Novel Antifungal Compound Z-705 Specifically Inhibits Protein Kinase C of Filamentous Fungi.

The cell wall integrity signaling (CWIS) pathway is involved in fungal cell wall biogenesis. This pathway is composed of sensor proteins, protein kinase C (PKC), and the mitogen-activated protein kinase (MAPK) pathway, and it controls the transcription of many cell wall-related genes. PKC plays a pivotal role in this pathway; deficiencies in PkcA in the model filamentous fungus Aspergillus nidulans and in MgPkc1p in the rice blast fungus Magnaporthe grisea are lethal. This suggests that PKC in filamentous fungi is a potential target for antifungal agents. In the present study, to search for MgPkc1p inhibitors, we carried out in silico screening by three-dimensional (3D) structural modeling and performed growth inhibition tests for M. grisea on agar plates. From approximately 800,000 candidate compounds, we selected Z-705 and evaluated its inhibitory activity against chimeric PKC expressed in Saccharomyces cerevisiae cells in which the kinase domain of native S. cerevisiae PKC was replaced with those of PKCs of filamentous fungi. Transcriptional analysis of MLP1, which encodes a downstream factor of PKC in S. cerevisiae, and phosphorylation analysis of the mitogen-activated protein kinase (MAPK) Mpk1p, which is activated downstream of PKC, revealed that Z-705 specifically inhibited PKCs of filamentous fungi. Moreover, the inhibitory activity of Z-705 was similar to that of a well-known PKC inhibitor, staurosporine. Interestingly, Z-705 inhibited melanization induced by cell wall stress in M. grisea We discuss the relationships between PKC and melanin biosynthesis.IMPORTANCE A candidate inhibitor of filamentous fungal protein kinase C (PKC), Z-705, was identified by in silico screening. A screening system to evaluate the effects of fungal PKC inhibitors was constructed in Saccharomyces cerevisiae Using this system, we found that Z-705 is highly selective for filamentous fungal PKC in comparison with S. cerevisiae PKC. Analysis of the AGS1 mRNA level, which is regulated by Mps1p mitogen-activated protein kinase (MAPK) via PKC, in the rice blast fungus Magnaporthe grisea revealed that Z-705 had a PKC inhibitory effect comparable to that of staurosporine. Micafungin induced hyphal melanization in M. grisea, and this melanization, which is required for pathogenicity of M. grisea, was inhibited by PKC inhibition by both Z-705 and staurosporine. The mRNA levels of 4HNR, 3HNR, and SCD1, which are essential for melanization in M. grisea, were suppressed by both PKC inhibitors.

Molecular Dynamics Simulations to Determine the Structure and Dynamics of Hepatitis B Virus Capsid Bound to a Novel Anti-viral Drug.

Hepatitis B virus (HBV) chronically infects millions of people worldwide and is a major cause of serious liver diseases, including liver cirrhosis and liver cancer. In our previous study, in silico screening was used to isolate new anti-viral compounds predicted to bind to the HBV capsid. Four of the isolated compounds have been reported to suppress the cellular multiplication of HBV experimentally. In the present study, molecular dynamics simulations of the HBV capsid were performed under rotational symmetry boundary conditions, to clarify how the structure and dynamics of the capsid are affected at the atomic level by the binding of one of the isolated compounds, C13. Two simulations of the free HBV capsid, two further simulations of the capsid-C13 complex, and one simulation of the capsid-AT-130 complex were performed. For statistical confidence, each set of simulations was repeated by five times, changing the simulation conditions. C13 continued to bind at the predicted binding site during the simulations, supporting the hypothesis that C13 is a capsid-binding compound. The structure and dynamics of the HBV capsid were greatly influenced by the binding and release of C13, and these effects were essentially identical to those seen for AT-130, indicating that C13 likely inhibits the function of the HBV capsid.

Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease.

BACKGROUND: Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. RESULTS: Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods. CONCLUSIONS: Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

Discovering novel direct acting antiviral agents for HBV using in silico screening.

The treatments for chronic hepatitis B (CHB) are interferon and nucleoside analogues reverse transcriptase (RT) inhibitors. Because both treatments are less than ideal, we conducted to identify novel anti-viral agents for HBV-reverse transcriptase (HBV-RT). We determined the ligand-binding site of the HBV-RT by conducting a homological search of the amino acid sequence and then we also determined not only structural arrangement of the target protein but the target protein-binding site of the ligand using known protein-ligand complexes in registered in the protein data bank (PDB). Finally we simulated binding between the ligand candidates and the HBV-RT and evaluated the degree of binding (in silico screening). PXB cells derived from human-mouse chimeric mouse liver, infected with HBV were administrated with the candidates, and HBVDNA in the culture medium was monitored by realtime qPCR. Among compounds from the AKosSamples database, twelve candidates that can inhibit RT were also identified, two of which seem to have the potential to control HBV replication in vitro.

