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Selenocysteine (Sec), the 21stamino acid specified by the genetic code, is a rare selenium-containing residue found in the catalytic site of selenoprotein oxidoreductases. Sec is analogous to the common cysteine (Cys) amino acid but its selenium atom offers physicalchemical properties not provided by the corresponding sulfur atom in Cys. Catalytic sites with Sec in selenoproteins of vertebrates are under strong purifying selection but one enzyme, Glutathione Peroxidase 6 (GPX6), independently exchanged Sec for Cys less than one hundred million years ago in several mammalian lineages. We reconstructed and assayed these ancient enzymes before and after Sec was lost and up to today, and found them to have lost their classic ability to reduce hydroperoxides using glutathione (GSH). This loss of function, however, was accompanied by bursts of amino acid changes in the catalytic domain, with protein sites concertedly changing under positive selection across distant lineages abandoning Sec in GPX6. This demonstrates that when sulfur in Cys impairs catalysis a narrow evolutionary path is followed, with epistasis and pleiotropy leading to convergent evolution and triggering enzymatic properties likely beyond those in classic GPXs. These findings are an unusual example of adaptive convergence towards unexplored oxidoreductase functions during mammalian evolution.

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.

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. We want to thank the Bigas’ and Espinosa’s lab members for constructive discussions and suggestions and technical support. This work has been funded by Instituto de Salud Carlos III FEDER (PI19/0013), Agencia Estatal de Investigación, Spain (PID2019-104695RB-I00) to A.B., NIH/GM085490 to G.G., BIO2017-83650-P to JVF and Generalitat de Catalunya 2017SGR135. LM is a predoctoral fellow of 2015FI-B00806 and 2016FI-B1 00110 and MF has financial support by the Universitat de Vic-Universitat Central de Catalunya PhD fellowships program. The authors thankfully acknowledge the computer resources at Pirineus and the technical support provided by the Spanish Supercomputer Network (BCV-2020-1-0001).

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 β2 -β3 , does not overlap with the forskolin binding site on adenylate cyclases. Proteins 2016; 84:1534-1548. © 2016 Wiley Periodicals, Inc.

Amyloid-β peptide (Aβ) 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 Aβ42 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 Aβ42 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 Aβ 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 Aβ oligomers. This occurs because of TPI nitrotyrosination, which affects both tyrosines associated with the catalytic center. This work was supported by the Spanish Ministry of Science and Innovation (SAF2010-16725; Fondo de Investigación Sanitaria PI10/00587 and PI10/01076; Red HERACLES RD12/0042/0014- FEDER Funds; CTQ2008-00755; BFU2006-28430-E/BMC and RETIC COMBIOMED RD07/0067/0001); the virtual physiological human (VPH) NoE (FP7-ICT-2007-2-223920); Generalitat de Catalunya (AGAUR BE-2 10240; SGR2009-1369); and La Marató de TV3 (Nº 100310 and 100610).

Supplemental Digital Content is available in the text, 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.

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 β2 -β3 , does not overlap with the forskolin binding site on adenylate cyclases. Proteins 2016; 84:1534-1548. © 2016 Wiley Periodicals, Inc.

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 β2 -β3 , does not overlap with the forskolin binding site on adenylate cyclases.

The estimation of ion channel permeability poses a considerable challenge for computer simulations because of the significant free energy barriers involved, but also offers valuable molecular information on the ion permeation process not directly available from experiments. In this article we determine the equilibrium free energy barrier for potassium ion permeability in Gramicidin A in an efficient way by atomistic forward-reverse non-equilibrium steered molecular dynamics simulations, opening the way for its use in more complex biochemical systems. Our results indicate that the tent-shaped energetics of translocation of K+ ions in Gramicidin A is dictated by the different polarization responses to the ion of the external bulk water and the less polar environment of the membrane.

Permeation of small molecules across membrane channels can be measured by a multiscale computational protocol based on Brownian dynamics and the potential of mean force formalism. In this article we look at ways to compute the potential of mean force by reusing pre-existing molecular dynamics trajectories via a protocol centered on instantaneous forward/reverse transformations. We apply the method to the energetics of water across the narrow channel formed by Gramicidin A and reproduce several features of the energy barrier across the channel albeit at a coarse level of detail due to limits imposed by the exponential averages intrinsic to the method and the small size of the channel. The implications for ions and less dense systems are briefly discussed.

Structural alignment of ligands in their biological conformation is a crucial step in the building of pharmacophoric models in structure-based drug design. In addition, docking algorithms are limited in some cases by the quality of the scoring functions and the limited flexibility of the environment that the different programs allow. On the other hand, GRID molecular interaction potentials (MIPs) have been used for a long time in 3D-QSAR studies. However, in most of these studies the alignment of the molecules is performed on the basis of geometrical or physico-chemical criteria that differ from the MIPs used in the partial least squares statistical analysis. We have previously developed a method to use the same scoring function for the molecular alignment and for 3D-QSAR studies. This methodology, based on the use of GRID potentials, consists in the weighted averaging of similarities of the relevant MIPs of the molecules to be aligned. Here we present a method to obtain the weights for the different GRID probes in the average based on the structural information on protein-ligand complexes for relevant systems. The method, implemented in MIPSIM, is shown to yield good accuracy in the prediction of the alignments for two systems: a set of three inhibitors of dihydrofolate reductase and a set of fifteen non-nucleoside HIV-1 reverse transcriptase inhibitors (NNRTIs). The smooth GRID potentials are shown to capture the flexible character of the active site, as opposed to traditional docking scoring energy functions.

