Ancient Loss of Catalytic Selenocysteine Spurred Convergent Adaptation in a Mammalian Oxidoreductase.

Selenocysteine, the 21st amino acid specified by the genetic code, is a rare selenium-containing residue found in the catalytic site of selenoprotein oxidoreductases. Selenocysteine is analogous to the common cysteine amino acid, but its selenium atom offers physical-chemical properties not provided by the corresponding sulfur atom in cysteine. Catalytic sites with selenocysteine in selenoproteins of vertebrates are under strong purifying selection, but one enzyme, glutathione peroxidase 6 (GPX6), independently exchanged selenocysteine for cysteine <100 million years ago in several mammalian lineages. We reconstructed and assayed these ancient enzymes before and after selenocysteine was lost and up to today and found them to have lost their classic ability to reduce hydroperoxides using glutathione. This loss of function, however, was accompanied by additional amino acid changes in the catalytic domain, with protein sites concertedly changing under positive selection across distant lineages abandoning selenocysteine in glutathione peroxidase 6. This demonstrates a narrow evolutionary range in maintaining fitness when sulfur in cysteine impairs the catalytic activity of this protein, with pleiotropy and epistasis likely driving the observed convergent evolution. We propose that the mutations shared across distinct lineages may trigger enzymatic properties beyond those in classic glutathione peroxidases, rather than simply recovering catalytic rate. These findings are an unusual example of adaptive convergence across mammalian selenoproteins, with the evolutionary signatures possibly representing the evolution of novel oxidoreductase functions.

Identification of Aggregation Mechanism of Acetylated PHF6* and PHF6 Tau Peptides Based on Molecular Dynamics Simulations and Markov State Modeling.

The microtubule-associated protein tau (MAPT) has a critical role in the development and preservation of the nervous system. However, tau's dysfunction and accumulation in the human brain can lead to several neurodegenerative diseases, such as Alzheimer's disease, Down's syndrome, and frontotemporal dementia. The microtubule binding (MTB) domain plays a significant, important role in determining the tau's pathophysiology, as the core of paired helical filaments PHF6* (275VQIINK280) and PHF6 (306VQIVYK311) of R2 and R3 repeat units, respectively, are formed in this region, which promotes tau aggregation. Post-translational modifications, and in particular lysine acetylation at K280 of PHF6* and K311 of PHF6, have been previously established to promote tau misfolding and aggregation. However, the exact aggregation mechanism is not known. In this study, we established an atomic-level nucleation-extension mechanism of the separated aggregation of acetylated PHF6* and PHF6 hexapeptides, respectively, of tau. We show that the acetylation of the lysine residues promotes the formation of ß-sheet enriched high-ordered oligomers. The Markov state model analysis of ac-PHF6* and ac-PHF6 aggregation revealed the formation of an antiparallel dimer nucleus which could be extended from both sides in a parallel manner to form mixed-oriented and high-ordered oligomers. Our study describes the detailed mechanism for acetylation-driven tau aggregation, which provides valuable insights into the effect of post-translation modification in altering the pathophysiology of tau hexapeptides.

CDK6 is activated by the atypical cyclin I to promote E2F-mediated gene expression and cancer cell proliferation.

Cyclin-dependent kinases (CDKs), together with their cyclin partners, are the master cell cycle regulators. Remarkably, the cyclin family was extended to include atypical cyclins, characterized by distinctive structural features, but their partner CDKs remain elusive. Here, we conducted a yeast two-hybrid screen to identify new atypical cyclin-CDK complexes. We identified 10 new complexes, including a complex between CDK6 and cyclin I (CCNI), which was found to be active against retinoblastoma protein. CCNI upregulation increased the proliferation of breast cancer cells in vitro and in vivo, with a magnitude similar to that seen upon cyclin D upregulation, an effect that was abrogated by CDK6 silencing or palbociclib treatment. In line with these findings, CCNI downregulation led to a decrease in cell number and a reduction in the percentage of cells reaching S phase. Finally, CCNI upregulation correlated with the high expression of E2F target genes in large panels of cancer cell lines and tissue samples from breast cancer patients. In conclusion, we unveil CCNI as a new player in the pathways that activate CDK6, enriching the wiring of cell cycle control.

