Regions Of Protein Surface Research Articles

A Fragment-Based Competitive 19F LB-NMR Platform For Hotspot-Directed Ligand Profiling.

Ligand binding hotspots are regions of protein surfaces that form particularly favourable interactions with small molecule pharmacophores. Targeting interactions with these hotspots maximises the efficiency of ligand binding. Existing methods are capable of identifying hotspots but often lack assays to quantify ligand binding and direct elaboration at these sites. Herein, we describe a fragment-based competitive 19F Ligand Based NMR (LB-NMR) screening platform that enables routine, quantitative ligand profiling focused at ligand-binding hotspots. As a proof of concept, the method was applied to 4'-phosphopantetheine adenylyltransferase (PPAT) from Mycobacterium abscessus (Mabs). X-ray crystallographic characterisation of the hits from a 960-member fragment screen identified three ligand-binding hotspots across the PPAT active site. From the fragment hits a collection of 19F reporter candidates were designed and synthesised. By rigorous prioritisation and use of optimisation workflows, a single 19F reporter molecule was generated for each hotspot. Profiling the binding of a set of structurally characterised ligands by competitive 19F LB-NMR with this suite of 19F reporters recapitulated the binding affinity and site ID assignments made by ITC and X-ray crystallography. This quantitative mapping of ligand binding events at hotspot level resolution establishes the utility of the fragment-based competitive 19F LB-NMR screening platform for hotspot-directed ligand profiling.

A Fragment‐Based Competitive 19F LB‐NMR Platform For Hotspot‐Directed Ligand Profiling

AbstractLigand binding hotspots are regions of protein surfaces that form particularly favourable interactions with small molecule pharmacophores. Targeting interactions with these hotspots maximises the efficiency of ligand binding. Existing methods are capable of identifying hotspots but often lack assays to quantify ligand binding and direct elaboration at these sites. Herein, we describe a fragment‐based competitive 19F Ligand Based NMR (LB‐NMR) screening platform that enables routine, quantitative ligand profiling focused at ligand‐binding hotspots. As a proof of concept, the method was applied to 4′‐phosphopantetheine adenylyltransferase (PPAT) from Mycobacterium abscessus (Mabs). X‐ray crystallographic characterisation of the hits from a 960‐member fragment screen identified three ligand‐binding hotspots across the PPAT active site. From the fragment hits a collection of 19F reporter candidates were designed and synthesised. By rigorous prioritisation and use of optimisation workflows, a single 19F reporter molecule was generated for each hotspot. Profiling the binding of a set of structurally characterised ligands by competitive 19F LB‐NMR with this suite of 19F reporters recapitulated the binding affinity and site ID assignments made by ITC and X‐ray crystallography. This quantitative mapping of ligand binding events at hotspot level resolution establishes the utility of the fragment‐based competitive 19F LB‐NMR screening platform for hotspot‐directed ligand profiling

In Silico Analysis of Dengue Virus Serotype 2 Mutations Detected at the Intrahost Level in Patients with Different Clinical Outcomes.

ABSTRACTIntrahost genetic diversity is thought to facilitate arbovirus adaptation to changing environments and hosts, and it may also be linked to viral pathogenesis. Intending to shed light on the viral determinants for severe dengue pathogenesis, we previously analyzed the DENV-2 intrahost genetic diversity in 68 patients clinically classified as dengue fever (n = 31), dengue with warning signs (n = 19), and severe dengue (n = 18), performing viral whole-genome deep sequencing from clinical samples with an amplicon-free approach. From it, we identified a set of 141 relevant mutations distributed throughout the viral genome that deserved further attention. Therefore, we employed molecular modeling to recreate three-dimensional models of the viral proteins and secondary RNA structures to map the mutations and assess their potential effects. Results showed that, in general lines, disruptive variants were identified primarily among dengue fever cases. In contrast, potential immune-escape variants were associated mainly with warning signs and severe cases, in line with the latter’s longer intrahost evolution times. Furthermore, several mutations were located on protein-surface regions, with no associated function. They could represent sites of further investigation, as the interaction of viral and host proteins is critical for both host immunomodulation and virus hijacking of the cellular machinery. The present analysis provides new information about the implications of the intrahost genetic diversity of DENV-2, contributing to the knowledge about the viral factors possibly involved in its pathogenesis within the human host. Strengthening our results with functional studies could allow many of these variants to be considered in the design of therapeutic or prophylactic compounds and the improvement of diagnostic assays.IMPORTANCE Previous evidence showed that intrahost genetic diversity in arboviruses may be linked to viral pathogenesis and that one or a few amino acid replacements within a single protein are enough to modify a biological feature of an RNA virus. To assess dengue virus serotype 2 determinants potentially involved in pathogenesis, we previously analyzed the intrahost genetic diversity of the virus in patients with different clinical outcomes and identified a set of 141 mutations that deserved further study. Thus, through a molecular modeling approach, we showed that disruptive variants were identified primarily among cases with mild dengue fever, while potential immune-escape variants were mainly associated with cases of greater severity. We believe that some of the variants pointed out in this study were attractive enough to be potentially considered in future intelligent designs of therapeutic or prophylactic compounds or the improvement of diagnostic tools. The present analysis provides new information about DENV-2 viral factors possibly involved in its pathogenesis within the human host.

