Protein structure fundamentally underpins the function and processes of numerous biological systems. Fold recognition algorithms offer a sensitive and robust tool to detect structural, and thereby functional, similarities between distantly related homologs. In the era of accurate structure prediction owing to advances in machine learning techniques and a wealth of experimentally determined structures, previously curated sequence databases have become a rich source of biological information. Here, we use bioinformatic fold recognition algorithms to scan the entire AlphaFold structure database to identify novel protein family members, infer function and group predicted protein structures. As an example of the utility of this approach, we identify novel, previously unknown members of various pore-forming protein families, including MACPFs, GSDMs and aerolysin-like proteins.

The roles of proteolytic cleavage have been intensively investigated and discussed during the past two decades. This irreversible chemical process has been frequently reported to influence a number of crucial biological processes (BPs), such as cell cycle, protein regulation and inflammation. A number of advanced studies have been published aiming at deciphering the mechanisms of proteolytic cleavage. Given its significance and the large number of functionally enriched substrates targeted by specific proteases, many computational approaches have been established for accurate prediction of protease-specific substrates and their cleavage sites. Consequently, there is an urgent need to systematically assess the state-of-the-art computational approaches for protease-specific cleavage site prediction to further advance the existing methodologies and to improve the prediction performance. With this goal in mind, in this article, we carefully evaluated a total of 19 computational methods (including 8 scoring function-based methods and 11 machine learning-based methods) in terms of their underlying algorithm, calculated features, performance evaluation and software usability. Then, extensive independent tests were performed to assess the robustness and scalability of the reviewed methods using our carefully prepared independent test data sets with 3641 cleavage sites (specific to 10 proteases). The comparative experimental results demonstrate that PROSPERous is the most accurate generic method for predicting eight protease-specific cleavage sites, while GPS-CCD and LabCaS outperformed other predictors for calpain-specific cleavage sites. Based on our review, we then outlined some potential ways to improve the prediction performance and ease the computational burden by applying ensemble learning, deep learning, positive unlabeled learning and parallel and distributed computing techniques. We anticipate that our study will serve as a practical and useful guide for interested readers to further advance next-generation bioinformatics tools for protease-specific cleavage site prediction.

MOTIVATION: Glycosylation is a ubiquitous type of protein post-translational modification (PTM) in eukaryotic cells, which plays vital roles in various biological processes (BPs) such as cellular communication, ligand recognition and subcellular recognition. It is estimated that >50% of the entire human proteome is glycosylated. However, it is still a significant challenge to identify glycosylation sites, which requires expensive/laborious experimental research. Thus, bioinformatics approaches that can predict the glycan occupancy at specific sequons in protein sequences would be useful for understanding and utilizing this important PTM. RESULTS: In this study, we present a novel bioinformatics tool called GlycoMine, which is a comprehensive tool for the systematic in silico identification of C-linked, N-linked, and O-linked glycosylation sites in the human proteome. GlycoMine was developed using the random forest algorithm and evaluated based on a well-prepared up-to-date benchmark dataset that encompasses all three types of glycosylation sites, which was curated from multiple public resources. Heterogeneous sequences and functional features were derived from various sources, and subjected to further two-step feature selection to characterize a condensed subset of optimal features that contributed most to the type-specific prediction of glycosylation sites. Five-fold cross-validation and independent tests show that this approach significantly improved the prediction performance compared with four existing prediction tools: NetNGlyc, NetOGlyc, EnsembleGly and GPP. We demonstrated that this tool could identify candidate glycosylation sites in case study proteins and applied it to identify many high-confidence glycosylation target proteins by screening the entire human proteome. AVAILABILITY AND IMPLEMENTATION: The webserver, Java Applet, user instructions, datasets, and predicted glycosylation sites in the human proteome are freely available at http://www.structbioinfor.org/Lab/GlycoMine/. CONTACT: [email protected] or [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Caspases and granzyme B (GrB) are important proteases involved in fundamental cellular processes and play essential roles in programmed cell death, necrosis and inflammation. Although a number of substrates for both types have been experimentally identified, the complete repertoire of caspases and granzyme B substrates remained to be fully characterized. Accordingly, systematic bioinformatics studies of known cleavage sites may provide important insights into their substrate specificity and facilitate the discovery of novel substrates. RESULTS: We develop a new bioinformatics tool, termed Cascleave 2.0, which builds on previous success of the Cascleave tool for predicting generic caspase cleavage sites. It can be efficiently used to predict potential caspase-specific cleavage sites for the human caspase-1, 3, 6, 7, 8 and GrB. In particular, we integrate heterogeneous sequence and protein functional information from various sources to improve the prediction accuracy of Cascleave 2.0. During classification, we use both maximum relevance minimum redundancy and forward feature selection techniques to quantify the relative contribution of each feature to prediction and thus remove redundant as well as irrelevant features. A systematic evaluation of Cascleave 2.0 using the benchmark data and comparison with other state-of-the-art tools using independent test data indicate that Cascleave 2.0 outperforms other tools on protease-specific cleavage site prediction of caspase-1, 3, 6, 7 and GrB. Cascleave 2.0 is anticipated to be used as a powerful tool for identifying novel substrates and cleavage sites of caspases and GrB and help understand the functional roles of these important proteases in human proteolytic cascades. AVAILABILITY AND IMPLEMENTATION: http://www.structbioinfor.org/cascleave2/.

