MOTIVATION: Up-to-date pathway knowledge is usually presented in scientific publications for human reading, making it difficult to utilize these resources for semantic integration and computational analysis of biological pathways. We here present an approach to mining knowledge graphs by combining manual curation with automated named entity recognition and automated relation extraction. This approach allows us to study pathway-related questions in detail, which we here show using the ketamine pathway, aiming to help improve understanding of the role of gut microbiota in the antidepressant effects of ketamine. RESULTS: The thus devised ketamine pathway 'KetPath' knowledge graph comprises five parts: (i) manually curated pathway facts from images; (ii) recognized named entities in biomedical texts; (iii) identified relations between named entities; (iv) our previously constructed microbiota and pre-/probiotics knowledge bases; and (v) multiple community-accepted public databases. We first assessed the performance of automated extraction of relations between named entities using the specially designed state-of-the-art tool BioKetBERT. The query results show that we can retrieve drug actions, pathway relations, co-occurring entities, and their relations. These results uncover several biological findings, such as various gut microbes leading to increased expression of BDNF, which may contribute to the sustained antidepressant effects of ketamine. We envision that the methods and findings from this research will aid researchers who wish to integrate and query data and knowledge from multiple biomedical databases and literature simultaneously. AVAILABILITY AND IMPLEMENTATION: Data and query protocols are available in the KetPath repository at https://dx.doi.org/10.5281/zenodo.8398941 and https://github.com/tingcosmos/KetPath.

MOTIVATION: The interactions between proteins and other molecules are essential to many biological and cellular processes. Experimental identification of interface residues is a time-consuming, costly and challenging task, while protein sequence data are ubiquitous. Consequently, many computational and machine learning approaches have been developed over the years to predict such interface residues from sequence. However, the effectiveness of different Deep Learning (DL) architectures and learning strategies for protein-protein, protein-nucleotide and protein-small molecule interface prediction has not yet been investigated in great detail. Therefore, we here explore the prediction of protein interface residues using six DL architectures and various learning strategies with sequence-derived input features. RESULTS: We constructed a large dataset dubbed BioDL, comprising protein-protein interactions from the PDB, and DNA/RNA and small molecule interactions from the BioLip database. We also constructed six DL architectures, and evaluated them on the BioDL benchmarks. This shows that no single architecture performs best on all instances. An ensemble architecture, which combines all six architectures, does consistently achieve peak prediction accuracy. We confirmed these results on the published benchmark set by Zhang and Kurgan (ZK448), and on our own existing curated homo- and heteromeric protein interaction dataset. Our PIPENN sequence-based ensemble predictor outperforms current state-of-the-art sequence-based protein interface predictors on ZK448 on all interaction types, achieving an AUC-ROC of 0.718 for protein-protein, 0.823 for protein-nucleotide and 0.842 for protein-small molecule. AVAILABILITY AND IMPLEMENTATION: Source code and datasets are available at https://github.com/ibivu/pipenn/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The ubiquitous availability of genome sequencing data explains the popularity of machine learning-based methods for the prediction of protein properties from their amino acid sequences. Over the years, while revising our own work, reading submitted manuscripts as well as published papers, we have noticed several recurring issues, which make some reported findings hard to understand and replicate. We suspect this may be due to biologists being unfamiliar with machine learning methodology, or conversely, machine learning experts may miss some of the knowledge needed to correctly apply their methods to proteins. Here, we aim to bridge this gap for developers of such methods. The most striking issues are linked to a lack of clarity: how were annotations of interest obtained; which benchmark metrics were used; how are positives and negatives defined. Others relate to a lack of rigor: If you sneak in structural information, your method is not sequence-based; if you compare your own model to "state-of-the-art," take the best methods; if you want to conclude that some method is better than another, obtain a significance estimate to support this claim. These, and other issues, we will cover in detail. These points may have seemed obvious to the authors during writing; however, they are not always clear-cut to the readers. We also expect many of these tips to hold for other machine learning-based applications in biology. Therefore, many computational biologists who develop methods in this particular subject will benefit from a concise overview of what to avoid and what to do instead.

