The Hypervariable Loops of Free TCRs Sample Multiple Distinct Metastable Conformations in Solution.

CD4+ and CD8+ αß T cell antigen recognition is determined by the interaction between the TCR Complementarity Determining Region (CDR) loops and the peptide-presenting MHC complex. These T cells are known for their ability to recognize multiple pMHC complexes, and for a necessary promiscuity that is required for their selection and function in the periphery. Crystallographic studies have previously elucidated the role of structural interactions in TCR engagement, but our understanding of the dynamic process that occurs during TCR binding is limited. To better understand the dynamic states that exist for TCR CDR loops in solution, and how this relates to their states when in complex with pMHC, we simulated the 2C T cell receptor in solution using all-atom molecular dynamics in explicit water and constructed a Markov State Model for each of the CDR3α and CDR3ß loops. These models reveal multiple metastable states for the CDR3 loops in solution. Simulation data and metastable states reproduce known CDR3ß crystal conformations, and reveal several novel conformations suggesting that CDR3ß bound states are the result of search processes from nearby pre-existing equilibrium conformational states. Similar simulations of the invariant, Type I Natural Killer T cell receptor NKT15, which engages the monomorphic, MHC-like CD1d ligand, demonstrate that iNKT TCRs also have distinct states, but comparatively restricted degrees of motion.

Advanced Modeling Reconciles Counterintuitive Decisions in Lead Optimization.

Lead optimization (LO) is essential to fulfill the efficacy and safety requirements of drug-based targeted therapy. The ease with which water may be locally removed from around the target protein crucially influences LO decisions. However, inferred binding sites often defy intuition and the resulting LO decisions are often counterintuitive, with nonpolar groups in the drug placed next to polar groups in the target. We first introduce biophysical advances to reconcile these apparent mismatches. We incorporate three-body energy terms that account for the net stabilization of preformed target structures upon removal of interfacial water concurrent with drug binding. These unexplored drug-induced environmental changes enhancing the target electrostatics are validated against drug-target affinity data, yielding superior computational accuracy required to improve drug design.

Drug leads for interactive protein targets with unknown structure.

The disruption of protein-protein interfaces (PPIs) remains a challenge in drug discovery. The problem becomes daunting when the structure of the target protein is unknown and is even further complicated when the interface is susceptible to disruptive phosphorylation. Based solely on protein sequence and information about phosphorylation-susceptible sites within the PPI, a new technology has been developed to identify drug leads to inhibit protein associations. Here we reveal this technology and contrast it with current structure-based technologies for the generation of drug leads. The novel technology is illustrated by a patented invention to treat heart failure. The success of this technology shows that it is possible to generate drug leads in the absence of target structure.

Purposely engineered drug-target mismatches for entropy-based drug optimization.

Proteins are dynamic objects that often undergo significant structural change and reduce their conformational possibilities upon ligand binding. Thus, unless dynamic information is incorporated, structure-based drug design becomes of limited applicability. Even within a dynamic approach, a rarely visited scenario arises as proteins increase their entropy content upon ligand binding by locally enhancing conformational exploration in the complex. In this opinion piece, we argue that this binding mode is of primary importance in drug development because it allows for drugs that are not optimized in the conventional way but feature mismatches with the target. Thus, we advocate entropy optimization that exploits dynamic information for drug design.

Dehydron analysis: quantifying the effect of hydrophobic groups on the strength and stability of hydrogen bonds.

In the past decade, research has demonstrated that defectively packed hydrogen bonds, or "dehydrons," play an important role in protein-ligand interactions and a host of other biochemical phenomena. These results are due in large part to the development of computational techniques to identify and analyze the hydrophobic microenvironments surrounding hydrogen bonds in protein structures. Here, we provide an introduction to the dehydron and the computational techniques that have been used to uncover its biological and biomedical significance. We then illustrate how dehydron-based computational analysis can be used as a basis for reengineering pharmaceutical compounds to improve their binding specificities.

Hydration profiles of amyloidogenic molecular structures.

Hydration shells of normal proteins display regions of highly structured water as well as patches of less structured bulk-like water. Recent studies suggest that isomers with larger surface densities of patches of bulk-like water have an increased propensity to aggregate. These aggregates are toxic to the cellular environment. Hence, the early detection of these toxic deposits is of paramount medical importance. We show that various morphological states of association of such isomers can be differentiated from the normal protein background based on the characteristic partition between bulk, caged, and surface hydration water and the magnetic resonance (MR) signals of this water. We derive simple mathematical equations relating the compartmentalization of water to the local hydration fraction and the packing density of the newly formed molecular assemblies. Then, we employ these equations to predict the MR response of water constrained by protein aggregation. Our results indicate that single units and compact aggregates that contain no water between constituents induce a shift of the MR signal from normal protein background to values in the hyperintensity domain (bright spots), corresponding to bulk water. In contrast, large plaques that cage significant amounts of water between constituents are likely to generate MR responses in the hypointensity domain (dark spots), typical for strongly correlated water. The implication of these results is that amyloids can display both dark and bright spots when compared to the normal gray background tissue on MR images. In addition, our findings predict that the bright spots are more likely to correspond to amyloids in their early stage of development. The results help explain the MR contrast patterns of amyloids and suggest a new approach for identifying unusual protein aggregation related to disease.

