Landscape of protein–small ligand binding modes

Elucidating the mechanisms of specific small‐molecule (ligand) recognition by proteins is a long‐standing conundrum. While the structures of these molecules, proteins and ligands, have been extensively studied, protein–ligand interactions, or binding modes, have not been comprehensively analyzed. Although methods for assessing similarities of binding site structures have been extensively developed, the methods for the computational treatment of binding modes have not been well established. Here, we developed a computational method for encoding the information about binding modes as graphs, and assessing their similarities. An all‐against‐all comparison of 20,040 protein–ligand complexes provided the landscape of the protein–ligand binding modes and its relationships with protein‐ and chemical spaces. While similar proteins in the same SCOP Family tend to bind relatively similar ligands with similar binding modes, the correlation between ligand and binding similarities was not very high (R² = 0.443). We found many pairs with novel relationships, in which two evolutionally distant proteins recognize dissimilar ligands by similar binding modes (757,474 pairs out of 200,790,780 pairs were categorized into this relationship, in our dataset). In addition, there were an abundance of pairs of homologous proteins binding to similar ligands with different binding modes (68,217 pairs). Our results showed that many interesting relationships between protein–ligand complexes are still hidden in the structure database, and our new method for assessing binding mode similarities is effective to find them.     

Engineering a high-affinity scaffold for non-chromatographic protein purification via intein-mediated cleavage

While protein purification has long been dominated by standard chromatography, the relatively high cost and complex scale‐up have promoted the development of alternative non‐chromatographic separation methods. Here we developed a new non‐chromatographic affinity method for the purification of proteins expressed in Escherichia coli. The approach is to genetically fuse the target proteins with an affinity tag. Direct purification and recovery can be achieved using a thermo‐responsive elastin‐like protein (ELP) scaffold containing the capturing domain. Naturally occurring cohesin–dockerin pairs, which are high‐affinity protein complex responsible for the formation of cellulosome in anaerobic bacteria, were used as the model. By exploiting the highly specific interaction between the dockerin and cohesin domain from Clostridium thermocellum and the reversible aggregation property of ELP, highly purified and active dockerin‐tagged proteins, such as the endoglucanase CelA, chloramphenicol acetyl transferase (CAT), and enhanced green fluorescence protein (EGFP), were recovered directly from crude cell extracts in a single thermal precipitation step with yields achieving over 90%. Incorporation of a self‐cleaving intein domain enabled rapid removal of the affinity tag from the target proteins, which was subsequently removed by another cycle of thermal precipitation. This method offers great flexibility as a wide range of affinity tags and ligands can be used. Biotechnol. Bioeng. 2012; 109: 2829–2835. © 2012 Wiley Periodicals, Inc.     

Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity

Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein–ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art “fixed backbone” design methods perform poorly on these tests, we develop a new “coupled moves” design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein – ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution.     

Optimized small‐molecule pull‐downs define MLBP1 as an acyl‐lipid‐binding protein

Abscisic acid (ABA) receptors belong to the START domain superfamily, which encompasses ligand‐binding proteins present in all kingdoms of life. START domain proteins contain a central binding pocket that, depending on the protein, can couple ligand binding to catalytic, transport or signaling functions. In Arabidopsis, the best characterized START domain proteins are the 14 PYR/PYL/RCAR ABA receptors, while the other members of the superfamily do not have assigned ligands. To address this, we used affinity purification of biotinylated proteins expressed transiently in Nicotiana benthamiana coupled to untargeted LC‐MS to identify candidate binding ligands. We optimized this method using ABA–PYL interactions and show that ABA co‐purifies with wild‐type PYL5 but not a binding site mutant. The K_d of PYL5 for ABA is 1.1 μm , which suggests that the method has sufficient sensitivity for many ligand–protein interactions. Using this method, we surveyed a set of 37 START domain‐related proteins, which resulted in the identification of ligands that co‐purified with MLBP1 (At4G01883) or MLP165 (At1G35260). Metabolite identification and the use of authentic standards revealed that MLBP1 binds to monolinolenin, which we confirmed using recombinant MLBP1. Monolinolenin also co‐purified with MLBP1 purified from transgenic Arabidopsis, demonstrating that the interaction occurs in a native context. Thus, deployment of this relatively simple method allowed us to define a protein–metabolite interaction and better understand protein–ligand interactions in plants.     

