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.

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.

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

Supplementary data are available at Bioinformatics online.

No abstract

Supplementary data are available at Bioinformatics online.

No abstract

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.