Search

The goal of assessment in a Universal Design for Learning (UDL) approach is to provide the kinds of information that will improve instruction for each learner. Whereas traditional tests and diagnostics tend to focus on identifying weaknesses and disabilities in the individual learner, diagnostics in a UDL approach focus much more on identifying weaknesses and barriers in the design of the learning context itself, making it possible to probe whether a different set of options, a different path, or a different design might lead to better learning for any given learner. A UDL approach to assessment assumes the fundamental centrality of emotions as part of the anticipated variability among all learners and asserts that when we place emotions front and center in assessment, we obtain more accurate and meaningful assessment results. In addition, a UDL approach incorporates recurring and flexible assessment throughout instruction to provide ongoing, actionable feedback to educators and students before failure takes place, when taking action can make a real difference for all. Teachers, students, parents, administrators, and assessment designers/developers all need accurate assessments and timely results to use as feedback to inform next steps. Instructional approaches with a foundation in UDL will reduce the inadvertent barriers to learning that many students currently face, making the assessment of progress toward expertise more accurate, informative, and useful, and enable the mosaic of all learners to become masters of learning itself.

Protein-protein interfaces are highly attractive targets for drug discovery because they are involved in a large number of disease pathways where therapeutic intervention would bring widespread benefit. Recent successes have challenged the widely held belief that these targets are 'undruggable'. The pocket finding algorithms described here show marked differences between the binding pockets that define protein-protein interactions (PPIs) and those that define protein-ligand interactions (PLIs) of currently marketed drugs. In the case of PPIs, drug discovery methods that simultaneously target several small pockets at the protein-protein interface are likely to increase the chances of success in this new and important field of therapeutics.

Structure Based Drug Design (SBDD) is a computational approach to lead discovery that uses the three-dimensional structure of a protein to fit drug-like molecules into a ligand binding site to modulate function. Identifying the location of the binding site is therefore a vital first step in this process, restricting the search space for SBDD or virtual screening studies. The detection and characterisation of functional sites on proteins has increasingly become an area of interest. Structural genomics projects are increasingly yielding protein structures with unknown functions and binding sites. Binding site prediction was pioneered by pocket detection, since the binding site is often found in the largest pocket. More recent methods involve phylogenetic analysis, identifying structural similarity with proteins of known function and identifying regions on the protein surface with a potential for high binding affinity. Binding site prediction has been used in several SBDD projects and has been incorporated into several docking tools. We discuss different methods of ligand binding site prediction, their strengths and weaknesses, and how they have been used in SBDD.

Unlike nuclear localization signals, there is no obvious consensus sequence for the targeting of proteins to the nucleolus. The nucleolus is a dynamic subnuclear structure which is crucial to the normal operation of the eukaryotic cell. Studying nucleolar trafficking signals is problematic as many nucleolar retention signals (NoRSs) are part of classical nuclear localization signals (NLSs). In addition, there is no known consensus signal with which to inform a study. The avian infectious bronchitis virus (IBV), coronavirus nucleocapsid (N) protein, localizes to the cytoplasm and the nucleolus. Mutagenesis was used to delineate a novel eight amino acid motif that was necessary and sufficient for nucleolar retention of N protein and colocalize with nucleolin and fibrillarin. Additionally, a classical nuclear export signal (NES) functioned to direct N protein to the cytoplasm. Comparison of the coronavirus NoRSs with known cellular and other viral NoRSs revealed that these motifs have conserved arginine residues. Molecular modelling, using the solution structure of severe acute respiratory (SARS) coronavirus N-protein, revealed that this motif is available for interaction with cellular factors which may mediate nucleolar localization. We hypothesise that the N-protein uses these signals to traffic to and from the nucleolus and the cytoplasm.