Development of novel hepatitis B virus capsid inhibitor using in silico screening.

Antiviral therapy for chronic hepatitis B that uses nucleos(t)ide analogue is considered effective. However, most drugs of this class frequently result in viral relapse after cessation of therapy as well as the emergence of resistance, thereby limiting their clinical use. In order to increase the therapeutic efficiency of chronic hepatitis B treatments, it is important to survey novel (chemical) reagents targeting other stages of the viral replication process. The aim of this study was to identify novel capsid inhibitor candidates using in silico screening. We discovered four such candidates that decreased the levels of HBV DNA and HBsAg in vitro. These four capsid inhibitor candidates did not induce cell toxicity even at high concentrations. Results from docking simulation showed that the candidates bounded with high affinity with the capsid protein hydrophobic binding site. Identifying direct acting HBV core protein inhibitors increases the likelihood that novel medicines can be developed that allows the combination of novel anti-viral drugs and nucleos(t)ide analogue or interferon for HBV treatment.

TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasis in non-small cell lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) remains lethal despite the development of numerous drug therapy technologies. About 85% to 90% of lung cancers are NSCLC and the 5-year survival rate is at best still below 50%. Thus, it is important to find drugable target genes for NSCLC to develop an effective therapy for NSCLC. RESULTS: Integrated analysis of publically available gene expression and promoter methylation patterns of two highly aggressive NSCLC cell lines generated by in vivo selection was performed. We selected eleven critical genes that may mediate metastasis using recently proposed principal component analysis based unsupervised feature extraction. The eleven selected genes were significantly related to cancer diagnosis. The tertiary protein structure of the selected genes was inferred by Full Automatic Modeling System, a profile-based protein structure inference software, to determine protein functions and to specify genes that could be potential drug targets. CONCLUSIONS: We identified eleven potentially critical genes that may mediate NSCLC metastasis using bioinformatic analysis of publically available data sets. These genes are potential target genes for the therapy of NSCLC. Among the eleven genes, TINAGL1 and B3GALNT1 are possible candidates for drug compounds that inhibit their gene expression.

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions.

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

Mechanism by which the lectin actinohivin blocks HIV infection of target cells.

Various lectins have attracted attention as potential microbicides to prevent HIV transmission. Their capacity to bind glycoproteins has been suggested as a means to block HIV binding and entry into susceptible cells. The previously undescribed lectin actinohivin (AH), isolated by us from an actinomycete, exhibits potent in vitro anti-HIV activity by binding to high-mannose (Man) type glycans (HMTGs) of gp120, an envelope glycoprotein of HIV. AH contains 114 aa and consists of three segments, all of which need to show high affinity to gp120 for the anti-HIV characteristic. To generate the needed mechanistic understanding of AH binding to HIV in anticipation of seeking approval for human testing as a microbicide, we have used multiple molecular tools to characterize it. AH showed a weak affinity to Man alpha(1-2)Man, Man alpha(1-2)Man alpha(1-2)Man, of HMTG (Man8 or Man9) or RNase B (which has a single HMTG), but exhibited a strong and highly specific affinity (K(d) = 3.4 x 10(-8) M) to gp120 of HIV, which contains multiple Man8 and/or Man9 units. We have compared AH to an alternative lectin, cyanovirin-N, which did not display similar levels of discrimination between high- and low-density HMTGs. X-ray crystal analysis of AH revealed a 3D structure containing three sugar-binding pockets. Thus, the strong specific affinity of AH to gp120 is considered to be due to multivalent interaction of the three sugar-binding pockets with three HMTGs of gp120 via the "cluster effect" of lectin. Thus, AH is a good candidate for investigation as a safe microbicide to help prevent HIV transmission.

A new method for induced fit docking (GENIUS) and its application to virtual screening of novel HCV NS3-4A protease inhibitors.

Hepatitis C virus (HCV) is an etiologic agent of chronic liver disease, and approximately 170 million people worldwide are infected with the virus. HCV NS3-4A serine protease is essential for the replication of this virus, and thus has been investigated as an attractive target for anti-HCV drugs. In this study, we developed our new induced-fit docking program (genius), and applied it to the discovery of a new class of NS3-4A protease inhibitors (IC(50)=1-10 µM including high selectivity index). The new inhibitors thus identified were modified, based on the docking models, and revealed preliminary structure-activity relationships. Moreover, the genius in silico screening performance was validated by using an enrichment factor. We believe our designed scaffold could contribute to the improvement of HCV chemotherapy.

