A self consistent mean field approach to simultaneous gap closure and side-chain positioning in homology modelling

Structure of an influenza group 2-neutralizing antibody targeting the hemagglutinin stem supersite

Several influenza antibodies with broad group 2 neutralization have recently been isolated. Here, we analyze the structure, class, and binding of one of these antibodies from an H7N9 vaccine trial, 315-19-1D12. The cryo-EM structure of 315-19-1D12 Fab in complex with the hemagglutinin (HA) trimer revealed the antibody to recognize the helix A region of the HA stem, at the supersite of vulnerability recognized by group 1-specific and by cross-group-neutralizing antibodies. 315-19-1D12 was derived from HV1-2 and KV2-28 genes and appeared to form a new antibody class. Bioinformatic analysis indicated its group 2 neutralization specificity to be a consequence of four key residue positions. We specifically tested the impact of the group 1-specific N33 glycan, which decreased but did not abolish group 2 binding of 315-19-1D12. Overall, this study highlights the recognition of a broad group 2-neutralizing antibody, revealing unexpected diversity in neutralization specificity for antibodies that recognize the HA stem supersite.

The Pfizer Crystal Structure Database: An essential tool for structure-based design at Pfizer

Pfizer's Crystal Structure Database (CSDB) is a key enabling technology that allows scientists on structure-based projects rapid access to Pfizer's vast library of in-house crystal structures, as well as a significant number of structures imported from the Protein Data Bank. In addition to capturing basic information such as the asymmetric unit coordinates, reflection data, and the like, CSDB employs a variety of automated methods to first ensure a standard level of annotations and error checking, and then to add significant value for design teams by processing the structures through a sequence of algorithms that prepares the structures for use in modeling. The structures are made available, both as the original asymmetric unit as submitted, as well as the final prepared structures, through REST-based web services that are consumed by several client desktop applications. The structures can be searched by keyword, sequence, submission date, ligand substructure and similarity search, and other common queries.

Sequence-Signature Optimization Enables Improved Identification of Human HV6-1-Derived Class Antibodies That Neutralize Diverse Influenza A Viruses

Sequence signatures of multidonor broadly neutralizing influenza antibodies can be used to quantify the prevalence of B cells with virus-neutralizing potential to accelerate development of broadly protective vaccine strategies. Antibodies of the same class share similar recognition modes and developmental pathways, and several antibody classes have been identified that neutralize diverse group 1- and group 2-influenza A viruses and have been observed in multiple human donors. One such multidonor antibody class, the HV6-1-derived class, targets the stem region of hemagglutinin with extraordinary neutralization breadth. Here, we use an iterative process to combine informatics, biochemical, and structural analyses to delineate an improved sequence signature for HV6-1-class antibodies. Based on sequence and structure analyses of known HV6-1 class antibodies, we derived a more inclusive signature (version 1), which we used to search for matching B-cell transcripts from published next-generation sequencing datasets of influenza vaccination studies. We expressed selected antibodies, evaluated their function, and identified amino acid-level requirements from which to refine the sequence signature (version 2). The cryo-electron microscopy structure for one of the signature-identified antibodies in complex with hemagglutinin confirmed motif recognition to be similar to known HV6-1-class members, MEDI8852 and 56.a.09, despite differences in recognition-loop length. Threading indicated the refined signature to have increased accuracy, and signature-identified heavy chains, when paired with the light chain of MEDI8852, showed neutralization comparable to the most potent members of the class. Incorporating sequences of additional class members thus enables an improved sequence signature for HV6-1-class antibodies, which can identify class members with increased accuracy.

The Effect of Virulence and Resistance Mechanisms on the Interactions between Parasitic Plants and Their Hosts

Parasitic plants have a unique heterotrophic lifestyle based on the extraction of water and nutrients from host plants. Some parasitic plant species, particularly those of the family Orobanchaceae, attack crops and cause substantial yield losses. The breeding of resistant crop varieties is an inexpensive way to control parasitic weeds, but often does not provide a long-lasting solution because the parasites rapidly evolve to overcome resistance. Understanding mechanisms underlying naturally occurring parasitic plant resistance is of great interest and could help to develop methods to control parasitic plants. In this review, we describe the virulence mechanisms of parasitic plants and resistance mechanisms in their hosts, focusing on obligate root parasites of the genera Orobanche and Striga. We noticed that the resistance (R) genes in the host genome often encode proteins with nucleotide-binding and leucine-rich repeat domains (NLR proteins), hence we proposed a mechanism by which host plants use NLR proteins to activate downstream resistance gene expression. We speculated how parasitic plants and their hosts co-evolved and discussed what drives the evolution of virulence effectors in parasitic plants by considering concepts from similar studies of plant-microbe interaction. Most previous studies have focused on the host rather than the parasite, so we also provided an updated summary of genomic resources for parasitic plants and parasitic genes for further research to test our hypotheses. Finally, we discussed new approaches such as CRISPR/Cas9-mediated genome editing and RNAi silencing that can provide deeper insight into the intriguing life cycle of parasitic plants and could potentially contribute to the development of novel strategies for controlling parasitic weeds, thereby enhancing crop productivity and food security globally.

