification of Behavior of Elastin-like Polypeptides by Changing Molecular Architecture

Elastin-like polypeptides (ELP) are environmentally responsive polymers that exhibit phase separation in response to external stimuli such as temperature, pH, light, and ionic strength. It has been shown that the sequence of the pentapeptide, its length, and the solution concentration are very important in the transition of the molecules from soluble to insoluble, but there has not been any detailed study of the effect of molecular architecture on the behavior of ELPs.In this study we designed, synthesized and characterized ELPs with different architectures and chemical identities to probe the effect of molecular design on the microscopic and macroscopic behavior of ELP molecules and to compare them to the linear ELP molecules. These new architectures also helped us better understand the theory of folding and aggregation of ELPs. The design was based on constructing three-armed star molecules by tagging a trimer forming oligomerization domain to the ELP chains. ELPs were chosen to have different chemical identities by changing the pentapetide sequence. The molecules were synthesized by molecular biology techniques and characterized by different methods.Our results show that capping the three ELP chains forces the chains to fold into more extended rod-like constructs prior to aggregation. A mathematical model was developed to predict the behavior of ELP chains at the transition temperature and it was shown that there is a difference between N- and C- terminal capping ELPs seem to fold at lower temperatures when their N-termini are held together. It was also shown that the constructs with both their ends capped can be designed such that they fold into a stable unit at much lower temperatures than the linear constructs without necessarily aggregation at higher temperatures. The trimer constructs were also used to make micellar aggregates that were characterized by dynamic and static light scattering. It was shown that the size of the micelles can be controlled by adjusting salt concentration or by making mixtures

FUNCTIONALISATION OF CARBON NANOMATERIALS WITH BIOMOLECULES

Carbon nanomaterials, including carbon nanotubes and graphene, with various unique physical and chemical properties are emerging as extraordinary materials for biomedical applications. The aim of this thesis was to functionalise single walled carbon nanotubes and reduced graphene oxide with range of biomolecules including, peptides, peptoids, and ribonucleosides. The first study investigated the noncovalent interaction between single walled carbon nanotubes and fluoro tagged nano-1 peptide with 19F NMR. In the second study single walled carbon nanotubes were noncovalently functionalised with a series of antibacterial, chiral, amphiphilic peptoids. The peptoids varied in the number of aromatic residues on the hydrophobic surface of the helix. It was found that peptoid’s ability to individually disperse single walled carbon nanotubes increased with increasing the number of aromatic residues. The third study presented the first experimental noncovalent interaction of ribonucleosides, nucleobases, and ribose with purified and oxidised single walled carbon nanotubes. It was found that cyclic and aromatic ribonucleosides and nucleobases are too small to disperse the hydrophobic nanotube surface by π-π stacking. Furthermore, results showed that the ribonucleosides dispersion ability towards nanotubes depends on the number of oxygen-containing functional groups on the nanotube surface. In the final research it was found that the flat rigid surface of reduced graphene oxide has a critical role in its noncovalent interaction with peptides and peptoids. Results showed that biomolecules with higher backbone flexibility can give a higher dispersion affinity towards reduced graphene oxide. Also, it was found that ribonucleosides and their nucleases, and ribose moieties have very limited dispersion affinity towards reduced graphene oxide. Finally, the covalent functionalisation of reduced graphene oxide with cell penetrating peptoid, thymidine, and adenosine was investigated

Development and characterization of dextrin based hydrogels use of non-catalityc domains for the modification of polysaccharides

