Exhaustive comparison and classification of ligand‐binding surfaces in proteins

Many proteins function by interacting with other small molecules (ligands). Identification of ligand‐binding sites (LBS) in proteins can therefore help to infer their molecular functions. A comprehensive comparison among local structures of LBSs was previously performed, in order to understand their relationships and to classify their structural motifs. However, similar exhaustive comparison among local surfaces of LBSs (patches) has never been performed, due to computational complexity. To enhance our understanding of LBSs, it is worth performing such comparisons among patches and classifying them based on similarities of their surface configurations and electrostatic potentials. In this study, we first developed a rapid method to compare two patches. We then clustered patches corresponding to the same PDB chemical component identifier for a ligand, and selected a representative patch from each cluster. We subsequently exhaustively as compared the representative patches and clustered them using similarity score, PatSim. Finally, the resultant PatSim scores were compared with similarities of atomic structures of the LBSs and those of the ligand‐binding protein sequences and functions. Consequently, we classified the patches into ～2000 well‐characterized clusters. We found that about 63% of these clusters are used in identical protein folds, although about 25% of the clusters are conserved in distantly related proteins and even in proteins with cross‐fold similarity. Furthermore, we showed that patches with higher PatSim score have potential to be involved in similar biological processes.     

Fold independent structural comparisons of protein-ligand binding sites for exploring functional relationships

The rapid growth in protein structural data and the emergence of structural genomics projects have increased the need for automatic structure analysis and tools for function prediction. Small molecule recognition is critical to the function of many proteins; therefore, determination of ligand binding site similarity is important for understanding ligand interactions and may allow their functional classification. Here, we present a binding sites database (SitesBase) that given a known protein-ligand binding site allows rapid retrieval of other binding sites with similar structure independent of overall sequence or fold similarity. However, each match is also annotated with sequence similarity and fold information to aid interpretation of structure and functional similarity. Similarity in ligand binding sites can indicate common binding modes and recognition of similar molecules, allowing potential inference of function for an uncharacterised protein or providing additional evidence of common function where sequence or fold similarity is already known. Alternatively, the resource can provide valuable information for detailed studies of molecular recognition including structure-based ligand design and in understanding ligand cross-reactivity. Here, we show examples of atomic similarity between superfamily or more distant fold relatives as well as between seemingly unrelated proteins. Assignment of unclassified proteins to structural superfamiles is also undertaken and in most cases substantiates assignments made using sequence similarity. Correct assignment is also possible where sequence similarity fails to find significant matches, illustrating the potential use of binding site comparisons for newly determined proteins.     

Geometric Similarities of Protein-Protein Interfaces at Atomic Resolution Are Only Observed within Homologous Families: An Exhaustive Structural Classification Study

To elucidate the structural basis of the diversity and universality in protein-protein interactions, an exhaustive all-against-all structural comparison of all known protein interfaces in the Protein Data Bank was performed at atomic resolution. After similar interfaces were clustered, approximately 20,000 structural motifs with at least two members were identified, out of which 3678 motifs consisted of at least 10 interfaces. Except for some trivial interfaces involving single α helices, almost all motifs were found to be confined within single protein families. Furthermore, the interaction partners of each motif were found to be very limited, and, accordingly, the interaction networks of the motifs tend to be small and are much more restricted than the binding sites for small ligand molecules. These findings suggest that, at the level of atomic structures, protein-protein interactions are precisely designed; hence, protein interfaces with multiple interacting partners should involve incompletely overlapping multiple interfaces and/or accommodate structural changes upon binding to their targets.     

