Characterization of myocardial injury phenotype by thermal liquid biopsy.

Background and aims: With the advent and implementation of high-sensitivity cardiac troponin assays, differentiation of patients with distinct types of myocardial injuries, including acute thrombotic myocardial infarction (TMI), acute non-thrombotic myocardial injury (nTMi), and chronic coronary atherosclerotic disease (cCAD), is of pressing clinical importance. Thermal liquid biopsy (TLB) emerges as a valuable diagnostic tool, relying on identifying thermally induced conformational changes of biomolecules in blood plasma. While TLB has proven useful in detecting and monitoring several cancers and autoimmune diseases, its application in cardiovascular diseases remains unexplored. In this proof-of-concept study, we sought to determine and characterize TLB profiles in patients with TMI, nTMi, and cCAD at multiple acute-phase time points (T 0 h, T 2 h, T 4 h, T 24 h, T 48 h) as well as a follow-up time point (Tfu) when the patient was in a stable state. Methods: TLB profiles were collected for 115 patients (60 with TMI, 35 with nTMi, and 20 with cCAD) who underwent coronary angiography at the event presentation and had subsequent follow-up. Medical history, physical, electrocardiographic, histological, biochemical, and angiographic data were gathered through medical records, standardized patient interviews, and core laboratory measurements. Results: Distinctive signatures were noted in the median TLB profiles across the three patient types. TLB profiles for TMI and nTMi patients exhibited gradual changes from T0 to Tfu, with significant differences during the acute and quiescent phases. During the quiescent phase, all three patient types demonstrated similar TLB signatures. An unsupervised clustering analysis revealed a unique TLB signature for the patients with TMI. TLB metrics generated from specific features of TLB profiles were tested for differences between patient groups. The first moment temperature (TFM) metric distinguished all three groups at time of presentation (T0). In addition, 13 other TLB-derived metrics were shown to have distinct distributions between patients with TMI and those with cCAD. Conclusion: Our findings demonstrated the use of TLB as a sensitive and data-rich technique to be explored in cardiovascular diseases, thus providing valuable insight into acute myocardial injury events.

Plasma Thermogram Parameters Differentiate Status and Overall Survival of Melanoma Patients.

Melanoma is the fifth most common cancer in the United States and the deadliest of all skin cancers. Even with recent advancements in treatment, there is still a 13% two-year recurrence rate, with approximately 30% of recurrences being distant metastases. Identifying patients at high risk for recurrence or advanced disease is critical for optimal clinical decision-making. Currently, there is substantial variability in the selection of screening tests and imaging, with most modalities characterized by relatively low accuracy. In the current study, we built upon a preliminary examination of differential scanning calorimetry (DSC) in the melanoma setting to examine its utility for diagnostic and prognostic assessment. Using regression analysis, we found that selected DSC profile (thermogram) parameters were useful for differentiation between melanoma patients and healthy controls, with more complex models distinguishing melanoma patients with no evidence of disease from patients with active disease. Thermogram features contributing to the third principal component (PC3) were useful for differentiation between controls and melanoma patients, and Cox proportional hazards regression analysis indicated that PC3 was useful for predicting the overall survival of active melanoma patients. With the further development and optimization of the classification method, DSC could complement current diagnostic strategies to improve screening, diagnosis, and prognosis of melanoma patients.

Sample Processing Considerations for Protein Stability Studies of Low Concentration Biofluid Samples using Differential Scanning Calorimetry.

BACKGROUND: The analysis of biofluid samples with low protein content (e.g., urine or saliva) can be challenging for downstream analysis methods with limited sensitivity. To circumvent this problem, sample processing methods are employed to increase the protein concentration in analyzed samples. However, for some techniques, like differential scanning calorimetry (DSC) that characterizes thermally-induced unfolding of biomolecules, sample processing must not affect native protein structure and stability. METHODS: We evaluated centrifugal concentration and stirred cell ultrafiltration, two common methods of sample concentration characterized by a low risk of protein denaturation, with the goal of establishing a protocol for DSC analysis of low concentration biospecimens. RESULTS: Our studies indicate that both methods can affect protein stability assessed by DSC and, even after optimization of several parameters, the obtained DSC profile (thermogram) suggested that sample processing affects the structure or intermolecular interactions of component proteins contributing to altered thermal stability detectable by DSC. We also found a relationship between changes in thermograms and low protein concentration, indicating that diluting biospecimens to concentrations below 0.1 mg/mL can perturb the intermolecular environment and affect the structure of proteins present in the solution. CONCLUSION: Dilution of samples below 0.1 mg/mL, as well as concentration of samples with low protein content, resulted in affected thermogram shapes suggesting changes in protein stability. This should be taken into account when concentrating dilute samples or employing techniques that lower the protein concentration (e.g., fractionation), when downstream applications include techniques, such as DSC, that require the preservation of native protein forms.

