Prediction of protein loop conformations using multiscale modeling methods with physical energy scoring functions

Disorder-Order Transitions in Conformational Selection of a Peptide by Ebola Virus Nucleoprotein

This study presents parallel-tempering lattice Monte Carlo simulations based on the side-chain-only (SICHO) model for calculating the conformational landscape of a 28-residue intrinsically disordered peptide extracted from the Ebola virus protein VP35. The central issue is the applicability of the SICHO potential energy function and in general coarse-grained (CG) representations of intermediate resolution for modeling large-scale conformational heterogeneity that includes both folded and unstructured peptide states. Crystallographic data shows that the peptide folds in a 4₁₀-helix-turn-3₁₀-helix topology upon complex formation with the Ebola virus nucleoprotein, whereas in isolation, the peptide transitions to a disordered conformational ensemble as observed in circular dichroism experiments. The simulation reveals a potential of mean force that displays conformational diversity along the helix-forming reaction coordinate consistent with disorder-order transitions, yet unexpectedly the bound topology is poorly sampled, and a population shift to an unstructured state incurs a significant free-energy penalty. Applying an elastic network interpolation model suggests a hybrid binding mechanism through conformational selection of the 4₁₀-helix followed by an induced fit of the 3₁₀-helix. A comparison of the CG model with previously reported all-atom CHARMM-based simulations highlights a lattice-based approach that is computationally fast and with the correct parameterization yields good resolution to modeling conformational plasticity.

New Deep Learning Methods for Protein Loop Modeling

Computational protein structure prediction is a long-standing challenge in bioinformatics. In the process of predicting protein 3D structures, it is common that parts of an experimental structure are missing or parts of a predicted structure need to be remodeled. The process of predicting local protein structures of particular regions is called loop modeling. In this paper, five new loop modeling methods based on machine learning techniques, called NearLooper, ConLooper, ResLooper, HyLooper1, and HyLooper2 are proposed. NearLooper is based on the nearest neighbor technique. ConLooper applies deep convolutional neural networks to predict ${\mathrm{C}}_{{{\alpha }}}$Cα atoms distance matrix as an orientation-independent representation of protein structure. ResLooper uses residual neural networks instead of deep convolutional neural networks. HyLooper1 combines the results of NearLooper and ConLooper while HyLooper2 combines NearLooper and ResLooper. Three commonly used benchmarks for loop modeling are used to compare the performance between these methods and existing state-of-the-art methods. The experiment results show promising performance in which our best method improves existing state-of-the-art methods by 28 and 54 percent of average RMSD on two datasets while being comparable on the other one.

Computational design of structured loops for new protein functions

The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.

The Role of the Active Site Flap in Streptavidin/Biotin Complex Formation

Obtaining a detailed description of how active site flap motion affects substrate or ligand binding will advance structure-based drug design (SBDD) efforts on systems including the kinases, HSP90, HIV protease, ureases, etc. Through this understanding, we will be able to design better inhibitors and better proteins that have desired functions. Herein we address this issue by generating the relevant configurational states of a protein flap on the molecular energy landscape using an approach we call MT_Flex-b and then following this with a procedure to estimate the free energy associated with the motion of the flap region. To illustrate our overall workflow, we explored the free energy changes in the streptavidin/biotin system upon introducing conformational flexibility in loop_3-4 in the biotin unbound ( apo) and bound ( holo) state. The free energy surfaces were created using the Movable Type free energy method, and for further validation, we compared them to potential of mean force (PMF) generated free energy surfaces using MD simulations employing the FF99SBILDN and FF14SB force fields. We also estimated the free energy thermodynamic cycle using an ensemble of closed-like and open-like end states for the ligand unbound and bound states and estimated the binding free energy to be approximately -16.2 kcal/mol (experimental -18.3 kcal/mol). The good agreement between MT_Flex-b in combination with the MT method with experiment and MD simulations supports the effectiveness of our strategy in obtaining unique insights into the motions in proteins that can then be used in a range of biological and biomedical applications.

