What is the relationship between the global structures of apo and holo proteins?

Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction

The rapid development of deep learning-based methods has considerably advanced the field of protein structure prediction. The accuracy of predicting the 3D structures of simple proteins is comparable to that of experimentally determined structures, providing broad possibilities for structure-based biological studies. Another critical question is whether and how multistate structures can be predicted from a given protein sequence. In this study, analysis of tens of two-state proteins demonstrated that deep learning-based contact map predictions contain structural information on both states, which suggests that it is probably appropriate to change the target of deep learning-based protein structure prediction from one specific structure to multiple likely structures. Furthermore, by combining deep learning- and physics-based computational methods, we developed a protocol for exploring alternative conformations from a known structure of a given protein, by which we successfully approached the holo-state conformations of multiple representative proteins from their apo-state structures.

Molecular docking in organic, inorganic, and hybrid systems: a tutorial review

Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review.

Graphical abstract

Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data

More than 61,000 proteins have up-to-date correspondence between their amino acid sequence (UniProtKB) and their 3D structures (PDB), enabled by the Structure Integration with Function, Taxonomy and Sequences (SIFTS) resource. SIFTS incorporates residue-level annotations from many other biological resources. SIFTS data is available in various formats like XML, CSV and TSV format or also accessible via the PDBe REST API but always maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. Here, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProtKB, Pfam, SCOP2, and CATH residue-level annotations directly into the PDBx/mmCIF files from the PDB archive. With the integrated UniProtKB annotations, these files now provide consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended dictionary yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at the residue level resulting in better data interoperability, supporting improved data analysis and visualisation.

Holo Protein Conformation Generation from Apo Structures by Ligand Binding Site Refinement

An important part in structure-based drug design is the selection of an appropriate protein structure. It has been revealed that a holo protein structure that contains a well-defined binding site is a much better choice than an apo structure in structure-based drug discovery. Therefore, it is valuable to obtain a holo-like protein conformation from apo structures in the case where no holo structure is available. Meeting the need, we present a robust approach to generate reliable holo-like structures from apo structures by ligand binding site refinement with restraints derived from holo templates with low homology. Our method was tested on a test set of 32 proteins from the DUD-E data set and compared with other approaches. It was shown that our method successfully refined the apo structures toward the corresponding holo conformations for 23 of 32 proteins, reducing the average all-heavy-atom RMSD of binding site residues by 0.48 Å. In addition, when evaluated against all the holo structures in the protein data bank, our method can improve the binding site RMSD for 14 of 19 cases that experience significant conformational changes. Furthermore, our refined structures also demonstrate their advantages over the apo structures in ligand binding mode predictions by both rigid docking and flexible docking and in virtual screening on the database of active and decoy ligands from the DUD-E. These results indicate that our method is effective in recovering holo-like conformations and will be valuable in structure-based drug discovery.

AHoJ: rapid, tailored search and retrieval of apo and holo protein structures for user-defined ligands

SUMMARY

Understanding the mechanism of action of a protein or designing better ligands for it, often requires access to a bound (holo) and an unbound (apo) state of the protein. Resources for the quick and easy retrieval of such conformations are severely limited. Apo-Holo Juxtaposition (AHoJ), is a web application for retrieving apo-holo structure pairs for user-defined ligands. Given a query structure and one or more user-specified ligands, it retrieves all other structures of the same protein that feature the same binding site(s), aligns them, and examines the superimposed binding sites to determine whether each structure is apo or holo, in reference to the query. The resulting superimposed datasets of apo-holo pairs can be visualized and downloaded for further analysis. AHoJ accepts multiple input queries, allowing the creation of customized apo-holo datasets.

AVAILABILITY AND IMPLEMENTATION

Freely available for non-commercial use at http://apoholo.cz. Source code available at https://github.com/cusbg/AHoJ-project.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design

Computational modeling is an essential component of modern drug discovery. One of its most important applications is to select promising drug candidates for pharmacologically relevant target proteins. Because of continuing advances in structural biology, putative binding sites for small organic molecules are being discovered in numerous proteins linked to various diseases. These valuable data offer new opportunities to build efficient computational models predicting binding molecules for target sites through the application of data mining and machine learning. In particular, deep neural networks are powerful techniques capable of learning from complex data in order to make informed drug binding predictions. In this communication, we describe Pocket2Drug, a deep graph neural network model to predict binding molecules for a given a ligand binding site. This approach first learns the conditional probability distribution of small molecules from a large dataset of pocket structures with supervised training, followed by the sampling of drug candidates from the trained model. Comprehensive benchmarking simulations show that using Pocket2Drug significantly improves the chances of finding molecules binding to target pockets compared to traditional drug selection procedures. Specifically, known binders are generated for as many as 80.5% of targets present in the testing set consisting of dissimilar data from that used to train the deep graph neural network model. Overall, Pocket2Drug is a promising computational approach to inform the discovery of novel biopharmaceuticals.

Enhanced Cellular Uptake of H-Chain Human Ferritin Containing Gold Nanoparticles

Gold nanoparticles (AuNP) capped with biocompatible layers have functional optical, chemical, and biological properties as theranostic agents in biomedicine. The ferritin protein containing in situ synthesized AuNPs has been successfully used as an effective and completely biocompatible nanocarrier for AuNPs in human cell lines and animal experiments in vivo. Ferritin can be uptaken by different cell types through receptor-mediated endocytosis. Despite these advantages, few efforts have been made to evaluate the toxicity and cellular internalization of AuNP-containing ferritin nanocages. In this work, we study the potential of human heavy-chain (H) and light-chain (L) ferritin homopolymers as nanoreactors to synthesize AuNPs and their cytotoxicity and cellular uptake in different cell lines. The results show very low toxicity of ferritin-encapsulated AuNPs on different human cell lines and demonstrate that efficient cellular ferritin uptake depends on the specific H or L protein chains forming the ferritin protein cage and the presence or absence of metallic cargo. Cargo-devoid apoferritin is poorly internalized in all cell lines, and the highest ferritin uptake was achieved with AuNP-loaded H-ferritin homopolymers in transferrin-receptor-rich cell lines, showing more than seven times more uptake than apoferritin.