Cholesterol Dependent Cytolysins (CDCs) are important bacterial virulence factors that form large (200-300 Å) membrane embedded pores in target cells. Currently, insights from X-ray crystallography, biophysical and single particle cryo-Electron Microscopy (cryo-EM) experiments suggest that soluble monomers first interact with the membrane surface via a C-terminal Immunoglobulin-like domain (Ig; Domain 4). Membrane bound oligomers then assemble into a prepore oligomeric form, following which the prepore assembly collapses towards the membrane surface, with concomitant release and insertion of the membrane spanning subunits. During this rearrangement it is proposed that Domain 2, a region comprising three β-strands that links the pore forming region (Domains 1 and 3) and the Ig domain, must undergo a significant yet currently undetermined, conformational change. Here we address this problem through a systematic molecular modeling and structural bioinformatics approach. Our work shows that simple rigid body rotations may account for the observed collapse of the prepore towards the membrane surface. Support for this idea comes from analysis of published cryo-EM maps of the pneumolysin pore, available crystal structures and molecular dynamics simulations. The latter data in particular reveal that Domains 1, 2 and 4 are able to undergo significant rotational movements with respect to each other. Together, our data provide new and testable insights into the mechanism of pore formation by CDCs.

MOTIVATION: The β-barrel is a ubiquitous fold that is deployed to accomplish a wide variety of biological functions including membrane-embedded pores. Key influences of β-barrel lumen diameter include the number of β-strands (n) and the degree of shear (S), the latter value measuring the extent to which the β-sheet is tilted within the β-barrel. Notably, it has previously been reported that the shear value for small antiparallel β-barrels (n≤24) typically ranges between n and 2n. Conversely, it has been suggested that the β-strands in giant antiparallel β-barrels, such as those formed by pore forming cholesterol-dependent cytolysins (CDC), are parallel relative to the axis of the β-barrel, i.e. S=0. The S=0 arrangement, however, has never been observed in crystal structures of small β-barrels. Therefore, the structural basis for how CDCs form a β-barrel and span a membrane remains to be understood. RESULTS: Through comparison of molecular models with experimental data, we are able to identify how giant CDC β-barrels utilize a 'near parallel' arrangement of β-strands where S=n/2. Furthermore, we show how side-chain packing within the β-barrel lumen is an important limiting factor with respect to the possible shear values for small β-barrels (n≤24 β-strands). In contrast, our models reveal no such limitation restricts the shear value of giant β-barrels (n>24 β-strands). Giant β-barrels can thus access a different architecture compared with smaller β-barrels.

SUMMARY: Protein topology diagrams are 2D representations of protein structure that are particularly useful in understanding and analysing complex protein folds. Generating such diagrams presents a major problem in graph drawing, with automatic approaches often resulting in errors or uninterpretable results. Here we apply a breakthrough in diagram layout to protein topology cartoons, providing clear, accurate, interactive and editable diagrams, which are also an interface to a structural search method. AVAILABILITY: Pro-origami is available via a web server at http://munk.csse.unimelb.edu.au/pro-origami CONTACT: [email protected]; [email protected].

