Pseudoalteromonas haloplanktis TAC125 as a cell factory for the production of recombinant proteins: strain improvement and novel engineering technologies

Pseudoalteromonas haloplanktis TAC125 (PhTAC125) represents a promising biological system for the recombinant production of high-quality proteins due to its profound differences in cellular physiochemical conditions in comparison to the commonly used mesophilic bacteria. The establishment of efficient constitutive and regulated gene expression systems, optimized culture media, mathematical metabolic models, and fermentative processes allowed the exploitation of this bacterium to produce complex eukaryotic proteins. In this scenario, this research project aimed to explore and extend the biotechnological capabilities of PhTAC125 as a cell factory. In the first part of my PhD project, I focused on the development of a mutant strain engineered to boost the performance of an IPTG-inducible expression system. The obtained strain, named KrPl lacY+, proved to be able to produce the E. coli lactose transporter and a truncated Lon protease devoid of its catalytic domain. The improvement in recombinant production derived from KrPl lacY+ was also demonstrated at low temperatures and encouraged further optimization toward cheaper and sustainable industrial processes. As described in the second chapter of this thesis, KrPl lacY+ was exploited for the recombinant production of the human partially IDP kinase CDKL5, unsuccessfully produced in other prokaryotic systems. Different strategies were applied to overcome the bottlenecks affecting the overall production yield, taking into account the translational efficiency, the optimization of the coding sequence and fusion partners, and the increase of expression plasmid copy number. The establishment of such an improved platform allowed the achievement of high production yields of CDKL5 in a full-length and active form, enabling its application for functional, structural as well as therapeutic studies. Finally, to further strengthen the exploitation of PhTAC125, an asRNA-mediated regulatory system was developed. The results described in the last chapter of the present study demonstrated the feasibility of conditional gene silencing in PhTAC125, opening new perspectives for manipulating marine psychrophilic bacteria in basic and applicative studies

Smart energy grids and renewable multi-generation systems

The current carbon-based energy system is undergoing a deep transformation, mostly aimed at reducing the energy-related emissions of carbon dioxide and other air pollutions. The evolutionary trend of such transition is toward smart energy networks, combining several energy technologies for energy production, storage and utilization, and including a plurality of energy producers, users and prosumers (i.e., producers and users at the same time). The main goal of this work is to explore a series of possible solutions addressing the development of sustainable smart energy networks, analyzing pros and cons of different layouts and technologies from energy, environmental and economic viewpoints, and providing criteria and guidelines for designers, stakeholder and policy makers. Note that the researches described within this thesis are based on researches published on peer-reviewed journals, which was coauthored by the author of this thesis. The studies are based on the use of a dynamic simulation approach. Dynamic simulations can mimic the real performance and behavior of the systems under evaluation, providing crucial information about such systems; this way, it is possible to evaluate their economic profitability and their capacity to reduce fossil energy consumptions and CO2 emissions, with respect to conventional systems. TRNSYS suite is adopted for carrying out such analyses and simulations. TRNSYS is a well-known and reliable tool, widely adopted in academic and commercial applications. Note that TRNSYS environment comes with a large library of components experimentally validated. Moreover, TRNSYS allows the user to adopt in-house and user-developed models. This tool exhibits high accuracy and reliability for the calculation of the dynamic performance of several solar systems. In addition, this software proved high accuracy and risibility in simulating building energy performance. Private mobility is the first sector analyzed in the thesis; in fact, it is currently recognized as one of the most important source of energy consumptions and related CO2 emissions. Different solutions to couple electric vehicles and renewable energy technologies were proposed and analyzed, highlighting that layouts including electric vehicles, residential buildings and renewable energies can be profitably included into smart energy networks. The second chapter is devoted to polygeneration systems. Such systems manage several energy sources, vectors and final users and energy vectors, and are therefore especially attractive for the development of smart energy networks. Polygeneration systems fed by renewables can produce several energy vectors with a very limited consumption of fossil energy. In particular, geothermal and solar energy were considered in the case studies developed and analyzed; such energy source, in fact, are largely available in Campania (South of Italy), where most of the systems evaluated are located. The production of freshwater through reverse osmosis driven by photovoltaic panels was also considered, aiming to match most needs of a given residential district. In addition, reverse osmosis can exploit the excess of photovoltaic power production, avoiding the problems related with the unbalancing of the local electric grid. Moreover, the coupling of photovoltaic energy and reverse osmosis is useful for reducing the dependence on water supply shipped by the mainland, in many isolated islands of the Mediterranean Sea. In Chapter 3, several layouts involving different energy networks were developed and analyzed, also coupling the energy demands of a residential district and that related to private mobility. A further smart energy network able to simultaneously provide thermal energy, electricity and drinkable water was assessed. Finally, the case of a micro energy network was also considered, referred to a hospital facility. Hospitals are highly energy intensive buildings and represent an important candidate for inclusion within a smart micro energy network: in fact, they are usually located nearby residential areas, and their energy facilities can be easily connected or expanded to the energy network serving such residential area. In this framework, several micro energy networks based on cogeneration internal combustion engine were analyzed. A hybrid layout based on photovoltaic and cogeneration was analyzed, too