Principal component analysis in protein tertiary structure prediction

We discuss applicability of principal component analysis (PCA) for protein tertiary structure prediction from amino acid sequence. The algorithm presented in this paper belongs to the category of protein refinement models and involves establishing a low-dimensional space where the sampling (and optimization) is carried out via particle swarm optimizer (PSO). The reduced space is found via PCA performed for a set of low-energy protein models previously found using different optimization techniques. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model. This term is aimed at providing high frequency details in the energy optimization. The goal of this research is to analyze how the dimensionality reduction affects the prediction capability of the PSO procedure. For that purpose, different proteins from the Critical Assessment of Techniques for Protein Structure Prediction experiments were modeled. In all the cases, both the energy of the best decoy and the distance to the native structure have decreased. Our analysis also shows how the predicted backbone structure of native conformation and of alternative low energy states varies with respect to the PCA dimensionality. Generally speaking, the reconstruction can be successfully achieved with 10 principal components and the high frequency term. We also provide a computational analysis of protein energy landscape for the inverse problem of reconstructing structure from the reduced number of principal components, showing that the dimensionality reduction alleviates the ill-posed character of this high-dimensional energy optimization problem. The procedure explained in this paper is very fast and allows testing different PCA expansions. Our results show that PSO improves the energy of the best decoy used in the PCA when the adequate number of PCA terms is considered.

Computational protein structure refinement: Almost there, yet still so far to go

Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.

Molecular Modeling of Transporters: From Low Resolution Cryo-Electron Microscopy Map to Conformational Exploration. The Example of TSPO

This chapter describes a protocol to establish a three-dimensional (3D) model of a protein and to explore its conformational landscape. It combines predictions from up-to-date bioinformatics methods with low-resolution experimental data. It also proposes to examine rapidly the dynamics of the protein using molecular dynamics simulations with a coarse-grained force field. Tools for analyzing these trajectories are suggested as well as those for constructing all-atoms models. Thus, starting from a protein sequence and using free software, the user can get important conformational information, which might improve the knowledge about the protein function.

Clustering and percolation in protein loop structures

Background

High precision protein loop modelling remains a challenge, both in template based and template independent approaches to protein structure prediction.

Method

We introduce the concepts of protein loop clustering and percolation, to develop a quantitative approach to systematically classify the modular building blocks of loops in crystallographic folded proteins. These fragments are all different parameterisations of a unique kink solution to a generalised discrete nonlinear Schrödinger (DNLS) equation. Accordingly, the fragments are also local energy minima of the ensuing energy function.

Results

We show how the loop fragments cover practically all ultrahigh resolution crystallographic protein structures in Protein Data Bank (PDB), with a 0.2 Ångström root-mean-square (RMS) precision. We find that no more than 12 different loop fragments are needed, to describe around 38 % of ultrahigh resolution loops in PDB. But there is also a large number of loop fragments that are either unique, or very rare, and examples of unique fragments are found even in the structure of a myoglobin.

Conclusions

Protein loops are built in a modular fashion. The loops are composed of fragments that can be modelled by the kink of the DNLS equation. The majority of loop fragments are also common, which are shared by many proteins. These common fragments are probably important for supporting the overall protein conformation. But there are also several fragments that are either unique to a given protein, or very rare. Such fragments are probably related to the function of the protein. Furthermore, we have found that the amino acid sequence does not determine the structure in a unique fashion. There are many examples of loop fragments with an identical amino acid sequence, but with a very different structure.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-015-0049-x) contains supplementary material, which is available to authorized users.

Hamiltonian replica-exchange simulations with adaptive biasing of peptide backbone and side chain dihedral angles

A Hamiltonian Replica-Exchange Molecular Dynamics (REMD) simulation method has been developed that employs a two-dimensional backbone and one-dimensional side chain biasing potential specifically to promote conformational transitions in peptides. To exploit the replica framework optimally, the level of the biasing potential in each replica was appropriately adapted during the simulations. This resulted in both high exchange rates between neighboring replicas and improved occupancy/flow of all conformers in each replica. The performance of the approach was tested on several peptide and protein systems and compared with regular MD simulations and previous REMD studies. Improved sampling of relevant conformational states was observed for unrestrained protein and peptide folding simulations as well as for refinement of a loop structure with restricted mobility of loop flanking protein regions.