New Mutations in HFE2 and TFR2 Genes Causing Non HFE-Related Hereditary Hemochromatosis.

Hereditary hemochromatosis (HH) is an iron metabolism disease clinically characterized by excessive iron deposition in parenchymal organs such as liver, heart, pancreas, and joints. It is caused by mutations in at least five different genes. HFE hemochromatosis is the most common type of hemochromatosis, while non-HFE related hemochromatosis are rare cases. Here, we describe six new patients of non-HFE related HH from five different families. Two families (Family 1 and 2) have novel nonsense mutations in the HFE2 gene have novel nonsense mutations (p.Arg63Ter and Asp36ThrfsTer96). Three families have mutations in the TFR2 gene, one case has one previously unreported mutation (Family A-p.Asp680Tyr) and two cases have known pathogenic mutations (Family B and D-p.Trp781Ter and p.Gln672Ter respectively). Clinical, biochemical, and genetic data are discussed in all these cases. These rare cases of non-HFE related hereditary hemochromatosis highlight the importance of an earlier molecular diagnosis in a specialized center to prevent serious clinical complications.

Dynamic chromatin association of IκBα is regulated by acetylation and cleavage of histone H4.

IκBs exert principal functions as cytoplasmic inhibitors of NF-kB transcription factors. Additional roles for IκB homologues have been described, including chromatin association and transcriptional regulation. Phosphorylated and SUMOylated IκBα (pS-IκBα) binds to histones H2A and H4 in the stem cell and progenitor cell compartment of skin and intestine, but the mechanisms controlling its recruitment to chromatin are largely unknown. Here, we show that serine 32-36 phosphorylation of IκBα favors its binding to nucleosomes and demonstrate that p-IκBα association with H4 depends on the acetylation of specific H4 lysine residues. The N-terminal tail of H4 is removed during intestinal cell differentiation by proteolytic cleavage by trypsin or chymotrypsin at residues 17-19, which reduces p-IκBα binding. Inhibition of trypsin and chymotrypsin activity in HT29 cells increases p-IκBα chromatin binding but, paradoxically, impaired goblet cell differentiation, comparable to IκBα deletion. Taken together, our results indicate that dynamic binding of IκBα to chromatin is a requirement for intestinal cell differentiation and provide a molecular basis for the understanding of the restricted nuclear distribution of p-IκBα in specific stem cell compartments.

SBMOpenMM: A Builder of Structure-Based Models for OpenMM.

Molecular dynamics (MD) simulations have become a standard tool to correlate the structure and function of biomolecules and significant advances have been made in the study of proteins and their complexes. A major drawback of conventional MD simulations is the difficulty and cost of obtaining converged results, especially when exploring potential energy surfaces containing considerable energy barriers. This limits the wide use of MD calculations to determine the thermodynamic properties of biomolecular processes. Indeed, this is true when considering the conformational entropy of such processes, which is ultimately critical in assessing the simulations' convergence. Alternatively, a wide range of structure-based models (SBMs) has been used in the literature to unravel the basic mechanisms of biomolecular dynamics. These models introduce simplifications that focus on the relevant aspects of the physical process under study. Because of this, SBMs incorporate the need to modify the force field definition and parameters to target specific biophysical simulations. Here we introduce SBMOpenMM, a Python library to build force fields for SBMs, that uses the OpenMM framework to create and run SBM simulations. The code is flexible and user-friendly and profits from the high customizability and performance provided by the OpenMM platform.

Proteoliposomal formulations of an HIV-1 gp41-based miniprotein elicit a lipid-dependent immunodominant response overlapping the 2F5 binding motif.

