<p>Nature is challenged to move charge efficiently over many length scales. From sub-nm to μm distances, electron-transfer proteins orchestrate energy conversion, storage, and release both inside and outside the cell. Uncovering the detailed mechanisms of biological electron-transfer reactions, which are often coupled to bond-breaking and bond-making events, is essential to designing durable, artificial energy conversion systems that mimic the specificity and efficiency of their natural counterparts. Here, we use theoretical modeling of long-distance charge hopping (Chapter 3), synthetic donor-bridge-acceptor molecules (Chapters 4, 5, and 6), and de novo protein design (Chapters 5 and 6) to investigate general principles that govern light-driven and electrochemically driven electron-transfer reactions in biology. We show that fast, μm-distance charge hopping along bacterial nanowires requires closely packed charge carriers with low reorganization energies (Chapter 3); singlet excited-state electronic polarization of supermolecular electron donors can attenuate intersystem crossing yields to lower-energy, oppositely polarized, donor triplet states (Chapter 4); the effective static dielectric constant of a small (~100 residue) de novo designed 4-helical protein bundle can change upon phototriggering an electron transfer event in the protein interior, providing a means to slow the charge-recombination reaction (Chapter 5); and a tightly-packed de novo designed 4-helix protein bundle can drastically alter charge-transfer driving forces of photo-induced amino acid radical formation in the bundle interior, effectively turning off a light-driven oxidation reaction that occurs in organic solvent (Chapter 6). This work leverages unique insights gleaned from proteins designed from scratch that bind synthetic donor-bridge-acceptor molecules that can also be studied in organic solvents, opening new avenues of exploration into the factors critical for protein control of charge flow in biology.</p> / Dissertation

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Human acidic fibroblast growth factor (FGF-1) is a member of the £]-trefoil superfamily and exhibits a characteristic three-fold tertiary structure symmetry. However, evidence of this symmetry is not readily apparent at the level of the primary structure. This suggests that while selective pressures may exist to retain (or converge upon) a symmetric tertiary structure, other selective pressures have resulted in divergence of the primary structure during evolution. Using intra-chain and homologue sequence comparisons for 19 members of this family of proteins, we have designed mutants of FGF-1 that constrain a subset of core-packing residues to three-fold symmetry at the level of the primary structure. The consequences of these mutations upon structure, stability, folding and unfolding kinetics have been evaluated using a combination of x-ray crystallography, differential scanning calorimetry, isothermal equilibrium denaturation and stopped flow protein refolding/unfolding kinetics. An alternative core packing group has been introduced into FGF-1. The alternative core is very similar from the wild type (WT) core with regard to structure, stability, folding and unfolding kinetics. The remaining asymmetry within the protein core is related to asymmetry in the tertiary structure. The removal of tertiary structure asymmetry greatly increases protein stability and results in a conversion from three-state to a two-state folding pathway. The tertiary structure asymmetry is intimately linked to functional regions of the protein. Surprisingly, upon deletion of the functional insertions, the mutant protein is approximately 80 times more potent than the wild type form as determined by functional bioassays. The results show that the ƒÒ-trefoil superfold is compatible with a three-fold symmetric constraint upon the core region, as might be the case if the superfold arose as a result of gene duplication/fusion events. Furthermore, this new protein arrangement can form the basis of a structural "building block" that can greatly simplify the de novo design of ƒÒ-trefoil proteins by utilizing symmetric structural complementarity. This study implies that a symmetric architecture of the £]-trefoil fold is kinetically and thermodynamically ¡§fit¡¨. / Dissertation / PhD

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Molecular self-assembly provides a simple and efficient route of constructing well-defined nanostructures which may serve as extra cellular matrix (ECM) mimics. This work focuses on two specific octapeptides: FEFEFKFK and FEFKFEFK (F: phenylalanine, E: glutamic acid, K: lysine) with alternating charge distribution. The peptides were shown to self-assemble in solution and form β-sheet rich nanofibres which, above a critical gelation concentration (CGC), entangle to form self-supporting hydrogels. The fibre morphology of the hydrogels was analysed using TEM and Cryo-SEM illustrating the dense fibrillar network of nanometer size fibres. Oscillatory rheology results showed that the hydrogels possesses viscoelastic properties. By varying peptide concentration and type hydrogel stiffness, viscosity, water content, fibre density and other mechanical properties were tailored to control cell interactions and subsequent tissue growth. Bovine chondrocytes were used to assess the biocompatibility of these novel scaffolds over 21 days under 2D and 3D cell culture conditions, particularly looking into cell morphology, proliferation and matrix deposition. 2D culture resulted in cell viability and collagen type I deposition. In 3D culture, the mechanically stable gel was shown to support viability, retention of cell morphology and collagen type II deposition. Subsequently, the scaffold may serve as a template for cartilage repair. In addition, this research also focused on developing novel injectable scaffold design with in situ gelation properties to encapsulate chondrocytes for cell culture applications.

