Search

Abstract Background The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties. Results We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php. Conclusions BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.

Abstract Comparative modeling is becoming an increasingly helpful technique in microbial cell factories as the knowledge of the three-dimensional structure of a protein would be an invaluable aid to solve problems on protein production. For this reason, an introduction to comparative modeling is presented, with special emphasis on the basic concepts, opportunities and challenges of protein structure prediction. This review is intended to serve as a guide for the biologist who has no special expertise and who is not involved in the determination of protein structure. Selected applications of comparative modeling in microbial cell factories are outlined, and the role of microbial cell factories in the structural genomics initiative is discussed.

Next-generation sequencing technology has created many new opportunities for clinical diagnostics, but it faces the challenge of functional annotation of identified mutations. Various algorithms have been developed to predict the impact of missense variants that influence oncogenic drivers. However, computational pipelines that handle biological data must integrate multiple software tools, which can add complexity and hinder non-specialist users from accessing the pipeline. Here, we have developed an online user-friendly web server tool PredictONCO that is fully automated and has a low barrier to access. The tool models the structure of the mutant protein in the first step. Next, it calculates the protein stability change, pocket level information, evolutionary conservation, and changes in ionisation of catalytic amino acid residues, and uses them as the features in the machine-learning predictor. The XGBoost-based predictor was validated on an independent subset of held-out data, demonstrating areas under the receiver operating characteristic curve (ROC) of 0.97 and 0.94, and the average precision from the precision-recall curve of 0.99 and 0.94 for structure-based and sequence-based predictions, respectively. Finally, PredictONCO calculates the docking results of small molecules approved by regulatory authorities. We demonstrate the applicability of the tool by presenting its usage for variants in two cancer-associated proteins, cellular tumour antigen p53 and fibroblast growth factor receptor FGFR1. Our free web tool will assist with the interpretation of data from next-generation sequencing and navigate treatment strategies in clinical oncology: https://loschmidt.chemi.muni.cz/predictonco/., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In understanding and redesigning the function of proteins in modern biochemistry, protein engineers are increasingly focusing on exploring regions in proteins called loops. Analyzing various characteristics of these regions helps the experts design the transfer of the desired function from one protein to another. This process is denoted as loop grafting. We designed a set of interactive visualizations that provide experts with visual support through all the loop grafting pipeline steps. The workflow is divided into several phases, reflecting the steps of the pipeline. Each phase is supported by a specific set of abstracted 2D visual representations of proteins and their loops that are interactively linked with the 3D View of proteins. By sequentially passing through the individual phases, the user shapes the list of loops that are potential candidates for loop grafting. Finally, the actual in-silico insertion of the loop candidates from one protein to the other is performed, and the results are visually presented to the user. In this way, the fully computational rational design of proteins and their loops results in newly designed protein structures that can be further assembled and tested through in-vitro experiments. We showcase the contribution of our visual support design on a real case scenario changing the enantiomer selectivity of the engineered enzyme. Moreover, we provide the readers with the experts' feedback.

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation's effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/ .Scientific contributionThis work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application., (© 2024. The Author(s).)

Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Computational study of the effect of drug candidates on intrinsically disordered biomolecules is challenging due to their vast and complex conformational space. Here, we developed a comparative Markov state analysis (CoVAMPnet) framework to quantify changes in the conformational distribution and dynamics of a disordered biomolecule in the presence and absence of small organic drug candidate molecules. First, molecular dynamics trajectories are generated using enhanced sampling, in the presence and absence of small molecule drug candidates, and ensembles of soft Markov state models (MSMs) are learned for each system using unsupervised machine learning. Second, these ensembles of learned MSMs are aligned across different systems based on a solution to an optimal transport problem. Third, the directional importance of inter-residue distances for the assignment to different conformational states is assessed by a discriminative analysis of aggregated neural network gradients. This final step provides interpretability and biophysical context to the learned MSMs. We applied this novel computational framework to assess the effects of ongoing phase 3 therapeutics tramiprosate (TMP) and its metabolite 3-sulfopropanoic acid (SPA) on the disordered Aβ42 peptide involved in Alzheimer's disease. Based on adaptive sampling molecular dynamics and CoVAMPnet analysis, we observed that both TMP and SPA preserved more structured conformations of Aβ42 by interacting nonspecifically with charged residues. SPA impacted Aβ42 more than TMP, protecting α-helices and suppressing the formation of aggregation-prone β-strands. Experimental biophysical analyses showed only mild effects of TMP/SPA on Aβ42 and activity enhancement by the endogenous metabolization of TMP into SPA. Our data suggest that TMP/SPA may also target biomolecules other than Aβ peptides. The CoVAMPnet method is broadly applicable to study the effects of drug candidates on the conformational behavior of intrinsically disordered biomolecules., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

