'Hybrid protein model' for optimally defining 3D protein structure fragments

MOTIVATION: Our aim is to develop a process that automatically defines a repertory of contiguous 3D protein structure fragments and can be used in homology modeling. We present here improvements to the method we introduced previously: the 'hybrid protein model' (de Brevern and Hazout, THEOR: Chem. Acc., 106, 36-47, (2001)) The hybrid protein learns a non-redundant databank encoded in a structural alphabet composed of 16 Protein Blocks (PBs; de Brevern et al., Proteins, 41, 271-287, (2000)). Every local fold is learned by looking for the most similar pattern present in the hybrid protein and modifying it slightly. Finally each position corresponds to a cluster of similar 3D local folds. RESULTS: In this paper, we describe improvements to our method for building an optimal hybrid protein: (i) 'baby training,' which is defined as the introduction of large structure fragments and the progressive reduction in the size of training fragments; and (ii) the deletion of the redundant parts of the hybrid protein. This repertory of contiguous 3D protein structure fragments should be a useful tool for molecular modeling.     

A protein lacking an essential input to its tertiary structure does fold into a compact globule

It is shown by equilibrium ultracentrifugation, velocity sedimentation, and viscometry that an N-truncated structural protein Caf1 (Cafl_13–149) of the Yersinia pestis capsular antigen fiber exists as a monomer in solution and is capable of folding from denatured state into a compact globular state by itself, without involvement of a chaperone or other subunits. This happens despite the fact that in the norm, important information on the tertiary structure of each Caf1 subunit (specifically, completion of its hydrophobic core) is provided by the “donor” segment Ala1-Thr12 of the neighboring fiber subunit.     

MATRAS: A program for protein 3D structure comparison

The recent accumulation of large amounts of 3D structural data warrants a sensitive and automatic method to compare and classify these structures. We developed a web server for comparing protein 3D structures using the program Matras (http://biunit.aist-nara.ac.jp/matras). An advantage of Matras is its structure similarity score, which is defined as the log-odds of the probabilities, similar to Dayhoff's substitution model of amino acids. This score is designed to detect evolutionarily related (homologous) structural similarities. Our web server has three main services. The first one is a pairwise 3D alignment, which is simply align two structures. A user can assign structures by either inputting PDB codes or by uploading PDB format files in the local machine. The second service is a multiple 3D alignment, which compares several protein structures. This program employs the progressive alignment algorithm, in which pairwise 3D alignments are assembled in the proper order. The third service is a 3D library search, which compares one query structure against a large number of library structures. We hope this server provides useful tools for insights into protein 3D structures.     

PROMALS3D: a tool for multiple protein sequence and structure alignments

Although multiple sequence alignments (MSAs) are essential for a wide range of applications from structure modeling to prediction of functional sites, construction of accurate MSAs for distantly related proteins remains a largely unsolved problem. The rapidly increasing database of spatial structures is a valuable source to improve alignment quality. We explore the use of 3D structural information to guide sequence alignments constructed by our MSA program PROMALS. The resulting tool, PROMALS3D, automatically identifies homologs with known 3D structures for the input sequences, derives structural constraints through structure-based alignments and combines them with sequence constraints to construct consistency-based multiple sequence alignments. The output is a consensus alignment that brings together sequence and structural information about input proteins and their homologs. PROMALS3D can also align sequences of multiple input structures, with the output representing a multiple structure-based alignment refined in combination with sequence constraints. The advantage of PROMALS3D is that it gives researchers an easy way to produce high-quality alignments consistent with both sequences and structures of proteins. PROMALS3D outperforms a number of existing methods for constructing multiple sequence or structural alignments using both reference-dependent and reference-independent evaluation methods.     

