Cation–π, amino–π, π–π, and H‐bond interactions stabilize antigen–antibody interfaces

The identification of immunogenic regions on the surface of antigens, which are able to stimulate an immune response, is a major challenge for the design of new vaccines. Computational immunology aims at predicting such regions—in particular B‐cell epitopes—but is far from being reliably applicable on a large scale. To gain understanding into the factors that contribute to the antigen–antibody affinity and specificity, we perform a detailed analysis of the amino acid composition and secondary structure of antigen and antibody surfaces, and of the interactions that stabilize the complexes, in comparison with the composition and interactions observed in other heterodimeric protein interfaces. We make a distinction between linear and conformational B‐cell epitopes, according to whether they consist of successive residues along the polypeptide chain or not. The antigen–antibody interfaces were shown to differ from other protein–protein interfaces by their smaller size, their secondary structure with less helices and more loops, and the interactions that stabilize them: more H‐bond, cation–π, amino–π, and π–π interactions, and less hydrophobic packing; linear and conformational epitopes can clearly be distinguished. Often, chains of successive interactions, called cation/amino–π and π–π chains, are formed. The amino acid composition differs significantly between the interfaces: antigen–antibody interfaces are less aliphatic and more charged, polar and aromatic than other heterodimeric protein interfaces. Moreover, paratopes and epitopes—albeit to a lesser extent—have amino acid compositions that are distinct from general protein surfaces. This specificity holds promise for improving B‐cell epitope prediction. Proteins 2014; 82:1734–1746. © 2014 Wiley Periodicals, Inc.     

Study on the Materials Formed by Self‐Assembling Hydrophobic,Aromatic Peptides Dedicated to Be Used for Regenerative Medicine

The aims of this study were to identify the short aromatic peptides which are able to form highly ordered amyloid‐like structures in self‐assembling processes, to test the influence of length of hydrophobic peptides on tendency to aggregation, and to check if aggregated peptides fulfill requirements expected for materials useful for scaffolding. All tested hydrophobic peptides were prepared on solid phase by using DMT/NMM/TsO^? as a coupling reagent. The progress of aggregation was studied by set of independent tests. All aggregated peptides were found stable under in vitro conditions. All fibrous material formed by self‐assembling of peptides does not show any cytotoxic effects on L929 fibroblast cells. Peptides containing tyrosine and tryptophan residues even effectively accelerated the proliferation and stimulated the activity of L929 fibroblasts.     

Importance of the interaction protein–protein of the CaM–PDE1A and CaM–MLCK complexes in the development of new anti‐CaM drugs

Protein–protein interactions play central roles in physiological and pathological processes. The bases of the mechanisms of drug action are relevant to the discovery of new therapeutic targets. This work focuses on understanding the interactions in protein–protein–ligands complexes, using proteins calmodulin (CaM), human calcium/calmodulin‐dependent 3′,5′‐cyclic nucleotide phosphodiesterase 1A active human (PDE1A), and myosin light chain kinase (MLCK) and ligands αII–spectrin peptide (αII–spec), and two inhibitors of CaM (chlorpromazine (CPZ) and malbrancheamide (MBC)). The interaction was monitored with a fluorescent biosensor of CaM (hCaM M124C–mBBr). The results showed changes in the affinity of CPZ and MBC depending on the CaM–protein complex under analysis. For the Ca²⁺–CaM, Ca²⁺–CaM–PDE1A, and Ca²⁺–CaM–MLCK complexes, CPZ apparent dissociation constants (K_ds) were 1.11, 0.28, and 0.55 μM, respectively; and for MBC K_ds were 1.43, 1.10, and 0.61 μM, respectively. In competition experiments the addition of calmodulin binding peptide 1 (αII–spec) to Ca²⁺–hCaM M124C–mBBr quenched the fluorescence (K_d = 2.55 ± 1.75 pM) and the later addition of MBC (up to 16 μM) did not affect the fluorescent signal. Instead, the additions of αII–spec to a preformed Ca²⁺–hCaM M124C–mBBr–MBC complex modified the fluorescent signal. However, MBC was able to displace the PDE1A and MLCK from its complex with Ca²⁺–CaM. In addition, docking studies were performed for all complexes with both ligands showing an excellent correlation with experimental data. These experiments may help to explain why in vivo many CaM drugs target prefer only a subset of the Ca²⁺–CaM regulated proteins and adds to the understanding of molecular interactions between protein complexes and small ligands. Copyright © 2013 John Wiley & Sons, Ltd.     