MOTIVATION: Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen's epitope region, as a special type of protein-protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments toward the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody. RESULTS: We collected and curated a high quality epitope dataset from the SAbDab database. Our generic PPI heterodimer predictor obtained an AUC-ROC of 0.666 when evaluated on the epitope test set. We then trained a random forest model specifically on the epitope dataset, reaching AUC 0.694. Further training on the combined heterodimer and epitope datasets, improves our final predictor to AUC 0.703 on the epitope test set. This is better than the best state-of-the-art sequence-based epitope predictor BepiPred-2.0. On one solved antibody-antigen structure of the COVID19 virus spike receptor binding domain, our predictor reaches AUC 0.778. We added the SeRenDIP-CE Conformational Epitope predictors to our webserver, which is simple to use and only requires a single antigen sequence as input, which will help make the method immediately applicable in a wide range of biomedical and biomolecular research. AVAILABILITY AND IMPLEMENTATION: Webserver, source code and datasets at www.ibi.vu.nl/programs/serendipwww/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Genetic interaction (GI) patterns are characterized by the phenotypes of interacting single and double mutated gene pairs. Uncovering the regulatory mechanisms of GIs would provide a better understanding of their role in biological processes, diseases and drug response. Computational analyses can provide insights into the underpinning mechanisms of GIs. RESULTS: In this study, we present a framework for exhaustive modelling of GI patterns using Petri nets (PN). Four-node models were defined and generated on three levels with restrictions, to enable an exhaustive approach. Simulations suggest ∼5 million models of GIs. Generalizing these we propose putative mechanisms for the GI patterns, inversion and suppression. We demonstrate that exhaustive PN modelling enables reasoning about mechanisms of GIs when only the phenotypes of gene pairs are known. The framework can be applied to other GI or genetic regulatory datasets. AVAILABILITY AND IMPLEMENTATION: The framework is available at http://www.ibi.vu.nl/programs/ExhMod. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: PRALINE 2 is a toolkit for custom multiple sequence alignment workflows. It can be used to incorporate sequence annotations, such as secondary structure or (DNA) motifs, into the alignment scoring, as well as to customize many other aspects of a progressive multiple alignment workflow. AVAILABILITY AND IMPLEMENTATION: PRALINE 2 is implemented in Python and available as open source software on GitHub: https://github.com/ibivu/PRALINE/.

MOTIVATION: Interpretation of ubiquitous protein sequence data has become a bottleneck in biomolecular research, due to a lack of structural and other experimental annotation data for these proteins. Prediction of protein interaction sites from sequence may be a viable substitute. We therefore recently developed a sequence-based random forest method for protein-protein interface prediction, which yielded a significantly increased performance than other methods on both homomeric and heteromeric protein-protein interactions. Here, we present a webserver that implements this method efficiently. RESULTS: With the aim of accelerating our previous approach, we obtained sequence conservation profiles by re-mastering the alignment of homologous sequences found by PSI-BLAST. This yielded a more than 10-fold speedup and at least the same accuracy, as reported previously for our method; these results allowed us to offer the method as a webserver. The web-server interface is targeted to the non-expert user. The input is simply a sequence of the protein of interest, and the output a table with scores indicating the likelihood of having an interaction interface at a certain position. As the method is sequence-based and not sensitive to the type of protein interaction, we expect this webserver to be of interest to many biological researchers in academia and in industry. AVAILABILITY AND IMPLEMENTATION: Webserver, source code and datasets are available at www.ibi.vu.nl/programs/serendipwww/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.

Motivation: Our society has become data-rich to the extent that research in many areas has become impossible without computational approaches. Educational programmes seem to be lagging behind this development. At the same time, there is a growing need not only for strong data science skills, but foremost for the ability to both translate between tools and methods on the one hand, and application and problems on the other. Results: Here we present our experiences with shaping and running a masters' programme in bioinformatics and systems biology in Amsterdam. From this, we have developed a comprehensive philosophy on how translation in training may be achieved in a dynamic and multidisciplinary research area, which is described here. We furthermore describe two requirements that enable translation, which we have found to be crucial: sufficient depth and focus on multidisciplinary topic areas, coupled with a balanced breadth from adjacent disciplines. Finally, we present concrete suggestions on how this may be implemented in practice, which may be relevant for the effectiveness of life science and data science curricula in general, and of particular interest to those who are in the process of setting up such curricula. Supplementary information: Supplementary data are available at Bioinformatics online.