The Utility of Differential Scanning Calorimetry Curves of Blood Plasma for Diagnosis, Subtype Differentiation and Predicted Survival in Lung Cancer.

Early detection of lung cancer (LC) significantly increases the likelihood of successful treatment and improves LC survival rates. Currently, screening (mainly low-dose CT scans) is recommended for individuals at high risk. However, the recent increase in the number of LC cases unrelated to the well-known risk factors, and the high false-positive rate of low-dose CT, indicate a need to develop new, non-invasive methods for LC detection. Therefore, we evaluated the use of differential scanning calorimetry (DSC) for LC patients' diagnosis and predicted survival. Additionally, by applying mass spectrometry, we investigated whether changes in O- and N-glycosylation of plasma proteins could be an underlying mechanism responsible for observed differences in DSC curves of LC and control subjects. Our results indicate selected DSC curve features could be useful for differentiation of LC patients from controls with some capable of distinction between subtypes and stages of LC. DSC curve features also correlate with LC patients' overall/progression free survival. Moreover, the development of classification models combining patients' DSC curves with selected plasma protein glycosylation levels that changed in the presence of LC could improve the sensitivity and specificity of the detection of LC. With further optimization and development of the classification method, DSC could provide an accurate, non-invasive, radiation-free strategy for LC screening and diagnosis.

Arsenite Exposure Displaces Zinc from ZRANB2 Leading to Altered Splicing.

Exposure to arsenic, a class I carcinogen, affects 200 million people globally. Skin is the major target organ, but the molecular etiology of arsenic-induced skin carcinogenesis remains unclear. Arsenite (As3+)-induced disruption of alternative splicing could be involved, but the mechanism is unknown. Zinc finger proteins play key roles in alternative splicing. As3+ can displace zinc (Zn2+) from C3H1 and C4 zinc finger motifs (zfm's), affecting protein function. ZRANB2, an alternative splicing regulator with two C4 zfm's integral to its structure and splicing function, was chosen as a candidate for this study. We hypothesized that As3+ could displace Zn2+ from ZRANB2, altering its structure, expression, and splicing function. As3+/Zn2+ binding and mutual displacement experiments were performed with synthetic apo-peptides corresponding to each ZRANB2 zfm, employing a combination of intrinsic fluorescence, ultraviolet spectrophotometry, zinc colorimetric assay, and liquid chromatography-tandem mass spectrometry. ZRANB2 expression in HaCaT cells acutely exposed to As3+ (0 or 5 µM, 0-72 h; or 0-5 µM, 6 h) was examined by RT-qPCR and immunoblotting. ZRANB2-dependent splicing of TRA2B mRNA, a known ZRANB2 target, was monitored by reverse transcription-polymerase chain reaction. As3+ bound to, as well as displaced Zn2+ from, each zfm. Also, Zn2+ displaced As3+ from As3+-bound zfm's acutely, albeit transiently. As3+ exposure induced ZRANB2 protein expression between 3 and 24 h and at all exposures tested but not ZRANB2 mRNA expression. ZRANB2-directed TRA2B splicing was impaired between 3 and 24 h post-exposure. Furthermore, ZRANB2 splicing function was also compromised at all As3+ exposures, starting at 100 nm. We conclude that As3+ exposure displaces Zn2+ from ZRANB2 zfm's, changing its structure and compromising splicing of its targets, and increases ZRANB2 protein expression as a homeostatic response both at environmental/toxicological exposures and therapeutically relevant doses.

Multi-group diagnostic classification of high-dimensional data using differential scanning calorimetry plasma thermograms.

