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We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural clues within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) clue is derived from templatebased modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing onE. coliand human protein-protein interaction (PPI) databases. A PrePPI database of ~1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of- the-art resource that offers an unprecedented structure-informed view of the human interactome.Graphic Abstract

We describe the Predicting Protein Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between nearly 7 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence and structural similarity-based metrics are established between template proteins in the Protein Data Bank, T, that bind small molecules, C, and proteins in the models database, Q. When these metrics pass a sequence threshold value, it is assumed that C also binds to Q with a probability derived from machine learning. If the relationship is based on structure, this probability is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto Coefficient identifies other small molecules that may bind to Q. A likelihood ratio for the binding of C to Q is obtained from naïve Bayesian statistics. The PrePCI algorithm performs well under different validations. It can be queried by entering a UniProt ID for a protein and obtaining a list of compounds predicted to bind to it along with associated probabilities. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database are described and a strategy is introduced to use PrePCI as a first step in a docking screen.

Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus–host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.

Dilated cardiomyopathy (DCM) belongs to the most frequent forms of cardiomyopathy mainly characterized by cardiac dilatation and reduced systolic function. Although most cases of DCM are classified as sporadic, 20–30% of cases show a heritable pattern. Familial forms of DCM are genetically heterogeneous, and mutations in several genes have been identified that most commonly play a role in cytoskeleton and sarcomere-associated processes. Still, a large number of familial cases remain unsolved. Here, we report five individuals from three independent families who presented with severe dilated cardiomyopathy during the neonatal period. Using whole-exome sequencing (WES), we identified causative, compound heterozygous missense variants in RPL3L (ribosomal protein L3-like) in all the affected individuals. The identified variants co-segregated with the disease in each of the three families and were absent or very rare in the human population, in line with an autosomal recessive inheritance pattern. They are located within the conserved RPL3 domain of the protein and were classified as deleterious by several in silico prediction software applications. RPL3L is one of the four non-canonical riboprotein genes and it encodes the 60S ribosomal protein L3-like protein that is highly expressed only in cardiac and skeletal muscle. Three-dimensional homology modeling and in silico analysis of the affected residues in RPL3L indicate that the identified changes specifically alter the interaction of RPL3L with the RNA components of the 60S ribosomal subunit and thus destabilize its binding to the 60S subunit. In conclusion, we report that bi-allelic pathogenic variants in RPL3L are causative of an early-onset, severe neonatal form of dilated cardiomyopathy, and we show for the first time that cytoplasmic ribosomal proteins are involved in the pathogenesis of non-syndromic cardiomyopathies.

The growing structural coverage of proteomes is making structural comparison a powerful tool for function annotation. Such template-based approaches are based on the observation that structural similarity is often sufficient to infer similar function. However, it seems clear that, in addition to structural similarity, the specific characteristics of a given protein should also be taken into account in predicting function. Here we describe PredUs 2.0, a method to predict regions on a protein surface likely to bind other proteins, that is, interfacial residues. PredUs 2.0 is based on the PredUs method that is entirely template-based and uses known binding sites in structurally similar proteins to predict interfacial residues. PredUs 2.0 uses a Bayesian approach to combine the template-based scoring of PredUs with a score that reflects the propensities of individual amino acids to be in interfaces. PredUs 2.0 includes a novel protein size dependent metric to determine the number of residues that should be reported as interfacial. PredUs 2.0 significantly outperforms PredUs as well as other published interface prediction methods.

We report a template-based method, LT-scanner, which scans the human proteome using protein structural alignment to identify proteins that are likely to bind ligands that are present in experimentally determined complexes. A scoring function that rapidly accounts for binding site similarities between the template and the proteins being scanned is a crucial feature of the method. The overall approach is first tested based on its ability to predict the residues on the surface of a protein that are likely to bind small-molecule ligands. The algorithm that we present, LBias, is shown to compare very favorably to existing algorithms for binding site residue prediction. LT-scanner's performance is evaluated based on its ability to identify known targets of Food and Drug Administration (FDA)-approved drugs and it too proves to be highly effective. The specificity of the scoring function that we use is demonstrated by the ability of LT-scanner to identify the known targets of FDA-approved kinase inhibitors based on templates involving other kinases. Combining sequence with structural information further improves LT-scanner performance. The approach we describe is extendable to the more general problem of identifying binding partners of known ligands even if they do not appear in a structurally determined complex, although this will require the integration of methods that combine protein structure and chemical compound databases.

