Different combinations of atomic interactions predict protein‐small molecule and protein‐DNA/RNA affinities with similar accuracy

Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three‐dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein?ligand complexes with associated binding‐affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein‐small molecule and protein‐DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small‐molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small‐molecule and DNA/RNA interactions, no statistical models were capable of predicting protein?protein affinity with >60% correlation. We demonstrate the potential usefulness of protein‐DNA/RNA binding prediction as a possible tool for high‐throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

Optimized distance‐dependent atom‐pair‐based potential DOOP for protein structure prediction

The DOcking decoy‐based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance‐dependent atom‐pair interactions. To optimize the atom‐pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand–receptor systems (or just pairs). Thus, a total of 8609 ligand–receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand–receptor systems, 1000 evenly sampled docking decoys with 0–10 Å interface root‐mean‐square‐deviation (iRMSD) were generated with a method used before for protein–protein docking. A neural network‐based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel‐like energy landscape for the interaction between these hypothetical ligand–receptor systems. Thus, our method hierarchically models the overall funnel‐like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom‐pair‐based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation‐dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand–receptor systems and their decoys are freely available at http://agknapp.chemie.fu‐berlin.de/doop/ . Proteins 2015; 83:881–890. © 2015 Wiley Periodicals, Inc.     

Structure-wise discrimination of cytosine,thymine, and uracil by proteins in terms of their nonbonded interactions

The molecular recognition and discrimination of very similar ligand moieties by proteins are important subjects in protein–ligand interaction studies. Specificity in the recognition of molecules is determined by the arrangement of protein and ligand atoms in space. The three pyrimidine bases, viz. cytosine, thymine, and uracil, are structurally similar, but the proteins that bind to them are able to discriminate them and form interactions. Since nonbonded interactions are responsible for molecular recognition processes in biological systems, our work attempts to understand some of the underlying principles of such recognition of pyrimidine molecular structures by proteins. The preferences of the amino acid residues to contact the pyrimidine bases in terms of nonbonded interactions; amino acid residue–ligand atom preferences; main chain and side chain atom contributions of amino acid residues; and solvent-accessible surface area of ligand atoms when forming complexes are analyzed. Our analysis shows that the amino acid residues, tyrosine and phenyl alanine, are highly involved in the pyrimidine interactions. Arginine prefers contacts with the cytosine base. The similarities and differences that exist between the interactions of the amino acid residues with each of the three pyrimidine base atoms in our analysis provide insights that can be exploited in designing specific inhibitors competitive to the ligands.     

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.     

Quantitative analysis and prediction of curvature in leucine‐rich repeat proteins

Leucine‐rich repeat (LRR) proteins form a large and diverse family. They have a wide range of functions most of which involve the formation of protein–protein interactions. All known LRR structures form curved solenoids, although there is large variation in their curvature. It is this curvature that determines the shape and dimensions of the inner space available for ligand binding. Unfortunately, large‐scale parameters such as the overall curvature of a protein domain are extremely difficult to predict. Here, we present a quantitative analysis of determinants of curvature of this family. Individual repeats typically range in length between 20 and 30 residues and have a variety of secondary structures on their convex side. The observed curvature of the LRR domains correlates poorly with the lengths of their individual repeats. We have, therefore, developed a scoring function based on the secondary structure of the convex side of the protein that allows prediction of the overall curvature with a high degree of accuracy. We also demonstrate the effectiveness of this method in selecting a suitable template for comparative modeling. We have developed an automated, quantitative protocol that can be used to predict accurately the curvature of leucine‐rich repeat proteins of unknown structure from sequence alone. This protocol is available as an online resource at http://www.bioinf.manchester.ac.uk/curlrr/ . Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Conformational diversity of ligands bound to proteins

The phenomenon of molecular recognition, which underpins almost all biological processes, is dynamic, complex and subtle. Establishing an interaction between a pair of molecules involves mutual structural rearrangements guided by a highly convoluted energy landscape, the accurate mapping of which continues to elude us. Increased understanding of the degree to which the conformational space of a ligand is restricted upon binding may have important implications for docking studies, structure refinement and for function prediction methods based on geometrical comparisons of ligands or their binding sites. Here, we present an analysis of the conformational variability exhibited by three of the most ubiquitous biological ligands in nature, ATP, NAD and FAD. First, we demonstrate qualitatively that these ligands bind to proteins in widely varying conformations, including several cases in which parts of the molecule assume energetically unfavourable orientations. Next, by comparing the distribution of bound ligand shapes with the set of all possible molecular conformations, we provide a quantitative assessment of previous observations that ligands tend to unfold when binding to proteins. We show that, while extended forms of ligands are indeed common in ligand-protein structures, instances of ligands in almost maximally compact arrangements can also be found. Thirdly, we compare the conformational variation in two sets of ligand molecules, those bound to homologous proteins, and those bound to unrelated proteins. Although most superfamilies bind ligands in a fairly conserved manner, we find several cases in which significant variation in ligand configuration is observed.     