Method for predicting homology modeling accuracy from amino acid sequence alignment: the power function.

We have devised a power function (PF) that can predict the accuracy of a three-dimensional (3D) structure model of a protein using only amino acid sequence alignments. This Power Function (PF) consists of three parts; (1) the length of a model, (2) a homology identity percent value and (3) the agreement rate between PSI-PRED secondary structure prediction and the secondary structure judgment of a reference protein. The PF value is mathematically computed from the execution process of homology search tools, such as FASTA or various BLAST programs, to obtain the amino acid sequence alignments. There is a high correlation between the global distance test-total score (GDT_TS) value of the protein model based upon the PF score and the GDT_TS(MAX) value used as an index of protein modeling accuracy in the international contest Critical Assessment of Techniques for Protein Structure Prediction (CASP). Accordingly, the PF method is valuable for constructing a highly accurate model without wasteful calculations of homology modeling that is normally performed by an iterative method to move the main chain and side chains in the modeling process. Moreover, a model with higher accuracy can be obtained by combining the models ordered by the PF score with models sorted by the size of the CIRCLE score. The CIRCLE software is a 3D-1D program, in which energetic stabilization is estimated based upon the experimental environment of each amino acid residue in the protein solution or protein crystals.

HUMAN FAMSD-BASE: high quality protein structure model database for the human genome using the FAMSD homology modeling method.

Almost all proteins express their biological functions through the structural conformation of their specific amino acid sequences. Therefore, acquiring the three-dimensional structures of proteins is very important to elucidate the role of a particular protein. We had built protein structure model databases, which is called RIKEN FAMSBASE (http://famshelp.gsc.riken.jp/famsbase/). The RIKEN FAMSBASE is a genome-wide protein structure model database that contains a large number of protein models from many organisms. The HUMAN FAMSBASE that is one part of the RIKEN FAMSBASE contains many protein models for human genes, which are significant in the pharmaceutical and medicinal fields. We have now implemented an update of the human protein modeling database consisting of 242918 constructed models against the number of 20743 human protein sequences with an improved modeling method called Full Automatic protein Modeling System Developed (FAMSD). The results of our benchmark test of the FAMSD method indicated that it has an excellent capability to pack amino acid side-chains with correct torsion angles in addition to the main-chain, while avoiding the formation of atom-atom collisions that are not found in experimental structures. This new protein structure model database for human genes, which is named HUMAN FAMSD-BASE, is open to the public as a component part of the RIKEN FAMSBASE at http://mammalia.gsc.riken.jp/human_famsd/. A significant improvement of the HUMAN FAMSD-BASE in comparison with the preceding HUMAN FAMSBASE was verified in the benchmark test of this paper. The HUMAN FAMSD-BASE will have an important impact on the progress of biological science.

New protein structure model evaluation methods that include a side-chain consensus score for the protein modeling.

Selecting the best quality model from a set of predicted structures is one of the most important aspects of protein structure prediction. We have developed model quality assessment programs that select high quality models which account for both the Calpha backbone and side-chain atom positions. The new methods are based on the consensus method with consideration of the side-chain environment of a protein structure and the secondary structure agreement. This Side-chain Environment Consensus (SEC) method is compared with the conventional consensus method, 3D-Jury (Ginalski K. et al., Bioinformatics, 19, 1015-1018 (2003)), which takes into account only the Calpha backbone atoms of the protein model. As the result, it was found that the SEC method selects the models with more accurate positioning of the side-chain atoms than the 3D-Jury method. When the SEC method was used in combination with the 3D-Jury method (3DJ+SEC), models were selected with improved quality both in the Calpha backbone and side-chain atom positions. Moreover, the CIRCLE (CCL) method (Terashi G. et al., Proteins, 69 (Suppl. 8), 98-107 (2007)) based on the 3D-1D profile score has been shown to select the best possible models that are the closest to the native structures from candidate models. Accordingly, the 3DJ+SEC+CCL method, in which CIRCLE is used after reducing the number of candidates by the 3DJ+SEC consensus method, was found to be very effective in selecting high quality models. Thus, the best method (the 3DJ+SEC+CCL method) includes the consensus approaches of the Calpha backbone and the side-chains, the secondary structure agreement and the 3D-1D profile score which corresponds to the free energy-like score in the residues of the protein model. In short, new algorithms are introduced in protein structure evaluation methods that are based on a side-chain consensus score. Additionally, in order to apply the 3DJ+SEC+CCL method and indicate the usefulness of this method, a model of human Cabin1, a protein associated with p53 function and cancer, is created using various internet modeling and alignment servers.