Unlike nuclear localization signals, there is no obvious consensus sequence for the targeting of proteins to the nucleolus. The nucleolus is a dynamic subnuclear structure which is crucial to the normal operation of the eukaryotic cell. Studying nucleolar trafficking signals is problematic as many nucleolar retention signals (NoRSs) are part of classical nuclear localization signals (NLSs). In addition, there is no known consensus signal with which to inform a study. The avian infectious bronchitis virus (IBV), coronavirus nucleocapsid (N) protein, localizes to the cytoplasm and the nucleolus. Mutagenesis was used to delineate a novel eight amino acid motif that was necessary and sufficient for nucleolar retention of N protein and colocalize with nucleolin and fibrillarin. Additionally, a classical nuclear export signal (NES) functioned to direct N protein to the cytoplasm. Comparison of the coronavirus NoRSs with known cellular and other viral NoRSs revealed that these motifs have conserved arginine residues. Molecular modelling, using the solution structure of severe acute respiratory (SARS) coronavirus N-protein, revealed that this motif is available for interaction with cellular factors which may mediate nucleolar localization. We hypothesise that the N-protein uses these signals to traffic to and from the nucleolus and the cytoplasm.

Angiotensin-converting enzyme-2 (ACE2) may play an important role in cardiorenal disease and it has also been implicated as a cellular receptor for the severe acute respiratory syndrome (SARS) virus. The ACE2 active-site model and its crystal structure, which was solved recently, highlighted key differences between ACE2 and its counterpart angiotensin-converting enzyme (ACE), which are responsible for their differing substrate and inhibitor sensitivities. In this study the role of ACE2 active-site residues was explored by site-directed mutagenesis. Arg273 was found to be critical for substrate binding such that its replacement causes enzyme activity to be abolished. Although both His505 and His345 are involved in catalysis, it is His345 and not His505 that acts as the hydrogen bond donor/acceptor in the formation of the tetrahedral peptide intermediate. The difference in chloride sensitivity between ACE2 and ACE was investigated, and the absence of a second chloride-binding site (CL2) in ACE2 confirmed. Thus ACE2 has only one chloride-binding site (CL1) whereas ACE has two sites. This is the first study to address the differences that exist between ACE2 and ACE at the molecular level. The results can be applied to future studies aimed at unravelling the role of ACE2, relative to ACE, in vivo.

A method is described for the rapid comparison of protein binding sites using geometric matching to detect similar three-dimensional structure. The geometric matching detects common atomic features through identification of the maximum common sub-graph or clique. These features are not necessarily evident from sequence or from global structural similarity giving additional insight into molecular recognition not evident from current sequence or structural classification schemes. Here we use the method to produce an all-against-all comparison of phosphate binding sites in a number of different nucleotide phosphate-binding proteins. The similarity search is combined with clustering of similar sites to allow a preliminary structural classification. Clustering by site similarity produces a classification of binding sites for the 476 representative local environments producing ten main clusters representing half of the representative environments. The similarities make sense in terms of both structural and functional classification schemes. The ten main clusters represent a very limited number of unique structural binding motifs for phosphate. These are the structural P-loop, di-nucleotide binding motif [FAD/ NAD(P)-binding and Rossman-like fold] and FAD-binding motif. Similar classification schemes for nucleotide binding proteins have also been arrived at independently by others using different methods.

Familial exudative vitreoretinopathy (FEVR) is an inherited blinding disorder of the retinal vascular system. Autosomal dominant FEVR is genetically heterogeneous, but its principal locus, EVR1, is on chromosome 11q13-q23. The gene encoding the Wnt receptor frizzled-4 (FZD4) was recently reported to be the EVR1 gene, but our mutation screen revealed fewer patients harboring mutations than expected. Here, we describe mutations in a second gene at the EVR1 locus, low-density-lipoprotein receptor–related protein 5 (LRP5), a Wnt coreceptor. This finding further underlines the significance of Wnt signaling in the vascularization of the eye and highlights the potential dangers of using multiple families to refine genetic intervals in gene-identification studies.