In silico modeling and experimental evidence of coagulant protein interaction with precursors for nanoparticle functionalization

The design of novel protein–nanoparticle hybrid systems has applications in many fields of science ranging from biomedicine, catalysis, water treatment, etc. The main barrier in devising such tool is lack of adequate information or poor understanding of protein–ligand chemistry. Here, we establish a new strategy based on computational modeling for protein and precursor linkers that can decorate the nanoparticles. Moringa oleifera (MO_2.1) seed protein that has coagulation and antimicrobial properties was used. Superparamagnetic nanoparticles (SPION) with precursor ligands were used for the protein–ligand interaction studies. The molecular docking studies reveal that there are two binding sites, one is located at the core binding site; tetraethoxysilane (TEOS) or 3-aminopropyl trimethoxysilane (APTES) binds to this site while the other one is located at the side chain residues where trisodium citrate (TSC) or Si₆₀ binds to this site. The protein–ligand distance profile analysis explains the differences in functional activity of the decorated SPION. Experimentally, TSC-coated nanoparticles showed higher coagulation activity as compared to TEOS- and APTES-coated SPION. To our knowledge, this is the first report on in vitro experimental data, which endorses the computational modeling studies as a powerful tool to design novel precursors for functionalization of nanomaterials; and develop interface hybrid systems for various applications.     

Combinatorial de novo design and application of a biomimetic affinity ligand for the purification of human anti‐HIV mAb 4E10 from transgenic tobacco

Monoclonal anti‐HIV antibody 4E10 (mAb 4E10) is one of the most broadly neutralizing antibodies against HIV, directed against a specific epitope on envelope protein gp41. In the present study, a combinatorial de novo design approach was used for the development of a biomimetic ligand for the affinity purification of mAb 4E10 from tobacco transgenic extract in a single chromatographic step. The biomimetic ligand (4E10lig) was based on a L ‐Phe/β‐Ala bi‐substituted 1,3,5‐triazine (Trz) scaffold (β‐Ala‐Trz‐L ‐Phe, 4E10lig) which potentially mimics the more pronounced electrostatic and hydrophobic interactions of mAb 4E10‐binding sequence determined by screening of a random peptide library. This library was comprised of Escherichia coli cells harboring a plasmid (pFlitrx) engineered to express a fusion protein containing random dodecapeptides that were inserted into the active loop of thioredoxin, which itself was inserted into the dispensable region of the flagellin gene. Adsorption equilibrium studies with this biomimetic ligand and mAb 4E10 determined a dissociation constant (K_D) of 0.41 ± 0.05 µM. Molecular modeling studies of the biomimetic ligand revealed that it can potentially occupy the same binding site as the natural binding core peptide epitope. The biomimetic affinity adsorbent was exploited in the development of a facile mAb 4E10 purification protocol, affording mAb 4E10 of high purity (approximately 95%) with good overall yield (60–80%). Analysis of the antibody preparation by SDS‐PAGE, enzyme‐linked immunosorbent assays (ELISA), and western blot showed that the mAb 4E10 was fully active and free of degraded variants, polyphenols, and alkaloids. Copyright © 2009 John Wiley & Sons, Ltd.     

Affinity of mouse immunoglobulin G subclasses for sialic acid derivatives immobilized on dextran-coated supports

High-performance liquid affinity chromatography is a powerful method for the purification of biological compounds owing to its specificity, rapidity and high resolution. In our laboratory, we develop chromatographic supports based on porous silica beads. However, in order to minimize non-specific interactions between the inorganic surface and proteins in aqueous solution, the silica beads are coated with modified dextran. As previously reported, many affinity ligands can be covalently grafted onto dextran-coated silica. In this study, N-acetylneuramic acid, which belongs to the sialic acid family and is present in immunoglobulin G (IgG) epitopes, is used as an active ligand. The interactions of this affinity support and IgG subclasses are analyzed. This immobilized ligand enables purification of IgG3 antibodies.     

Computational approaches for protein function prediction: A combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein–ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.     