PredictONCO 1.0 is a unique web server that analyzes effects of mutations on proteins frequently altered in various cancer types. The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at https://loschmidt.chemi.muni.cz/predictonco., (© The Author(s) 2023. Published by Oxford University Press.)

Summary: The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library., Availability and Implementation: SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib., (© The Author(s) 2023. Published by Oxford University Press.)

Background: Apolipoprotein E (ApoE) ε4 genotype is the most prevalent risk factor for late-onset Alzheimer's Disease (AD). Although ApoE4 differs from its non-pathological ApoE3 isoform only by the C112R mutation, the molecular mechanism of its proteinopathy is unknown., Methods: Here, we reveal the molecular mechanism of ApoE4 aggregation using a combination of experimental and computational techniques, including X-ray crystallography, site-directed mutagenesis, hydrogen-deuterium mass spectrometry (HDX-MS), static light scattering and molecular dynamics simulations. Treatment of ApoE ε3/ε3 and ε4/ε4 cerebral organoids with tramiprosate was used to compare the effect of tramiprosate on ApoE4 aggregation at the cellular level., Results: We found that C112R substitution in ApoE4 induces long-distance (> 15 Å) conformational changes leading to the formation of a V-shaped dimeric unit that is geometrically different and more aggregation-prone than the ApoE3 structure. AD drug candidate tramiprosate and its metabolite 3-sulfopropanoic acid induce ApoE3-like conformational behavior in ApoE4 and reduce its aggregation propensity. Analysis of ApoE ε4/ε4 cerebral organoids treated with tramiprosate revealed its effect on cholesteryl esters, the storage products of excess cholesterol., Conclusions: Our results connect the ApoE4 structure with its aggregation propensity, providing a new druggable target for neurodegeneration and ageing., (© 2023. The Author(s).)