Development of a novel anti-hepatitis B virus agent via Sp1.

Nucleos(t)ide analog (NA) therapy has proven effective in treating chronic hepatitis B. However, NAs frequently result in viral relapse after the cessation of therapy. This is because NAs cannot fully eliminate the viral episomal covalently closed circular DNA (cccDNA) in the nucleus. In this study, we identified small molecular compounds that control host factors related to viral replication using in silico screening with simulated annealing based on bioinformatics for protein-ligand flexible docking. Twelve chemical compound candidates for alpha-glucosidase (AG) inhibitors were identified from a library of chemical compounds and used to treat fresh human hepatocytes infected with HBV. They were then monitored for their anti-viral effects. HBV replication was inhibited by one candidate (1-[3-(4-tert-butylcyclohexyl)oxy-2-hydroxypropyl]-2,2,6,6-tetramethylpiperidin-4-ol) in a dose-dependent manner. This compound significantly reduced ccc DNA production, compared to Entecavir (p < 0.05), and had a lower anti-AG effect. Gene expression analysis and structural analysis of this compound showed that its inhibitive effect on HBV was via interaction with Sp1. The nuclear transcription factor Sp1 acts on multiple regions of HBV to suppress HBV replication. Identifying candidates that control nuclear transcription factors facilitate the development of novel therapies. Drugs with a mechanism different from NA are promising for the elimination of HBV.

Author Correction: Development of a novel anti-hepatitis B virus agent via Sp1.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Dynamic influence of the two membrane-proximal immunoglobulin-like domains upon the peptide-binding platform domain in class I and class II major histocompatibility complexes: normal mode analysis.

Major histocompatibility complexes (MHCs) mainly fall into class I and class II. The two classes have similar structures, with two membrane-proximal immunoglobulin-like domains and a peptide-binding platform domain, though their organizations are different. We simulated the dynamics of a whole and partial model deficient in either of the two membrane-proximal domains for class I and class II using normal mode analysis. Our study showed that the influence of the two membrane-proximal domains upon the dynamics of the platform domain were decisively different between class II and class I. Both membrane-proximal domains (the alpha2 and beta2 domains) of class II MHC, especially the alpha2 domain, influenced the most important pocket that accommodates a large hydrophobic anchor side chain of the N-terminal side of the bound peptide, though the pocket was not in the alpha2 domain neighborhood. By contrast, the two membrane-proximal domains (the alpha3 and beta2m domains) of class I MHC had little influence upon the most important pocket that accommodates the N-terminal residue of the bound peptide. These results suggest that the two membrane-proximal domains of class II MHC have a greater influence upon peptide-binding than those of class I MHC.

FAMSD: A powerful protein modeling platform that combines alignment methods, homology modeling, 3D structure quality estimation and molecular dynamics.

The prediction of a protein three-dimensional (3D) structure is one of the most important challenges in computational structural biology. We have developed an automatic protein 3D structure prediction method called FAMSD. FAMSD is based on a comparative modeling method which consists of the following four steps: (1) generating and selecting sequence alignments between target and template proteins; (2) constructing 3D structure models based on each selected alignment; (3) selecting the best 3D structure model and (4) refining the selected model. In the FAMSD method, sequence alignment programs such as a series of BLAST programs, SP3 and SPARKS2 programs, the homology modeling program FAMS (Full Automatic Modeling System), the model quality estimation program CIRCLE and the molecular dynamics program APRICOT were used in combination to construct high quality protein models. To assess the FAMSD method we have participated in the 8th Critical Assessment of Techniques for Protein Structure Prediction (CASP8) experiment. The results of our original assessment indicate that the FAMSD method offers excellent capability in packing side-chains with the correct torsion angles while avoiding the formation of atom-atom collisions. Since side-chain packing plays a significant role in defining the biological function of proteins, this method is a valuable resource in biological, pharmaceutical and medicinal research efforts.

A prospective compound screening contest identified broader inhibitors for Sirtuin 1.

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

Ubiquitin-dependent proteolysis of CXCL7 leads to posterior longitudinal ligament ossification.