Tese de Doutoramento em Engenharia Química e BiológicaTissue engineering (TE) has emerged as a promising approach to circumvent the limitations of the existing therapies for the treatment of tissue loss or organ failure. In a parallel route, continuous advances in biotechnology led to the availability of complex natural molecules for the treatment of the 20th Century diseases, such as AIDS, Alzheimer and cancer. These molecules, far more challenging to deliver than the classical therapeutic agents, were the driven force for the development of a new frontier research – the controlled drug delivery (CDD). TE and CDD have soon become interdisciplinary branches of science, gathering concepts from engineering, material and life sciences to develop new generations of biomedical tools which allowed overcoming clinical limitations such as donor scarcity, immunological rejection or drawbacks associated with surgery, thereby increasing patient compliance. The development of biomedical devices has focused on the design of three-dimensional structures made from natural or synthetic materials, termed scaffolds. Hydrogels are a class of hydrophilic polymeric scaffolds, with appealing features from the perspective of biological mimicking. They have a good biocompatibility, degradability and appropriate mechanical properties, allowing for a favorable controlled interaction with living systems. Hydrogels can be used in TE, as scaffolds to support and promote tissue regeneration, and as attractive systems for the controlled release of pharmaceutically active molecules. The goal of this thesis was to functionalize the biomaterial – Dextrin – to produce a hydrogel, as a potential alternative to the commonly used polymers for biomedical applications, namely as controlled release devices. Dextrin is a polymer composed of -(1 4) D-glucose units, produced by partial hydrolysis of starch. The transesterification of dextrin with vinyl acrylate (VA) was carried out in anhydrous dimethylsulfoxide (DMSO), being C2 and C3 the preferred acylation positions, as revealed by solid state-NMR (nuclear magnetic resonance) analysis. Different degrees of substitution (DS) ranging from ca. 10 to 70% were obtained by controlling the molar ratio of VA to dextrin and gels were obtained by free radical polymerization of aqueous solutions of dextrin-VA. A preliminary analysis on the potential of these hydrogels for the controlled release of bioactive molecules was carried out. The protein (bovine serum albumin-BSA) diffusion coefficients on the hydrogels were calculated using the lag-time analysis. Values in range 10-7 cm2/s were obtained for DS 20 and DS 40 and a smaller value of 10-8 cm2/s arised upon DS increasing to 70%, revealing the dependence of the diffusivity on the crosslinking density. Further investigation has shown that the degradation is very slow under physiological conditions. However, the hydrogels could be rendered degradable through the incorporation of the enzyme amyloglucosidase, which prove to be an effective route to modulate the release profiles. Nevertheless, an alternative approach, which included the functionalization of the polymer with a methacrylate ester (HEMA), was also performed. It was possible to obtain hydrogels with distinct mechanical properties, resulting in more desirable degradation kinetics, as revealed through a rheologic analysis of the viscoelastic behavior. Finally, the biocompatibility of the hydrogels has been evaluated in vitro, using mouse embryo fibroblasts. The adhesion, proliferation and morphology of the cells on the hydrogel were studied. The extracts obtained from the hydrogels, only slightly reduced the proliferation of fibroblasts (~15%). It was possible to observe that the direct seeding of the cells onto the hydrogels surfaces resulted in a reduction in the proliferation rate, as compared to tissue culture polystyrene plate. However, the results show that, although with a delay, cells are effectively able to grow, indicating that no deleterious effects are produced by dextrin hydrogels. Cellulose is the most abundant polysaccharide on Earth. Its hydrolysis is handled by a variety of different enzymes, known as cellulases. Cellulases, hemicellulases and other polysaccharide-degrading enzymes are widely used in a variety of applications, namelly in pulp and paper industries. Despite its wide utilization, several drawbacks result from enzyme utilization. Taking the paper treatment as an example, the drawbacks include the extensive hydrolysis of polysaccharides that leads to a reduction of both fiber strength and mass. In this context, the application of carbohydrate-binding modules (CBMs) allows overcoming the limitations associated with the enzyme technology. CBMs are non-catalytic modules present in several cellulases and hemicellulases. Several studies indicated that treatment of cellulose fibers with CBMs alters the interfacial properties of the fibers. In this work, the effect of recombinant cellulose-binding domains (CBD) on the properties of secondary paper fiber was evaluated. Two recombinant family 3 CBDs, from Clostridium thermocellum scaffoldin protein CipA (CBDCipA) and Cellobiohydrolase A (CbhA) were used. The CbhA CBD was used either alone (CBDCbhA) or fused with the internal fibronectin (FN31,2) module (FN31-FN32-CBDCbhA). Additionally, the CBDs were chemically conjugated with an activated polyethylene glycol (PEG). The data showed that the CBDCipA-PEG conjugate leads to a change on the properties of secondary fibers, as revealed by the improvement in both pulp drainage (Shopper-Riegler degree (ºSR) decreased up to 15%) and paper tensile strength. This effect is attributed to the presence of the PEG molecule, since CBDs lacking PEG were unable to modify pulp and paper properties. It is suggested that PEG mimetizes the glycosidic fraction of fungal CBDs, which is absent in the highly purified bacterial modules used here. It is concluded that the improved drainability of the pulp is attributed to the hydration and stabilization of the fibers.A engenharia de tecidos (TE) surgiu como uma forma promissora de contornar as limitações das terapias existentes, utilizadas no tratamento do mau funcionamento ou perda total de funções de um órgão ou tecido. Numa linha de investigação