The HIV-1 gp41 Membrane Proximal External Region (MPER) is recognized by broadly neutralizing antibodies and represents a promising vaccine target. However, MPER immunogenicity and antibody activity are influenced by membrane lipids. To evaluate lipid modulation of MPER immunogenicity, we generated a 1-Palmitoyl-2-oleoylphosphatidylcholine (POPC)-based proteoliposome collection containing combinations of phosphatidylserine (PS), GM3 ganglioside, cholesterol (CHOL), sphingomyelin (SM) and the TLR4 agonist monophosphoryl lipid A (MPLA). A recombinant gp41-derived miniprotein (gp41-MinTT) exposing the MPER and a tetanus toxoid (TT) peptide that favors MHC-II presentation, was successfully incorporated into lipid mixtures (>85%). Immunization of mice with soluble gp41-MinTT exclusively induced responses against the TT peptide, while POPC proteoliposomes generated potent anti-gp41 IgG responses using lower protein doses. The combined addition of PS and GM3 or CHOL/SM to POPC liposomes greatly increased gp41 immunogenicity, which was further enhanced by the addition of MPLA. Responses generated by all proteoliposomes targeted the N-terminal moiety of MPER overlapping the 2F5 neutralizing epitope. Our data show that lipids impact both, the epitope targeted and the magnitude of the response to membrane-dependent antigens, helping to improve MPER-based lipid carriers. Moreover, the identification of immunodominant epitopes allows for the redesign of immunogens targeting MPER neutralizing determinants.

Computational exploration of the binding mode of heme-dependent stimulators into the active catalytic domain of soluble guanylate cyclase.

Soluble guanylate cyclase (sGC), the main target of nitric oxide (NO), has been proven to have a significant role in coronary artery disease, pulmonary hypertension, erectile dysfunction, and myocardial infarction. One of its agonists, BAY 41-2272 (Riociguat), has been recently approved for treatment of pulmonary arterial hypertension (PHA), while some others are in clinical phases of development. However, the location of the binding sites for the two known types of agonists, heme-dependent stimulators and heme-independent activators, is a matter of debate, particularly for the first group where both a location on the regulatory (H-NOX) and on the catalytic domain have been suggested by different authors. Here, we address its potential location on the catalytic domain, the unique well characterized at the structural level, by an "in silico" approach. Homology models of the catalytic domain of sGC in "inactive" or "active" conformations were constructed using the structure of previously described crystals of the catalytic domains of "inactive" sGCs (2WZ1, 3ET6) and of "active" adenylate cyclase (1CJU). Each model was submitted to six independent molecular dynamics simulations of about 1 µs. Docking of YC-1, a classic heme-dependent stimulator, to all frames of representative trajectories of "inactive" and "active" conformations, followed by calculation of absolute binding free energies with the linear interaction energy (LIE) method, revealed a potential high-affinity binding site on the "active" structure. The site, located between the pseudo-symmetric and the catalytic site just over the loop ß2 -ß3 , does not overlap with the forskolin binding site on adenylate cyclases. Proteins 2016; 84:1534-1548. © 2016 Wiley Periodicals, Inc.

Antiretroviral therapy suppressed participants with low CD4+ T-cell counts segregate according to opposite immunological phenotypes.

BACKGROUND: The failure to increase CD4 T-cell counts in some antiretroviral therapy suppressed participants (immunodiscordance) has been related to perturbed CD4 T-cell homeostasis and impacts clinical evolution. METHODS: We evaluated different definitions of immunodiscordance based on CD4 T-cell counts (cutoff) or CD4 T-cell increases from nadir value (ΔCD4) using supervised random forest classification of 74 immunological and clinical variables from 196 antiretroviral therapy suppressed individuals. Unsupervised clustering was performed using relevant variables identified in the supervised approach from 191 individuals. RESULTS: Cutoff definition of CD4 cell count 400 cells/µl performed better than any other definition in segregating immunoconcordant and immunodiscordant individuals (85% accuracy), using markers of activation, nadir and death of CD4 T cells. Unsupervised clustering of relevant variables using this definition revealed large heterogeneity between immunodiscordant individuals and segregated participants into three distinct subgroups with distinct production, programmed cell-death protein-1 (PD-1) expression, activation and death of T cells. Surprisingly, a nonnegligible number of immunodiscordant participants (22%) showed high frequency of recent thymic emigrants and low CD4 T-cell activation and death, very similar to immunoconcordant participants. Notably, human leukocyte antigen - antigen D related (HLA-DR) PD-1 and CD45RA expression in CD4 T cells allowed reproducing subgroup segregation (81.4% accuracy). Despite sharp immunological differences, similar and persistently low CD4 values were maintained in these participants over time. CONCLUSION: A cutoff value of CD4 T-cell count 400 cells/µl classified better immunodiscordant and immunoconcordant individuals than any ΔCD4 classification. Immunodiscordance may present several, even opposite, immunological patterns that are identified by a simple immunological follow-up. Subgroup classification may help clinicians to delineate diverse approaches that may be needed to boost CD4 T-cell recovery.