3D-Jury: a simple approach to improve protein structure predictions

MOTIVATION: Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable sets of models obtained from diverse sources. The resulting protocol should help to improve the quality of structural annotations of novel proteins. Results: The 3D-Jury system generates meta-predictions from sets of models created using variable methods. It is not necessary to know prior characteristics of the methods. The system is able to utilize immediately new components (additional prediction providers). The accuracy of the system is comparable with other well-tuned prediction servers. The algorithm resembles methods of selecting models generated using ab initio folding simulations. It is simple and offers a portable solution to improve the accuracy of other protein structure prediction protocols. AVAILABILITY: The 3D-Jury system is available via the Structure Prediction Meta Server (http://BioInfo.PL/Meta/) to the academic community. SUPPLEMENTARY INFORMATION: 3D-Jury is coupled to the continuous online server evaluation program, LiveBench (http://BioInfo.PL/LiveBench/)     

Motif3D: Relating protein sequence motifs to 3D structure

Motif3D is a web-based protein structure viewer designed to allow sequence motifs, and in particular those contained in the fingerprints of the PRINTS database, to be visualised on three-dimensional (3D) structures. Additional functionality is provided for the rhodopsin-like G protein-coupled receptors, enabling fingerprint motifs of any of the receptors in this family to be mapped onto the single structure available, that of bovine rhodopsin. Motif3D can be used via the web interface available at: http://www.bioinf.man.ac.uk/dbbrowser/motif3d/motif3d.html.     

I-TASSER server for protein 3D structure prediction

Background  

Prediction of 3-dimensional protein structures from amino acid sequences represents one of the most important problems in computational structural biology. The community-wide Critical Assessment of Structure Prediction (CASP) experiments have been designed to obtain an objective assessment of the state-of-the-art of the field, where I-TASSER was ranked as the best method in the server section of the recent 7th CASP experiment. Our laboratory has since then received numerous requests about the public availability of the I-TASSER algorithm and the usage of the I-TASSER predictions.     

MUFOLD: A new solution for protein 3D structure prediction

There have been steady improvements in protein structure prediction during the past 2 decades. However, current methods are still far from consistently predicting structural models accurately with computing power accessible to common users. Toward achieving more accurate and efficient structure prediction, we developed a number of novel methods and integrated them into a software package, MUFOLD. First, a systematic protocol was developed to identify useful templates and fragments from Protein Data Bank for a given target protein. Then, an efficient process was applied for iterative coarse‐grain model generation and evaluation at the Cα or backbone level. In this process, we construct models using interresidue spatial restraints derived from alignments by multidimensional scaling, evaluate and select models through clustering and static scoring functions, and iteratively improve the selected models by integrating spatial restraints and previous models. Finally, the full‐atom models were evaluated using molecular dynamics simulations based on structural changes under simulated heating. We have continuously improved the performance of MUFOLD by using a benchmark of 200 proteins from the Astral database, where no template with >25% sequence identity to any target protein is included. The average root‐mean‐square deviation of the best models from the native structures is 4.28 Å, which shows significant and systematic improvement over our previous methods. The computing time of MUFOLD is much shorter than many other tools, such as Rosetta. MUFOLD demonstrated some success in the 2008 community‐wide experiment for protein structure prediction CASP8. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

A protein with missing information about its tertiary structure folds into the compact globular structure

It has been shown by the methods of hydrodynamics (equilibrium ultracentrifugation, velocity sedimentation, intrinsic viscosity) that a fragment of the structural protein Cafl (Cafl 13.149) from the pili-like fibril Yersinia pestis is in the monomeric state and is capable of forming the compact ternary structure spontaneously, without the involvement of chaperone or other subunits. This happens despite the fact that some information about the ternary structure of this protein is provided in fibrils by the "sticky" segment Alal-Thr12 of the neighboring subunit. As previously shown, it is this segment of another molecule in the norm that is an important element of the ternary structure of the subunit itself since it interacts with the acceptor cleft and participates in the formation of the hydrophobic core.     