Subunit-specific backbone NMR assignments of a 64 kDa trp repressor/DNA complex: A role for N-terminal residues in tandem binding

Deuterium decoupled, triple resonance NMR spectroscopy was used to analyze complexes of ²H,¹⁵N,¹³C labelled intact and (des2–7) trp repressor (2–7 trpR) from E. coli bound in tandem to an idealized 22 basepair trp operator DNA fragment and the corepressor 5-methyltryptophan. The DNA sequence used here binds two trpR dimers in tandem resulting in chemically nonequivalent environments for the two subunits of each dimer. Sequence- and subunit-specific NMR resonance assignments were made for backbone ¹HN, ¹⁵N, ¹³C positions in both forms of the protein and for¹³ C in the intact repressor. The differences in backbone chemical shifts between the two subunits within each dimer of 2–7 trpR reflect dimer-dimer contacts involving the helix-turn-helix domains and N-terminal residues consistent with a previously determined crystal structure [Lawson and Carey (1993) Nature, 366, 178–182]. Comparison of the backbone chemical shifts of DNA-bound 2–7 trpR with those of DNA-bound intact trpR reveals significant changes for those residues involved in N-terminal-mediated interactions observed in the crystal structure. In addition, our solution NMR data contain three sets of resonances for residues 2–12 in intact trpR suggesting that the N-terminus has multiple conformations in the tandem complex. Analysis of C chemical shifts using a chemical shift index (CSI) modified for deuterium isotope effects has allowed a comparison of the secondary structure of intact and 2–7 tprR. Overall these data demonstrate that NMR backbone chemical shift data can be readily used to study specific structural details of large protein complexes.     

An ancient anion‐binding structural module in RNA and DNA helicases

RNA and DNA helicases manipulate or translocate along single strands of nucleic acids by grasping them using a conserved structural motif. We have examined the available crystal structures of helicases of the two principal superfamilies, SF1 and SF2, and observed that the most conserved interactions with the nucleic acid occur between the phosphosugar backbone of a trinucleotide and the three strand‐helix loops within a (β‐strand/α‐helix)₃ structural module. At the first and third loops is a conserved hydrogen‐bonded feature called a thr‐motif, often seen at α‐helical N‐termini, with the threonine as the N‐cap residue. These loops can be aligned with few insertions or deletions, and their main chain atoms are structurally congruent amongst the family members and between the two modules found as tandem pairs in all SF1 and SF2 proteins. The other highly conserved interactions with nucleic acid involve mainchain NH groups, often at the helical N‐termini, interacting with phosphate groups. We comment on how the sequence motifs that are commonly used to identify helicases map to locations on the module and discuss the implications of the conserved orientation of nucleic acid on the surface of the module for directional stepping along DNA or RNA. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

pH‐sensitive residues in the p19 RNA silencing suppressor protein from carnation Italian ringspot virus affect siRNA binding stability

Tombusviruses, such as Carnation Italian ringspot virus (CIRV), encode a protein homodimer called p19 that is capable of suppressing RNA silencing in their infected hosts by binding to and sequestering short‐interfering RNA (siRNA) away from the RNA silencing pathway. P19 binding stability has been shown to be sensitive to changes in pH but the specific amino acid residues involved have remained unclear. Using constant pH molecular dynamics simulations, we have identified key pH‐dependent residues that affect CIRV p19–siRNA binding stability at various pH ranges based on calculated changes in the free energy contribution from each titratable residue. At high pH, the deprotonation of Lys60, Lys67, Lys71, and Cys134 has the largest effect on the binding stability. Similarly, deprotonation of several acidic residues (Asp9, Glu12, Asp20, Glu35, and/or Glu41) at low pH results in a decrease in binding stability. At neutral pH, residues Glu17 and His132 provide a small increase in the binding stability and we find that the optimal pH range for siRNA binding is between 7.0 and 10.0. Overall, our findings further inform recent experiments and are in excellent agreement with data on the pH‐dependent binding profile.     

Pathway mapping and development of disease‐specific biomarkers: protein‐based network biomarkers

It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network‐based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein–protein or gene–gene interactions that can be monitored and evaluated at different stages and time‐points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue‐generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases‐specific, stage‐specific, severity‐specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed.     

