The dataset for protein–RNA binding affinity

We have developed a non‐redundant protein–RNA binding benchmark dataset derived from the available protein–RNA structures in the Protein Database Bank. It consists of 73 complexes with measured binding affinity. The experimental conditions (pH and temperature) for binding affinity measurements are also listed in our dataset. This binding affinity dataset can be used to compare and develop protein–RNA scoring functions. The predicted binding free energy of the 73 complexes from three available scoring functions for protein–RNA docking has a low correlation with the binding Gibbs free energy calculated from K_d. © 2013 The Protein Society     

Using collections of structural models to predict changes of binding affinity caused by mutations in protein–protein interactions

Protein–protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state‐of‐the‐art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state‐of‐art methods.     

Interplay between binding affinity and kinetics in protein–protein interactions

To clarify the interplay between the binding affinity and kinetics of protein–protein interactions, and the possible role of intrinsically disordered proteins in such interactions, molecular simulations were carried out on 20 protein complexes. With bias potential and reweighting techniques, the free energy profiles were obtained under physiological affinities, which showed that the bound‐state valley is deep with a barrier height of 12 ? 33 RT. From the dependence of the affinity on interface interactions, the entropic contribution to the binding affinity is approximated to be proportional to the interface area. The extracted dissociation rates based on the Arrhenius law correlate reasonably well with the experimental values (Pearson correlation coefficient R = 0.79). For each protein complex, a linear free energy relationship between binding affinity and the dissociation rate was confirmed, but the distribution of the slopes for intrinsically disordered proteins showed no essential difference with that observed for ordered proteins. A comparison with protein folding was also performed. Proteins 2016; 84:920–933. © 2016 Wiley Periodicals, Inc.     

DNA‐binding affinity and nuclease activity of two cytotoxic copper terpyridine complexes

Two copper(II) terpyridine complexes, [Cu(atpy)(NO₃)(H₂O)](NO₃) ? 3H₂O ( 1 ) and [Cu(ttpy)(NO₃)₂] ( 2 ) (atpy = 4′‐p‐N9‐adeninylmethyl‐phenyl‐2,2′:6,2″‐terpyridine; ttpy = 4′‐p‐tolyl‐2,2′:6,2″‐terpyridine) exhibited high cytotoxicity, with average ten times more potency than cisplatin against the human cervix carcinoma cell line (HeLa), the human liver carcinoma cell line (HepG2), the human galactophore carcinoma cell line (MCF7), and the human prostate carcinoma cell line (PC‐3). The cytotoxicity of the complex 1 was lower than that of the complex 2 . Both complexes showed more efficient oxidative DNA cleavage activity under irradiation with UV light at 260 nm than in the presence of ascorbic acid. Especially, complex 1 exhibited evident photoinduced double‐stranded DNA cleavage activity. The preliminary mechanism experiments revealed that hydrogen peroxide was involved in the oxidative DNA damage induced by both complexes. From the absorption titration data, the DNA‐binding affinity of the complexes with surpersoiled plasmid pUC19 DNA, polydAdT, and polydGdC was calculated and complex 2 showed higher binding affinity than complex 1 with all these substrates. The DNA cleavage ability and DNA‐binding affinity of both complexes depended on the substituent group on the terpyrdine ligands. © 2009 Wiley Periodicals, Inc. J Biochem Mol Toxicol 23:295–302, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/jbt.20292     

Quantitative prediction of protein–protein binding affinity with a potential of mean force considering volume correction

Quantitative prediction of protein–protein binding affinity is essential for understanding protein–protein interactions. In this article, an atomic level potential of mean force (PMF) considering volume correction is presented for the prediction of protein–protein binding affinity. The potential is obtained by statistically analyzing X‐ray structures of protein–protein complexes in the Protein Data Bank. This approach circumvents the complicated steps of the volume correction process and is very easy to implement in practice. It can obtain more reasonable pair potential compared with traditional PMF and shows a classic picture of nonbonded atom pair interaction as Lennard‐Jones potential. To evaluate the prediction ability for protein–protein binding affinity, six test sets are examined. Sets 1–5 were used as test set in five published studies, respectively, and set 6 was the union set of sets 1–5, with a total of 86 protein–protein complexes. The correlation coefficient (R) and standard deviation (SD) of fitting predicted affinity to experimental data were calculated to compare the performance of ours with that in literature. Our predictions on sets 1–5 were as good as the best prediction reported in the published studies, and for union set 6, R = 0.76, SD = 2.24 kcal/mol. Furthermore, we found that the volume correction can significantly improve the prediction ability. This approach can also promote the research on docking and protein structure prediction.     

