Successful examples of the application of novel iterative trainable algorithms to guide rational mutation strategies for enzyme engineering: From prediction to lab testing to algorithm retraining

Both natural mutations occurring in a homologous enzyme family and mutations engineered in a given protein can have a tremendous impact in the activity and binding behavior of the enzyme towards substrates or other molecules. Binding and catalytic properties can be modified by rationally mutating selected amino acids in a protein. For instance, new specificity properties can be engineered into existing enzymes, which can be applied to the rational design of mutations to alter its catalysis. Although this approach has been largely used, the modifications introduced in the target protein have not been exempt of deleterious effects on protein function, binding or physicochemical properties. Much finer tuned modifications should be designed in order to alter the desired catalytic or binding properties of a protein and simultaneously not affecting other protein properties or functions. These engineered mutations usually require a thorough knowledge of the relevant structure-function relationships in the protein molecule. If no precise structure-function information is available for a protein, the amount of possible amino acid mutations to be tested precludes a direct search. Furthermore, in many cases a directed evolution strategy cannot be successfully used to achieve the desired results due to the unavailability of suitable screening tests. In the last years, we have developed new and powerful in silico methodologies to automatically propose, test and redesign mutagenesis strategies for a target protein, based only on evolutionarily conserved physicochemical properties of amino acids in a protein family where the target protein belongs, and on structural properties, including calculation of vibrational entropies, if available, with no need of explicit structure-function relationships. This methodology identifies amino acid positions that are putatively responsible for function, specificity, stability or binding interactions in a family of proteins and calculates amino acid propensity and distributions at each position. Not only conserved amino acid positions in a protein family can be labelled as functionally relevant, but also non-conserved amino acid positions can be identified to have a meaningful functional effect, and even amino acid substitutions that are unobserved in nature. These results can be used to predict if a given mutation can have a functional implication and which mutation is most likely to be functionally silent for a protein. Through several rounds of mutation suggestions, laboratory testing of the mutants and feedback of results to retrain the algorithms, our methodology can be used to rapidly and automatically discard any irrelevant mutation and guide the research focus toward functionally significant mutations. In this work, we will show how we have successfully used our publicly available methods to guide mutant design in enzyme engineering applied to xylanases (producing an improved octuple mutant in a single mutagenesis round), proteases, glucanases, ubiquitin ligases and other enzymes, to alter protein function, stability or thermodynamic properties independently of their catalytic properties in vitro and in vivo. We will also show how the predictions of these methods have been employed to shift chromatographic elution profiles of xylanases and ferritin nanocages for better purification without affecting their activity and to obtain ferritin variants with better properties to be used in nanotechnological applications, including modifications to the external and internal surface of the protein to change its interaction properties, improve its recombinant production, alter the characteristics of nanoparticles within or change its organic molecule carrier capacity. Finally, we will show how a similar approach has been integrated in an artificial intelligence classification scheme to identify somatic mutations in the human VHL gene that are related to renal clear-cell cancer and to predict the clinical outcome and prognosis of pVHL mutation and malfunction in humans, based on specific disruption of interactions with VHL binding partners. Clearly, our techniques show promising performance as a valuable and powerful bioinformatics tool to aid in the computer-aided design of engineered enzyme variants and in the understanding of function-structure, binding and affinity relationships in enzymes and other proteins

A multi-group SEIRA model for the spread of COVID-19 among heterogeneous populations

The outbreak and propagation of COVID-19 have posed a considerable challenge to modern society. In particular, the different restrictive actions taken by governments to prevent the spread of the virus have changed the way humans interact and conceive interaction. Due to geographical, behavioral, or economic factors, different sub-groups among a population are more (or less) likely to interact, and thus to spread/acquire the virus. In this work, we present a general multi-group SEIRA model for representing the spread of COVID-19 among a heterogeneous population and test it in a numerical case of study. By highlighting its applicability and the ease with which its general formulation can be adapted to particular studies, we expect our model to lead us to a better understanding of the evolution of this pandemic and to better public-health policies to control it

Efficient microbial bioconversion of brown macroalgae obtained through profitable high-density sea cultivation using modified microbial strains to produce commodity and specialty chemicals: A developing blue chemical industry in Chile