Ligand-Binding-Site Refinement to Generate Reliable Holo Protein Structure Conformations from Apo Structures

The first important step in a structure-based virtual screening is the judicious selection of a receptor protein. In cases where the holo protein receptor structure is unavailable, significant reduction in virtual screening performance has been reported. In this work, we present a robust method to generate reliable holo protein structure conformations from apo structures using molecular dynamics (MD) simulation with restraints derived from holo structure binding-site templates. We perform benchmark tests on two different datasets: 40 structures from a directory of useful decoy-enhanced (DUD-E) and 84 structures from the Gunasekaran dataset. Our results show successful refinement of apo binding-site structures toward holo conformations in 82% of the test cases. In addition, virtual screening performance of 40 DUD-E structures is significantly improved using our MD-refined structures as receptors with an average enrichment factor (EF), an EF_1% value of 6.2 compared to apo structures with 3.5. Docking of native ligands to the refined structures shows an average ligand root mean square deviation (RMSD) of 1.97 Å (DUD-E dataset and Gunasekaran dataset) relative to ligands in the holo crystal structures, which is comparable to the self-docking (i.e., docking of the native ligand back to its crystal structure receptor) average, 1.34 Å (DUD-E dataset) and 1.36 Å (Gunasekaran dataset). On the other hand, docking to the apo structures yields an average ligand RMSD of 3.65 Å (DUD-E) and 2.90 Å (Gunasekaran). These results indicate that our method is robust and can be useful to improve virtual screening performance of apo structures.

Exploring the different states of wild-type T-cell receptor and mutant conformational changes towards understanding the antigen recognition

Recognition of proteolytic peptide fragments presented by major histocompatibility complex (MHC) on target cells by T-cell receptor (TCR) is among the most important interactions in the adaptive immune system. Several computational studies have been performed to investigate conformational and dynamical properties of TCRs for enhanced immunogenicity. Here, we present the large-scale molecular dynamics (MD) simulation studies of the two comprehensive systems consisting of the wild-type and mutant IG4 TCR in complex with the tumor epitope NY-ESO peptide (SLLMWITQC) and analyzed for mapping conformational changes of TCR in the states prior to antigen binding, upon antigen binding and after the antigen was released. All of the simulations were performed with different states of TCRs for each 1000 ns of simulation time, providing six simulations for time duration of 6000 ns (6µs). We show that rather than undergoing most critical conformational changes upon antigen binding, the high proportion of complementarity-determining region (CDR) loops change by comparatively small amount. The hypervariable CDRα3 and CDRβ3 loops showed significant structural changes. Interestingly, the TCR β chain loops showed the least changes, which is reliable with recent implications that β domain of TCR may propel antigen interaction. The mutant shows higher rigidity than wild-type even in released state; expose an induced fit mechanism occurring from the re-structuring of CDRα3 loop and can allow enhanced binding affinity of the peptide antigen. Additionally, we show that CDRα3 loop and peptide contacts are an adaptive feature of affinity enhanced mutant TCR.Communicated by Ramaswamy H. Sarma.

The nature of the ligand's side chain interacting with the S1'-subsite of metallocarboxypeptidase T (from Thermoactinomyces vulgaris) determines the geometry of the tetrahedral transition complex

The carboxypeptidase T (CPT) from Thermoactinomyces vulgaris has an active site structure and 3D organization similar to pancreatic carboxypeptidases A and B (CPA and CPB), but differs in broader substrate specificity. The crystal structures of CPT complexes with the transition state analogs N-sulfamoyl-L-leucine and N-sulfamoyl-L-glutamate (SLeu and SGlu) were determined and compared with previously determined structures of CPT complexes with N-sulfamoyl-L-arginine and N-sulfamoyl-L-phenylalanine (SArg and SPhe). The conformations of residues Tyr255 and Glu270, the distances between these residues and the corresponding ligand groups, and the Zn-S gap between the zinc ion and the sulfur atom in the ligand's sulfamoyl group that simulates a distance between the zinc ion and the tetrahedral sp3-hybridized carbon atom of the converted peptide bond, vary depending on the nature of the side chain in the substrate's C-terminus. The increasing affinity of CPT with the transition state analogs in the order SGlu, SArg, SPhe, SLeu correlates well with a decreasing Zn-S gap in these complexes and the increasing efficiency of CPT-catalyzed hydrolysis of the corresponding tripeptide substrates (ZAAL > ZAAF > ZAAR > ZAAE). Thus, the side chain of the ligand that interacts with the primary specificity pocket of CPT, determines the geometry of the transition complex, the relative orientation of the bond to be cleaved by the catalytic groups of the active site and the catalytic properties of the enzyme. In the case of CPB, the relative orientation of the catalytic amino acid residues, as well as the distance between Glu270 and SArg/SPhe, is much less dependent on the nature of the corresponding side chain of the substrate. The influence of the nature of the substrate side chain on the structural organization of the transition state determines catalytic activity and broad substrate specificity of the carboxypeptidase T.