Soft regions of protein surface are potent for stable dimer formation

By having knowledge about the characteristics of protein interaction interfaces, we will be able to manipulate protein complexes for therapies. Dimer state is considered as the primary alphabet of the most proteins’ quaternary structure. The properties of binding interface between subunits and of noninterface region define the specificity and stability of the intended protein complex. Considering some topological properties and amino acids’ affinity for binding in interfaces of protein dimers, we construct the interface-specific recurrence plots. The data obtained from recurrence quantitative analysis, and accessibility-related metrics help us to classify the protein dimers into four distinct classes. Some mechanical properties of binding interfaces are computed for each predefined class of the dimers. The computed mechanical characteristics of binding patch region are compared with those of nonbinding region of proteins. Our observations indicate that the mechanical properties of protein binding sites have a decisive impact on determining the dimer stability. We introduce a new concept in analyzing protein structure by considering mechanical properties of protein structure. We conclude that the interface region between subunits of stable dimers is usually mechanically softer than the interface of unstable protein dimers.AbbreviationsAABaverage affinity for bindingANManisotropic network modelAPCaffinity propagation clusteringASAaccessible surface areaCCDinter residues distanceCSCcomplex stability codeDMdistance matrixΔGdissPISA-computed dissociation free energyGNMGaussian normal mode analysisNMAnormal mode analysisPBPprotein binding patchPISAproteins, interfaces, structures and assembliesrASArelative accessible area in respect to unfolded state of residuesRMrecurrence matrixrPrelative protrusionRPrecurrence plotRQArecurrence quantitative analysisSEMstandard error of meanCommunicated by Ramaswamy H. Sarma

All-Hydrocarbon Staples and Their Effect over Peptide Conformation under Different Force Fields.

Olefinic staples enhance α-helical content and conformational stability in peptides, maintaining a structural scaffold that allows the emulation of specific regions of protein surfaces for therapeutical purposes. The ability to anticipate the efficacy of adding a staple to a peptide through computational simulations may contribute to lowering the costs associated with rational drug design. We evaluated the capabilities of different force fields to reproduce the effect of all-hydrocarbon staples in molecular dynamics simulations. Using the AMBER99SB-ILDN, CHARMM36, and GROMOS54A7 force fields and two distinct initial conformations, we compared our results to experimentally obtained circular dichroism data. The GROMOS54A7 united-atom force field seems to be more accurate compared with all-atom force fields, despite being unable to reproduce the effect of the staple in some of the simulated systems. With further force field enhancements, MD simulations may be used to anticipate conformational effects of all-hydrocarbon staples in peptides.

Computational analysis of interactions in structurally available protein-glycosaminoglycan complexes.