Comparative Protein Structure Modeling Using MODELLER

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.

Comparative Protein Structure Modeling Using MODELLER

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.

Comparative Protein Structure Modeling Using MODELLER

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

Protein structure modeling in the proteomics era

Structural proteomics aims to understand the structural basis of protein interactions and functions. A prerequisite for this is the availability of 3D protein structures that mediate the biochemical interactions. The explosion in the number of available gene sequences set the stage for the next step in genome-scale projects -- to obtain 3D structures for each protein. To achieve this ambitious goal, the slow and costly structure determination experiments are supplemented with theoretical approaches. The current state and recent advances in structure modeling approaches are reviewed here, with special emphasis on comparative protein structure modeling techniques.

Comparative Modeling of Drug Target Proteins☆

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.

The H-factor as a novel quality metric for homology modeling

Background

Drug discovery typically starts with the identification of a potential target that is then tested and validated either through high-throughput screening against a library of drug compounds or by rational drug design. When the putative target is a protein, the latter approach requires the knowledge of its structure. Finding the structure of a protein is however a difficult task. Significant progress has come from high-resolution techniques such as X-ray crystallography and NMR; there are many proteins however whose structure have not yet been solved. Computational techniques for structure prediction are viable alternatives to experimental techniques for these cases. However, the proper validation of the structural models they generate remains an issue.

Findings

In this report, we focus on homology modeling techniques and introduce the H-factor, a new indicator for assessing the quality of protein structure models generated with these techniques. The H-factor is meant to mimic the R-factor used in X-ray crystallography. The method for computing the H-factor is fully described with a demonstration of its effectiveness on a test set of target proteins.

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.

Structural modelling and dynamics of proteins for insights into drug interactions

Proteins are the workhorses of biomolecules and their function is affected by their structure and their structural rearrangements during ligand entry, ligand binding and protein-protein interactions. Hence, the knowledge of protein structure and, importantly, the dynamic behaviour of the structure are critical for understanding how the protein performs its function. The predictions of the structure and the dynamic behaviour can be performed by combinations of structure modelling and molecular dynamics simulations. The simulations also need to be sensitive to the constraints of the environment in which the protein resides. Standard computational methods now exist in this field to support the experimental effort of solving protein structures. This review presents a comprehensive overview of the basis of the calculations and the well-established computational methods used to generate and understand protein structure and function and the study of their dynamic behaviour with the reference to lung-related targets.

Protein loop closure using orientational restraints from NMR data

Protein loops often play important roles in biological functions. Modeling loops accurately is crucial to determining the functional specificity of a protein. Despite the recent progress in loop prediction approaches, which led to a number of algorithms over the past decade, few rigorous algorithmic approaches exist to model protein loops using global orientational restraints, such as those obtained from residual dipolar coupling (RDC) data in solution nuclear magnetic resonance (NMR) spectroscopy. In this article, we present a novel, sparse data, RDC-based algorithm, which exploits the mathematical interplay between RDC-derived sphero-conics and protein kinematics, and formulates the loop structure determination problem as a system of low-degree polynomial equations that can be solved exactly, in closed-form. The polynomial roots, which encode the candidate conformations, are searched systematically, using provable pruning strategies that triage the vast majority of conformations, to enumerate or prune all possible loop conformations consistent with the data; therefore, completeness is ensured. Results on experimental RDC datasets for four proteins, including human ubiquitin, FF2, DinI, and GB3, demonstrate that our algorithm can compute loops with higher accuracy, a three- to six-fold improvement in backbone RMSD, versus those obtained by traditional structure determination protocols on the same data. Excellent results were also obtained on synthetic RDC datasets for protein loops of length 4, 8, and 12 used in previous studies. These results suggest that our algorithm can be successfully applied to determine protein loop conformations, and hence, will be useful in high-resolution protein backbone structure determination, including loops, from sparse NMR data. Proteins 2012. © 2011 Wiley Periodicals, Inc.

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.

Improved side-chain modeling by coupling clash-detection guided iterative search with rotamer relaxation

MOTIVATION

Side-chain modeling has seen wide applications in computational structure biology. Most of the popular side-chain modeling programs explore the conformation space using discrete rigid rotamers for speed and efficiency. However, in the tightly packed environments of protein interiors, these methods will inherently lead to atomic clashes and hinder the prediction accuracy.

