Automated protein model building combined with iterative structure refinement.

In protein crystallography, much time and effort are often required to trace an initial model from an interpretable electron density map and to refine it until it best agrees with the crystallographic data. Here, we present a method to build and refine a protein model automatically and without user intervention, starting from diffraction data extending to resolution higher than 2.3 A and reasonable estimates of crystallographic phases. The method is based on an iterative procedure that describes the electron density map as a set of unconnected atoms and then searches for protein-like patterns. Automatic pattern recognition (model building) combined with refinement, allows a structural model to be obtained reliably within a few CPU hours. We demonstrate the power of the method with examples of a few recently solved structures.     

DINAMO: interactive protein alignment and model building.

MOTIVATION: To facilitate the process of structure prediction by both comparative modeling and fold recognition, we describe DINAMO, an interactive protein alignment building and model evaluation tool that dynamically couples a multiple sequence alignment editor to a molecular graphics display. DINAMO allows the user to optimize the alignment and model to satisfy the known heuristics of protein structure by means of a set of analysis tools. The analysis tools return information to both the alignment editor and graphics model in the form of visual cues (color, shape), allowing for rapid evaluation. Several analysis tools may be employed, including residue conservation, residue properties (charge, hydrophobicity, volume), residue environmental preference, and secondary structure propensity. RESULTS: We demonstrate DINAMO by building a model for submission in the 3rd annual Critical Assessment of Techniques for Protein Structure Prediction (CASP3) contest. AVAILABILITY: DINAMO is freely available as a local application or Web-based Java applet at http://tito.ucsc.edu/dinamo     

Comparative modeling for protein structure prediction

With the progression of structural genomics projects, comparative modeling remains an increasingly important method of choice. It helps to bridge the gap between the available sequence and structure information by providing reliable and accurate protein models. Comparative modeling based on more than 30% sequence identity is now approaching its natural template-based limits and further improvements require the development of effective refinement techniques capable of driving models toward native structure. For difficult targets, for which the most significant progress in recent years has been observed, optimal template selection and alignment accuracy are still the major problems.     

HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

Sequence-based protein function and structure prediction depends crucially on sequence-search sensitivity and accuracy of the resulting sequence alignments. We present an open-source, general-purpose tool that represents both query and database sequences by profile hidden Markov models (HMMs): 'HMM-HMM-based lightning-fast iterative sequence search' (HHblits; http://toolkit.genzentrum.lmu.de/hhblits/). Compared to the sequence-search tool PSI-BLAST, HHblits is faster owing to its discretized-profile prefilter, has 50-100% higher sensitivity and generates more accurate alignments.     

Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments

MOTIVATION: Two major bottlenecks in advancing comparative protein structure modeling are the efficient combination of multiple template structures and the generation of a correct input target-template alignment. RESULTS: A novel method, Multiple Mapping Method with Multiple Templates (M4T) is introduced that implements an algorithm to automatically select and combine Multiple Template structures (MT) and an alignment optimization protocol (Multiple Mapping Method, MMM). The MT module of M4T selects and combines multiple template structures through an iterative clustering approach that takes into account the 'unique' contribution of each template, their sequence similarity among themselves and to the target sequence, and their experimental resolution. MMM is a sequence-to-structure alignment method that optimally combines alternatively aligned regions according to their fit in the structural environment of the template structure. The resulting M4T alignment is used as input to a comparative modeling module. The performance of M4T has been benchmarked on CASP6 comparative modeling target sequences and on a larger independent test set, and showed favorable performance to current state of the art methods.     

Database searching by flexible protein structure alignment

We have recently developed a flexible protein structure alignment program (FATCAT) that identifies structural similarity, at the same time accounting for flexibility of protein structures. One of the most important applications of a structure alignment method is to aid in functional annotations by identifying similar structures in large structural databases. However, none of the flexible structure alignment methods were applied in this task because of a lack of significance estimation of flexible alignments. In this paper, we developed an estimate of the statistical significance of FATCAT alignment score, allowing us to use it as a database-searching tool. The results reported here show that (1) the distribution of the similarity score of FATCAT alignment between two unrelated protein structures follows the extreme value distribution (EVD), adding one more example to the current collection of EVDs of sequence and structure similarities; (2) introducing flexibility into structure comparison only slightly influences the sensitivity and specificity of identifying similar structures; and (3) the overall performance of FATCAT as a database searching tool is comparable to that of the widely used rigid-body structure comparison programs DALI and CE. Two examples illustrating the advantages of using flexible structure alignments in database searching are also presented. The conformational flexibilities that were detected in the first example may be involved with substrate specificity, and the conformational flexibilities detected in the second example may reflect the evolution of structures by block building.     

