Anna Vangone

Updates to the Integrated Protein–Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall and r=0.72 for the rigid complexes.

COCOMAPS: a web application to analyze and visualize contacts at the interface of biomolecular complexes.

SUMMARY Herein we present COCOMAPS, a novel tool for analyzing, visualizing and comparing the interface in protein-protein and protein-nucleic acids complexes. COCOMAPS combines traditional analyses and 3D visualization of the interface with the effectiveness of intermolecular contact maps. AVAILABILITY COCOMAPS is accessible as a public web tool at http://www.molnac.unisa.it/BioTools/cocomaps CONTACT romina.oliva@uniparthenope.it; lcavallo@unisa.it.

Contacts-based prediction of binding affinity in protein–protein complexes

Almost all critical functions in cells rely on specific protein–protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein–protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein–protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods. DOI: http://dx.doi.org/10.7554/eLife.07454.001

PRODIGY: a web server for predicting the binding affinity of protein–protein complexes

Gaining insights into the structural determinants of protein-protein interactions holds the key for a deeper understanding of biological functions, diseases and development of therapeutics. An important aspect of this is the ability to accurately predict the binding strength for a given protein-protein complex. Here we present PROtein binDIng enerGY prediction (PRODIGY), a web server to predict the binding affinity of protein-protein complexes from their 3D structure. The PRODIGY server implements our simple but highly effective predictive model based on intermolecular contacts and properties derived from non-interface surface. AVAILABILITY AND IMPLEMENTATION PRODIGY is freely available at: http://milou.science.uu.nl/services/PRODIGY CONTACT: a.m.j.j.bonvin@uu.nl, a.vangone@uu.nl.

Ranking multiple docking solutions based on the conservation of inter‐residue contacts

Molecular docking is the method of choice for investigating the molecular basis of recognition in a large number of functional protein complexes. However, correctly scoring the obtained docking solutions (decoys) to rank native‐like (NL) conformations in the top positions is still an open problem. Herein we present CONSRANK, a simple and effective tool to rank multiple docking solutions, which relies on the conservation of inter‐residue contacts in the analyzed decoys ensemble. First it calculates a conservation rate for each inter‐residue contact, then it ranks decoys according to their ability to match the more frequently observed contacts. We applied CONSRANK to 102 targets from three different benchmarks, RosettaDock, DOCKGROUND, and Critical Assessment of PRedicted Interactions (CAPRI). The method performs consistently well, both in terms of NL solutions ranked in the top positions and of values of the area under the receiver operating characteristic curve. Its ideal application is to solutions coming from different docking programs and procedures, as in the case of CAPRI targets. For all the analyzed CAPRI targets where a comparison is feasible, CONSRANK outperforms the CAPRI scorers. The fraction of NL solutions in the top ten positions in the RosettaDock, DOCKGROUND, and CAPRI benchmarks is enriched on average by a factor of 3.0, 1.9, and 9.9, respectively. Interestingly, CONSRANK is also able to specifically single out the high/medium quality (HMQ) solutions from the docking decoys ensemble: it ranks 46.2 and 70.8% of the total HMQ solutions available for the RosettaDock and CAPRI targets, respectively, within the top 20 positions. Proteins 2013.

CONS-COCOMAPS: a novel tool to measure and visualize the conservation of inter-residue contacts in multiple docking solutions

BackgroundThe development of accurate protein-protein docking programs is making this kind of simulations an effective tool to predict the 3D structure and the surface of interaction between the molecular partners in macromolecular complexes. However, correctly scoring multiple docking solutions is still an open problem. As a consequence, the accurate and tedious screening of many docking models is usually required in the analysis step.MethodsAll the programs under CONS-COCOMAPS have been written in python, taking advantage of python libraries such as SciPy and Matplotlib. CONS-COCOMAPS is freely available as a web tool at the URL:http://www.molnac.unisa.it/BioTools/conscocomaps/.ResultsHere we presented CONS-COCOMAPS, a novel tool to easily measure and visualize the consensus in multiple docking solutions. CONS-COCOMAPS uses the conservation of inter-residue contacts as an estimate of the similarity between different docking solutions. To visualize the conservation, CONS-COCOMAPS uses intermolecular contact maps.ConclusionsThe application of CONS-COCOMAPS to test-cases taken from recent CAPRI rounds has shown that it is very efficient in highlighting even a very weak consensus that often is biologically meaningful.

CONSRANK: a server for the analysis, comparison and ranking of docking models based on inter-residue contacts

SUMMARY Herein, we present CONSRANK, a web tool for analyzing, comparing and ranking protein-protein and protein-nucleic acid docking models, based on the conservation of inter-residue contacts and its visualization in 2D and 3D interactive contact maps. AVAILABILITY AND IMPLEMENTATION CONSRANK is accessible as a public web tool at https://www.molnac.unisa.it/BioTools/consrank/. CONTACT romina.oliva@uniparthenope.it.

Using a consensus approach based on the conservation of inter-residue contacts to rank CAPRI models

Herein we propose the use of a consensus approach, CONSRANK, for ranking CAPRI models. CONSRANK relies on the conservation of inter‐residue contacts in the analyzed decoys ensemble. Models are ranked according to their ability to match the most frequently observed contacts. We applied CONSRANK to 19 CAPRI protein–protein targets, covering a wide range of prediction difficulty and involved in a variety of biological functions. CONSRANK results are consistently good, both in terms of native‐like (NL) solutions ranked in the top positions and of values of the Area Under the receiver operating characteristic Curve (AUC). For targets having a percentage of NL solutions above 3%, an excellent performance is found, with AUC values approaching 1. For the difficult target T46, having only 3.4% NL solutions, the number of NL solutions in the top 5 and 10 ranked positions is enriched by a factor 30, and the AUC value is as high as 0.997. AUC values below 0.8 are only found for targets featuring a percentage of NL solutions within 1.1%. Remarkably, a false consensus emerges only in one case, T42, which happens to be an artificial protein, whose assembly details remain uncertain, based on controversial experimental data. We also show that CONSRANK still performs very well on a limited number of models, provided that more than 1 NL solution is included in the ensemble, thus extending its applicability to cases where few dozens of models are available. Proteins 2013; 81:2210‐2220.

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.

A recurrent Gly43Asp substitution in coagulation Factor X rigidifies its catalytic pocket and impairs catalytic activity and intracellular trafficking

Factor X (FX) deficiency is one of the most severe among recessively inherited coagulation disorders. The homozygous Gly222Asp mutation (Gly43Asp in the chymotrypsinogen numbering) on the FX gene was found in fifteen patients with severe FX deficiency (FX:C <1%). The Gly(43) residue is located at the highly conserved 42-58 residues region shared among all trypsinogen-like proteins. In vitro expression studies showed that the replacement of a neutral Gly by a charged Asp residue into the so-called loop-40 of the FX (comprising residues from 42 to 58) causes an impairment of its catalytic competence as well as a secretion defect. Steady state kinetic studies showed also a severe defect of activation by FVIIa of the FX43Asp. Surprisingly, molecular dynamics studies clearly indicated that the Gly43Asp mutation neither disrupts nor destabilizes the FXa native structure of the catalytic site. Rather, it makes it more rigid, by thickening the H-bonding network around its catalytic site. Altogether, the defects of FX43Asp explain the severe bleeding symptoms in the patients and outline the relevance of the plasticity in the FXa catalytic pocket for maintenance of its catalytic competence.