Glycosaminoglycans represent a class of linear anionic periodic polysaccharides, which play a key role in a variety of biological processes in the extracellular matrix via interactions with their protein targets. Computationally, glycosaminoglycans are very challenging due to their high flexibility, periodicity and electrostatics-driven nature of the interactions with their protein counterparts. In this work, we carry out a detailed computational characterization of the interactions in protein-glycosaminoglycan complexes from the Protein Data Bank (PDB), which are split into two subsets accounting for their intrinsic nature: non-enzymatic-protein-glycosaminoglycan and enzyme-glycosaminoglycan complexes. We apply molecular dynamics to analyze the differences in these two subsets in terms of flexibility, retainment of the native interactions in the simulations, free energy components of binding and contributions of protein residue types to glycosaminoglycan binding. Furthermore, we systematically demonstrate that protein electrostatic potential calculations, previously found to be successful for glycosaminoglycan binding sites prediction for individual systems, are in general very useful for proposing protein surface regions as putative glycosaminoglycan binding sites, which can be further used for local docking calculations with these particular polysaccharides. Finally, the performance of six different docking programs (Autodock 3, Autodock Vina, MOE, eHiTS, FlexX and Glide), some of which proved to perform well for particular protein-glycosaminoglycan complexes in previous work, is evaluated on the complete protein-glycosaminoglycan data set from the PDB. This work contributes to widen our knowledge of protein-glycosaminoglycan molecular recognition and could be useful to steer a choice of the strategies to be applied in theoretical studies of these systems.

Investigation of protein selectivity in multimodal chromatography using in silico designed Fab fragment variants.

In this study, a unique set of antibody Fab fragments was designed in silico and produced to examine the relationship between protein surface properties and selectivity in multimodal chromatographic systems. We hypothesized that multimodal ligands containing both hydrophobic and charged moieties would interact strongly with protein surface regions where charged groups and hydrophobic patches were in close spatial proximity. Protein surface property characterization tools were employed to identify the potential multimodal ligand binding regions on the Fab fragment of a humanized antibody and to evaluate the impact of mutations on surface charge and hydrophobicity. Twenty Fab variants were generated by site-directed mutagenesis, recombinant expression, and affinity purification. Column gradient experiments were carried out with the Fab variants in multimodal, cation-exchange, and hydrophobic interaction chromatographic systems. The results clearly indicated that selectivity in the multimodal system was different from the other chromatographic modes examined. Column retention data for the reduced charge Fab variants identified a binding site comprising light chain CDR1 as the main electrostatic interaction site for the multimodal and cation-exchange ligands. Furthermore, the multimodal ligand binding was enhanced by additional hydrophobic contributions as evident from the results obtained with hydrophobic Fab variants. The use of in silico protein surface property analyses combined with molecular biology techniques, protein expression, and chromatographic evaluations represents a previously undescribed and powerful approach for investigating multimodal selectivity with complex biomolecules.

Methanol concentration dependent protein denaturing ability of guanidinium/methanol mixed solution.

Mixtures of osmolytes are present in the cell. Therefore, the understanding of the interplay of mixed osmolyte molecules and their combined effects on protein structure is of fundamental importance. In this article, the structure stability of a model protein (BdpA) in the mixture of guanidinium thiocyanate (GdmSCN) and methanol (MeOH) was investigated by molecular dynamics simulation. It was observed that guanidinium (Gdm(+)) is driven to protein surface by favorable electrostatic interactions and MeOH is driven by both favorable electrostatic and VDW interactions, respectively. The mixture of Gdm(+) and MeOH doesnot affect the electrostatic energy distribution of Gdm(+) but does reduce the difference in VDW energy of MeOH between the regions of protein surface and bulk solution. As a result, the accumulation level of Gdm(+) is not influenced, but the accumulation level of MeOH is lowered in mixed solution. The tertiary structure stability of protein is determined by the accumulated strength of VDW interactions from MeOH to protein side chain, and the secondary structure stability is correlated to the strength of combined electrostatic energies from solvent (water) and cosolvent (Gdm(+) and MeOH) to protein backbone, particularly in hydrogen bonding part. The mixture of GdmSCN with low-concentrated MeOH stabilizes native structure of BdpA whereas the further increase of MeOH concentration denatures native structure of protein to expanded unfolded structure. The present study together with our previous study on the mixture of GdmSCN and 2,2,2-trifluoroethanol (TFE) provides novel insights into the effects of mixed osmolytes on protein structure.

