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.

Registration quality filtering improves robustness of voxel-wise analyses to the choice of brain template.

MOTIVATION: Many clinical and scientific conclusions that rely on voxel-wise analyses of neuroimaging depend on the accurate comparison of corresponding anatomical regions. Such comparisons are made possible by registration of the images of subjects of interest onto a common brain template, such as the Johns Hopkins University (JHU) template. However, current image registration algorithms are prone to errors that are distributed in a template-dependent manner. Therefore, the results of voxel-wise analyses can be sensitive to template choice. Despite this problem, the issue of appropriate template choice for voxel-wise analyses is not generally addressed in contemporary neuroimaging studies, which may lead to the reporting of spurious results. RESULTS: We present a novel approach to determine the suitability of a brain template for voxel-wise analysis. The approach is based on computing a "distance" between automatically-generated atlases of the subjects of interest and templates that is indicative of the extent of subject-to-template registration errors. This allows for the filtering of subjects and candidate templates based on a quantitative measure of registration quality. We benchmark our approach by evaluating alternative templates for a voxel-wise analysis that reproduces the well-known decline in fractional anisotropy (FA) with age. Our results show that filtering registrations minimizes errors and decreases the sensitivity of voxel-wise analysis to template choice. In addition to carrying important implications for future neuroimaging studies, the developed framework of template induction can be used to evaluate robustness of data analysis methods to template choice.

Discovery of receptor-ligand interfaces in the immunoglobulin superfamily.

Cell-surface-anchored immunoglobulin superfamily (IgSF) proteins are widespread throughout the human proteome, forming crucial components of diverse biological processes including immunity, cell-cell adhesion, and carcinogenesis. IgSF proteins generally function through protein-protein interactions carried out between extracellular, membrane-bound proteins on adjacent cells, known as trans-binding interfaces. These protein-protein interactions constitute a class of pharmaceutical targets important in the treatment of autoimmune diseases, chronic infections, and cancer. A molecular-level understanding of IgSF protein-protein interactions would greatly benefit further drug development. A critical step toward this goal is the reliable identification of IgSF trans-binding interfaces. We propose a novel combination of structure and sequence information to identify trans-binding interfaces in IgSF proteins. We developed a structure-based binding interface prediction approach that can identify broad regions of the protein surface that encompass the binding interfaces and suggests that IgSF proteins possess binding supersites. These interfaces could theoretically be pinpointed using sequence-based conservation analysis, with performance approaching the theoretical upper limit of binding interface prediction accuracy, but achieving this in practice is limited by the current ability to identify an appropriate multiple sequence alignment for conservation analysis. However, an important contribution of combining the two orthogonal methods is that agreement between these approaches can estimate the reliability of the predictions. This approach was benchmarked on the set of 22 IgSF proteins with experimentally solved structures in complex with their ligands. Additionally, we provide structure-based predictions and reliability scores for the 62 IgSF proteins with known structure but yet uncharacterized binding interfaces.

The choice of sequence homologs included in multiple sequence alignments has a dramatic impact on evolutionary conservation analysis.

Motivation: The analysis of sequence conservation patterns has been widely utilized to identify functionally important (catalytic and ligand-binding) protein residues for over a half-century. Despite decades of development, on average state-of-the-art non-template-based functional residue prediction methods must predict ∼25% of a protein's total residues to correctly identify half of the protein's functional site residues. The overwhelming proportion of false positives results in reported 'F-Scores' of ∼0.3. We investigated the limits of current approaches, focusing on the so-far neglected impact of the specific choice of homologs included in multiple sequence alignments (MSAs). Results: The limits of conservation-based functional residue prediction were explored by surveying the binding sites of 1023 proteins. A straightforward conservation analysis of MSAs composed of randomly selected homologs sampled from a PSI-BLAST search achieves average F-Scores of ∼0.3, a performance matching that reported by state-of-the-art methods, which often consider additional features for the prediction in a machine learning setting. Interestingly, we found that a simple combinatorial MSA sampling algorithm will in almost every case produce an MSA with an optimal set of homologs whose conservation analysis reaches average F-Scores of ∼0.6, doubling state-of-the-art performance. We also show that this is nearly at the theoretical limit of possible performance given the agreement between different binding site definitions. Additionally, we showcase the progress in this direction made by Selection of Alignment by Maximal Mutual Information (SAMMI), an information-theory-based approach to identifying biologically informative MSAs. This work highlights the importance and the unused potential of optimally composed MSAs for conservation analysis. Supplementary information: Supplementary data are available at Bioinformatics online.

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.

Identifying functionally informative evolutionary sequence profiles.

Motivation: Multiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually. Results: We present Selection of Alignment by Maximal Mutual Information (SAMMI), an automated, sequence-based approach to objectively select an optimal MSA from a large set of alternatives sampled from a general sequence database search. The hypothesis of this approach is that the mutual information among MSA columns will be maximal for those MSAs that contain the most diverse set possible of the most structurally and functionally homogeneous protein sequences. SAMMI was tested to select MSAs for functional site residue prediction by analysis of conservation patterns on a set of 435 proteins obtained from protein-ligand (peptides, nucleic acids and small substrates) and protein-protein interaction databases. Availability and implementation: A freely accessible program, including source code, implementing SAMMI is available at https://github.com/nelsongil92/SAMMI.git. Contact: andras.fiser@einstein.yu.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Development and Evaluation of a Web-Based Dermatology Teaching Tool for Preclinical Medical Students.

INTRODUCTION: There is growing interest in, and emphasis on, electronic teaching tools in medicine. Despite relevant testing on the United States Medical Licensing Examination (USMLE), American medical schools offer limited training in skin disorders. Teaching visual topics like dermatology in classroom formats is challenging. We hypothesized that an electronic module would enhance students' dermatology competency. METHODS: A self-directed, case-based module was created. To test its efficacy, 40 medical students were randomized to have module access (interventional group) or none (conventional group). Learning outcomes were compared using a multiple-choice exam, including questions relevant and irrelevant to the module. Outcomes included proportions of correctly answered module questions (module scores) and nonmodule questions (nonmodule scores). Difference scores were calculated: (module score) - (nonmodule score). Positive values indicated that knowledge of module questions surpassed that of nonmodule questions. If there were a training effect, the interventional group's difference score should exceed that of the conventional group. RESULTS: The interventional group scored significantly higher than did the conventional group on module questions-75% (interquartile range [IQR], 69-88) versus 50% (IQR, 38-63), p < .001-and nonmodule questions-85% (IQR, 69-92) versus 69% (IQR, 54-77), p = .02. The Hodges-Lehman median difference estimate of the training effect was 13.0 (95% confidence interval, 0.5-25.5). DISCUSSION: This e-module is effective at enhancing students' competency in dermatology while emphasizing detailed pathophysiology that prepares them for USMLE Step 1. A module-based curriculum may enhance learning in supplement to traditional teaching modalities.