Biomedical subjects

Haim J Wolfson

Publications and source records attributed to Haim J Wolfson.

12 recordsLinked to original sources

PubMed2004

A method for simultaneous alignment of multiple protein structures.

Here, we present MultiProt, a fully automated highly efficient technique to detect multiple structural alignments of protein structures. MultiProt finds the common geometrical cores between input molecules. To date, most methods for multiple alignment start from the pairwise alignment solutions. This may lead to a small overall alignment. In contrast, our method derives multiple alignments from simultaneous superpositions of input molecules. Further, our method does not require that all input molecules participate in the alignment. Actually, it efficiently detects high scoring partial multiple alignments for all possible number of molecules in the input. To demonstrate the power of MultiProt, we provide a number of case studies. First, we demonstrate known multiple alignments of protein structures to illustrate the performance of MultiProt. Next, we present various biological applications. These include: (1) a partial alignment of hinge-bent domains; (2) identification of functional groups of G-proteins; (3) analysis of binding sites; and (4) protein-protein interface alignment. Some applications preserve the sequence order of the residues in the alignment, whereas others are order-independent. It is their residue sequence order-independence that allows application of MultiProt to derive multiple alignments of binding sites and of protein-protein interfaces, making MultiProt an extremely useful structural tool.

Algorithms↗

PubMed2004

BioInfo3D: a suite of tools for structural bioinformatics.

Here, we describe BioInfo3D, a suite of freely available web services for protein structural analysis. The FlexProt method performs flexible structural alignment of protein molecules. FlexProt simultaneously detects the hinge regions and aligns the rigid subparts of the molecules. It does not require an a priori knowledge of the flexible hinge regions. MultiProt and MASS perform simultaneous comparison of multiple protein structures. PatchDock performs prediction of protein-protein and protein-small molecule interactions. The input to all services is either protein PDB codes or protein structures uploaded to the server. All the services are available at http://bioinfo3d.cs.tau.ac.il.

Algorithms↗

PubMed2004

Recognition of functional sites in protein structures.

Recognition of regions on the surface of one protein, that are similar to a binding site of another is crucial for the prediction of molecular interactions and for functional classifications. We first describe a novel method, SiteEngine, that assumes no sequence or fold similarities and is able to recognize proteins that have similar binding sites and may perform similar functions. We achieve high efficiency and speed by introducing a low-resolution surface representation via chemically important surface points, by hashing triangles of physico-chemical properties and by application of hierarchical scoring schemes for a thorough exploration of global and local similarities. We proceed to rigorously apply this method to functional site recognition in three possible ways: first, we search a given functional site on a large set of complete protein structures. Second, a potential functional site on a protein of interest is compared with known binding sites, to recognize similar features. Third, a complete protein structure is searched for the presence of an a priori unknown functional site, similar to known sites. Our method is robust and efficient enough to allow computationally demanding applications such as the first and the third. From the biological standpoint, the first application may identify secondary binding sites of drugs that may lead to side-effects. The third application finds new potential sites on the protein that may provide targets for drug design. Each of the three applications may aid in assigning a function and in classification of binding patterns. We highlight the advantages and disadvantages of each type of search, provide examples of large-scale searches of the entire Protein Data Base and make functional predictions.

Algorithms↗

PubMed2004

Identification of the N-terminal peptide binding site of glucose-regulated protein 94.

Because the stress protein GRP94 can augment presentation of peptides to T cells, it is important to define how it, as well as all other HSP90 family members, binds peptides. Having previously shown that the N-terminal half of GRP94 can account for the peptide binding activity of the full-length protein, we now locate this binding site by testing predictions of a molecular docking model. The best predicted site was on the opposite face of the beta sheet from the pan-HSP90 radicicol-binding pocket, in close proximity to a deep hydrophobic pocket. The peptide and radicicol-binding sites are distinct, as shown by the ability of a radicicol-refractive mutant to bind peptide. When the fluorophore acrylodan is attached to Cys117 within the hydrophobic pocket, its fluorescence is reduced upon peptide binding, consistent with proximity of the two ligands. Substitution of His125, which contacts the bound peptide, compromises peptide-binding activity. We conclude that peptide binds to the concave face of the beta sheet of the N-terminal domain, where binding is regulated during the action cycle of the chaperone.

Amino Acid Sequence↗

PubMed2004

FlexProt: alignment of flexible protein structures without a predefinition of hinge regions.