Interfaces between organelles are emerging as critical platforms for many biological responses in eukaryotic cells. In yeast, the ERMES complex is an endoplasmic reticulum (ER)–mitochondria tether composed of four proteins, three of which contain a SMP (synaptotagmin-like mitochondrial-lipid binding protein) domain. No functional ortholog for any ERMES protein has been identified in metazoans. Here, we identified PDZD8 as an ER protein present at ER-mitochondria contacts. The SMP domain of PDZD8 is functionally orthologous to the SMP domain found in yeast Mmm1. PDZD8 was necessary for the formation of ER-mitochondria contacts in mammalian cells. In neurons, PDZD8 was required for calcium ion (Ca 2+ ) uptake by mitochondria after synaptically induced Ca 2+ -release from ER and thereby regulated cytoplasmic Ca 2+ dynamics. Thus, PDZD8 represents a critical ER-mitochondria tethering protein in metazoans. We suggest that ER-mitochondria coupling is involved in the regulation of dendritic Ca 2+ dynamics in mammalian neurons.

International audience; Background: The DiGeorge syndrome, the most common of the microdeletion syndromes, affects multiple organs, including the heart, the nervous system, and the kidney. It is caused by deletions on chromosome 22q11.2; the genetic driver of the kidney defects is unknown.Methods: We conducted a genomewide search for structural variants in two cohorts: 2080 patients with congenital kidney and urinary tract anomalies and 22,094 controls. We performed exome and targeted resequencing in samples obtained from 586 additional patients with congenital kidney anomalies. We also carried out functional studies using zebrafish and mice.Results: We identified heterozygous deletions of 22q11.2 in 1.1% of the patients with congenital kidney anomalies and in 0.01% of population controls (odds ratio, 81.5; P=4.5×10-14). We localized the main drivers of renal disease in the DiGeorge syndrome to a 370-kb region containing nine genes. In zebrafish embryos, an induced loss of function in snap29, aifm3, and crkl resulted in renal defects; the loss of crkl alone was sufficient to induce defects. Five of 586 patients with congenital urinary anomalies had newly identified, heterozygous protein-altering variants, including a premature termination codon, in CRKL. The inactivation of Crkl in the mouse model induced developmental defects similar to those observed in patients with congenital urinary anomalies.Conclusions: We identified a recurrent 370-kb deletion at the 22q11.2 locus as a driver of kidney defects in the DiGeorge syndrome and in sporadic congenital kidney and urinary tract anomalies. Of the nine genes at this locus, SNAP29, AIFM3, and CRKL appear to be critical to the phenotype, with haploinsufficiency of CRKL emerging as the main genetic driver. (Funded by the National Institutes of Health and others.).

We outline a set of strategies to infer protein function from structure. The overall approach depends on extensive use of homology modeling, the exploitation of a wide range of global and local geometric relationships between protein structures and the use of machine learning techniques. The combination of modeling with broad searches of protein structure space defines a “structural BLAST” approach to infer function with high genomic coverage. Applications are described to the prediction of protein–protein and protein–ligand interactions. In the context of protein–protein interactions, our structure-based prediction algorithm, PrePPI, has comparable accuracy to high-throughput experiments. An essential feature of PrePPI involves the use of Bayesian methods to combine structure-derived information with non-structural evidence (e.g. co-expression) to assign a likelihood for each predicted interaction. This, combined with a structural BLAST approach significantly expands the range of applications of protein structure in the annotation of protein function, including systems level biological applications where it has previously played little role.

Protein–protein interactions, essential for understanding how a cell functions, are predicted using a new method that combines protein structure with other computationally and experimentally derived clues. The analysis of protein-interaction networks is essential to an understanding of the regulatory processes in a living cell. Many methods have been developed with a view to predicting protein–protein interactions (PPIs) at a genome-wide level, although the differences obtained using these approaches suggest that there are still factors unaccounted for. Barry Honig and colleagues have developed a new way of predicting PPIs that is based on the proteins' three-dimensional structures and functional data. Tests of several predictions of the new algorithm, known as PREPPI, confirm the accuracy of the results. The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms1,2. Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification3, as well as from manual curation of experiments on individual systems4. A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein–protein interactions (PPIs)5,6. Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages7,8,9. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.