Structural model of ρ1 GABAC receptor based on evolutionary analysis: Testing of predicted protein–protein interactions involved in receptor assembly and function

The homopentameric ρ1 GABA_C receptor is a ligand‐gated ion channel with a binding pocket for γ‐aminobutyric acid (GABA) at the interfaces of N‐terminal extracellular domains. We combined evolutionary analysis, structural modeling, and experimental testing to study determinants of GABA_C receptor assembly and channel gating. We estimated the posterior probability of selection pressure at amino acid residue sites measured as ω‐values and built a comparative structural model, which identified several polar residues under strong selection pressure at the subunit interfaces that may form intersubunit hydrogen bonds or salt bridges. At three selected sites (R111, T151, and E55), mutations disrupting intersubunit interactions had strong effects on receptor folding, assembly, and function. We next examined the role of a predicted intersubunit salt bridge for residue pair R158–D204. The mutant R158D, where the positively charged residue is replaced by a negatively charged aspartate, yielded a partially degraded receptor and lacked membrane surface expression. The membrane surface expression was rescued by the double mutant R158D–D204R, where positive and negative charges are switched, although the mutant receptor was inactive. The single mutants R158A, D204R, and D204A exhibited diminished activities and altered kinetic profiles with fast recovery kinetics, suggesting that R158–D204 salt bridge perhaps stabilizes the open state of the GABA_C receptor. Our results emphasize the functional importance of highly conserved polar residues at the protein–protein interfaces in GABA_C ρ1 receptors and demonstrate how the integration of computational and experimental approaches can aid discovery of functionally important interactions.     

Probing the role of interfacial waters in protein–DNA recognition using a hybrid implicit/explicit solvation model

When proteins bind to their DNA target sites, ordered water molecules are often present at the protein–DNA interface bridging protein and DNA through hydrogen bonds. What is the role of these ordered interfacial waters? Are they important determinants of the specificity of DNA sequence recognition, or do they act in binding in a primarily nonspecific manner, by improving packing of the interface, shielding unfavorable electrostatic interactions, and solvating unsatisfied polar groups that are inaccessible to bulk solvent? When modeling details of structure and binding preferences, can fully implicit solvent models be fruitfully applied to protein–DNA interfaces, or must the individualistic properties of these interfacial waters be accounted for? To address these questions, we have developed a hybrid implicit/explicit solvation model that specifically accounts for the locations and orientations of small numbers of DNA‐bound water molecules, while treating the majority of the solvent implicitly. Comparing the performance of this model with that of its fully implicit counterpart, we find that explicit treatment of interfacial waters results in a modest but significant improvement in protein side‐chain placement and DNA sequence recovery. Base‐by‐base comparison of the performance of the two models highlights DNA sequence positions whose recognition may be dependent on interfacial water. Our study offers large‐scale statistical evidence for the role of ordered water for protein–DNA recognition, together with detailed examination of several well‐characterized systems. In addition, our approach provides a template for modeling explicit water molecules at interfaces that should be extensible to other systems. Proteins 2013; 81:1318–1329. © 2013 Wiley Periodicals, Inc.     

BiPPred: Combined sequence‐ and structure‐based prediction of peptide binding to the Hsp70 chaperone BiP

Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi‐scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence‐based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence‐based prediction models were fitted using this and other peptide binding data. A structure‐based position‐specific scoring matrix (SB‐PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB‐PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA‐based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi‐scale pipeline can readily be applied to other protein‐peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence‐based prediction models is not available. Proteins 2016; 84:1390–1407. © 2016 Wiley Periodicals, Inc.     

DNABind: A hybrid algorithm for structure‐based prediction of DNA‐binding residues by combining machine learning‐ and template‐based approaches

Accurate prediction of DNA‐binding residues has become a problem of increasing importance in structural bioinformatics. Here, we presented DNABind, a novel hybrid algorithm for identifying these crucial residues by exploiting the complementarity between machine learning‐ and template‐based methods. Our machine learning‐based method was based on the probabilistic combination of a structure‐based and a sequence‐based predictor, both of which were implemented using support vector machines algorithms. The former included our well‐designed structural features, such as solvent accessibility, local geometry, topological features, and relative positions, which can effectively quantify the difference between DNA‐binding and nonbinding residues. The latter combined evolutionary conservation features with three other sequence attributes. Our template‐based method depended on structural alignment and utilized the template structure from known protein–DNA complexes to infer DNA‐binding residues. We showed that the template method had excellent performance when reliable templates were found for the query proteins but tended to be strongly influenced by the template quality as well as the conformational changes upon DNA binding. In contrast, the machine learning approach yielded better performance when high‐quality templates were not available (about 1/3 cases in our dataset) or the query protein was subject to intensive transformation changes upon DNA binding. Our extensive experiments indicated that the hybrid approach can distinctly improve the performance of the individual methods for both bound and unbound structures. DNABind also significantly outperformed the state‐of‐art algorithms by around 10% in terms of Matthews's correlation coefficient. The proposed methodology could also have wide application in various protein functional site annotations. DNABind is freely available at http://mleg.cse.sc.edu/DNABind/ . Proteins 2013; 81:1885–1899. © 2013 Wiley Periodicals, Inc.     

