Cold spots are universal in protein–protein interactions

Proteins interact with each other through binding interfaces that differ greatly in size and physico‐chemical properties. Within the binding interface, a few residues called hot spots contribute the majority of the binding free energy and are hence irreplaceable. In contrast, cold spots are occupied by suboptimal amino acids, providing possibility for affinity enhancement through mutations. In this study, we identify cold spots due to cavities and unfavorable charge interactions in multiple protein–protein interactions (PPIs). For our cold spot analysis, we first use a small affinity database of PPIs with known structures and affinities and then expand our search to nearly 4000 homo‐ and heterodimers in the Protein Data Bank (PDB). We observe that cold spots due to cavities are present in nearly all PPIs unrelated to their binding affinity, while unfavorable charge interactions are relatively rare. We also find that most cold spots are located in the periphery of the binding interface, with high‐affinity complexes showing fewer centrally located colds spots than low‐affinity complexes. A larger number of cold spots is also found in non‐cognate interactions compared to their cognate counterparts. Furthermore, our analysis reveals that cold spots are more frequent in homo‐dimeric complexes compared to hetero‐complexes, likely due to symmetry constraints imposed on sequences of homodimers. Finally, we find that glycines, glutamates, and arginines are the most frequent amino acids appearing at cold spot positions. Our analysis emphasizes the importance of cold spot positions to protein evolution and facilitates protein engineering studies directed at enhancing binding affinity and specificity in a wide range of applications.     

A metadynamic approach to understand the recognition mechanism of the histone H3 tail with the ATRXADD domain

The binding affinity between the histone 3 (H3) tail and the ADD domain of ATRX (ATRX_ADD) increases with the subsequent addition of methyl groups on lysine 9 on H3. To improve our understanding of how the difference in methylation state affects binding between H3 and the ATRX_ADD, we adopted a metadynamic approach to explore the recognition mechanism between the two proteins and identify the key intermolecular interactions that mediate this protein-peptide interaction (PPI). The non-methylated H3 peptide is recognized only by the PHD finger of ATRX_ADD while mono-, di-, and trimethylated H3 is recognized by both the PHD and GATA-like zinc finger of the domain. Furthermore, water molecules play an important role in orienting the lysine 9 anchor towards the GATA-like zinc finger, which results in stabilizing the lysine 9 binding pocket on ATRX_ADD. We compared our computational results against experimentally determined NMR and X-ray structures by demonstrating the RMSD, order parameter S² and hydration number of the complex. The metadynamics data provide new insight into roles of water-bridges and the mechanisms through which K9 hydration stabilizes the H3K9me3:ATRX_ADD PPI, providing context for the high affinity demonstrated between this protein and peptide.     

An unexpected way forward: towards a more accurate and rigorous protein-protein binding affinity scoring function by eliminating terms from an already simple scoring function

A fundamental and unsolved problem in biophysical chemistry is the development of a computationally simple, physically intuitive, and generally applicable method for accurately predicting and physically explaining protein–protein binding affinities from protein–protein interaction (PPI) complex coordinates. Here, we propose that the simplification of a previously described six-term PPI scoring function to a four term function results in a simple expression of all physically and statistically meaningful terms that can be used to accurately predict and explain binding affinities for a well-defined subset of PPIs that are characterized by (1) crystallographic coordinates, (2) rigid-body association, (3) normal interface size, and hydrophobicity and hydrophilicity, and (4) high quality experimental binding affinity measurements. We further propose that the four-term scoring function could be regarded as a core expression for future development into a more general PPI scoring function. Our work has clear implications for PPI modeling and structure-based drug design.     

Histone H3 Inhibits Ubiquitin-Ubiquitin Intermolecular Interactions to Enhance Binding to DNA Methyl Transferase 1