RESULTS

We present a side-chain modeling method (CIS-RR), which couples a novel clash-detection guided iterative search (CIS) algorithm with continuous torsion space optimization of rotamers (RR). Benchmark testing shows that compared with the existing popular side-chain modeling methods, CIS-RR removes atomic clashes much more effectively and achieves comparable or even better prediction accuracy while having comparable computational cost. We believe that CIS-RR could be a useful method for accurate side-chain modeling.

AVAILABILITY

CIS-RR is available to non-commercial users at our website: http://jianglab.ibp.ac.cn/lims/cisrr/cisrr.html.

MOLS sampling and its applications in structural biophysics

This review describes the MOLS method and its applications. This computational method has been developed in our laboratory primarily to explore the conformational space of small peptides and identify features of interest, particularly the minima, i.e., the low energy conformations. A systematic "brute-force" search through the vast conformational space for such features faces the insurmountable problem of combinatorial explosion, whilst other techniques, e.g., Monte Carlo searches, are somewhat limited in their region of exploration and may be considered inexhaustive. The MOLS method, on the other hand, uses a sampling technique commonly employed in experimental design theory to identify a small sample of the conformational space that nevertheless retains information about the entire space. The information is extracted using a technique that is a variant of the self-consistent mean field technique, which has been used to identify, for example, the optimal set of side-chain conformations in a protein. Applications of the MOLS method to understand peptide structure, predict the structures of loops in proteins, predict three-dimensional structures of small proteins, and arrive at the best conformation, orientation, and positions of a small molecule ligand in a protein receptor site have all yielded satisfactory results.

MEDELLER: homology-based coordinate generation for membrane proteins

MOTIVATION

Membrane proteins (MPs) are important drug targets but knowledge of their exact structure is limited to relatively few examples. Existing homology-based structure prediction methods are designed for globular, water-soluble proteins. However, we are now beginning to have enough MP structures to justify the development of a homology-based approach specifically for them.

RESULTS

We present a MP-specific homology-based coordinate generation method, MEDELLER, which is optimized to build highly reliable core models. The method outperforms the popular structure prediction programme Modeller on MPs. The comparison of the two methods was performed on 616 target-template pairs of MPs, which were classified into four test sets by their sequence identity. Across all targets, MEDELLER gave an average backbone root mean square deviation (RMSD) of 2.62 Å versus 3.16 Å for Modeller. On our 'easy' test set, MEDELLER achieves an average accuracy of 0.93 Å backbone RMSD versus 1.56 Å for Modeller.

AVAILABILITY AND IMPLEMENTATION

http://medeller.info; Implemented in Python, Bash and Perl CGI for use on Linux systems; Supplementary data are available at http://www.stats.ox.ac.uk/proteins/resources.

Template-based protein structure modeling

Functional characterization of a protein is often facilitated by its 3D structure. However, the fraction of experimentally known 3D models is currently less than 1% due to the inherently time-consuming and complicated nature of structure determination techniques. Computational approaches are employed to bridge the gap between the number of known sequences and that of 3D models. Template-based protein structure modeling techniques rely on the study of principles that dictate the 3D structure of natural proteins from the theory of evolution viewpoint. Strategies for template-based structure modeling will be discussed with a focus on comparative modeling, by reviewing techniques available for all the major steps involved in the comparative modeling pipeline.

A self-organizing algorithm for modeling protein loops

Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A variety of algorithms have been developed to tackle the loop closure problem, ranging from inverse kinematics to knowledge-based approaches that utilize pre-existing fragments extracted from known protein structures. However, many of these approaches focus on the generation of conformations that mainly satisfy the fixed end point condition, leaving the steric constraints to be resolved in subsequent post-processing steps. In the present work, we describe a simple solution that simultaneously satisfies not only the end point and steric conditions, but also chirality and planarity constraints. Starting from random initial atomic coordinates, each individual conformation is generated independently by using a simple alternating scheme of pairwise distance adjustments of randomly chosen atoms, followed by fast geometric matching of the conformationally rigid components of the constituent amino acids. The method is conceptually simple, numerically stable and computationally efficient. Very importantly, additional constraints, such as those derived from NMR experiments, hydrogen bonds or salt bridges, can be incorporated into the algorithm in a straightforward and inexpensive way, making the method ideal for solving more complex multi-loop problems. The remarkable performance and robustness of the algorithm are demonstrated on a set of protein loops of length 4, 8, and 12 that have been used in previous studies.

Protein loops play an important role in protein function, such as ligand binding, recognition, and allosteric regulation. However, due to their flexibility, it is notoriously difficult to determine their 3D structures using traditional experimental techniques. As a result, one can often find protein structures with missing loops in the Protein Data Bank. Their sequence variability also presents a particular challenge for homology modeling methods, which can only yield good overall structures given sufficient sequence identity and good experimental reference structures. Despite extensive research, the construction of protein loop 3D structures remains an open problem, since a sensible conformation should seamlessly bridge the anchor points without introducing steric clashes within the loop itself or between the loop and its surroundings environment. Here, we present a conceptually simple, mathematically straightforward, numerically robust and computationally efficient approach for building protein loop conformations that simultaneously satisfy end-point, steric, planar and chiral constraints. More importantly, additional constraints derived from experimental sources can be incorporated in a straightforward manner, allowing the processing of more complex structures involving multiple interlocking loops.