A homology/ab initio hybrid algorithm for sampling near-native protein conformations

One of the major challenges for protein tertiary structure prediction strategies is the quality of conformational sampling algorithms, which can effectively and readily search the protein fold space to generate near-native conformations. In an effort to advance the field by making the best use of available homology as well as fold recognition approaches along with ab initio folding methods, we have developed Bhageerath-H Strgen, a homology/ab initio hybrid algorithm for protein conformational sampling. The methodology is tested on the benchmark CASP9 dataset of 116 targets. In 93% of the cases, a structure with TM-score ≥ 0.5 is generated in the pool of decoys. Further, the performance of Bhageerath-H Strgen was seen to be efficient in comparison with different decoy generation methods. The algorithm is web enabled as Bhageerath-H Strgen web tool which is made freely accessible for protein decoy generation (http://www.scfbio-iitd.res.in/software/Bhageerath-HStrgen1.jsp).

Conformational sampling in template-free protein loop structure modeling: an overview

Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a "mini protein folding problem" under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increasing number of known structures available in PDB. This mini review provides an overview on the recent computational approaches for loop structure modeling. In particular, we focus on the approaches of sampling loop conformation space, which is a critical step to obtain high resolution models in template-free methods. We review the potential energy functions for loop modeling, loop buildup mechanisms to satisfy geometric constraints, and loop conformation sampling algorithms. The recent loop modeling results are also summarized.

Large loop conformation sampling using the activation relaxation technique, ART-nouveau method

We present an adaptation of the ART-nouveau energy surface sampling method to the problem of loop structure prediction. This method, previously used to study protein folding pathways and peptide aggregation, is well suited to the problem of sampling the conformation space of large loops by targeting probable folding pathways instead of sampling exhaustively that space. The number of sampled conformations needed by ART nouveau to find the global energy minimum for a loop was found to scale linearly with the sequence length of the loop for loops between 8 and about 20 amino acids. Considering the linear scaling dependence of the computation cost on the loop sequence length for sampling new conformations, we estimate the total computational cost of sampling larger loops to scale quadratically compared to the exponential scaling of exhaustive search methods.

Protein Loop Modeling with Optimized Backbone Potential Functions

We represented protein backbone potential as a Fourier series. The parameters of the backbone dihedral potential were initialized to random values and optimized by Monte Carlo simulations so that generated native-like loop decoys had a lower energy than non-native decoys. The low energy regions of the optimized backbone potential were consistent with observed Ramachandran plots derived from crystal structures. The backbone potential was then used for the prediction of loop conformations (OSCAR-loop) combining with the previously described OSCAR force field, which has been shown to be very accurate in side chain modeling. As a result, the accuracy of OSCAR-loop was improved by local energy minimization based on the complete force field. The average accuracies were 0.40, 0.70, 1.10, 2.08, and 3.58 Å for 4, 6, 8, 10, and 12-residue loops, respectively, with each size being represented by 325 to 2809 targets. The accuracy was better than that of other loop modeling algorithms for short loops (<10 residues). For longer loops, the prediction accuracy was improved by concurrently sampling with a fragment-based method, Spanner. OSCAR-loop is available for download at http://sysimm.ifrec.osaka-u.ac.jp/OSCAR/ .

The importance of slow motions for protein functional loops

Loops in proteins that connect secondary structures such as alpha-helix and beta-sheet, are often on the surface and may play a critical role in some functions of a protein. The mobility of loops is central for the motional freedom and flexibility requirements of active-site loops and may play a critical role for some functions. The structures and behaviors of loops have not been studied much in the context of the whole structure and its overall motions, especially how these might be coupled. Here we investigate loop motions by using coarse-grained structures (C(α) atoms only) to solve the motions of the system by applying Lagrange equations with elastic network models to learn about which loops move in an independent fashion and which move in coordination with domain motions, faster and slower, respectively. The normal modes of the system are calculated using eigen-decomposition of the stiffness matrix. The contribution of individual modes and groups of modes is investigated for their effects on all residues in each loop by using Fourier analyses. Our results indicate overall that the motions of functional sets of loops behave in similar ways as the whole structure. But overall only a relatively few loops move in coordination with the dominant slow modes of motion, and these are often closely related to function.

Affinity maturation by semi-rational approaches

Rational engineering methods can be applied with success to optimize physicochemical characteristics of antibodies. Application of in silico analysis and prediction methods to antibody Fv regions can help to find residues affecting antibody-antigen affinity when high-resolution antibody structures or antibody-antigen complex structures are known. In these cases, the identification of residues affecting affinity can facilitate the selection of candidates for guided maturation by PCR using degenerate oligonucleotides. Here, we describe the utilization of a semi-rational approach to enhance the affinity of antibodies by combining in silico and traditional wet lab-based methods.