Interaction between bound water molecules and local protein structures: A statistical analysis of the hydrogen bond structures around bound water molecules

Water molecules play an important role in protein folding and protein interactions through their structural association with proteins. Examples of such structural association can be found in protein crystal structures, and can often explain protein functionality in the context of structure. We herein report the systematic analysis of the local structures of proteins interacting with water molecules, and the characterization of their geometric features. We first examined the interaction of water molecules with a large local interaction environment by comparing the preference of water molecules in three regions, namely, the protein–protein interaction (PPI) interfaces, the crystal contact (CC) interfaces, and the non‐interfacial regions. High preference of water molecules to the PPI and CC interfaces was found. In addition, the bound water on the PPI interface was more favorably associated with the complex interaction structure, implying that such water‐mediated structures may participate in the shaping of the PPI interface. The pairwise water‐mediated interaction was then investigated, and the water‐mediated residue–residue interaction potential was derived. Subsequently, the types of polar atoms surrounding the water molecules were analyzed, and the preference of the hydrogen bond acceptor was observed. Furthermore, the geometries of the structures interacting with water were analyzed, and it was found that the major structure on the protein surface exhibited planar geometry rather than tetrahedral geometry. Several previously undiscovered characteristics of water–protein interactions were unfolded in this study, and are expected to lead to a better understanding of protein structure and function. Proteins 2016; 84:43–51. © 2015 Wiley Periodicals, Inc.     

Structure-based prediction of DNA-binding sites on proteins using the empirical preference of electrostatic potential and the shape of molecular surfaces

Protein-DNA interactions play an essential role in the genetic activities of life. Many structures of protein-DNA complexes are already known, but the common rules on how and where proteins bind to DNA have not emerged. Many attempts have been made to predict protein-DNA interactions using structural information, but the success rate is still about 80%. We analyzed 63 protein-DNA complexes by focusing our attention on the shape of the molecular surface of the protein and DNA, along with the electrostatic potential on the surface, and constructed a new statistical evaluation function to make predictions of DNA interaction sites on protein molecular surfaces. The shape of the molecular surface was described by a combination of local and global average curvature, which are intended to describe the small convex and concave and the large-scale concave curvatures of the protein surface preferentially appearing at DNA-binding sites. Using these structural features, along with the electrostatic potential obtained by solving the Poisson-Boltzmann equation numerically, we have developed prediction schemes with 86% and 96% accuracy for DNA-binding and non-DNA-binding proteins, respectively.     

Intramolecular interactions at protein surfaces and their impact on protein function

Protein surfaces play a key role in the biological function of proteins. Consequently, structural features of protein surfaces are the basis for predicting function from structure. A well-established principle of binding by proteins is that ligands must compete with water and other small molecules to form interactions with protein surfaces. A less obvious issue, and the emphasis of this article, is that ligands must also compete with interactions among residues at protein surfaces. Results from structural surveys, a variety of experimental studies and computations suggest that intramolecular interactions are present at protein surfaces and that the energetics of these interactions can change when proteins bind to other molecules.     

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.     

DRoP: Automated detection of conserved solvent-binding sites on proteins

Water and ligand binding play critical roles in the structure and function of proteins, yet their binding sites and significance are difficult to predict a priori. Multiple solvent crystal structures (MSCS) is a method where several X-ray crystal structures are solved, each in a unique solvent environment, with organic molecules that serve as probes of the protein surface for sites evolved to bind ligands, while the first hydration shell is essentially maintained. When superimposed, these structures contain a vast amount of information regarding hot spots of protein-protein or protein-ligand interactions, as well as conserved water-binding sites retained with the change in solvent properties. Optimized mining of this information requires reliable structural data and a consistent, objective analysis tool. Detection of related solvent positions (DRoP) was developed to automatically organize and rank the water or small organic molecule binding sites within a given set of structures. It is a flexible tool that can also be used in conserved water analysis given multiple structures of any protein independent of the MSCS method. The DRoP output is an HTML format list of the solvent sites ordered by conservation rank in its population within the set of structures, along with renumbered and recolored PDB files for visualization and facile analysis. Here, we present a previously unpublished set of MSCS structures of bovine pancreatic ribonuclease A (RNase A) and use it together with published structures to illustrate the capabilities of DRoP.     