Understanding the basic principles of structural biology is becoming a major subject of study in most undergraduate level programs in biology. In the genomic and proteomic age, it is becoming indispensable for biology students to master concepts related to the sequence and structure of proteins in order to develop skills that may be useful in a wide range of applications. Within this context, this article shows a scheme for teaching structural biology based on hands-on computer approaches, including computational genomics, and structural bioinformatics tools. The students learn to use most of the leading computer programs available for the complete path that goes from sequence to structure and eventually function. During the course, they are taught to build models of proteins based on sequence and structure information. The students are also provided with a critical point of view on automatic procedures and learn to discern between likely and unlikely structures of their final models.

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.

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., M.M. is an investigator of the Sara Borrell program (CD09/00421). E.L.A. is a recipient of a predoctoral fellowship from the Department of Education, Universities and Research of the Basque Government (BFI-2011), and V.R. was a recipient of FIMIM predoctoral fellowship. L.E. is an investigator at the Carlos III program. This work was supported by a grant from the Instituto de Salud Carlos III (PI041890), Fundación Mutua Madrileña, AGAUR (SGR23), and RTICCS/FEDER (RD06/0020/0098 and RD12/0036/0054). M.D. was funded by the Ministerio de Ciencia e Innovación (BFU2009-09074 and MEC-CONSOLIDER CSD2007-00023), Generalitat Valenciana (PROMETEO 2008/134), and a grant from European Union Research (UE-HEALH-F2-2008-201666). J.V-F. was funded by a MICINN grant (CTQ2008-00755) and the EC VPH (no. FP7-2007-IST-223920).

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. P.M. is a recipient of a FPU fellowship (AP2009-2892) and M.C.M. is funded by the ‘‘Sara Borrell’’ Program from MICIN (CD09/00421). This work was supported by Fondo de Investigaciones Sanitarias (PI07/0778 and PI10/01128), AGAUR (2009SGR23), Fondos Feder RD06/0020/0098 and RD09/0076/00036, and Xarxa de Bancs de tumors sponsored by Pla Director d’Oncologia de/nCatalunya (XBTC). M.W.M. was supported by funds from the NIH/NCI R01 CA104397

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.

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 T ool K it ’—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 T ool K it 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.

Alzheimer's disease neuropathology is characterized by neuronal death, amyloid beta-peptide deposits and neurofibrillary tangles composed of paired helical filaments of tau protein. Although crucial for our understanding of the pathogenesis of Alzheimer's disease, the molecular mechanisms linking amyloid beta-peptide and paired helical filaments remain unknown. Here, we show that amyloid beta-peptide-induced nitro-oxidative damage promotes the nitrotyrosination of the glycolytic enzyme triosephosphate isomerase in human neuroblastoma cells. Consequently, nitro-triosephosphate isomerase was found to be present in brain slides from double transgenic mice overexpressing human amyloid precursor protein and presenilin 1, and in Alzheimer's disease patients. Higher levels of nitro-triosephosphate isomerase (P0.05) were detected, by Western blot, in immunoprecipitates from hippocampus (9 individuals) and frontal cortex (13 individuals) of Alzheimer's disease patients, compared with healthy subjects (4 and 9 individuals, respectively). Triosephosphate isomerase nitrotyrosination decreases the glycolytic flow. Moreover, during its isomerase activity, it triggers the production of the highly neurotoxic methylglyoxal (n = 4; P0.05). The bioinformatics simulation of the nitration of tyrosines 164 and 208, close to the catalytic centre, fits with a reduced isomerase activity. Human embryonic kidney (HEK) cells overexpressing double mutant triosephosphate isomerase (Tyr164 and 208 by Phe164 and 208) showed high methylglyoxal production. This finding correlates with the widespread glycation immunostaining in Alzheimer's disease cortex and hippocampus from double transgenic mice overexpressing amyloid precursor protein and presenilin 1. Furthermore, nitro-triosephosphate isomerase formed large beta-sheet aggregates in vitro and in vivo, as demonstrated by turbidometric analysis and electron microscopy. Transmission electron microscopy (TEM) and atomic force microscopy studies have demonstrated that nitro-triosephosphate isomerase binds tau monomers and induces tau aggregation to form paired helical filaments, the characteristic intracellular hallmark of Alzheimer's disease brains. Our results link oxidative stress, the main etiopathogenic mechanism in sporadic Alzheimer's disease, via the production of peroxynitrite and nitrotyrosination of triosephosphate isomerase, to amyloid beta-peptide-induced toxicity and tau pathology.

