Metabolic Fluxes Using Deep Learning Based on Enzyme Variations: .

Metabolic pathway modeling, essential for understanding organism metabolism, is pivotal in predicting genetic mutation effects, drug design, and biofuel development. Enhancing these modeling techniques is crucial for achieving greater prediction accuracy and reliability. However, the limited experimental data or the complexity of the pathway makes it challenging for researchers to predict phenotypes.

Peptipedia v2.0: a peptide sequence database and user-friendly web platform. A major update.

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides.

The mutual and dynamic role of TSPO and ligands in their binding process: An example with PK-11195.

Translocator protein (TSPO) is an 18 kDa transmembrane protein, localized primarily on the outer mitochondrial membrane. It has been found to be involved in various physiological processes and pathophysiological conditions. Though studies on its structure have been performed only recently, there is little information on the nature of dynamics and doubts about some structures referenced in the literature, especially the NMR structure of mouse TSPO.

Resistance against two lytic phage variants attenuates virulence and antibiotic resistance in .

Background: Bacteriophage therapy is becoming part of mainstream Western medicine since antibiotics of clinical use tend to fail. It involves applying lytic bacteriophages that self-replicate and induce cell lysis, thus killing their hosts. Nevertheless, bacterial killing promotes the selection of resistant clones which sometimes may exhibit a decrease in bacterial virulence or antibiotic resistance.

To cheat or not to cheat: cheatable and non-cheatable virulence factors in Pseudomonas aeruginosa.

Important bacterial pathogens such as Pseudomonas aeruginosa produce several exoproducts such as siderophores, degradative enzymes, biosurfactants, and exopolysaccharides that are used extracellularly, benefiting all members of the population, hence being public goods. Since the production of public goods is a cooperative trait, it is in principle susceptible to cheating by individuals in the population who do not invest in their production, but use their benefits, hence increasing their fitness at the expense of the cooperators' fitness. Among the most studied virulence factors susceptible to cheating are siderophores and exoproteases, with several studies in vitro and some in animal infection models.

General Trends of the Antibody VHs Domain Dynamics.

Conformational flexibility plays an essential role in antibodies' functional and structural stability. They facilitate and determine the strength of antigen-antibody interactions. Camelidae express an interesting subtype of single-chain antibody, named Heavy Chain only Antibody.

Regioselective glucosylation of (+)-catechin using a new variant of sucrose phosphorylase from .

Mutation Q345F in sucrose phosphorylase from (SP) has shown to allow efficient (+)-catechin glucosylation yielding a regioisomeric mixture: (+)-catechin-3'--α-D-glucopyranoside, (+)-catechin-5--α-D-glucopyranoside and (+)-catechin-3',5--α-D-diglucopyranoside with a ratio of 51 : 25 : 24. Here, we efficiently increased the control of (+)-catechin glucosylation regioselectivity with a new variant Q345F/P134D. The same products were obtained with a ratio of 82 : 9 : 9.

Quality assessment of VH models.

Heavy Chain Only Antibodies are specific to Camelid species. Despite the lack of the light chain variable domain, their heavy chain variable domain (VH) domain, named VH or nanobody, has promising potential applications in research and therapeutic fields. The structural study of VH is therefore of great interest.

Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model.

The use of machine learning (ML) in life sciences has gained wide interest over the past years, as it speeds up the development of high performing models. Important modeling tools in biology have proven their worth for pathway design, such as mechanistic models and metabolic networks, as they allow better understanding of mechanisms involved in the functioning of organisms. However, little has been done on the use of ML to model metabolic pathways, and the degree of non-linearity associated with them is not clear.

Learning Strategies in Protein Directed Evolution.

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons.

VH Structural Modelling Approaches: A Critical Review.

VH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies.

Insights into Comparative Modeling of VH Domains.

In the particular case of the family, immunoglobulin proteins have evolved into a unique and more simplified architecture with only heavy chains. The variable domains of these chains, named VHs, have a number of Complementary Determining Regions (CDRs) reduced by half, and can function as single domains making them good candidates for molecular tools. 3D structure prediction of these domains is a beneficial and advantageous step to advance their developability as molecular tools.

Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.

Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena.

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches.

Metabolic pathway modeling plays an increasing role in drug design by allowing better understanding of the underlying regulation and controlling networks in the metabolism of living organisms. However, despite rapid progress in this area, pathway modeling can become a real nightmare for researchers, notably when few experimental data are available or when the pathway is highly complex. Here, three different approaches were developed to model the second part of glycolysis of E.

Versatile Dimerisation Process of Translocator Protein (TSPO) Revealed by an Extensive Sampling Based on a Coarse-Grained Dynamics Study.

Translocator protein (TSPO), a mitochondrial membrane protein, has been extensively studied, and its role is still debated and continues to be enigmatic. From a structural perspective, despite availability of atomic structures from different species, the possible oligomeric state and its 3D structure remains elusive. In the present study, we attempted to study dynamics of TSPO from the perspective of oligomerization.

A glance into the evolution of template-free protein structure prediction methodologies.

Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modelling protocols, whereas strategies that involve template-free modelling still lag behind, specifically for larger proteins (>150 a.a.

Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction.

Antigen binding by antibodies requires precise orientation of the complementarity- determining region (CDR) loops in the variable domain to establish the correct contact surface. Members of the family Camelidae have a modified form of immunoglobulin gamma (IgG) with only heavy chains, called Heavy Chain only Antibodies (HCAb). Antigen binding in HCAbs is mediated by only three CDR loops from the single variable domain (VH) at the N-terminus of each heavy chain.

Structural variations within proteins can be as large as variations observed across their homologues.

Understanding the structural plasticity of proteins is key to understanding the intricacies of their functions and mechanistic basis. In the current study, we analyzed the available multiple crystal structures of the same protein for the structural differences. For this purpose we used an abstraction of protein structures referred as Protein Blocks (PBs) that was previously established.

One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators.

Enzymes are biological catalysts with many industrial applications, but natural enzymes are usually unsuitable for industrial processes because they are not optimized for the process conditions. The properties of enzymes can be improved by directed evolution, which involves multiple rounds of mutagenesis and screening. By using mathematical models to predict the structure-activity relationship of an enzyme, and by defining the optimal combination of mutations in silico, we can significantly reduce the number of bench experiments needed, and hence the time and investment required to develop an optimized product.

Flux prediction using artificial neural network (ANN) for the upper part of glycolysis.

The selection of optimal enzyme concentration in multienzyme cascade reactions for the highest product yield in practice is very expensive and time-consuming process. The modelling of biological pathways is a difficult process because of the complexity of the system. The mathematical modelling of the system using an analytical approach depends on the many parameters of enzymes which rely on tedious and expensive experiments.

A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes.

Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants.

Application of fourier transform and proteochemometrics principles to protein engineering.

Background: Connecting the dots between the protein sequence and its function is of fundamental interest for protein engineers. In-silico methods are useful in this quest especially when structural information is not available. In this study we propose a mutant library screening tool called iSAR (innovative Sequence Activity Relationship) that relies on the physicochemical properties of the amino acids, digital signal processing and partial least squares regression to uncover these sequence-function correlations.