A new method is presented that docks molecular fragments to a rigid protein receptor. It uses a probabilistic procedure based on statistical thermodynamic principles to place ligand atom triplets at the lowest energy sites. The probabilistic method ranks receptor binding modes so that the lowest energy ones are sampled first. This allows constraints to be introduced to limit the depth of the search leading to a computationally efficient method of sampling low energy conformational space. This is combined with energy minimization of the initial fragment placement to arrive at a low energy conformation for the molecular fragment. Two different search methods are tested involving (i) geometric hashing and (ii) pose clustering methods. Ten molecular fragments were docked that have commonly been used to test docking methods. The success rate was 8/10 and 10/10 for generating a close solution ranked first using the two different sampling procedures. In general, all five of the top ranked solutions reproduce the observed binding mode, which increases confidence in the predictions. A set of ten molecular fragments that have previously been identified as problematic were docked. Success was achieved in 3/10 and 4/10 using the two different methods. Again there is a high level of agreement between the two methods and again in the successful cases the top ranked solutions are correct whilst in the case of the failures none are. The geometric hashing and pose clustering methods are fast averaging ∼ 13 and ∼ 11 s per placement respectively using conservative parameters. The results are very encouraging and will facilitate the process of finding novel small molecule lead compounds by virtual screening of chemical databases.

Methods for the prediction of protein function from structure are of growing importance in the age of structural genomics. Here, we focus on the problem of identifying sites of potential serine protease inhibitor interactions on the surface of proteins of known structure. Given that there is no sequence conservation within canonical loops from different inhibitor families, we first compare representative loops to all fragments of equal length among proteins of known structure by calculating main-chain RMS deviation. Fragments with RMS deviation below a certain threshold (hits) are removed if residues have solvent accessibilities appreciably lower than those observed in the search structure. These remaining hits are further filtered to remove those occurring largely within secondary structure elements. Likely functional significance is restricted further by considering only extracellular protein domains. By comparing different canonical loop structures to the protein structure database, we show that the method is able to detect previously known inhibitors. In addition, we discuss potentially new canonical loop structures found in secreted hydrolases, toxins, viral proteins, cytokines and other proteins. We discuss the possible functional significance of several of the examples found, and comment on implications for the prediction of function from protein 3D structure.

A computationally tractable strategy has been developed to refine protein-protein interfaces that models the effects of side-chain conformational change, solvation and limited rigid-body movement of the subunits. The proteins are described at the atomic level by a multiple copy representation of side-chains modelled according to a rotamer library on a fixed peptide backbone. The surrounding solvent environment is described by “soft” sphere Langevin dipoles for water that interact with the protein via electrostatic, van der Waals and field-dependent hydrophobic terms. Energy refinement is based on a two-step process in which (1) a probability-based conformational matrix of the protein side-chains is refined iteratively by a mean field method. A side-chain interacts with the protein backbone and the probability-weighted average of the surrounding protein side-chains and solvent molecules. The resultant protein conformations then undergo (2) rigid-body energy minimization to relax the protein interface. Steps (1) and (2) are repeated until convergence of the interaction energy. The influence of refinement on side-chain conformation starting from unbound conformations found improvement in the RMSD of side-chains in the interface of protease-inhibitor complexes, and shows that the method leads to an improvement in interface geometry. In terms of discriminating between docked structures, the refinement was applied to two classes of protein-protein complex: five protease-protein inhibitor and four antibody-antigen complexes. A large number of putative docked complexes have already been generated for the test systems using our rigid-body docking program, FTDOCK. They include geometries that closely resemble the crystal complex, and therefore act as a test for the refinement procedure. In the protease-inhibitors, geometries that resemble the crystal complex are ranked in the top four solutions for four out of five systems when solvation is included in the energy function, against a background of between 26 and 364 complexes in the data set. The results for the antibody-antigen complexes are not as encouraging, with only two of the four systems showing discrimination. It would appear that these results reflect the somewhat different binding mechanism dominant in the two types of protein-protein complex. Binding in the protease-inhibitors appears to be “lock and key” in nature. The fixed backbone and mobile side-chain representation provide a good model for binding. Movements in the backbone geometry of antigens on binding represent an “induced-fit” and provides more of a challenge for the model. Given the limitations of the conformational sampling, the ability of the energy function to discriminate between native and non-native states is encouraging. Development of the approach to include greater conformational sampling could lead to a more general solution to the protein docking problem.