Comparison between self-guided Langevin dynamics and molecular dynamics simulations for structure refinement of protein loop conformations

This article presents a comparative analysis of two replica-exchange simulation methods for the structure refinement of protein loop conformations, starting from low-resolution predictions. The methods are self-guided Langevin dynamics (SGLD) and molecular dynamics (MD) with a Nosé-Hoover thermostat. We investigated a small dataset of 8- and 12-residue loops, with the shorter loops placed initially from a coarse-grained lattice model and the longer loops from an enumeration assembly method (the Loopy program). The CHARMM22 + CMAP force field with a generalized Born implicit solvent model (molecular-surface parameterized GBSW2) was used to explore conformational space. We also assessed two empirical scoring methods to detect nativelike conformations from decoys: the all-atom distance-scaled ideal-gas reference state (DFIRE-AA) statistical potential and the Rosetta energy function. Among the eight-residue loop targets, SGLD out performed MD in all cases, with a median of 0.48 Å reduction in global root-mean-square deviation (RMSD) of the loop backbone coordinates from the native structure. Among the more challenging 12-residue loop targets, SGLD improved the prediction accuracy over MD by a median of 1.31 Å, representing a substantial improvement. The overall median RMSD for SGLD simulations of 12-residue loops was 0.91 Å, yielding refinement of a median 2.70 Å from initial loop placement. Results from DFIRE-AA and the Rosetta model applied to rescoring conformations failed to improve the overall detection calculated from the CHARMM force field. We illustrate the advantage of SGLD over the MD simulation model by presenting potential-energy landscapes for several loop predictions. Our results demonstrate that SGLD significantly outperforms traditional MD in the generation and populating of nativelike loop conformations and that the CHARMM force field performs comparably to other empirical force fields in identifying these conformations from the resulting ensembles.

Open conformation of adipokinetic hormone receptor from the malaria mosquito facilitates hormone binding

Insect flight requires rapid mobilization of energy reserves during flight, which is mediated and regulated by hormonal control via adipokinetic hormones. The structure of the G-protein receptors to which these hormones bind, are crucial in understanding many of the physiological processes in which they play a central role. To date no 3D structure of an insect G-protein coupled receptor (GPCR) is available. Here, the first models of the 3D structures of a GPCR from the malaria mosquito are presented. Homology modeling of the receptor identified from the genome of Anopheles gambiae was used to construct two models of the receptor. The 7 transmembrane helical bundles of these two models are based on the crystal structures of beta2-adrenergic receptor and rhodopsin. The flexible loop regions were modeled using high temperature simulated annealing and constrained molecular dynamic simulations. The two receptor models differ in a number of critical features, the most important of which is that the rhodopsin-based model has a 'closed' structure while the beta2-based structure is 'open'. The 'open' conformation provides easy access of the hormone to the binding pocket. Docking calculations with the insect adipokinetic hormones, AKH-1 (pGlu-Leu-Thr-Phe-Thr-Pro-Ala-Trp-NH(2)) from the malaria mosquito and Del-CC (pGlu-Lys-Asn-Phe-Ser-Pro-Asn-Trp-Gly-Asn-NH(2)) from the blister beetle showed that while the binding motif of the two is similar, AKH-1 has more than 30 times higher affinity than Del-CC, which strongly suggests that the binding is specific, and that the correct binding site was identified. Using these models it is possible to design antagonists, which block the binding site and are thus species-specific insecticides.

A quality metric for homology modeling: the H-factor

BACKGROUND

The analysis of protein structures provides fundamental insight into most biochemical functions and consequently into the cause and possible treatment of diseases. As the structures of most known proteins cannot be solved experimentally for technical or sometimes simply for time constraints, in silico protein structure prediction is expected to step in and generate a more complete picture of the protein structure universe. Molecular modeling of protein structures is a fast growing field and tremendous works have been done since the publication of the very first model. The growth of modeling techniques and more specifically of those that rely on the existing experimental knowledge of protein structures is intimately linked to the developments of high resolution, experimental techniques such as NMR, X-ray crystallography and electron microscopy. This strong connection between experimental and in silico methods is however not devoid of criticisms and concerns among modelers as well as among experimentalists.

