Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data

Pyrimethamine Based Anti-protozoan Agents from Isostere and Heuristic Structure-similarity Search

Aims: To generate new medicaments for control and treatment of the parasitic protozoan Toxoplasma gondii. Study Design: Structure similarity search and isostere search was conducted over a broad range of structure categories. Correlation and highest similarity scores were implemented to select the best drug candidates. Place and Duration of Study: University of Nebraska, Department of Chemistry, Durham Science Center, 6001 Dodge Street, Omaha Nebraska 68182, from June 2016 to February 2017. Methodology: Utilizing pyrimethamine as the parent compound, a broad range of similar structures and isosteres were found by applying search methods. The compounds having the highest correlation and similarity scores were selected for the study of molecular properties. The molecular properties were determined and examined for underlying relationships by pattern recognition hierarchical cluster analysis and K-means cluster analysis. Results: Thirty compounds were identified to have a very high level of structure similarity or isosteric relationship to pyrimethamine. The molecular structures and molecular properties are presented for all compounds, inclusive of pyrimethamine. Hierarchical cluster analysis and K-means cluster analysis indicated compounds with highest underlying similarity to pyrimethamine. Box plots showed the over-all distribution of important pharmaceutical properties, such as molecular weight, Log P, polar surface area, number of rotatable bonds, molecular volume, and number of hydrogen bond donors. Structure components are compared to elucidate potential clinical activity. Multiple regression is applied on all compounds to generate a numerical relationship for prediction of similar compounds. Save for only one isostere, all compounds showed zero violations of the Rule of 5, indicating favorable drug-likeness and bioavailability. Conclusion: Thirty compounds highly analogous to pyrimethamine were identified following heuristic search course. The molecular properties were determined for all compounds and indicated genuine potential for treatment of toxoplasmosis. Correlation of structure and pattern recognition methods indicated 30 compounds of clinical potential and property analogy to pyrimethamine

Deep learning for in vitro prediction of pharmaceutical formulations

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error approach by individual experiences of pharmaceutical scientists, which is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of this research is to use deep learning to predict pharmaceutical formulations. In this paper, two different types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assessing the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. The result shows the accuracies of both two deep neural networks were above 80% and higher than other machine learning models, which showed good prediction in pharmaceutical formulations. In summary, deep learning with the automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was firstly developed for the prediction of pharmaceutical formulations. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical researches from experience-dependent studies to data-driven methodologies