TRIFORCE: Tessellated Semianalytical Solvent Exposed Surface Areas and Derivatives.

We present a new approach to the calculation of solvent-accessible surface areas of molecules with potential application to surface area based methods for determination of solvation free energies. As in traditional analytical and statistical approaches, this new algorithm, called TRIFORCE, reports both component areas and derivatives as a function of the atomic coordinates and radii. Unique to TRIFORCE are the rapid and scalable approaches for the determination of sphere intersection points and numerical estimation of the surface areas, derivatives, and other properties that can be associated with the surface area facets. The algorithm performs a special tessellation and semianalytical integration that uses a precomputed look-up table. This provides a simple way to balance numerical accuracy and memory usage. TRIFORCE calculates derivatives in the same manner, enabling application in force-dependent activities such as molecular geometry minimization. TRIFORCE is available free of charge for academic purposes as both a C++ library, which can be directly interfaced to existing molecular simulation packages, and a web-accessible application.

Methylglyoxal produced by amyloid-ß peptide-induced nitrotyrosination of triosephosphate isomerase triggers neuronal death in Alzheimer's disease.

Amyloid-ß peptide (Aß) aggregates induce nitro-oxidative stress, contributing to the characteristic neurodegeneration found in Alzheimer's disease (AD). One of the most strongly nitrotyrosinated proteins in AD is the triosephosphate isomerase (TPI) enzyme which regulates glycolytic flow, and its efficiency decreased when it is nitrotyrosinated. The main aims of this study were to analyze the impact of TPI nitrotyrosination on cell viability and to identify the mechanism behind this effect. In human neuroblastoma cells (SH-SY5Y), we evaluated the effects of Aß42 oligomers on TPI nitrotyrosination. We found an increased production of methylglyoxal (MG), a toxic byproduct of the inefficient nitro-TPI function. The proapoptotic effects of Aß42 oligomers, such as decreasing the protective Bcl2 and increasing the proapoptotic caspase-3 and Bax, were prevented with a MG chelator. Moreover, we used a double mutant TPI (Y165F and Y209F) to mimic nitrosative modifications due to Aß action. Neuroblastoma cells transfected with the double mutant TPI consistently triggered MG production and a decrease in cell viability due to apoptotic mechanisms. Our data show for the first time that MG is playing a key role in the neuronal death induced by Aß oligomers. This occurs because of TPI nitrotyrosination, which affects both tyrosines associated with the catalytic center.

Chromatin-bound IκBα regulates a subset of polycomb target genes in differentiation and cancer.

IκB proteins are the primary inhibitors of NF-κB. Here, we demonstrate that sumoylated and phosphorylated IκBα accumulates in the nucleus of keratinocytes and interacts with histones H2A and H4 at the regulatory region of HOX and IRX genes. Chromatin-bound IκBα modulates Polycomb recruitment and imparts their competence to be activated by TNFα. Mutations in the Drosophila IκBα gene cactus enhance the homeotic phenotype of Polycomb mutants, which is not counteracted by mutations in dorsal/NF-κB. Oncogenic transformation of keratinocytes results in cytoplasmic IκBα translocation associated with a massive activation of Hox. Accumulation of cytoplasmic IκBα was found in squamous cell carcinoma (SCC) associated with IKK activation and HOX upregulation.

A truncated form of IKKα is responsible for specific nuclear IKK activity in colorectal cancer.

Nuclear IKKα regulates gene transcription by phosphorylating specific substrates and has been linked to cancer progression and metastasis. However, the mechanistic connection between tumorigenesis and IKKα activity remains poorly understood. We have now analyzed 288 human colorectal cancer samples and found a significant association between the presence of nuclear IKK and malignancy. Importantly, the nucleus of tumor cells contains an active IKKα isoform with a predicted molecular weight of 45 kDa (p45-IKKα) that includes the kinase domain but lacks several regulatory regions. Active nuclear p45-IKKα forms a complex with nonactive IKKα and NEMO that mediates phosphorylation of SMRT and histone H3. Proteolytic cleavage of FL-IKKα into p45-IKKα is required for preventing the apoptosis of CRC cells in vitro and sustaining tumor growth in vivo. Our findings identify a potentially druggable target for treating patients with advance refractory CRC.

