matchms - processing and similarity evaluation of mass spectrometry data

Mass spectrometry data is at the heart of numerous applications in the biomedical and lifesciences. With growing use of high-throughput techniques, researchers need to analyze largerand more complex datasets. In particular through joint effort in the research community,fragmentation mass spectrometry datasets are growing in size and number. Platforms such asMassBank (Horai et al., 2010), GNPS (Wang et al., 2016) or MetaboLights (Haug et al., 2020)serve as an open-access hub for sharing of raw, processed, or annotated fragmentation massspectrometry data. Without suitable tools, however, exploitation of such datasets remainsoverly challenging. In particular, large collected datasets contain data acquired using differentinstruments and measurement conditions, and can further contain a significant fraction ofinconsistent, wrongly labeled, or incorrect metadata (annotations)

An overview of data‐driven HADDOCK strategies in CAPRI rounds 38-45

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models

The pdb2sql Python Package: Parsing, Manipulation and Analysis of PDB Files Using SQL Queries

Fast and versatile biomolecular structure PDB file parser using SQL queries

The pdb2sql Python Package: Parsing, Manipulation and Analysis of PDB Files Using SQL Queries

Fast and versatile biomolecular structure PDB file parser using SQL queries

iScore: Support Vector Machine on Graph Kernel for Ranking Protein-Protein Docking Models

iScore allows to score protein-protein interface using graph kernels and support vector machine

Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes

Reliable prediction of binding affinity changes (ΔΔG) upon mutations in protein complexes relies not only on the performance of computational methods but also on the availability and quality of experimental data. Binding affinity changes can be measured by various experimental methods with different accuracies and limitations. To understand the impact of these on the prediction of binding affinity change, we present the Database of binding Affinity Change Upon Mutation (DACUM), a database of 1872 binding affinity changes upon single-point mutations, a subset of the SKEMPI database (Moal,I.H. and Fernández-Recio,J. Bioinformatics, 2012;28:2600-2607) extended with information on the experimental methods used for ΔΔG measurements. The ΔΔG data were classified into different data sets based on the experimental method used and the position of the mutation (interface and non-interface). We tested the prediction performance of the original HADDOCK score, a newly trained version of it and mutation Cutoff Scanning Matrix (Pires,D.E.V., Ascher,D.B. and Blundell,T.L. Bioinformatics 2014;30:335-342), one of the best reported ΔΔG predictors so far, on these various data sets. Our results demonstrate a strong impact of the experimental methods on the performance of binding affinity change predictors for protein complexes. This underscores the importance of properly considering and carefully choosing experimental methods in the development of novel binding affinity change predictors. The DACUM database is available online at https://github.com/haddocking/DACUM