Modulating drug impact by wrapping target proteins.

Molecular therapy requires a careful control of specificity. The authors review the recent advances in this regard focusing on a novel marker for ligand-target interaction, the solvent-exposed hydrogen bond or dehydron. Dehydrons promote their own dehydration and are not conserved across homolog proteins. Thus, the so-called wrapping technology is geared at enhancing drug specificity and hinges on an analysis of interfacial dehydrons in target-ligand complexes to assess microenvironmental changes occurring on association. Dehydron differences across purported targets have been exploited to redesign drugs in order to enhance selectivity. Tested wrapping modifications to cancer drugs are reviewed. Distance matrices defined by comparing dehydron patterns across targets correlate strongly with pharmacologic distances. This fact suggests a broad applicability of the wrapping technology, ultimately leading to molecular therapies with tighter control of side effects.

Linear independence of pairwise comparisons of DNA microarray data.

MOTIVATION: For DNA microarrays, the gain in certainty by performing multiple experimental repeats is offset by the high cost of each experiment. In a typical experiment, two independent measurements (that is, data from two separate arrays) are combined to yield a single comparative index per gene. Thus, although one uses 2n arrays and performs 2n independent measurements, one obtains only n comparative measurements. We addressed the question: how many of the potential n2 comparisons derivable from such data are actually independent, and what effect do these additional comparisons have on the false positive rate. RESULTS: We show there are precisely 2n - 1 independent comparisons available from among the n2 possibilities. Applying these additional n - 1 independent comparisons to experimental and simulated data reduced the false positive rate by as much as 10-fold, with excellent agreement between experimental and theoretical false positive rates.

Adherence of packing defects in soluble proteins.

For protein structure to prevail in water, its backbone hydrogen bonds must be shielded from water attack, requiring a cluster of "wrapping" nonpolar groups. Thus, underwrapped regions are adhesive, as exogenous removal of surrounding water becomes thermodynamically favorable. Here we measure the average adhesive force exerted by an underwrapped hydrogen bond on a test hydrophobe and thus define a new interactivity constant.

Structural defects and the diagnosis of amyloidogenic propensity.

Disease-related amyloidogenic propensity has been unexpectedly found in proteins driven to adopt a monomeric uncomplexed state at high concentrations under near-physiological conditions. This situation occasionally arises in new health treatments, such as kidney dialysis. Assuming that under such conditions a partial retention of native structure takes place, this work identifies a structural characteristic indicating amyloidogenic propensity: a high density of backbone hydrogen bonds exposed to water attack in monomeric structure. On this basis, we propose a diagnostic tool based on the identification of hydrogen bonds with a paucity of intramolecular dehydration or "wrapping." We use this predictor to identify potentially pathogenic mutations that foster amyloidogenic propensity in human prions. Such mutations either enhance the intramolecular dehydration of beta-sheet hydrogen bonds, thus stabilizing the nucleus for rearrangement into the scrapie fold, or contribute to the destabilization of the cellular form by introducing additional underwrapped hydrogen bonds. Our predictions are consistent with known disease-related mutations and lead to a cogent explanation of the pathogenic nature of specific mutations affecting the cellular prion protein structural wrapping. On the other hand, a different wrapping of a very similar fold, mouse doppel, induces a dramatically different level of amyloidogenic propensity, suggesting that the packing within the fold, and not the fold itself, contains the signal for aggregation.

Correct identification of genes from serial analysis of gene expression tag sequences.

SAGE (serial analysis of gene expression) is a remarkable technique for genome-wide analysis of gene expression. It is crucial to understand the extent to which SAGE can accurately indicate a gene or expressed sequence tag (EST) with a single tag. We analyzed the effect of the size of SAGE tag on gene identification. Our observation indicates that SAGE tags are in general not long enough to achieve the degree of uniqueness of identification originally envisaged. Our observations also indicate that the limitation of using SAGE tag to identify a gene can be overcome by converting SAGE tags into longer 3' EST sequences with the generation of longer cDNA fragments from SAGE tages for gene identification (GLGI) method.