An estimate of the numbers and density of low-energy structures (or decoys) in the conformational landscape of proteins

BACKGROUND

The conformational energy landscape of a protein, as calculated by known potential energy functions, has several minima, and one of these corresponds to its native structure. It is however difficult to comprehensively estimate the actual numbers of low energy structures (or decoys), the relationships between them, and how the numbers scale with the size of the protein.

METHODOLOGY

We have developed an algorithm to rapidly and efficiently identify the low energy conformers of oligo peptides by using mutually orthogonal Latin squares to sample the potential energy hyper surface. Using this algorithm, and the ECEPP/3 potential function, we have made an exhaustive enumeration of the low-energy structures of peptides of different lengths, and have extrapolated these results to larger polypeptides.

CONCLUSIONS AND SIGNIFICANCE

We show that the number of native-like structures for a polypeptide is, in general, an exponential function of its sequence length. The density of these structures in conformational space remains more or less constant and all the increase appears to come from an expansion in the volume of the space. These results are consistent with earlier reports that were based on other models and techniques.

Docking and scoring with alternative side-chain conformations

We describe a scoring and modeling procedure for docking ligands into protein models that have either modeled or flexible side-chain conformations. Our methodical contribution comprises a procedure for generating new potentials of mean force for the ROTA scoring function which we have introduced previously for optimizing side-chain conformations with the tool IRECS. The ROTA potentials are specially trained to tolerate small-scale positional errors of atoms that are characteristic of (i) side-chain conformations that are modeled using a sparse rotamer library and (ii) ligand conformations that are generated using a docking program. We generated both rigid and flexible protein models with our side-chain prediction tool IRECS and docked ligands to proteins using the scoring function ROTA and the docking programs FlexX (for rigid side chains) and FlexE (for flexible side chains). We validated our approach on the forty screening targets of the DUD database. The validation shows that the ROTA potentials are especially well suited for estimating the binding affinity of ligands to proteins. The results also show that our procedure can compensate for the performance decrease in screening that occurs when using protein models with side chains modeled with a rotamer library instead of using X-ray structures. The average runtime per ligand of our method is 168 seconds on an Opteron V20z, which is fast enough to allow virtual screening of compound libraries for drug candidates.

Incorporating dipolar solvents with variable density in Poisson-Boltzmann electrostatics

We describe a new way to calculate the electrostatic properties of macromolecules that goes beyond the classical Poisson-Boltzmann treatment with only a small extra CPU cost. The solvent region is no longer modeled as a homogeneous dielectric media but rather as an assembly of self-orienting interacting dipoles of variable density. The method effectively unifies both the Poisson-centric view and the Langevin Dipole model. The model results in a variable dielectric constant epsilon(r) in the solvent region and also in a variable solvent density rho(r) that depends on the nature of the closest exposed solute atoms. The model was calibrated using small molecules and ions solvation data with only two adjustable parameters, namely the size and dipolar moment of the solvent. Hydrophobicity scales derived from the solvent density profiles agree very well with independently derived hydrophobicity scales, both at the atomic or residue level. Dimerization interfaces in homodimeric proteins or lipid-binding regions in membrane proteins clearly appear as poorly solvated patches on the solute accessible surface. Comparison of the thermally averaged solvent density of this model with the one derived from molecular dynamics simulations shows qualitative agreement on a coarse-grained level. Because this calculation is much more rapid than that from molecular dynamics, applications of a density-profile-based solvation energy to the identification of the true structure among a set of decoys become computationally feasible. Various possible improvements of the model are discussed, as well as extensions of the formalism to treat mixtures of dipolar solvents of different sizes.

Comparative protein structure modeling using MODELLER

Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

Comparative protein structure modeling using Modeller

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

Rod-derived Cone Viability Factor-2 is a novel bifunctional-thioredoxin-like protein with therapeutic potential

Background

Cone degeneration is the hallmark of the inherited retinal disease retinitis pigmentosa. We have previously identified a trophic factor "Rod-derived Cone Viability Factor (RdCVF) that is secreted by rods and promote cone viability in a mouse model of the disease.

Results

Here we report the bioinformatic identification and the experimental analysis of RdCVF2, a second trophic factor belonging to the Rod-derived Cone Viability Factor family. The mouse RdCVF gene is known to be bifunctional, encoding both a long thioredoxin-like isoform (RdCVF-L) and a short isoform with trophic cone photoreceptor viability activity (RdCVF-S). RdCVF2 shares many similarities with RdCVF in terms of gene structure, expression in a rod-dependent manner and protein 3D structure. Furthermore, like RdCVF, the RdCVF2 short isoform exhibits cone rescue activity that is independent of its putative thiol-oxydoreductase activity.

