Protein binding affinity prediction using support vector regression and interfecial features

In understanding biology at the molecular level, analysis of protein interactions and protein binding affinity is a challenge. It is an important problem in computational and structural biology. Experimental measurement of binding affinity in the wet-lab is expensive and time consuming. Therefore, machine learning approaches are widely used to predict protein interactions and binding affinities by learning from specific properties of existing complexes. In this work, we propose an innovative computational model to predict binding affinities and interaction based on sequence, structural and interface features of the interacting proteins that are robust to binding associated conformational changes. We modeled the prediction of binding affinity as classification and regression problem with least-squared and support vector regression models using structure and sequence features of proteins. Specifically, we have used the number and composition of interacting residues at protein complexes interface as features and sequence features. We evaluated the performance of our prediction models using Affinity Benchmark Dataset version 2.0 which contains a diverse set of both bound and unbound protein complex structures with known binding affinities. We evaluated our regression performance results with root mean square error (RMSE) as well as Spearman and Pearson's correlation coefficients using a leave-one-out cross-validation protocol. We evaluate classification results with AUC-ROC and AUC-PR Our results show that Support Vector Regression performs significantly better than other models with a Spearman Correlation coefficient of 0.58, Pearson Correlation score of 0.55 and RMSE of 2.41 using 3-mer and sequence feature. It is interesting to note that simple features based on 3-mer features and the properties of the interface of a protein complex are predictive of its binding affinity. These features, together with support vector regression achieve higher accuracy than existing sequence based methods

Gene silencing and large-scale domain structure of the E. coli genome

The H-NS chromosome-organizing protein in E. coli can stabilize genomic DNA loops, and form oligomeric structures connected to repression of gene expression. Motivated by the link between chromosome organization, protein binding and gene expression, we analyzed publicly available genomic data sets of various origins, from genome-wide protein binding profiles to evolutionary information, exploring the connections between chromosomal organization, genesilencing, pseudo-gene localization and horizontal gene transfer. We report the existence of transcriptionally silent contiguous areas corresponding to large regions of H-NS protein binding along the genome, their position indicates a possible relationship with the known large-scale features of chromosome organization

Method for Computing Protein Binding Affinity

A Monte Carlo method is given to compute the binding affinity of a ligand to a protein. The method involves extending configuration space by a discrete variable indicating whether the ligand is bound to the protein and a special Monte Carlo move which allows transitions between the unbound and bound states. Provided that an accurate protein structure is given, that the protein-ligand binding site is known, and that an accurate chemical force field together with a continuum solvation model is used, this method provides a quantitative estimate of the free energy of binding.Comment: RevTex, 10 pages with 5 figures. Explanatory figure adde

Pre-hybridisation: an efficient way of suppressing endogenous biotin-binding activity inherent to biotin–streptavidin detection system

Endogenous biotin or biotinylated protein binding activity is a major drawback to biotin-avidin/streptavidin detection system. The avidin/streptavidin conjugate used to detect the complex of the biotinylated secondary antibody and the primary antibody binds to endogenous biotin or biotinylated proteins leading to non-specific signals. In Western blot, the endogenous biotin or biotinylated protein binding activity is usually manifested in the form of ~72kDa, ~75kDa and ~150kDa protein bands, which often mask the signals of interest. To overcome this problem, a method based on prior hybridisation of the biotinylated secondary antibody and the streptavidin conjugate was developed. The method was tested alongside the conventional biotin-streptavidin method on proteins extracted from zebrafish (Danio rerio) embryos. Results showed that the newly developed method efficiently suppresses the endogenous biotin or biotinylated protein binding activity inherent to the biotin-streptavidin detection system