New model of cystic fibrosis transmembrane conductance regulator proposes active channel-like conformation.

The cystic fibrosis transmembrane conductance regulator (CFTR) is an unusual ABC transporter, functioning as a chloride channel critical for fluid homeostasis in multiple organs. Disruption of CFTR function is associated with cystic fibrosis making it an attractive therapeutic target. In addition, CFTR blockers are being developed as potential antidiarrheals. CFTR drug discovery is hampered by the lack of high resolution structural data, and considerable efforts have been invested in modeling the channel structure. Although previously published CFTR models that have been made publicly available mostly agree with experimental data relating to the overall structure, they present the channel in an outward-facing conformation that does not agree with expected properties of a "channel-like" structure. Here, we make available a model of CFTR in such a "channel-like" conformation, derived by a unique modeling approach combining restrained homology modeling and ROSETTA refinement. In contrast to others, the present model is in agreement with expected channel properties such as pore shape, dimensions, solvent accessibility, and experimentally derived distances. We have used the model to explore the interaction of open channel blockers within the pore, revealing a common binding mode and ionic interaction with K95, in agreement with experimental data. The binding-site was further validated using a virtual screening enrichment experiment, suggesting the model might be suitable for drug discovery. In addition, we subjected the model to a molecular dynamics simulation, revealing previously unaddressed salt-bridge interactions that may be important for structure stability and pore-lining residues that may take part in Cl(-) conductance.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine.

BACKGROUND: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. RESULTS: To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. CONCLUSIONS: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene--disease and gene--compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Multiscale molecular dynamics of protein aggregation.

The 60's gave birth to the practical implementation of classical mechanics to unravel the dynamics and energetics of biomolecules. In the 70's the use of generalized force fields and more advanced integrative solutions to the microscopic understanding of nature (like hybrid QM/MM) were introduced. During the 80's, algorithms to obtain free energy values were further developed and in the 90's practical integration schemes of molecular mechanics force fields with other levels of detail (QM on one extreme and advances in implicit solvation on the other) were implemented in widely spread software. In the first decade of the XXIst century a considerable effort has been put in two seemingly discordant models for the simulation of biomolecules. On the one hand, extraordinary advances in computing technologies (both in terms of processor power and of new efficient parallel and distributed computing schemas) have allowed researchers to deal with bigger systems and longer simulations, reaching molecular processes including millions of particles or lying in the microsecond scale. On the other hand, the realization that the relevant answers to many biomolecular problems are not homogeneously distributed through the molecular structure, something already envisioned by the QM/MM pioneers more than three decades ago, has led researchers to find smart ways of putting different emphases on different ranges of the spatial or system time scale. In this context, e.g., molecular aggregation represents a paradigm for multiscalability, as molecular recognition can be understood with simple (semi-)macroscopic terms when the two fragments are far apart, while the atomic interactions need to be considered in full detail upon close distances. In this manuscript the current status of the techniques that use multiple scale representations of biomolecules are reviewed, and the findings are synthesized in a modular schema that can be extensively used when studying aggregation processes. It is shown that a smart alternative to brute force and massive computation of uninteresting regions in the all atom potential energy surface is the consideration of a simplified reference potential, explored thoroughly in the relevant regions, combined with a free energy perturbation approach that transforms this simple representation to a full atom representation.

Simulation methods with extended stability for stiff biochemical Kinetics.