Conclusion

Taken together, these findings define a new family of bifunctional genes which are: expressed in vertebrate retina, encode trophic cone viability factors, and have major therapeutic potential for human retinal neurodegenerative diseases such as retinitis pigmentosa.

The psychrophilic bacterium Pseudoalteromonas halosplanktis TAC125 possesses a gene coding for a cold-adapted feruloyl esterase activity that shares homology with esterase enzymes from γ-proteobacteria and yeast

The complete genome of the psychrophilic bacteria Pseudoalteromonas haloplanktis TAC 125, recently published, owns a gene coding for a putative esterase activity corresponding to the ORF PSHAa1385, also classified in the Carbohydrate Active Enzymes database (CAZY) belonging to family 1 of carbohydrate esterase proteins. This ORF is 843 bp in length and codes for a protein of 280 amino acid residues. In this study we characterized and cloned the PSHAa1385 gene in Escherichia coli. We also characterized the recombinant protein by biochemical and biophysical methodologies. The PSHAa1385 gene sequence showed a significant homology with several carboxyl-esterase and acetyl-esterase genes from gamma-proteobacteria genera and yeast. The recombinant protein exhibited a significant activity towards pNP-acetate, alpha-and beta-naphthyl acetate as generic substrates, and 4-methylumbelliferyl p-trimethylammonio cinnamate chloride (MUTMAC) as a specific substrate, indicating that the protein exhibits a feruloyl esterase activity that it is displayed by similar enzymes present in other organisms. Finally, a three-dimensional model of the protein was built and the amino acid residues involved in the catalytic function of the protein were identified.

Exploring the conformational space of protein loops using a mean field technique with MOLS sampling

We have recently developed a computational technique that uses mutually orthogonal Latin square sampling to explore the conformational space of oligopeptides in an exhaustive manner. In this article, we report its use to analyze the conformational spaces of 120 protein loop sequences in proteins, culled from the PDB, having the length ranging from 5 to 10 residues. The force field used did not have any information regarding the sequences or structures that flanked the loop. The results of the analyses show that the native structure of the loop, as found in the PDB falls at one of the low energy points in the conformational landscape of the sequences. Thus, a large portion of the structural determinants of the loop may be considered intrinsic to the sequence, regardless of either adjacent sequences or structures, or the interactions that the atoms of the loop make with other residues in the protein or in neighboring proteins.

An evaluation of automated homology modelling methods at low target template sequence similarity

MOTIVATION

There are two main areas of difficulty in homology modelling that are particularly important when sequence identity between target and template falls below 50%: sequence alignment and loop building. These problems become magnified with automatic modelling processes, as there is no human input to correct mistakes. As such we have benchmarked several stand-alone strategies that could be implemented in a workflow for automated high-throughput homology modelling. These include three new sequence-structure alignment programs: 3D-Coffee, Staccato and SAlign, plus five homology modelling programs and their respective loop building methods: Builder, Nest, Modeller, SegMod/ENCAD and Swiss-Model. The SABmark database provided 123 targets with at least five templates from the same SCOP family and sequence identities </=50%.

RESULTS

When using Modeller as the common modelling program, 3D-Coffee outperforms Staccato and SAlign using both multiple templates and the best single template, and across the sequence identity range 20-50%. The mean model RMSD generated from 3D-Coffee using multiple templates is 15 and 28% (or using single templates, 3 and 13%) better than those generated by Staccato and Salign, respectively. 3D-Coffee gives equivalent modelling accuracy from multiple and single templates, but Staccato and SAlign are more successful with single templates, their quality deteriorating as additional lower sequence identity templates are added. Evaluating the different homology modelling programs, on average Modeller performs marginally better in overall modelling than the others tested. However, on average Nest produces the best loops with an 8% improvement by mean RMSD compared to the loops generated by Builder.

Comparative Modeling of Drug Target Proteins

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state of the art by a number of specific examples.

Plant NBS-LRR proteins: adaptable guards

Plant nucleotide-binding site leucine-rich repeat (NBS-LRR) proteins are a large family involved in disease resistance; they may monitor the status of proteins targeted by pathogens.

The majority of disease resistance genes in plants encode nucleotide-binding site leucine-rich repeat (NBS-LRR) proteins. This large family is encoded by hundreds of diverse genes per genome and can be subdivided into the functionally distinct TIR-domain-containing (TNL) and CC-domain-containing (CNL) subfamilies. Their precise role in recognition is unknown; however, they are thought to monitor the status of plant proteins that are targeted by pathogen effectors.