The Bead blot: a method for selecting small molecule ligands for protein capture and purification

We present a new method for selecting peptide ligands that are useful for protein purification, protein targeting and exploring protein-ligand interactions, and which requires no prior protein purification or derivatization. In the Bead blot, a complex mixture containing the target protein, for example, plasma, is incubated with a combinatorial library of peptide ligands synthesized on beads. The proteins are fractionated and purified on their respective ligands and the beads with their bound proteins are immobilized in a gel. The proteins are eluted from the ligands by capillary action and captured on a membrane so that their position on the membrane corresponds to the position of the beads in the gel. The protein is detected on the membrane, generating spots on an autoradiography film, the spots on the film are aligned with the beads in the gel, and the beads that bound the protein are recovered. The ligand on the bead(s) can be sequenced and synthesized at large scale for protein purification. The Bead blot can be completed in several hours with overnight pause steps if desired. On average, 5 prospective ligands are selected per 50,000 beads screened using this method. Unlike other ligand identification methods, the target protein does not have to be purified or labeled, and the Bead blot allows ligands for multiple proteins to be selected in a single experiment.     

Dead‐end elimination with perturbations (DEEPer): A provable protein design algorithm with continuous sidechain and backbone flexibility

Computational protein and drug design generally require accurate modeling of protein conformations. This modeling typically starts with an experimentally determined protein structure and considers possible conformational changes due to mutations or new ligands. The DEE/A* algorithm provably finds the global minimum‐energy conformation (GMEC) of a protein assuming that the backbone does not move and the sidechains take on conformations from a set of discrete, experimentally observed conformations called rotamers. DEE/A* can efficiently find the overall GMEC for exponentially many mutant sequences. Previous improvements to DEE/A* include modeling ensembles of sidechain conformations and either continuous sidechain or backbone flexibility. We present a new algorithm, DEEPer (D ead‐E nd E limination with Per turbations), that combines these advantages and can also handle much more extensive backbone flexibility and backbone ensembles. DEEPer provably finds the GMEC or, if desired by the user, all conformations and sequences within a specified energy window of the GMEC. It includes the new abilities to handle arbitrarily large backbone perturbations and to generate ensembles of backbone conformations. It also incorporates the shear, an experimentally observed local backbone motion never before used in design. Additionally, we derive a new method to accelerate DEE/A*‐based calculations, indirect pruning, that is particularly useful for DEEPer. In 67 benchmark tests on 64 proteins, DEEPer consistently identified lower‐energy conformations than previous methods did, indicating more accurate modeling. Additional tests demonstrated its ability to incorporate larger, experimentally observed backbone conformational changes and to model realistic conformational ensembles. These capabilities provide significant advantages for modeling protein mutations and protein–ligand interactions. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Towards in silico lead optimization: scores from ensembles of protein/ligand conformations reliably correlate with biological activity

Accurately ranking protein/ligand interactions and distinguishing subtle differences between homologous compounds in a virtual focused library in silico is essential in a structure-based drug discovery program. In order to establish a predictive model to design novel inhibitors of dihydrofolate reductase (DHFR) from the parasitic protozoa, Cryptosporidium hominis, we docked a series of 30 DHFR inhibitors with measured inhibition constants against the crystal structure of the protein. By including protein flexibility and averaging the energies of the 25 lowest protein/ligand conformers we obtained more accurate total nonbonded energies from which we calculated a predicted biological activity. The calculated and measured biological activities showed reliable correlations of 72.9%. Additionally, visual analysis of the ensemble of protein/ligand conformations revealed alternative ligand binding pockets in the active site. Using the same principles we then created a homology model of DHFR from Toxoplasma gondii and docked 11 inhibitors. A correlation of 50.2% between docking score and activity validates both the method and the model. The correlations presented here are particularly compelling considering the high structural similarity of the ligands and the fact that we have used structures derived from crystallographic data and homology modeling. These docking principles may be useful in any lead optimization study where accurate ranking of similar compounds is desired.     

Galactosyl-mimodye ligands for Pseudomonas fluorescens beta-galactose dehydrogenase.