Using RNA secondary structures to guide sequence motif finding towards single-stranded regions

RNA binding proteins recognize RNA targets in a sequence specific manner. Apart from the sequence, the secondary structure context of the binding site also affects the binding affinity. Binding sites are often located in single-stranded RNA regions and it was shown that the sequestration of a binding motif in a double-strand abolishes protein binding. Thus, it is desirable to include knowledge about RNA secondary structures when searching for the binding motif of a protein. We present the approach MEMERIS for searching sequence motifs in a set of RNA sequences and simultaneously integrating information about secondary structures. To abstract from specific structural elements, we precompute position-specific values measuring the single-strandedness of all substrings of an RNA sequence. These values are used as prior knowledge about the motif starts to guide the motif search. Extensive tests with artificial and biological data demonstrate that MEMERIS is able to identify motifs in single-stranded regions even if a stronger motif located in double-strand parts exists. The discovered motif occurrences in biological datasets mostly coincide with known protein-binding sites. This algorithm can be used for finding the binding motif of single-stranded RNA-binding proteins in SELEX or other biological sequence data.     

Similar binding sites and different partners: implications to shared proteins in cellular pathways

We studied a data set of structurally similar interfaces that bind to proteins with different binding-site structures and different functions. Our multipartner protein interface clusters enable us to address questions like: What makes a given site bind different proteins? How similar/different are the interactions? And, what drives the apparently less-specific association? We find that proteins with common binding-site motifs preferentially use conserved interactions at similar interface locations, despite the different partners. Helices are major vehicles for binding different partners, allowing alternate ways to achieve favorable association. The binding sites are characterized by imperfect packing, planar architectures, bridging water molecules, and, on average, smaller size. Interestingly, analysis of the connectivity of these proteins illustrates that they have more interactions with other proteins. These findings are important in predicting "date hubs," if we assume that "date hubs" are shared proteins with binding sites capable of transient binding to multipartners, linking higher-order networks.     

Prediction of carbohydrate binding sites on protein surfaces with 3-dimensional probability density distributions of interacting atoms

Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall prediction MCC was 0.49. Independent tests on anti-carbohydrate antibodies showed that the carbohydrate antigen binding sites were predicted with comparable accuracy. These results demonstrate that the predictors are among the best in carbohydrate binding site predictions to date.     

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

Ligand–protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED‐SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED‐SMA is an automated and fast method to classify binding sites. It is based on MED‐SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora‐A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED‐SMA approach is demonstrated as it gathers binding sites of proteins with similar structure‐activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target‐based drug design and prediction of cross‐reactivity and therefore potential toxic side effects.     

Prefoldin recognition motifs in the nonhomologous proteins of the actin and tubulin families

Nascent actin and tubulin molecules undergo a series of complex interactions with chaperones and are thereby guided to their native conformation. These cytoskeletal proteins have the initial part of the pathway in common: both interact with prefoldin and with the cytosolic chaperonin containing tailless complex polypeptide 1. Little is understood with regard to how these chaperones and, in particular, prefoldin recognize the non-native forms of these target proteins. Using mutagenesis, we provide evidence that beta-actin and alpha-tubulin each have two prefoldin interaction sites. The most amino-terminally located site of both proteins shows striking sequence similarity, although these proteins are nonhomologous. Very similar motifs are present in beta- and gamma-tubulin and in the newly identified prefoldin target protein actin-related protein 1. Actin-related proteins 2 and 3 have related motifs, but these have altered charge properties. The latter two proteins do not bind prefoldin, although we identify them here as target proteins for the cytosolic chaperonin. Actin fragments containing the two prefoldin interaction regions compete efficiently with actin for prefoldin binding. In addition, they also compete with tubulins, suggesting that these target proteins contact similar prefoldin subunits.     

Understand the Functions of Scaffold Proteins in Cell Signaling by a Mesoscopic Simulation Method