RESULTS

In this paper, we focus on homology-modeling and more specifically, we review how it is perceived by the structural biology community and what can be done to impress on the experimentalists that it can be a valuable resource to them. We review the common practices and provide a set of guidelines for building better models. For that purpose, we introduce the H-factor, a new indicator for assessing the quality of homology models, mimicking the R-factor in X-ray crystallography. The methods for computing the H-factor is fully described and validated on a series of test cases.

CONCLUSIONS

We have developed a web service for computing the H-factor for models of a protein structure. This service is freely accessible at http://koehllab.genomecenter.ucdavis.edu/toolkit/h-factor.

Development of a new physics-based internal coordinate mechanics force field and its application to protein loop modeling

We report the development of internal coordinate mechanics force field (ICMFF), new force field parameterized using a combination of experimental data for crystals of small molecules and quantum mechanics calculations. The main features of ICMFF include: (a) parameterization for the dielectric constant relevant to the condensed state (ε = 2) instead of vacuum, (b) an improved description of hydrogen-bond interactions using duplicate sets of van der Waals parameters for heavy atom-hydrogen interactions, and (c) improved backbone covalent geometry and energetics achieved using novel backbone torsional potentials and inclusion of the bond angles at the C(α) atoms into the internal variable set. The performance of ICMFF was evaluated through loop modeling simulations for 4-13 residue loops. ICMFF was combined with a solvent-accessible surface area solvation model optimized using a large set of loop decoys. Conformational sampling was carried out using the biased probability Monte Carlo method. Average/median backbone root-mean-square deviations of the lowest energy conformations from the native structures were 0.25/0.21 Å for four residues loops, 0.84/0.46 Å for eight residue loops, and 1.16/0.73 Å for 12 residue loops. To our knowledge, these results are significantly better than or comparable with those reported to date for any loop modeling method that does not take crystal packing into account. Moreover, the accuracy of our method is on par with the best previously reported results obtained considering the crystal environment. We attribute this success to the high accuracy of the new ICM force field achieved by meticulous parameterization, to the optimized solvent model, and the efficiency of the search method.

Loop simulations

Loop modeling is crucial for high-quality homology model construction outside conserved secondary structure elements. Dozens of loop modeling protocols involving a range of database and ab initio search algorithms and a variety of scoring functions have been proposed. Knowledge-based loop modeling methods are very fast and some can successfully and reliably predict loops up to about eight residues long. Several recent ab initio loop simulation methods can be used to construct accurate models of loops up to 12-13 residues long, albeit at a substantial computational cost. Major current challenges are the simulations of loops longer than 12-13 residues, the modeling of multiple interacting flexible loops, and the sensitivity of the loop predictions to the accuracy of the loop environment.

Improving predicted protein loop structure ranking using a Pareto-optimality consensus method

Background

Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction.

Results

We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of ~20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD < 0.5A from the native) as top-ranked, and selecting at least one near-native model in the top-5-ranked models, respectively. Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops. Furthermore, the POC method outperforms the other popularly-used consensus strategies in model ranking, such as rank-by-number, rank-by-rank, rank-by-vote, and regression-based methods.

Conclusions

By integrating multiple knowledge- and physics-based scoring functions based on Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set.

Modeling the possible conformations of the extracellular loops in G-protein-coupled receptors

This study presents the results of a de novo approach modeling possible conformational dynamics of the extracellular (EC) loops in G-protein-coupled receptors (GPCRs), specifically in bovine rhodopsin (bRh), squid rhodopsin (sRh), human beta-2 adrenergic receptor (beta2AR), turkey beta-1 adrenergic receptor (beta1AR), and human A2 adenosine receptor (A2AR). The approach deliberately sacrificed a detailed description of any particular 3D structure of the loops in GPCRs in favor of a less precise description of many possible structures. Despite this, the approach found ensembles of the low-energy conformers of the EC loops that contained structures close to the available X-ray snapshots. For the smaller EC1 and EC3 loops (6-11 residues), our results were comparable with the best recent results obtained by other authors using much more sophisticated techniques. For the larger EC2 loops (25-34 residues), our results consistently yielded structures significantly closer to the X-ray snapshots than the results of the other authors for loops of similar size. The results suggested possible large-scale movements of the EC loops in GPCRs that might determine their conformational dynamics. The approach was also validated by accurately reproducing the docking poses for low-molecular-weight ligands in beta2AR, beta1AR, and A2AR, demonstrating the possible influence of the conformations of the EC loops on the binding sites of ligands. The approach correctly predicted the system of disulfide bridges between the EC loops in A2AR and elucidated the probable pathways for forming this system.