The thermoanalytical technique differential scanning calorimetry (DSC) has been applied to characterize protein denaturation patterns (thermograms) in blood plasma samples and relate these to a subject's health status. The analysis and classification of thermograms is challenging because of the high-dimensionality of the dataset. There are various methods for group classification using high-dimensional data sets; however, the impact of using high-dimensional data sets for cancer classification has been poorly understood. In the present article, we proposed a statistical approach for data reduction and a parametric method (PM) for modeling of high-dimensional data sets for two- and three- group classification using DSC and demographic data. We compared the PM to the non-parametric classification method K-nearest neighbors (KNN) and the semi-parametric classification method KNN with dynamic time warping (DTW). We evaluated the performance of these methods for multiple two-group classifications: (i) normal versus cervical cancer, (ii) normal versus lung cancer, (iii) normal versus cancer (cervical + lung), (iv) lung cancer versus cervical cancer as well as for three-group classification: normal versus cervical cancer versus lung cancer. In general, performance for two-group classification was high whereas three-group classification was more challenging, with all three methods predicting normal samples more accurately than cancer samples. Moreover, specificity of the PM method was mostly higher or the same as KNN and DTW-KNN with lower sensitivity. The performance of KNN and DTW-KNN decreased with the inclusion of demographic data, whereas similar performance was observed for the PM which could be explained by the fact that the PM uses fewer parameters as compared to KNN and DTW-KNN methods and is thus less susceptible to the risk of overfitting. More importantly the accuracy of the PM can be increased by using a greater number of quantile data points and by the inclusion of additional demographic and clinical data, providing a substantial advantage over KNN and DTW-KNN methods.

Thermal liquid biopsy for monitoring melanoma patients under surveillance during treatment: A pilot study.

BACKGROUND: Differential Scanning Calorimetry (DSC) is a technique traditionally used to study thermally induced macromolecular transitions, and it has recently been proposed as a novel approach for diagnosis and monitoring of several diseases. We report a pilot study applying Thermal Liquid Biopsy (TLB, DSC thermograms of plasma samples) as a new clinical approach for diagnostic assessment of melanoma patients. METHODS: Multiparametric analysis of DSC thermograms of patient plasma samples collected during treatment and surveillance (63 samples from 10 patients) were compared with clinical and diagnostic imaging assessment to determine the utility of thermograms for diagnostic assessment in melanoma. Nine of the ten patients were stage 2 or 3 melanoma subjects receiving adjuvant therapy after surgical resection of their melanomas. The other patient had unresectable stage 4 melanoma and was treated with immunotherapy. Two reference groups were used: (A) 36 healthy subjects and (B) 13 samples from 8 melanoma patients who had completed successful surgical management of their disease and were determined by continued clinical assessment to have no evidence of disease. RESULTS: Plasma thermogram analysis applied to melanoma patients generally agrees with clinical evaluation determined by physical assessment or diagnostic imaging (~80% agreement). No false negatives were obtained from DSC thermograms. Importantly, this methodology was able to detect changes in disease status before it was identified clinically. CONCLUSIONS: Thermal Liquid Biopsy could be used in combination with current clinical assessment for the earlier detection of melanoma recurrence and metastasis. GENERAL SIGNIFICANCE: TLB offers advantages over current diagnostic techniques (PET/CT imaging), limited in frequency by radiation burden and expense, in providing a minimally-invasive, low-risk, low-cost clinical test for more frequent personalized patient monitoring to assess recurrence and facilitate clinical decision-making.

Application and interpretation of functional data analysis techniques to differential scanning calorimetry data from lupus patients.