Inherent versus induced protein flexibility: Comparisons within and between apo and holo structures

Understanding how ligand binding influences protein flexibility is important, especially in rational drug design. Protein flexibility upon ligand binding is analyzed herein using 305 proteins with 2369 crystal structures with ligands (holo) and 1679 without (apo). Each protein has at least two apo and two holo structures for analysis. The inherent variation in structures with and without ligands is first established as a baseline. This baseline is then compared to the change in conformation in going from the apo to holo states to probe induced flexibility. The inherent backbone flexibility across the apo structures is roughly the same as the variation across holo structures. The induced backbone flexibility across apo-holo pairs is larger than that of the apo or holo states, but the increase in RMSD is less than 0.5 Å. Analysis of χ1 angles revealed a distinctly different pattern with significant influences seen for ligand binding on side-chain conformations in the binding site. Within the apo and holo states themselves, the variation of the χ1 angles is the same. However, the data combining both apo and holo states show significant displacements. Upon ligand binding, χ1 angles are frequently pushed to new orientations outside the range seen in the apo states. Influences on binding-site variation could not be easily attributed to features such as ligand size or x-ray structure resolution. By combining these findings, we find that most binding site flexibility is compatible with the common practice in flexible docking, where backbones are kept rigid and side chains are allowed some degree of flexibility.

An Augmented Pocketome: Detection and Analysis of Small-Molecule Binding Pockets in Proteins of Known 3D Structure

Protein-ligand interactions form the basis of most cellular events. Identifying ligand binding pockets in proteins will greatly facilitate rationalizing and predicting protein function. Ligand binding sites are unknown for many proteins of known three-dimensional (3D) structure, creating a gap in our understanding of protein structure-function relationships. To bridge this gap, we detect pockets in proteins of known 3D structures, using computational techniques. This augmented pocketome (PocketDB) consists of 249,096 pockets, which is about seven times larger than what is currently known. We deduce possible ligand associations for about 46% of the newly identified pockets. The augmented pocketome, when subjected to clustering based on similarities among pockets, yielded 2,161 site types, which are associated with 1,037 ligand types, together providing fold-site-type-ligand-type associations. The PocketDB resource facilitates a structure-based function annotation, delineation of the structural basis of ligand recognition, and provides functional clues for domains of unknown functions, allosteric proteins, and druggable pockets.

Structural changes of homodimers in the PDB

Protein complexes are involved in various biological phenomena. These complexes are intrinsically flexible, and structural changes are essential to their functions. To perform a large-scale automated analysis of the structural changes of complexes, we combined two original methods. An application, SCPC, compares two structures of protein complexes and decides the match of binding mode. Another application, Motion Tree, identifies rigid-body motions in various sizes and magnitude from the two structural complexes with the same binding mode. This approach was applied to all available homodimers in the Protein Data Bank (PDB). We defined two complex-specific motions: interface motion and subunit-spanning motion. In the former, each subunit of a complex constitutes a rigid body, and the relative movement between subunits occurs at the interface. In the latter, structural parts from distinct subunits constitute a rigid body, providing the relative movement spanning subunits. All structural changes were classified and examined. It was revealed that the complex-specific motions were common in the homodimers, detected in around 40% of families. The dimeric interfaces were likely to be small and flat for interface motion, while large and rugged for subunit-spanning motion. Interface motion was accompanied by a drastic change in contacts at the interface, while the change in the subunit-spanning motion was moderate. These results indicate that the interface properties of homodimers correlated with the type of complex-specific motion. The study demonstrates that the pipeline of SCPC and Motion Tree is useful for the massive analysis of structural change of protein complexes.

Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2

Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds. We tested a number of pose prediction methods as part of the challenge; we found that docking methods that incorporate protein flexibility (Induced Fit Docking) outperformed methods that treated the protein as rigid. We also found that using binding pose metadynamics, a molecular dynamics based method, to score docked poses provided the best predictions of our methods with an average RMSD of 2.01 Å. We tested both structure-based (e.g. docking) and ligand-based methods (e.g. QSAR) in the affinity prediction portion of the competition. We found that our structure-based methods based on docking with Smina (Spearman ρ = 0.614), performed slightly better than our ligand-based methods (ρ = 0.543), and had equivalent performance with the other top methods in the competition. Despite the overall good performance of our methods in comparison to other participants in the challenge, there exists significant room for improvement especially in cases such as these where protein flexibility plays such a large role.

Elastic network model of learned maintained contacts to predict protein motion

We present a novel elastic network model, lmcENM, to determine protein motion even for localized functional motions that involve substantial changes in the protein's contact topology. Existing elastic network models assume that the contact topology remains unchanged throughout the motion and are thus most appropriate to simulate highly collective function-related movements. lmcENM uses machine learning to differentiate breaking from maintained contacts. We show that lmcENM accurately captures functional transitions unexplained by the classical ENM and three reference ENM variants, while preserving the simplicity of classical ENM. We demonstrate the effectiveness of our approach on a large set of proteins covering different motion types. Our results suggest that accurately predicting a "deformation-invariant" contact topology offers a promising route to increase the general applicability of ENMs. We also find that to correctly predict this contact topology a combination of several features seems to be relevant which may vary slightly depending on the protein. Additionally, we present case studies of two biologically interesting systems, Ferric Citrate membrane transporter FecA and Arachidonate 15-Lipoxygenase.

Dynamical persistence of active sites identified in maltose-binding protein

This study identifies dynamical properties of maltose-binding protein (MBP) useful in unveiling active site residues susceptible to ligand binding. The described methodology has been previously used in support of novel topological techniques of persistent homology and statistical inference in complex, multi-scale, high-dimensional data often encountered in computational biophysics. Here we outline a computational protocol that is based on the anisotropic elastic network models of 14 all-atom three-dimensional protein structures. We introduce the notion of dynamical distance matrices as a measure of correlated interactions among 370 amino acid residues that constitute a single protein. The dynamical distance matrices serve as an input for a persistent homology suite of codes to further distinguish a small subset of residues with high affinity for ligand binding and allosteric activity. In addition, we show that ligand-free closed MBP structures require lower deformation energies than open MBP structures, which may be used in categorization of time-evolving molecular dynamics structures. Analysis of the most probable allosteric coupling pathways between active site residues and the protein exterior is also presented.