Protein domain family PF11267 (DUF3067) is a family of proteins of unknown function found in both bacteria and eukaryotes. Here we present the solution NMR structure of the 102-residue Alr2454 protein from Nostoc sp. PCC 7120, which constitutes the first structural representative from this conserved protein domain family. The structure of Nostoc sp. Alr2454 adopts a novel protein fold.

With the advent of Systems Biology, the prediction of whether two proteins form a complex has become a problem of increased importance. A variety of experimental techniques have been applied to the problem, but three-dimensional structural information has not been widely exploited. Here we explore the range of applicability of such information by analyzing the extent to which the location of binding sites on protein surfaces is conserved among structural neighbors. We find, as expected, that interface conservation is most significant among proteins that have a clear evolutionary relationship, but that there is a significant level of conservation even among remote structural neighbors. This finding is consistent with recent evidence that information available from structural neighbors, independent of classification, should be exploited in the search for functional insights. The value of such structural information is highlighted through the development of a new protein interface prediction method, PredUs, that identifies what residues on protein surfaces are likely to participate in complexes with other proteins. The performance of PredUs, as measured through comparisons with other methods, suggests that relationships across protein structure space can be successfully exploited in the prediction of protein-protein interactions.

It has become increasingly apparent that geometric relationships often exist between regions of two proteins that have quite different global topologies or folds. In this article, we examine whether such relationships can be used to infer a functional connection between the two proteins in question. We find, by considering a number of examples involving metal and cation binding, sugar binding, and aromatic group binding, that geometrically similar protein fragments can share related functions, even if they have been classified as belonging to different folds and topologies. Thus, the use of classifications inevitably limits the number of functional inferences that can be obtained from the comparative analysis of protein structures. In contrast, the development of interactive computational tools that recognize the “continuous” nature of protein structure/function space, by increasing the number of potentially meaningful relationships that are considered, may offer a dramatic enhancement in the ability to extract information from protein structure databases. We introduce the MarkUs server, that embodies this strategy and that is designed for a user interested in developing and validating specific functional hypotheses.

We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI’s coverage of the human protein interactome., Author Summary Complexes formed between a structured domain on one protein and an unstructured peptide on another are ubiquitous. However, they are often quite difficult to detect experimentally. The development of computational approaches to predict domain-motif interactions is therefore an important goal. We report a method to predict domain-motif interactions using a Bayesian approach to integrate evidence from a variety of sources, including three-dimensional structural and non-structural information. The method was applied to the entire human proteome and showed significant improvement over existing methods. The method was incorporated into PrePPI, a computational pipeline for the prediction of protein-protein interactions that relies heavily on structural information. Approximately 80,000 new interactions were detected. The new PrePPI database provides easy access to about 400,000 human protein-protein interactions and should thus constitute a valuable resource in a variety of biological applications including the characterization of molecular interaction networks and, more generally, in the study of interactions mediated by proteins in families that may not be extensively studied experimentally.

In recent years, there has been significant progress in the ability to predict the three-dimensional structure of proteins from their amino acid sequence. Progress has been due to new methods to extract the growing amount of information in sequence and structure databases and improved computational descriptions of protein energetics. This review summarizes recent advances in these areas and describes a number of novel biological applications made possible by structure prediction. Despite remaining challenges, protein structure prediction is becoming an extremely useful tool in understanding phenomena in modern molecular and cell biology.