Ossification of the posterior longitudinal ligament (OPLL), a spinal ligament, reduces the range of motion in limbs. No treatment is currently available for OPLL, which is why therapies are urgently needed. OPLL occurs in obesity, is more common in men, and has an onset after 40 years of age. The mechanisms underlying OPLL remain unclear. In this study, we performed a serum proteomic analysis in both OPLL patients and healthy subjects to identify factors potentially involved in the development of OPLL, and found reduced levels of a protein that might underlie the pathology of OPLL. We isolated the protein, determined its amino acid sequence, and identified it as chemokine (C-X-C motif) ligand 7 (CXCL7). Based on these proteomics findings, we generated a CXCL7 knockout mouse model to study the molecular mechanisms underlying OPLL. CXCL7-null mice presented with a phenotype of OPLL, showing motor impairment, heterotopic ossification in the posterior ligament tissue, and osteoporosis in vertebrate tissue. To identify the mechanisms of CXCL7 deficiency in OPLL, we searched for single nucleotide polymorphisms and altered DNA exons, but no abnormalities were found. Although miR-340 levels were found to be high in an miRNA array, they were insufficient to reduce CXCL7 levels. Ubiquitin C-terminal hydrolase1 (UCHL1) was found to be overexpressed in CXCL7-null mice and in the sera of patients with OPLL, and was correlated with OPLL severity. Post-translational modifications of proteins with ubiquitin and ubiquitin-like modifiers, orchestrated by a cascade of specialized ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2), and ubiquitin ligase (E3) enzymes, are thought to control a wide range of cellular processes, and alterations in the ubiquitin-proteasome system have been associated with several degenerative disorders. In addition, the OPLL tissue of CXCL7-null mouse and its primary cells expressed the antibody to ubiquitin (linkage-specific K48). Our data clearly show decreased CXCL7 levels in patients with OPLL, and that OPLL developed in mice lacking CXCL7. Tumor necrosis factor receptor-associated factor (TRAF)6 expression was decreased in CXCL7-null mouse primary cells. Furthermore, K48 polyubiquitination was found in posterior longitudinal ligament ossified tissue and primary cells from CXCL7-null mice. We performed a phosphoproteomics analysis in CXCL7-deficient mice and identified increased phosphorylation of mitogen-activated protein kinase kinase (ME3K)15, ubiquitin protein ligase E3C (UBE3C) and protein kinase C (PKC) alpha, suggesting that ubiquitin-dependent degradation is involved in CXCL7 deficiency. Future studies in the CXCL7-null mouse model are, therefore, warranted to investigate the role of ubiquitination in the onset of OPLL. In conclusion, CXCL7 levels may be useful as a serum marker for the progression of OPLL. This study also suggests that increasing CXCL7 levels in patients can serve as an effective therapeutic strategy for the treatment of OPLL.

The SKE-DOCK server and human teams based on a combined method of shape complementarity and free energy estimation.

We participated in rounds 6-12 of the critical assessment of predicted interaction (CAPRI) contest as the SKE-DOCK server and human teams. The SKE-DOCK server is based on simple geometry docking and a knowledge base scoring function. The procedure is summarized in the following three steps: (1) protein docking according to shape complementarity, (2) evaluating complex models, and (3) repacking side-chain of models. The SKE-DOCK server did not make use of biological information. On the other hand, the human team tried various intervention approaches. In this article, we describe in detail the processes of the SKE-DOCK server, together with results and reasons for success and failure. Good predicted models were obtained for target 25 by both the SKE-DOCK server and human teams. When the modeled receptor proteins were superimposed on the experimental structures, the smallest Ligand-rmsd values corresponding to the rmsd between the model and experimental structures were 3.307 and 3.324 A, respectively. Moreover, the two teams obtained 4 and 2 acceptable models for target 25. The overall result for both the SKE-DOCK server and human teams was medium accuracy for one (Target 25) out of nine targets.

Fams-ace: a combined method to select the best model after remodeling all server models.

During Critical Assessment of Protein Structure Prediction (CASP7, Pacific Grove, CA, 2006), fams-ace was entered in the 3D coordinate prediction category as a human expert group. The procedure can be summarized by the following three steps. (1) All the server models were refined and rebuilt utilizing our homology modeling method. (2) Representative structures were selected from each server, according to a model quality evaluation, based on a 3D1D profile score (like Verify3D). (3) The top five models were selected and submitted in the order of the consensus-based score (like 3D-Jury). Fams-ace is a fully automated server and does not require human intervention. In this article, we introduce the methodology of fams-ace and discuss the successes and failures of this approach during CASP7. In addition, we discuss possible improvements for the next CASP.