A protein docking study was performed for two classes of biomolecular complexes: six enzyme/inhibitor and four antibody/antigen. Biomolecular complexes for which crystal structures of both the complexed and uncomplexed proteins are available were used for eight of the ten test systems. Our docking experiments consist of a global search of translational and rotational space followed by refinement of the best predictions. Potential complexes are scored on the basis of shape complementarity and favourable electrostatic interactions using Fourier correlation theory. Since proteins undergo conformational changes upon binding, the scoring function must be sufficiently soft to dock unbound structures successfully. Some degree of surface overlap is tolerated to account for side-chain flexibility. Similarly for electrostatics, the interaction of the dispersed point charges of one protein with the Coulombic field of the other is measured rather than precise atomic interactions. We tested our docking protocol using the native rather than the complexed forms of the proteins to address the more scientifically interesting problem of predictive docking. In all but one of our test cases, correctly docked geometries (interface Calpha RMS deviation

The CASP blind trials (Critical Assessment of techniques for protein Structure Prediction) assess the accuracy of protein prediction that includes evaluation of comparative model building of protein structures. Comparative models of four proteins (T0001, T0003, T0017, and T0028) for CASP2 (held during 1996) were constructed using computer algorithms combined with visual inspection. Essentially the main-chain modelling involves construction of the target structure from rigid-body segments of homologues and loop fragments extracted from homologous and nonredundant databases. Side-chains were initially constructed by inheritance from the parent or from a rotamer library. Side-chain conformations were then refined using a novel mean field approach that includes solvation. Comparison of the models with the subsequently released X-ray structures identified the successes and limitations of our approach. The most problematic area is the quality of the sequence alignments between parent(s) and target. In this respect the overinterpretation of the conserved features within homologous families can be misleading. Several features of our approach have a positive effect on the accuracy of the models. For T0003, inspection correctly identified that a lower sequence identity parent provides the best framework for this model. Loop selection worked well where a homologous protein fragment was used, but that the use of nonredundant fragment library remains problematic for hinge movements and displacements in secondary structure elements relative to the parent. Side-chain refinement improved residue conformations relative to the initial model. Use of limited energy minimization improved the stereochemical quality of the model without increasing the RMS deviation. This study has identified methods that are effective and areas requiring further attention to improve model building by comparison. Proteins, Suppl. 1:59–67, 1997. © 1998 Wiley-Liss, Inc.

We develop a Bayesian model for the alignment of two point configurations under the full similarity transformations of rotation, translation and scaling. Other work in this area has concentrated on rigid body transformations, where scale information is preserved, motivated by problems involving molecular data; this is known as form analysis. We concentrate on a Bayesian formulation for statistical shape analysis. We generalize the model introduced by Green and Mardia [Biometrika 93 (2006) 235-254] for the pairwise alignment of two unlabeled configurations to full similarity transformations by introducing a scaling factor to the model. The generalization is not straightforward, since the model needs to be reformulated to give good performance when scaling is included. We illustrate our method on the alignment of rat growth profiles and a novel application to the alignment of protein domains. Here, scaling is applied to secondary structure elements when comparing protein folds; additionally, we find that one global scaling factor is not in general sufficient to model these data and, hence, we develop a model in which multiple scale factors can be included to handle different scalings of shape components., Published in at http://dx.doi.org/10.1214/12-AOAS615 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Crystallization of the 1:1 molecular complex between the beta-lactamase TEM-1 and the beta-lactamase inhibitory protein BLIP has provided an opportunity to put a stringent test on current protein-docking algorithms. Prior to the successful determination of the structure of the complex, nine laboratory groups were given the refined atomic coordinates of each of the native molecules. Other than the fact that BLIP is an effective inhibitor of a number of beta-lactamase enzymes (KI for TEM-1 approximately 100 pM) no other biochemical or structural data were available to assist the practitioners in their molecular docking. In addition, it was not known whether the molecules underwent conformational changes upon association or whether the inhibition was competitive or non-competitive. All six of the groups that accepted the challenge correctly predicted the general mode of association of BLIP and TEM-1.