Local functional descriptors for surface comparison based binding prediction

BackgroundMolecular recognition in proteins occurs due to appropriate arrangements of physical, chemical, and geometric properties of an atomic surface. Similar surface regions should create similar binding interfaces. Effective methods for comparing surface regions can be used in identifying similar regions, and to predict interactions without regard to the underlying structural scaffold that creates the surface.ResultsWe present a new descriptor for protein functional surfaces and algorithms for using these descriptors to compare protein surface regions to identify ligand binding interfaces. Our approach uses descriptors of local regions of the surface, and assembles collections of matches to compare larger regions. Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface. Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand. The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface. We demonstrate the effectiveness of the approach on a number of benchmarks, demonstrating performance that is comparable to the state-of-the-art, with an approach with more generality than these prior methods.ConclusionsLocal functional descriptors offer a new method for protein surface comparison that is sufficiently flexible to serve in a variety of applications.

SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity

Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes) is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP). Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP.

Identification of binding pockets in protein structures using a knowledge-based potential derived from local structural similarities

BackgroundThe identification of ligand binding sites is a key task in the annotation of proteins with known structure but uncharacterized function. Here we describe a knowledge-based method exploiting the observation that unrelated binding sites share small structural motifs that bind the same chemical fragments irrespective of the nature of the ligand as a whole.ResultsPDBinder compares a query protein against a library of binding and non-binding protein surface regions derived from the PDB. The results of the comparison are used to derive a propensity value for each residue which is correlated with the likelihood that the residue is part of a ligand binding site. The method was applied to two different problems: i) the prediction of ligand binding residues and ii) the identification of which surface cleft harbours the binding site. In both cases PDBinder performed consistently better than existing methods.PDBinder has been trained on a non-redundant set of 1356 high-quality protein-ligand complexes and tested on a set of 239 holo and apo complex pairs. We obtained an MCC of 0.313 on the holo set with a PPV of 0.413 while on the apo set we achieved an MCC of 0.271 and a PPV of 0.372.ConclusionsWe show that PDBinder performs better than existing methods. The good performance on the unbound proteins is extremely important for real-world applications where the location of the binding site is unknown. Moreover, since our approach is orthogonal to those used in other programs, the PDBinder propensity value can be integrated in other algorithms further increasing the final performance.

New data on biochemical mechanism of programmed senescence of organisms and antioxidant defense of mitochondria

Much evidence has recently been reported suggesting that reactive oxygen species (ROS) produced in mitochondria play a crucial role in the programmed senescence of organisms. In particular, it has been shown that antioxidants addressed to mitochondria slow down the appearance of symptoms of senescence and development of senile diseases and increase the median lifespan of various organisms from fungi to mammals. At the biochemical level, the mechanism of action of such rechargeable antioxidants as plastoquinonyldecyltriphenyl phosphonium (SkQ1) includes, in particular, prevention of oxidation of mitochondrial cardiolipin by ROS. The hormone melatonin also exhibits a number of such effects, and decrease in its level with age could explain the weakening of antioxidant protection upon aging. According to Moosmann et al., there exists a natural mechanism of antioxidant protection that, like SkQ1, is localized in the internal mitochondrial membrane and is rechargeable. It involves methionine residues in the surface regions of proteins encoded by mitochondrial DNA. It appears that in organisms with high respiratory metabolism the genetic code in the mitochondrial system of protein biosynthesis has changed. In these organisms (including some yeasts, insects, crustaceans, and vertebrates), the AUA codon codes for methionine rather than isoleucine, as in the case of synthesis of proteins encoded either in the nucleus or in mitochondria of organisms with lower rates of metabolism (other yeast species, sponges, and echinoderms). Methionine quenches ROS, being converted to methionine sulfoxide, which is re-reduced to the initial methionine by NADPH.

Combining interface core and whole interface descriptors in postscan processing of protein‐protein docking models

Computational protein-protein docking scans commonly produce correct and nearly correct interaction models, but sometimes these models are ranked low. We present here postscan processing procedures that dramatically enhance the distinction between nearly correct and false predictions. The procedures employ propensity descriptors calculated for the interface core, an interface-core clusters count, solvation energy, and a geometric-electrostatic-hydrophobic complementarity score. The various descriptors rank high different selections of false models (shuffling effect), and therefore, are used as Boolean yes/no classifiers in soft intersection filters, which eliminate large proportions of false models. Furthermore, the standardized descriptors are used in new scoring functions that highlight nearly correct models (NCMs). All the tests are performed on unbound docking models produced with MolFit without use of external data. We find that the discrimination between nearly correct and false models by the various descriptors is class dependent; hence, our postscan processing is class specific. The filters reduce the number of putative models from 10,726, 12,517, and 11,054 to 758, 157, and 1218 for enzyme-inhibitor, antibody-antigen, and nonclassified systems. When combined with the new scoring functions, they improve the average rank of the highest ranking NCMs from 673 to 122. Application to 23 CAPRI targets demonstrates the effectiveness of the postscan procedures in cases where external information is used in the production of the putative models. Our new per-molecule residue propensity descriptors show that interacting interfaces are enriched with high propensity residues except for antigenic sites, which resemble more the noninteracting regions of protein surfaces.