A rapid protein structure alignment algorithm based on a text modeling technique

Structural alignment of proteins is widely used in various fields of structural biology. In order to further improve the quality of alignment, we describe an algorithm for structural alignment based on text modelling techniques. The technique firstly superimposes secondary structure elements of two proteins and then, models the 3D-structure of the protein in a sequence of alphabets. These sequences are utilized by a step-by-step sequence alignment procedure to align two protein structures. A benchmark test was organized on a set of 200 non-homologous proteins to evaluate the program and compare it to state of the art programs, e.g. CE, SAL, TM-align and 3D-BLAST. On average, the results of all-against-all structure comparison by the program have a competitive accuracy with CE and TM-align where the algorithm has a high running speed like 3D-BLAST.     

Multiple protein structure alignment.

A method was developed to compare protein structures and to combine them into a multiple structure consensus. Previous methods of multiple structure comparison have only concatenated pairwise alignments or produced a consensus structure by averaging coordinate sets. The current method is a fusion of the fast structure comparison program SSAP and the multiple sequence alignment program MULTAL. As in MULTAL, structures are progressively combined, producing intermediate consensus structures that are compared directly to each other and all remaining single structures. This leads to a hierarchic "condensation," continually evaluated in the light of the emerging conserved core regions. Following the SSAP approach, all interatomic vectors were retained with well-conserved regions distinguished by coherent vector bundles (the structural equivalent of a conserved sequence position). Each bundle of vectors is summarized by a resultant, whereas vector coherence is captured in an error term, which is the only distinction between conserved and variable positions. Resultant vectors are used directly in the comparison, which is weighted by their error values, giving greater importance to the matching of conserved positions. The resultant vectors and their errors can also be used directly in molecular modeling. Applications of the method were assessed by the quality of the resulting sequence alignments, phylogenetic tree construction, and databank scanning with the consensus. Visual assessment of the structural superpositions and consensus structure for various well-characterized families confirmed that the consensus had identified a reasonable core.     

Refinement of protein structures by iterative comparative modeling and CryoEM density fitting

We developed a method for structure characterization of assembly components by iterative comparative protein structure modeling and fitting into cryo-electron microscopy (cryoEM) density maps. Specifically, we calculate a comparative model of a given component by considering many alternative alignments between the target sequence and a related template structure while optimizing the fit of a model into the corresponding density map. The method relies on the previously developed Moulder protocol that iterates over alignment, model building, and model assessment. The protocol was benchmarked using 20 varied target-template pairs of known structures with less than 30% sequence identity and corresponding simulated density maps at resolutions from 5A to 25A. Relative to the models based on the best existing sequence profile alignment methods, the percentage of C(alpha) atoms that are within 5A of the corresponding C(alpha) atoms in the superposed native structure increases on average from 52% to 66%, which is half-way between the starting models and the models from the best possible alignments (82%). The test also reveals that despite the improvements in the accuracy of the fitness function, this function is still the bottleneck in reducing the remaining errors. To demonstrate the usefulness of the protocol, we applied it to the upper domain of the P8 capsid protein of rice dwarf virus that has been studied by cryoEM at 6.8A. The C(alpha) root-mean-square deviation of the model based on the remotely related template, bluetongue virus VP7, improved from 8.7A to 6.0A, while the best possible model has a C(alpha) RMSD value of 5.3A. Moreover, the resulting model fits better into the cryoEM density map than the initial template structure. The method is being implemented in our program MODELLER for protein structure modeling by satisfaction of spatial restraints and will be applicable to the rapidly increasing number of cryoEM density maps of macromolecular assemblies.     