Very few methods address the problem of predicting beta-barrel membrane proteins directly from sequence. One reason is that only very few high-resolution structures for transmembrane beta-barrel (TMB) proteins have been determined thus far. Here we introduced the design, statistics and results of a novel profile-based hidden Markov model for the prediction and discrimination of TMBs. The method carefully attempts to avoid over-fitting the sparse experimental data. While our model training and scoring procedures were very similar to a recently published work, the architecture and structure-based labelling were significantly different. In particular, we introduced a new definition of beta- hairpin motifs, explicit state modelling of transmembrane strands, and a log-odds whole-protein discrimination score. The resulting method reached an overall four-state (up-, down-strand, periplasmic-, outer-loop) accuracy as high as 86%. Furthermore, accurately discriminated TMB from non-TMB proteins (45% coverage at 100% accuracy). This high precision enabled the application to 72 entirely sequenced Gram-negative bacteria. We found over 164 previously uncharacterized TMB proteins at high confidence. Database searches did not implicate any of these proteins with membranes. We challenge that the vast majority of our 164 predictions will eventually be verified experimentally. All proteome predictions and the PROFtmb prediction method are available at http://www.rostlab.org/ services/PROFtmb/.

We develop a protocol for estimating the free energy difference between different conformations of the same polypeptide chain. The conformational free energy evaluation combines the CHARMM force field with a continuum treatment of the solvent. In almost all cases studied, experimentally determined structures are predicted to be more stable than misfolded "decoys." This is due in part to the fact that the Coulomb energy of the native protein is consistently lower than that of the decoys. The solvation free energy generally favors the decoys, although the total electrostatic free energy (sum of Coulomb and solvation terms) favors the native structure. The behavior of the solvation free energy is somewhat counterintuitive and, surprisingly, is not correlated with differences in the burial of polar area between native structures and decoys. Rather. the effect is due to a more favorable charge distribution in the native protein, which, as is discussed, will tend to decrease its interaction with the solvent. Our results thus suggest, in keeping with a number of recent studies, that electrostatic interactions may play an important role in determining the native topology of a folded protein. On this basis, a simplified scoring function is derived that combines a Coulomb term with a hydrophobic contact term. This function performs as well as the more complete free energy evaluation in distinguishing the native structure from misfolded decoys. Its computational efficiency suggests that it can be used in protein structure prediction applications, and that it provides a physically well-defined alternative to statistically derived scoring functions.

Here we carry out an examination of shape complementarity as a criterion in protein- protein docking and binding. Specifically, we examine the quality of shape complementarity as a critical determinant not only in the docking of 26 protein-protein ''bound'' complexed cases, but in particular, of 19 ''unbound'' protein-protein cases, where the structures have been determined sepa- rately. In all cases, entire molecular surfaces are utilized in the docking, with no consideration of the location of the active site, or of particular residues/ atoms in either the receptor or the ligand that participate in the binding. To evaluate the goodness of the strictly geometry-based shape complementar- ity in the docking process as compared to the main favorable and unfavorable energy components, we study systematically a potential correlation be- tween each of these components and the root mean square deviation (RMSD) of the ''unbound'' protein-protein cases. Specifically, we examine the non-polar buried surface area, polar buried sur- face area, buried surface area relating to groups bearing unsatisfied buried charges, and the number of hydrogen bonds in all docked protein-protein interfaces. For these cases, where the two proteins have been crystallized separately, and where entire molecular surfaces are considered without a predefi- nition of the binding site, no correlation is observed. None of these parameters appears to consistently improve on shape complementarity in the docking of unbound molecules. These findings argue that simplicity in the docking process, utilizing geo- metrical shape criteria may capture many of the essential features in protein-protein docking. In particular, they further reinforce the long held no- tion of the importance of molecular surface shape complementarity in the binding, and hence in dock- ing. This is particularly interesting in light of the fact that the structures of the docked pairs have been determined separately, allowing side chains on the surface of the proteins to move relatively freely.

We describe MarkUs, a web server for analysis and comparison of the structural and functional properties of proteins. In contrast to a 'structure in/function out' approach to protein function annotation, the server is designed to be highly interactive and to allow flexibility in the examination of possible functions, suggested either automatically by various similarity measures or specified by a user directly. This is combined with tools that allow a user to assess independently whether or not a suggested function is consistent with the bioinformatic and biophysical properties of a given query structure, further allowing the user to generate testable hypotheses. The server is available at http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:Mark-Us.