Intrinsic protein dynamics contribute to their biological functions. Rational engineering of protein dynamics is extremely challenging with only a handful of successful examples. Hydrogen/deuterium exchange coupled to mass spectrometry (HDX-MS) represents a powerful technique for quantitative analysis of protein dynamics. Here we provide a detailed description of the preparation of protein samples, collection of high-quality data, and their in-depth analysis using various computational tools. We illustrate the application of HDX-MS for the study of protein dynamics in the rational engineering of flexible loops in the reconstructed ancestor of haloalkane dehalogenase and Renilla luciferase. These experiments provided unique and valuable data rigorously describing the modification of protein dynamics upon grafting of the loop-helix element. Tips and tricks are provided to stimulate the wider use of HDX-MS to study and engineer protein dynamics., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The transplantation of loops between structurally related proteins is a compelling method to improve the activity, specificity and stability of enzymes. However, despite the interest of loop regions in protein engineering, the available methods of loop-based rational protein design are scarce. One particular difficulty related to loop engineering is the unique dynamism that enables them to exert allosteric control over the catalytic function of enzymes. Thus, when engaging in a transplantation effort, such dynamics in the context of protein structure need consideration. A second practical challenge is identifying successful excision points for the transplantation or grafting. Here, we present LoopGrafter (https://loschmidt.chemi.muni.cz/loopgrafter/), a web server that specifically guides in the loop grafting process between structurally related proteins. The server provides a step-by-step interactive procedure in which the user can successively identify loops in the two input proteins, calculate their geometries, assess their similarities and dynamics, and select a number of loops to be transplanted. All possible different chimeric proteins derived from any existing recombination point are calculated, and 3D models for each of them are constructed and energetically evaluated. The obtained results can be interactively visualized in a user-friendly graphical interface and downloaded for detailed structural analyses., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Anterior gradient 2 (AGR2) is an endoplasmic reticulum (ER)-resident protein disulfide isomerase (PDI) known to be overexpressed in many human epithelial cancers and is involved in cell migration, cellular transformation, angiogenesis, and metastasis. This protein inhibits the activity of the tumor suppressor p53, and its expression levels can be used to predict cancer patient outcome. However, the precise network of AGR2-interacting partners and clients remains to be fully characterized. Herein, we used label-free quantification and also stable isotope labeling with amino acids in cell culture-based LC-MS/MS analyses to identify proteins interacting with AGR2. Functional annotation confirmed that AGR2 and its interaction partners are associated with processes in the ER that maintain intracellular metabolic homeostasis and participate in the unfolded protein response, including those associated with changes in cellular metabolism, energy, and redox states in response to ER stress. As a proof of concept, the interaction between AGR2 and PDIA3, another ER-resident PDI, was studied in more detail. Pathway analysis revealed that AGR2 and PDIA3 play roles in protein folding in ER, including post-translational modification and in cellular response to stress. We confirmed the AGR2-PDIA3 complex formation in cancer cells, which was enhanced in response to ER stress. Accordingly, molecular docking characterized potential quaternary structure of this complex; however, it remains to be elucidated whether AGR2 rather contributes to PDIA3 maturation in ER, the complex directly acts in cellular signaling, or mediates AGR2 secretion. Our study provides a comprehensive insight into the protein-protein interaction network of AGR2 by identifying functionally relevant proteins and related cellular and biochemical pathways associated with the role of AGR2 in cancer cells., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

In the process of understanding and redesigning the function of proteins in modern biochemistry, protein engineers are increasingly focusing on the exploration of regions in proteins called loops. Analyzing various characteristics of these regions helps the experts to design the transfer of the desired function from one protein to another. This process is denoted as loop grafting. As this process requires extensive manual treatment and currently there is no proper visual support for it, we designed LoopGrafter: a web-based tool that provides experts with visual support through all the loop grafting pipeline steps. The tool is logically divided into several phases, starting with the definition of two input proteins and ending with a set of grafted proteins. Each phase is supported by a specific set of abstracted 2D visual representations of loaded proteins and their loops that are interactively linked with the 3D view onto proteins. By sequentially passing through the individual phases, the user is shaping the list of loops that are potential candidates for loop grafting. In the end, the actual in-silico insertion of the loop candidates from one protein to the other is performed and the results are visually presented to the user. In this way, the fully computational rational design of proteins and their loops results in newly designed protein structures that can be further assembled and tested through in-vitro experiments. LoopGrafter was designed in tight collaboration with protein engineers, and its final appearance reflects many testing iterations. We showcase the contribution of LoopGrafter on a real case scenario and provide the readers with the experts' feedback, confirming the usefulness of our tool.

Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Protein dynamics are often invoked in explanations of enzyme catalysis, but their design has proven elusive. Here we track the role of dynamics in evolution, starting from the evolvable and thermostable ancestral protein Anc HLD-RLuc which catalyses both dehalogenase and luciferase reactions. Insertion-deletion (InDel) backbone mutagenesis of Anc HLD-RLuc challenged the scaffold dynamics. Screening for both activities reveals InDel mutations localized in three distinct regions that lead to altered protein dynamics (based on crystallographic B-factors, hydrogen exchange, and molecular dynamics simulations). An anisotropic network model highlights the importance of the conformational flexibility of a loop-helix fragment of Renilla luciferases for ligand binding. Transplantation of this dynamic fragment leads to lower product inhibition and highly stable glow-type bioluminescence. The success of our approach suggests that a strategy comprising (i) constructing a stable and evolvable template, (ii) mapping functional regions by backbone mutagenesis, and (iii) transplantation of dynamic features, can lead to functionally innovative proteins.