Protein or DNA motifs are sequence regions which possess biological importance. These regions are often highly conserved among homologous sequences. The generation of multiple sequence alignments (MSAs) with a correct alignment of the conserved sequence motifs is still difficult to achieve, due to the fact that the contribution of these typically short fragments is overshadowed by the rest of the sequence. Here we extended the PRALINE multiple sequence alignment program with a novel motif-aware MSA algorithm in order to address this shortcoming. This method can incorporate explicit information about the presence of externally provided sequence motifs, which is then used in the dynamic programming step by boosting the amino acid substitution matrix towards the motif. The strength of the boost is controlled by a parameter, α. Using a benchmark set of alignments we confirm that a good compromise can be found that improves the matching of motif regions while not significantly reducing the overall alignment quality. By estimating α on an unrelated set of reference alignments we find there is indeed a strong conservation signal for motifs. A number of typical but difficult MSA use cases are explored to exemplify the problems in correctly aligning functional sequence motifs and how the motif-aware alignment method can be employed to alleviate these problems.

MOTIVATION: Genome sequencing is producing an ever-increasing amount of associated protein sequences. Few of these sequences have experimentally validated annotations, however, and computational predictions are becoming increasingly successful in producing such annotations. One key challenge remains the prediction of the amino acids in a given protein sequence that are involved in protein-protein interactions. Such predictions are typically based on machine learning methods that take advantage of the properties and sequence positions of amino acids that are known to be involved in interaction. In this paper, we evaluate the importance of various features using Random Forest (RF), and include as a novel feature backbone flexibility predicted from sequences to further optimise protein interface prediction. RESULTS: We observe that there is no single sequence feature that enables pinpointing interacting sites in our Random Forest models. However, combining different properties does increase the performance of interface prediction. Our homomeric-trained RF interface predictor is able to distinguish interface from non-interface residues with an area under the ROC curve of 0.72 in a homomeric test-set. The heteromeric-trained RF interface predictor performs better than existing predictors on a independent heteromeric test-set. We trained a more general predictor on the combined homomeric and heteromeric dataset, and show that in addition to predicting homomeric interfaces, it is also able to pinpoint interface residues in heterodimers. This suggests that our random forest model and the features included capture common properties of both homodimer and heterodimer interfaces. AVAILABILITY AND IMPLEMENTATION: The predictors and test datasets used in our analyses are freely available ( http://www.ibi.vu.nl/downloads/RF_PPI/ ). CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Biological pathways play a key role in most cellular functions. To better understand these functions, diverse computational and cell biology researchers use biological pathway data for various analysis and modeling purposes. For specifying these biological pathways, a community of researchers has defined BioPAX and provided various tools for creating, validating and visualizing BioPAX models. However, a generic software framework for simulating BioPAX models is missing. Here, we attempt to fill this gap by introducing a generic simulation framework for BioPAX. The framework explicitly separates the execution model from the model structure as provided by BioPAX, with the advantage that the modelling process becomes more reproducible and intrinsically more modular; this ensures natural biological constraints are satisfied upon execution. The framework is based on the principles of discrete event systems and multi-agent systems, and is capable of automatically generating a hierarchical multi-agent system for a given BioPAX model. RESULTS: To demonstrate the applicability of the framework, we simulated two types of biological network models: a gene regulatory network modeling the haematopoietic stem cell regulators and a signal transduction network modeling the Wnt/β-catenin signaling pathway. We observed that the results of the simulations performed using our framework were entirely consistent with the simulation results reported by the researchers who developed the original models in a proprietary language. AVAILABILITY AND IMPLEMENTATION: The framework, implemented in Java, is open source and its source code, documentation and tutorial are available at http://www.ibi.vu.nl/programs/BioASF CONTACT: [email protected].