High‐resolution crystal structures leverage protein binding affinity predictions

Predicting protein binding affinities from structural data has remained elusive, a difficulty owing to the variety of protein binding modes. Using the structure‐affinity‐benchmark (SAB, 144 cases with bound/unbound crystal structures and experimental affinity measurements), prediction has been undertaken either by fitting a model using a handfull of predefined variables, or by training a complex model from a large pool of parameters (typically hundreds). The former route unnecessarily restricts the model space, while the latter is prone to overfitting. We design models in a third tier, using 12 variables describing enthalpic and entropic variations upon binding, and a model selection procedure identifying the best sparse model built from a subset of these variables. Using these models, we report three main results. First, we present models yielding a marked improvement of affinity predictions. For the whole dataset, we present a model predicting K_d within 1 and 2 orders of magnitude for 48% and 79% of cases, respectively. These statistics jump to 62% and 89% respectively, for the subset of the SAB consisting of high resolution structures. Second, we show that these performances owe to a new parameter encoding interface morphology and packing properties of interface atoms. Third, we argue that interface flexibility and prediction hardness do not correlate, and that for flexible cases, a performance matching that of the whole SAB can be achieved. Overall, our work suggests that the affinity prediction problem could be partly solved using databases of high resolution complexes whose affinity is known. Proteins 2016; 84:9–20. © 2015 Wiley Periodicals, Inc.     

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.     

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.     

Balance between DNA‐binding affinity and specificity enables selective recognition of longer target sequences in vivo

Although genome‐editing enzymes such as TALEN and CRISPR/Cas9 are being widely used, they have an essential limitation in that their relatively high‐molecular weight makes them difficult to be delivered to cells. To develop a novel genome‐editing enzyme with a smaller molecular weight, we focused on the engrailed homeodomain (EHD). We designed and constructed proteins composed of two EHDs connected by a linker to increase sequence specificity. In bacterial one‐hybrid assays and electrophoresis mobility shift assay analyses, the created proteins exhibited good affinity for DNA sequences consisting of two tandemly aligned EHD target sequences. However, they also bound to individual EHD targets. To avoid binding to single target sites, we introduced amino acid mutations to reduce the protein–DNA affinity of each EHD monomer and successfully created a small protein with high specificity for tandem EHD target sequences.     

Non‐interacting surface solvation and dynamics in protein–protein interactions

Protein–protein interactions control a plethora of cellular processes, including cell proliferation, differentiation, apoptosis, and signal transduction. Understanding how and why proteins interact will inevitably lead to novel structure‐based drug design methods, as well as design of de novo binders with preferred interaction properties. At a structural and molecular level, interface and rim regions are not enough to fully account for the energetics of protein–protein binding, even for simple lock‐and‐key rigid binders. As we have recently shown, properties of the global surface might also play a role in protein–protein interactions. Here, we report on molecular dynamics simulations performed to understand solvent effects on protein–protein surfaces. We compare properties of the interface, rim, and non‐interacting surface regions for five different complexes and their free components. Interface and rim residues become, as expected, less mobile upon complexation. However, non‐interacting surface appears more flexible in the complex. Fluctuations of polar residues are always lower compared with charged ones, independent of the protein state. Further, stable water molecules are often observed around polar residues, in contrast to charged ones. Our analysis reveals that (a) upon complexation, the non‐interacting surface can have a direct entropic compensation for the lower interface and rim entropy and (b) the mobility of the first hydration layer, which is linked to the stability of the protein–protein complex, is influenced by the local chemical properties of the surface. These findings corroborate previous hypotheses on the role of the hydration layer in shielding protein–protein complexes from unintended protein–protein interactions. Proteins 2015; 83:445–458. © 2014 Wiley Periodicals, Inc.     

His‐tagged protein purification by metal‐chelate affinity extraction with nickel‐chelate reverse micelles

Di(2‐ethylhexyl) phosphoric acid (HDEHP) was used as a transition metal ion chelator and introduced to the nonionic reverse micellar system composed of equimolar Triton X‐45 and Span 80 at a total concentration of 30 mmol/L. Ni(II) ions were chelated to the HDEHP dimers in the reverse micelles, forming a complex denoted as Ni(II)R₂. The Ni(II)‐chelate reverse micelles were characterized for the purification of recombinant hexahistidine‐tagged enhanced green fluorescent protein (EGFP) expressed in Escherichia coli. The affinity binding of EGFP to Ni(II)R₂ was proved by investigation of the forward and back extraction behaviors of purified EGFP. Then, EGFP was purified with the affinity reverse micelles. It was found that the impurities in the feedstock impeded EGFP transfer to the reverse micelles, though they were little solubilized in the organic phase. The high specificity of the chelated Ni²⁺ ions toward the histidine tag led to the production of electrophoretically pure EGFP, which was similar to that purified by immobilized metal affinity chromatography. A two‐stage purification by the metal‐chelate affinity extraction gave rise to 87% recovery of EGFP. Fluorescence spectrum analysis suggests the preservation of native protein structure after the separation process, indicating the system was promising for protein purification. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Biological and protein‐binding studies of newly synthesized polymer–cobalt(III) complexes

