Estimating the accuracy of pharmacophore-based detection of cognate receptor-ligand pairs in the immunoglobulin superfamily.

Secreted and membrane-bound members of the immunoglobulin superfamily (IgSF) encompass a large, diverse array of proteins that play central roles in immune response and neural development, and are implicated in diseases ranging from cancer to rheumatoid arthritis. Despite the potential biomedical benefits of understanding IgSF:IgSF cognate receptor-ligand interactions, relatively little about them is known at a molecular level, and experimentally probing all possible receptor-ligand pairs is prohibitively costly. The Protein Ligand Interface Design (ProtLID) algorithm is a computational pharmacophore-based approach to identify cognate receptor-ligand pairs that was recently validated in a pilot study on a small set of IgSF complexes. Although ProtLID has shown a success rate of 61% at identifying at least one cognate ligand for a given receptor, it currently lacks any form of confidence measure that can prioritize individual receptor-ligand predictions to pursue experimentally. In this study, we expanded the application of ProtLID to cover all IgSF complexes with available structural data. In addition, we introduced an approach to estimate the confidence of predictions made by ProtLID based on a statistical analysis of how the ProtLID-constructed pharmacophore matches the structures of candidate ligands. The confidence score combines the physicochemical compatibility, spatial consistency, and mathematical skewness of the distribution of matches throughout a set of candidate ligands. Our results suggest that a subset of cases meeting stringent confidence criteria will always have at least one successful receptor-ligand prediction.

Assessing the accuracy of contact predictions in CASP13.

The accuracy of sequence-based tertiary contact predictions was assessed in a blind prediction experiment at the CASP13 meeting. After 4 years of significant improvements in prediction accuracy, another dramatic advance has taken place since CASP12 was held 2 years ago. The precision of predicting the top L/5 contacts in the free modeling category, where L is the corresponding length of the protein in residues, has exceeded 70%. As a comparison, the best-performing group at CASP12 with a 47% precision would have finished below the top 1/3 of the CASP13 groups. Extensively trained deep neural network approaches dominate the top performing algorithms, which appear to efficiently integrate information on coevolving residues and interacting fragments or possibly utilize memories of sequence similarities and sometimes can deliver accurate results even in the absence of virtually any target specific evolutionary information. If the current performance is evaluated by F-score on L contacts, it stands around 24% right now, which, despite the tremendous impact and advance in improving its utility for structure modeling, also suggests that there is much room left for further improvement.

Assessment of chemical-crosslink-assisted protein structure modeling in CASP13.

With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches. This largest-to-date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases. However, in a broader context, it also suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows.

A fragmentation and reassembly method for ab initio phasing.

Ab initio phasing with de novo models has become a viable approach for structural solution from protein crystallographic diffraction data. This approach takes advantage of the known protein sequence information, predicts de novo models and uses them for structure determination by molecular replacement. However, even the current state-of-the-art de novo modelling method has a limit as to the accuracy of the model predicted, which is sometimes insufficient to be used as a template for successful molecular replacement. A fragment-assembly phasing method has been developed that starts from an ensemble of low-accuracy de novo models, disassembles them into fragments, places them independently in the crystallographic unit cell by molecular replacement and then reassembles them into a whole structure that can provide sufficient phase information to enable complete structure determination by automated model building. Tests on ten protein targets showed that the method could solve structures for eight of these targets, although the predicted de novo models cannot be used as templates for successful molecular replacement since the best model for each target is on average more than 4.0 Šaway from the native structure. The method has extended the applicability of the ab initio phasing by de novo models approach. The method can be used to solve structures when the best de novo models are still of low accuracy.

Improving fragment quality for de novo structure prediction.

De novo structure prediction can be defined as a search in conformational space under the guidance of an energy function. The most successful de novo structure prediction methods, such as Rosetta, assemble the fragments from known structures to reduce the search space. Therefore, the fragment quality is an important factor in structure prediction. In our study, a method is proposed to generate a new set of fragments from the lowest energy de novo models. These fragments were subsequently used to predict the next-round of models. In a benchmark of 30 proteins, the new set of fragments showed better performance when used to predict de novo structures. The lowest energy model predicted using our method was closer to native structure than Rosetta for 22 proteins. Following a similar trend, the best model among top five lowest energy models predicted using our method was closer to native structure than Rosetta for 20 proteins. In addition, our experiment showed that the C-alpha root mean square deviation was improved from 5.99 to 5.03 Å on average compared to Rosetta when the lowest energy models were picked as the best predicted models.