Plant biomass is considered a promising feedstock for large scale sustainable bio-based green chemistry. However, only the use of agricultural or forestry residues is viable, since they do not compete for land with feed crops and have competitive costs. Moreover, carbohydrate recovery from these sources is always difficult due to their high lignin content. Alternatively, macroalgae are competitive sources of carbohydrate-rich biomass not requiring land or fresh water for its production. Macrocystis pyrifera is one of the fastest-growing macroalgal species with high CO2 fixation efficiency, highly-abundant and accessible carbohydrates. We demonstrated that it can be cultured in temperate seas, yielding 124 ton/Ha/yr, and can be economically profitable at a 10-hectare scale 1,2. Microbial and enzymatic algal biomass bioprocessing has been also undertaken by our group. We demonstrated the technical feasibility of producing ethanol at a pilot industrial scale by fermenting algal carbohydrates with a genetically modified Escherichia coli 3. However, ethanol production, even with high productivities, was not commercially viable. To make algal biomass bioconversion profitable, we performed a large metabolic engineering and synthetic biology project to discover combinations of metabolic pathways, regulation, carbohydrate sources –algal or not– and alternative bioproducts that maximize microbial efficiency and commercial viability. Using a genome-scale reconstruction of Saccharomyces cerevisiae’s metabolism, we demonstrated that redox ratio constraints and the preferential use of NADH or NADPH for alginate metabolism were key for S. cerevisiae conversion of alginate:mannitol carbohydrate sources 4. However, yeast use makes chemical processes technically and economically unfeasible for low value products due to their inability to produce extracellular enzymes for alginate lysis. By means of dynamic metabolic models developed for E. coli, we demonstrated that the main metabolic process bottleneck is microbial carbohydrate metabolization and that algal carbohydrate composition is a key determinant of fermentation efficiency. Using a multi-objective optimization strategy focused on microorganism growth, energy levels and redox ratio conservation, we also showed that ethanol production from algal biomass is incompatible with E. coli’s metabolism, due to low energetic and redox efficiencies obtained from alginate using host microorganism metabolic pathways. We then used high-performance parallel computing to develop a metabolic potentiality map for E. coli in which we explored more than 10.000 possible combinations of metabolic pathways that could be built in our strain to convert brown macroalgae carbohydrates with high efficiency, considering the best combinations of knock-outs and overexpressions to be introduced in E. coli’s central metabolic pathways. With this technique, we identified other valuable chemicals, such as succinic, aspartic, gluconic and levulinic acids, and complex aromatic and aliphatic biomolecules can be efficiently produced from Macrocystis with specifically modified strains for each product. The bulk of our research fostering algal feedstock production and industrial bioconversion in Chile will be presented in this work. 1. Buschmann, A. H. et al. The Status of Kelp Exploitation and Marine Agronomy, with Emphasis on Macrocystis pyrifera, in Chile. Advances in Botanical Research 71, 161–188 (2014). 2. Camus, C., Infante, J. & Buschmann, A. H. Overview of 3 year precommercial seafarming of Macrocystis pyrifera along the Chilean coast. Reviews in Aquaculture 10, 543–559 (2018). 3. Camus, C. et al. Scaling up bioethanol production from the farmed brown macroalga Macrocystis pyrifera in Chile. Biofuels, Bioproducts and Biorefining 10, 673–685 (2016). 4. Contador, C. A. et al. Analyzing redox balance in a synthetic yeast platform to improve utilization of brown macroalgae as feedstock. Metabolic Engineering Communications 2, 76–84 (2015)

Mutagenesis Objective Search and Selection Tool (MOSST): an algorithm to predict structure-function related mutations in proteins