The polymer–cobalt(III) complexes, [Co(bpy)(dien)BPEI]Cl₃ · 4H₂O (bpy = 2,2′‐bipyridine, dien = diethylentriamine, BPEI = branched polyethyleneimine) were synthesized and characterized. The interaction of these complexes with human serum albumin (HSA) and bovine serum albumin (BSA) was investigated under physiological conditions using various physico‐chemical techniques. The results reveal that the fluorescence quenching of serum albumins by polymer–cobalt(III) complexes took place through static quenching. The binding of these complexes changed the molecular conformation of the protein considerably. The polymer–cobalt(III) complex with x = 0.365 shows antimicrobial activity against several human pathogens. This complex also induces cytotoxicity against MCF‐7 through apoptotic induction. However, further studies are needed to decipher the molecular mode of action of polymer–cobalt(III) complex and for its possible utilization in anticancer therapy. Copyright © 2015 John Wiley & Sons, Ltd.     

AB‐Bind: Antibody binding mutational database for computational affinity predictions

Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high‐affinity and high‐specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody‐Bind (AB‐Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. Using the AB‐Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16–0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < −1.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.     

Computational design of second‐site suppressor mutations at protein–protein interfaces

The importance of a protein–protein interaction to a signaling pathway can be established by showing that amino acid mutations that weaken the interaction disrupt signaling, and that additional mutations that rescue the interaction recover signaling. Identifying rescue mutations, often referred to as second‐site suppressor mutations, controls against scenarios in which the initial deleterious mutation inactivates the protein or disrupts alternative protein–protein interactions. Here, we test a structure‐based protocol for identifying second‐site suppressor mutations that is based on a strategy previously described by Kortemme and Baker. The molecular modeling software Rosetta is used to scan an interface for point mutations that are predicted to weaken binding but can be rescued by mutations on the partner protein. The protocol typically identifies three types of specificity switches: knob‐in‐to‐hole redesigns, switching hydrophobic interactions to hydrogen bond interactions, and replacing polar interactions with nonpolar interactions. Computational predictions were tested with two separate protein complexes; the G‐protein Gα_i1 bound to the RGS14 GoLoco motif, and UbcH7 bound to the ubiquitin ligase E6AP. Eight designs were experimentally tested. Swapping a buried hydrophobic residue with a polar residue dramatically weakened binding affinities. In none of these cases were we able to identify compensating mutations that returned binding to wild‐type affinity, highlighting the challenges inherent in designing buried hydrogen bond networks. The strongest specificity switches were a knob‐in‐to‐hole design (20‐fold) and the replacement of a charge–charge interaction with nonpolar interactions (55‐fold). In two cases, specificity was further tuned by including mutations distant from the initial design. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

A bone substitute with high affinity for vitamin D‐binding protein―relationship with niche of osteoclasts

The biological activity of osteoblasts and osteoclasts is regulated not only by hormones but also by local growth factors, which are expressed in neighbouring cells or included in bone matrix. Previously, we developed hydroxyapatite (HA) composed of rod‐shaped particles using applied hydrothermal methods (HHA), and it revealed mild biodegradability and potent osteoclast homing activity. Here, we compared serum proteins adsorbed to HHA with those adsorbed to conventional HA composed of globular‐shaped particles (CHA). The two ceramics adsorbed serum albumin and γ‐globulin to similar extents, but affinity for γ‐globulin was much greater than that to serum albumin. The chemotactic activity for macrophages of serum proteins adsorbed to HHA was significantly higher than that of serum proteins adsorbed to CHA. Quantitative proteomic analysis of adsorbed serum proteins revealed preferential binding of vitamin D‐binding protein (DBP) and complements C3 and C4B with HHA. When implanted with the femur of 8‐week‐old rats, HHA contained significantly larger amount of DBP than CHA. The biological activity of DBP was analysed and it was found that the chemotactic activity for macrophages was weak. However, DBP‐macrophage activating factor, which is generated by the digestion of sugar chains of DBP, stimulated osteoclastogenesis. These results confirm that the microstructure of hydroxyapatite largely affects the affinity for serum proteins, and suggest that DBP preferentially adsorbed to HA composed of rod‐shaped particles influences its potent osteoclast homing activity and local bone metabolism.     