Mapping the 3D structures of small molecule binding sites

Background

Analysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery. Binding site comparison (BSC) studies can be employed to elucidate the function of orphan proteins or to predict the potential for polypharmacology. Many previous binding site analyses only consider binding sites surrounding an experimentally observed bound ligand.

Results

To encompass potential protein–ligand binding sites that do not have ligands known to bind, we have incorporated fpocket cavity detection software and assessed the impact of this inclusion on BSC performance. Using fpocket, we generated a database of ligand-independent potential binding sites and applied the BSC tool, SiteHopper, to analyze similarity relationships between protein binding sites. We developed a method for clustering potential binding sites using a curated dataset of structures for six therapeutically relevant proteins from diverse protein classes in the protein data bank. Two clustering methods were explored; hierarchical clustering and a density-based method adept at excluding noise and outliers from a dataset. We introduce circular plots to visualize binding site structure space. From the datasets analyzed in this study, we highlight a structural relationship between binding sites of cationic trypsin and prothrombin, protein targets known to bind structurally similar small molecules, exemplifying the potential utility of objectively and holistically mapping binding site space from the structural proteome.

Conclusions

We present a workflow for the objective mapping of potential protein–ligand binding sites derived from the currently available structural proteome. We show that ligand-independent binding site detection tools can be introduced without excessive penalty on BSC performance. Clustering combined with intuitive visualization tools can be applied to map relationships between the 3D structures of protein binding sites.Graphical abstract

In silico prediction and in vivo analysis of antiestrogenic potential of 2-isopropylthioxanthone (2-ITX) in juvenile goldfish (Carassius auratus)

Previous studies have shown both anti-estrogenic and anti-androgenic activities of 2-isopropylthioxanthone (2-ITX), a well known food contaminant, in in vitro assays. However, no data are available on the anti-estrogenic potentials and risks of 2-ITX in aquatic organisms. This work evaluated the potential endocrine disrupting effects of 2-ITX at the level of estrogen receptor (ER) signaling cascade using juvenile goldfish (Carassius auratus) as model. Firstly, we investigated the ligand binding efficiency of 2-ITX to the ligand binding domains (LBD) of goldfish ER subtypes using a molecular docking approach. Secondly, we assessed the effects of 2-ITX on E2-induced hepatic expression of ERα1, ERβ1, ERβ2, and vitellogenin (VTG) in vivo. Crosstalk between ER-VTG and aryl hydrocarbon receptor 2 (AhR2)-cytochrome P4501A (CYP1A) was also investigated. Fish were injected with increasing doses of 2-ITX ranging from 2 to 10µg/g BW, and results were compared to the effect of tamoxifen, a well-known ER modulator. We observed that compared to ERβ, the interaction potentials of 2-ITX to goldfish ERα1 LBD was more stable in the inactive receptor conformation. The in silico docking simulation analysis also revealed that 2-ITX acted as agonist for the goldfish AhR2 LBDs suggesting the ability of this compound to activate the cross-talk between the ERα- and AhR-signaling pathways. In vivo experiments confirm in silico simulation predictions demonstrating that 2-ITX reduced the estrogenicity of E2 at both transcriptional and post-transcriptional levels, indicating a clear anti-estrogenic effect. Co-exposure of E2 and 2-ITX also resulted in a significant decrease of CYP1A gene expression with respect to 2-ITX alone. Results from these studies collectively revealed that the antiestrogenic property of 2-ITX can be ascribed to a combination of effects on multiple signaling pathways suggesting the potential for this environmental contaminant to affect the hormonal control of reproductive processes in fish.

Addressing the Role of Conformational Diversity in Protein Structure Prediction

Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.

Disorder transitions and conformational diversity cooperatively modulate biological function in proteins

Structural differences between conformers sustain protein biological function. Here, we studied in a large dataset of 745 intrinsically disordered proteins, how ordered-disordered transitions modulate structural differences between conformers as derived from crystallographic data. We found that almost 50% of the proteins studied show no transitions and have low conformational diversity while the rest show transitions and a higher conformational diversity. In this last subset, 60% of the proteins become more ordered after ligand binding, while 40% more disordered. As protein conformational diversity is inherently connected with protein function our analysis suggests differences in structure-function relationships related to order-disorder transitions.

Evolutionary Conserved Positions Define Protein Conformational Diversity

Conformational diversity of the native state plays a central role in modulating protein function. The selection paradigm sustains that different ligands shift the conformational equilibrium through their binding to highest-affinity conformers. Intramolecular vibrational dynamics associated to each conformation should guarantee conformational transitions, which due to its importance, could possibly be associated with evolutionary conserved traits. Normal mode analysis, based on a coarse-grained model of the protein, can provide the required information to explore these features. Herein, we present a novel procedure to identify key positions sustaining the conformational diversity associated to ligand binding. The method is applied to an adequate refined dataset of 188 paired protein structures in their bound and unbound forms. Firstly, normal modes most involved in the conformational change are selected according to their corresponding overlap with structural distortions introduced by ligand binding. The subspace defined by these modes is used to analyze the effect of simulated point mutations on preserving the conformational diversity of the protein. We find a negative correlation between the effects of mutations on these normal mode subspaces associated to ligand-binding and position-specific evolutionary conservations obtained from multiple sequence-structure alignments. Positions whose mutations are found to alter the most these subspaces are defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. These positions are shown to be evolutionary conserved, mostly buried aliphatic residues localized in regular structural regions of the protein like β-sheets and α-helix.