BACKGROUND: Protein families participating in protein-protein interactions may contain sub-families that have different binding characteristics, ranging from right binding to showing no interaction at all. Composition differences at the sequence level in these sub-families are often decisive to their differential functional interaction. Methods to predict interface sites from protein sequences typically exploit conservation as a signal. Here, instead, we provide proof of concept that the sequence specificity between interacting versus non-interacting groups can be exploited to recognise interaction sites. RESULTS: We collected homodimeric and monomeric proteins and formed homologous groups, each having an interacting (homodimer) subgroup and a non-interacting (monomer) subgroup. We then compiled multiple sequence alignments of the proteins in the homologous groups and identified compositional differences between the homodimeric and monomeric subgroups for each of the alignment positions. Our results show that this specificity signal distinguishes interface and other surface residues with 40.9% recall and up to 25.1% precision. CONCLUSIONS: To our best knowledge, this is the first large scale study that exploits sequence specificity between interacting and non-interacting homologs to predict interaction sites from sequence information only. The performance obtained indicates that this signal contains valuable information to identify protein-protein interaction sites.

MOTIVATION: To assess whether two proteins will interact under physiological conditions, information on the interaction free energy is needed. Statistical learning techniques and docking methods for predicting protein-protein interactions cannot quantitatively estimate binding free energies. Full atomistic molecular simulation methods do have this potential, but are completely unfeasible for large-scale applications in terms of computational cost required. Here we investigate whether applying coarse-grained (CG) molecular dynamics simulations is a viable alternative for complexes of known structure. RESULTS: We calculate the free energy barrier with respect to the bound state based on molecular dynamics simulations using both a full atomistic and a CG force field for the TCR-pMHC complex and the MP1-p14 scaffolding complex. We find that the free energy barriers from the CG simulations are of similar accuracy as those from the full atomistic ones, while achieving a speedup of >500-fold. We also observe that extensive sampling is extremely important to obtain accurate free energy barriers, which is only within reach for the CG models. Finally, we show that the CG model preserves biological relevance of the interactions: (i) we observe a strong correlation between evolutionary likelihood of mutations and the impact on the free energy barrier with respect to the bound state; and (ii) we confirm the dominant role of the interface core in these interactions. Therefore, our results suggest that CG molecular simulations can realistically be used for the accurate prediction of protein-protein interaction strength. AVAILABILITY AND IMPLEMENTATION: The python analysis framework and data files are available for download at http://www.ibi.vu.nl/downloads/bioinformatics-2013-btt675.tgz.

Teaching students with very diverse backgrounds can be extremely challenging. This article uses the Bioinformatics and Systems Biology MSc in Amsterdam as a case study to describe how the knowledge gap for students with heterogeneous backgrounds can be bridged. We show that a mix in backgrounds can be turned into an advantage by creating a stimulating learning environment for the students. In the MSc Programme, conversion classes help to bridge differences between students, by mending initial knowledge and skill gaps. Mixing students from different backgrounds in a group to solve a complex task creates an opportunity for the students to reflect on their own abilities. We explain how a truly interdisciplinary approach to teaching helps students of all backgrounds to achieve the MSc end terms. Moreover, transferable skills obtained by the students in such a mixed study environment are invaluable for their later careers.

MOTIVATION: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes. RESULTS: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as 'stepping stones' for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or 'trigger' is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Understanding the processes involved in multi-cellular pattern formation is a central problem of developmental biology, hopefully leading to many new insights, e.g. in the treatment of various diseases. Defining suitable computational techniques for development modelling, able to perform in silico simulation experiments, is an open and challenging problem. RESULTS: Previously, we proposed a coarse-grained, quantitative approach based on the basic Petri net formalism, to mimic the behaviour of the biological processes during multicellular differentiation. Here, we apply our modelling approach to the well-studied process of Caenorhabditis elegans vulval development. We show that our model correctly reproduces a large set of in vivo experiments with statistical accuracy. It also generates gene expression time series in accordance with recent biological evidence. Finally, we modelled the role of microRNA mir-61 during vulval development and predict its contribution in stabilizing cell pattern formation.

