The docking field has come of age. The time is ripe to present the principles of docking, reviewing the current state of the field. Two reasons are largely responsible for the maturity of the computational docking area. First, the early optimism that the very presence of the "correct" native conformation within the list of predicted docked conformations signals a near solution to the docking problem, has been replaced by the stark realization of the extreme difficulty of the next scoring/ranking step. Second, in the last couple of years more realistic approaches to handling molecular flexibility in docking schemes have emerged. As in folding, these derive from concepts abstracted from statistical mechanics, namely, populations. Docking and folding are interrelated. From the purely physical standpoint, binding and folding are analogous processes, with similar underlying principles. Computationally, the tools developed for docking will be tremendously useful for folding. For large, multidomain proteins, domain docking is probably the only rational way, mimicking the hierarchical nature of protein folding. The complexity of the problem is huge. Here we divide the computational docking problem into its two separate components. As in folding, solving the docking problem involves efficient search (and matching) algorithms, which cover the relevant conformational space, and selective scoring functions, which are both efficient and effectively discriminate between native and non-native solutions. It is universally recognized that docking of drugs is immensely important. However, protein-protein docking is equally so, relating to recognition, cellular pathways, and macromolecular assemblies. Proteins function when they are bound to other molecules. Consequently, we present the review from both the computational and the biological points of view. Although large, it covers only partially the extensive body of literature, relating to small (drug) and to large protein-protein molecule docking, to rigid and to flexible. Unfortunately, when reviewing these, a major difficulty in assessing the results is the non-uniformity in the formats in which they are presented in the literature. Consequently, we further propose a way to rectify it here.

Principles of docking: An overview of search algorithms and a guide to scoring functions

Multiple structural alignment is a fundamental problem in structural genomics. In this article, we define a reliable and robust algorithm, MUSTANG (MUltiple STructural AligNment AlGorithm), for the alignment of multiple protein structures. Given a set of protein structures, the program constructs a multiple alignment using the spatial information of the C(alpha) atoms in the set. Broadly based on the progressive pairwise heuristic, this algorithm gains accuracy through novel and effective refinement phases. MUSTANG reports the multiple sequence alignment and the corresponding superposition of structures. Alignments generated by MUSTANG are compared with several handcurated alignments in the literature as well as with the benchmark alignments of 1033 alignment families from the HOMSTRAD database. The performance of MUSTANG was compared with DALI at a pairwise level, and with other multiple structural alignment tools such as POSA, CE-MC, MALECON, and MultiProt. MUSTANG performs comparably to popular pairwise and multiple structural alignment tools for closely related proteins, and performs more reliably than other multiple structural alignment methods on hard data sets containing distantly related proteins or proteins that show conformational changes.

/pdf/mustang-a-multiple-structural-alignment-algorithm-xbynp82pxh.pdf

MUSTANG: a multiple structural alignment algorithm.

Polar residue hot spots have been observed at protein–protein binding sites. Here we show that hot spots occur predominantly at the interfaces of macromolecular complexes, distinguishing binding sites from the remainder of the surface. Consequently, hot spots can be used to define binding epitopes. We further show a correspondence between energy hot spots and structurally conserved residues. The number of structurally conserved residues, particularly of high ranking energy hot spots, increases with the binding site contact size. This finding may suggest that effectively dispersing hot spots within a large contact area, rather than compactly clustering them, may be a strategy to sustain essential key interactions while still allowing certain protein flexibility at the interface. Thus, most conserved polar residues at the binding interfaces confer rigidity to minimize the entropic cost on binding, whereas surrounding residues form a flexible cushion. Furthermore, our finding that similar residue hot spots occur across different protein families suggests that affinity and specificity are not necessarily coupled: higher affinity does not directly imply greater specificity. Conservation of Trp on the protein surface indicates a highly likely binding site. To a lesser extent, conservation of Phe and Met also imply a binding site. For all three residues, there is a significant conservation in binding sites, whereas there is no conservation on the exposed surface. A hybrid strategy, mapping sequence alignment onto a single structure illustrates the possibility of binding site identification around these three residues.

https://www.pnas.org/content/pnas/100/10/5772.full.pdf

Protein–protein interactions: Structurally conserved residues distinguish between binding sites and exposed protein surfaces

Prediction of protein-protein interactions by docking methods

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.

/pdf/a-method-for-simultaneous-alignment-of-multiple-protein-wej2ginenk.pdf

A method for simultaneous alignment of multiple protein structures

Here we present an algorithm designed to carry out multiple structure alignment and to detect recurring substructural motifs. So far we have implemented it for comparison of protein structures. How...

MUSTA - A General, Efficient, Automated Method for Multiple Structure Alignment and Detection of Common Motifs: Application to Proteins

While a number of approaches have been geared toward multiple sequence alignments, to date there have been very few approaches to multiple structure alignment and detection of a recurring substructural motif Among these, none performs both multiple structure comparison and motif detection simultaneously Further, none considers all structures at the same time, rather than initiating from pairwise molecular comparisons We present such a multiple structural alignment algorithm Given an ensemble of protein structures, the algorithm automatically finds the largest common substructure (core) of Cα atoms that appears in all the molecules in the ensemble The detection of the core and the structural alignment are done simultaneously Additional structural alignments also are obtained and are ranked by the sizes of the substructural motifs, which are present in the entire ensemble The method is based on the geometric hashing paradigm As in our previous structural comparison algorithms, it compares the structures in an amino acid sequence order-independent way, and hence the resulting alignment is unaffected by insertions, deletions and protein chain directionality As such, it can be applied to protein surfaces, protein–protein interfaces and protein cores to find the optimally, and suboptimally spatially recurring substructural motifs There is no predefinition of the motif We describe the algorithm, demonstrating its efficiency In particular, we present a range of results for several protein ensembles, with different folds and belonging to the same, or to different, families Since the algorithm treats molecules as collections of points in three-dimensional space, it can also be applied to other molecules, such as RNA, or drugs Proteins 2001;43:235–245 © 2001 Wiley-Liss, Inc

/pdf/automated-multiple-structure-alignment-and-detection-of-a-4aw8x5yf9s.pdf

Automated multiple structure alignment and detection of a common substructural motif.

A Multiple Structural Alignment algorithm is presented. The algorithm accepts an ensemble of pro- tein structures and finds the largest substructure (core) of Ca atoms whose geometric configuration appear in all the molecules of the ensemble (core). Both the detec- tion of this core and the resulting structural alignment are done simultaneously. Other large enough multi- structural superimpositions are detected as well. Our method is based on the Geometric Hashing paradigm and a superimposition clustering technique which repre- sents superimpositions by sets of matching atoms. The algorithm proved to be efficient on real data in a se- ries of experiments. The same method can be applied to any ensemble of molecules (not necessarily proteins) since our basic technique is sequence order independent.

/pdf/multiple-structural-alignment-and-core-detection-by-cq1hsmoy9e.pdf

Multiple Structural Alignment and Core Detection by Geometric Hashing

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MUSTA - A General, Efficient, Automated Method for Multiple Structure Alignment and Detection of Common Motifs: Application to Proteins

Automated multiple structure alignment and detection of a common substructural motif.

Multiple Structural Alignment and Core Detection by Geometric Hashing