A Quantitative Measure of Conformational Changes in Apo, Holo and Ligand-Bound Forms of Enzymes

Determination of the native geometry of the enzymes and ligand complexes is a key step in the process of structure-based drug designing. Enzymes and ligands show flexibility in structural behavior as they come in contact with each other. When ligand binds with active site of the enzyme, in the presence of cofactor some structural changes are expected to occur in the active site. Motivation behind this study is to determine the nature of conformational changes as well as regions where such changes are more pronounced. To measure the structural changes due to cofactor and ligand complex, enzyme in apo, holo and ligand-bound forms is selected. Enzyme data set was retrieved from protein data bank. Fifteen triplet groups were selected for the analysis of structural changes based on selection criteria. Structural features for selected enzymes were compared at the global as well as local region. Accessible surface area for the enzymes in entire triplet set was calculated, which describes the change in accessible surface area upon binding of cofactor and ligand with the enzyme. It was observed that some structural changes take place during binding of ligand in the presence of cofactor. This study will helps in understanding the level of flexibility in protein-ligand interaction for computer-aided drug designing.

A quantitative measure of conformational changes in Apo, holo and ligand bound form of enzymes

Determination of the native geometry of the enzymes and ligand complexe is a key step in the process of structure based drug designing. Enzymes and ligands show flexibility in structural behavior as they come in contact with each other. When ligand binds with active site of the enzyme, in presence of cofactor some structural changes are expected to occur in the active site. Motivation behind this study is to determine the nature of conformational changes as well as regions where such changes are more pronounced. To measure the structural changes due to cofactor and ligand complex, enzyme in Apo, holo and ligand bound form is selected. Enzyme data set was retrieved from protein data bank (PDB). 15 triplet groups were selected for the analysis of structural changes based on selection criteria. Structural features for selected enzymes were compared at the global as well as local region. Accessible surface area for the enzymes in entire triplet set was calculated, which describes the change in accessible surface area upon binding of cofactor and ligand with the enzyme. It was observed that some structural changes take place during binding of ligand in presence of cofactor. This study will helps in understanding the level of flexibility in protein-ligand interaction for computer aided drug designing.

Quantitative method for the assignment of hinge and shear mechanism in protein domain movements

Motivation: A popular method for classification of protein domain movements apportions them into two main types: those with a ‘hinge’ mechanism and those with a ‘shear’ mechanism. The intuitive assignment of domain movements to these classes has limited the number of domain movements that can be classified in this way. Furthermore, whether intended or not, the term ‘shear’ is often interpreted to mean a relative translation of the domains.

Results: Numbers of occurrences of four different types of residue contact changes between domains were optimally combined by logistic regression using the training set of domain movements intuitively classified as hinge and shear to produce a predictor for hinge and shear. This predictor was applied to give a 10-fold increase in the number of examples over the number previously available with a high degree of precision. It is shown that overall a relative translation of domains is rare, and that there is no difference between hinge and shear mechanisms in this respect. However, the shear set contains significantly more examples of domains having a relative twisting movement than the hinge set. The angle of rotation is also shown to be a good discriminator between the two mechanisms.

Availability and implementation: Results are free to browse at http://www.cmp.uea.ac.uk/dyndom/interface/.

Contact:sjh@cmp.uea.ac.uk.

Supplementary information:Supplementary data are available at Bioinformatics online.

Antigen exposure leads to rigidification of germline antibody combining site

Immune complexes involving diverse antigens and corresponding antibodies were analyzed for mapping conformational transitions of an antibody before antigen binding, upon antigen binding and after antigen release. Molecular dynamics simulations of the two comprehensive datasets consisting of the antigen-free and antigen-bound structures of the germline antibodies 36-65 and BBE6.12H3 provided mechanistic model of antigen encounter by primary antibodies. While native germline antibodies exhibit substantial mobility in the antigen-combining sites, their antigen-bound states exhibit relatively rigid conformations, even in the absence of the antigen suggesting preservation of the structural state after antigen release. It is proposed that acquired rigidity by a germline antibody upon antigen binding may be the first step in affinity maturation in favor of that antigen.

Specific non-local interactions are not necessary for recovering native protein dynamics

The elastic network model (ENM) is a widely used method to study native protein dynamics by normal mode analysis (NMA). In ENM we need information about all pairwise distances, and the distance between contacting atoms is restrained to the native value. Therefore ENM requires O(N²) information to realize its dynamics for a protein consisting of N amino acid residues. To see if (or to what extent) such a large amount of specific structural information is required to realize native protein dynamics, here we introduce a novel model based on only O(N) restraints. This model, named the ‘contact number diffusion’ model (CND), includes specific distance restraints for only local (along the amino acid sequence) atom pairs, and semi-specific non-local restraints imposed on each atom, rather than atom pairs. The semi-specific non-local restraints are defined in terms of the non-local contact numbers of atoms. The CND model exhibits the dynamic characteristics comparable to ENM and more correlated with the explicit-solvent molecular dynamics simulation than ENM. Moreover, unrealistic surface fluctuations often observed in ENM were suppressed in CND. On the other hand, in some ligand-bound structures CND showed larger fluctuations of buried protein atoms interacting with the ligand compared to ENM. In addition, fluctuations from CND and ENM show comparable correlations with the experimental B-factor. Although there are some indications of the importance of some specific non-local interactions, the semi-specific non-local interactions are mostly sufficient for reproducing the native protein dynamics.

Coevolutionary signals across protein lineages help capture multiple protein conformations

A long-standing problem in molecular biology is the determination of a complete functional conformational landscape of proteins. This includes not only proteins' native structures, but also all their respective functional states, including functionally important intermediates. Here, we reveal a signature of functionally important states in several protein families, using direct coupling analysis, which detects residue pair coevolution of protein sequence composition. This signature is exploited in a protein structure-based model to uncover conformational diversity, including hidden functional configurations. We uncovered, with high resolution (mean ~1.9 Å rmsd for nonapo structures), different functional structural states for medium to large proteins (200-450 aa) belonging to several distinct families. The combination of direct coupling analysis and the structure-based model also predicts several intermediates or hidden states that are of functional importance. This enhanced sampling is broadly applicable and has direct implications in protein structure determination and the design of ligands or drugs to trap intermediate states.