Conformational selection in protein binding and function

Protein binding and function often involves conformational changes. Advanced NMR experiments indicate that these conformational changes can occur in the absence of ligand molecules (or with bound ligands), and that the ligands may 'select' protein conformations for binding (or unbinding). In this review, we argue that this conformational selection requires transition times for ligand binding and unbinding that are small compared to the dwell times of proteins in different conformations, which is plausible for small ligand molecules. Such a separation of timescales leads to a decoupling and temporal ordering of binding/unbinding events and conformational changes. We propose that conformational-selection and induced-change processes (such as induced fit) are two sides of the same coin, because the temporal ordering is reversed in binding and unbinding direction. Conformational-selection processes can be characterized by a conformational excitation that occurs prior to a binding or unbinding event, while induced-change processes exhibit a characteristic conformational relaxation that occurs after a binding or unbinding event. We discuss how the ordering of events can be determined from relaxation rates and effective on- and off-rates determined in mixing experiments, and from the conformational exchange rates measured in advanced NMR or single-molecule FRET experiments. For larger ligand molecules such as peptides, conformational changes and binding events can be intricately coupled and exhibit aspects of conformational-selection and induced-change processes in both binding and unbinding direction.Comment: review article; 10 pages, 4 figures, Protein Sci. 201

Blood Protein Binding of Cyclosporine in Transplant Patients

The objective of this study was to compare the binding of cyclosporine to blood proteins between four healthy subjects and five liver and eight renal transplant patients. Fresh heparinized blood was obtained, to which sufficient quantities of tritium-labelled cyclosporine and unlabelled cyclosporine were added to blood samples or red blood cell (RBC) suspensions. Concentrations of cyclosporine in whole blood, plasma, RBC suspension, and phosphate buffer were estimated by liquid scintigraphy. The blood:plasma ratio of cyclosporine in transplant patients was significantly lower (P < .05) than that in healthy volunteers. The RBC:buffer ratio, a measure of affinity of RBCs for cyclosporine, was highest in those with liver transplants and lowest in those with kidney transplants. The unbound fraction of cyclosporine in plasma was less in transplant patients than in healthy volunteers. The results of this study indicate that there are differences in blood protein binding of cyclosporine between transplant patients that may contribute to the differences in the pharmacokinetics and pharmacodynamics of this drug

RNA–protein binding kinetics in an automated microfluidic reactor

Microfluidic chips can automate biochemical assays on the nanoliter scale, which is of considerable utility for RNA–protein binding reactions that would otherwise require large quantities of proteins. Unfortunately, complex reactions involving multiple reactants cannot be prepared in current microfluidic mixer designs, nor is investigation of long-time scale reactions possible. Here, a microfluidic ‘Riboreactor’ has been designed and constructed to facilitate the study of kinetics of RNA–protein complex formation over long time scales. With computer automation, the reactor can prepare binding reactions from any combination of eight reagents, and is optimized to monitor long reaction times. By integrating a two-photon microscope into the microfluidic platform, 5-nl reactions can be observed for longer than 1000 s with single-molecule sensitivity and negligible photobleaching. Using the Riboreactor, RNA–protein binding reactions with a fragment of the bacterial 30S ribosome were prepared in a fully automated fashion and binding rates were consistent with rates obtained from conventional assays. The microfluidic chip successfully combines automation, low sample consumption, ultra-sensitive fluorescence detection and a high degree of reproducibility. The chip should be able to probe complex reaction networks describing the assembly of large multicomponent RNPs such as the ribosome

Fluctuations in Mass-Action Equilibrium of Protein Binding Networks

We consider two types of fluctuations in the mass-action equilibrium in protein binding networks. The first type is driven by relatively slow changes in total concentrations (copy numbers) of interacting proteins. The second type, to which we refer to as spontaneous, is caused by quickly decaying thermodynamic deviations away from the equilibrium of the system. As such they are amenable to methods of equilibrium statistical mechanics used in our study. We investigate the effects of network connectivity on these fluctuations and compare them to their upper and lower bounds. The collective effects are shown to sometimes lead to large power-law distributed amplification of spontaneous fluctuations as compared to the expectation for isolated dimers. As a consequence of this, the strength of both types of fluctuations is positively correlated with the overall network connectivity of proteins forming the complex. On the other hand, the relative amplitude of fluctuations is negatively correlated with the abundance of the complex. Our general findings are illustrated using a real network of protein-protein interactions in baker's yeast with experimentally determined protein concentrations.Comment: 4 pages, 3 figure