DNA methyltransferase 1 (Dnmt1) is crucial for cell maintenance and preferentially methylates hemimethylated DNA. Recently, a study revealed that Dnmt1 is timely and site-specifically activated by several types of two-mono-ubiquitinated histone H3 tails (H3Ts). However, the molecular mechanism of Dnmt1 activation has not yet been determined, in addition to the role of H3T. Based on experimental data, two-mono-ubiquitinated H3Ts activate Dnmt1 by binding, with different binding affinities. In contrast, ubiquitin molecules unlinked with H3T do not bind to Dnmt1. Despite the existence of experimental data, it is unclear why the binding affinities for Dnmt1 are different. To obtain new insights into the activation mechanism of Dnmt1, we performed all-atom molecular dynamics (MD) simulations on three systems: (1) K14/K18, (2) K14/K23 mono-ubiquitinated H3Ts, and (3) two ubiquitin molecules unlinked with H3T. As an analysis of our MD trajectories, these ubiquitylation patterns modulated ubiquitin-ubiquitin intermolecular interactions. More specifically, the intermolecular contacts between a pair of ubiquitin molecules linked with H3T became weak in the presence of H3T, indicating that H3T makes a cleft between them to inhibit their intermolecular interactions. For these three systems, the intermolecular interactions between the ubiquitin molecules calculated by our MD simulations are in good agreement with the binding affinities for Dnmt1 experimentally measured in a previous study. Therefore, we conclude that H3T acts as a spacer to inhibit ubiquitin-ubiquitin intermolecular interactions, enhancing binding to Dnmt1.     

The enantiomer of octreotate binds to all five somatostatin receptors with almost equal micromolar affinity--a comparison with SANDOSTATIN

Octreotate (1b) is the octreotide (SANDOSTATIN; 1a) analogue, carrying a C-terminal CO(2)H (Thr) instead of the CH(2)OH (threoninol) group. In pursuit of our interest in unnatural peptides, we have now synthesized (by the solid-phase Fmoc method) the enantiomeric form 2 of octreotate and determined its affinity for the five human somatostatin (SRIF) receptors (hsst(1-5)). The binding was found to be 9.1, 4.1, 1.0, 1.4, and 4.2 microM, respectively. This almost equal one-digit micromolar affinity of ent-octreotate (2) to all five receptors contrasts with the behavior of most other somatostatin mimics including SANDOSTATIN (octreotide; 1a) and [Tyr(3)]-octreotate (1c), which have affinities for the various receptors differing up to and above 10(4)-fold. Thus, the structure of the new compound does not prevent binding, albeit more weakly than its pseudo-enantiomer octreotide, and there is hardly any selectivity of the peptide-protein interaction (PPI) for any one of the five SRIF G-protein coupled receptors (GPCRs). Since the detailed structure(s) of these membrane-embedded receptors is unknown (no X-ray structure!), the result described here may be useful for modeling structures by comparing the affinities of the numerous known somatostatin mimics.     

Action-at-a-distance interactions enhance protein binding affinity

The identification of protein mutations that enhance binding affinity may be achieved by computational or experimental means, or by a combination of the two. Sources of affinity enhancement may include improvements to the net balance of binding interactions of residues forming intermolecular contacts at the binding interface, such as packing and hydrogen-bonding interactions. Here we identify noncontacting residues that make substantial contributions to binding affinity and that also provide opportunities for mutations that increase binding affinity of the TEM1 beta-lactamase (TEM1) to the beta-lactamase inhibitor protein (BLIP). A region of BLIP not on the direct TEM1-binding surface was identified for which changes in net charge result in particularly large increases in computed binding affinity. Some mutations to the region have previously been characterized, and our results are in good correspondence with this results of that study. In addition, we propose novel mutations to BLIP that were computed to improve binding significantly without contacting TEM1 directly. This class of noncontacting electrostatic interactions could have general utility in the design and tuning of binding interactions.     

Different combinations of atomic interactions predict protein‐small molecule and protein‐DNA/RNA affinities with similar accuracy

Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three‐dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein?ligand complexes with associated binding‐affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein‐small molecule and protein‐DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small‐molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small‐molecule and DNA/RNA interactions, no statistical models were capable of predicting protein?protein affinity with >60% correlation. We demonstrate the potential usefulness of protein‐DNA/RNA binding prediction as a possible tool for high‐throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

Selection is more intelligent than design: improving the affinity of a bivalent ligand through directed evolution