Classification of domain movements in proteins using dynamic contact graphs

A new method for the classification of domain movements in proteins is described and applied to 1822 pairs of structures from the Protein Data Bank that represent a domain movement in two-domain proteins. The method is based on changes in contacts between residues from the two domains in moving from one conformation to the other. We argue that there are five types of elemental contact changes and that these relate to five model domain movements called: “free”, “open-closed”, “anchored”, “sliding-twist”, and “see-saw.” A directed graph is introduced called the “Dynamic Contact Graph” which represents the contact changes in a domain movement. In many cases a graph, or part of a graph, provides a clear visual metaphor for the movement it represents and is a motif that can be easily recognised. The Dynamic Contact Graphs are often comprised of disconnected subgraphs indicating independent regions which may play different roles in the domain movement. The Dynamic Contact Graph for each domain movement is decomposed into elemental Dynamic Contact Graphs, those that represent elemental contact changes, allowing us to count the number of instances of each type of elemental contact change in the domain movement. This naturally leads to sixteen classes into which the 1822 domain movements are classified.

Prediction and experimental validation of enzyme substrate specificity in protein structures

Structural Genomics aims to elucidate protein structures to identify their functions. Unfortunately, the variation of just a few residues can be enough to alter activity or binding specificity and limit the functional resolution of annotations based on sequence and structure; in enzymes, substrates are especially difficult to predict. Here, large-scale controls and direct experiments show that the local similarity of five or six residues selected because they are evolutionarily important and on the protein surface can suffice to identify an enzyme activity and substrate. A motif of five residues predicted that a previously uncharacterized Silicibacter sp. protein was a carboxylesterase for short fatty acyl chains, similar to hormone-sensitive-lipase-like proteins that share less than 20% sequence identity. Assays and directed mutations confirmed this activity and showed that the motif was essential for catalysis and substrate specificity. We conclude that evolutionary and structural information may be combined on a Structural Genomics scale to create motifs of mixed catalytic and noncatalytic residues that identify enzyme activity and substrate specificity.

eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands

Molecular structures and functions of the majority of proteins across different species are yet to be identified. Much needed functional annotation of these gene products often benefits from the knowledge of protein-ligand interactions. Towards this goal, we developed eFindSite, an improved version of FINDSITE, designed to more efficiently identify ligand binding sites and residues using only weakly homologous templates. It employs a collection of effective algorithms, including highly sensitive meta-threading approaches, improved clustering techniques, advanced machine learning methods and reliable confidence estimation systems. Depending on the quality of target protein structures, eFindSite outperforms geometric pocket detection algorithms by 15-40 % in binding site detection and by 5-35 % in binding residue prediction. Moreover, compared to FINDSITE, it identifies 14 % more binding residues in the most difficult cases. When multiple putative binding pockets are identified, the ranking accuracy is 75-78 %, which can be further improved by 3-4 % by including auxiliary information on binding ligands extracted from biomedical literature. As a first across-genome application, we describe structure modeling and binding site prediction for the entire proteome of Escherichia coli. Carefully calibrated confidence estimates strongly indicate that highly reliable ligand binding predictions are made for the majority of gene products, thus eFindSite holds a significant promise for large-scale genome annotation and drug development projects. eFindSite is freely available to the academic community at http://www.brylinski.org/efindsite .

Are predicted protein structures of any value for binding site prediction and virtual ligand screening?

The recently developed field of ligand homology modeling (LHM) that extends the ideas of protein homology modeling to the prediction of ligand binding sites and for use in virtual ligand screening has emerged as a powerful new approach. Unlike traditional docking methodologies, LHM can be applied to low-to-moderate resolution predicted as well as experimental structures with little if any diminution in performance; thereby enabling ≈ 75% of an average proteome to have potentially significant virtual screening predictions. In large scale benchmarking, LHM is able to predict off-target ligand binding. Thus, despite the widespread belief to the contrary, low-to-moderate resolution predicted structures have considerable utility for biochemical function prediction.

Conformational heterogeneity within the LID domain mediates substrate binding to Escherichia coli adenylate kinase: function follows fluctuations

Proteins exist as dynamic ensembles of molecules, implying that protein amino acid sequences evolved to code for both the ground state structure as well as the entire energy landscape of excited states. Accumulating theoretical and experimental evidence suggests that enzymes use such conformational fluctuations to facilitate allosteric processes important for substrate binding and possibly catalysis. This phenomenon can be clearly demonstrated in Escherichia coli adenylate kinase, where experimentally observed local unfolding of the LID subdomain, as opposed to a more commonly postulated rigid-body opening motion, is related to substrate binding. Because "entropy promoting" glycine mutations designed to increase specifically the local unfolding of the LID domain also affect substrate binding, changes in the excited energy landscape effectively tune the function of this enzyme without changing the ground state structure or the catalytic site. Thus, additional thermodynamic information, above and beyond the single folded structure of an enzyme-substrate complex, is likely required for a full and quantitative understanding of how enzymes work.

Ligand-guided receptor optimization

Receptor models generated by homology or even obtained by crystallography often have their binding pockets suboptimal for ligand docking and virtual screening applications due to insufficient accuracy or induced fit bias. Knowledge of previously discovered receptor ligands provides key information that can be used for improving docking and screening performance of the receptor. Here, we present a comprehensive ligand-guided receptor optimization (LiBERO) algorithm that exploits ligand information for selecting the best performing protein models from an ensemble. The energetically feasible protein conformers are generated through normal mode analysis and Monte Carlo conformational sampling. The algorithm allows iteration of the conformer generation and selection steps until convergence of a specially developed fitness function which quantifies the conformer's ability to select known ligands from decoys in a small-scale virtual screening test. Because of the requirement for a large number of computationally intensive docking calculations, the automated algorithm has been implemented to use Linux clusters allowing easy parallel scaling. Here, we will discuss the setup of LiBERO calculations, selection of parameters, and a range of possible uses of the algorithm which has already proven itself in several practical applications to binding pocket optimization and prospective virtual ligand screening.

Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison

Functional elucidation of proteins is one of the essential tasks in biology. Function of a protein, specifically, small ligand molecules that bind to a protein, can be predicted by finding similar local surface regions in binding sites of known proteins. Here, we developed an alignment free local surface comparison method for predicting a ligand molecule which binds to a query protein. The algorithm, named Patch-Surfer, represents a binding pocket as a combination of segmented surface patches, each of which is characterized by its geometrical shape, the electrostatic potential, the hydrophobicity, and the concaveness. Representing a pocket by a set of patches is effective to absorb difference of global pocket shape while capturing local similarity of pockets. The shape and the physicochemical properties of surface patches are represented using the 3D Zernike descriptor, which is a series expansion of mathematical 3D function. Two pockets are compared using a modified weighted bipartite matching algorithm, which matches similar patches from the two pockets. Patch-Surfer was benchmarked on three datasets, which consist in total of 390 proteins that bind to one of 21 ligands. Patch-Surfer showed superior performance to existing methods including a global pocket comparison method, Pocket-Surfer, which we have previously introduced. Particularly, as intended, the accuracy showed large improvement for flexible ligand molecules, which bind to pockets in different conformations.

PSCDB: a database for protein structural change upon ligand binding

Proteins are flexible molecules that undergo structural changes to function. The Protein Data Bank contains multiple entries for identical proteins determined under different conditions, e.g. with and without a ligand molecule, which provides important information for understanding the structural changes related to protein functions. We gathered 839 protein structural pairs of ligand-free and ligand-bound states from monomeric or homo-dimeric proteins, and constructed the Protein Structural Change DataBase (PSCDB). In the database, we focused on whether the motions were coupled with ligand binding. As a result, the protein structural changes were classified into seven classes, i.e. coupled domain motion (59 structural changes), independent domain motion (70), coupled local motion (125), independent local motion (135), burying ligand motion (104), no significant motion (311) and other type motion (35). PSCDB provides lists of each class. On each entry page, users can view detailed information about the motion, accompanied by a morphing animation of the structural changes. PSCDB is available at http://idp1.force.cs.is.nagoya-u.ac.jp/pscdb/.

Ligand-induced conformational change of a protein reproduced by a linear combination of displacement vectors obtained from normal mode analysis

The conformational change of a protein upon ligand binding was examined by normal mode analysis (NMA) based on an elastic-network model (ENM) for a full-atom system using dihedral angles as independent variables. Specifically, we investigated the extent to which conformational change vectors of atoms from an apo form to a holo form of a protein can be represented by a linear combination of the displacement vectors of atoms in the apo form calculated for the lowest-frequency m normal modes (m=1, 2,…, 20). In this analysis, the latter vectors were best fitted to the former ones by the least-squares method. Twenty-two paired proteins in the holo and apo forms, including three dimer pairs, were examined. The results showed that, in most cases, the conformational change vectors were reproduced well by a linear combination of the displacement vectors of a small number of low-frequency normal modes. The conformational change around an active site was reproduced as well as the entire conformational change, except for some proteins that only undergo significant conformational changes around active sites. The weighting factors for 20 normal modes optimized by the least-squares fitting characterize the conformational changes upon ligand binding for these proteins. The conformational changes sampled around the apo form of a protein by the linear combination of the displacement vectors obtained by ENM-based NMA may help solve the flexible-docking problem of a protein with another molecule because the results presented herein suggest that they have a relatively high probability of being involved in an actual conformational change.

Why not consider a spherical protein? Implications of backbone hydrogen bonding for protein structure and function

The intrinsic ability of protein structures to exhibit the geometric features required for molecular function in the absence of evolution is examined in the context of three systems: the reference set of real, single domain protein structures, a library of computationally generated, compact homopolypeptides, artificial structures with protein-like secondary structural elements, and quasi-spherical random proteins packed at the same density as proteins but lacking backbone secondary structure and hydrogen bonding. Without any evolutionary selection, the library of artificial structures has similar backbone hydrogen bonding, global shape, surface to volume ratio and statistically significant structural matches to real protein global structures. Moreover, these artificial structures have native like ligand binding cavities, and a tiny subset has interfacial geometries consistent with native-like protein-protein interactions and DNA binding. In contrast, the quasi-spherical random proteins, being devoid of secondary structure, have a lower surface to volume ratio and lack ligand binding pockets and intermolecular interaction interfaces. Surprisingly, these quasi-spherical random proteins exhibit protein like distributions of virtual bond angles and almost all have a statistically significant structural match to real protein structures. This implies that it is local chain stiffness, even without backbone hydrogen bonding, and compactness that give rise to the likely completeness of the library solved single domain protein structures. These studies also suggest that the packing of secondary structural elements generates the requisite geometry for intermolecular binding. Thus, backbone hydrogen bonding plays an important role not only in protein structure but also in protein function. Such ability to bind biological molecules is an inherent feature of protein structure; if combined with appropriate protein sequences, it could provide the non-zero background probability for low-level function that evolution requires for selection to occur.

Structural analysis of heme proteins: implications for design and prediction

BACKGROUND

Heme is an essential molecule and plays vital roles in many biological processes. The structural determination of a large number of heme proteins has made it possible to study the detailed chemical and structural properties of heme binding environment. Knowledge of these characteristics can provide valuable guidelines in the design of novel heme proteins and help us predict unknown heme binding proteins.