On the molecular basis of the high affinity binding of basic amino acids to LAOBP,a periplasmic binding protein from Salmonella typhimurium

The rational designing of binding abilities in proteins requires an understanding of the relationship between structure and thermodynamics. However, our knowledge of the molecular origin of high‐affinity binding of ligands to proteins is still limited; such is the case for l ‐lysine–l ‐arginine–l ‐ornithine periplasmic binding protein (LAOBP), a periplasmic binding protein from Salmonella typhimurium that binds to l ‐arginine, l ‐lysine, and l ‐ornithine with nanomolar affinity and to l ‐histidine with micromolar affinity. Structural studies indicate that ligand binding induces a large conformational change in LAOBP. In this work, we studied the thermodynamics of l ‐histidine and l ‐arginine binding to LAOBP by isothermal titration calorimetry. For both ligands, the affinity is enthalpically driven, with a binding ΔCp of ~?300 cal mol^?1 K^?1, most of which arises from the burial of protein nonpolar surfaces that accompanies the conformational change. Osmotic stress measurements revealed that several water molecules become sequestered upon complex formation. In addition, LAOBP prefers positively charged ligands in their side chain. An energetic analysis shows that the protein acquires a thermodynamically equivalent state with both ligands. The 1000‐fold higher affinity of LAOBP for l ‐arginine as compared with l ‐histidine is mainly of enthalpic origin and can be ascribed to the formation of an extra pair of hydrogen bonds. Periplasmic binding proteins have evolved diverse energetic strategies for ligand recognition. STM4351, another arginine binding protein from Salmonella, shows an entropy‐driven micromolar affinity toward l ‐arginine. In contrast, our data show that LAOBP achieves nanomolar affinity for the same ligand through enthalpy optimization. Copyright © 2015 John Wiley & Sons, Ltd.     

Side-chain flexibility in protein-ligand binding: the minimal rotation hypothesis

The goal of this work is to learn from nature about the magnitudes of side-chain motions that occur when proteins bind small organic molecules, and model these motions to improve the prediction of protein-ligand complexes. Following analysis of protein side-chain motions upon ligand binding in 63 complexes, we tested the ability of the docking tool SLIDE to model these motions without being restricted to rotameric transitions or deciding which side chains should be considered as flexible. The model tested is that side-chain conformational changes involving more atoms or larger rotations are likely to be more costly and less prevalent than small motions due to energy barriers between rotamers and the potential of large motions to cause new steric clashes. Accordingly, SLIDE adjusts the protein and ligand side groups as little as necessary to achieve steric complementarity. We tested the hypothesis that small motions are sufficient to achieve good dockings using 63 ligands and the apo structures of 20 different proteins and compared SLIDE side-chain rotations to those experimentally observed. None of these proteins undergoes major main-chain conformational change upon ligand binding, ensuring that side-chain flexibility modeling is not required to compensate for main-chain motions. Although more frugal in the number of side-chain rotations performed, this model substantially mimics the experimentally observed motions. Most side chains do not shift to a new rotamer, and small motions are both necessary and sufficient to predict the correct binding orientation and most protein-ligand interactions for the 20 proteins analyzed.     

Structural analysis of protein‐ligand interactions: the binding of endogenous compounds and of synthetic drugs

The large number of macromolecular structures deposited with the Protein Data Bank (PDB) describing complexes between proteins and either physiological compounds or synthetic drugs made it possible a systematic analysis of the interactions occurring between proteins and their ligands. In this work, the binding pockets of about 4000 PDB protein‐ligand complexes were investigated and amino acid and interaction types were analyzed. The residues observed with lowest frequency in protein sequences, Trp, His, Met, Tyr, and Phe, turned out to be the most abundant in binding pockets. Significant differences between drug‐like and physiological compounds were found. On average, physiological compounds establish with respect to drugs about twice as many hydrogen bonds with protein atoms, whereas drugs rely more on hydrophobic interactions to establish target selectivity. The large number of PDB structures describing homologous proteins in complex with the same ligand made it possible to analyze the conservation of binding pocket residues among homologous protein structures bound to the same ligand, showing that Gly, Glu, Arg, Asp, His, and Thr are more conserved than other amino acids. Also in the cases in which the same ligand is bound to unrelated proteins, the binding pockets showed significant conservation in the residue types. In this case, the probability of co‐occurrence of the same amino acid type in the binding pockets could be up to thirteen times higher than that expected on a random basis. The trends identified in this study may provide an useful guideline in the process of drug design and lead optimization. Copyright © 2014 John Wiley & Sons, Ltd.     

