Protein-protein binding-sites prediction by protein surface structure conservation

A new algorithm to predict protein-protein binding sites using conservation of both protein surface structure and physical-chemical properties in structurally similar proteins is developed. Binding-site residues in proteins are known to be more conserved than the rest of the surface, and finding local surface similarities by comparing a protein to its structural neighbors can potentially reveal the location of binding sites on this protein. This approach, which has previously been used to predict binding sites for small ligands, is now extended to predict protein-protein binding sites. Examples of binding-site predictions for a set of proteins, which have previously been studied for sequence conservation in protein-protein interfaces, are given. The predicted binding sites and the actual binding sites are in good agreement. Our algorithm for finding conserved surface structures in a set of similar proteins is a useful tool for the prediction of protein-protein binding sites.     

Exploring the effectiveness of the TSR-based protein 3-D structural comparison method for protein clustering,and structural motif identification and discovery of protein kinases,hydrolases, and SARS-CoV-2’s protein via the application of amino acid grouping

Development of protein 3-D structural comparison methods is essential for understanding protein functions. Some amino acids share structural similarities while others vary considerably. These structures determine the chemical and physical properties of amino acids. Grouping amino acids with similar structures potentially improves the ability to identify structurally conserved regions and increases the global structural similarity between proteins. We systematically studied the effects of amino acid grouping on the numbers of Specific/specific, Common/common, and statistically different keys to achieve a better understanding of protein structure relations. Common keys represent substructures found in all types of proteins and Specific keys represent substructures exclusively belonging to a certain type of proteins in a data set. Our results show that applying amino acid grouping to the Triangular Spatial Relationship (TSR)-based method, while computing structural similarity among proteins, improves the accuracy of protein clustering in certain cases. In addition, applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. The results from the principal component analysis (PCA) demonstrate that applying amino acid grouping captures slightly more structural variation than when amino acid grouping is not used, indicating that amino acid grouping reduces structure diversity as predicted. The TSR-based method uniquely identifies and discovers binding sites for drugs or interacting proteins. The binding sites of nsp16 of SARS-CoV-2, SARS-CoV and MERS-CoV that we have defined will aid future antiviral drug design for improving therapeutic outcome. This approach for incorporating the amino acid grouping feature into our structural comparison method is promising and provides a deeper insight into understanding of structural relations of proteins.     

Parallel‐ProBiS: Fast parallel algorithm for local structural comparison of protein structures and binding sites

The ProBiS algorithm performs a local structural comparison of the query protein surface against the nonredundant database of protein structures. It finds proteins that have binding sites in common with the query protein. Here, we present a new parallelized algorithm, Parallel‐ProBiS, for detecting similar binding sites on clusters of computers. The obtained speedups of the parallel ProBiS scale almost ideally with the number of computing cores up to about 64 computing cores. Scaling is better for larger than for smaller query proteins. For a protein with almost 600 amino acids, the maximum speedup of 180 was achieved on two interconnected clusters with 248 computing cores. Source code of Parallel‐ProBiS is available for download free for academic users at http://probis.cmm.ki.si/download . © 2012 Wiley Periodicals, Inc.     

Phosphate recognition in structural biology

Drug-discovery research in the past decade has seen an increased selection of targets with phosphate recognition sites, such as protein kinases and phosphatases, in the past decade. This review attempts, with the help of database-mining tools, to give an overview of the most important principles in molecular recognition of phosphate groups by enzymes. A total of 3003 X-ray crystal structures from the RCSB Protein Data Bank with bound organophosphates has been analyzed individually, in particular for H-bonding interactions between proteins and ligands. The various known binding motifs for phosphate binding are reviewed, and similarities to phosphate complexation by synthetic receptors are highlighted. An analysis of the propensities of amino acids in various classes of phosphate-binding enzymes showed characteristic distributions of amino acids used for phosphate binding. This review demonstrates that structure-based lead development and optimization should carefully address the phosphate-binding-site environment and also proposes new alternatives for filling such sites.     