Molecular cloning,expression profile,odorant affinity,and stability of two odorant‐binding proteins in Macrocentrus cingulum Brischke (Hymenoptera: Braconidae)

The polyembryonic endoparasitoid wasp Macrocentrus cingulum Brischke (Hymenoptera: Braconidae) is deployed successfully as a biocontrol agent for corn pest insects from the Lepidopteran genus Ostrinia in Europe and throughout Asia, including Japan, Korea, and China. The odorants are recognized, bound, and solubilized by odorant‐binding protein (OBP) in the initial biochemical recognition steps in olfaction that transport them across the sensillum lymph to initiate behavioral response. In the present study, we examine the odorant‐binding effects on thermal stability of McinOBP2, McinOBP3, and their mutant form that lacks the third disulfide bonds. Real‐time PCR experiments indicate that these two are expressed mainly in adult antennae, with expression levels differing by sex. Odorant‐binding affinities of aldehydes, terpenoids, and aliphatic alcohols were measured with circular dichroism spectroscopy based on changes in the thermal stability of the proteins upon their affinities to odorants. The obtained results reveal higher affinity of trans‐caryophelle, farnesene, and cis‐3‐Hexen‐1‐ol exhibits to both wild and mutant McinOBP2 and McinOBP3. Although conformational flexibility of the mutants and shape of binding cavity make differences in odorant affinity between the wild‐type and mutant, it suggested that lacking the third disulfide bond in mutant proteins may have chance to incorrect folded structures that reduced the affinity to these odorants. In addition, CD spectra clearly indicate proteins enriched with α‐helical content.     

Weak conservation of structural features in the interfaces of homologous transient protein–protein complexes

Residue types at the interface of protein–protein complexes (PPCs) are known to be reasonably well conserved. However, we show, using a dataset of known 3‐D structures of homologous transient PPCs, that the 3‐D location of interfacial residues and their interaction patterns are only moderately and poorly conserved, respectively. Another surprising observation is that a residue at the interface that is conserved is not necessarily in the interface in the homolog. Such differences in homologous complexes are manifested by substitution of the residues that are spatially proximal to the conserved residue and structural differences at the interfaces as well as differences in spatial orientations of the interacting proteins. Conservation of interface location and the interaction pattern at the core of the interfaces is higher than at the periphery of the interface patch. Extents of variability of various structural features reported here for homologous transient PPCs are higher than the variation in homologous permanent homomers. Our findings suggest that straightforward extrapolation of interfacial nature and inter‐residue interaction patterns from template to target could lead to serious errors in the modeled complex structure. Understanding the evolution of interfaces provides insights to improve comparative modeling of PPC structures.     

Investigating the linkage between disease‐causing amino acid variants and their effect on protein stability and binding

Single amino acid variations (SAV) occurring in human population result in natural differences between individuals or cause diseases. It is well understood that the molecular effect of SAV can be manifested as changes of the wild type characteristics of the corresponding protein, among which are the protein stability and protein interactions. Typically the effect of SAV on protein stability and interactions was assessed via the changes of the wild type folding and binding free energies. However, in terms of SAV affecting protein functionally and disease susceptibility, one wants to know to what extend the wild type function is perturbed by the SAV. Here it is demonstrated that relative, rather than the absolute, change of the folding and binding free energy serves as a good indicator for SAV association with disease. Using HumVar as a source for disease‐causing SAV and experimentally determined free energy changes from ProTherm and SKEMPI databases, correlation coefficients (CC) between the disease index and relative folding and binding probability indexes, respectively, was achieved. The obtained CCs demonstrated the applicability of the proposed approach and it served as good indicator for SAV association with disease. Proteins 2016; 84:232–239. © 2015 Wiley Periodicals, Inc.     

Norovirus‐binding proteins recovered from activated sludge micro‐organisms with an affinity to a noroviral capsid peptide

Aims: Transmission routes of noroviruses, leading aetiological agents of acute gastroenteritis, are rarely verified when outbreaks occur. Because the destination of norovirus particles being firmly captured by micro‐organisms could be totally different from that of those particles moving freely, micro‐organisms with natural affinity ligands such as virus‐binding proteins would affect the fate of viruses in environment, if such microbial affinity ligands exist. The aim of this study is to identify norovirus‐binding proteins (NoVBPs) that are presumably working as natural ligands for norovirus particles in water environments. Methods and Results: NoVBPs were recovered from activated sludge micro‐organisms by an affinity chromatography technique in which a capsid peptide of norovirus genogroup II (GII) was immobilized. The recovered NoVBPs bind to norovirus‐like particles (NoVLPs) of norovirus GII, and this adsorption was stronger than that to NoVLPs of norovirus genogroup I. The profile of two‐dimensional electrophoresis of NoVBPs showed that the recovered NoVBPs included at least seven spots of protein. The determination of N‐terminal amino acid sequences of these NoVBPs revealed that hydrophobic interactions could contribute to the adsorption between NoVBPs and norovirus particles. Conclusions: NoVBPs conferring a high affinity to norovirus GII were successfully isolated from activated sludge micro‐organisms. Significance and Impact of the Study: NoVBPs could be natural viral ligands and play an important role in the NoV transmission.