LightDock: a new multi-scale approach to protein–protein docking

Computational prediction of protein–protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed. We describe here a new multi-scale protein–protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigid-body docking, especially in flexible cases.B.J-G was supported by a FPI fellowship from the Spanish Ministry of Economy and Competitiveness. This work was supported by I+D+I Research Project grants BIO2013-48213-R and BIO2016-79930-R from the Spanish Ministry of Economy and Competitiveness. This work is partially supported by the European Union H2020 program through HiPEAC (GA 687698), by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology (TIN2015-65316-P) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programaciói Entorns d’Execució Paral·lels (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

pyDock performance in 5th CAPRI edition: from docking and scoring to binding affinity predictions and other challenges

Proteins form the executive machinery underlying all the biological processes that occur within and between cells, from DNA replication to protein degradation. Although genome-scale technologies enable to clarify their large, intricate and highly dynamics networks, they fail to elucidate the detailed molecular mechanism that underlies the protein association process. Therefore, one of the most challenging objectives in biological research is to functionally characterize protein interactions by solving 3D complex structures. This is, however, not a trivial task as confirmed by the large gap that exist between the number of complexes identified by large-scale proteomics efforts and those for which high-resolution 3D experimental structures are available. For these reasons, computational docking methods, aimed to predict the binding mode of two proteins starting from the coordinates of the individual subunits, are bound to become a complementary approach to solve the structural interactome. Given its importance, the field of protein docking has experienced an explosion in recent years partially propelled by CAPRI (http://www.ebi.ac.uk/msd-srv/capri/). CAPRI (Critical Assessment of PRedicted Interaction) is a community-wide blind experiment aimed at objectively assessing the performance of computational methods for modeling protein interactions by inviting developers to test their algorithms on the same target system and quantitatively evaluating the results. In order to test pyDock,1 a docking scoring algorithm developed in our group, the PID (Protein Interaction and Docking) group of the BSC Life Science Department, we have participated in all the 15 targets (T46 to T58) of the 5th CAPRI edition (2010-2012). Our automated protocol confirmed to be highly successful to provide correct models in easy-to-medium difficulty protein-protein docking cases placing among the Top5 ranked groups out of more than 60 participants. Key words: Complex structure, CAPRI, protein-protein docking, pyDock, protein interactions

pyDockDNA: a new approach for protein-DNA docking

Here we present pyDockDNA, which is based on the pyDock program, with a new module for reading and parsing DNA molecules. The protocol is composed of two major steps: sampling and scoring. The first sampling step consists in the generation of 10,000 protein-DNA docking models by FTDock [5]. This program takes a protein and a nucleic acid coordinate file, discretizes the molecules into corresponding 3D grids, and computes their geometric correlation by using Fast Fourier Transform algorithms to speed up the translations between the two molecules

pyDock performance in 5th CAPRI edition: from docking and scoring to binding affinity predictions and other challenges

Proteins form the executive machinery underlying all the biological processes that occur within and between cells, from DNA replication to protein degradation. Although genome-scale technologies enable to clarify their large, intricate and highly dynamics networks, they fail to elucidate the detailed molecular mechanism that underlies the protein association process. Therefore, one of the most challenging objectives in biological research is to functionally characterize protein interactions by solving 3D complex structures. This is, however, not a trivial task as confirmed by the large gap that exist between the number of complexes identified by large-scale proteomics efforts and those for which high-resolution 3D experimental structures are available. For these reasons, computational docking methods, aimed to predict the binding mode of two proteins starting from the coordinates of the individual subunits, are bound to become a complementary approach to solve the structural interactome. Given its importance, the field of protein docking has experienced an explosion in recent years partially propelled by CAPRI (http://www.ebi.ac.uk/msd-srv/capri/). CAPRI (Critical Assessment of PRedicted Interaction) is a community-wide blind experiment aimed at objectively assessing the performance of computational methods for modeling protein interactions by inviting developers to test their algorithms on the same target system and quantitatively evaluating the results. In order to test pyDock,1 a docking scoring algorithm developed in our group, the PID (Protein Interaction and Docking) group of the BSC Life Science Department, we have participated in all the 15 targets (T46 to T58) of the 5th CAPRI edition (2010-2012). Our automated protocol confirmed to be highly successful to provide correct models in easy-to-medium difficulty protein-protein docking cases placing among the Top5 ranked groups out of more than 60 participants. Key words: Complex structure, CAPRI, protein-protein docking, pyDock, protein interactions

SKEMPI 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation

Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering. Results: We present SKEMPI 2.0, a major update to our database of binding free energy changes upon mutation for structurally resolved protein–protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations, which abolish detectable binding.This work has been supported by the European Molecular Biology Laboratory [I.H.M.]; Biotechnology and Biological Sciences Research Council [Future Leader Fellowship BB/N011600/1 to I.H.M.]; Spanish Ministry of Economy and Competitiveness (MINECO) [BIO2016-79930-R to J.F.R.]; Interreg POCTEFA [EFA086/15 to J.F.R.]; European Commission [H2020 grant 676556 (MuG)].Peer ReviewedPostprint (published version