Assessing a novel approach for predicting local 3D protein structures from sequence

We developed a novel approach for predicting local protein structure from sequence. It relies on the Hybrid Protein Model (HPM), an unsupervised clustering method we previously developed. This model learns three-dimensional protein fragments encoded into a structural alphabet of 16 protein blocks (PBs). Here, we focused on 11-residue fragments encoded as a series of seven PBs and used HPM to cluster them according to their local similarities. We thus built a library of 120 overlapping prototypes (mean fragments from each cluster), with good three-dimensional local approximation, i.e., a mean accuracy of 1.61 A Calpha root-mean-square distance. Our prediction method is intended to optimize the exploitation of the sequence-structure relations deduced from this library of long protein fragments. This was achieved by setting up a system of 120 experts, each defined by logistic regression to optimize the discrimination from sequence of a given prototype relative to the others. For a target sequence window, the experts computed probabilities of sequence-structure compatibility for the prototypes and ranked them, proposing the top scorers as structural candidates. Predictions were defined as successful when a prototype <2.5 A from the true local structure was found among those proposed. Our strategy yielded a prediction rate of 51.2% for an average of 4.2 candidates per sequence window. We also proposed a confidence index to estimate prediction quality. Our approach predicts from sequence alone and will thus provide valuable information for proteins without structural homologs. Candidates will also contribute to global structure prediction by fragment assembly.     

Improved hybrid optimization algorithm for 3D protein structure prediction

Diuron, a chlorine-substituted dimethyl herbicide, is widely used in agriculture. Though the degradation of diuron in water has been studied much with experiments, little is known about the detailed degradation mechanism from the molecular level. In this work, the degradation mechanisms for OH-induced reactions of diuron in water phase are investigated at the MPWB1K/6–311+G(3df,2p)//MPWB1K/6–31+G(d,p) level with polarizable continuum model (PCM) calculation. Three reaction types including H-atom abstraction, addition, and substitution are identified. For H-atom abstraction reactions, the calculation results show that the reaction abstracting H atom from the methyl group has the lowest energy barrier; the potential barrier of ortho- H (H1’) abstraction is higher than the meta- H abstraction, and the reason is possibly that part of the potential energy is to overcome the side chain torsion for the H1’ abstraction reaction. For addition pathways, the ortho- site (C (2) atom) is the most favorable site that OH may first attack; the potential barriers for OH additions to the ortho- sites (pathways R7 and R8) and the chloro-substituted para- site (R10) are lower than other sites, indicating the ortho- and para- sites are more favorable to be attacked, matching well with the -NHCO- group as an ortho-para directing group.

Fragment-HMM: a new approach to protein structure prediction

We designed a simple position-specific hidden Markov model to predict protein structure. Our new framework naturally repeats itself to converge to a final target, conglomerating fragment assembly, clustering, target selection, refinement, and consensus, all in one process. Our initial implementation of this theory converges to within 6 A of the native structures for 100% of decoys on all six standard benchmark proteins used in ROSETTA (discussed by Simons and colleagues in a recent paper), which achieved only 14%-94% for the same data. The qualities of the best decoys and the final decoys our theory converges to are also notably better.     

RNA and protein 3D structure modeling: similarities and differences

In analogy to proteins, the function of RNA depends on its structure and dynamics, which are encoded in the linear sequence. While there are numerous methods for computational prediction of protein 3D structure from sequence, there have been very few such methods for RNA. This review discusses template-based and template-free approaches for macromolecular structure prediction, with special emphasis on comparison between the already tried-and-tested methods for protein structure modeling and the very recently developed “protein-like” modeling methods for RNA. We highlight analogies between many successful methods for modeling of these two types of biological macromolecules and argue that RNA 3D structure can be modeled using “protein-like” methodology. We also highlight the areas where the differences between RNA and proteins require the development of RNA-specific solutions.     