Bayesian statistical approach for protein residue-residue contact prediction

Despite continuous efforts in automating experimental structure determination and systematic target selection in structural genomics projects, the gap between the number of known amino acid sequences and solved 3D structures for proteins is constantly widening. While DNA sequencing technologies are advancing at an extraordinary pace, thereby constantly increasing throughput while at the same time reducing costs, protein structure determination is still labour intensive, time-consuming and expensive. This trend illustrates the essential importance of complementary computational approaches in order to bridge the so-called sequence-structure gap. About half of the protein families lack structural annotation and therefore are not amenable to techniques that infer protein structure from homologs. These protein families can be addressed by de novo structure prediction approaches that in practice are often limited by the immense computational costs required to search the conformational space for the lowest-energy conformation. Improved predictions of contacts between amino acid residues have been demonstrated to sufficiently constrain the overall protein fold and thereby extend the applicability of de novo methods to larger proteins. Residue-residue contact prediction is based on the idea that selection pressure on protein structure and function can lead to compensatory mutations between spatially close residues. This leaves an echo of correlation signatures that can be traced down from the evolutionary record. Despite the success of contact prediction methods, there are several challenges. The most evident limitation lies in the requirement of deep alignments, which excludes the majority of protein families without associated structural information that are the focus for contact guided de novo structure prediction. The heuristics applied by current contact prediction methods pose another challenge, since they omit available coevolutionary information. This work presents two different approaches for addressing the limitations of contact prediction methods. Instead of inferring evolutionary couplings by maximizing the pseudo-likelihood, I maximize the full likelihood of the statistical model for protein sequence families. This approach performed with comparable precision up to minor improvements over the pseudo-likelihood methods for protein families with few homologous sequences. A Bayesian statistical approach has been developed that provides posterior probability estimates for residue-residue contacts and eradicates the use of heuristics. The full information of coevolutionary signatures is exploited by explicitly modelling the distribution of statistical couplings that reflects the nature of residue-residue interactions. Surprisingly, the posterior probabilities do not directly translate into more precise predictions than obtained by pseudo-likelihood methods combined with prior knowledge. However, the Bayesian framework offers a statistically clean and theoretically solid treatment for the contact prediction problem. This flexible and transparent framework provides a convenient starting point for further developments, such as integrating more complex prior knowledge. The model can also easily be extended towards the Derivation of probability estimates for residue-residue distances to enhance the precision of predicted structures

Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language Model

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% non-redundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold, and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials

A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design

Therapeutic antibodies are an essential and rapidly expanding drug modality. The binding specificity between antibodies and antigens is decided by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a hierarchical training paradigm (HTP) for the antibody sequence-structure co-design. HTP consists of four levels of training stages, each corresponding to a specific protein modality within a particular protein domain. Through carefully crafted tasks in different stages, HTP seamlessly and effectively integrates geometric graph neural networks (GNNs) with large-scale protein language models to excavate evolutionary information from not only geometric structures but also vast antibody and non-antibody sequence databases, which determines ligand binding pose and strength. Empirical experiments show that HTP sets the new state-of-the-art performance in the co-design problem as well as the fix-backbone design. Our research offers a hopeful path to unleash the potential of deep generative architectures and seeks to illuminate the way forward for the antibody sequence and structure co-design challenge

Computational Analysis of Microbial Sequence Data Using Statistics and Machine Learning