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification

STRUCTURAL BIOMEDICINE: CHARACTERIZATION OF THE STRUCTURAL BASIS IN PROTEIN-DRUG RECOGNITION IN DIFFERENT HUMAN DISEASES

[ES] La cristalografía de rayos X es una potente técnica para la resolución de la estructura atómica de macromoléculas. La información generada, tiene gran impacto sobre diferentes campos relacionados con la investigación básica y aplicada, como son la biomedicina y diseño de fármacos, al igual que en el desarrollo de aplicaciones nanotecnológicas y biotecnológicas. Esta Tesis se centra en determinadas problemáticas actuales y en las proteínas involucradas en las mismas (TryR, eEF1A2 y CBDP35), siendo éstas sujeto de desarrollo biotecnológico en los campos de la biomedicina, farmacia y de la industria alimentaria, en el que la cristalografía de rayos X juega un papel crucial para dilucidar sus estructuras atómicas y funciones. En consideración a la biomedicina y diseño de fármacos, hemos resuelto la estructura de la Tripanotión reductasa (TryR) de Leishmania infantum en complejo con potentes inhibidores de su actividad oxidorreductasa, con potencial de desarrollo como fármacos. Así, se ha caracterizado la unión y mecanismo de acción de éstos inhibidores. TryR es una reconocida diana farmacológica para el tratamiento de la enfermedad de Chagas, la Tripanosomiasis Humana Africana y la leishmaniosis, ya que desempeña un papel crucial y esencial en el metabolismo redox de los parásitos de la familia Trypanosomatidae. Además, se han analizado los parámetros de cristalización y difracción de novedosos inhibidores de la dimerización de TryR, cuyo diseño racional se basa en la unión a la interfaz de dimerización de la misma. La oncoproteína eEF1A2, involucrada en múltiples funciones celulares y sujeto de numerosas modificaciones post-traduccionales, se une al fármaco anticancerígeno plitidepsina. La cristalografía de rayos X, combinada con experimentos de espectrometría de masas, se han utilizado como herramientas para identificar nuevas modificaciones post-traduccionales y características estructurales en eEF1A2:GDP. Una modificación única, la adición de etanolamina fosfoglicerol (EPG) a aminoácidos conservados (Glu301 y Glu374 en mamíferos), se ha observado aquí por primera vez. El análisis estructural de estos hallazgos facilita la comprensión de las múltiples funciones y regulaciones de eEF1A2. La adquisición de una muestra conformacionalmente homogénea de eEF1A2:GTP, necesaria para la unión a la plitidepsina, ha sido evaluada en ensayos de cristalización del complejo terciario de eEF1A2: GTP: plitidepsina. Con respecto al dominio de unión a la pared celular de la endolisina PlyP35 codificada por el fago P35 de Listeria monocytogenes (CBDP35), hemos resuelto la estructura cristalina de CBDP35 en un complejo con ácido teicoico natural de L. monocytogenes serovar 1/2a. Esta estructura es el primer módulo de unión a la pared celular en complejo con ácidos teicoicos jamás dilucidado. El análisis estructural reveló los principales determinantes para la unión de la pared celular bacteriana, en particular, el mecanismo molecular del reconocimiento de N-acetil-d-glucosamina, una decoración de carácter glicosídico en ácidos teicoicos de serovares patógenos de L. monocytogenes. Estos hallazgos arrojan luz sobre el desarrollo biotecnológico de nuevas herramientas en la industria alimentaria y las terapias derivadas de fagos para detectar y tratar infecciones bacterianas.[CA] La cristal·lografia de raig X és una potent tècnica per a la resolució de l'estructura atòmica de macromolècules. La informació generada té gran impacte sobre diferents camps relacionats amb la investigació bàsica i aplicada, com són la biomedicina i disseny de fàrmacs, igual que en el desenvolupament d'aplicacions nanotecnológiques i biotecnològiques. Aquesta Tesi es centra en determinades problemàtiques actuals i en les proteïnes involucrades en les mateixes (TryR, eEF1A2 i CBDP35), sent estes subjecte de desenvolupament biotecnològic en els camps de la biomedicina, farmàcia i de la indústria alimentària, en el que la cristal·lografia de raig X juga un paper crucial per a dilucidar les seues estructures atòmiques i funcions. En consideració a la biomedicina i disseny de fàrmacs, hem resolt l'estructura de la Tripanotión reductasa (TryR) de Leishmania infantum en complex amb potents inhibidors de la seua activitat oxidorreductasa, amb potencial de desenrotllament com a fàrmacs. Així, s'ha caracteritzat la unió i mecanisme d'acció d'estos inhibidors. TryR és una reconeguda diana farmacològica per al tractament de la malaltia de Chagas, la Tripanosomiasi Humana Africana i la leishmaniosi, ja que exerceix un paper crucial i essencial en el metabolisme redox dels paràsits de la família Trypanosomatidae. A més, s'han analitzat els paràmetres de cristal·lització i difracció de nous inhibidors de la dimerizació de TryR, el disseny racional dels quals es basa en la unió a la interfície de dimerización de la mateixa. L'oncoproteína eEF1A2, involucrada en múltiples funcions cel·lulars i subjecte de nombroses modificacions