SkyLine, a high-throughput homology modeling pipeline tool, detects and models true sequence homologs to a given protein structure. Structures and models are stored in SkyBase with links to computational function annotation, as calculated by MarkUs. The SkyLine/SkyBase/MarkUs technology represents a novel structure-based approach that is more objective and versatile than other protein classification resources. This structure-centric strategy provides a multi-dimensional organization and coverage of protein space at the levels of family, function, and genome. The concept of "modelability", the ability to model sequences on related structures, provides a reliable criterion for membership in a protein family ("leverage") and underlies the unique success of this approach. The overall procedure is illustrated by its application to START domains, which comprise a Biomedical Theme for the Northeast Structural Genomics Consortium as part of the Protein Structure Initiative. START domains are typically involved in the non-vesicular transport of lipids. While 19 experimentally determined structures are available, the family, whose evolutionary hierarchy is not well determined, is highly sequence diverse, and the ligand-binding potential of many family members is unknown. The SkyLine/SkyBase/MarkUs approach provides significant insights and predicts: (1) many more family members (approximately 4,000) than any other resource; (2) the function for a large number of unannotated proteins; (3) instances of START domains in genomes from which they were thought to be absent; and (4) the existence of two types of novel proteins, those containing dual START domain and those containing N-terminal START domains.

The current nonredundant protein sequence database contains over seven million entries and the number of individual functional domains is significantly larger than this value. The vast quantity of data associated with these proteins poses enormous challenges to any attempt at function annotation. Classification of proteins into sequence and structural groups has been widely used as an approach to simplifying the problem. In this article we question such strategies. We describe how the multifunctionality and structural diversity of even closely related proteins confounds efforts to assign function on the basis of overall sequence or structural similarity. Rather, we suggest that strategies that avoid classification may offer a more robust approach to protein function annotation.

Activated KRAS interacts directly and indirectly with numerous downstream effector pathways that are still elusive or poorly characterized. Furthermore, it has become clear over the past few years that the KRAS regulatory gene network (interactome) is highly cell-context dependent and thus tumor specific. We sought to computationally elucidate novel KRAS effectors, modulators, and interactors in Lung Adenocarcinomas (LUAD) and Colon Adenocarcinomas (COAD) by integrating computational and biochemical reverse engineering methods. Specifically, results from ARACNe and MINDy based analysis of KRAS networks were combined with Protein-protein interaction (PPI) predictions based on the PrePPI algorithm. These algorithms detect, respectively, transcriptional, post-translational, and protein-protein interactions respectively. The MARINa/VIPER algorithm was used to analyze the networks resulting from these analyses to identify key modulators and effectors of KRAS activity, including modulators harboring genetic alterations that phenocopy aberrant KRAS activity. In addition, we collaborated with the Jackson (Stanford) and Vidal (Harvard) labs to include high-throughput tandem affinity mass spectrometry data and yeast 2 hybrid screens in these analyses. Validation of computationally inferred effectors, using pooled shRNA silencing assays in primary lung cancer cells isolated from a KrasLSL/p53f/f mouse model of LUAD validated novel genes as likely KRASmut effectors and putative synthetic lethal genes. At least two hairpins against RNF43, E4F1, HMCN1, MYO9A, RHOT2, SETD1B and MAP3K2 scored in this assay. These results suggest that KRAS network analyses using computational methods can identify novel members of the KRAS interactome. Citation Format: Joshua Broyde, David Simpson, David A. Wah, Federico M. Giorgi, Donald Petrey, Mariano J. Alvarez, Antonina Silkov, Alexander Lachmann, David E. Hill, Marc Vidal, Peter Jackson, Barry Honig, Alejandro Sweet-Cordero, Andrea Califano. Systems and structural biology approaches to elucidate new effectors in KRAS mutant tumors. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR13.

The identification of geometric relationships between protein structures offers a powerful approach to predicting the structure and function of proteins. Methods to detect such relationships range from human pattern recognition to a variety of mathematical algorithms. A number of schemes for the classification of protein structure have found widespread use and these implicitly assume the organization of protein structure space into discrete categories. Recently, an alternative view has emerged in which protein fold space is seen as continuous and multidimensional. Significant relationships have been observed between proteins that belong to what have been termed different 'folds'. There has been progress in the use of these relationships in the prediction of protein structure and function.