BACKGROUND: With increasing computer power, simulating the dynamics of complex systems in chemistry and biology is becoming increasingly routine. The modelling of individual reactions in (bio)chemical systems involves a large number of random events that can be simulated by the stochastic simulation algorithm (SSA). The key quantity is the step size, or waiting time, tau, whose value inversely depends on the size of the propensities of the different channel reactions and which needs to be re-evaluated after every firing event. Such a discrete event simulation may be extremely expensive, in particular for stiff systems where tau can be very short due to the fast kinetics of some of the channel reactions. Several alternative methods have been put forward to increase the integration step size. The so-called tau-leap approach takes a larger step size by allowing all the reactions to fire, from a Poisson or Binomial distribution, within that step. Although the expected value for the different species in the reactive system is maintained with respect to more precise methods, the variance at steady state can suffer from large errors as tau grows. RESULTS: In this paper we extend Poisson tau-leap methods to a general class of Runge-Kutta (RK) tau-leap methods. We show that with the proper selection of the coefficients, the variance of the extended tau-leap can be well-behaved, leading to significantly larger step sizes. CONCLUSIONS: The benefit of adapting the extended method to the use of RK frameworks is clear in terms of speed of calculation, as the number of evaluations of the Poisson distribution is still one set per time step, as in the original tau-leap method. The approach paves the way to explore new multiscale methods to simulate (bio)chemical systems.

Statistical analysis of global connectivity and activity distributions in cellular networks.

Various molecular interaction networks have been claimed to follow power-law decay for their global connectivity distribution. It has been proposed that there may be underlying generative models that explain this heavy-tailed behavior by self-reinforcement processes such as classical or hierarchical scale-free network models. Here, we analyze a comprehensive data set of protein-protein and transcriptional regulatory interaction networks in yeast, an Escherichia coli metabolic network, and gene activity profiles for different metabolic states in both organisms. We show that in all cases the networks have a heavy-tailed distribution, but most of them present significant differences from a power-law model according to a stringent statistical test. Those few data sets that have a statistically significant fit with a power-law model follow other distributions equally well. Thus, while our analysis supports that both global connectivity interaction networks and activity distributions are heavy-tailed, they are not generally described by any specific distribution model, leaving space for further inferences on generative models. Supplementary Material is available online at www.liebertonline.com.

The Virtual Physiological Human ToolKit.

The Virtual Physiological Human (VPH) is a major European e-Science initiative intended to support the development of patient-specific computer models and their application in personalized and predictive healthcare. The VPH Network of Excellence (VPH-NoE) project is tasked with facilitating interaction between the various VPH projects and addressing issues of common concern. A key deliverable is the 'VPH ToolKit'--a collection of tools, methodologies and services to support and enable VPH research, integrating and extending existing work across Europe towards greater interoperability and sustainability. Owing to the diverse nature of the field, a single monolithic 'toolkit' is incapable of addressing the needs of the VPH. Rather, the VPH ToolKit should be considered more as a 'toolbox' of relevant technologies, interacting around a common set of standards. The latter apply to the information used by tools, including any data and the VPH models themselves, and also to the naming and categorizing of entities and concepts involved. Furthermore, the technologies and methodologies available need to be widely disseminated, and relevant tools and services easily found by researchers. The VPH-NoE has thus created an online resource for the VPH community to meet this need. It consists of a database of tools, methods and services for VPH research, with a Web front-end. This has facilities for searching the database, for adding or updating entries, and for providing user feedback on entries. Anyone is welcome to contribute.

Computational study on the carboligation reaction of acetohidroxyacid synthase: new approach on the role of the HEThDP- intermediate.

Acetohydroxyacid synthase (AHAS) is a thiamin diphosphate dependent enzyme that catalyses the decarboxylation of pyruvate to yield the hydroxyethyl-thiamin diphosphate (ThDP) anion/enamine intermediate (HEThDP(-)). This intermediate reacts with a second ketoacid to form acetolactate or acetohydroxybutyrate as products. Whereas the mechanism involved in the formation of HEThDP(-) from pyruvate is well understood, the role of the enzyme in controlling the carboligation reaction of HEThDP(-) has not been determined yet. In this work, molecular dynamics (MD) simulations were employed to identify the aminoacids involved in the carboligation stage. These MD studies were carried out over the catalytic subunit of yeast AHAS containing the reaction intermediate (HEThDP(-)) and a second pyruvate molecule. Our results suggest that additional acid-base ionizable groups are not required to promote the catalytic cycle, in contrast with earlier proposals. This finding leads us to postulate that the formation of acetolactate relies on the acid-base properties of the HEThDP(-) intermediate itself. PM3 semiempirical calculations were employed to obtain the energy profile of the proposed mechanism on a reduced model of the active site. These calculations confirm the role of HEThDP(-) intermediate as the ionizable group that promotes the carboligation and product formation steps of the catalytic cycle.