Protein interactions play a crucial role among the different functions of a cell and are central to our understanding of cellular processes both in health and disease. Here we present Galaxy InteractoMIX (http://galaxy.interactomix.com), a platform composed of 13 different computational tools each addressing specific aspects of the study of protein-protein interactions, ranging from large-scale cross-species protein-wide interactomes to atomic resolution level of protein complexes. Galaxy InteractoMIX provides an intuitive interface where users can retrieve consolidated interactomics data distributed across several databases or uncover links between diseases and genes by analyzing the interactomes underlying these diseases. The platform makes possible large-scale prediction and curation protein interactions using the conservation of motifs, interology, or presence or absence of key sequence signatures. The range of structure-based tools includes modeling and analysis of protein complexes, delineation of interfaces and the modeling of peptides acting as inhibitors of protein-protein interactions. Galaxy InteractoMIX includes a range of ready-to-use workflows to run complex analyses requiring minimal intervention by users. The potential range of applications of the platform covers different aspects of life science, biomedicine, biotechnology and drug discovery where protein associations are studied., Competing Interests: Declaration of Competing Interest None declared., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The roles of protein conformational dynamics and allostery in function are well-known. However, the roles that interdomain dynamics have in function are not entirely understood. We used matrix metalloprotease-1 (MMP1) as a model system to study the relationship between interdomain dynamics and activity because MMP1 has diverse substrates. Here we focus on fibrin, the primary component of a blood clot. Water-soluble fibrinogen, following cleavage by thrombin, self-polymerize to form water-insoluble fibrin. We studied the interdomain dynamics of MMP1 on fibrin without crosslinks using single-molecule Forster Resonance Energy Transfer (smFRET). We observed that the distance between the catalytic and hemopexin domains of MMP1 increases or decreases as the MMP1 activity increases or decreases, respectively. We modulated the activity using (1) an active site mutant (E219Q) of MMP1, (2) MMP9, another member of the MMP family that increases the activity of MMP1, and (3) tetracycline, an inhibitor of MMP1. We fitted the histograms of smFRET values to a sum of two Gaussians and the autocorrelations to an exponential and power law. We modeled the dynamics as a two-state Poisson process and calculated the kinetic rates from the histograms and autocorrelations. Activity-dependent interdomain dynamics may enable allosteric control of the MMP1 function.

An understanding of the structure-dynamics relationship is essential for understanding how a protein works. Prior research has shown that the activity of a protein correlates with intradomain dynamics occurring at picosecond to millisecond timescales. However, the correlation between interdomain dynamics and the function of a protein is poorly understood. Here, we show that communications between the catalytic and hemopexin domains of matrix metalloprotease-1 (MMP1) on type 1 collagen fibrils correlate with its activity. Using single-molecule Förster resonance energy transfer, we identified functionally relevant open conformations in which the two MMP1 domains are well separated, which were significantly absent for catalytically inactive point mutant (E219Q) of MMP1 and could be modulated by an inhibitor or an enhancer of activity. The observed relevance of open conformations resolves the debate about the roles of open and closed MMP1 structures in function. We fitted the histograms of single-molecule Förster resonance energy transfer values to a sum of two Gaussians and the autocorrelations to an exponential and power law. We used a two-state Poisson process to describe the dynamics and calculate the kinetic rates from the fit parameters. All-atom and coarse-grained simulations reproduced some of the experimental features and revealed substrate-dependent MMP1 dynamics. Our results suggest that an interdomain separation facilitates opening up the catalytic pocket so that the collagen chains come closer to the MMP1 active site. Coordination of functional conformations at different parts of MMP1 occurs via allosteric communications that can take place via interactions mediated by collagen even if the linker between the domains is absent. Modeling dynamics as a Poisson process enables connecting the picosecond timescales of molecular dynamics simulations with the millisecond timescales of single-molecule measurements. Water-soluble MMP1 interacting with water-insoluble collagen fibrils poses challenges for biochemical studies that the single-molecule tracking can overcome for other insoluble substrates. Interdomain communications are likely important for multidomain proteins., (Copyright © 2020 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Angiotensin Converting Enzyme (ACE) expression and activity is associated with obesity. ACE is a circulating factor that predicts sustained weight loss over a time frame of months. Here, we evaluate whether ACE might also be an early marker (over a 24-hour period) for weight loss. 32 participants (78% females; BMI 28.47 ± 4.87kg/m2) followed a 1200KCal diet with an optional daily (<250KCal) snack and were asked to use an in-house generated health platform to provide recordings of food intake, physical activity and urine collection time and volume. Following a day of dieting, ACE levels in urine negatively correlated with weight loss ( p = 0.015 ). This reduction in ACE levels was significantly more robust in individuals with a BMI > 25 ( p = 0.0025 ). This study demonstrated that ACE levels correlate with BMI and weight loss as early as after 1 day of dieting, and thus ACE could be a potential early "biofeedback" marker for weight loss and diet efficiency., Competing Interests: “The authors declare no conflict of interest.”

