Isothermal titration calorimetry (ITC) is a powerful classical method that enables researchers in many fields to study the thermodynamics of molecular interactions. Primary ITC data comprise the temporal evolution of differential power reporting the heat of reaction during a series of injections of aliquots of a reactant into a sample cell. By integration of each injection peak, an isotherm can be constructed of total changes in enthalpy as a function of changes in solution composition, which is rich in thermodynamic information on the reaction. However, the signals from the injection peaks are superimposed by the stochastically varying time-course of the instrumental baseline power, limiting the precision of ITC isotherms. Here, we describe a method for automated peak assignment based on peak-shape analysis via singular value decomposition in combination with detailed least-squares modeling of local pre- and post-injection baselines. This approach can effectively filter out contributions of short-term noise and adventitious events in the power trace. This method also provides, for the first time, statistical error estimates for the individual isotherm data points. In turn, this results in improved detection limits for high-affinity or low-enthalpy binding reactions and significantly higher precision of the derived thermodynamic parameters.

The protein refractive index increment, dn/dc, is an important parameter underlying the concentration determination and the biophysical characterization of proteins and protein complexes in many techniques. In this study, we examine the widely used assumption that most proteins have dn/dc values in a very narrow range, and reappraise the prediction of dn/dc of unmodified proteins based on their amino acid composition. Applying this approach in large scale to the entire set of known and predicted human proteins, we obtain, for the first time, to our knowledge, an estimate of the full distribution of protein dn/dc values. The distribution is close to Gaussian with a mean of 0.190 ml/g (for unmodified proteins at 589 nm) and a standard deviation of 0.003 ml/g. However, small proteins <10 kDa exhibit a larger spread, and almost 3000 proteins have values deviating by more than two standard deviations from the mean. Due to the widespread availability of protein sequences and the potential for outliers, the compositional prediction should be convenient and provide greater accuracy than an average consensus value for all proteins. We discuss how this approach should be particularly valuable for certain protein classes where a high dn/dc is coincidental to structural features, or may be functionally relevant such as in proteins of the eye.

Isothermal titration calorimetry experiments can provide significantly more detailed information about molecular interactions when combined in global analysis. For example, global analysis can improve the precision of binding affinity and enthalpy, and of possible linkage parameters, even for simple bimolecular interactions, and greatly facilitate the study of multi-site and multi-component systems with competition or cooperativity. A pre-requisite for global analysis is the departure from the traditional binding model, including an ‘n’-value describing unphysical, non-integral numbers of sites. Instead, concentration correction factors can be introduced to account for either errors in the concentration determination or for the presence of inactive fractions of material. SEDPHAT is a computer program that embeds these ideas and provides a graphical user interface for the seamless combination of biophysical experiments to be globally modeled with a large number of different binding models. It offers statistical tools for the rigorous determination of parameter errors, correlations, as well as advanced statistical functions for global ITC (gITC) and global multi-method analysis (GMMA). SEDPHAT will also take full advantage of error bars of individual titration data points determined with the unbiased integration software NITPIC. The present communication reviews principles and strategies of global analysis for ITC and its extension to GMMA in SEDPHAT. We will also introduce a new graphical tool for aiding experimental design by surveying the concentration space and generating simulated data sets, which can be subsequently statistically examined for their information content. This procedure can replace the ‘c’-value as an experimental design parameter, which ceases to be helpful for multi-site systems and in the context of gITC.

Membrane‐less organelles in cells are large, dynamic protein/protein or protein/RNA assemblies that have been reported in some cases to have liquid droplet properties. However, the molecular interactions underlying the recruitment of components are not well understood. Herein, we study how the ability to form higher‐order assemblies influences the recruitment of the speckle‐type POZ protein (SPOP) to nuclear speckles. SPOP, a cullin‐3‐RING ubiquitin ligase (CRL3) substrate adaptor, self‐associates into higher‐order oligomers; that is, the number of monomers in an oligomer is broadly distributed and can be large. While wild‐type SPOP localizes to liquid nuclear speckles, self‐association‐deficient SPOP mutants have a diffuse distribution in the nucleus. SPOP oligomerizes through its BTB and BACK domains. We show that BTB‐mediated SPOP dimers form linear oligomers via BACK domain dimerization, and we determine the concentration‐dependent populations of the resulting oligomeric species. Higher‐order oligomerization of SPOP stimulates CRL3SPOP ubiquitination efficiency for its physiological substrate Gli3, suggesting that nuclear speckles are hotspots of ubiquitination. Dynamic, higher‐order protein self‐association may be a general mechanism to concentrate functional components in membrane‐less cellular bodies.

Isothermal titration calorimetry (ITC) is a powerful and widely used method to measure the energetics of macromolecular interactions by recording a thermogram of differential heating power during a titration. However, traditional ITC analysis is limited by stochastic thermogram noise and by the limited information content of a single titration experiment. Here we present a protocol for bias-free thermogram integration based on automated shape analysis of the injection peaks, followed by combination of isotherms from different calorimetric titration experiments into a global analysis, statistical analysis of binding parameters and graphical presentation of the results. This is performed using the integrated public-domain software packages NITPIC, SEDPHAT and GUSSI. The recently developed low-noise thermogram integration approach and global analysis allow for more precise parameter estimates and more reliable quantification of multisite and multicomponent cooperative and competitive interactions. Titration experiments typically take 1-2.5 h each, and global analysis usually takes 10-20 min.

Significant progress in the interpretation of analytical ultracentrifugation (AUC) data in the last decade has led to profound changes in the practice of AUC, both for sedimentation velocity (SV) and sedimentation equilibrium (SE). Modern computational strategies have allowed for the direct modeling of the sedimentation process of heterogeneous mixtures, resulting in SV size-distribution analyses with significantly improved detection limits and strongly enhanced resolution. These advances have transformed the practice of SV, rendering it the primary method of choice for most existing applications of AUC, such as the study of protein self- and hetero-association, the study of membrane proteins, and applications in biotechnology. New global multi-signal modeling and mass conservation approaches in SV and SE, in conjunction with the effective-particle framework for interpreting the sedimentation boundary structure of interacting systems, as well as tools for explicit modeling of the reaction/diffusion/sedimentation equations to experimental data, have led to more robust and more powerful strategies for the study of reversible protein interactions and multi-protein complexes. Furthermore, modern mathematical modeling capabilities have allowed for a detailed description of many experimental aspects of the acquired data, thus enabling novel experimental opportunities, with important implications for both sample preparation and data acquisition. The goal of the current commentary is to supplement previous AUC protocols, Current Protocols in Protein Science 20.3 (1999) and 20.7 (2003), and 7.12 (2008), and provide an update describing the current tools for the study of soluble proteins, detergent-solubilized membrane proteins and their interactions by SV and SE.

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This chapter presents an introduction to the kinetic analysis of SPR biosensor data for the determination of affinity and kinetic rate constants of biomolecular interactions between an immobilized and a soluble binding partner. The need to be aware of and critically tests the assumptions underlying the analysis models is emphasized and the consequences for the experimental design are discussed. The two most common sources of deviation in SPR surface binding kinetics from the ideal pseudo-first order binding kinetics of bimolecular reactions are mass transport limitations and the heterogeneity of the surface sites. These problems are intrinsic to the use of a biosensor surface for characterizing interactions. The effect of these factors on the observed binding kinetics, and strategies to account for them are reviewed, both in the context of mathematical data analysis, as well as the design of the experiments and controls.