MatAlign: precise protein structure comparison by matrix alignment

We propose a detailed protein structure alignment method named "MatAlign". It is a two-step algorithm. Firstly, we represent 3D protein structures as 2D distance matrices, and align these matrices by means of dynamic programming in order to find the initially aligned residue pairs. Secondly, we refine the initial alignment iteratively into the optimal one according to an objective scoring function. We compare our method against DALI and CE, which are among the most accurate and the most widely used of the existing structural comparison tools. On the benchmark set of 68 protein structure pairs by Fischer et al., MatAlign provides better alignment results, according to four different criteria, than both DALI and CE in a majority of cases. MatAlign also performs as well in structural database search as DALI does, and much better than CE does. MatAlign is about two to three times faster than DALI, and has about the same speed as CE. The software and the supplementary information for this paper are available at http://xena1.ddns.comp.nus.edu.sg/~genesis/MatAlign/.     

Studies in the assessment of folding quality for protein modeling and structure prediction

A diagnostic for assessing the quality of a fold has been developed to which further criteria can be progressively added. The goal is to create a measure that can follow the status of a protein structure in a simulation or modeling process, when the answer (the experimental structure) is not known in advance, rather than simply reject deliberate misfolds. This places greater emphasis on the need to study, and calibrate against, marginal cases, i.e., unusual native structures, incomplete structures, partially erroneous X-ray structures, good models, poor models, and the effect of cofactors. The first three terms introduced in the diagnostic are appropriate core-forming properties or noncore properties of residues in relation to tertiary structure, appropriate neighboring structure density for each residue in relation to tertiary structure, and secondary structure consistency. While the method emerges as a useful simulation analysis tool, we find a need for further fine-tuning to diminish sensitivity to minor conformational changes that retain essential features of the fold, balanced against the need to obtain a more sensitive response when a conformational change involves less physically meaningful interatomic interactions. This dual utility is difficult to obtain: the investigation highlights some of the issues. Initial attempts to obtain it have led to terms in the diagnostic that are admittedly complex: simplifications must also be explored.     

A holistic approach to protein structure alignment

A method of protein structure comparison developed previously is extended to incorporate other aspects of protein structure in addition to the inter-atomic vectors on which it was originally based. Each additional aspect, which induced hydrogen bonding, solvent exposure, torsional angles and sequence, was introduced separately and evaluated for its ability to improve alignment quality. The components were then combined, suitably weighted, to produce a more holistic comparison method. The method was tested on a group of remotely related beta/alpha type proteins that share a common feature in their overall chain fold. The results indicated that while the original inter-atomic vector component was sufficient to give the correct alignment of most pairs of topologically equivalent proteins, the inclusion of hydrogen bonds, torsion angles and a measure of solvent exposure led to improvements in the more difficult comparisons. Consideration of amino acid properties, including hydrophobicity, had no beneficial effect. The failure of the latter component was not unexpected considering the almost total lack of sequence similarity among the proteins considered.     

A rapid method of protein structure alignment

A reduction in the time required to compare two protein structures has been achieved for a previously developed structure alignment method, by reducing the number of residue pair comparisons which must be performed between the two structures. Subsets of residue pairs are selected by an iterative procedure. Initially, selection is based on similarities in solvent accessible surface areas or torsional angles or a combination of both properties, giving subsets containing approximately 2% of the total number of residue pairs. Using these subsets, a rough comparison of the two structures is generated by the structural alignment program. The information returned from this can be used to identify more accurately topologically equivalent residues in the two proteins, thus enabling a new and much smaller subset (less than 0.2% of the total number of residue pairs) to be selected. The process of iterative refinement of the residue pair subsets is repeated once more, when in 95% of the structure comparisons tested, the correct alignment of the proteins was obtained. Times required to compare the structures using the refined subsets are insignificant compared to the initial comparison, so that considerable increases in speed are possible. The method was tested on two groups of proteins, a set of remotely related alpha/beta nucleotide proteins and the variable and constant domains of the immunoglobulins. Increases in speed ranging from 50-fold to greater than 150-fold were obtained depending on the degree of similarity of the two structures. In some comparisons the alignment was improved due to the reduction in noise obtained by comparing mainly equivalent residues.     

