Rational enzyme engineering of different active sites on a xylanase

In the current times, many processes in industry are switching from using chemical compounds, requiring extreme conditions, to enzymatic technology, which is a more sustainable and greener alternative [1]. In the paper industry, xylanases are enzymes used to facilitate the removal of residual lignin, which gives a dark colour to the paper pulp since these enzymes carry out the depolymerization of the xylan [2]. Xylan polymer consists of a chain of xylose monomers from where several different radicals hang. One of these radical molecules is arabinose, and, in addition, there is sometimes a ferulate bonded to those arabinoses. Usually, to cleave the ferulate from xylan, an enzyme called feruloyl esterase is used, an esterase that gives as a product ferulic acid. This compound is similar to a hydroxycinnamic acid existing in plant materials, mainly used in pharmaceuticals, cosmetics, and food industries owing to its antioxidant, antiinflammatory, and anti-cancer biological activities [3]. In this work, several mutations are proposed in an endoxylanase to generate a second catalytic centre, different from the wild-type one, with feruloyl esterase activity. This would allow the enzyme to cut the bond between arabinose and ferulate and thus substantially improve the usage of the enzyme in industry, as only one enzyme should be needed to perform two different reactions [4]

Mapping potential determinants of peroxidative activity in an evolved fungal peroxygenase from agrocybe aegerita

Fungal unspecific peroxygenases (UPOs) are hybrid biocatalysts with peroxygenative activity that insert oxygen into non-activated compounds, while also possessing convergent peroxidative activity for one electron oxidation reactions. In several ligninolytic peroxidases, the site of peroxidative activity is associated with an oxidizable aromatic residue at the protein surface that connects to the buried heme domain through a long-range electron transfer (LRET) pathway. However, the peroxidative activity of these enzymes may also be initiated at the heme access channel. In this study, we examined the origin of the peroxidative activity of UPOs using an evolved secretion variant (PaDa-I mutant) from Agrocybe aegerita as our point of departure. After analyzing potential radical-forming aromatic residues at the PaDa-I surface by QM/MM, independent saturation mutagenesis libraries of Trp24, Tyr47, Tyr79, Tyr151, Tyr265, Tyr281, Tyr293 and Tyr325 were constructed and screened with both peroxidative and peroxygenative substrates. These mutant libraries were mostly inactive, with only a few functional clones detected, none of these showing marked differences in the peroxygenative and peroxidative activities. By contrast, when the flexible Gly314-Gly318 loop that is found at the outer entrance to the heme channel was subjected to combinatorial saturation mutagenesis and computational analysis, mutants with improved kinetics and a shift in the pH activity profile for peroxidative substrates were found, while they retained their kinetic values for peroxygenative substrates. This striking change was accompanied by a 4.5°C enhancement in kinetic thermostability despite the variants carried up to four consecutive mutations. Taken together, our study proves that the origin of the peroxidative activity in UPOs, unlike other ligninolytic peroxidases described to date, is not dependent on a LRET route from oxidizable residues at the protein surface, but rather it seems to be exclusively located at the heme access channel.This work was supported by the Comunidad de Madrid Synergy CAM Project Y2018/BIO-4738-EVOCHIMERA-CM, the PID2019-106166RB-100-OXYWAVE and the PID2019-106370RB-I00-PZymes grants from the Spanish Ministry of Science and Innovation and the CSIC Project PIE-201580E042. AB-N thanks the PhD Programme in Molecular Biosciences, Doctoral School, Universidad Autónoma de Madrid.Peer ReviewedPostprint (published version

Enhancing backbone sampling in Monte Carlo simulations using Internal Coordinates Normal Mode Analysis

Normal mode methods are becoming a popular alternative to sample the conformational landscape of proteins. In this study, we describe the implementation of an internal coordinate normal mode analysis method and its application in exploring protein flexibility by using the Monte Carlo method PELE. This new method alternates two different stages, a perturbation of the backbone through the application of torsional normal modes, and a resampling of the side chains. We have evaluated the new approach using two test systems, ubiquitin and c-Src kinase, and the differences to the original ANM method are assessed by comparing both results to reference molecular dynamics simulations. The results suggest that the sampled phase space in the internal coordinate approach is closer to the molecular dynamics phase space than the one coming from a Cartesian coordinate anisotropic network model. In addition, the new method shows a great speedup (∼∼5-7x), making it a good candidate for future normal mode implementations in Monte Carlo methods.The authors thank D. E. Shaw Research lab. for providing the kinase MD coordinates and Dr. López Blanco for sharing the code developed in his thesis and for providing useful comments. This work was supported by the CTQ-48287-R projects of the Spanish Ministry of Economy and Competitiveness (MINECO) and the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Esterases computational study