Protein molecular modelling and ligand docking were employed for the design of anthraquinone galactosyl-biomimetic dye ligands (galactosyl-mimodyes) for the target enzyme galactose dehydrogenase (GaDH). Using appropriate modelling methodology, a GaDH model was build based on a glucose-fructose oxidoreductase (GFO) protein template. Subsequent computational analysis predicted chimaeric mimodye-ligands comprising a NAD-pseudomimetic moiety (anthraquinone diaminobenzosulfonic acid) and a galactosyl-mimetic moiety (2-amino-2-deoxygalactose or shikimic acid) bearing an aliphatic 'linker' molecule. In addition, the designed mimodye ligands had an appropriate in length and chemical nature 'spacer' molecule via which they can be attached onto a chromatographic support without steric clashes upon interaction with GaDH. Following their synthesis, purification and analysis, the ligands were immobilized to agarose. The respective affinity adsorbents, compared to other conventional adsorbents, were shown to be superior affinity chromatography materials for the target enzyme, Pseudomonas fluorescensbeta-galactose dehydrogenase. In addition, these mimodye affinity adsorbents displayed good selectivity, binding low amounts of enzymes other than GaDH. Further immobilized dye-ligands, comprising different linker and/or spacer molecules, or not having a biomimetic moiety, had inferior chromatographic behavior. Therefore, these new mimodyes suggested by computational analysis, are candidates for application in affinity labeling and structural studies as well as for purification of galactose dehydrogenase.     

Purification of animal immunoglobulin G (IgG) using peptoid affinity ligands

The availability of highly pure animal antibodies is critical in the production of diagnostic tools and biosensors. The peptoid PL16, previously isolated from an ensemble of peptoid variants of the IgG-binding peptide HWRGWV, was utilized in this work as affinity ligand on WorkBeads resin for the purification of immunoglobulin G (IgG) from a variety of mammalian sources and chicken immunoglobulin Y (IgY). The chromatographic protocol initially optimized for murine serum and ascites was subsequently employed for processing rabbit, goat and sheep, donkey, llama, and chicken sera. The PL16-WorkBeads resin proved able to recover all antibody targets with values of yield between 50 and 90%, and purity consistently above 90%. Notably, PL16 not only binds a broader spectrum of animal immunoglobulins than the reference ligands Protein A and G, but it also binds equally well with all their subclasses. Unlike the protein ligands, in fact, PL16 afforded excellent values of yield and purity of mammalian polyclonal IgG, namely murine (47 and 94%), rabbit (66.5 and 91.7%), caprine IgG (63 and 91–95%), donkey, and llama (93 and 97%), as well as chicken IgY (42 and 92%). Of notice, it is also the ability of PL16 to target monomeric IgG without binding aggregated IgG; when challenged with a mixture of monomeric and aggregated murine IgG, PL16 eluted <3% of fed aggregates, against 11–13% eluted by Protein A and G. Collectively, these results prove the potential of the proposed peptoid ligand for large-scale purification of animal immunoglobulins.     

Mathematical modelling and computational study of two-dimensional and three-dimensional dynamics of receptor–ligand interactions in signalling response mechanisms

Cell signalling processes involve receptor trafficking through highly connected networks of interacting components. The binding of surface receptors to their specific ligands is a key factor for the control and triggering of signalling pathways. But the binding process still presents many enigmas and, by analogy with surface catalytic reactions, two different mechanisms can be conceived: the first mechanism is related to the Eley–Rideal (ER) mechanism, i.e. the bulk-dissolved ligand interacts directly by pure three-dimensional (3D) diffusion with the specific surface receptor; the second mechanism is similar to the Langmuir–Hinshelwood (LH) process, i.e. 3D diffusion of the ligand to the cell surface followed by reversible ligand adsorption and subsequent two-dimensional (2D) surface diffusion to the receptor. A situation where both mechanisms simultaneously contribute to the signalling process could also occur. The aim of this paper is to perform a computational study of the behavior of the signalling response when these different mechanisms for ligand-receptor interactions are integrated into a model for signal transduction and ligand transport. To this end, partial differential equations have been used to develop spatio-temporal models that show trafficking dynamics of ligands, cell surface components, and intracellular signalling molecules through the different domains of the system. The mathematical modeling developed for these mechanisms has been applied to the study of two situations frequently found in cell systems: (a) dependence of the signal response on cell density; and (b) enhancement of the signalling response in a synaptic environment.     

The harnessing of peptide–monolith constructs for single step plasmid DNA purification

The availability of synthetic peptides has paved the way for their use in tailor-made interactions with biomolecules. In this study, a 16mer LacI-based peptide was used as an affinity ligand to examine the scale up feasibility for plasmid DNA purification. First, the peptide was designed and characterized for the affinity purification of lacO containing plasmid DNA, to be employed as a high affinity ligand for the potential capturing of plasmid DNA in a single unit operation. It was found there were no discernible interactions with a control plasmid that did not encode the lacO nucleotide sequence. The dissociation equilibrium constant of the binding between the 16mer peptide and target pUC19 was 5.0 ± 0.5 × 10⁻⁸ M as assessed by surface plasmon resonance. This selectivity and moderated affinity indicate that the 16mer is suitable for the adsorption and chromatographic purification of plasmid DNA. The suitability of this peptide was then evaluated using a chromatography system with the 16mer peptide immobilized to a customized monolith to purify plasmid DNA, obtaining preferential purification of supercoiled pUC19. The results demonstrate the applicability of peptide–monolith supports to scale up the purification process for plasmid DNA using designed ligands via a biomimetic approach.     