RESULTS

In this paper, we constructed a non-redundant dataset of 125 heme-binding protein chains and found that these heme proteins encompass at least 31 different structural folds with all-α class as the dominating scaffold. Heme binding pockets are enriched in aromatic and non-polar amino acids with fewer charged residues. The differences between apo and holo forms of heme proteins in terms of the structure and the binding pockets have been investigated. In most cases the proteins undergo small conformational changes upon heme binding. We also examined the CP (cysteine-proline) heme regulatory motifs and demonstrated that the conserved dipeptide has structural implications in protein-heme interactions.

CONCLUSIONS

Our analysis revealed that heme binding pockets show special features and that most of the heme proteins undergo small conformational changes after heme binding, suggesting the apo structures can be used for structure-based heme protein prediction and as scaffolds for future heme protein design.

Classification and annotation of the relationship between protein structural change and ligand binding

The causal relationship between protein structural change and ligand binding was classified and annotated for 839 nonredundant pairs of crystal structures in the Protein Data Bank-one with and the other without a bound low-molecular-weight ligand molecule. Protein structural changes were first classified into either domain or local motions depending on the size of the moving protein segments. Whether the protein motion was coupled with ligand binding was then evaluated based on the location of the ligand binding site and by application of the linear response theory of protein structural change. Protein motions coupled with ligand binding were further classified into either closure or opening motions. This classification revealed the following: (i) domain motions coupled with ligand binding are dominated by closure motions, which can be described by the linear response theory; (ii) local motions frequently accompany order-disorder or α-helix-coil conformational transitions; and (iii) transferase activity (Enzyme Commission number 2) is the predominant function among coupled domain closure motions. This could be explained by the closure motion acting to insulate the reaction site of these enzymes from environmental water.

Real-time ligand binding pocket database search using local surface descriptors

Because of the increasing number of structures of unknown function accumulated by ongoing structural genomics projects, there is an urgent need for computational methods for characterizing protein tertiary structures. As functions of many of these proteins are not easily predicted by conventional sequence database searches, a legitimate strategy is to utilize structure information in function characterization. Of particular interest is prediction of ligand binding to a protein, as ligand molecule recognition is a major part of molecular function of proteins. Predicting whether a ligand molecule binds a protein is a complex problem due to the physical nature of protein-ligand interactions and the flexibility of both binding sites and ligand molecules. However, geometric and physicochemical complementarity is observed between the ligand and its binding site in many cases. Therefore, ligand molecules which bind to a local surface site in a protein can be predicted by finding similar local pockets of known binding ligands in the structure database. Here, we present two representations of ligand binding pockets and utilize them for ligand binding prediction by pocket shape comparison. These representations are based on mapping of surface properties of binding pockets, which are compactly described either by the two-dimensional pseudo-Zernike moments or the three-dimensional Zernike descriptors. These compact representations allow a fast real-time pocket searching against a database. Thorough benchmark studies employing two different datasets show that our representations are competitive with the other existing methods. Limitations and potentials of the shape-based methods as well as possible improvements are discussed.

M-ORBIS: mapping of molecular binding sites and surfaces

M-ORBIS is a Molecular Cartography approach that performs integrative high-throughput analysis of structural data to localize all types of binding sites and associated partners by homology and to characterize their properties and behaviors in a systemic way. The robustness of our binding site inferences was compared to four curated datasets corresponding to protein heterodimers and homodimers and protein–DNA/RNA assemblies. The Molecular Cartographies of structurally well-detailed proteins shows that 44% of their surfaces interact with non-solvent partners. Residue contact frequencies with water suggest that ∼86% of their surfaces are transiently solvated, whereas only 15% are specifically solvated. Our analysis also reveals the existence of two major binding site families: specific binding sites which can only bind one type of molecule (protein, DNA, RNA, etc.) and polyvalent binding sites that can bind several distinct types of molecule. Specific homodimer binding sites are for instance nearly twice as hydrophobic than previously described and more closely resemble the protein core, while polyvalent binding sites able to form homo and heterodimers more closely resemble the surfaces involved in crystal packing. Similarly, the regions able to bind DNA and to alternatively form homodimers, are more hydrophobic and less polar than previously described DNA binding sites.

fpocket: online tools for protein ensemble pocket detection and tracking

Computational small-molecule binding site detection has several important applications in the biomedical field. Notable interests are the identification of cavities for structure-based drug discovery or functional annotation of structures. fpocket is a small-molecule pocket detection program, relying on the geometric alpha-sphere theory. The fpocket web server allows: (i) candidate pocket detection--fpocket; (ii) pocket tracking during molecular dynamics, in order to provide insights into pocket dynamics--mdpocket; and (iii) a transposition of mdpocket to the combined analysis of homologous structures--hpocket. These complementary online tools allow to tackle various questions related to the identification and annotation of functional and allosteric sites, transient pockets and pocket preservation within evolution of structural families. The server and documentation are freely available at http://bioserv.rpbs.univ-paris-diderot.fr/fpocket.

Q-Dock(LHM): Low-resolution refinement for ligand comparative modeling

The success of ligand docking calculations typically depends on the quality of the receptor structure. Given improvements in protein structure prediction approaches, approximate protein models now can be routinely obtained for the majority of gene products in a given proteome. Structure-based virtual screening of large combinatorial libraries of lead candidates against theoretically modeled receptor structures requires fast and reliable docking techniques capable of dealing with structural inaccuracies in protein models. Here, we present Q-Dock(LHM), a method for low-resolution refinement of binding poses provided by FINDSITE(LHM), a ligand homology modeling approach. We compare its performance to that of classical ligand docking approaches in ligand docking against a representative set of experimental (both holo and apo) as well as theoretically modeled receptor structures. Docking benchmarks reveal that unlike all-atom docking, Q-Dock(LHM) exhibits the desired tolerance to the receptor's structure deformation. Our results suggest that the use of an evolution-based approach to ligand homology modeling followed by fast low-resolution refinement is capable of achieving satisfactory performance in ligand-binding pose prediction with promising applicability to proteome-scale applications.