Accounting for loop flexibility during protein-protein docking

Although reliable docking can now be achieved for systems that do not undergo important induced conformational change upon association, the presence of flexible surface loops, which must adapt to the steric and electrostatic properties of a partner, generally presents a major obstacle. We report here the first docking method that allows large loop movements during a systematic exploration of the possible arrangements of the two partners in terms of position and rotation. Our strategy consists in taking into account an ensemble of possible loop conformations by a multi-copy representation within a reduced protein model. The docking process starts from regularly distributed positions and orientations of the ligand around the whole receptor. Each starting configuration is submitted to energy minimization during which the best-fitting loop conformation is selected based on the mean-field theory. Trials were carried out on proteins with significant differences in the main-chain conformation of the binding loop between isolated form and complexed form, which were docked to their partner considered in their bound form. The method is able to predict complexes very close to the crystal complex both in terms of relative position of the two partners and of the geometry of the flexible loop. We also show that introducing loop flexibility on the isolated protein form during systematic docking largely improves the predictions of relative position of the partners in comparison with rigid-body docking.

All are not equal: a benchmark of different homology modeling programs

Modeling a protein structure based on a homologous structure is a standard method in structural biology today. In this process an alignment of a target protein sequence onto the structure of a template(s) is used as input to a program that constructs a 3D model. It has been shown that the most important factor in this process is the correctness of the alignment and the choice of the best template structure(s), while it is generally believed that there are no major differences between the best modeling programs. Therefore, a large number of studies to benchmark the alignment qualities and the selection process have been performed. However, to our knowledge no large-scale benchmark has been performed to evaluate the programs used to transform the alignment to a 3D model. In this study, a benchmark of six different homology modeling programs- Modeller, SegMod/ENCAD, SWISS-MODEL, 3D-JIGSAW, nest, and Builder-is presented. The performance of these programs is evaluated using physiochemical correctness and structural similarity to the correct structure. From our analysis it can be concluded that no single modeling program outperform the others in all tests. However, it is quite clear that three modeling programs, Modeller, nest, and SegMod/ ENCAD, perform better than the others. Interestingly, the fastest and oldest modeling program, SegMod/ ENCAD, performs very well, although it was written more than 10 years ago and has not undergone any development since. It can also be observed that none of the homology modeling programs builds side chains as well as a specialized program (SCWRL), and therefore there should be room for improvement.

Full cyclic coordinate descent: solving the protein loop closure problem in Calpha space

BACKGROUND

Various forms of the so-called loop closure problem are crucial to protein structure prediction methods. Given an N- and a C-terminal end, the problem consists of finding a suitable segment of a certain length that bridges the ends seamlessly. In homology modelling, the problem arises in predicting loop regions. In de novo protein structure prediction, the problem is encountered when implementing local moves for Markov Chain Monte Carlo simulations. Most loop closure algorithms keep the bond angles fixed or semi-fixed, and only vary the dihedral angles. This is appropriate for a full-atom protein backbone, since the bond angles can be considered as fixed, while the (phi, psi) dihedral angles are variable. However, many de novo structure prediction methods use protein models that only consist of Calpha atoms, or otherwise do not make use of all backbone atoms. These methods require a method that alters both bond and dihedral angles, since the pseudo bond angle between three consecutive Calpha atoms also varies considerably.

RESULTS

Here we present a method that solves the loop closure problem for Calpha only protein models. We developed a variant of Cyclic Coordinate Descent (CCD), an inverse kinematics method from the field of robotics, which was recently applied to the loop closure problem. Since the method alters both bond and dihedral angles, which is equivalent to applying a full rotation matrix, we call our method Full CCD (FCDD). FCCD replaces CCD's vector-based optimization of a rotation around an axis with a singular value decomposition-based optimization of a general rotation matrix. The method is easy to implement and numerically stable.

CONCLUSION

We tested the method's performance on sets of random protein Calpha segments between 5 and 30 amino acids long, and a number of loops of length 4, 8 and 12. FCCD is fast, has a high success rate and readily generates conformations close to those of real loops. The presence of constraints on the angles only has a small effect on the performance. A reference implementation of FCCD in Python is available as supplementary information.

Docking macromolecules with flexible segments

We address a major obstacle to macromolecular docking algorithms by presenting a new method that takes into account the induced conformational adjustment of flexible loops situated at a protein/macromolecule interface. The method, MC2, is based on a multiple copy representation of the loops, coupled with a Monte Carlo conformational search of the relative position of the macromolecules and their side chain conformations. The selection of optimal loop conformations takes place during Monte Carlo cycling by the iterative adjustment of the weight of each copy. We describe here the parameterization of the method and trials on a protein-DNA complex of known 3-D structure, involving the Drosophila prd paired domain protein and its target oligonucleotide Wenqing, X. et al., Cell 1995, 80, 639. We demonstrate that our algorithm can correctly configure and position this protein, despite its relatively complex interactions with both grooves of DNA.