We use molecular docking and free energy calculations to estimate the relative free energy of binding of six arylamide compounds designed to inhibit the hDM2-p53 interaction. We show that using docking methods to predict or rank the binding affinity of a series of arylamide inhibitors of the hDM2-p53 interaction is problematic. However, using free energy calculations, we show that we can achieve levels of accuracy that can guide the development of novel arylamide compounds. We perform alchemical free energy calculations using the Desmond molecular dynamics package with the same arylamide inhibitors of the hDM2-p53 system and illustrate the challenges of performing accurate free energy calculations for realistic systems. To our knowledge, these are the first calculations for inhibitors of the hDM2 system that employ a full treatment of statistical mechanics including explicit water representation and full protein flexibility. We show that mutating three functional groups in a single transformation can be more efficient than mutating the groups one by one if proper intermediates are used. We also show that Hamiltonian exchanges can improve the efficiency of the calculation compared to standard alchemical methods, with a novel use of the phase space overlap to monitor sampling extent. We show that, despite sampling limitations, this approach can achieve levels of accuracy sufficient to bias further inhibitor modification toward binding, and identifies antiparallel configurations as stable or more stable than the parallel configurations that are typically considered.

We report a computational approach that integrates structural bioinformatics, molecular modelling and systems biology to construct a drug-target network on a structural proteome-wide scale. The approach has been applied to the genome of Mycobacterium tuberculosis (M.tb), the causative agent of one of today's most widely spread infectious diseases. The resulting drug-target interaction network for all structurally characterized approved drugs bound to putative M.tb receptors, we refer to as the ‘TB-drugome’. The TB-drugome reveals that approximately one-third of the drugs examined have the potential to be repositioned to treat tuberculosis and that many currently unexploited M.tb receptors may be chemically druggable and could serve as novel anti-tubercular targets. Furthermore, a detailed analysis of the TB-drugome has shed new light on the controversial issues surrounding drug-target networks [1]–[3]. Indeed, our results support the idea that drug-target networks are inherently modular, and further that any observed randomness is mainly caused by biased target coverage. The TB-drugome (http://funsite.sdsc.edu/drugome/TB) has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally the methodology may be applied to other pathogens of interest with results improving as more of their structural proteomes are determined through the continued efforts of structural biology/genomics., Author Summary The worldwide increase in multi-drug resistant TB poses a great threat to human health and highlights the need to identify new anti-tubercular agents. We have developed a computational strategy to link the structural proteome of Mycobacterium tuberculosis, the causative agent of tuberculosis, to all structurally characterized approved drugs, and hence construct a proteome-wide drug-target network – the TB-drugome. The TB-drugome has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally, the proteome-wide and multi-scale view of target and drug space may facilitate a systematic drug discovery process, which concurrently takes into account the disease mechanism and druggability of targets, the drug-likeness and ADMET properties of chemical compounds, and the genetic dispositions of individuals. Ultimately it may help to reduce the high attrition rate in drug development through a better understanding of drug-receptor interactions on a large scale.