posttraduccionals, s'unieix al fàrmac anticancerigen plitidepsina. La cristal·lografia de raig X, combinada amb experiments d'espectrometria de masses, s'han utilitzat com a ferramentes per a identificar noves modificacions posttraduccionals i característiques estructurals en eEF1A2:GDP. Una modificació única, l'addició d'etanolamina fosfoglicerol (EPG) a aminoàcids conservats (Glu301 i Glu374 en mamífers), s'ha observat ací per primera vegada. L'anàlisi estructural d'estes troballes facilita la comprensió de les múltiples funcions i regulacions d'eEF1A2. L'adquisició d'una mostra conformacionalmente homogènia d'eEF1A2:GTP, necessària per a la unió a la plitidepsina, ha sigut avaluada en assajos de cristal·lització del complex terciari d'eEF1A2: GTP: plitidepsina. Respecte al domini d'unió a la paret cel·lular de l'endolisina PlyP35 codificada pel fago P35 de Listeria monocytogenes (CBDP35), hem resolt l'estructura cristal·lina de CBDP35 en un complex amb àcid teicoico natural de L. monocytogenes serovar 1/2a. Esta estructura és el primer mòdul d'unió a la paret cel·lular en complex amb àcids teicoicos mai dilucidat. L'anàlisi estructural va revelar els principals determinants per a la unió de la paret cel·lular bacteriana, en particular, el mecanisme molecular del reconeixement de N-acetil-d-glucosamina, una decoració de caràcter glicosídico en àcids teicoicos de serovares patògens de L. monocytogenes. Estes troballes fan llum sobre el desenrotllament biotecnològic de noves ferramentes en la indústria alimentària i les teràpies derivades de fagos per a detectar i tractar infeccions bacterianes.[EN] X-ray crystallography is a powerful technique for atomic structure resolution of macromolecules. The information generated impacts different fields involving basic and applied research on biomedicine and drug design and the development of nanotechnology and biotechnological applications. This dissertation focuses on current problematics and the target proteins involved (TryR, eEF1A2 and CBDP35) that are in sight for biotechnological development in the biomedical, pharmaceutical and food industry fields, in which X-ray crystallography plays a crucial role in the elucidation of their atomic structures and functions. Attaining to biomedical and drug design problematics, we have solved the structure of Leishmania infantum TryR in complex with potent oxidoreductase inhibitors prone to further development as anti-trypanosomal drugs, thereby characterizing their binding and mechanism of action. This protein is a long recognized drug target for the treatment of Chagas disease, Human African Trypanosomiasis and leishmaniasis, as it plays a crucial and essential role in the redox-metabolism of the Trypanosomatidae parasites. Moreover, the crystallization and diffraction parameters of novel TryR dimerization disruptors have been assayed for inhibitors which have been rationally designed to bind the dimerization interface of TryR. The "moonlighting" oncoprotein eEF1A2 is known to be highly post-translationally modified and to bind the anticancer drug plitidepsin. X-ray crystallography, combined with mass-spectrometry experiments, have been used as tools to identify novel post-translational modifications and structural features in eEF1A2:GDP. A unique modification, namely the addition of ethanolamine phosphoglycerol (EPG) to conserved glutamic residues (Glu301 and Glu374 in mammals), has been here observed for the first time. Structural analysis of these findings facilitate the understanding of eEF1A2's multiple functions and regulations. The acquirement of a conformationally homogenous eEF1A2:GTP sample, necessary for plitidepsin binding, has been has been assayed for eEF1A2:GTP:plitidepsin complex crystallization. Regarding the cell wall binding domain of Listeria monocytogenes phage-encoded endolysin PlyP35 (CBDP35), we have solved the crystal structure of CBDP35 in complex with natural Listeria serovar 1/2a teichoic acid. This structure is the first cell wall binding module in complex with teichoic acids ever elucidated. Structural analysis revealed the main determinants for bacterial cell-wall binding, in particular, the molecular mechanism of N-acetyl-d-glucosamine recognition, a glycosidic moiety in teichoic acids of pathogenic serovars of L. monocytogenes. These findings shed light upon the biotechnological development of new tools in the food industry and phage-derived therapies to detect and treat bacterial infections.Agradecer al Ministerio de Educación, Cultura y Deporte por haberme proporcionado el contrato FPU (FPU14/03190) que me ha permitido desarrollar esta Tesis Doctoral en el Instituto de Química-Física “Rocasolano” del Consejo Superior de Investigaciones Científicas (IQFR-CSIC), así como la financiación otorgada para poder realizar mi estancia predoctoral en el laboratorio del Prof. Hammershmidt, en Greifswald, Alemania (EST17/00751).Carriles Linares, AÁ. (2019). STRUCTURAL BIOMEDICINE: CHARACTERIZATION OF THE STRUCTURAL BASIS IN PROTEIN-DRUG RECOGNITION IN DIFFERENT HUMAN DISEASES [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/130844TESI