A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties.

Identification of favorable biophysical properties for protein therapeutics as part of developability assessment is a crucial part of the preclinical development process. Successful prediction of such properties and bioassay results from calculated in silico features has potential to reduce the time and cost of delivering clinical-grade material to patients, but nevertheless has remained an ongoing challenge to the field. Here, we demonstrate an automated and flexible machine learning workflow designed to compare and identify the most powerful features from computationally derived physiochemical feature sets, generated from popular commercial software packages. We implement this workflow with medium-sized datasets of human and humanized IgG molecules to generate predictive regression models for two key developability endpoints, hydrophobicity and poly-specificity. The most important features discovered through the automated workflow corroborate several previous literature reports, and newly discovered features suggest directions for further research and potential model improvement.

Error-estimation-guided rebuilding of de novo models increases the success rate of ab initio phasing.

Recent advancements in computational methods for protein-structure prediction have made it possible to generate the high-quality de novo models required for ab initio phasing of crystallographic diffraction data using molecular replacement. Despite those encouraging achievements in ab initio phasing using de novo models, its success is limited only to those targets for which high-quality de novo models can be generated. In order to increase the scope of targets to which ab initio phasing with de novo models can be successfully applied, it is necessary to reduce the errors in the de novo models that are used as templates for molecular replacement. Here, an approach is introduced that can identify and rebuild the residues with larger errors, which subsequently reduces the overall C(α) root-mean-square deviation (CA-RMSD) from the native protein structure. The error in a predicted model is estimated from the average pairwise geometric distance per residue computed among selected lowest energy coarse-grained models. This score is subsequently employed to guide a rebuilding process that focuses on more error-prone residues in the coarse-grained models. This rebuilding methodology has been tested on ten protein targets that were unsuccessful using previous methods. The average CA-RMSD of the coarse-grained models was improved from 4.93 to 4.06 Å. For those models with CA-RMSD less than 3.0 Å, the average CA-RMSD was improved from 3.38 to 2.60 Å. These rebuilt coarse-grained models were then converted into all-atom models and refined to produce improved de novo models for molecular replacement. Seven diffraction data sets were successfully phased using rebuilt de novo models, indicating the improved quality of these rebuilt de novo models and the effectiveness of the rebuilding process. Software implementing this method, called MORPHEUS, can be downloaded from http://www.riken.jp/zhangiru/software.html.

Durandal: fast exact clustering of protein decoys.

In protein folding, clustering is commonly used as one way to identify the best decoy produced. Initializing the pairwise distance matrix for a large decoy set is computationally expensive. We have proposed a fast method that works even on large decoy sets. This method is implemented in a software called Durandal. Durandal has been shown to be consistently faster than other software performing fast exact clustering. In some cases, Durandal can even outperform the speed of an approximate method. Durandal uses the triangular inequality to accelerate exact clustering, without compromising the distance function. Recently, we have further enhanced the performance of Durandal by incorporating a Quaternion-based characteristic polynomial method that has increased the speed of Durandal between 13% and 27% compared with the previous version. Durandal source code is available under the GNU General Public License at http://www.riken.jp/zhangiru/software/durandal_released_qcp.tgz. Alternatively, a compiled version of Durandal is also distributed with the nightly builds of the Phenix (http://www.phenix-online.org/) crystallographic software suite (Adams et al., Acta Crystallogr Sect D 2010, 66, 213).

Entropy-accelerated exact clustering of protein decoys.

MOTIVATION: Clustering is commonly used to identify the best decoy among many generated in protein structure prediction when using energy alone is insufficient. Calculation of the pairwise distance matrix for a large decoy set is computationally expensive. Typically, only a reduced set of decoys using energy filtering is subjected to clustering analysis. A fast clustering method for a large decoy set would be beneficial to protein structure prediction and this still poses a challenge. RESULTS: We propose a method using propagation of geometric constraints to accelerate exact clustering, without compromising the distance measure. Our method can be used with any metric distance. Metrics that are expensive to compute and have known cheap lower and upper bounds will benefit most from the method. We compared our method's accuracy against published results from the SPICKER clustering software on 40 large decoy sets from the I-TASSER protein folding engine. We also performed some additional speed comparisons on six targets from the 'semfold' decoy set. In our tests, our method chose a better decoy than the energy criterion in 25 out of 40 cases versus 20 for SPICKER. Our method also was shown to be consistently faster than another fast software performing exact clustering named Calibur. In some cases, our approach can even outperform the speed of an approximate method. AVAILABILITY: Our C++ software is released under the GNU General Public License. It can be downloaded from http://www.riken.jp/zhangiru/software/durandal_released.tgz.