Haloalkane dehalogenases are enzymes with a broad application potential in biocatalysis, bioremediation, biosensing and cell imaging. The new haloalkane dehalogenase DmxA originating from the psychrophilic bacterium Marinobacter sp. ELB17 surprisingly possesses the highest thermal stability (apparent melting temperature T m,app = 65.9 °C) of all biochemically characterized wild type haloalkane dehalogenases belonging to subfamily II. The enzyme was successfully expressed and its crystal structure was solved at 1.45 Å resolution. DmxA structure contains several features distinct from known members of haloalkane dehalogenase family: (i) a unique composition of catalytic residues; (ii) a dimeric state mediated by a disulfide bridge; and (iii) narrow tunnels connecting the enzyme active site with the surrounding solvent. The importance of narrow tunnels in such paradoxically high stability of DmxA enzyme was confirmed by computational protein design and mutagenesis experiments.

Mutations in the RHO gene encoding for the visual pigment protein, rhodopsin, are among the most common cause of autosomal dominant retinitis pigmentosa (ADRP). Previous studies of ADRP mutations in different domains of rhodopsin have indicated that changes that lead to more instability in rhodopsin structure are responsible for more severe disease in patients. Here, we further test this hypothesis by comparing side-by-side and therefore quantitatively two RHO mutations, N15S and P23H, both located in the N-terminal intradiscal domain. The in vitro biochemical properties of these two rhodopsin proteins, expressed in stably transfected tetracycline-inducible HEK293S cells, their UV-visible absorption, their Fourier transform infrared, circular dichroism and Metarhodopsin II fluorescence spectroscopy properties were characterized. As compared to the severely impaired P23H molecular function, N15S is only slightly defective in structure and stability. We propose that the molecular basis for these structural differences lies in the greater distance of the N15 residue as compared to P23 with respect to the predicted rhodopsin folding core. As described previously for WT rhodopsin, addition of the cytoplasmic allosteric modulator chlorin e6 stabilizes especially the P23H protein, suggesting that chlorin e6 may be generally beneficial in the rescue of those ADRP rhodopsin proteins whose stability is affected by amino acid replacement., Competing Interests: The authors have declared that no competing interests exist.