Inhibition of amyloid fibril formation by stabilization of the native form of the protein transthyretin (TTR) is a viable approach for the treatment of familial amyloid polyneuropathy that has been gaining momentum in the field of amyloid research. The TTR stabilizer molecules discovered to date have shown efficacy at inhibiting fibrilization in vitro but display impairing issues of solubility, affinity for TTR in the blood plasma and/or adverse effects. In this study we present a benchmark of four protein- and ligand-based virtual screening (VS) methods for identifying novel TTR stabilizers: (i) two-dimensional (2D) similarity searches with chemical hashed, pharmacophore, and UNITY fingerprints, (ii) 3D searches based on shape, chemical, and electrostatic similarity, (iii) LigMatch, a new ligand-based method which uses multiple templates and combines 3D geometric hashing with a 2D preselection process, and (iv) molecular docking to consensus X-ray crystal structures of TTR. We illustrate the potential of the best-performing VS protocols to retrieve promising new leads by ranking a tailored library of 2.3 million commercially available compounds. Our predictions show that the top-scoring molecules possess distinctive features from the known TTR binders, holding better solubility, fraction of halogen atoms, and binding affinity profiles. To the best of our knowledge, this is the first attempt to rationalize the utilization of a large battery of in silico screening techniques toward the identification of a new generation of TTR amyloid inhibitors.

One of the key ingredients in drug discovery is the derivation of conceptual templates called pharmacophores. A pharmacophore model characterizes the physicochemical properties common to all active molecules, called ligands, bound to a particular protein receptor, together with their relative spatial arrangement. Motivated by this important application, we develop a Bayesian hierarchical model for the derivation of pharmacophore templates from multiple configurations of point sets, partially labeled by the atom type of each point. The model is implemented through a multistage template hunting algorithm that produces a series of templates that capture the geometrical relationship of atoms matched across multiple configurations. Chemical information is incorporated by distinguishing between atoms of different elements, whereby different elements are less likely to be matched than atoms of the same element. We illustrate our method through examples of deriving templates from sets of ligands that all bind structurally related protein active sites and show that the model is able to retrieve the key pharmacophore features in two test cases.

The proposal that the active site vacuole of NAD(+)-S-lactate dehydrogenase is unable to accommodate any imbalance in electrostatic charge was tested by genetically manipulating the cDNA coding for human muscle lactate dehydrogenase to make a protein with an aspartic acid introduced at position 140 instead of the wild-type asparagine. The Asn 140-Asp mutant enzyme has the same kcat as the wild type (Asn 140) at low pH (4.5), and at higher pH the Km for pyruvate increases 10-fold for each unit increase in pH up to pH 9. We conclude that the anion of Asp 140 is completely inactive and that it binds pyruvate with a Km that is over 1,000 times that of the Km of the neutral, protonated aspartic-140. Experimental results and molecular modeling studies indicate the pKa of the active site histidine-195 in the enzyme-NADH complex is raised to greater than 10 by the presence of the anion at position 140. Energy minimization and molecular dynamics studies over 36 ps suggest that the anion at position 140 promotes the opening of and the entry of mobile solvent beneath the polypeptide loop (98-110), which normally seals off the internal active site vacuole from external bulk solvent.

Current homology-modelling methods do not consider small molecules in their automated processes. Therefore, the development of a reliable tool for protein-ligand homology modelling is an important next step in generating plausible models for molecular interactions. Two automated protein-ligand homology-modelling strategies, requiring no expert knowledge from the user, are investigated here. Both employ the "induced fit" concept with flexibility in side chains and ligand. The most successful strategy superimposes the new ligand over the original ligand before homology modelling, allowing the new ligand to be taken into consideration during protein modelling (rather than after), facilitating conformational change in the local backbone if necessary. We show that this approach results in successful modelling of the ligand and key binding-site residues of angiotensin-converting enzyme 2 (ACE2) from its homologue ACE, which is not possible via conventional homology modelling or by homology modelling followed by docking. Several other difficult target complexes are also successfully modelled, reproducing native protein-ligand contacts with significantly different biological substrates and different binding-site conformations. These include the modelling of Cdk5 (cyclin-dependent kinase 5) from Cdk2, thymidine phosphorylase from a bacterial homologue, and dihydrofolate reductase from a recombinant variant with a markedly different inhibitor. In terms of average modelling quality across 82 targets, the ligand RMSD with respect to the experimental structure is 1.4 A (and 2.0 A for the protein binding site) for "easy" cases and 2.9 A for the ligand (and 2.7 A for the protein binding site) in "hard" cases. This demonstrates the importance of selecting an optimal template. Ligand-modelling accuracy is strongly dependent on target-template ligand structural similarity, rather than target-template sequence identity. However, protein-modelling accuracy is dependent on both. Our automated protein-ligand homology-modelling strategy generates a higher degree of accuracy than homology modelling followed by docking, generating an average ligand RMSD that is 1-2 A better than docking with homology models.