Accelerating ab initio phasing with de novo models.

Ab initio phasing is one of the remaining challenges in protein crystallography. Recent progress in computational structure prediction has enabled the generation of de novo models with high enough accuracy to solve the phase problem ab initio. This `ab initio phasing with de novo models' method first generates a huge number of de novo models and then selects some lowest energy models to solve the phase problem using molecular replacement. The amount of CPU time required is huge even for small proteins and this has limited the utility of this method. Here, an approach is described that significantly reduces the computing time required to perform ab initio phasing with de novo models. Instead of performing molecular replacement after the completion of all models, molecular replacement is initiated during the course of each simulation. The approach principally focuses on avoiding the refinement of the best and the worst models and terminating the entire simulation early once suitable models for phasing have been obtained. In a benchmark data set of 20 proteins, this method is over two orders of magnitude faster than the conventional approach. It was observed that in most cases molecular-replacement solutions were determined soon after the coarse-grained models were turned into full-atom representations. It was also found that all-atom refinement was hardly able to change the models sufficiently to enable successful molecular replacement if the coarse-grained models were not very close to the native structure. Therefore, it remains critical to generate good-quality coarse-grained models to enable subsequent all-atom refinement for successful ab initio phasing by molecular replacement.

Residue-based pharmacophore approaches to study protein-protein interactions.

This review focuses on pharmacophore approaches in researching protein interfaces that bind protein ligands. Pharmacophore descriptions of binding interfaces that employ molecular dynamics simulation can account for effects of solvation and conformational flexibility. In addition, these calculations provide an approximation to entropic considerations and as such, a better approximation of the free energy of binding. Residue-based pharmacophore approaches can facilitate a variety of drug discovery tasks such as the identification of receptor-ligand partners, identifying their binding poses, designing protein interfaces for selectivity, or defining a reduced mutational combinatorial exploration for subsequent experimental engineering techniques by orders of magnitudes.

Allosteric regulation of binding specificity of HVEM for CD160 and BTLA ligands upon G89F mutation.

Molecular interactions mediated by engagement of the Herpes virus entry mediator (HVEM) with members of TNF and Ig superfamily generate distinct signals in T cell activation pathways that modulate inflammatory and inhibitory responses. HVEM interacts with CD160 and B and T lymphocyte attenuator (BTLA), both members of the immunoglobulin (Ig) superfamily, which share a common binding site that is unique from that of LIGHT, a TNF ligand. BTLA or CD160 engagement with HVEM deliver inhibitory or stimulatory signals to the host immune response in a context dependent fashion, whereas HVEM engagement with LIGHT results in pro-inflammatory responses. We identified a mutation in human HVEM, G89F, which directly interferes with the human LIGHT interaction, but interestingly, also differentially modulates the binding of human BTLA and CD160 via an apparent allosteric mechanism involving recognition surfaces remote from the site of the mutation. Specifically, the G89F mutation enhances binding of CD160, while decreasing that of BTLA to HVEM in cell-based assays. Molecular dynamics simulations for wild-type and G89F mutant HVEM, bound to different sets of ligands, were performed to define the molecular basis of this unexpected allosteric effect. These results were leveraged to design additional human HVEM mutants with altered binding specificities.

Redesigning HVEM Interface for Selective Binding to LIGHT, BTLA, and CD160.