Resistance to microtubule-stabilising agents following point mutation of human βI-tubulin

Marine environments represent a rich source of bioactive secondary metabolites that may be harnessed for use in a therapeutic context. Two novel compounds, peloruside A and laulimalide, isolated from the marine sponges Mycale hentsheli and Cacospongia mycofijiensis, respectively, both demonstrate useful pharmacological properties in mammalian cells. These compounds share major similarities with microtubule-stabilising agents. Like other agents in this class, peloruside A and laulimalide bind to the β-tubulin subunit of microtubules, the primary cytoskeletal element of eukaryotic cells. These compounds enhance polymerisation dynamics between ternary microtubule structures and severely hinder necessary cytoskeletal rearrangements within the cell. Over the course of a patient’s treatment, cancerous cells may develop multi-drug resistance phenotypes. P-glycoprotein drug efflux pumps play a major role in the development of therapy resistance in many cancers, as the current generation microtubule-stabilising agents are easily removed from diseased cells by upregulated efflux mechanisms. Unlike agents already in clinical application, both peloruside A and laulimalide are poor substrates for removal by these mechanisms, making them and their synthetic derivatives interesting as potential treatments for drug-resistant tumours. Peloruside A and laulimalide exhibit potent nanomolar anti-mitotic activities in vitro and arrest cell cycle progression in G₂/M phase, leading to cell death – a characteristic mode of action among microtubule-stabilising agents. Unlike all known agents in this class, peloruside A and laulimalide share a secondary, unique binding region in β-tubulin. In the past decade our understanding of this region has developed, revealing a second, unique mechanism for stabilisation of microtubules. Using mammalian cells to model physiological tubulin, the present study investigates the predicted role of aspartic acid 297 of human βI-tubulin in the binding association of both peloruside A and laulimalide. This particular amino acid is predicted to hydrogen bond with both compounds, contributing to their activity as stabilisers. It was revealed that the introduction of a point mutation in D297 resulted in a small but highly consistent resistance phenotype to both compounds, but not to microtubule-stabilising agents that bind to the traditional, taxoid site on β-tubulin. It was concluded that aspartic acid 297 is likely to be one of the amino acids directly involved in the binding association of peloruside A and laulimalide to β-tubulin, contributing partial compound stabilisation. The rational synthesis of future analogues may benefit from these findings in the design of molecules with enhanced interactions at this particular amino acid residue

Massively-Parallel Computational Identification of Novel Broad Spectrum Antivirals to Combat Coronavirus Infection

Philosophiae Doctor - PhDGiven the significant disease burden caused by human coronaviruses, the discovery of an effective antiviral strategy is paramount, however there is still no effective therapy to combat infection. This thesis details the in silica exploration of ligand libraries to identify candidate lead compounds that, based on multiple criteria, have a high probability of inhibiting the 3 chymotrypsin-like protease (3CUro) of human coronaviruses. Atomistic models of the 3CUro were obtained from the Protein Data Bank or theoretical models were successfully generated by homology modelling. These structures served the basis of both structure- and ligand-based drug design studies. Consensus molecular docking and pharmacophore modelling protocols were adapted to explore the ZINC Drugs-Now dataset in a high throughput virtual screening strategy to identify ligands which computationally bound to the active site of the 3CUro . Molecular dynamics was further utilized to confirm the binding mode and interactions observed in the static structure- and ligand-based techniques were correct via analysis of various parameters in a IOns simulation. Molecular docking and pharmacophore models identified a total of 19 ligands which displayed the potential to computationally bind to all 3CUro included in the study. Strategies employed to identify these lead compounds also indicated that a known inhibitor of the SARS-Co V 3CUro also has potential as a broad spectrum lead compound. Further analysis by molecular dynamic simulations largely confirmed the binding mode and ligand orientations identified by the former techniques. The comprehensive approach used in this study improves the probability of identifying experimental actives and represents a cost effective pipeline for the often expensive and time consuming process of lead discovery. These identified lead compounds represent an ideal starting point for assays to confirm in vitro activity, where experimentally confirmed actives will be proceeded to subsequent studies on lead optimization

From existing data to novel hypotheses : design and application of structure-based Molecular Class Specific Information Systems