Since the discovery of the double helix of DNA in 1953, modern molecular biology has opened the door to a better understanding of how genes control chemical processes within cells, including protein synthesis. Although we are still far from claiming a complete understanding, recent advances in sequencing technologies, increased computational capacity, and more sophisticated computational methods have allowed the development of various new applications that provide further insight into DNA sequence data and how the information they encode impacts living organisms and their environment. Sequencing data can now be used to start identifying the relationships between microorganisms, where they live, and in some cases how they affect their host organisms. We introduce and compare methods used for this bioinformatics application, and develop a machine learning model that can be used to effectively predict environmental factors associated with these microorganisms. Codon Usage Bias (CUB), which refers to the highly non-uniform usage of codons that code for the same amino acid has been known to reflect the expression level of a protein-coding gene under the evolutionary theory that selection favors certain synonymous codons. Traditional methods used to estimate CUB and its relation with protein translation have been proven effective on single-celled organisms such as yeast and E. coli, but their applications are limited when it comes to more complex multi-cellular organisms such as plants and animals. To extend our abilities to further understand the relations between codon usage patterns and the protein translation processes in these organisms, we develop a novel deep learning model that can discover patterns in codon usage bias between different species using only their DNA sequences

Métodos computacionais para a caracterização de genes e extração de conhecimento genómico

Doutoramento conjunto MAPi em Ciências da ComputaçãoMotivation: Medicine and health sciences are changing from the classical symptom-based to a more personalized and genetics-based paradigm, with an invaluable impact in health-care. While advancements in genetics were already contributing significantly to the knowledge of the human organism, the breakthrough achieved by several recent initiatives provided a comprehensive characterization of the human genetic differences, paving the way for a new era of medical diagnosis and personalized medicine. Data generated from these and posterior experiments are now becoming available, but its volume is now well over the humanly feasible to explore. It is then the responsibility of computer scientists to create the means for extracting the information and knowledge contained in that data. Within the available data, genetic structures contain significant amounts of encoded information that has been uncovered in the past decades. Finding, reading and interpreting that information are necessary steps for building computational models of genetic entities, organisms and diseases; a goal that in due course leads to human benefits. Aims: Numerous patterns can be found within the human variome and exome. Exploring these patterns enables the computational analysis and manipulation of digital genomic data, but requires specialized algorithmic approaches. In this work we sought to create and explore efficient methodologies to computationally calculate and combine known biological patterns for various purposes, such as the in silico optimization of genetic structures, analysis of human genes, and prediction of pathogenicity from human genetic variants. Results: We devised several computational strategies to evaluate genes, explore genomes, manipulate sequences, and analyze patients’ variomes. By resorting to combinatorial and optimization techniques we were able to create and combine sequence redesign algorithms to control genetic structures; by combining the access to several web-services and external resources we created tools to explore and analyze available genetic data and patient data; and by using machine learning we developed a workflow for analyzing human mutations and predicting their pathogenicity.Motivação: A medicina e as ciências da saúde estão atualmente num processo de alteração que muda o paradigma clássico baseado em sintomas para um personalizado e baseado na genética. O valor do impacto desta mudança nos cuidados da saúde é inestimável. Não obstante as contribuições dos avanços na genética para o conhecimento do organismo humano até agora, as descobertas realizadas recentemente por algumas iniciativas forneceram uma caracterização detalhada das diferenças genéticas humanas, abrindo o caminho a uma nova era de diagnóstico médico e medicina personalizada. Os dados gerados por estas e outras iniciativas estão disponíveis mas o seu volume está muito para lá do humanamente explorável, e é portanto da responsabilidade dos cientistas informáticos criar os meios para extrair a informação e conhecimento contidos nesses dados. Dentro dos dados disponíveis estão estruturas genéticas que contêm uma quantidade significativa de informação codificada que tem vindo a ser descoberta nas últimas décadas. Encontrar, ler e interpretar essa informação são passos necessários para construir modelos computacionais de entidades genéticas, organismos e doenças; uma meta que, em devido tempo, leva a benefícios humanos. Objetivos: É possível encontrar vários padrões no varioma e exoma humano. Explorar estes padrões permite a análise e manipulação computacional de dados genéticos digitais, mas requer algoritmos especializados. Neste trabalho procurámos criar e explorar metodologias eficientes para o cálculo e combinação de padrões biológicos conhecidos, com a intenção de realizar otimizações in silico de estruturas genéticas, análises de genes humanos, e previsão da patogenicidade a partir de diferenças genéticas humanas. Resultados: Concebemos várias estratégias computacionais para avaliar genes, explorar genomas, manipular sequências, e analisar o varioma de pacientes. Recorrendo a técnicas combinatórias e de otimização criámos e conjugámos algoritmos de redesenho de sequências para controlar estruturas genéticas; através da combinação do acesso a vários web-services e recursos externos criámos ferramentas para explorar e analisar dados genéticos, incluindo dados de pacientes; e através da aprendizagem automática desenvolvemos um procedimento para analisar mutações humanas e prever a sua patogenicidade