A spectral approach to protein structure alignment

A new intrinsic geometry based on a spectral analysis is used to motivate methods for aligning protein folds. The geometry is induced by the fact that a distance matrix can be scaled so that its eigenvalues are positive. We provide a mathematically rigorous development of the intrinsic geometry underlying our spectral approach and use it to motivate two alignment algorithms. The first uses eigenvalues alone and dynamic programming to quickly compute a fold alignment. Family identification results are reported for the Skolnick40 and Proteus300 data sets. The second algorithm extends our spectral method by iterating between our intrinsic geometry and the 3D geometry of a fold to make high-quality alignments. Results and comparisons are reported for several difficult fold alignments. The second algorithm's ability to correctly identify fold families in the Skolnick40 and Proteus300 data sets is also established.     

Comparative modeling of the three-dimensional structure of type II antifreeze protein.

Type II antifreeze proteins (AFP), which inhibit the growth of seed ice crystals in the blood of certain fishes (sea raven, herring, and smelt), are the largest known fish AFPs and the only class for which detailed structural information is not yet available. However, a sequence homology has been recognized between these proteins and the carbohydrate recognition domain of C-type lectins. The structure of this domain from rat mannose-binding protein (MBP-A) has been solved by X-ray crystallography (Weis WI, Drickamer K, Hendrickson WA, 1992, Nature 360:127-134) and provided the coordinates for constructing the three-dimensional model of the 129-amino acid Type II AFP from sea raven, to which it shows 19% sequence identity. Multiple sequence alignments between Type II AFPs, pancreatic stone protein, MBP-A, and as many as 50 carbohydrate-recognition domain sequences from various lectins were performed to determine reliably aligned sequence regions. Successive molecular dynamics and energy minimization calculations were used to relax bond lengths and angles and to identify flexible regions. The derived structure contains two alpha-helices, two beta-sheets, and a high proportion of amino acids in loops and turns. The model is in good agreement with preliminary NMR spectroscopic analyses. It explains the observed differences in calcium binding between sea raven Type II AFP and MBP-A. Furthermore, the model proposes the formation of five disulfide bridges between Cys 7 and Cys 18, Cys 35 and Cys 125, Cys 69 and Cys 100, Cys 89 and Cys 111, and Cys 101 and Cys 117.(ABSTRACT TRUNCATED AT 250 WORDS)     

SPINFAST: using structure alignment profiles to enhance the accuracy and assess the reliability of protein side-chain modeling

We present a novel, knowledge-based method for the side-chain addition step in protein structure modeling. The foundation of the method is a conditional probability equation, which specifies the probability that a side-chain will occupy a specific rotamer state, given a set of evidence about the rotamer states adopted by the side-chains at aligned positions in structurally homologous crystal structures. We demonstrate that our method increases the accuracy of homology model side-chain addition when compared with the widely employed practice of preserving the side-chain conformation from the homology template to the target at conserved residue positions. Furthermore, we demonstrate that our method accurately estimates the probability that the correct rotamer state has been selected. This interesting result implies that our method can be used to understand the reliability of each and every side-chain in a protein homology model.     

SSEP-Domain: protein domain prediction by alignment of secondary structure elements and profiles

MOTIVATION: The prediction of protein domains is a crucial task for functional classification, homology-based structure prediction and structural genomics. In this paper, we present the SSEP-Domain protein domain prediction approach, which is based on the application of secondary structure element alignment (SSEA) and profile-profile alignment (PPA) in combination with InterPro pattern searches. SSEA allows rapid screening for potential domain regions while PPA provides us with the necessary specificity for selecting significant hits. The combination with InterPro patterns allows finding domain regions without solved structural templates if sequence family definitions exist. RESULTS: A preliminary version of SSEP-Domain was ranked among the top-performing domain prediction servers in the CASP 6 and CAFASP 4 experiments. Evaluation of the final version shows further improvement over these results together with a significant speed-up. AVAILABILITY: The server is available at http://www.bio.ifi.lmu.de/SSEP/     

Simple protein model building tool

An interactive protein model building program, named Alpha, running on the Evans & Sutherland PS340, is presented. It has two prominent features: flexible construction and an informative display of the protein model. These characteristics arise from the adoption and analysis of the α-carbon representation of a protein. Although its concept and program are simple. Alpha is a useful tool for investigation of the 3D structure of a protein, whether or not it is elucidated.