Scaffold proteins are central players in regulating the spatial-temporal organization of many important signaling pathways in cells. They offer physical platforms to downstream signaling proteins so that their transient interactions in a crowded and heterogeneous environment of cytosol can be greatly facilitated. However, most scaffold proteins tend to simultaneously bind more than one signaling molecule, which leads to the spatial assembly of multimeric protein complexes. The kinetics of these protein oligomerizations are difficult to quantify by traditional experimental approaches. To understand the functions of scaffold proteins in cell signaling, we developed a, to our knowledge, new hybrid simulation algorithm in which both spatial organization and binding kinetics of proteins were implemented. We applied this new technique to a simple network system that contains three molecules. One molecule in the network is a scaffold protein, whereas the other two are its binding targets in the downstream signaling pathway. Each of the three molecules in the system contains two binding motifs that can interact with each other and are connected by a flexible linker. By applying the new simulation method to the model, we show that the scaffold proteins will promote not only thermodynamics but also kinetics of cell signaling given the premise that the interaction between the two signaling molecules is transient. Moreover, by changing the flexibility of the linker between two binding motifs, our results suggest that the conformational fluctuations in a scaffold protein play a positive role in recruiting downstream signaling molecules. In summary, this study showcases the capability of computational simulation in understanding the general principles of scaffold protein functions.     

Motif-pattern dependence of biomolecular phase separation driven by specific interactions

Eukaryotic cells partition a wide variety of important materials and processes into biomolecular condensates—phase-separated droplets that lack a membrane. In addition to nonspecific electrostatic or hydrophobic interactions, phase separation also depends on specific binding motifs that link together constituent molecules. Nevertheless, few rules have been established for how these ubiquitous specific, saturating, motif-motif interactions drive phase separation. By integrating Monte Carlo simulations of lattice-polymers with mean-field theory, we show that the sequence of heterotypic binding motifs strongly affects a polymer’s ability to phase separate, influencing both phase boundaries and condensate properties (e.g. viscosity and polymer diffusion). We find that sequences with large blocks of single motifs typically form more inter-polymer bonds, which promotes phase separation. Notably, the sequence of binding motifs influences phase separation primarily by determining the conformational entropy of self-bonding by single polymers. This contrasts with systems where the molecular architecture primarily affects the energy of the dense phase, providing a new entropy-based mechanism for the biological control of phase separation.     

The MASH pipeline for protein function prediction and an algorithm for the geometric refinement of 3D motifs.

The development of new and effective drugs is strongly affected by the need to identify drug targets and to reduce side effects. Resolving these issues depends partially on a thorough understanding of the biological function of proteins. Unfortunately, the experimental determination of protein function is expensive and time consuming. To support and accelerate the determination of protein functions, algorithms for function prediction are designed to gather evidence indicating functional similarity with well studied proteins. One such approach is the MASH pipeline, described in the first half of this paper. MASH identifies matches of geometric and chemical similarity between motifs, representing known functional sites, and substructures of functionally uncharacterized proteins (targets). Observations from several research groups concur that statistically significant matches can indicate functionally related active sites. One major subproblem is the design of effective motifs, which have many matches to functionally related targets (sensitive motifs), and few matches to functionally unrelated targets (specific motifs). Current techniques select and combine structural, physical, and evolutionary properties to generate motifs that mirror functional characteristics in active sites. This approach ignores incidental similarities that may occur with functionally unrelated proteins. To address this problem, we have developed Geometric Sieving (GS), a parallel distributed algorithm that efficiently refines motifs, designed by existing methods, into optimized motifs with maximal geometric and chemical dissimilarity from all known protein structures. In exhaustive comparison of all possible motifs based on the active sites of 10 well-studied proteins, we observed that optimized motifs were among the most sensitive and specific.     

Non-covalent binding of membrane lipids to membrane proteins

Polar lipids and membrane proteins are major components of biological membranes, both cell membranes and membranes of enveloped viruses. How these two classes of membrane components interact with each other to influence the function of biological membranes is a fundamental question that has attracted intense interest since the origins of the field of membrane studies. One of the most powerful ideas that driven the field is the likelihood that lipids bind to membrane proteins at specific sites, modulating protein structure and function. However only relatively recently has high resolution structure determination of membrane proteins progressed to the point of providing atomic level structure of lipid binding sites on membrane proteins. Analysis of X-ray diffraction, electron crystallography and NMR data over 100 specific lipid binding sites on membrane proteins. These data demonstrate tight lipid binding of both phospholipids and cholesterol to membrane proteins. Membrane lipids bind to membrane proteins by their headgroups, or by their acyl chains, or binding is mediated by the entire lipid molecule. When headgroups bind, binding is stabilized by polar interactions between lipid headgroups and the protein. When acyl chains bind, van der Waals effects dominate as the acyl chains adopt conformations that complement particular sites on the rough protein surface. No generally applicable motifs for binding have yet emerged. Previously published biochemical and biophysical data link this binding with function. This Article is Part of a Special Issue Entitled: Membrane Structure and Function: Relevance in the Cell's Physiology, Pathology and Therapy.     