Analysis of loop boundaries using different local structure assignment methods

Loops connect regular secondary structures. In many instances, they are known to play important biological roles. Analysis and prediction of loop conformations depend directly on the definition of repetitive structures. Nonetheless, the secondary structure assignment methods (SSAMs) often lead to divergent assignments. In this study, we analyzed, both structure and sequence point of views, how the divergence between different SSAMs affect boundary definitions of loops connecting regular secondary structures. The analysis of SSAMs underlines that no clear consensus between the different SSAMs can be easily found. Because these latter greatly influence the loop boundary definitions, important variations are indeed observed, that is, capping positions are shifted between different SSAMs. On the other hand, our results show that the sequence information in these capping regions are more stable than expected, and, classical and equivalent sequence patterns were found for most of the SSAMs. This is, to our knowledge, the most exhaustive survey in this field as (i) various databank have been used leading to similar results without implication of protein redundancy and (ii) the first time various SSAMs have been used. This work hence gives new insights into the difficult question of assignment of repetitive structures and addresses the issue of loop boundaries definition. Although SSAMs give very different local structure assignments capping sequence patterns remain efficiently stable.

SuperLooper--a prediction server for the modeling of loops in globular and membrane proteins

SuperLooper provides the first online interface for the automatic, quick and interactive search and placement of loops in proteins (LIP). A database containing half a billion segments of water-soluble proteins with lengths up to 35 residues can be screened for candidate loops. A specified database containing 180 000 membrane loops in proteins (LIMP) can be searched, alternatively. Loop candidates are scored based on sequence criteria and the root mean square deviation (RMSD) of the stem atoms. Searching LIP, the average global RMSD of the respective top-ranked loops to the original loops is benchmarked to be <2 Å, for loops up to six residues or <3 Å for loops shorter than 10 residues. Other suitable conformations may be selected and directly visualized on the web server from a top-50 list. For user guidance, the sequence homology between the template and the original sequence, proline or glycine exchanges or close contacts between a loop candidate and the remainder of the protein are denoted. For membrane proteins, the expansions of the lipid bilayer are automatically modeled using the TMDET algorithm. This allows the user to select the optimal membrane protein loop concerning its relative orientation to the lipid bilayer. The server is online since October 2007 and can be freely accessed at URL: http://bioinformatics.charite.de/superlooper/

Simulated mutagenesis of the hypervariable loops of a llama VHH domain for the recovery of canonical conformations

In this work, wildtype and mutated hypervariable regions of an anti-hCG llama VHH antibody were simulated via a molecular dynamics replica exchange method (REM). Seven mutants were simulated with the goal of identifying structural determinants that return the noncanonical H1 loop of the wildtype antibody to the type 1 canonical structure predicted by database methods formulated for conventional antibodies. Two cases with three point mutations yielded a stable type 1 H1 structure. In addition, other mutants with fewer mutations showed evidence of such conformations. Overall, the mutagenesis results suggest a marked influence of interloop interactions on the attainment of canonical conformations for this antibody. On the methodological front, a novel REM scheme was developed to quickly screen diverse mutants based on their relative propensities for attaining favorable structures. This multimutant REM (MMREM) was used to successfully identify mutations that stabilize a canonical H1 loop grafted on the llama antibody scaffold. The use of MMREM and REM for screening mutants and assessing structural stability may be useful in the rational design of antibody hypervariable loops.

The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications

Structural genomics efforts contribute new protein structures that often lack significant sequence and fold similarity to known proteins. Traditional sequence and structure-based methods may not be sufficient to annotate the molecular functions of these structures. Techniques that combine structural and functional modeling can be valuable for functional annotation. FEATURE is a flexible framework for modeling and recognition of functional sites in macromolecular structures. Here, we present an overview of the main components of the FEATURE framework, and describe the recent developments in its use. These include automating training sets selection to increase functional coverage, coupling FEATURE to structural diversity generating methods such as molecular dynamics simulations and loop modeling methods to improve performance, and using FEATURE in large-scale modeling and structure determination efforts.