A graph-theory algorithm for rapid protein side-chain prediction

Fast and accurate side-chain conformation prediction is important for homology modeling, ab initio protein structure prediction, and protein design applications. Many methods have been presented, although only a few computer programs are publicly available. The SCWRL program is one such method and is widely used because of its speed, accuracy, and ease of use. A new algorithm for SCWRL is presented that uses results from graph theory to solve the combinatorial problem encountered in the side-chain prediction problem. In this method, side chains are represented as vertices in an undirected graph. Any two residues that have rotamers with nonzero interaction energies are considered to have an edge in the graph. The resulting graph can be partitioned into connected subgraphs with no edges between them. These subgraphs can in turn be broken into biconnected components, which are graphs that cannot be disconnected by removal of a single vertex. The combinatorial problem is reduced to finding the minimum energy of these small biconnected components and combining the results to identify the global minimum energy conformation. This algorithm is able to complete predictions on a set of 180 proteins with 34342 side chains in <7 min of computer time. The total chi(1) and chi(1 + 2) dihedral angle accuracies are 82.6% and 73.7% using a simple energy function based on the backbone-dependent rotamer library and a linear repulsive steric energy. The new algorithm will allow for use of SCWRL in more demanding applications such as sequence design and ab initio structure prediction, as well addition of a more complex energy function and conformational flexibility, leading to increased accuracy.

Identifying residue-residue clashes in protein hybrids by using a second-order mean-field approach

In this article, a second-order mean-field-based approach is introduced for characterizing the complete set of residue-residue couplings consistent with a given protein structure. This information is subsequently used to classify protein hybrids with respect to their potential to be functional based on the presenceabsence and severity of clashing residue-residue interactions. First, atomistic representations of both the native and denatured states are used to calculate rotamer-backbone, rotamer-intrinsic, and rotamer-rotamer conformational energies. Next, this complete conformational energy table is coupled with a second-order mean-field description to elucidate the probabilities of all possible rotamer-rotamer combinations in a minimum Helmholtz free-energy ensemble. Computational results for the dihydrofolate reductase family reveal correlation in substitution patterns between not only contacting but also distal second-order structural elements. Residue-residue clashes in hybrid proteins are quantified by contrasting the ensemble probabilities of protein hybrids against the ones of the original parental sequences. Good agreement with experimental data is demonstrated by superimposing these clashes against the functional crossover profiles of bidirectional incremental truncation libraries for Escherichia coli and human glycinamide ribonucleotide transformylases.

Enhancement of protein modeling by human intervention in applying the automatic programs 3D-JIGSAW and 3D-PSSM

Fourteen models were constructed and analyzed for the comparative modeling section of Critical Assessment of Techniques for Protein Structure Prediction (CASP4). Sequence identity between each target and the best possible parent(s) ranged between 55 and 13%, and the root-mean-square deviation between model and target was from 0.8 to 17.9 A. In the fold recognition section, 10 of the 11 remote homologues were recognized. The modeling protocols are a combination of automated computer algorithms, 3D-JIGSAW (for comparative modeling) and 3D-PSSM (for fold recognition), with human intervention at certain critical stages. In particular, intervention is required to check superfamily assignment, best possible parents from which to model, sequence alignments to those parents and take-off regions for modeling variable regions. There now is a convergence of algorithms for comparative modeling and fold recognition, particularly in the region of remote homology.

A divide and conquer approach to fast loop modeling

We describe a fast ab initio method for modeling local segments in protein structures. The algorithm is based on a divide and conquer approach and uses a database of precalculated look-up tables, which represent a large set of possible conformations for loop segments of variable length. The target loop is recursively decomposed until the resulting conformations are small enough to be compiled analytically. The algorithm, which is not restricted to any specific loop length, generates a ranked set of loop conformations in 20-180 s on a desktop PC. The prediction quality is evaluated in terms of global RMSD. Depending on loop length the top prediction varies between 1.06 A RMSD for three-residue loops and 3.72 A RMSD for eight-residue loops. Due to its speed the method may also be useful to generate alternative starting conformations for complex simulations.

Structure activity relationships of new inhibitors of mammalian 2,3-oxidosqualene cyclase designed from isoquinoline derivatives

We have designed more potent inhibitors from the previously reported LF 05-0038, a 6-isoquinolinol based inhibitor of 2,3-oxidosqualene cyclase (IC50: 1.1 microM). Replacement of the 3-OH group by various 3-substituted amino groups, and modification of the alkyl chain borne by the endocyclic nitrogen led to inhibitors with IC50 in the range of 0.15 to 1 microM. In a second step, opening of the bicyclic ring system afforded the corresponding aminoalkylpiperidines which were slightly more potent. Finally, introduction of suitable aromatic containing moieties on the piperidine nitrogen yielded very potent inhibitors such as 20x (IC50 = 18 nM) easy to synthesize and achiral. The recent availability of the crystal structure of squalene-hopene cyclase allowed us to construct a three-dimensional (3D) model of the related 2,3-oxidosqualene cyclase (OSC) which was tentatively used to describe the possible mode of binding of our compounds and which can be useful for designing new inhibitors.