FlexProt is a novel technique for the alignment of flexible proteins. Unlike all previous algorithms designed to solve the problem of structural comparisons allowing hinge-bending motions, FlexProt does not require an a priori knowledge of the location of the hinge(s). FlexProt carries out the flexible alignment, superimposing the matching rigid subpart pairs, and detects the flexible hinge regions simultaneously. A large number of methods are available to handle rigid structural alignment. However, proteins are flexible molecules, which may appear in different conformations. Hence, protein structural analysis requires algorithms that can deal with molecular flexibility. Here, we present a method addressing specifically a flexible protein alignment task. First, the method efficiently detects maximal congruent rigid fragments in both molecules. Transforming the task into a graph theoretic problem, our method proceeds to calculate the optimal arrangement of previously detected maximal congruent rigid fragments. The fragment arrangement does not violate the protein sequence order. A clustering procedure is performed on fragment-pairs which have the same 3-D rigid transformation regardless of insertions and deletions (such as loops and turns) which separate them. Although the theoretical worst case complexity of the algorithm is O(n(6)), in practice FlexProt is highly efficient. It performs a structural comparison of a pair of proteins 300 amino acids long in about seven seconds on a standard desktop PC (400 MHz Pentium II processor with 256MB internal memory). We have performed extensive experiments with the algorithm. An assortment of these results is presented here. FlexProt can be accessed via WWW at bioinfo3d.cs.tau.ac.il/FlexProt/.

Computational Biology↗

PubMed2004

Predicting molecular interactions in silico: II. Protein-protein and protein-drug docking.

This article reviews the docking field starting from basic docking algorithms and describing the latest advances. We present the algorithmic framework and classify the state of-the-art methods. We point out the bottlenecks of the methods, like flexibility, absence of absolute scoring functions and explain what types of information can potentially be added to improve the results.

Algorithms↗

PubMed2004

Predicting molecular interactions in silico: I. A guide to pharmacophore identification and its applications to drug design.

A major goal in contemporary drug design is to develop new ligands with high affinity of binding toward a given protein receptor. Pharmacophore, which is the three-dimensional arrangement of essential features that enable a molecule to exert a particular biological effect, is a very useful model for achieving this goal. If the three dimensional structure of the receptor is known, pharmacophore is a complementary tool to standard techniques, such as docking. However, frequently the structure of the receptor protein is unknown and only a set of ligands together with their measured binding affinities towards the receptor is available. In such a case, a pharmacophore based strategy is one of the few applicable tools. Here we present a broad, yet concise guide to pharmacophore identification and review a sample of applications for drug design. In particular, we present the framework of the algorithms, classify their modules and point out their advantages and challenges.

Algorithms↗

PubMed2003

Taking geometry to its edge: fast unbound rigid (and hinge-bent) docking.

We present a very efficient rigid "unbound" soft docking methodology, which is based on detection of geometric shape complementarity, allowing liberal steric clash at the interface. The method is based on local shape feature matching, avoiding the exhaustive search of the 6D transformation space. Our experiments at CAPRI rounds 1 and 2 show that although the method does not perform an exhaustive search of the 6D transformation space, the "correct" solution is never lost. However, such a solution might rank low for large proteins, because there are alternatives with significantly larger geometrically compatible interfaces. In many cases this problem can be resolved by successful a priori focusing on the vicinity of potential binding sites as well as the extension of the technique to flexible (hinge-bent) docking. This is demonstrated in the experiments performed as a lesson from our CAPRI experience.

Algorithms↗

PubMed2003

Protein structure prediction via combinatorial assembly of sub-structural units.

Following the hierarchical nature of protein folding, we propose a three-stage scheme for the prediction of a protein structure from its sequence. First, the sequence is cut to fragments that are each assigned a structure. Second, the assigned structures are combinatorially assembled to form the overall 3D organization. Third, highly ranked predicted arrangements are completed and refined. This work focuses on the second stage of this scheme: the combinatorial assembly. We present CombDock, a combinatorial docking algorithm. CombDock gets an ordered set of protein sub-structures and predicts the inter-contacts that define their overall organization. We reduce the combinatorial assembly to a graph-theory problem, and give a heuristic polynomial solution to this computationally hard problem. We applied CombDock to various examples of structural units of two types: protein domains and building blocks, which are relatively stable sub-structures of domains. Moreover, we tested CombDock using increasingly distorted input, where the native structural units were replaced by similarly folded units extracted from homologous proteins and, in the more difficult cases, from globally unrelated proteins. The algorithm is robust, showing low sensitivity to input distortion. This suggests that CombDock is a useful tool in protein structure prediction that may be applied to large target proteins.

Algorithms↗

PubMed2003

Multiple structural alignment by secondary structures: algorithm and applications.