Accelerating screening of 3D protein data with a graph theoretical approach

MOTIVATION: The Dictionary of Interfaces in Proteins (DIP) is a database collecting the 3D structure of interacting parts of proteins that are called patches. It serves as a repository, in which patches similar to given query patches can be found. The computation of the similarity of two patches is time consuming and traversing the entire DIP requires some hours. In this work we address the question of how the patches similar to a given query can be identified by scanning only a small part of DIP. The answer to this question requires the investigation of the distribution of the similarity of patches. RESULTS: The score values describing the similarity of two patches can roughly be divided into three ranges that correspond to different levels of spatial similarity. Interestingly, the two iso-score lines separating the three classes can be determined by two different approaches. Applying a concept of the theory of random graphs reveals significant structural properties of the data in DIP. These can be used to accelerate scanning the DIP for patches similar to a given query. Searches for very similar patches could be accelerated by a factor of more than 25. Patches with a medium similarity could be found 10 times faster than by brute-force search.     

Enlarged FAMSBASE: protein 3D structure models of genome sequences for 41 species

Enlarged FAMSBASE is a relational database of comparative protein structure models for the whole genome of 41 species, presented in the GTOP database. The models are calculated by Full Automatic Modeling System (FAMS). Enlarged FAMSBASE provides a wide range of query keys, such as name of ORF (open reading frame), ORF keywords, Protein Data Bank (PDB) ID, PDB heterogen atoms and sequence similarity. Heterogen atoms in PDB include cofactors, ligands and other factors that interact with proteins, and are a good starting point for analyzing interactions between proteins and other molecules. The data may also work as a template for drug design. The present number of ORFs with protein 3D models in FAMSBASE is 183 805, and the database includes an average of three models for each ORF. FAMSBASE is available at http://famsbase.bio.nagoya-u.ac.jp/famsbase/.     

HYPLOSP: a knowledge-based approach to protein local structure prediction

Local structure prediction can facilitate ab initio structure prediction, protein threading, and remote homology detection. However, the accuracy of existing methods is limited. In this paper, we propose a knowledge-based prediction method that assigns a measure called the local match rate to each position of an amino acid sequence to estimate the confidence of our method. Empirically, the accuracy of the method correlates positively with the local match rate; therefore, we employ it to predict the local structures of positions with a high local match rate. For positions with a low local match rate, we propose a neural network prediction method. To better utilize the knowledge-based and neural network methods, we design a hybrid prediction method, HYPLOSP (HYbrid method to Protein LOcal Structure Prediction) that combines both methods. To evaluate the performance of the proposed methods, we first perform cross-validation experiments by applying our knowledge-based method, a neural network method, and HYPLOSP to a large dataset of 3,925 protein chains. We test our methods extensively on three different structural alphabets and evaluate their performance by two widely used criteria, Maximum Deviation of backbone torsion Angle (MDA) and Q(N), which is similar to Q(3) in secondary structure prediction. We then compare HYPLOSP with three previous studies using a dataset of 56 new protein chains. HYPLOSP shows promising results in terms of MDA and Q(N) accuracy and demonstrates its alphabet-independent capability.     

ABACUS, a direct method for protein NMR structure computation via assembly of fragments

The ABACUS algorithm obtains the protein NMR structure from unassigned NOESY distance restraints. ABACUS works as an integrated approach that uses the complete set of available NMR experimental information in parallel and yields spin system typing, NOE spin pair identities, sequence specific resonance assignments, and protein structure, all at once. The protocol starts from unassigned molecular fragments (including single amino acid spin systems) derived from triple-resonance (1)H/(13)C/(15)N NMR experiments. Identifications of connected spin systems and NOEs precede the full sequence specific resonance assignments. The latter are obtained iteratively via Monte Carlo-Metropolis and/or probabilistic sequence selections, molecular dynamics structure computation and BACUS filtering (A. Grishaev and M. Llinás, J Biomol NMR 2004;28:1-10). ABACUS starts from scratch, without the requirement of an initial approximate structure, and improves iteratively the NOE identities in a self-consistent fashion. The procedure was run as a blind test on data recorded on mth1743, a 70-amino acid genomic protein from M. thermoautotrophicum. It converges to a structure in ca. 15 cycles of computation on a 3-GHz processor PC. The calculated structures are very similar to the ones obtained via conventional methods (1.22 A backbone RMSD). The success of ABACUS on mth1743 further validates BACUS as a NOESY identification protocol.