Module walking using an SH3-like cell-wall-binding domain leads to a new GH184 family of muramidases.

Muramidases (also known as lysozymes) hydrolyse the peptidoglycan component of the bacterial cell wall and are found in many glycoside hydrolase (GH) families. Similar to other glycoside hydrolases, muramidases sometimes have noncatalytic domains that facilitate their interaction with the substrate. Here, the identification, characterization and X-ray structure of a novel fungal GH24 muramidase from Trichophaea saccata is first described, in which an SH3-like cell-wall-binding domain (CWBD) was identified by structure comparison in addition to its catalytic domain.

Accurate positioning of functional residues with robotics-inspired computational protein design.

SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods.

Multitasking in the gut: the X-ray structure of the multidomain BbgIII from Bifidobacterium bifidum offers possible explanations for its alternative functions.

β-Galactosidases catalyse the hydrolysis of lactose into galactose and glucose; as an alternative reaction, some β-galactosidases also catalyse the formation of galactooligosaccharides by transglycosylation. Both reactions have industrial importance: lactose hydrolysis is used to produce lactose-free milk, while galactooligosaccharides have been shown to act as prebiotics. For some multi-domain β-galactosidases, the hydrolysis/transglycosylation ratio can be modified by the truncation of carbohydrate-binding modules.

Computational design of a modular protein sense-response system.

Sensing and responding to signals is a fundamental ability of living systems, but despite substantial progress in the computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here, we describe a generalizable computational strategy for designing sensor-actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation through split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds.

Engineering a light-activated caspase-3 for precise ablation of neurons in vivo.

The circuitry of the brain is characterized by cell heterogeneity, sprawling cellular anatomy, and astonishingly complex patterns of connectivity. Determining how complex neural circuits control behavior is a major challenge that is often approached using surgical, chemical, or transgenic approaches to ablate neurons. However, all these approaches suffer from a lack of precise spatial and temporal control.

A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design.

The development and validation of computational macromolecular modeling and design methods depend on suitable benchmark datasets and informative metrics for comparing protocols. In addition, if a method is intended to be adopted broadly in diverse biological applications, there needs to be information on appropriate parameters for each protocol, as well as metrics describing the expected accuracy compared to experimental data. In certain disciplines, there exist established benchmarks and public resources where experts in a particular methodology are encouraged to supply their most efficient implementation of each particular benchmark.

Increasing the precision of orthology-based complex prediction through network alignment.

Macromolecular assemblies play an important role in almost all cellular processes. However, despite several large-scale studies, our current knowledge about protein complexes is still quite limited, thus advocating the use of in silico predictions to gather information on complex composition in model organisms. Since protein-protein interactions present certain constraints on the functional divergence of macromolecular assemblies during evolution, it is possible to predict complexes based on orthology data.

Scientific benchmarks for guiding macromolecular energy function improvement.

Accurate energy functions are critical to macromolecular modeling and design. We describe new tools for identifying inaccuracies in energy functions and guiding their improvement, and illustrate the application of these tools to the improvement of the Rosetta energy function. The feature analysis tool identifies discrepancies between structures deposited in the PDB and low-energy structures generated by Rosetta; these likely arise from inaccuracies in the energy function.

NetAligner--a network alignment server to compare complexes, pathways and whole interactomes.

The many ongoing genome sequencing initiatives are delivering comprehensive lists of the individual molecular components present in an organism, but these reveal little about how they work together. Follow-up initiatives are revealing thousands of interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Recently, we developed NetAligner, a novel network alignment tool that allows the identification of conserved protein complexes and pathways across organisms, providing valuable hints as to how those interaction networks evolved.

The role of structural disorder in the rewiring of protein interactions through evolution.

Structurally disordered regions play a key role in protein-protein interaction networks and the evolution of highly connected proteins, enabling the molecular mechanisms for multiple binding. However, the role of protein disorder in the evolution of interaction networks has only been investigated through the analysis of individual proteins, making it impossible to distinguish its specific impact in the (re)shaping of their interaction environments. Now, the availability of large interactomes for several model organisms permits exploration of the role of disorder in protein interaction networks not only at the level of the interacting proteins but of the interactions themselves.

A novel framework for the comparative analysis of biological networks.

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology.

Uncovering new substrates for Aurora A kinase.

Aurora A is a serine/threonine kinase that is essential for a wide variety of cell-cycle-related events, but only a small number of its substrates are known. We present and validate a strategy by which to identify Aurora A substrates and their phosphorylation sites. We developed a computational approach integrating various types of biological information to generate a list of 90 potential Aurora substrates, with a prediction accuracy of about 80%.

Systematic bioinformatics and experimental validation of yeast complexes reduces the rate of attrition during structural investigations.

For high-throughput structural studies of protein complexes of composition inferred from proteomics data, it is crucial that candidate complexes are selected accurately. Herein, we exemplify a procedure that combines a bioinformatics tool for complex selection with in vivo validation, to deliver structural results in a medium-throughout manner. We have selected a set of 20 yeast complexes, which were predicted to be feasible by either an automated bioinformatics algorithm, by manual inspection of primary data, or by literature searches.

Dynamic interactions of proteins in complex networks: a more structured view.

Virtually every process in a cell is carried out by macromolecular complexes whose actions need to be perfectly orchestrated. The synchronization and regulation of these biological functions is indeed critical and is usually carried out by complex networks of transient protein interactions. Here, we review some of the many strategies that proteins in regulatory networks use to achieve the dynamic plasticity necessary to rapidly respond to diverse cellular needs.

Exploiting gene deletion fitness effects in yeast to understand the modular architecture of protein complexes under different growth conditions.

Background: Understanding how individual genes contribute towards the fitness of an organism is a fundamental problem in biology. Although recent genome-wide screens have generated abundant data on quantitative fitness for single gene knockouts, very few studies have systematically integrated other types of biological information to understand how and why deletion of specific genes give rise to a particular fitness effect. In this study, we combine quantitative fitness data for single gene knock-outs in yeast with large-scale interaction discovery experiments to understand the effect of gene deletion on the modular architecture of protein complexes, under different growth conditions.

Incorporating high-throughput proteomics experiments into structural biology pipelines: identification of the low-hanging fruits.

The last years have seen the emergence of many large-scale proteomics initiatives that have identified thousands of new protein interactions and macromolecular assemblies. However, unfortunately, only a few among the discovered complexes meet the high-quality standards required to be promptly used in structural studies. This has thus created an increasing gap between the number of known protein interactions and complexes and those for which a high-resolution 3-D structure is available.

Towards a molecular characterisation of pathological pathways.

The dominant conceptual reductionism in drug discovery has resulted in many promising drug candidates to fail during the last clinical phases, mainly due to a lack of knowledge about the patho-physiological pathways they are acting on. Consequently, to increase the revenues of the drug discovery process, we need to improve our understanding of the molecular mechanisms underlying complex cellular processes and consider each potential drug target in its full biological context. Here, we review several strategies that combine computational and experimental techniques, and suggest a systems pathology approach that will ultimately lead to a better comprehension of the molecular bases of disease.