Multivalent molecular interactions can be exploited to dramatically enhance the performance of an affinity reagent. The enhancement in affinity and specificity achieved with a multivalent construct depends critically on the effectiveness of the scaffold that joins the ligands, as this determines their positions and orientations with respect to the target molecule. Currently, no generalizable design rules exist for construction of an optimal multivalent ligand for targets with known structures, and the design challenge remains an insurmountable obstacle for the large number of proteins whose structures are not known. As an alternative to such design-based strategies, we report here a directed evolution-based method for generating optimal bivalent aptamers. To demonstrate this approach, we fused two thrombin aptamers with a randomized DNA sequence and used a microfluidic in vitro selection strategy to isolate scaffolds with exceptionally high affinities. Within five rounds of selection, we generated a bivalent aptamer that binds thrombin with an apparent dissociation constant (K_d) <10 pM, representing a ∼200-fold improvement in binding affinity over the monomeric aptamers and a ∼15-fold improvement over the best designed bivalent construct. The process described here can be used to produce high-affinity multivalent aptamers and could potentially be adapted to other classes of biomolecules.     

Sliding of Proteins Non-specifically Bound to DNA: Brownian Dynamics Studies with Coarse-Grained Protein and DNA Models

DNA binding proteins efficiently search for their cognitive sites on long genomic DNA by combining 3D diffusion and 1D diffusion (sliding) along the DNA. Recent experimental results and theoretical analyses revealed that the proteins show a rotation-coupled sliding along DNA helical pitch. Here, we performed Brownian dynamics simulations using newly developed coarse-grained protein and DNA models for evaluating how hydrodynamic interactions between the protein and DNA molecules, binding affinity of the protein to DNA, and DNA fluctuations affect the one dimensional diffusion of the protein on the DNA. Our results indicate that intermolecular hydrodynamic interactions reduce 1D diffusivity by 30%. On the other hand, structural fluctuations of DNA give rise to steric collisions between the CG-proteins and DNA, resulting in faster 1D sliding of the protein. Proteins with low binding affinities consistent with experimental estimates of non-specific DNA binding show hopping along the CG-DNA. This hopping significantly increases sliding speed. These simulation studies provide additional insights into the mechanism of how DNA binding proteins find their target sites on the genome.     

Feature selection and classification of protein–protein complexes based on their binding affinities using machine learning approaches

Protein–protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein–protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein–protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein–protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein‐protein complexes into high and low affinity groups based on their K_d values. Our results showed a 10‐fold cross‐validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein–protein interaction networks and human–pathogen interactions based on the strength of interactions. Proteins 2014; 82:2088–2096. © 2014 Wiley Periodicals, Inc.     

Smoothing molecular interactions: the "kinetic buffer" effect of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) widely participate in molecular recognition and signaling processes in cells by interacting with other molecules. Compared with ordered proteins, IDPs usually possess stronger intermolecular interactions in binding. As a result, the interface structure of IDPs in complexes is distinct from that of ordered-protein complexes, and this difference may have essential effect on the response to various perturbations in a cell. In this study, we examined the perturbations of intermolecular interactions and temperature on the coupled folding and binding processes of pKID to KIX domains by performing molecular dynamics simulations. By comparing a series of virtual pKID systems with various degree of disorder, we found that the complex stability and the binding kinetics of the disordered systems were less sensitive to the perturbations than the ordered systems. The origin of the lower response sensitivity of IDPs was attributed to their higher flexibility in the complex interface, which was further supported by an analysis on protein complex structures. On the basis of our simulations and results from the literature, we speculate IDPs may not only interact with their biological partners with high specificity and low affinity but also may be resistant to the perturbations in the environment and transmit signals fast and smooth. We proposed to name it the "kinetic buffer" effect.     

Computational Assessment of Protein–protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes

The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein–protein interactions.     

Effect of four-alpha-helix bundle cavity size on volatile anesthetic binding energetics

Currently, it is thought that inhalational anesthetics cause anesthesia by binding to ligand-gated ion channels. This is being investigated using four-alpha-helix bundles, small water-soluble analogues of the transmembrane domains of the "natural" receptor proteins. The study presented here specifically investigates how multiple alanine-to-valine substitutions (which each decrease the volume of the internal binding cavity by 38 A(3)) affect structure, stability, and anesthetic binding affinity of the four-alpha-helix bundles. Structure remains essentially unchanged when up to four alanine residues are changed to valine. However, stability increases as the number of these substitutions is increased. Anesthetic binding affinities are also affected. Halothane binds to the four-alpha-helix bundle variants with 0, 1, and 2 substitutions with equivalent affinities but binds to the variants with 3 and 4 more tightly. The same order of binding affinities was observed for chloroform, although for a particular variant, chloroform was bound less tightly. The observed differences in binding affinities may be explained in terms of a modulation of van der Waals and hydrophobic interactions between ligand and receptor. These, in turn, could result from increased four-alpha-helix bundle binding cavity hydrophobicity, a decrease in cavity size, or improved ligand/receptor shape complementarity.     