Background: Numerous diets, apps and websites help guide and monitor dietary behaviour with the goal of losing weight, yet dieting success is highly dependent on personal preferences and circumstances. To enable a more quantitative approach to dieting, we developed an integrated platform that allows tracking of life-style information alongside molecular biofeedback measurements (lactate and insulin)., Methods: To facilitate weight loss, participants (≥18 years) omitted one main meal from the usual three-meal routine. Daily caloric intake was restricted to ~1200KCal with one optional snack ≤250KCal. A mobile health platform (personalhealth.warwick.ac.uk) was developed and used to maintain diaries of food intake, weight, urine collection and volume. A survey was conducted to understand participants' willingness to collect samples, motivation for taking part in the study and reasons for dropout., Results: Meal skipping resulted in weight loss after a 24 h period in contrast to 3-meal control days regardless of the meal that was skipped, breakfast, lunch or dinner ( p  < 0.001). Common reasons for engagement were interest in losing weight and personal metabolic profile. Total insulin and lactate values varied significantly between healthy and obese individuals at p  = 0.01 and 0.05 respectively., Conclusion: In a proof of concept study with a meal-skipping diet, we show that insulin and lactate values in urine correlate with weight loss, making these molecules potential candidates for quantitative feedback on food intake behaviour to people dieting., Competing Interests: Not applicable.The authors declare that they have no competing interest.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The interaction between cardiolipin (CL) and cytochrome c (cyt-c) results in a gain of function of peroxidase activity by cyt-c. Despite intensive research, disagreements on nature and molecular details of this interaction remain. In particular, it is still not known how the interaction triggers the onset of apoptosis. Enzymatic characterization of peroxidase activity has highlighted the need for a critical threshold concentration of CL, a finding of profound physiological relevance in vivo. Using solution NMR, fluorescence spectroscopy, and in silico modeling approaches we here confirm that full binding of cyt-c to the membrane requires a CL:cyt-c threshold ratio of 5:1. Among three binding sites, the simultaneous binding of two sites, at two opposing sides of the heme, provides a mechanism to open the heme crevice to substrates. This results in "productive binding" in which cyt-c then sequesters CL, inducing curvature in the membrane. Membrane perturbation along with lipid peroxidation, due to interactions of heme/CL acyl chains, initiates the next step in the apoptotic pathway of making the membrane leaky. The third CL binding site while allowing interaction with the membrane, does not cluster CL or induce subsequent events, making this interaction "unproductive"., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Motivation: The characterization of the protein-protein association mechanisms is crucial to understanding how biological processes occur. It has been previously shown that the early formation of non-specific encounters enhances the realization of the stereospecific (i.e. native) complex by reducing the dimensionality of the search process. The association rate for the formation of such complex plays a crucial role in the cell biology and depends on how the partners diffuse to be close to each other. Predicting the binding free energy of proteins provides new opportunities to modulate and control protein-protein interactions. However, existing methods require the 3D structure of the complex to predict its affinity, severely limiting their application to interactions with known structures., Results: We present a new approach that relies on the unbound protein structures and protein docking to predict protein-protein binding affinities. Through the study of the docking space (i.e. decoys), the method predicts the binding affinity of the query proteins when the actual structure of the complex itself is unknown. We tested our approach on a set of globular and soluble proteins of the newest affinity benchmark, obtaining accuracy values comparable to other state-of-art methods: a 0.4 correlation coefficient between the experimental and predicted values of ΔG and an error < 3 Kcal/mol., Availability and Implementation: The binding affinity predictor is implemented and available at http://sbi.upf.edu/BADock and https://github.com/badocksbi/BADock., Contact: j.planas-iglesias@warwick.ac.uk or baldo.oliva@upf.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2017. Published by Oxford University Press.)

Since its discovery 75years ago, a wealth of knowledge has accumulated on the role of cardiolipin, the hallmark phospholipid of mitochondria, in bioenergetics and particularly on the structural organization of the inner mitochondrial membrane. A surge of interest in this anionic doubly-charged tetra-acylated lipid found in both prokaryotes and mitochondria has emerged based on its newly discovered signaling functions. Cardiolipin displays organ, tissue, cellular and transmembrane distribution asymmetries. A collapse of the membrane asymmetry represents a pro-mitophageal mechanism whereby externalized cardiolipin acts as an "eat-me" signal. Oxidation of cardiolipin's polyunsaturated acyl chains - catalyzed by cardiolipin complexes with cytochrome c. - is a pro-apoptotic signal. The messaging functions of myriads of cardiolipin species and their oxidation products are now being recognized as important intracellular and extracellular signals for innate and adaptive immune systems. This newly developing field of research exploring cardiolipin signaling is the main subject of this review. This article is part of a Special Issue entitled: Lipids of Mitochondria edited by Guenther Daum., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA&#95;Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Virtually all the biological processes that occur inside or outside cells are mediated by protein-protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com)., (© 2016 Authors; published by Portland Press Limited.)