Application of coevolution-based methods and deep learning for structure prediction of protein complexes

The three-dimensional structures of proteins play a critical role in determining their biological functions and interactions. Experimental determination of protein and protein complex structures can be expensive and difficult. Computational prediction of protein and protein complex structures has therefore been an open challenge for decades. Recent advances in computational structure prediction techniques have resulted in increasingly accurate protein structure predictions. These techniques include methods that leverage information about coevolving residues to predict residue interactions and that apply deep learning techniques to enable better prediction of residue contacts and protein structures. Prior to the work outlined in this thesis, coevolution-based methods and deep learning had been shown to improve the prediction of single protein domains or single protein chains. Most proteins in living organisms do not function on their own but interact with other proteins either through transient interactions or by forming stable protein complexes. Knowledge of protein complex structures can be useful for biological and disease research, drug discovery and protein engineering. Unfortunately, a large number of protein complexes do not have experimental structures or close homolog structures that can be used as templates. In this thesis, methods previously developed and applied to the de novo prediction of single protein domains or protein monomer chains were modified and leveraged for the prediction of protein heterodimer and homodimer complexes. A number of coevolution-based tools and deep learning methods are explored for the purpose of predicting inter-chain and intra-chain residue contacts in protein dimers. These contacts are combined with existing protein docking methods to explore the prediction of homodimers and heterodimers. Overall, the work in this thesis demonstrates the promise of leveraging coevolution and deep-learning for the prediction of protein complexes, shows improvements in protein complex prediction tasks achieved using coevolution based methods and deep learning methods, and demonstrates remaining challenges in protein complex prediction

Development of a novel platform for high-throughput gene design and artificial gene synthesis to produce large libraries of recombinant venom peptides for drug discovery