Kinetic and Structural Studies on the Interactions of Heparin and Proteins of Human Seminal Plasma using Surface Plasmon Resonance

Heparin is naturally occurring polysaccharides which interacts with seminal plasma proteins and regulate multiple steps in fertilization process. Qualitative and quantitative information regarding the affinity for heparin-seminal plasma proteins interactions is not generally well documented and there are no reports of a comprehensive analysis of these interactions in human seminal plasma. Such information should improve our understanding of how GAGs especially heparin present in the reproductive tract regulate fertilization. In this study, we use SPR to study interactions of heparin with various seminal plasma heparin-binding proteins (HBPs). HBPs like lactoferrin (LF), fibronectin fragment (FNIII), semenogelinI (SGI) and prostate specific antigen (PSA) all bind heparin with different binding kinetics and affinities. Kinetic data suggests that FNIII binds heparin with a high affinity (KD=3.2 nM), while PSA binds heparin with a micromolar affinity (KD=11.1 μM). Preincubation of SGI with heparin inhibits the binding of SGI to immobilized PSA in a dosedependent manner, while FNIII incubated with heparin binds with an increased affinity to PSA. Solution-competition studies show that the minimum size of a heparin oligosaccharide capable of binding with PSA is greater than a tetrasaccharide, with LF and SGI is larger than a hexasaccharide and for FNIII is larger than an octasaccharide.     

Structural Basis of Chemokine Receptor Function—A Model for Binding Affinity and Ligand Selectivity

Chemokine receptors play fundamental roles in human physiology from embryogenesis to inflammatory response. The receptors belong to the G-protein coupled receptor class, and are activated by chemokine ligands with a range of specificities and affinities that result in a complicated network of interactions. The molecular basis for function is largely a black box, and can be directly attributed to the lack of structural information on the receptors. Studies to date indicate that function can be best described by a two-site model, that involves interactions between the receptor N-domain and ligand N-terminal loop residues (site-I), and between receptor extracellular loop and the ligand N-terminal residues (site-II). In this review, we describe how the two-site model could modulate binding affinity and ligand selectivity, and also highlight some of the unique chemokine receptor features, and their role in function.     

Influence of Protein Abundance on High-Throughput Protein-Protein Interaction Detection

Experimental protein-protein interaction (PPI) networks are increasingly being exploited in diverse ways for biological discovery. Accordingly, it is vital to discern their underlying natures by identifying and classifying the various types of deterministic (specific) and probabilistic (nonspecific) interactions detected. To this end, we have analyzed PPI networks determined using a range of high-throughput experimental techniques with the aim of systematically quantifying any biases that arise from the varying cellular abundances of the proteins. We confirm that PPI networks determined using affinity purification methods for yeast and Eschericia coli incorporate a correlation between protein degree, or number of interactions, and cellular abundance. The observed correlations are small but statistically significant and occur in both unprocessed (raw) and processed (high-confidence) data sets. In contrast, the yeast two-hybrid system yields networks that contain no such relationship. While previously commented based on mRNA abundance, our more extensive analysis based on protein abundance confirms a systematic difference between PPI networks determined from the two technologies. We additionally demonstrate that the centrality-lethality rule, which implies that higher-degree proteins are more likely to be essential, may be misleading, as protein abundance measurements identify essential proteins to be more prevalent than nonessential proteins. In fact, we generally find that when there is a degree/abundance correlation, the degree distributions of nonessential and essential proteins are also disparate. Conversely, when there is no degree/abundance correlation, the degree distributions of nonessential and essential proteins are not different. However, we show that essentiality manifests itself as a biological property in all of the yeast PPI networks investigated here via enrichments of interactions between essential proteins. These findings provide valuable insights into the underlying natures of the various high-throughput technologies utilized to detect PPIs and should lead to more effective strategies for the inference and analysis of high-quality PPI data sets.