In our project, we are focusing on two different esterases called LAE6 and LAE5. LAE6 is very promiscuous since it presents activity towards a wide range of different substrates, whereas LAE5 presents activity towards few substrates. This promiscuity can be due to the location of the binding cavity: in LAE6 the binding cavity is located buried inside the protein, so the substrates can be retained longer, correlating with a higher reactive probability . LAE5 binding cavity is situated on the surface of the protein, in direct contact with the solvent, which, in our working hypothesis, is the main reason for its much less promiscuity. We are using several computational techniques to extract important descriptors in order to construct a mathematical model to predict the activity of these two enzymes towards different substrates

A platform for antibody design

Antibodies are specialized proteins produced by our adaptive immune system to identify and neutralize specific molecules (antigens) of foreign objects, such as pathogenic microorganisms or infected cells. Due to their chemical specificity and sensitivity, antibodies are widely used in biomedical research and to treat many diseases due to their high efficiency and low risk of adverse events. Nowadays, it is possible to produce a wide variety of artificial antibodies; however, the process of obtaining therapeutic antibodies remains empirical and time-consuming with low success rates. The structural and energetic information of the antibodyantigen complexes, together with computational simulations, complement the experimental results to work towards a more effective and faster “a la carte” antibody design. In this framework, the project aims to develop a general platform that assists in the antibody design and helps the user overcome common challenges such as the lack of structural data of the antibody and the antibody-antigen complex and the flexibility of the antibody complementary determining regions (CDRs). The platform workflow (Fig. 1) will consist of the following steps. First, if there is no structural data available, DeepAb [1] will be used to generate the 3D structure of the antibody from its amino acid sequence; otherwise, we can jump to the minimization step. In the minimization step, the system will be relaxed to either remove clashes, if it is a predicted structure, or remove crystal artifacts, if we dispose of a crystal structure. Subsequently, we can jump to the point mutation step if we already have information on how the antibody binds the antigen (e.g. crystal of the antibody-antigen complex). Otherwise, we will run the in-house Consensus Docking [2], a pipeline designed to cluster protein-protein poses (PPPs) obtained using several rigid-body protein docking methodologies, which has shown to have a higher success rate than using the docking methodologies individually. This technique extracts 20-30 representative structures from the top 5 most populated clusters that fulfill the consensus among all the docking methodologies used. Then these structures will be refined with the in-house Monte Carlo software, PELE from Protein Energy Landscape Exploration [3], to study if they tend to converge into 2-5 stable binding structures and use these structures to start the affinity maturation of the antibody towards that antigen. The affinity maturation step will mainly consist on using UEP [4], a fast and accurate in-house predictor of detrimental and beneficial point mutations of the binding energy of protein-protein interactions. For the screening of mutations, we will use the 2-5 binding poses found by PELE or the crystal structure of the complex if it is available. The selection of the most beneficial point mutations identified by UEP will be introduced in the binding structures to perform a local PELE refinement with an exhaustive sampling on the CDRs loops. This refinement will allow the study of the local conformational changes due to the mutation and evaluate the binding energy between antibody-antigen and the system’s total energy. Finally, the workflow will output the refined structures with the most beneficial point mutations and a report with the energetic profiles for each mutation. This protocol will be validated in the future while improving the stability and binding affinity of industryrelevant antibodies

Adaptive simulations, towards interactive protein-ligand modeling

Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.We thank Drs Anders Hogner and Christoph Grebner, from AstraZeneca, and Jorge Estrada, from BSC, for fruitful discussions and feedback on the manuscript. We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by the CTQ2016-79138-R grant from the Spanish Government. D.L. acknowledges the support of SEV-2011-00067, awarded by the Spanish Government.Peer ReviewedPostprint (published version