BACKGROUND: DSC is used to determine thermally-induced conformational changes of biomolecules within a blood plasma sample. Recent research has indicated that DSC curves (or thermograms) may have different characteristics based on disease status and, thus, may be useful as a monitoring and diagnostic tool for some diseases. Since thermograms are curves measured over a range of temperature values, they are considered functional data. In this paper we apply functional data analysis techniques to analyze differential scanning calorimetry (DSC) data from individuals from the Lupus Family Registry and Repository (LFRR). The aim was to assess the effect of lupus disease status as well as additional covariates on the thermogram profiles, and use FD analysis methods to create models for classifying lupus vs. control patients on the basis of the thermogram curves. METHODS: Thermograms were collected for 300 lupus patients and 300 controls without lupus who were matched with diseased individuals based on sex, race, and age. First, functional regression with a functional response (DSC) and categorical predictor (disease status) was used to determine how thermogram curve structure varied according to disease status and other covariates including sex, race, and year of birth. Next, functional logistic regression with disease status as the response and functional principal component analysis (FPCA) scores as the predictors was used to model the effect of thermogram structure on disease status prediction. The prediction accuracy for patients with Osteoarthritis and Rheumatoid Arthritis but without Lupus was also calculated to determine the ability of the classifier to differentiate between Lupus and other diseases. Data were divided 1000 times into separate 2/3 training and 1/3 test data for evaluation of predictions. Finally, derivatives of thermogram curves were included in the models to determine whether they aided in prediction of disease status. RESULTS: Functional regression with thermogram as a functional response and disease status as predictor showed a clear separation in thermogram curve structure between cases and controls. The logistic regression model with FPCA scores as the predictors gave the most accurate results with a mean 79.22% correct classification rate with a mean sensitivity = 79.70%, and specificity = 81.48%. The model correctly classified OA and RA patients without Lupus as controls at a rate of 75.92% on average with a mean sensitivity = 79.70% and specificity = 77.6%. Regression models including FPCA scores for derivative curves did not perform as well, nor did regression models including covariates. CONCLUSION: Changes in thermograms observed in the disease state likely reflect covalent modifications of plasma proteins or changes in large protein-protein interacting networks resulting in the stabilization of plasma proteins towards thermal denaturation. By relating functional principal components from thermograms to disease status, our Functional Principal Component Analysis model provides results that are more easily interpretable compared to prior studies. Further, the model could also potentially be coupled with other biomarkers to improve diagnostic classification for lupus.

Characterization and classification of lupus patients based on plasma thermograms.

OBJECTIVE: Plasma thermograms (thermal stability profiles of blood plasma) are being utilized as a new diagnostic approach for clinical assessment. In this study, we investigated the ability of plasma thermograms to classify systemic lupus erythematosus (SLE) patients versus non SLE controls using a sample of 300 SLE and 300 control subjects from the Lupus Family Registry and Repository. Additionally, we evaluated the heterogeneity of thermograms along age, sex, ethnicity, concurrent health conditions and SLE diagnostic criteria. METHODS: Thermograms were visualized graphically for important differences between covariates and summarized using various measures. A modified linear discriminant analysis was used to segregate SLE versus control subjects on the basis of the thermograms. Classification accuracy was measured based on multiple training/test splits of the data and compared to classification based on SLE serological markers. RESULTS: Median sensitivity, specificity, and overall accuracy based on classification using plasma thermograms was 86%, 83%, and 84% compared to 78%, 95%, and 86% based on a combination of five antibody tests. Combining thermogram and serology information together improved sensitivity from 78% to 86% and overall accuracy from 86% to 89% relative to serology alone. Predictive accuracy of thermograms for distinguishing SLE and osteoarthritis / rheumatoid arthritis patients was comparable. Both gender and anemia significantly interacted with disease status for plasma thermograms (p<0.001), with greater separation between SLE and control thermograms for females relative to males and for patients with anemia relative to patients without anemia. CONCLUSION: Plasma thermograms constitute an additional biomarker which may help improve diagnosis of SLE patients, particularly when coupled with standard diagnostic testing. Differences in thermograms according to patient sex, ethnicity, clinical and environmental factors are important considerations for application of thermograms in a clinical setting.

Select human cancer mutants of NRMT1 alter its catalytic activity and decrease N-terminal trimethylation.

A subset of B-cell lymphoma patients have dominant mutations in the histone H3 lysine 27 (H3K27) methyltransferase EZH2, which change it from a monomethylase to a trimethylase. These mutations occur in aromatic resides surrounding the active site and increase growth and alter transcription. We study the N-terminal trimethylase NRMT1 and the N-terminal monomethylase NRMT2. They are 50% identical, but differ in key aromatic residues in their active site. Given how these residues affect EZH2 activity, we tested whether they are responsible for the distinct catalytic activities of NRMT1/2. Additionally, NRMT1 acts as a tumor suppressor in breast cancer cells. Its loss promotes oncogenic phenotypes but sensitizes cells to DNA damage. Mutations of NRMT1 naturally occur in human cancers, and we tested a select group for altered activity. While directed mutation of the aromatic residues had minimal catalytic effect, NRMT1 mutants N209I (endometrial cancer) and P211S (lung cancer) displayed decreased trimethylase and increased monomethylase/dimethylase activity. Both mutations are located in the peptide-binding channel and indicate a second structural region impacting enzyme specificity. The NRMT1 mutants demonstrated a slower rate of trimethylation and a requirement for higher substrate concentration. Expression of the mutants in wild type NRMT backgrounds showed no change in N-terminal methylation levels or growth rates, demonstrating they are not acting as dominant negatives. Expression of the mutants in cells lacking endogenous NRMT1 resulted in minimal accumulation of N-terminal trimethylation, indicating homozygosity could help drive oncogenesis or serve as a marker for sensitivity to DNA damaging chemotherapeutics or γ-irradiation.