In Silico Analysis of Phosphoproteome Data Suggests a Rich-get-richer Process of Phosphosite Accumulation over Evolution

Recent phosphoproteome analyses using mass spectrometry-based technologies have provided new insights into the extensive presence of protein phosphorylation in various species and have raised the interesting question of how this protein modification was gained evolutionarily on such a large scale. We investigated this issue by using human and mouse phosphoproteome data. We initially found that phosphoproteins followed a power-law distribution with regard to their number of phosphosites: most of the proteins included only a few phosphosites, but some included dozens of phosphosites. The power-law distribution, unlike more commonly observed distributions such as normal and log-normal distributions, is considered by the field of complex systems science to be produced by a specific rich-get-richer process called preferential attachment growth. Therefore, we explored the factors that may have promoted the rich-get-richer process during phosphosite evolution. We conducted a bioinformatics analysis to evaluate the relationship of amino acid sequences of phosphoproteins with the positions of phosphosites and found an overconcentration of phosphosites in specific regions of protein surfaces and implications that in many phosphoproteins these clusters of phosphosites are activated simultaneously. Multiple phosphosites concentrated in limited spaces on phosphoprotein surfaces may therefore function biologically as cooperative modules that are resistant to selective pressures during phosphoprotein evolution. We therefore proposed a hypothetical model by which the modularization of multiple phosphosites has been resistant to natural selection and has driven the rich-get-richer process of the evolutionary growth of phosphosite numbers.

Influence of pH and Ionic Strength on the Steric Mass-Action Model Parameters around the Isoelectric Point of Protein

The ion-exchange equilibrium and the dependence of the parameters in the steric mass-action (SMA) model on salt concentration and buffer pH around the isoelectric point of protein were studied. Bovine serum albumin (BSA, isoelectric point = 5.4) was used as a model protein and DEAE Sepharose FF as an ion exchanger. Finite batch adsorption experiments and isocratic elution chromatography were performed for the determination of the model parameters (i.e., characteristic charge, equilibrium constant, and steric factor). The results showed that pH had significant effects on the parameters. With an increase of pH from 4.5 to 6.5, the characteristic charge increased from 0.9 to 3.0 and leveled off as a plateau at pH above 5.5. The charge groups in the contact region of protein surface were considered to play a crucial role on the characteristic charge. The decrease of pH and increase of salt concentration lowered the absolute value of the zeta potential of the protein surface and led to a decrease of the equilibrium constant. The steric factor remained unchanged at about 31 at pH 5.5 and 6.0 and increased to 44.5 at pH 5.0 and 96.8 at pH 4.5, mainly as a result of the lower adsorption capacity of BSA at pH <5.5. Furthermore, the increase of the molecular volume of BSA at pH 4.5 would be an additional reason for the increase of the steric factor. Taking into account the effect of the pH and salt concentration on these parameters, the SMA model described the ion exchange equilibrium of protein more accurately.

Rapid comparison of properties on protein surface

The mapping of physicochemical characteristics onto the surface of a protein provides crucial insights into its function and evolution. This information can be further used in the characterization and identification of similarities within protein surface regions. We propose a novel method which quantitatively compares global and local properties on the protein surface. We have tested the method on comparison of electrostatic potentials and hydrophobicity. The method is based on 3D Zernike descriptors, which provides a compact representation of a given property defined on a protein surface. Compactness and rotational invariance of this descriptor enable fast comparison suitable for database searches. The usefulness of this method is exemplified by studying several protein families including globins, thermophilic and mesophilic proteins, and active sites of TIM beta/alpha barrel proteins. In all the cases studied, the descriptor is able to cluster proteins into functionally relevant groups. The proposed approach can also be easily extended to other surface properties. This protein surface-based approach will add a new way of viewing and comparing proteins to conventional methods, which compare proteins in terms of their primary sequence or tertiary structure.