Carbohydrate binding sites are considered important for cellular recognition and adhesion and are important targets for drug design. In this paper we present a new method called InCa-SiteFinder for predicting non-covalent inositol and carbohydrate binding sites on the surface of protein structures. It uses the van der Waals energy of a protein–probe interaction and amino acid propensities to locate and predict carbohydrate binding sites. The protein surface is searched for continuous volume envelopes that correspond to a favorable protein–probe interaction. These volumes are subsequently analyzed to demarcate regions of high cumulative propensity for binding a carbohydrate moiety based on calculated amino acid propensity scores. InCa-SiteFinder 1 was tested on an independent test set of 80 protein–ligand complexes. It efficiently identifies carbohydrate binding sites with high specificity and sensitivity. It was also tested on a second test set of 80 protein–ligand complexes containing 40 known carbohydrate binders (having 40 carbohydrate binding sites) and 40 known drug-like compound binders (having 58 known drug-like compound binding sites) for the prediction of the location of the carbohydrate binding sites and to distinguish these from the drug-like compound binding sites. At 73% sensitivity the method showed 98% specificity. Almost all of the carbohydrate and drug-like compound binding sites were correctly identified with an overall error rate of 12%.

Aicardi-Goutieres syndrome is a mendelian mimic of congenital infection and also shows overlap with systemic lupus erythematosus at both a clinical and biochemical level. The recent identification of mutations in TREX1 and genes encoding the RNASEH2 complex and studies of the function of TREX1 in DNA metabolism have defined a previously unknown mechanism for the initiation of autoimmunity by interferon-stimulatory nucleic acid. Here we describe mutations in SAMHD1 as the cause of AGS at the AGS5 locus and present data to show that SAMHD1 may act as a negative regulator of the cell-intrinsic antiviral response.

The development and validation of a new knowledge based scoring function (SIScoreJE) to predict binding energy between proteins and ligands is presented. SIScoreJE efficiently predicts the binding energy between a small molecule and its protein receptor. Protein−ligand atomic contact information was derived from a Non-Redundant Data set (NRD) of over 3000 X-ray crystal structures of protein−ligand complexes. This information was classified for individual “atom contact pairs” (ACP) which is used to calculate the atomic contact preferences. In addition to the two schemes generated in this study we have assessed a number of other common atom-type classification schemes. The preferences were calculated using an information theoretic relationship of joint entropy. Among 18 different atom-type classification schemes “ScoreJE Atom Type set2” (SATs2) was found to be the most suitable for our approach. To test the sensitivity of the method to the inclusion of solvent, Single-body Solvation Potentials (SSP) were al...

The structure of the type I fructose 1,6-bisphosphate aldolase from human muscle has been extended from 3 A to 2 A resolution. The improvement in the resulting electron density map is such that the 20 or so C-terminal residues, known to be associated with activity and isozyme specificity, have been located. The side-chain of the Schiff's base-forming lysine 229 is located towards the centre of an eight-stranded β-barrel type structure. The C-terminal “tail” extends from the rim of the β-barrel towards lysine 229, thus forming part of the active site of the enzyme. This structural arrangement appears to explain the difference in activity and specificity of the three tissue-specific human aldolases and helps with our understanding of the type I aldolase reaction mechanism.