While opto-genetics has proven to have tremendous value in revealing the functions of the macromolecular machinery in cells, it is not amenable to exploration of small molecules such as phospholipids (PLs). Here, we describe a redox opto-lipidomics approach based on a combination of high affinity light-sensitive ligands to specific PLs in mitochondria with LC-MS based redox lipidomics/bioinformatics analysis for the characterization of pro-apoptotic lipid signals. We identified the formation of mono-oxygenated derivatives of C18:2-containing cardiolipins (CLs) in mitochondria after the exposure of 10-nonylacridine orange bromide (NAO)-loaded cells to light. We ascertained that these signals emerge as an immediate opto-lipidomics response, but they decay long before the commencement of apoptotic cell death. We found that a protonophoric uncoupler caused depolarization of mitochondria and prevented the mitochondrial accumulation of NAO, inhibited the formation of C18:2-CL oxidation product,s and protected cells from death. Redox opto-lipidomics extends the power of opto-biologic protocols to studies of small PL molecules resilient to opto-genetic manipulations.

Maintenance of energy homeostasis depends on the highly regulated storage and release of triacylglycerol primarily in adipose tissue, and excessive storage is a feature of common metabolic disorders. CIDEA is a lipid droplet (LD)-protein enriched in brown adipocytes promoting the enlargement of LDs, which are dynamic, ubiquitous organelles specialized for storing neutral lipids. We demonstrate an essential role in this process for an amphipathic helix in CIDEA, which facilitates embedding in the LD phospholipid monolayer and binds phosphatidic acid (PA). LD pairs are docked by CIDEA trans-complexes through contributions of the N-terminal domain and a C-terminal dimerization region. These complexes, enriched at the LD-LD contact site, interact with the cone-shaped phospholipid PA and likely increase phospholipid barrier permeability, promoting LD fusion by transference of lipids. This physiological process is essential in adipocyte differentiation as well as serving to facilitate the tight coupling of lipolysis and lipogenesis in activated brown fat.

Cardiolipins (CL) represent unique phospholipids of bacteria and eukaryotic mitochondria with four acyl chains and two phosphate groups that have been implicated in numerous functions from energy metabolism to apoptosis. Many proteins are known to interact with CL, and several cocrystal structures of protein-CL complexes exist. In this work, we describe the collection of the first systematic and, to the best of our knowledge, the comprehensive gold standard data set of all known CL-binding proteins. There are 62 proteins in this data set, 21 of which have nonredundant crystal structures with bound CL molecules available. Using binding patch analysis of amino acid frequencies, secondary structures and loop supersecondary structures considering phosphate and acyl chain binding regions together and separately, we gained a detailed understanding of the general structural and dynamic features involved in CL binding to proteins. Exhaustive docking of CL to all known structures of proteins experimentally shown to interact with CL demonstrated the validity of the docking approach, and provides a rich source of information for experimentalists who may wish to validate predictions., (Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Motivation: The remodeling of short fragment(s) of the protein backbone to accommodate new function(s), fine-tune binding specificities or change/create novel protein interactions is a common task in structure-based computational design. Alternative backbone conformations can be generated de novo or by redeploying existing fragments extracted from protein structures i.e. knowledge-based. We present Frag'r'Us, a web server designed to sample alternative protein backbone conformations in loop regions. The method relies on a database of super secondary structural motifs called smotifs. Thus, sampling of conformations reflects structurally feasible fragments compiled from existing protein structures. Availability and implementation Frag'r'Us has been implemented as web application and is available at http://www.bioinsilico.org/FRAGRUS., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

