Protein structure discovery: A software package to computer proteomics tasks (Review)

A software information system called Protein Structure Discovery was developed. The system can be used to solve a wide range of tasks in the field of computer proteomics, including prediction of function, structure, and immune properties of proteins. A special section of the system allows the evaluation of quantitative and qualitative effects of mutations on the structural and functional properties of proteins. There are 19 different programs integrated into the system, including: PDBSite, a database of protein functional sites; PDBSiteScan, a program to predict functional sites in three-dimensional structures of proteins; and a Web-ProAnalyst program to quantitatively analysis the structure-activity relationships of proteins. The Protein Structure Discovery has a Web interface and is available for users via the Internet (http://www-bionet.sscc.ru/psd/). For example, the binding sites of zinc ion and ADP showed a high stability of the method to errors in the reconstruction of spatial structures of proteins in the recognition of functional sites in model structures.     

An empirical approach for structure-based prediction of carbohydrate-binding sites on proteins

A computer program system was developed to predict carbohydrate-binding sites on three-dimensional (3D) protein structures. The programs search for binding sites by referring to the empirical rules derived from the known 3D structures of carbohydrate-protein complexes. A total of 80 non-redundant carbohydrate-protein complex structures were selected from the Protein Data Bank for the empirical rule construction. The performance of the prediction system was tested on 50 known complex structures to determine whether the system could detect the known binding sites. The known monosaccharide-binding sites were detected among the best three predictions in 59% of the cases, which covered 69% of the polysaccharide-binding sites in the target proteins, when the performance was evaluated by the overlap between residue patches of predicted and known binding sites.     

HotPatch: a statistical approach to finding biologically relevant features on protein surfaces

We describe a fully automated algorithm for finding functional sites on protein structures. Our method finds surface patches of unusual physicochemical properties on protein structures, and estimates the patches' probability of overlapping functional sites. Other methods for predicting the locations of specific types of functional sites exist, but in previous analyses, it has been difficult to compare methods when they are applied to different types of sites. Thus, we introduce a new statistical framework that enables rigorous comparisons of the usefulness of different physicochemical properties for predicting virtually any kind of functional site. The program's statistical models were trained for 11 individual properties (electrostatics, concavity, hydrophobicity, etc.) and for 15 neural network combination properties, all optimized and tested on 15 diverse protein functions. To simulate what to expect if the program were run on proteins of unknown function, as might arise from structural genomics, we tested it on 618 proteins of diverse mixed functions. In the higher-scoring top half of all predictions, a functional residue could typically be found within the first 1.7 residues chosen at random. The program may or may not use partial information about the protein's function type as an input, depending on which statistical model the user chooses to employ. If function type is used as an additional constraint, prediction accuracy usually increases, and is particularly good for enzymes, DNA-interacting sites, and oligomeric interfaces. The program can be accessed online (at http://hotpatch.mbi.ucla.edu).     

ProSAT: functional annotation of protein 3D structures

ProSAT (for Protein Structure Annotation Tool) is a tool to facilitate interactive visualization of non-structure-based functional annotations in protein 3D structures. It performs automated mapping of the functional annotations onto the protein structure and allows functional sites to be readily identified upon visualization. The current version of ProSAT can be applied to large datasets of protein structures for fast visual identification of active and other functional sites derived from the SwissProt and Prosite databases.     

Protein structure prediction: making AWSEM AWSEM‐ER by adding evolutionary restraints

Protein sequences have evolved to fold into functional structures, resulting in families of diverse protein sequences that all share the same overall fold. One can harness protein family sequence data to infer likely contacts between pairs of residues. In the current study, we combine this kind of inference from coevolutionary information with a coarse‐grained protein force field ordinarily used with single sequence input, the Associative memory, Water mediated, Structure and Energy Model (AWSEM), to achieve improved structure prediction. The resulting Associative memory, Water mediated, Structure and Energy Model with Evolutionary Restraints (AWSEM‐ER) yields a significant improvement in the quality of protein structure prediction over the single sequence prediction from AWSEM when a sufficiently large number of homologous sequences are available. Free energy landscape analysis shows that the addition of the evolutionary term shifts the free energy minimum to more native‐like structures, which explains the improvement in the quality of structures when performing predictions using simulated annealing. Simulations using AWSEM without coevolutionary information have proved useful in elucidating not only protein folding behavior, but also mechanisms of protein function. The success of AWSEM‐ER in de novo structure prediction suggests that the enhanced model opens the door to functional studies of proteins even when no experimentally solved structures are available.     

FLORA: A Novel Method to Predict Protein Function from Structure in Diverse Superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues.     