Aprendiendo a programar. Nuevos retos, nuevas propuestas

La enseñanza de la programación en el ámbito universitario, a pesar de toda la experiencia acumulada, presenta muchos retos aún por alcanzar. Son diversos los elementos que añaden complejidad a este empeño y diversos también los estudios y propuestas didácticas que se proponen para abordarlos. El reto es aún mayor desde que, recientemente, se han incorporado al panorama universitario nuevos programas de especialidad con asignaturas de programación en ámbitos multidisciplinares como la bioinformática o la ciencia de datos. En este artículo se describe la experiencia de dos asignaturas de nivel de máster: “Programación para la bioinformática” y “Programación para la Ciencia de Datos”, ambas introductorias a la programación en el lenguaje Python, pero orientadas cada una de ellas a la resolución de los problemas específicos que se plantean en cada uno de estos dos ámbitos. Se trata de un objetivo tremendamente complejo, sobre todo si tenemos en cuenta el perfil heterogéneo de entrada de los estudiantes, poco o nada acostumbrados a la programación.In the university context and despite all the accumulated experience over the past decades, teaching computer programming is still challenging. The different approaches to accomplish this goal are diverse and complex, with many different didactic proposals. New challenges have aroused in recent times with the development of new and more specialized courses for multidisciplinary programs, such as bioinformatics or data science. In this work we describe the experience obtained in two MSc programs: Programming for Bioinformatics and Programming for Data Science, both of them with an introductory aim at programming in the Python language and oriented to solve specific problems and challenges in the two different scopes. This is an extremely complex goal, considering the heterogeneous background of the students, not familiar with coding

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.Peer ReviewedPostprint (author's final draft

Next Generation Flow for highly sensitive and standardized detection of minimal residual disease in multiple myeloma

[EN]Flow cytometry has become a highly valuable method to monitor minimal residual disease (MRD) and evaluate the depth of complete response (CR) in bone marrow (BM) of multiple myeloma (MM) after therapy. However, current flow-MRD has lower sensitivity than molecular methods and lacks standardization. Here we report on a novel next generation flow (NGF) approach for highly sensitive and standardized MRD detection in MM. An optimized 2-tube 8-color antibody panel was constructed in five cycles of design-evaluation-redesign. In addition, a bulk-lysis procedure was established for acquisition of ⩾107 cells/sample, and novel software tools were constructed for automatic plasma cell gating. Multicenter evaluation of 110 follow-up BM from MM patients in very good partial response (VGPR) or CR showed a higher sensitivity for NGF-MRD vs conventional 8-color flow-MRD -MRD-positive rate of 47 vs 34% (P=0.003)-. Thus, 25% of patients classified as MRD-negative by conventional 8-color flow were MRD-positive by NGF, translating into a significantly longer progression-free survival for MRD-negative vs MRD-positive CR patients by NGF (75% progression-free survival not reached vs 7 months; P=0.02). This study establishes EuroFlow-based NGF as a highly sensitive, fully standardized approach for MRD detection in MM which overcomes the major limitations of conventional flow-MRD methods and is ready for implementation in routine diagnostics.This work has been supported by the International Myeloma Foundation-Black Swan Research Initiative, the Red Temática de Investigación Cooperativa en Cáncer (RTICC); grant SA079U14 from the Consejería de Educación, Junta de Castilla y León, Valladolid, Spain and; grant DTS15/00119 from Instituto de Salud Carlos III, Ministerio de Economía y Competitividad, Madrid, Spain

Stabilization and reversal of child obesity in Andalusia using objective anthropometric measures by socioeconomic status

Background: Childhood obesity continues to be a significant public health issue worldwide. Recent national studies in Spain show a stable picture. However, prevalence and trends differ by socio-economic status, age, and region. We present the trend in childhood excess weight prevalence, aged 8–15 years, in Andalusia from 2011-2012 to 2015–2016 by socio-economic status. Results: Overall prevalence of excess weight decreased from 42.0% in 2011–2012 to 35.4% in 2015–2016. Overweight decreased from 28.2 to 24.2% and obesity from 13.8 to 11.2%. In 2011–2012 the prevalence of excess weight in boys was 46.0%and 37.9% in girls; in 2015–2016 the difference became significant with 41% of boys with excess weight compared with 30% in girls. Conclusions: Childhood excess weight prevalence in Andalusia has decreased slightly between 2011-2012 and 2015–2016. Notably, a decrease in obesity prevalence in girls aged 8–15 years was recorded. In 2011–2012 a social gradient for excess weight prevalence across three SES indicators was observed: in 2015–2016 this gradient disappeared. Nonetheless, prevalence remains too high