Synthetic Ligands Able to Interact with the P53 Tetramerization Domain. Towards Understanding a Protein Surface Recognition Event

The applied interaction of synthetic molecules with defined regions of protein surfaces is an emerging strategy for the modulation of protein activity and/or stability. In spite of recent advances, the design of these molecules is not trivial. Among the most challenging aspects in designing these compounds is that they must compete with water molecules for interaction with polar patches of protein surfaces. Herein is reported the preparation of an arginine-rich peptide that interacts in aqueous solution with a very hydrophilic patch at the surface of the tetramerization domain of the tumor suppressor protein p53. The interaction has been studied by several complementary techniques. By using this peptide as a template, a library of peptides has been prepared and evaluated in order to examine the different factors that contribute to the recognition event. The conclusions extracted from this work could be useful for the design of ligands directed at highly hydrophilic protein surface patches.

SiteEngines: recognition and comparison of binding sites and protein-protein interfaces

Protein surface regions with similar physicochemical properties and shapes may perform similar functions and bind similar binding partners. Here we present two web servers and software packages for recognition of the similarity of binding sites and interfaces. Both methods recognize local geometrical and physicochemical similarity, which can be present even in the absence of overall sequence or fold similarity. The first method, SiteEngine (), receives as an input two protein structures and searches the complete surface of one protein for regions similar to the binding site of the other. The second, Interface-to-Interface (I2I)-SiteEngine (), compares protein–protein interfaces, which are regions of interaction between two protein molecules. It receives as an input two structures of protein–protein complexes, extracts the interfaces and finds the three-dimensional transformation that maximizes the similarity between two pairs of interacting binding sites. The output of both servers consists of a superimposition in PDB file format and a list of physicochemical properties shared by the compared entities. The methods are highly efficient and the freely available software packages are suitable for large-scale database searches of the entire PDB.

Computational approaches for identification of conserved/unique binding pockets in the A chain of ricin

Specific and sensitive ligand-based protein detection assays that employ antibodies or small molecules such as peptides, aptamers or other small molecules require that the corresponding surface region of the protein be accessible and that there be minimal cross-reactivity with non-target proteins. To reduce the time and cost of laboratory screening efforts for diagnostic reagents, we developed new methods for evaluating and selecting protein surface regions for ligand targeting. We devised combined structure- and sequence-based methods for identifying 3D epitopes and binding pockets on the surface of the A chain of ricin that are conserved with respect to a set of ricin A chains and unique with respect to other proteins. We (1) used structure alignment software to detect structural deviations and extracted from this analysis the residue-residue correspondence, (2) devised a method to compare corresponding residues across sets of ricin structures and structures of closely related proteins, (3) devised a sequence-based approach to determine residue infrequency in local sequence context and (4) modified a pocket-finding algorithm to identify surface crevices in close proximity to residues determined to be conserved/unique based on our structure- and sequence-based methods. In applying this combined informatics approach to ricin A, we identified a conserved/unique pocket in close proximity (but not overlapping) the active site that is suitable for bi-dentate ligand development. These methods are generally applicable to identification of surface epitopes and binding pockets for development of diagnostic reagents, therapeutics and vaccines.

A Fourier Fingerprint-Based Method for Protein Surface Representation

A crucial enabling technology for structural genomics is the development of algorithms that can predict the putative function of novel protein structures: the proposed functions can subsequently be experimentally tested by functional studies. Testable assignments of function can be made if it is possible to attribute a putative, or indeed probable, function on the basis of the shapes of the binding sites on the surface of a protein structure. However the comparison of the surfaces of 3D protein structures is a computationally demanding task. Here we present four surface representations that can be used locally to describe the global shape of specifically bounded local region models. The most successful of these representations is obtained by a Fourier analysis of the distribution of surface curvature on concentric spheres around a surface point and summarizes a 24 A diameter spherically clipped region of protein surface by a fingerprint of 18 Fourier amplitude values. Searching experiments using these fingerprints on a set of 366 proteins demonstrate that this provides an effective and an efficient technique for the matching of protein surfaces.