Identification of the ligand binding sites on the molecular surface of proteins

Identification of protein biochemical functions based on their three-dimensional structures is now required in the post-genome-sequencing era. Ligand binding is one of the major biochemical functions of proteins, and thus the identification of ligands and their binding sites is the starting point for the function identification. Previously we reported our first trial on structure-based function prediction, based on the similarity searches of molecular surfaces against the functional site database. Here we describe the extension of our first trial by expanding the search database to whole heteroatom binding sites appearing within the Protein Data Bank (PDB) with the new analysis protocol. In addition, we have determined the similarity threshold line, by using 10 structure pairs with solved free and complex structures. Finally, we extensively applied our method to newly determined hypothetical proteins, including some without annotations, and evaluated the performance of our methods.     

Prediction of in-vivo modification sites of proteins from their primary structures

In order to make better use of the information contained in rapidly expanding amino acid sequence data, a new method to predict various modification sites of proteins from their primary structures is presented. It is also applicable to the prediction of other functional sites in proteins. Here we show the examples of N-glycosylation and serine/threonine phosphorylation sites. The method is essentially an elaboration of consensus sequence pattern matching based on stepwise discriminant analysis. The occurring amino acids near a potential modification site are represented by six numerical values which reflect various properties of amino acids. Longer-range effects around these sites are also considered. The stepwise procedure enabled us to automatically select effective features for discrimination. A computer program with our method first identifies potential modification sites by a sequence pattern, NX(S/T) for N-glycosylation or (S/T) for phosphorylation, and then decides by discriminant analysis whether a potential site is likely to be a true modification site. The prediction accuracy in the second step of discrimination was about 60% for glycosylation sites and about 80% for phosphorylation sites.     

Prediction of interaction sites from apo 3D structures when the holo conformation is different

The predictability of catalytic and binding sites from apo structures is addressed for proteins that undergo significant conformational change upon binding. Theoretical microscopic titration curves (THEMATICS), an electrostatics-based method for the prediction of functional sites, is performed on a test set of 24 proteins with both apo and holo structures available. For 23 of these 24 proteins (96%), THEMATICS predicts the correct catalytic or binding site for both the apo and holo forms. For only one of the 24 proteins, THEMATICS makes the correct prediction for the holo structure but fails for the apo structure. The metrics used by THEMATICS to identify functional residues generally are larger in absolute value for the functional residues in the holo forms compared to the corresponding residues in the apo forms. However, even in the apo forms, these identifying metrics are still statistically significantly larger for functional residues than for residues not involved in catalysis or binding. This indicates that some of the unusual electrostatic properties of functional residues are preserved in the apo conformation. Evidence is presented that certain residues immediately surrounding the active catalytic and binding residues impart functionally important chemical and electrostatic properties to the active residues. At least parts of these microenvironments exist in the unbound conformations, such that THEMATICS is able to distinguish the functional residues even in the apo structures.     

Length-dependent prediction of protein intrinsic disorder

Background  

Due to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (≤30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions.     

Are predicted structures good enough to preserve functional sites?

BACKGROUND: A principal goal of structure prediction is the elucidation of function. We have studied the ability of computed models to preserve the microenvironments of functional sites. In particular, 653 model structures of a calcium-binding protein (generated using an ab initio folding protocol) were analyzed, and the degree to which calcium-binding sites were recognizable was assessed. RESULTS: While some model structures preserve the calcium-binding microenvironments, many others, including some with low root mean square deviations (rmsds) from the crystal structure of the native protein, do not. There is a very weak correlation between the overall rmsd of a structure and the preservation of calcium-binding sites. Only when the quality of the model structure is high (rmsd less than 2 A for atoms in the 7 A local neighborhood around calcium) does the modeling of the binding sites become reliable. CONCLUSIONS: Protein structure prediction methods need to be assessed in terms of their preservation of functional sites. High-resolution structures are necessary for identifying binding sites such as calcium-binding sites.     

Prediction of loop regions in protein sequence

We suggest an algorithm that inputs a protein sequence and outputs a decomposition of the protein chain into a regular part including secondary structures and a nonregular part corresponding to loop regions. We have analyzed loop regions in a protein dataset of 3,769 globular domains and defined the optimal parameters for this prediction: the threshold between regular and nonregular regions and the optimal window size for averaging procedures using the scale of the expected number of contacts in a globular state and entropy scale as the number of degrees of freedom for the angles phi, psi, and chi for each amino acid. Comparison with known methods demonstrates that our method gives the same results as the well-known ALB method based on physical properties of amino acids (the percentage of true predictions is 64% against 66%), and worse prediction for regular and nonregular regions than PSIPRED (Protein Structure Prediction Server) without alignment of homologous proteins (the percentage of true predictions is 73%). The potential advantage of the suggested approach is that the predicted set of loops can be used to find patterns of rigid and flexible loops as possible candidates to play a structure/function role as well as a role of antigenic determinants.     