<p>Abstract</p> <p>Background</p> <p>Functionally relevant artificial or natural mutations are difficult to assess or predict if no structure-function information is available for a protein. This is especially important to correctly identify functionally significant non-synonymous single nucleotide polymorphisms (nsSNPs) or to design a site-directed mutagenesis strategy for a target protein. A new and powerful methodology is proposed to guide these two decision strategies, based only on conservation rules of physicochemical properties of amino acids extracted from a multiple alignment of a protein family where the target protein belongs, with no need of explicit structure-function relationships.</p> <p>Results</p> <p>A statistical analysis is performed over each amino acid position in the multiple protein alignment, based on different amino acid physical or chemical characteristics, including hydrophobicity, side-chain volume, charge and protein conformational parameters. The variances of each of these properties at each position are combined to obtain a global statistical indicator of the conservation degree of each property. Different types of physicochemical conservation are defined to characterize relevant and irrelevant positions. The differences between statistical variances are taken together as the basis of hypothesis tests at each position to search for functionally significant mutable sites and to identify specific mutagenesis targets. The outcome is used to statistically predict physicochemical consensus sequences based on different properties and to calculate the amino acid propensities at each position in a given protein. Hence, amino acid positions are identified that are putatively responsible for function, specificity, stability or binding interactions in a family of proteins. Once these key functional positions are identified, position-specific statistical distributions are applied to divide the 20 common protein amino acids in each position of the protein's primary sequence into a group of functionally non-disruptive amino acids and a second group of functionally deleterious amino acids.</p> <p>Conclusions</p> <p>With this approach, not only conserved amino acid positions in a protein family can be labeled as functionally relevant, but also non-conserved amino acid positions can be identified to have a physicochemically meaningful functional effect. These results become a discriminative tool in the selection and elaboration of rational mutagenesis strategies for the protein. They can also be used to predict if a given nsSNP, identified, for instance, in a genomic-scale analysis, can have a functional implication for a particular protein and which nsSNPs are most likely to be functionally silent for a protein. This analytical tool could be used to rapidly and automatically discard any irrelevant nsSNP and guide the research focus toward functionally significant mutations. Based on preliminary results and applications, this technique shows promising performance as a valuable bioinformatics tool to aid in the development of new protein variants and in the understanding of function-structure relationships in proteins.</p

Mathematical modeling of the dynamic storage of iron in ferritin

<p>Abstract</p> <p>Background</p> <p>Iron is essential for the maintenance of basic cellular processes. In the regulation of its cellular levels, ferritin acts as the main intracellular iron storage protein. In this work we present a mathematical model for the dynamics of iron storage in ferritin during the process of intestinal iron absorption. A set of differential equations were established considering kinetic expressions for the main reactions and mass balances for ferritin, iron and a discrete population of ferritin species defined by their respective iron content.</p> <p>Results</p> <p>Simulation results showing the evolution of ferritin iron content following a pulse of iron were compared with experimental data for ferritin iron distribution obtained with purified ferritin incubated <it>in vitro </it>with different iron levels. Distinctive features observed experimentally were successfully captured by the model, namely the distribution pattern of iron into ferritin protein nanocages with different iron content and the role of ferritin as a controller of the cytosolic labile iron pool (cLIP). Ferritin stabilizes the cLIP for a wide range of total intracellular iron concentrations, but the model predicts an exponential increment of the cLIP at an iron content > 2,500 Fe/ferritin protein cage, when the storage capacity of ferritin is exceeded.</p> <p>Conclusions</p> <p>The results presented support the role of ferritin as an iron buffer in a cellular system. Moreover, the model predicts desirable characteristics for a buffer protein such as effective removal of excess iron, which keeps intracellular cLIP levels approximately constant even when large perturbations are introduced, and a freely available source of iron under iron starvation. In addition, the simulated dynamics of the iron removal process are extremely fast, with ferritin acting as a first defense against dangerous iron fluctuations and providing the time required by the cell to activate slower transcriptional regulation mechanisms and adapt to iron stress conditions. In summary, the model captures the complexity of the iron-ferritin equilibrium, and can be used for further theoretical exploration of the role of ferritin in the regulation of intracellular labile iron levels and, in particular, as a relevant regulator of transepithelial iron transport during the process of intestinal iron absorption.</p

Cell cycle and protein complex dynamics in discovering signaling pathways

Signaling pathways are responsible for the regulation of cell processes, such as monitoring the external environment, transmitting information across membranes, and making cell fate decisions. Given the increasing amount of biological data available and the recent discoveries showing that many diseases are related to the disruption of cellular signal transduction cascades, in silico discovery of signaling pathways in cell biology has become an active research topic in past years. However, reconstruction of signaling pathways remains a challenge mainly because of the need for systematic approaches for predicting causal relationships, like edge direction and activation/inhibition among interacting proteins in the signal flow. We propose an approach for predicting signaling pathways that integrates protein interactions, gene expression, phenotypes, and protein complex information. Our method first finds candidate pathways using a directed-edge-based algorithm and then defines a graph model to include causal activation relationships among proteins, in candidate pathways using cell cycle gene expression and phenotypes to infer consistent pathways in yeast. Then, we incorporate protein complex coverage information for deciding on the final predicted signaling pathways. We show that our approach improves the predictive results of the state of the art using different ranking metrics