Bioinformatics 25(16), 2049–2056 We regret that Figure 5 on page 4 of this paper was incorrect and should appear as below.

MOTIVATION: Membrane-bound proteins are a special class of proteins. The regions that insert into the cell-membrane have a profoundly different hydrophobicity pattern compared with soluble proteins. Multiple alignment techniques use scoring schemes tailored for sequences of soluble proteins and are therefore in principle not optimal to align membrane-bound proteins. RESULTS: Transmembrane (TM) regions in protein sequences can be reliably recognized using state-of-the-art sequence prediction techniques. Furthermore, membrane-specific scoring matrices are available. We have developed a new alignment method, called PRALINETM, which integrates these two features to enhance multiple sequence alignment. We tested our algorithm on the TM alignment benchmark set by Bahr et al. (2001), and showed that the quality of TM alignments can be significantly improved compared with the quality produced by a standard multiple alignment technique. The results clearly indicate that the incorporation of these new elements into current state-of-the-art alignment methods is crucial for optimizing the alignment of TM proteins. AVAILABILITY: A webserver is available at http://www.ibi.vu.nl/programs/pralinewww.

MOTIVATION: Identification of residues that account for protein function specificity is crucial, not only for understanding the nature of functional specificity, but also for protein engineering experiments aimed at switching the specificity of an enzyme, regulator or transporter. Available algorithms generally use multiple sequence alignments to identify residue positions conserved within subfamilies but divergent in between. However, many biological examples show a much subtler picture than simple intra-group conservation versus inter-group divergence. RESULTS: We present multi-RELIEF, a novel approach for identifying specificity residues that is based on RELIEF, a state-of-the-art Machine-Learning technique for feature weighting. It estimates the expected 'local' functional specificity of residues from an alignment divided in multiple classes. Optionally, 3D structure information is exploited by increasing the weight of residues that have high-weight neighbors. Using ROC curves over a large body of experimental reference data, we show that (a) multi-RELIEF identifies specificity residues for the seven test sets used, (b) incorporating structural information improves prediction for specificity of interaction with small molecules and (c) comparison of multi-RELIEF with four other state-of-the-art algorithms indicates its robustness and best overall performance. AVAILABILITY: A web-server implementation of multi-RELIEF is available at www.ibi.vu.nl/programs/multirelief. Matlab source code of the algorithm and data sets are available on request for academic use.

BACKGROUND: Protein structural alignment provides a fundamental basis for deriving principles of functional and evolutionary relationships. It is routinely used for structural classification and functional characterization of proteins and for the construction of sequence alignment benchmarks. However, the available techniques do not fully consider the implications of protein structural diversity and typically generate a single alignment between sequences. RESULTS: We have taken alternative protein crystal structures and generated simulation snapshots to explicitly investigate the impact of structural changes on the alignments. We show that structural diversity has a significant effect on structural alignment. Moreover, we observe alignment inconsistencies even for modest spatial divergence, implying that the biological interpretation of alignments is less straightforward than commonly assumed. A salient example is the GroES 'mobile loop' where sub-Angstrom variations give rise to contradictory sequence alignments. CONCLUSION: A comprehensive treatment of ambiguous alignment regions is crucial for further development of structural alignment applications and for the representation of alignments in general. For this purpose we have developed an on-line database containing our data and new ways of visualizing alignment inconsistencies, which can be found at http://www.ibi.vu.nl/databases/stralivari.

Multiple sequence alignments are often used to reveal functionally important residues within a protein family. In particular, they can be very useful for identification of key residues that determine functional differences between protein subclasses (subtype specific sites). This paper proposes a new algorithm for selecting subtype specific sites from a set of aligned protein sequences. The algorithm combines a feature selection technique with neighbor position information for selecting and ranking a set of putative relevant sites. The algorithm is applied to a dataset of protein sequences from the MH2 domain of the SMAD family of transcriptor factors. Validation of the results on the basis of the known interaction and function of the sites shows that the algorithm successfully identifies the known (from literature) subtype specific sites and new putative ones