Hidden partners: Using cross‐docking calculations to predict binding sites for proteins with multiple interactions

Protein‐protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein‐protein interactions. Cross‐docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross‐docking simulations of 358 proteins with 2 different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity‐sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross‐docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, that is, partners not included in the original cross‐docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.     

Dynamics of protein folding and cofactor binding monitored by single-molecule force spectroscopy

Many proteins in living cells require cofactors to carry out their biological functions. To reach their functional states, these proteins need to fold into their unique three-dimensional structures in the presence of their cofactors. Two processes, folding of the protein and binding of cofactors, intermingle with each other, making the direct elucidation of the folding mechanism of proteins in the presence of cofactors challenging. Here we use single-molecule atomic force microscopy to directly monitor the folding and cofactor binding dynamics of an engineered metal-binding protein G6-53 at the single-molecule level. Using the mechanical stability of different conformers of G6-53 as sensitive probes, we directly identified different G6-53 conformers (unfolded, apo- and Ni²⁺-bound) populated along the folding pathway of G6-53 in the presence of its cofactor Ni²⁺. By carrying out single-molecule atomic force microscopy refolding experiments, we monitored kinetic evolution processes of these different conformers. Our results suggested that the majority of G6-53 folds through a binding-after-folding mechanism, whereas a small fraction follows a binding-before-folding pathway. Our study opens an avenue to utilizing force spectroscopy techniques to probe the folding dynamics of proteins in the presence of cofactors at the single-molecule level, and we anticipated that this method can be used to study a wide variety of proteins requiring cofactors for their function.     

Genetic separation of Escherichia coli recA functions for SOS mutagenesis and repressor cleavage.

Evidence is presented that recA functions which promote the SOS functions of mutagenesis, LexA protein proteolysis, and lambda cI repressor proteolysis are each genetically separable from the others. This separation was observed in recombination-proficient recA mutants and rec+ (F' recA56) heterodiploids. recA430, recA433, and recA435 mutants and recA+ (F' recA56) heterodiploids were inducible for only one or two of the three functions and defective for mutagenesis. recA80 and recA432 mutants were constitutively activated for two of the three functions in that these mutants did not have to be induced to express the functions. We propose that binding of RecA protein to damaged DNA and subsequent interaction with small inducer molecules gives rise to conformational changes in RecA protein. These changes promote surface-surface interactions with other target proteins, such as cI and LexA proteins. By this model, the recA mutants are likely to have incorrect amino acids substituted as sites in the RecA protein structure which affect surface regions required for protein-protein interactions. The constitutively activated mutants could likewise insert altered amino acids at sites in RecA which are involved in the activation of RecA protein by binding small molecules or polynucleotides which metabolically regulate RecA protein.     

Multisite phosphorylation disrupts arginine-glutamate salt bridge networks required for binding of cytoplasmic linker-associated protein 2 (CLASP2) to end-binding protein 1 (EB1)