During the last decade, there is an increasing interest in applying deep learning in de novo drug design. In this thesis, a tool is developed to address the specific needs for generating small library for lead optimization. The optimization of small molecules is conducted given an input scaffold with defined attachment points. Various chemical fragments are proposed by the generative model and reinforcement learning is used to guide the generation to produce a library of molecules that satisfy user-defined properties. The generation is also constrained to follow user-defined reactions which makes synthesis controllable. Several experiments are executed to find the optimal hyperparameters, make comparison of different learning strategies, demonstrate the superiority of slicing molecules based on defined reactions compared to RECAP rules, showcase the model’s ability to follow different synthetic routes as well as its capability of decorating scaffolds with various attachment points. Results have shown that DAP learning strategy outperforms all other learning strategies. The use of reaction based slicing is superior than utilising RECAP rules slicing, it helps the model to learn the reaction filter faster. Also, the model was capable of satisfying different reaction filters and decorating scaffolds with various attachment points. In conclusion, the model is able to rapidly generate a molecular library which contains a large number of molecules sharing the same scaffold, with desirable properties and can be synthesised under specified reactions.

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No description available.

Peptide binders are short proteins that bind to larger proteins. Due to peptide binders having high specificity and being cheap to synthesize, they are a prime candidate for drug design. Creating new proteins in silico can be divided into three steps: protein backbone generation, sequence design, and computational filtering. With the release of AlphaFold2 (AF2), protein structure prediction is possible with atomic accuracy, even for peptide-protein complexes. Structure predictions enables some important computational filtering, which saves time and resources before doing experimental validation. After the release of AF2 together with the advancements in generative AI, new computational methods for the first two design steps have been developed. In this report, three different methods for backbone generation and sequence design were evaluated and compared: EvoBind, RFdiffusion, and ProteinMPNN. The latter two were developed solely for protein design. However, their broad application capabilities allowed for peptide development, which was implemented in this report. In total, 5500 peptides for 55 different protein targets were designed by each method, with the purpose to evaluate the performance and identify advantages of the methods. Combining the three methods in unexplored ways allowed for additional evaluation as well as gaining deeper understanding of how the methods worked. Whilst not being one-shot design approaches, all methods used in the report showed potential of being able to design de novo peptide binders with varying degree of in silico success. The methods’ peptide design success rate ranged from 16% to 2.6%. The direct evolution approach applied with EvoBind generated most peptide binder backbones with close binding to the specified interfaces. Using the message passing neural network (MPNN) in ProteinMPNN, the sequences designed were optimized for binding affinity and resulted in sequences that were easier for AF2 to predict. The methods allow for the potential development of peptide binder therapeutics to become more cost- and time efficient, on the basis that AF2’s predictions are aligned with the expressed peptides’ bindings and structures.

This PhD thesis is the result of the combination of experimental and computational techniques with the aim of understanding the mechanism of action of de novo cyclic decapeptides with high antimicrobial activity. By experimental techniques the influence of the replacement of the phenylalanine for tryptophan residue in their antimicrobial activity was tested and the stability in human serum was also analyzed, in order to evaluate their potential therapeutic application as antitumor agents. On the other hand, the interaction amongst the peptide BPC194 c(KKLKKFKKLQ), the best candidate from the whole library of cyclic peptides, and a model anionic membrane was simulated. The results showed a structure-function relationship derived from the stable conformation of the peptides involved in the membrane permeabilization. As a result, a rational design was performed being BPC490 the peptide with best antimicrobial activity compared with the best active peptide from the original library. / Aquesta tesi doctoral resulta de la combinació d’estudis mitjançant tècniques experimentals i computacionals amb l’objectiu d’entendre el mecanisme d’acció de "de novo" decapèptids cíclics amb elevada activitat antimicrobiana. Experimentalment, es va avaluar la influència de la substitució dels residus de fenilalanina per triptòfan en la seva activitat antimicrobiana i també la seva estabilitat sèrum humà, per tal de valorar la seva possible aplicació terapèutica envers el càncer. Per altra banda, es va simular la interacció del pèptid BPC194 c(KKLKKFKKLQ), millor candidat de la biblioteca de pèptids cíclics, amb models aniònics de bicapa lipídica. Els resultats van posar en manifest una relació estructura-funció derivada de la conformació estable dels pèptids que participen directament en la permeabilització de la membrana. Es va procedir doncs al disseny racional de nous pèptids cíclics sent el pèptid BPC490 el que va presentar una millor activitat bacteriana en comparació amb el pèptid més actiu de la llibreria original.