Evidence that vitronectin is a potent migration-enhancing factor for cancer cells chaperoned by fibrinogen: a novel view of the metastasis of cancer cells to low-fibrinogen lymphatics and body cavities.

Diluted (1%) plasma induces migration of malignant cell lines much more strongly than potent pro-metastatic factors. To characterize the factor(s) present in diluted plasma responsible for this phenomenon we performed i) heat inactivation, ii) dialysis, iii) proteinase K treatment, and iv) molecular size filtration studies. We found that this remarkable pro-migratory activity of diluted normal plasma is associated with a ~50-100-kD protein that interacts with GαI protein-coupled receptors and activates p42/44 MAPK and AKT signaling in target cells. Since this pro-migratory activity of 1% plasma decreases at higher plasma concentrations (> 20%), but is retained in serum, we hypothesized that fibrinogen may be involved as a chaperone of the protein(s). To identify the pro-migratory protein(s) present in diluted plasma and fibrinogen-depleted serum, we performed gel filtration and hydrophobic interaction chromatography followed by mass spectrometry analysis. We identified several putative protein candidates that were further tested in in vitro experiments. We found that this pro-migratory factor chaperoned by fibrinogen is vitronectin, which activates uPAR, and that this effect can be inhibited by fibrinogen. These results provide a novel mechanism for the metastasis of cancer cells to lymphatics and body cavities, in which the concentration of fibrinogen is low, and thus suggests that free vitronectin stimulates migration of tumor cells.

Differential scanning calorimetry as a complementary diagnostic tool for the evaluation of biological samples.

BACKGROUND: Differential scanning calorimetry (DSC) is a tool for measuring the thermal stability profiles of complex molecular interactions in biological fluids. DSC profiles (thermograms) of biofluids provide specific signatures which are being utilized as a new diagnostic approach for characterizing disease but the development of these approaches is still in its infancy. METHODS: This article evaluates several approaches for the analysis of thermograms which could increase the utility of DSC for clinical application. Thermograms were analyzed using localized thermogram features and principal components (PCs). The performance of these methods was evaluated alongside six models for the classification of a data set comprised of 300 systemic lupus erythematosus (SLE) patients and 300 control subjects obtained from the Lupus Family Registry and Repository (LFRR). RESULTS: Classification performance was substantially higher using the penalized algorithms relative to localized features/PCs alone. The models were grouped into two sets, the first having smoother solution vectors but lower classification accuracies than the second with seemingly noisier solution vectors. CONCLUSIONS: Coupling thermogram technology with modern classification algorithms provides a powerful diagnostic approach for analysis of biological samples. The solution vectors from the models may reflect important information from the thermogram profiles for discriminating between clinical groups. GENERAL SIGNIFICANCE: DSC thermograms show sensitivity to changes in the bulk plasma proteome that correlate with clinical status. To move this technology towards clinical application the development of new approaches is needed to extract discriminatory parameters from DSC profiles for the comparison and diagnostic classification of patients.

Clinical application of plasma thermograms. Utility, practical approaches and considerations.

Differential scanning calorimetry (DSC) studies of blood plasma are part of an emerging area of the clinical application of DSC to biofluid analysis. DSC analysis of plasma from healthy individuals and patients with various diseases has revealed changes in the thermal profiles of the major plasma proteins associated with the clinical status of the patient. The sensitivity of DSC to the concentration of proteins, their interactions with other proteins or ligands, or their covalent modification underlies the potential utility of DSC analysis. A growing body of literature has demonstrated the versatility and performance of clinical DSC analysis across a range of biofluids and in a number of disease settings. The principles, practice and challenges of DSC analysis of plasma are described in this article.

Detection of cervical cancer biomarker patterns in blood plasma and urine by differential scanning calorimetry and mass spectrometry.