Characterization of the SRP68/72 interface of human signal recognition particle by systematic site‐directed mutagenesis

The signal recognition particle (SRP) is a ribonucleoprotein complex which is crucial for the delivery of proteins to cellular membranes. Among the six proteins of the eukaryotic SRP, the two largest, SRP68 and SRP72, form a stable SRP68/72 heterodimer of unknown structure which is required for SRP function. Fragments 68e′ (residues 530 to 620) and 72b′ (residues 1 to 166) participate in the SRP68/72 interface. Both polypeptides were expressed in Escherichia coli and assembled into a complex which was stable at high ionic strength. Disruption of 68e′/72b′ and SRP68/72 was achieved by denaturation using moderate concentrations of urea. The four predicted tetratricopeptide repeats (TPR1 to TPR4) of 72b′ were required for stable binding of 68e′. Site‐directed mutagenesis suggested that they provide the structural framework for the binding of SRP68. Deleting the region between TPR3 and TPR4 (h120) also prevented the formation of a heterodimer, but this predicted alpha‐helical region appeared to engage several of its amino acid residues directly at the interface with 68e′. A 39‐residue polypeptide (68h, residues 570–605), rich in prolines and containing an invariant aspartic residue at position 585, was found to be active. Mutagenesis scanning of the central region of 68h demonstrated that D585 was solely responsible for the formation of the heterodimer. Coexpression experiments suggested that 72b′ protects 68h from proteolytic digestion consistent with the assertion that 68h is accommodated inside a groove formed by the superhelically arranged four TPRs of the N‐terminal region of SRP72.     

Experimental and computational studies of enantioseparation of structurally similar chiral compounds on amylose tris(3,5‐dimethylphenylcarbamate)

The enantioseparation of 14 structurally similar chiral solutes, with one or two chiral centers, are studied for a commercially important polysaccharide‐based chiral stationary phase, amylose tris(3,5‐dimethylphenylcarbamate) (ADMPC). Among these solutes, only two solutes show significant enantioresolutions of 2 to 2.5 in n‐hexane/2‐propanol (90/10, v/v) at 298 K. The retention factors of the chiral solutes vary significantly from 0.7 to 7.0, and they are compared with those of simpler nonchiral solutes having similar but fewer functional groups. The sorbent–solute H‐bonding interactions between the solute functional groups and the polymer C?O and NH functional groups are probed with attenuated total reflection infrared spectroscopy (ATR‐IR). The H‐bonding interactions of the polymer C?O and NH groups with the solutes result in changes in the IR amide band wavenumbers of ADMPC upon solute adsorption. The nanostructure of an ADMPC cavity and the potential interactions with the chiral solutes are proposed based on the sorbent–solute–solvent HPLC data, the sorbent–solute IR data, and the sorbent–solute molecular dynamics (MD) simulations. The results are consistent with the three point attachment hypothesis and indicate that a significant enantioresolution in ADMPC requires at least three different interaction sites for simultaneous H‐bonds and phenyl–phenyl interactions for phenylpropylamine (PPA) and various structurally similar chiral solutes. Chirality 2010. © 2009 Wiley‐Liss, Inc.     