The widespread use of the original version of GRASP revealed the importance of the visualization of physicochemical and structural properties on the molecular surface. This chapter describes a new version of GRASP that contains many new capabilities. In terms of analysis tools, the most notable new features are sequence and structure analysis and alignment tools and the graphical integration of sequence and structural information. Not all the new GRASP2 could be described here and more capabilities are continually being added. An on-line manual, details on obtaining the software, and technical notes about the program and the Troll software library can be found at the Honig laboratory Web site (http://trantor.bioc.columbia.edu).

We participated in the fold recognition and homology sections of CASP5 using primarily in-house software. The central feature of our structure prediction strategy involved the ability to generate good sequence-to-structure alignments and to quickly transform them into models that could be evaluated both with energy-based methods and manually. The in-house tools we used include: a) HMAP (Hybrid Multidimensional Alignment Profile)-a profile-to-profile alignment method that is derived from sequence-enhanced multiple structure alignments in core regions, and sequence motifs in non-structurally conserved regions. b) NEST-a fast model building program that applies an "artificial evolution" algorithm to construct a model from a given template and alignment. c) GRASP2-a new structure and alignment visualization program incorporating multiple structure superposition and domain database scanning modules. These methods were combined with model evaluation based on all atom and simplified physical-chemical energy functions. All of these methods were under development during CASP5 and consequently a great deal of manual analysis was carried out at each stage of the prediction process. This interactive model building procedure has several advantages and suggests important ways in which our and other methods can be improved, examples of which are provided.

Publisher Summary This chapter describes the new version of Graphical Representation and Analysis of Surface Properties (GRASP), which contains many new capabilities. The widespread use of the original version of GRASP revealed the importance of the visualization of physicochemical and structural properties on the molecular surface. The GRASP program was written a number of years ago; it was the first program specifically designed to display the molecular surface. It also provided several unique analytical tools. For example, electrostatic potentials calculated using the finite-difference Poisson–Boltzmann (FDPB) method could be displayed on the molecular surface. The new version of GRASP has been written using the object-oriented programming language C++, making the algorithms and code easily accessible and modifiable. Also, molecular graphics functionality is carried out using the platform-independent graphics language OpenGL, making it more portable (GRASP2 currently runs on PCs running Microsoft Windows). The surface of a biological macromolecule is the region of the structure with which other molecules can interact. Consequently, the visualization and analysis of surface properties can contribute significantly to the understanding of the physical determinants of protein function, from chemical catalysis to allosteric properties to protein–protein, protein–ligand, and protein–membrane interactions.

The methods of continuum electrostatics are used to calculate the binding free energies of a set of protein-protein complexes including experimentally determined structures as well as other orientations generated by a fast docking algorithm. In the native structures, charged groups that are deeply buried were often found to favor complex formation (relative to isosteric nonpolar groups), whereas in nonnative complexes generated by a geometric docking algorithm, they were equally likely to be stabilizing as destabilizing. These observations were used to design a new filter for screening docked conformations that was applied, in conjunction with a number of geometric filters that assess shape complementarity, to 15 antibody-antigen complexes and 14 enzyme-inhibitor complexes. For the bound docking problem, which is the major focus of this paper, native and near-native solutions were ranked first or second in all but two enzyme-inhibitor complexes. Less success was encountered for antibody-antigen complexes, but in all cases studied, the more complete free energy evaluation was able to identify native and near-native structures. A filter based on the enrichment of tyrosines and tryptophans in antibody binding sites was applied to the antibody-antigen complexes and resulted in a native and near-native solution being ranked first and second in all cases. A clear improvement over previously reported results was obtained for the unbound antibody-antigen examples as well. The algorithm and various filters used in this work are quite efficient and are able to reduce the number of plausible docking orientations to a size small enough so that a final more complete free energy evaluation on the reduced set becomes computationally feasible.

Corrigendum: Structure-based prediction of protein–protein interactions on a genome-wide scale

The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms. To date, structural information has had only limited impact on genome-scale efforts to predict protein–protein interactions (PPIs). A new algorithm, PrePPI, will be introduced that combines structural information with nonstructural clues and that is comparable in accuracy to high-throughput experiments. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models and the exploitation of both close and remote geometric relationships between proteins. More generally, the “structural BLAST” approach encapsulated in PrePPI significantly expands the range of application of protein structure in the annotation of protein function.