Herpes virus entry mediator (HVEM) regulates positive and negative signals for T cell activation through co-signaling pathways. Dysfunction of the HVEM co-signaling network is associated with multiple pathologies related to autoimmunity, infectious disease, and cancer, making the associated molecules biologically and therapeutically attractive targets. HVEM interacts with three ligands from two different superfamilies using two different binding interfaces. The engagement with ligands CD160 and B- and T-lymphocyte attenuator (BTLA), members of immunoglobulin superfamily, is associated with inhibitory signals, whereas inflammatory responses are regulated through the interaction with LIGHT from the TNF superfamily. We computationally redesigned the HVEM recognition interfaces using a residue-specific pharmacophore approach, ProtLID, to achieve switchable-binding specificity. In subsequent cell-based binding assays the new interfaces, designed with only single or double mutations, exhibited selective binding to only one or two out of the three cognate ligands.

Computational Redesign of PD-1 Interface for PD-L1 Ligand Selectivity.

Chronic or persistent stimulation of the programmed cell death-1 (PD-1) pathway prevents T cells from mounting anti-tumor and anti-viral immune responses. Blockade of this inhibitory checkpoint pathway has shown therapeutic importance by rescuing T cells from their exhausted state. Cognate ligands of the PD-1 receptor include the tissue-specific PD-L1 and PD-L2 proteins. Engineering a human PD-1 interface specific for PD-L1 or PD-L2 can provide a specific reagent and therapeutic advantage for tissue-specific disruption of the PD-1 pathway. We utilized ProtLID, a computational framework, which constitutes a residue-based pharmacophore approach, to custom-design a human PD-1 interface specific to human PD-L1 without any significant affinity to PD-L2. In subsequent cell assay experiments, half of all single-point mutant designs proved to introduce a statistically significant selectivity, with nine of these maintaining a close to wild-type affinity to PD-L1. This proof-of-concept study suggests a general approach to re-engineer protein interfaces for specificity.

Fragger: a protein fragment picker for structural queries.

Protein modeling and design activities often require querying the Protein Data Bank (PDB) with a structural fragment, possibly containing gaps. For some applications, it is preferable to work on a specific subset of the PDB or with unpublished structures. These requirements, along with specific user needs, motivated the creation of a new software to manage and query 3D protein fragments. Fragger is a protein fragment picker that allows protein fragment databases to be created and queried. All fragment lengths are supported and any set of PDB files can be used to create a database. Fragger can efficiently search a fragment database with a query fragment and a distance threshold. Matching fragments are ranked by distance to the query. The query fragment can have structural gaps and the allowed amino acid sequences matching a query can be constrained via a regular expression of one-letter amino acid codes. Fragger also incorporates a tool to compute the backbone RMSD of one versus many fragments in high throughput. Fragger should be useful for protein design, loop grafting and related structural bioinformatics tasks.

The crystal and solution structure of YdiE from Escherichia coli.

Iron-containing porphyrins are essential for all life as electron carriers. Since iron is poorly available in an oxidizing environment, bacterial growth may be restricted by iron limitation, and this has led to the evolution of a huge variety of iron-uptake systems. Among pathogens, iron scavenging from the haemoglobin of an animal host is a common means of acquiring sufficient iron for growth. The Isd system of Staphylococcus aureus is a well studied example; the bacterium devotes considerable resources to the construction of surface proteins that deftly remove haem from haemoglobin and pass it along a chain of related proteins, eventually delivering the haem to the cytoplasm, where it can be utilized or degraded. All organisms, however, must deal with haem and related molecules, which are by their nature hydrophobic and prone to precipitate, and which tend to promote the formation of reactive oxygen species. Chaperones are an obvious solution to the problem of maintaining a pool of haem for insertion into cytochromes without allowing naked haem to cause damage. YdiE is a very small protein from Escherichia coli of only 63 residues which may play a role in haem trafficking. Here, NMR analysis and the crystal structure of the protein to high resolution are reported.

ModuleSearch: finding functional modules in a protein-protein interaction network.

Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein-protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein-protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.

A probabilistic fragment-based protein structure prediction algorithm.

Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained decoys on a benchmark of [Formula: see text] proteins. The best coarse-grained models produced by both methods were refined into all-atom models and used in molecular replacement. All atom decoys produced out of EdaFold's decoy set reach high enough accuracy to solve the crystallographic phase problem by molecular replacement for some test proteins. EdaFold showed a higher success rate in molecular replacement when compared to Rosetta. Our study suggests that improving low resolution coarse-grained decoys allows computational methods to avoid subsequent sampling issues during all-atom refinement and to produce better all-atom models. EdaFold can be downloaded from http://www.riken.jp/zhangiru/software.html [corrected].