LuTHy: a double‐readout bioluminescence‐based two‐hybrid technology for quantitative mapping of protein–protein interactions in mammalian cells

Information on protein–protein interactions (PPIs) is of critical importance for studying complex biological systems and developing therapeutic strategies. Here, we present a double‐readout bioluminescence‐based two‐hybrid technology, termed LuTHy, which provides two quantitative scores in one experimental procedure when testing binary interactions. PPIs are first monitored in cells by quantification of bioluminescence resonance energy transfer (BRET) and, following cell lysis, are again quantitatively assessed by luminescence‐based co‐precipitation (LuC). The double‐readout procedure detects interactions with higher sensitivity than traditional single‐readout methods and is broadly applicable, for example, for detecting the effects of small molecules or disease‐causing mutations on PPIs. Applying LuTHy in a focused screen, we identified 42 interactions for the presynaptic chaperone CSPα, causative to adult‐onset neuronal ceroid lipofuscinosis (ANCL), a progressive neurodegenerative disease. Nearly 50% of PPIs were found to be affected when studying the effect of the disease‐causing missense mutations L115R and ?L116 in CSPα with LuTHy. Our study presents a robust, sensitive research tool with high utility for investigating the molecular mechanisms by which disease‐associated mutations impair protein activity in biological systems.     

Flexible docking of peptides to proteins using CABS‐dock

Molecular docking of peptides to proteins can be a useful tool in the exploration of the possible peptide binding sites and poses. CABS‐dock is a method for protein–peptide docking that features significant conformational flexibility of both the peptide and the protein molecules during the peptide search for a binding site. The CABS‐dock has been made available as a web server and a standalone package. The web server is an easy to use tool with a simple web interface. The standalone package is a command‐line program dedicated to professional users. It offers a number of advanced features, analysis tools and support for large‐sized systems. In this article, we outline the current status of the CABS‐dock method, its recent developments, applications, and challenges ahead.     

Modulatory effect of Lactobacillus acidophilus KLDS 1.0738 on intestinal short‐chain fatty acids metabolism and GPR41/43 expression in β‐lactoglobulin–sensitized mice

We investigated the correlation between the beneficial effect of Lactobacillus acidophilus on gut microbiota composition, metabolic activities, and reducing cow's milk protein allergy. Mice sensitized with β‐lactoglobulin (β‐Lg) were treated with different doses of L. acidophilus KLDS 1.0738 for 4 weeks, starting 1 week before allergen induction. The results showed that intake of L. acidophilus significantly suppressed the hypersensitivity responses, together with increased fecal microbiota diversity and short‐chain fatty acids (SCFAs) concentration (including propionate, butyrate, isobutyrate, and isovalerate) when compared with the allergic group. Moreover, treatment with L. acidophilus induced the expression of SCFAs receptors, G‐protein–coupled receptors 41 (GPR41) and 43 (GPR43), in the spleen and colon of the allergic mice. Further analysis revealed that the GPR41 and GPR43 messenger RNA expression both positively correlated with the serum concentrations of transforming growth factor‐β and IFN‐γ (p < .05), but negatively with the serum concentrations of IL‐17, IL‐4, and IL‐6 in the L. acidophilus–treated group compared with the allergic group (p < .05). These results suggested that L. acidophilus protected against the development of allergic inflammation by improving the intestinal flora, as well as upregulating SCFAs and their receptors GPR41/43.     

On the satisfaction of backbone‐carbonyl lone pairs of electrons in protein structures

Protein structures are stabilized by a variety of noncovalent interactions (NCIs), including the hydrophobic effect, hydrogen bonds, electrostatic forces and van der Waals’ interactions. Our knowledge of the contributions of NCIs, and the interplay between them remains incomplete. This has implications for computational modeling of NCIs, and our ability to understand and predict protein structure, stability, and function. One consideration is the satisfaction of the full potential for NCIs made by backbone atoms. Most commonly, backbone‐carbonyl oxygen atoms located within α‐helices and β‐sheets are depicted as making a single hydrogen bond. However, there are two lone pairs of electrons to be satisfied for each of these atoms. To explore this, we used operational geometric definitions to generate an inventory of NCIs for backbone‐carbonyl oxygen atoms from a set of high‐resolution protein structures and associated molecular‐dynamics simulations in water. We included more‐recently appreciated, but weaker NCIs in our analysis, such as n→π* interactions, Cα‐H bonds and methyl‐H bonds. The data demonstrate balanced, dynamic systems for all proteins, with most backbone‐carbonyl oxygen atoms being satisfied by two NCIs most of the time. Combinations of NCIs made may correlate with secondary structure type, though in subtly different ways from traditional models of α‐ and β‐structure. In addition, we find examples of under‐ and over‐satisfied carbonyl‐oxygen atoms, and we identify both sequence‐dependent and sequence‐independent secondary‐structural motifs in which these reside. Our analysis provides a more‐detailed understanding of these contributors to protein structure and stability, which will be of use in protein modeling, engineering and design.