paralela, os contínuos avanços biotecnológicos, conduziram ao aparecimento de uma vasta gama de moléculas complexas para o tratamento de emergentes doenças do Século XX, tais como SIDA, Alzheimer e cancro. A forma de libertação destas novas moléculas no organismo constituiu um desafio, que acabaria por ser a força impulsionadora de uma nova fronteira de investigação – a libertação controlada de fármacos (CDD). TE e CDD cedo se tornaram ramos científicos interdisciplinares. Aplicando conceitos de engenharia e ciências da vida, uniram esforços no sentido de desenvolver novas gerações de produtos biomédicos que permitissem ultrapassar alguns dos urgentes problemas associados à prática clínica, como a escassez de dadores, a rejeição imunológica ou as desvantagens da cirurgia, melhorando os cuidados de saúde. O desenvolvimento dos novos produtos biomédicos foi direccionado no sentido da produção de estruturas tridimensionais a partir de materiais naturais ou sintéticos, denominados scaffolds. Neste contexto surgem os hidrogéis, como uma classe de scaffolds poliméricos e hidrofilicos, com características apelativas da perspectiva do mimetismo de condições biológicas naturais, das quais se destacam a biocompatibilidade, a degradabilidade e as propriedades mecânicas, permitindo uma interacção favorável e controlada com os sistemas vivos. Os hidrogéis são utilizados em TE como suportes para promover a regeneração de tecidos, podendo também ser usados como atractivos sistemas de libertação controlada de fármacos. Um dos principais objectivos deste trabalho consistia na funcionalização de um biomaterial – Dextrino – para a produção de um hidrogel, como alternativa aos polímeros actualmente utilizados em aplicações biomédicas, nomeadamente como sistema de libertação controlada. O dextrino é um polímero de unidades de -(1 4) D-glucose, produzido pela hidrólise parcial do amido. A sua transesterificação com vinil acrilato (VA) foi efectuada em dimetilsulfoxido anidro (DMSO), sendo as posições C2 e C3, os locais preferenciais de acilação, revelados por ressonância magnética nuclear (NMR) de sólidos. Diferentes graus de substituição (DS) (entre 10 e 70%) foram obtidos através da alteração da razão molar VA/dextrino e os hidrogéis foram obtidos por polimerização radicalar de soluções aquosas de dextrino- VA. A avaliação preliminar do potencial destes hidrogéis como sistemas de libertação controlada, foi efectuada utilizando a proteína albumina sérica de bovino (BSA), tendo os coeficientes de difusão sido calculado por análise do lag-time. Valores na ordem de 10-7 cm2/s foram obtidos para géis DS 20 e DS 40. Verificou-se, no entanto, uma diminuição para 10-8 cm2/s, aquando do aumento do DS para 70%, assinalando a dependência da difusão na densidade de reticulação do hidrogel. Apesar de investigação subsequente ter revelado que a degradação dos hidrogéis ocorre de forma lenta em condições fisiológicas, foi possível torná-los degradáveis através da incorporação da enzima amiloglucosidase, sendo uma forma efectiva de modular os perfis de libertação. Não obstante, foi realizada uma abordagem alternativa, passando pela utilização do ester metacrilato (HEMA) na funcionalização do polímero. A avaliação reológica do comportamento visco-elástico revelou ser possível a obtenção de hidrogéis com propriedades mecânicas distintas, resultando em cinéticas de degradação mais apropriadas. Finalmente, a biocompatibilidade dos hidrogéis foi avaliada in vitro, em fibroblastos embrionários de rato, através da análise da adesão, proliferação e morfologia celulares. Os resultados demonstraram que os extractos obtidos a partir dos hidrogéis induziram apenas uma ligeira redução da proliferação celular (~15%). Foi ainda possível observar que o cultivo directo das células na superfície dos hidrogéis, resulta numa redução na taxa de proliferação quando comparada com a cultura controlo. No entanto, foi demonstrado que as células são efectivamente capazes de crescer, indicando que a presença do hidrogel não produz efeitos deletérios. A celulose é o polissacarídeo mais abundante na Terra. A sua hidrólise é levada a cabo por diferentes enzimas, conhecidas como celulases. As celulases, hemicelulases e outras enzimas responsáveis pela degradação de polissacarídeos, têm uma vasta aplicação, nomeadamente na indústria de polpa e papel. Apesar de amplamente utilizadas, vários inconvenientes resultam da acção das enzimas. No caso do tratamento de papel destaca-se a hidrólise extensiva dos polissacarídeos, que resulta numa redução de massa e resistência das fibras. Neste contexto, a aplicação de módulos de ligação a carbohidratos (CBMs), surge como uma alternativa viável, evitando as desvantagens da tecnologia enzimática. Os CBMs consistem em módulos não-catalíticos, presentes em várias celulases e hemicelulases. Vários estudos indicam que o tratamento de fibras de celulose com CBMs provoca alterações nas propriedades interfaciais das mesmas. Neste trabalho foi avaliado o efeito de domínios de ligação a celulose (CBDs) recombinantes nas propriedades de fibras de papel secundárias. Foram utilizados dois CBDs (família 3) recombinantes de Clostridium thermocellum, pertencentes a dois complexos enzimáticos: scaffoldin protein A (CipA/CBDCipA) e Cellobiohydrolase A (CbhA/CBDCbhA). O CBDCipA foi utilizado isolado ou em fusão com o módulo interno de fibronectina (FN31-FN32-CBDCbhA). Procedeu-se ainda à conjugação química dos CBDs com uma molécula activada de polietileno glicol (PEG). Os resultados obtidos demonstraram que o conjugado CBDCipA-PEG provoca alterações nas fibras secundárias, que resultam no melhoramento da drenabilidadedas polpas (diminuição do grau de Shopper-Riegler (ºSR) até 15%), bem como da resistência à tensão do papel. Este efeito é atribuído à presença da molécula de PEG, uma vez que na ausência deste, os CBDs isolados não são capazes de provocar alterações nas propriedades da polpa e do papel, sugerindo que a molécula de PEG mimetiza o efeito da fracção glicosídica dos CBDs fungícos, que está ausente nos módulos bacterianos puros. Conclui-se que o melhoramento na drenabilidade da polpa está relacionado com a hidratação e estabilização das fibras.Fundação para a Ciência e Tecnologia (FCT), bolsa de investigação SFRH/BD/17482/2004