Improved methods for the accurate identification of both the presence and severity of cervical intraepithelial neoplasia (CIN) and extent of spread of invasive carcinomas of the cervix (IC) are needed. Differential scanning calorimetry (DSC) has recently been shown to detect specific changes in the thermal behavior of blood plasma proteins in several diseases. This methodology is being explored to provide a complementary approach for screening of cervical disease. The present study evaluated the utility of DSC in differentiating between healthy controls, increasing severity of CIN and early and advanced IC. Significant discrimination was apparent relative to the extent of disease with no clear effect of demographic factors such as age, ethnicity, smoking status and parity. Of most clinical relevance, there was strong differentiation of CIN from healthy controls and IC, and amongst patients with IC between FIGO Stage I and advanced cancer. The observed disease-specific changes in DSC profiles (thermograms) were hypothesized to reflect differential expression of disease biomarkers that subsequently bound to and affected the thermal behavior of the most abundant plasma proteins. The effect of interacting biomarkers can be inferred from the modulation of thermograms but cannot be directly identified by DSC. To investigate the nature of the proposed interactions, mass spectrometry (MS) analyses were employed. Quantitative assessment of the low molecular weight protein fragments of plasma and urine samples revealed a small list of peptides whose abundance was correlated with the extent of cervical disease, with the most striking plasma peptidome data supporting the interactome theory of peptide portioning to abundant plasma proteins. The combined DSC and MS approach in this study was successful in identifying unique biomarker signatures for cervical cancer and demonstrated the utility of DSC plasma profiles as a complementary diagnostic tool to evaluate cervical cancer health.

Calorimetric analysis of the plasma proteome: identification of type 1 diabetes patients with early renal function decline.

BACKGROUND: Microalbuminuria (MA) has been questioned as a predictor of progressive renal dysfunction in patients with type 1 diabetes (T1D). Consequently, new clinical end points are needed that identify or predict patients that are at risk for early renal function decline (ERFD). The potential clinical utility of differential scanning calorimetry (DSC) analysis of blood plasma and other biofluids has recently been reported. This method provides an alternate physical basis with which to study disease-associated changes in the bulk plasma proteome. METHODS: DSC analysis of blood plasma was applied to identify unique signatures of ERFD in subjects enrolled in the 1st Joslin Study of the Natural History of Microalbuminuria in Type 1 Diabetes, a prospective cohort study of T1D patients. Recent data suggests that differences in the plasma peptidome of these patients correlate with longitudinal measures of renal function. Differences in DSC profile (thermogram) features were evaluated between T1D MA individuals exhibiting ERFD (n=15) and matched control subjects (n=14). RESULTS: The average control group thermogram resembled a previously defined healthy thermogram. Differences were evident between ERFD and control individuals. Heat capacity values of the main two transitions were found to be significant discriminators of patient status. CONCLUSIONS: Results from this pilot study suggest the potential utility of DSC proteome analysis to prognostic indicators of renal disease in T1D. GENERAL SIGNIFICANCE: DSC shows sensitivity to changes in the bulk plasma proteome that correlate with clinical status in T1D providing additional support for the utility of DSC profiling in clinical diagnostics.

Thermodynamic studies for drug design and screening.

INTRODUCTION: A key part of drug design and development is the optimization of molecular interactions between an engineered drug candidate and its binding target. Thermodynamic characterization provides information about the balance of energetic forces driving binding interactions and is essential for understanding and optimizing molecular interactions. AREAS COVERED: This review discusses the information that can be obtained from thermodynamic measurements and how this can be applied to the drug development process. Current approaches for the measurement and optimization of thermodynamic parameters are presented, specifically higher throughput and calorimetric methods. Relevant literature for this review was identified in part by bibliographic searches for the period 2004 - 2011 using the Science Citation Index and PUBMED and the keywords listed below. EXPERT OPINION: The most effective drug design and development platform comes from an integrated process utilizing all available information from structural, thermodynamic and biological studies. Continuing evolution in our understanding of the energetic basis of molecular interactions and advances in thermodynamic methods for widespread application are essential to realize the goal of thermodynamically driven drug design. Comprehensive thermodynamic evaluation is vital early in the drug development process to speed drug development toward an optimal energetic interaction profile while retaining good pharmacological properties. Practical thermodynamic approaches, such as enthalpic optimization, thermodynamic optimization plots and the enthalpic efficiency index, have now matured to provide proven utility in the design process. Improved throughput in calorimetric methods remains essential for even greater integration of thermodynamics into drug design.

Structure and stability of higher-order human telomeric quadruplexes.