As the active component of many biological systems, proteins are of great interest to life scientists. Proteins are used in a large number of different applications such as the production of precursors and compounds, for bioremediation, as drug targets, to diagnose patients suffering from genetic disorders, etc. Many research projects have therefore focused on the characterization of proteins and on improving the understanding of the functional and mechanistic properties of proteins. Studies have examined folding mechanisms, reaction mechanisms, stability under stress, effects of mutations, etc. All these research projects have resulted in an enormous amount of available data in lots of different formats that are difficult to retrieve, combine, and use efficiently. The main topic of this thesis is the 3DM platform that was developed to generate Molecular Class Specific Information Systems (3DM systems) for protein superfamilies. These superfamily systems can be used to collect and interlink heterogeneous data sets based on structure based multiple sequence alignments. 3DM systems can be used to integrate protein, structure, mutation, reaction, conservation, correlation, contact, and many other types of data. Data is visualized using websites, directly in protein structures using YASARA, and in literature using Utopia Documents. 3DM systems contain a number of modules that can be used to analyze superfamily characteristics namely Comulator for correlated mutation analyses, Mutator for mutation retrieval, and Validator for mutant pathogenicity prediction. To be able to determine the characteristics of subsets of proteins and to be able to compare the characteristics of different subsets a powerful filtering mechanism is available. 3DM systems can be used as a central knowledge base for projects in protein engineering, DNA diagnostics, and drug design. The scientific and technical background of the 3DM platform is described in the first two chapters. Chapter 1 describes the scientific background, starting with an overview of the foundations of the 3DM platform. Alignment methods and tools for both structure and sequence alignments, and the techniques used in the 3DM modules are described in detail. Alternative methods are also described with the advantages and disadvantages of the various strategies. Chapter 2 contains a technical description of the implementation of the 3DM platform and the 3DM modules. A schematic overview of the database used to store the data is provided together with a description of the various tables and the steps required to create new 3DM systems. The techniques used in the Comulator, Mutator and Validator modules of the 3DM platforms are discussed in more detail. Chapter 3 contains a concise overview of the 3DM platform, its capabilities, and the results of protein engineering projects using 3DM systems. Thirteen 3DM systems were generated for superfamilies such as the PEPM/ICL and Nuclear Receptors. These systems are available online for further examination. Protein engineering studies aimed at optimizing substrate specificity, enzyme activity, or thermostability were designed targeting proteins from these superfamilies. Preliminary results of drug design and DNA diagnostics projects are also included to highlight the diversity of projects 3DM systems can be applied to. Project HOPE: a biomedical tool to predict the effect of a mutation on the structure of a protein is described in chapter 4. Project HOPE is developed at the Radboud University Nijmegen Medical Center under supervision of H. Venselaar. Project HOPE employs webservices to optimally reuse existing databases and computing facilities. After selection of a mutant in a protein, data is collected from various sources such as UniProt and PISA. A homology model is created to determine features such as contacts and side-chain accessibility directly in the structure. Using a decision tree, the available data is evaluated to predict the effects of the mutation on the protein. Chapter 5 describes Comulator: the 3DM module for correlated mutation analyses. Two positions in an alignment correlate when they co-evolve, that is they mutate simultaneously or not at all. Comulator uses a statistical coupling algorithm to calculate correlated mutation analyses. Correlated mutations are visualized using heatmaps, or directly in protein structures using YASARA. Analyses of correlated mutations in various superfamilies showed that positions that correlate are often found in networks and that the positions in these networks often share a common function. Using these networks, mutants were predicted to increase the specificity or activity of proteins. Mutational studies confirmed that correlated mutation analyses are a valuable tool for rational design of proteins. Mutator, the text mining tool used to incorporate mutations into 3DM systems is described in chapter 6. Mutator was designed to automatically retrieve mutations from literature and store these mutations in a 3DM system. A PubMed search using keywords from the 3DM system is used to preselect articles of interest. These articles are retrieved from the internet, converted to text, and parsed for mutations. Mutations are then grounded to proteins and stored in a 3DM database. Mutation retrieval was tested on the alpha-amylase superfamily as this superfamily contains the enzyme involved in Fabry’s disease: an x linked lysosomal storage disease. Compared to existing mutant databases, such as the HGMD and SwissProt, Mutator retrieved 30% more mutations from literature. A major problem in DNA diagnostics is the differentiation between natural variants and pathogenic mutations. To distinguish between pathogenic mutations and natural variation in proteins the Validator modules was added to 3DM. Validator uses the data available in a 3DM system to predict the pathogenicity of a mutant using, for example, the residue conservation of the mutants alignment position, side-chain accessibility of the mutant in the structure, and the number of mutations found in literature for the alignment position. Mutator and Validator can be used to study mutants found in disorder related genes. Although these tools are not the definitive solution for DNA diagnostics they can hopefully be used to increase our understanding of the molecular basis of disorders. Chapter 7 and 8 describe applied research projects using 3DM systems containg proteins of potential commercial interest. A 3DM system for the a/b-beta hydrolases superfamily is described in chapter 7. This superfamily consists of almost 20,000 proteins with a diverse range of functions. Superfamily alignments were generated for the common beta-barrel fold shared by all superfamily members, and for five distinct subtypes within the superfamily. Due to the size and functional diversity of the superfamily, there is a lot of potential for industrial application of superfamily members. Chapter 8 describes a study focusing on a sucrose phosphorylase enzyme from the a-amylase superfamily. This enzyme can be potentially used in an industrial setting for the transfer of glucose to a wide variety of molecules. The aim of the study was to increase the stability of the protein at higher temperatures. A combination of rational design using a 3DM system, and in-depth study of the protein structure, led to a series of mutations that resulted in more than doubling the half-life of the protein at 60°C. 3DM systems have been successfully applied in a wide range of protein engineering and DNA diagnostics studies. Currently, 3DM systems are applied most successfully in project studying a single protein family or monogenetic disorder. In the future, we hope to be able to apply 3DM to more complex scenarios such as enzyme factories and polygenetic disorders by combining multiple 3DM systems for interacting proteins.</p