Model-based monitoring of industrial reversed phase chromatography to predict insulin variants

Downstream processing in the manufacturing biopharmaceutical industry is a multistep process separating the desired product from process- and product-related impurities. However, removing product-related impurities, such as product variants, without compromising the product yield or prolonging the process time due to extensive quality control analytics, remains a major challenge. Here, we show how mechanistic model-based monitoring, based on analytical quality control data, can predict product variants by modeling their chromatographic separation during product polishing with reversed phase chromatography. The system was described by a kinetic dispersive model with a modified Langmuir isotherm. Solely quality control analytical data on product and product variant concentrations were used to calibrate the model. This model-based monitoring approach was developed for an insulin purification process. Industrial materials were used in the separation of insulin and two insulin variants, one eluting at the product peak front and one eluting at the product peak tail. The model, fitted to analytical data, used one component to simulate each protein, or two components when a peak displayed a shoulder. This monitoring approach allowed the prediction of the elution patterns of insulin and both insulin variants. The results indicate the potential of using model-based monitoring in downstream polishing at industrial scale to take pooling decisions.     

Affinity chromatographic purification of human immunoglobulin M from human B lymphocyte cell culture supernatant

Compared to immunoglobulin G purification with extensively studied affinity ligands such as protein A and protein G, little work has been done on affinity chromatographic purification of immunoglobulin M. Hexamer peptide ligand HWRGWV, previously shown to bind specifically to the Fc fragment of IgG, also demonstrated potential for IgM purification. This study presents further characterization and investigation of this ligand for its potential for purification of IgM. Different running conditions were employed in order to improve the recovery and purity of IgM. The final recovery and purity of the antibody is feedstock dependent, but can reach levels of both recovery and purity as high as 95%. The dependence of the recovery and purity on total loading amount and initial IgM concentration were investigated and discussed. Although relatively low dynamic binding capacities (DBC) in the range of 4.6–13.1 mg IgM/mL resin at linear flow rates from 173 to 35 cm/h were obtained for IgM compared to IgG because of the large molecular weight of IgM, the DBC value of HWRGWV for IgM is much greater than protein-based IgM affinity ligands found in the literature and is competitive with current commercially available affinity ligands, such as KAPTIVE-M, CaptureSelect IgM and Ultralink Immobilized Mannan Binding Protein.     

The Bead blot: a method for identifying ligand-protein and protein-protein interactions using combinatorial libraries of peptide ligands

Small molecules that bind proteins can be used as ligands for protein purification and for investigating protein-protein and protein-drug interactions. Unfortunately, many methods used to identify new ligands to desired proteins suffer from common shortcomings, including the requirement that the target protein be purified and/or the requirement that the ligands be selected under conditions different from those under which it will be used. We have developed a new method called the Bead blot that can (i) select ligands to unpurified proteins, including trace proteins, present in complex materials (e.g., unfractionated plasma); (ii) select ligands to multiple proteins under a variety of conditions in a single experiment; and (iii) be used with libraries of different types of ligands. In the Bead blot, a library of ligands, synthesized on chromatography resin beads, is incubated with a starting material containing a target protein for which a ligand is sought. The proteins in the material bind to their complementary ligands according to specific affinity interactions. Then the protein-loaded beads are immobilized in a porous matrix, and the proteins are directionally eluted from the beads and captured on a membrane superimposed on the beads. The location of the target protein on the membrane is determined, and because the position of the protein(s) on the membrane reflects the position of the bead(s) in the matrix, the bead that originally bound the protein is identified, with subsequent elucidation of the ligand sequence. Ligands to several targets can be identified in one experiment. Here we demonstrate the broad utility of this method by the selection of ligands that purify plasma protein complexes or that remove pathogens from whole blood with very high affinity constants. We also select ligands to a protein based on competitive elution.