Model structures of Helicobacter pylori UreD(H) domains: a putative molecular recognition platform

The analysis of the sequence of Helicobacter pylori UreD(H), an accessory protein involved in the activation of urease through the assembly of the Ni(2+)-containing active site, revealed the presence of two domains. The structure of these domains was calculated using threading and modeling algorithms. A search for putative binding sites on the protein surface was carried out using dedicated algorithms sensitive to either sequence conservation or structural similarity based on geometry and physicochemical properties. The results suggest that UreD(H) acts as a multifunctional molecular recognition platform facilitating the interaction between apo-urease and the ancillary proteins UreG, UreF, and UreE, responsible for nickel trafficking and delivering.     

A fourier fingerprint-based method for protein surface representation

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

Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening

Structure Based Drug Design (SBDD) is a computational approach to lead discovery that uses the three-dimensional structure of a protein to fit drug-like molecules into a ligand binding site to modulate function. Identifying the location of the binding site is therefore a vital first step in this process, restricting the search space for SBDD or virtual screening studies. The detection and characterisation of functional sites on proteins has increasingly become an area of interest. Structural genomics projects are increasingly yielding protein structures with unknown functions and binding sites. Binding site prediction was pioneered by pocket detection, since the binding site is often found in the largest pocket. More recent methods involve phylogenetic analysis, identifying structural similarity with proteins of known function and identifying regions on the protein surface with a potential for high binding affinity. Binding site prediction has been used in several SBDD projects and has been incorporated into several docking tools. We discuss different methods of ligand binding site prediction, their strengths and weaknesses, and how they have been used in SBDD.     

Protein surface conservation in binding sites

An algorithm is described which uses the conservation of the 3D structure of protein surfaces, as opposed to their sequences, to detect protein-protein binding sites. The protein in which protein-protein binding sites are sought is compared with structures of multiple structurally related proteins and the surface that is conserved at least once is considered to be a part of the binding site. The binding site predictions obtained in this way for a set of protein-protein complexes correspond well with the actual protein-protein binding sites. A comparison of this method with an algorithm using the support vector machine approach for predicting protein-protein binding sites shows structural conservation to be an important characteristic that distinguishes binding sites from the remainder of protein surfaces.     

Creating functional artificial proteins

Much is now known about how protein folding occurs, through the sequence analysis of proteins of known folding geometry and the sequence/structural analysis of proteins and their mutants. This has allowed not only the modification of natural proteins but also the construction of de novo polypeptides with predictable folding patterns. Structure/function analysis of natural proteins is used to construct derived versions that retain a degree of biological activity. The constructed versions made of either natural or artificial sequences contain critical residues for activity such as receptor binding. In some cases, the functionality is introduced by incorporating binding sites for other elements, such as organic cofactors or transition metals, into the protein scaffold. While these modified proteins can mimic the function of natural proteins, they can also be constructed to have novel activities. Recently engineered photoactive proteins are good examples of such systems in which a light-induced electron transfer can be established in normally light-insensitive proteins. The present review covers some aspects of protein design that have been used to investigate protein receptor binding, cofactor binding and biological electron transfer.     

Fabrication of Pascal-triangle Lattice of Proteins by Inducing Ligand Strategy

A protein Pascal triangle has been constructed as new type of supramolecular architecture by using the inducing ligand strategy that we previously developed for protein assemblies. Although mathematical studies on this famous geometry have a long history, no work on such Pascal triangles fabricated from native proteins has been reported so far due to their structural complexity. In this work, by carefully tuning the specific interactions between the native protein building block WGA and the inducing ligand R-SL , a 2D Pascal-triangle lattice with three types of triangular voids has been assembled. Moreover, a 3D crystal structure was obtained based on the 2D Pascal triangles. The distinctive carbohydrate binding sites of WGA and the intralayer as well as interlayer dimerization of RhB was the key to facilitate nanofabrication in solution. This strategy may be applied to prepare and explore various sophisticated assemblies based on native proteins.     