Gene regulation plays a major role in the control of developmental processes. Pattern formation, for example, is thought to be regulated by a limited number genes translated into transcription factors that control the differential expression of other genes in different cells in a given tissue. We focused on the Notch pathway during the formation of chess-like patterns along development. Simplified models exist of the patterning by lateral inhibition due to the Notch-Delta signalling cascade. We show here how parameters from the literature are able to explain the steady-state behavior of model tissuesof several sizes, although they are not able to reproduce time series of experiments. In order to refine the parameters set for data from real experiments we propose a practical implementation of an optimal experimental design protocol that combines parameter estimation tools with sensitivity analysis, in order to minimize the number of additional experiments to perform.

We describe a model for the data storage, retrieval and manipulation requirements of complex system applications based on pervasive, integrated, application specific databases shared over a peer to peer network. Such a model can significannotly increase productivity through transparent data sharing and querying as well as aid collaborations. We show a proof of concept of this approach as implemented in the Adun molecular simulation application together with a discussion of its limitations and possible extensions.

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. Several drugs that increase the activity of this enzyme are now in clinical phase of development: some of them are heme-dependent and might interact with the catalytic domain and others are heme-independent and supposedly bind to the sensory domain. The absence of reliable structural information is one of the factors that have precluded knowledge of the precise site of interaction of these molecules and of the mechanism of activation of the enzyme.

Finding why protein–protein interactions (PPIs) are so specific can provide a valuable tool in a variety of fields. Statistical surveys of so-called transient complexes (like those relevant for signal transduction mechanisms) have shown a tendency of polar residues to participate in the interaction region. Following this scheme, residues in the unbound partners have to compete between interacting with water or interacting with other residues of the protein. On the other hand, several works have shown that the notion of active site electrostatic preorganization can be used to interpret the high efficiency in enzyme reactions. This preorganization can be related to the instability of the residues important for catalysis. In some enzymes, in addition, conformational changes upon binding to other proteins lead to an increase in the activity of the enzymatic partner. In this article the linear response approximation version of the semimacroscopic protein dipoles Langevin dipoles (PDLD/S-LRA) model is used to evaluate the stability of several residues in two phosphate hydrolysis enzymes upon complexation with their activating partners. In particular, the residues relevant for PPI and for phosphate hydrolysis in the CDK2/Cyclin A and Ras/GAP complexes are analyzed. We find that the evaluation of the stability of residues in these systems can be used to identify not only active site regions but it can also be used as a guide to locate “hot spots” for PPIs. We also show that conformational changes play a major role in positioning interfacing residues in a proper “energetic” orientation, ready to interact with the residues in the partner protein surface. Thus, we extend the preorganization theory to PPIs, extrapolating the results we obtained from the above-mentioned complexes to a more general case. We conclude that the correlation between stability of a residue in the surface and the likelihood that it participates in the interaction can be a general fact for transient PPIs. Proteins 2006. © 2005 Wiley-Liss, Inc.

The effective design of catalytic antibodies represents a major conceptual and practical challenge. It is implicitly assumed that a proper transition state analogue (TSA) can elicit a catalytic antibody (CA) that will catalyze the given reaction in a similar way to an enzyme that would evolve (or was evolved) to catalyze this reaction. However, in most cases it was found that the TSA used produced CAs with relatively low rate enhancement as compared to the corresponding enzymes, when these exist. The present work explores the origin of this problem, by developing two approaches that examine the similarity of the TSA and the corresponding transition state (TS). These analyses are used to assess the proficiency of the CA generated by the given TSA. Both approaches focus on electrostatic effects that have been found to play a major role in enzymatic reactions. The first method uses molecular interaction potentials to look for the similarity between the TSA and the TS and, in principle, to help in designing new haptens by using 3D quantitative structure-activity relationships. The second and more quantitative approach generates a grid of Langevin dipoles, which are polarized by the TSA, and then uses the grid to bind the TS. Comparison of the resulting binding energy with the binding energy of the TS to the grid that was polarized by the TS provides an estimate of the proficiency of the given CA. Our methods are used in examining the origin of the difference between the catalytic power of the 1F7 CA and chorismate mutase. It is demonstrated that the relatively small changes in charge and structure between the TS and TSA are sufficient to account for the difference in proficiency between the CA and the enzyme. Apparently the environment that was preorganized to stabilize the TSA charge distribution does not provide a sufficient stabilization to the TS. The general implications of our findings and the difficulties in designing a perfect TSA are discussed. Finally, the possible use of our approach in screening for an optimal TSA is pointed out.

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 milisecond 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 electrostatic 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. Fil: Avila, Cesar Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina Fil: Dreschel, Nils J. D.. Universitat Pompeu Fabra; España Fil: Alcántara, Raúl. Universitat Pompeu Fabra; España Fil: Villà Freixa, Jordi. Universitat Pompeu Fabra; España

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.

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, τ, 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 τ 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 τ-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 τ grows. Results In this paper we extend Poisson τ-leap methods to a general class of Runge-Kutta (RK) τ-leap methods. We show that with the proper selection of the coefficients, the variance of the extended τ-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 τ-leap method. The approach paves the way to explore new multiscale methods to simulate (bio)chemical systems.