Tese de Doutoramento em Ciências Veterinárias na Especialidade de Ciências Biológicas e BiomédicasAnimal venoms are complex mixtures of biologically active molecules that, while presenting low immunogenicity, target with high selectivity and efficacy a variety of membrane receptors. It is believed that animal venoms comprise a natural library of more than 40 million different natural compounds that have been continuously fine-tuned during the evolutionary process to disturb cellular function. Within animal venoms, reticulated peptides are the most attractive class of molecules for drug discovery. However, the use of animal venoms to develop novel pharmacological compounds is still hampered by difficulties in obtaining these low molecular mass cysteine-rich polypeptides in sufficient amounts. Here, a high-throughput gene synthesis platform was developed to produce synthetic genes encoding venom peptides. The final goal of this project is the production of large libraries of recombinant venom peptides that can be screened for drug discovery. A robust and efficient Polymerase Chain Reaction (PCR) methodology was refined to assemble overlapping oligonucleotides into small artificial genes (< 500 bp) with high-fidelity. In addition, two bioinformatics tools were constructed to design multiple optimized genes (ATGenium) and overlapping oligonucleotides (NZYOligo designer), in order to allow automation of the high-throughput gene synthesis platform. The platform can assemble 96 synthetic genes encoding venom peptides simultaneously, with an error rate of 1.1 mutations per kb. To decrease the error rate associated with artificial gene synthesis, an error removal step using phage T7 endonuclease I was designed and integrated into the gene synthesis methodology. T7 endonuclease I was shown to be highly effective to specifically recognize and cleave DNA mismatches allowing a dramatically reduction of error frequency in large synthetic genes, from 3.45 to 0.43 errors per kb. Combining the knowledge acquired in the initial stages of the work, a comprehensive study was performed to investigate the influence of gene design, presence of fusion tags, cellular localization of expression, and usage of Tobacco Etch Virus (TEV) protease for tag removal, on the recombinant expression of disulfide-rich venom peptides in Escherichia coli. Codon usage dramatically affected the levels of recombinant expression in E. coli. In addition, a significant pressure in the usage of the two cysteine codons suggests that both need to be present at equivalent levels in genes designed de novo to ensure high levels of expression. This study also revealed that DsbC was the best fusion tag for recombinant expression of disulfide-rich peptides, in particular when expression of the fusion peptide was directed to the bacterial periplasm. TEV protease was highly effective for efficient tag removal and its recognition sites can tolerate all residues at its C-terminal, with exception of proline, confirming that no extra residues need to be incorporated at the N-terminus of recombinant venom peptides. This study revealed that E. coli is a convenient heterologous host for the expression of soluble and potentially functional venom peptides. Thus, this novel high-throughput gene synthesis platform was used to produce ~5,000 synthetic genes with a low error rate. This genetic library supported the production of the largest library of recombinant venom peptides constructed until now. The library contains 2736 animal venom peptides and it is presently being screened for the discovery of novel drug leads related to different diseases.RESUMO - Desenvolvimento de uma nova plataforma de alta capacidade para desenhar e sintetizar genes artificiais, para a produção de péptidos venómicos recombinantes - Os venenos animais são misturas complexas de moléculas biologicamente activas que se ligam com elevada selectividade e eficácia a uma grande variedade de receptores de membrana. Embora apresentem baixa imunogenicidade, os venenos podem afectar a função celular actuando ao nível dos seus receptores. Actualmente, pensa-se que os venenos de animais constituam uma biblioteca natural de mais de 40 milhões de moléculas diferentes que têm sido continuamente aperfeiçoadas ao longo do processo evolutivo. Tendo em conta a composição dos venenos, os péptidos reticulados são a classe mais atractiva de moléculas com interesse farmacológico. No entanto, a utilização de venenos para o desenvolvimento de novos fármacos está limitada por dificuldades em obter estas moléculas em quantidades adequadas ao seu estudo. Neste trabalho desenvolveu-se uma plataforma de alta capacidade para a síntese de genes sintéticos codificadores de péptidos venómicos, com o objectivo de produzir bibliotecas de péptidos venómicos recombinantes que possam ser rastreadas para a descoberta de novos medicamentos. Com o objectivo de sintetizar genes pequenos (< 500 pares de bases) com elevada fidelidade e em simultâneo, desenvolveu-se uma metodologia de PCR (polymerase chain reaction) robusta e eficiente, que se baseia na extensão de oligonucleótidos sobrepostos. Para possibilitar a automatização da plataforma de síntese de genes, foram construídas duas ferramentas bioinformáticas para desenhar simultaneamente dezenas a milhares de genes optimizados para a expressão em Escherichia coli (ATGenium) e os respectivos oligonucleótios sobrepostos (NZYOligo designer). Esta plataforma foi optimizada para sintetizar em simultâneo 96 genes sintéticos, tendo-se obtido uma taxa de erro de 1.1 mutações por kb de DNA sintetizado. A fim de diminuir a taxa de erro associada à produção de genes sintéticos, desenvolveu-se um método para remoção de erros utilizando a enzima T7 endonuclease I. A enzima T7 endonuclease I mostrou-se muito eficaz no reconhecimento e clivagem de moléculas DNA que apresentam emparelhamentos incorrectos, reduzindo drasticamente a frequência de erros identificados em genes grandes, de 3.45 para 0.43 erros por kb de DNA sintetizado. Investigou-se também a influência do desenho dos genes, da presença de tags de fusão, da localização celular da expressão e da actividade da protease Tobacco Etch Virus (TEV) para a remoção eficiente de tags, na expressão de péptidos venómicos ricos em cisteínas em E. coli. A utilização de codões meticulosamente escolhidos afectou drasticamente os níveis de expressão em E. coli. Para além disso, os resultados mostram que existe uma pressão significativa no uso dos dois codões que codificam para o resíduo cisteína, o que sugere que ambos os codões têm de estar presentes, em níveis equivalentes, nos genes que foram desenhados e optimizados para garantir elevados níveis de expressão. Este trabalho indicou também que o tag de fusão DsbC foi o mais apropriado para a expressão eficiente de péptidos venómicos ricos em cisteínas, particularmente quando os péptidos recombinantes foram expressos no periplasma bacteriano. Confirmou-se que a protease TEV é eficaz na remoção de tags de fusão, podendo o seu local de reconhecimento conter quaisquer aminoácidos na extremidade C-terminal, com excepção da prolina. Desta forma, verificou-se não ser necessário incorporar qualquer aminoácido extra na extremidade N-terminal dos péptidos venómicos recombinantes. Reunindo todos os resultados, verificou-se que a E. coli é um hospedeiro adequado para a expressão, na forma solúvel, de péptidos venómicos potencialmente funcionais. Por último, foram produzidos, com uma taxa de erro reduzida, ~5000 genes sintéticos codificadores de péptidos venómicos utilizando a nova plataforma de elevada capacidade para a síntese de genes aqui desenvolvida. A nova biblioteca de genes sintéticos foi usada para produzir a maior biblioteca de péptidos venómicos recombinantes construída até agora, que inclui 2736 péptidos venómicos. Esta biblioteca recombinante está presentemente a ser rastreada com o objectivo de descobrir novas drogas com interesse para a saúde humana