MOTIVATION: Identification of functionally equivalent genes in different species is essential to understand the evolution of biological pathways and processes. At the same time, identification of strings of conserved orthologous genes helps identify complex genomic rearrangements across different organisms. Such an insight is particularly useful, for example, in the transfer of experimental results between different experimental systems such as Drosophila and mammals. RESULTS: Here, we describe the Encapsulated Gene-by-gene Matching (EGM) approach, a method that employs a graph matching strategy to identify gene orthologs and conserved gene segments. Given a pair of genomes, EGM constructs a global gene match for all genes taking into account gene context and family information. The Hungarian method for identifying the maximum weight matching in bipartite graphs is employed, where the resulting matching reveals one-to-one correspondences between nodes (genes) in a manner that maximizes the gene similarity and context. CONCLUSION: We tested our approach by performing several comparisons including a detailed Human versus Mouse genome mapping. We find that the algorithm is robust and sensitive in detecting orthologs and conserved gene segments. EGM can sensitively detect rearrangements within large and small chromosomal segments. The EGM tool is fully automated and easy to use compared to other more complex methods that also require extensive manual intervention and input. AVAILABILITY: The EGM software, Supplementary information and other tools are available online from http://vbc.med.monash.edu.au/ approximately kmahmood/EGM.

MOTIVATION: The caspase family of cysteine proteases play essential roles in key biological processes such as programmed cell death, differentiation, proliferation, necrosis and inflammation. The complete repertoire of caspase substrates remains to be fully characterized. Accordingly, systematic computational screening studies of caspase substrate cleavage sites may provide insight into the substrate specificity of caspases and further facilitating the discovery of putative novel substrates. RESULTS: In this article we develop an approach (termed Cascleave) to predict both classical (i.e. following a P(1) Asp) and non-typical caspase cleavage sites. When using local sequence-derived profiles, Cascleave successfully predicted 82.2% of the known substrate cleavage sites, with a Matthews correlation coefficient (MCC) of 0.667. We found that prediction performance could be further improved by incorporating information such as predicted solvent accessibility and whether a cleavage sequence lies in a region that is most likely natively unstructured. Novel bi-profile Bayesian signatures were found to significantly improve the prediction performance and yielded the best performance with an overall accuracy of 87.6% and a MCC of 0.747, which is higher accuracy than published methods that essentially rely on amino acid sequence alone. It is anticipated that Cascleave will be a powerful tool for predicting novel substrate cleavage sites of caspases and shedding new insights on the unknown caspase-substrate interactivity relationship. AVAILABILITY: http://sunflower.kuicr.kyoto-u.ac.jp/ approximately sjn/Cascleave/ CONTACT: [email protected]; [email protected]; james; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Determining the X-ray crystallographic structures of proteins using the technique of molecular replacement (MR) can be a time and labor-intensive trial-and-error process, involving evaluating tens to hundreds of possible solutions to this complex 3D jigsaw puzzle. For challenging cases indicators of success often do not appear until the later stages of structure refinement, meaning that weeks or even months could be wasted evaluating MR solutions that resist refinement and do not lead to a final structure. In order to improve the chances of success as well as decrease this timeframe, we have developed a novel grid computing approach that performs many MR calculations in parallel, speeding up the process of structure determination from weeks to hours. This high-throughput approach also allows parameter sweeps to be performed in parallel, improving the chances of MR success.

Structural biology research places significant demands upon computing and informatics infrastructure. Protein production, crystallization and X-ray data collection require solutions to data management, annotation, target tracking and remote experiment monitoring. Structure elucidation is computationally demanding and requires user-friendly interfaces to high-performance computing resources. Here we discuss how these challenges are being met at the Protein Crystallography Unit at Monash University. Specifically, we have developed informatics solutions for each stage in the structural biology pipeline, from DNA cloning through to protein structure determination. This infrastructure will be pivotal for accelerating the process of structural discovery and will be of significant interest to other laboratories worldwide.