G-quadruplex formation in the sequences 5'-(TTAGGG)(n) and 5'(TTAGGG)(n)TT (n = 4, 8, 12) was studied using circular dichroism, sedimentation velocity, differential scanning calorimetry, and molecular dynamics simulations. Sequences containing 8 and 12 repeats formed higher-order structures with two and three contiguous quadruplexes, respectively. Plausible structures for these sequences were determined by molecular dynamics simulations followed by experimental testing of predicted hydrodynamic properties by sedimentation velocity. These structures featured folding of the strand into contiguous quadruplexes with mixed hybrid conformations. Thermodynamic studies showed the strands folded spontaneous to contain the maximum number contiguous quadruplexes. For the sequence 5'(TTAGGG)(12)TT, more than 90% of the strands contained completely folded structures with three quadruplexes. Statistical mechanical-based deconvolution of thermograms for three quadruplex structures showed that each quadruplex melted independently with unique thermodynamic parmameters. Thermodynamic analysis revealed further that quadruplexes in higher-ordered structures were destabilized relative to their monomeric counterparts, with unfavorable coupling free energies. Quadruplex stability thus depends critically on the sequence and structural context.

Allosteric interaction of nucleotides and tRNA(ala) with E. coli alanyl-tRNA synthetase.

Alanyl-tRNA synthetase, a dimeric class 2 aminoacyl-tRNA synthetase, activates glycine and serine at significant rates. An editing activity hydrolyzes Gly-tRNA(ala) and Ser-tRNA(ala) to ensure fidelity of aminoacylation. Analytical ultracentrifugation demonstrates that the enzyme is predominately a dimer in solution. ATP binding to full length enzyme (ARS875) and to an N-terminal construct (ARS461) is endothermic (ΔH = 3-4 kcal mol(-1)) with stoichiometries of 1:1 for ARS461 and 2:1 for full-length dimer. Binding of aminoacyl-adenylate analogues, 5'-O-[N-(L-alanyl)sulfamoyl]adenosine (ASAd) and 5'-O-[N-(L-glycinyl)sulfamoyl]adenosine (GSAd), are exothermic; ASAd exhibits a large negative heat capacity change (ΔC(p) = 0.48 kcal mol(-1) K(-1)). Modification of alanyl-tRNA synthetase with periodate-oxidized tRNA(ala) (otRNA(ala)) generates multiple, covalent, enzyme-tRNA(ala) products. The distribution of these products is altered by ATP, ATP and alanine, and aminoacyl-adenylate analogues (ASAd and GSAd). Alanyl-tRNA synthetase was modified with otRNA(ala), and tRNA-peptides from tryptic digests were purified by ion exchange chromatography. Six peptides linked through a cyclic dehydromoropholino structure at the 3'-end of tRNA(ala) were sequenced by mass spectrometry. One site lies in the N-terminal adenylate synthesis domain (residue 74), two lie in the opening to the editing site (residues 526 and 585), and three (residues 637, 639, and 648) lie on the back side of the editing domain. At least one additional modification site was inferred from analysis of modification of ARS461. The location of the sites modified by otRNA(ala) suggests that there are multiple modes of interaction of tRNA(ala) with the enzyme, whose distribution is influenced by occupation of the ATP binding site.

Statistical analysis of plasma thermograms measured by differential scanning calorimetry.

Melting curves of human plasma measured by differential scanning calorimetry (DSC), known as thermograms, have the potential to markedly impact diagnosis of human diseases. A general statistical methodology is developed to analyze and classify DSC thermograms to analyze and classify thermograms. Analysis of an acquired thermogram involves comparison with a database of empirical reference thermograms from clinically characterized diseases. Two parameters, a distance metric, P, and correlation coefficient, r, are combined to produce a 'similarity metric,' ρ, which can be used to classify unknown thermograms into pre-characterized categories. Simulated thermograms known to lie within or fall outside of the 90% quantile range around a median reference are also analyzed. Results verify the utility of the methods and establish the apparent dynamic range of the metric ρ. Methods are then applied to data obtained from a collection of plasma samples from patients clinically diagnosed with SLE (lupus). High correspondence is found between curve shapes and values of the metric ρ. In a final application, an elementary classification rule is implemented to successfully analyze and classify unlabeled thermograms. These methods constitute a set of powerful yet easy to implement tools for quantitative classification, analysis and interpretation of DSC plasma melting curves.