A group of diverse proteins reversibly binds to growing microtubule plus ends through interactions with end-binding proteins (EBs). These +TIPs control microtubule dynamics and microtubule interactions with other intracellular structures. Here, we use cytoplasmic linker-associated protein 2 (CLASP2) binding to EB1 to determine how multisite phosphorylation regulates interactions with EB1. The central, intrinsically disordered region of vertebrate CLASP proteins contains two SXIP EB1 binding motifs that are required for EB1-mediated plus-end-tracking in vitro. In cells, both EB1 binding motifs can be functional, but most of the binding free energy results from nearby electrostatic interactions. By employing molecular dynamics simulations of the EB1 interaction with a minimal CLASP2 plus-end-tracking module, we find that conserved arginine residues in CLASP2 form extensive hydrogen-bond networks with glutamate residues predominantly in the unstructured, acidic C-terminal tail of EB1. Multisite phosphorylation of glycogen synthase kinase 3 (GSK3) sites near the EB1 binding motifs disrupts this electrostatic "molecular Velcro." Molecular dynamics simulations and (31)P NMR spectroscopy indicate that phosphorylated serines participate in intramolecular interactions with and sequester arginine residues required for EB1 binding. Multisite phosphorylation of these GSK3 motifs requires priming phosphorylation by interphase or mitotic cyclin-dependent kinases (CDKs), and we find that CDK- and GSK3-dependent phosphorylation completely disrupts CLASP2 microtubule plus-end-tracking in mitosis.     

Searching for protein signatures using a multilevel alphabet

Short motifs are known to play diverse roles in proteins, such as in mediating the interactions with other molecules, binding to membranes, or conducting a specific biological function. Standard approaches currently employed to detect short motifs in proteins search for enrichment of amino acid motifs considering mostly the sequence information. Here, we presented a new approach to search for common motifs (protein signatures) which share both physicochemical and structural properties, looking simultaneously at different features. Our method takes as an input an amino acid sequence and translates it to a new alphabet that reflects its intrinsic structural and chemical properties. Using the MEME search algorithm, we identified the proteins signatures within subsets of protein which encompass common sequence and structural information. We demonstrated that we can detect enriched structural motifs, such as the amphipathic helix, from large datasets of linear sequences, as well as predicting common structural properties (such as disorder, surface accessibility, or secondary structures) of known functional‐motifs. Finally, we applied the method to the yeast protein interactome and identified novel putative interacting motifs. We propose that our approach can be applied for de novo protein function prediction given either sequence or structural information. Proteins 2013; © 2012 Wiley Periodicals, Inc.     

Mapping of ligand‐binding cavities in proteins

The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterize and map the ligand‐binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity, and charge). This approach can provide valuable information on the similarities and dissimilarities, of binding cavities due to mutations, between‐species differences and flexibility upon ligand‐binding. The presented results show that information on ligand‐binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand‐binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterization and mapping of “orphan structures”, selection of protein structures for docking studies in structure‐based design, and identification of proteins for selectivity screens in drug design programs. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Treble clef finger--a functionally diverse zinc-binding structural motif

Detection of similarity is particularly difficult for small proteins and thus connections between many of them remain unnoticed. Structure and sequence analysis of several metal-binding proteins reveals unexpected similarities in structural domains classified as different protein folds in SCOP and suggests unification of seven folds that belong to two protein classes. The common motif, termed treble clef finger in this study, forms the protein structural core and is 25-45 residues long. The treble clef motif is assembled around the central zinc ion and consists of a zinc knuckle, loop, beta-hairpin and an alpha-helix. The knuckle and the first turn of the helix each incorporate two zinc ligands. Treble clef domains constitute the core of many structures such as ribosomal proteins L24E and S14, RING fingers, protein kinase cysteine-rich domains, nuclear receptor-like fingers, LIM domains, phosphatidylinositol-3-phosphate-binding domains and His-Me finger endonucleases. The treble clef finger is a uniquely versatile motif adaptable for various functions. This small domain with a 25 residue structural core can accommodate eight different metal-binding sites and can have many types of functions from binding of nucleic acids, proteins and small molecules, to catalysis of phosphodiester bond hydrolysis. Treble clef motifs are frequently incorporated in larger structures or occur in doublets. Present analysis suggests that the treble clef motif defines a distinct structural fold found in proteins with diverse functional properties and forms one of the major zinc finger groups.