Enhanced Cellular Uptake of H-Chain Human Ferritin Containing Gold Nanoparticles

Gold nanoparticles (AuNP) capped with biocompatible layers have functional optical, chemical, and biological properties as theranostic agents in biomedicine. The ferritin protein containing in situ synthesized AuNPs has been successfully used as an effective and completely biocompatible nanocarrier for AuNPs in human cell lines and animal experiments in vivo. Ferritin can be uptaken by different cell types through receptor-mediated endocytosis. Despite these advantages, few efforts have been made to evaluate the toxicity and cellular internalization of AuNP-containing ferritin nanocages. In this work, we study the potential of human heavy-chain (H) and light-chain (L) ferritin homopolymers as nanoreactors to synthesize AuNPs and their cytotoxicity and cellular uptake in different cell lines. The results show very low toxicity of ferritin-encapsulated AuNPs on different human cell lines and demonstrate that efficient cellular ferritin uptake depends on the specific H or L protein chains forming the ferritin protein cage and the presence or absence of metallic cargo. Cargo-devoid apoferritin is poorly internalized in all cell lines, and the highest ferritin uptake was achieved with AuNP-loaded H-ferritin homopolymers in transferrin-receptor-rich cell lines, showing more than seven times more uptake than apoferritin

Enhanced Cellular Uptake of H-Chain Human Ferritin Containing Gold Nanoparticles

Gold nanoparticles (AuNP) capped with biocompatible layers have functional optical, chemical, and biological properties as theranostic agents in biomedicine. The ferritin protein containing in situ synthesized AuNPs has been successfully used as an effective and completely biocompatible nanocarrier for AuNPs in human cell lines and animal experiments in vivo. Ferritin can be uptaken by different cell types through receptor-mediated endocytosis. Despite these advantages, few efforts have been made to evaluate the toxicity and cellular internalization of AuNP-containing ferritin nanocages. In this work, we study the potential of human heavy-chain (H) and light-chain (L) ferritin homopolymers as nanoreactors to synthesize AuNPs and their cytotoxicity and cellular uptake in different cell lines. The results show very low toxicity of ferritin-encapsulated AuNPs on different human cell lines and demonstrate that efficient cellular ferritin uptake depends on the specific H or L protein chains forming the ferritin protein cage and the presence or absence of metallic cargo. Cargo-devoid apoferritin is poorly internalized in all cell lines, and the highest ferritin uptake was achieved with AuNP-loaded H-ferritin homopolymers in transferrin-receptor-rich cell lines, showing more than seven times more uptake than apoferritin

Cold Adaptation, Ca²⁺ Dependency and Autolytic Stability Are Related Features in a Highly Active Cold-Adapted Trypsin Resistant to Autoproteolysis Engineered for Biotechnological Applications

<div><p>Pig trypsin is routinely used as a biotechnological tool, due to its high specificity and ability to be stored as an inactive stable zymogen. However, it is not an optimum enzyme for conditions found in wound debriding for medical uses and trypsinization processes for protein analysis and animal cell culturing, where low Ca²⁺ dependency, high activity in mild conditions and easy inactivation are crucial. We isolated and thermodynamically characterized a highly active cold-adapted trypsin for medical and laboratory use that is four times more active than pig trypsin at 10^° C and at least 50% more active than pig trypsin up to 50^° C. Contrary to pig trypsin, this enzyme has a broad optimum pH between 7 and 10 and is very insensitive to Ca²⁺ concentration. The enzyme is only distantly related to previously described cryophilic trypsins. We built and studied molecular structure models of this trypsin and performed molecular dynamic calculations. Key residues and structures associated with calcium dependency and cryophilicity were identified. Experiments indicated that the protein is unstable and susceptible to autoproteolysis. Correlating experimental results and structural predictions, we designed mutations to improve the resistance to autoproteolysis and conserve activity for longer periods after activation. One single mutation provided around 25 times more proteolytic stability. Due to its cryophilic nature, this trypsin is easily inactivated by mild denaturation conditions, which is ideal for controlled proteolysis processes without requiring inhibitors or dilution. We clearly show that cold adaptation, Ca²⁺ dependency and autolytic stability in trypsins are related phenomena that are linked to shared structural features and evolve in a concerted fashion. Hence, both structurally and evolutionarily they cannot be interpreted and studied separately as previously done.</p> </div