The function of a protein is determined by its three-dimensional structure, which is formed by regular (i.e. β-strands and α-helices) and non-periodic structural units such as loops. Compared to regular structural elements, non-periodic, non-repetitive conformational units enclose a much higher degree of variability--raising difficulties in the identification of regularities, and yet represent an important part of the structure of a protein. Indeed, loops often play a pivotal role in the function of a protein and different aspects of protein folding and dynamics. Therefore, the structural classification of protein loops is an important subject with clear applications in homology modelling, protein structure prediction, protein design (e.g. enzyme design and catalytic loops) and function prediction. ArchDB, the database presented here (freely available at http://sbi.imim.es/archdb), represents such a resource and has been an important asset for the scientific community throughout the years. In this article, we present a completely reworked and updated version of ArchDB. The new version of ArchDB features a novel, fast and user-friendly web-based interface, and a novel graph-based, computationally efficient, clustering algorithm. The current version of ArchDB classifies 149,134 loops in 5739 classes and 9608 subclasses.

Summary: Protein-protein interactions play a critical role in many biological processes. Despite that, the number of servers that provide an easy and comprehensive method to predict them is still limited. Here, we present iLoops, a web server that predicts whether a pair of proteins can interact using local structural features. The inputs of the server are as follows: (i) the sequences of the query proteins and (ii) the pairs to be tested. Structural features are assigned to the query proteins by sequence similarity. Pairs of structural features (formed by loops or domains) are classified according to their likelihood to favor or disfavor a protein-protein interaction, depending on their observation in known interacting and non-interacting pairs. The server evaluates the putative interaction using a random forest classifier., Availability: iLoops is available at http://sbi.imim.es/iLoops.php, Contact: baldo.oliva@upf.edu, Supplementary Information: Supplementary data are available at Bioinformatics online.

Protein-protein interactions (PPIs) play a relevant role among the different functions of a cell. Identifying the PPI network of a given organism (interactome) is useful to shed light on the key molecular mechanisms within a biological system. In this work, we show the role of structural features (loops and domains) to comprehend the molecular mechanisms of PPIs. A paradox in protein-protein binding is to explain how the unbound proteins of a binary complex recognize each other among a large population within a cell and how they find their best docking interface in a short timescale. We use interacting and non-interacting protein pairs to classify the structural features that sustain the binding (or non-binding) behavior. Our study indicates that not only the interacting region but also the rest of the protein surface are important for the interaction fate. The interpretation of this classification suggests that the balance between favoring and disfavoring structural features determines if a pair of proteins interacts or not. Our results are in agreement with previous works and support the funnel-like intermolecular energy landscape theory that explains PPIs. We have used these features to score the likelihood of the interaction between two proteins and to develop a method for the prediction of PPIs. We have tested our method on several sets with unbalanced ratios of interactions and non-interactions to simulate real conditions, obtaining accuracies higher than 25% in the most unfavorable circumstances., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Cells exploit signaling pathways during responses to environmental changes, and these processes are often modulated during disease. Particularly, relevant human pathologies such as cancer or viral infections require downregulating apoptosis signaling pathways to progress. As a result, the identification of proteins responsible for these changes is essential for the diagnostics and development of therapeutics. Transferring functional annotation within protein interaction networks has proven useful to identify such proteins, although this is not a trivial task. Here, we used different scoring methods to transfer annotation from 53 well-studied members of the human apoptosis pathways (as known by 2005) to their protein interactors. All scoring methods produced significant predictions (compared to a random negative model), but its number was too large to be useful. Thus, we made a final prediction using specific combinations of scoring methods and compared it to the proteins related to apoptosis signaling pathways during the last 5 years. We propose 273 candidate proteins that may be relevant in apoptosis signaling pathways. Although some of them have known functions consistent with their proposed apoptotsis involvement, the majority have not been annotated yet, leaving room for further experimental studies. We provide our predictions at http://sbi.imim.es/web/Apoptosis.php.