Phosphate binding sites identification in protein structures

Nearly half of known protein structures interact with phosphate-containing ligands, such as nucleotides and other cofactors. Many methods have been developed for the identification of metal ions-binding sites and some for bigger ligands such as carbohydrates, but none is yet available for the prediction of phosphate-binding sites. Here we describe Pfinder, a method that predicts binding sites for phosphate groups, both in the form of ions or as parts of other non-peptide ligands, in proteins of known structure. Pfinder uses the Query3D local structural comparison algorithm to scan a protein structure for the presence of a number of structural motifs identified for their ability to bind the phosphate chemical group. Pfinder has been tested on a data set of 52 proteins for which both the apo and holo forms were available. We obtained at least one correct prediction in 63% of the holo structures and in 62% of the apo. The ability of Pfinder to recognize a phosphate-binding site in unbound protein structures makes it an ideal tool for functional annotation and for complementing docking and drug design methods. The Pfinder program is available at http://pdbfun.uniroma2.it/pfinder.     

Prediction of enzyme function based on 3D templates of evolutionarily important amino acids

Background  

Structural genomics projects such as the Protein Structure Initiative (PSI) yield many new structures, but often these have no known molecular functions. One approach to recover this information is to use 3D templates – structure-function motifs that consist of a few functionally critical amino acids and may suggest functional similarity when geometrically matched to other structures. Since experimentally determined functional sites are not common enough to define 3D templates on a large scale, this work tests a computational strategy to select relevant residues for 3D templates.     

Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM

Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.     

Recognizing complex, asymmetric functional sites in protein structures using a Bayesian scoring function

The increase in known three-dimensional protein structures enables us to build statistical profiles of important functional sites in protein molecules. These profiles can then be used to recognize sites in large-scale automated annotations of new protein structures. We report an improved FEATURE system which recognizes functional sites in protein structures. FEATURE defines multi-level physico-chemical properties and recognizes sites based on the spatial distribution of these properties in the sites' microenvironments. It uses a Bayesian scoring function to compare a query region with the statistical profile built from known examples of sites and control nonsites. We have previously shown that FEATURE can accurately recognize calcium-binding sites and have reported interesting results scanning for calcium-binding sites in the entire Protein Data Bank. Here we report the ability of the improved FEATURE to characterize and recognize geometrically complex and asymmetric sites such as ATP-binding sites and disulfide bond-forming sites. FEATURE does not rely on conserved residues or conserved residue geometry of the sites. We also demonstrate that, in the absence of a statistical profile of the sites, FEATURE can use an artificially constructed profile based on a priori knowledge to recognize the sites in new structures, using redoxin active sites as an example.     

A Physical Approach to Protein Structure Prediction

We describe our global optimization method called Stochastic Perturbation with Soft Constraints (SPSC), which uses information from known proteins to predict secondary structure, but not in the tertiary structure predictions or in generating the terms of the physics-based energy function. Our approach is also characterized by the use of an all atom energy function that includes a novel hydrophobic solvation function derived from experiments that shows promising ability for energy discrimination against misfolded structures. We present the results obtained using our SPSC method and energy function for blind prediction in the 4^th Critical Assessment of Techniques for Protein Structure Prediction competition, and show that our approach is more effective on targets for which less information from known proteins is available. In fact our SPSC method produced the best prediction for one of the most difficult targets of the competition, a new fold protein of 240 amino acids.     

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.     

The Protein Structure Initiative Structural Biology Knowledgebase Technology Portal: a structural biology web resource

The Technology Portal of the Protein Structure Initiative Structural Biology Knowledgebase (PSI SBKB; http://technology.sbkb.org/portal/ ) is a web resource providing information about methods and tools that can be used to relieve bottlenecks in many areas of protein production and structural biology research. Several useful features are available on the web site, including multiple ways to search the database of over 250 technological advances, a link to videos of methods on YouTube, and access to a technology forum where scientists can connect, ask questions, get news, and develop collaborations. The Technology Portal is a component of the PSI SBKB ( http://sbkb.org ), which presents integrated genomic, structural, and functional information for all protein sequence targets selected by the Protein Structure Initiative. Created in collaboration with the Nature Publishing Group, the SBKB offers an array of resources for structural biologists, such as a research library, editorials about new research advances, a featured biological system each month, and a functional sleuth for searching protein structures of unknown function. An overview of the various features and examples of user searches highlight the information, tools, and avenues for scientific interaction available through the Technology Portal.