Selective immobilization of multivalent ligands for surface plasmon resonance and fluorescence microscopy

Cell surface multivalent ligands, such as proteoglycans and mucins, are often tethered by a single attachment point. In vitro, however, it is difficult to immobilize multivalent ligands at single sites due to their heterogeneity. Moreover, multivalent ligands often lack a single group with reactivity orthogonal to other functionality in the ligand. Biophysical analyses of multivalent ligand-receptor interactions would benefit from the availability of strategies for uniform immobilization of multivalent ligands. To this end, we report the design and synthesis of a multivalent ligand that has a single terminal orthogonal functional group and we demonstrate that this material can be selectively immobilized onto a surface suitable for surface plasmon resonance (SPR) experiments. The polymeric ligand we generated displays multiple copies of 3,6-disulfogalactose, and it can bind to the cell adhesion molecules P- and L-selectin. Using SPR measurements, we found that surfaces displaying our multivalent ligands bind specifically to P- and L-selectin. The affinities of P- and L-selectin for surfaces displaying the multivalent ligand are five- to sixfold better than the affinities for a surface modified with the corresponding monovalent ligand. In addition to binding soluble proteins, surfaces bearing immobilized polymers bound to cells displaying L-selectin. Cell binding was confirmed by visualizing adherent cells by fluorescence microscopy. Together, our results indicate that synthetic surfaces can be created by selective immobilization of multivalent ligands and that these surfaces are capable of binding soluble and cell-surface-associated receptors with high affinity.     

Structure-based mechanism of ligand binding for periplasmic solute-binding protein of the Bug family

Bug proteins form a large family of periplasmic solute-binding proteins well represented in beta-proteobacteria. They adopt a characteristic Venus flytrap fold with two globular domains bisected by a ligand-binding cleft. The structures of two liganded Bug proteins have revealed that the family is specific for carboxylated solutes, with a characteristic mode of binding involving two highly conserved beta strand-beta turn-alpha helix motifs originating from each domain. These two motifs form hydrogen bonds with a carboxylate group of the ligand, both directly and via conserved water molecules, and have thus been termed the carboxylate pincers. In both crystallized Bug proteins, the ligands were found enclosed between the two domains and inaccessible to solvent, suggesting an inter-domain hinge-bending motion upon ligand binding. We report here the first structures of an open, unliganded Bug protein and of the same protein with a citrate ion bound in the open cavity. One of the ligand carboxylate groups is bound to one half of the carboxylate pincers by the beta strand-beta turn-alpha helix motif from domain 1, and the citrate ion forms several additional interactions with domain 1. The ligand is accessible to solvent and has very few contacts with domain 2. In this open, liganded structure, the second part of the carboxylate pincers originating from domain 2 is not stabilized by ligand binding, and a loop replaces the beta turn. In the unliganded structure, both motifs of the carboxylate pincers are highly mobile, and neither of the two beta turns is formed. Thus, ligand recognition is performed by domain 1, with the carboxylate group serving as an initial anchoring point. Stabilization of the closed conformation requires proper interactions to be established with domain 2, and thus domain 2 discriminates between productively and non-productively bound ligands.     

Preference of small molecules for local minimum conformations when binding to proteins

It is well known that small molecules (ligands) do not necessarily adopt their lowest potential energy conformations when binding to proteins. Analyses of protein-bound ligand crystal structures have reportedly shown that many of them do not even adopt the conformations at local minima of their potential energy surfaces (local minimum conformations). The results of these analyses raise a concern regarding the validity of virtual screening methods that use ligands in local minimum conformations. Here we report a normal-mode-analysis (NMA) study of 100 crystal structures of protein-bound ligands. Our data show that the energy minimization of a ligand alone does not automatically stop at a local minimum conformation if the minimum of the potential energy surface is shallow, thus leading to the folding of the ligand. Furthermore, our data show that all 100 ligand conformations in their protein-bound ligand crystal structures are nearly identical to their local minimum conformations obtained from NMA-monitored energy minimization, suggesting that ligands prefer to adopt local minimum conformations when binding to proteins. These results both support virtual screening methods that use ligands in local minimum conformations and caution about possible adverse effect of excessive energy minimization when generating a database of ligand conformations for virtual screening.