Virtual interaction profiles of proteins

We have developed a new method for the prediction of peptide sequences that bind to a protein, given a three-dimensional structure of the protein in complex with a peptide. By applying a recently developed sequence prediction algorithm and a novel ensemble averaging calculation, we generate a diverse collection of peptide sequences that are predicted to have significant affinity for the protein. Using output from the simulations, we create position-specific scoring matrices, or virtual interaction profiles (VIPs). Comparison of VIPs for a collection of binding motifs to sequences determined experimentally indicates that the prediction algorithm is accurate and applicable to a diverse range of structures. With these VIPs, one can scan protein sequence databases rapidly to seek binding partners of potential biological significance. Overall, this method can significantly enhance the information contained within a protein- peptide crystal structure, and enrich the data obtained by experimental selection methods such as phage display.

Rigorous classical-mechanical derivation of a multiple-copy algorithm for sampling statistical mechanical ensembles

We derive a rigorous, multiple-copy simulation algorithm that is formally equivalent to conventional classical molecular dynamics for an ensemble of systems, but may be used for rapid geometry optimizations. The derivation is accomplished by starting from an ensemble of copies of the entire system and applying a point coordinate transformation to a large subsystem defined as the bath. After the transformation, each atom of the bath is described by one "major" set of coordinates located at the average position of the ensemble of equivalent atoms and a set of "minor" coordinates that when combined with the "major" coordinates represent exact dynamics. Neglecting the "minor" set of coordinates results in a Hamiltonian and a probability density equivalent to those used in existing multiple-copy methods. Neglecting Hamilton's equations of motion for the minor variables gives the equations of motion for locally enhanced sampling. Numerical tests indicate that the algorithm can recover exact molecular dynamics of the ensemble, conventional multiple-copy dynamics, or results of intermediate accuracy. Thus, the algorithm provides a rigorous basis for multiple-copy dynamics, resolves many of the uncertainties associated with their current implementations, and offers the potential for calculating ensemble average properties in conjunction with finding a system's global minimum energy geometry.

Implicit solvation in the self-consistent mean field theory method: sidechain modelling and prediction of folding free energies of protein mutants

The Atomic Solvation Parameter (ASP) model is one of the simplest models of solvation, in which the solvation free energy of a molecule is proportional to the solvent accessible surface area (SAS) of its atoms. However, until now this model had not been incorporated into the Self-Consistent Mean Field Theory (SCMFT) method for modelling sidechain conformations in proteins. The reason for this is that SAS is a many-body quantity and, thus, it is not obvious how to define it within the Mean Field (MF) framework, where multiple copies of each sidechain exist simultaneously. Here, we present a method for incorporating an SAS-based potential, such as the ASP model, into SCMFT. The theory on which the method is based is exact within the MF framework, that is, it does not depend on a pairwise or any other approximation of SAS. Therefore, SAS can be calculated to arbitrary accuracy. The method is computationally very efficient: only 7.6% slower on average than the method without solvation. We applied the method to the prediction of sidechain conformation, using as a test set high-quality solution structures of 11 proteins. Solvation was found to substantially improve the prediction accuracy of well-defined surface sidechains. We also investigated whether the methodology can be applied to prediction of folding free energies of protein mutants, using a set of barnase mutants. For apolar mutants, the modest correlation observed between calculated and observed folding free energies without solvation improved substantially when solvation was included, allowing the prediction of trends in the folding free energies of this type of mutants. For polar mutants, correlation was not significant even with solvation. Several other factors also responsible for the correlation were identified and analysed. From this analysis, future directions for applying and improving the present methodology are discussed.

Meanfield approach to the thermodynamics of protein-solvent systems with application to p53

We present a meanfield theoretical approach for studying protein-solvent interactions. Starting with the partition function of the system, we develop a field theory by introducing densities for the different components of the system. At this point, protein-solvent interactions are introduced following the inhomogeneous Flory-Huggins model for polymers. Finally, we calculate the free energy in a meanfield approximation. We apply this method to study the stability of the tetramerization domain of the tumor suppressor protein p53 when subjected to site-directed mutagenesis. The four chains of this protein are held together by hydrophobic interactions, and some mutations can weaken this bond while preserving the secondary structure of the single protein chains. We find good qualitative agreement between our numerical results and experimental data, thus encouraging the use of this method as a guide in designing experiments.

Comparative protein structure modeling of genes and genomes

Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are increasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are necessary in recognizing weak sequence-structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.