Fabrication of Pascal‐triangle Lattice of Proteins by Inducing Ligand Strategy

A protein Pascal triangle has been constructed as new type of supramolecular architecture by using the inducing ligand strategy that we previously developed for protein assemblies. Although mathematical studies on this famous geometry have a long history, no work on such Pascal triangles fabricated from native proteins has been reported so far due to their structural complexity. In this work, by carefully tuning the specific interactions between the native protein building block WGA and the inducing ligand R‐SL , a 2D Pascal‐triangle lattice with three types of triangular voids has been assembled. Moreover, a 3D crystal structure was obtained based on the 2D Pascal triangles. The distinctive carbohydrate binding sites of WGA and the intralayer as well as interlayer dimerization of RhB was the key to facilitate nanofabrication in solution. This strategy may be applied to prepare and explore various sophisticated assemblies based on native proteins.     

Protein-protein binding sites prediction by 3D structural similarities

Identifying the location of binding sites on proteins is of fundamental importance for a wide range of applications including molecular docking, de novo drug design, structure identification, and comparison of functional sites. In this paper, we develop an efficient approach for finding binding sites between proteins. Our approach consists of four steps: local sequence alignment, protein surface detection, 3D structure comparison, and candidate binding site selection. A comparison of our method with the LSA algorithm shows that the binding sites predicted by our method are somewhat closer to the actual binding sites in the protein-protein complexes. The software package is available at http://sites.google.com/site/guofeics/pro-bs for noncommercial use.     

A combined molecular modeling study on gelatinases and their potent inhibitors

Zinc‐dependent matrix metalloproteinase (MMP) family is considered to be an attractive target because of its important role in many physiological and pathological processes. In the present work, a molecular modeling study combining protein‐, ligand‐ and complex‐based computational methods was performed to analyze a new series of β‐N‐biaryl ether sulfonamide hydroxamates as potent inhibitors of gelatinase A (MMP‐2) and gelatinase B (MMP‐9). Firstly, the similarities and differences between the binding sites of MMP‐2 and MMP‐9 were analyzed through sequence alignment and structural superimposition. Secondly, in order to extract structural features influencing the activities of these inhibitors, quantitative structure‐activity relationship (QSAR) models using genetic algorithm‐multiple linear regression (GA‐MLR), comparative molecular field (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed. The proposed QSAR models could give good predictive ability for the studied inhibitors. Thirdly, docking study was employed to further explore the binding mode between the ligand and protein. The results from all the above analyses could provide the information about the similarities and differences of the binding mode between the MMP‐2, MMP‐9 and their potent inhibitors. The obtained results can provide very useful information for the design of new potential inhibitors. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010     

The effect of tightly bound water molecules on the structural interpretation of ligand-derived pharmacophore models

The importance of the consideration of water molecules in the structural interpretation of ligand-derived pharmacophore models is explored. We compare and combine results from recently introduced methods for bound-water molecule identification in protein binding sites and ligand-superposition-based pharmacophore derivation, for the interpretation of ligand-derived pharmacophore models. In the analysis of thymidine kinase (HSV-1) and poly (ADP-ribose) polymerase (PARP), the concurrent application of both methods leads to an agreement in the prediction of tightly bound water molecules as key pharmacophoric points in the binding site of these proteins. This agreement has implications for approaching binding site analysis and consensus drug design, as it highlights how pharmacophore-based models of binding sites can include interaction features not only with protein groups but also with bound water molecules.     

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.     

Comparison of non-sequential sets of protein residues

A methodology for performing sequence-free comparison of functional sites in protein structures is introduced. The method is based on a new notion of similarity among superimposed groups of amino acid residues that evaluates both geometry and physico-chemical properties. The method is specifically designed to handle disconnected and sparsely distributed sets of residues. A genetic algorithm is employed to find the superimposition of protein segments that maximizes their similarity. The method was evaluated by performing an all-to-all comparison on two separate sets of ligand-binding sites, comprising 47 protein-FAD (Flavin-Adenine Dinucleotide) and 64 protein-NAD (Nicotinamide-Adenine Dinucleotide) complexes, and comparing the results with those of an existing sequence-based structural alignment tool (TM-Align). The quality of the two methodologies is judged by the methods’ capacity to, among other, correctly predict the similarities in the protein-ligand contact patterns of each pair of binding sites. The results show that using a sequence-free method significantly improves over the sequence-based one, resulting in 23 significant binding-site homologies being detected by the new method but ignored by the sequence-based one.