We present MASS (Multiple Alignment by Secondary Structures), a novel highly efficient method for structural alignment of multiple protein molecules and detection of common structural motifs. MASS is based on a two-level alignment, using both secondary structure and atomic representation. Utilizing secondary structure information aids in filtering out noisy solutions and achieves efficiency and robustness. Currently, only a few methods are available for addressing the multiple structural alignment task. In addition to using secondary structure information, the advantage of MASS as compared to these methods is that it is a combination of several important characteristics: (1) While most existing methods are based on series of pairwise comparisons, and thus might miss optimal global solutions, MASS is truly multiple, considering all the molecules simultaneously; (2) MASS is sequence order-independent and thus capable of detecting nontopological structural motifs; (3) MASS is able to detect not only structural motifs, shared by all input molecules, but also motifs shared only by subsets of the molecules. Here, we show the application of MASS to various protein ensembles. We demonstrate its ability to handle a large number (order of tens) of molecules, to detect nontopological motifs and to find biologically meaningful alignments within nonpredefined subsets of the input. In particular, we show how by using conserved structural motifs, one can guide protein-protein docking, which is a notoriously difficult problem. MASS is freely available at http://bioinfo3d.cs.tau.ac.il/MASS/.

Algorithms↗

PubMed2002

Flexible protein alignment and hinge detection.

Here we present a novel technique for the alignment of flexible proteins. The method does not require an a priori knowledge of the flexible hinge regions. The FlexProt algorithm simultaneously detects the hinge regions and aligns the rigid subparts of the molecules. Our technique is not sensitive to insertions and deletions. Numerous methods have been developed to solve rigid structural comparisons. Unlike FlexProt, all previously developed methods designed to solve the protein flexible alignment require an a priori knowledge of the hinge regions. The FlexProt method is based on 3-D pattern-matching algorithms combined with graph theoretic techniques. The algorithm is highly efficient. For example, it performs a structural comparison of a pair of proteins with 300 amino acids in about 7 s on a 400-MHz desktop PC. We provide experimental results obtained with this algorithm. First, we flexibly align pairs of proteins taken from the database of motions. These are extended by taking additional proteins from the same SCOP family. Next, we present some of the results obtained from exhaustive all-against-all flexible structural comparisons of 1329 SCOP family representatives. Our results include relatively high-scoring flexible structural alignments between the C-terminal merozoite surface protein vs. tissue factor; class II aminoacyl-tRNA synthase, histocompatibility antigen vs. neonatal FC receptor; tyrosine-protein kinase C-SRC vs. haematopoetic cell kinase (HCK); tyrosine-protein kinase C-SRC vs. titine protein (autoinhibited serine kinase domain); and tissue factor vs. hormone-binding protein. These are illustrated and discussed, showing the capabilities of this structural alignment algorithm, which allows un-predefined hinge-based motions.

Algorithms↗

PubMed2002

Multiple diverse ligands binding at a single protein site: a matter of pre-existing populations.

Here, we comment on the steadily increasing body of data showing that proteins with specificity actually bind ligands of diverse shapes, sizes, and composition. Such a phenomenon is not surprising when one considers that binding is a dynamic process with populations in equilibrium and that the shape of the binding site is strongly influenced by the molecular partner. It derives implicitly from the concept of populations. All proteins, specific and nonspecific, exist in ensembles of substates. If the library of ligands in solution is large enough, favorably matching ligands with altered shapes and sizes can be expected to bind, with a redistribution of the protein populations. Point mutations at spatially distant sites may exert large conformational rearrangements and hinge effects, consistent with mutations away from the binding site leading to population shifts and (cross-)drug resistance. A similar effect is observed in protein superfamilies, in which different sequences with similar topologies display similar large-scale dynamic motions. The hinges are frequently at analogous sites, yet with different substrate specificity. Similar topologies yield similar conformational isomers, although with different distributions of population times, owing to the change in the conditions, that is, the change in the sequences. In turn, different distributions relate to binding of different sizes and shapes. Hence, the binding site shape and size are defined by the ligand. They are not independent entities of fixed proportions and cannot be analyzed independently of the binding partner. Such a proposition derives from viewing proteins as dynamic distributions, presenting to the incoming ligands a range of binding site shapes. It illustrates how presumably specific binding molecules can bind multiple ligands. In terms of drug design, the ability of a single receptor to recognize many dissimilar ligands shows the need to consider more diverse molecules. It provides a rationale for higher affinity inhibitors that are not derived from substrates at their transition states and indicates flexible docking schemes.

Binding Sites↗