Binding Pocket Optimization by Computational Protein Design

Engineering specific interactions between proteins and small molecules is extremely useful for biological studies, as these interactions are essential for molecular recognition. Furthermore, many biotechnological applications are made possible by such an engineering approach, ranging from biosensors to the design of custom enzyme catalysts. Here, we present a novel method for the computational design of protein-small ligand binding named PocketOptimizer. The program can be used to modify protein binding pocket residues to improve or establish binding of a small molecule. It is a modular pipeline based on a number of customizable molecular modeling tools to predict mutations that alter the affinity of a target protein to its ligand. At its heart it uses a receptor-ligand scoring function to estimate the binding free energy between protein and ligand. We compiled a benchmark set that we used to systematically assess the performance of our method. It consists of proteins for which mutational variants with different binding affinities for their ligands and experimentally determined structures exist. Within this test set PocketOptimizer correctly predicts the mutant with the higher affinity in about 69% of the cases. A detailed analysis of the results reveals that the strengths of PocketOptimizer lie in the correct introduction of stabilizing hydrogen bonds to the ligand, as well as in the improved geometric complemetarity between ligand and binding pocket. Apart from the novel method for binding pocket design we also introduce a much needed benchmark data set for the comparison of affinities of mutant binding pockets, and that we use to asses programs for in silico design of ligand binding.     

Shared human-chimpanzee pattern of perinatal femoral shaft morphology and its implications for the evolution of hominin locomotor adaptations

Background

Acquisition of bipedality is a hallmark of human evolution. How bipedality evolved from great ape-like locomotor behaviors, however, is still highly debated. This is mainly because it is difficult to infer locomotor function, and even more so locomotor kinematics, from fossil hominin long bones. Structure-function relationships are complex, as long bone morphology reflects phyletic history, developmental programs, and loading history during an individual’s lifetime. Here we discriminate between these factors by investigating the morphology of long bones in fetal and neonate great apes and humans, before the onset of locomotion.

Methodology/Principal Findings

Comparative morphometric analysis of the femoral diaphysis indicates that its morphology reflects phyletic relationships between hominoid taxa to a greater extent than taxon-specific locomotor adaptations. Diaphyseal morphology in humans and chimpanzees exhibits several shared-derived features, despite substantial differences in locomotor adaptations. Orangutan and gorilla morphologies are largely similar, and likely represent the primitive hominoid state.

Conclusions/Significance

These findings are compatible with two possible evolutionary scenarios. Diaphyseal morphology may reflect retained adaptive traits of ancestral taxa, hence human-chimpanzee shared-derived features may be indicative of the locomotor behavior of our last common ancestor. Alternatively, diaphyseal morphology might reflect evolution by genetic drift (neutral evolution) rather than selection, and might thus be more informative about phyletic relationships between taxa than about locomotor adaptations. Both scenarios are consistent with the hypothesis that knuckle-walking in chimpanzees and gorillas resulted from convergent evolution, and that the evolution of human bipedality is unrelated to extant great ape locomotor specializations.     

Altitudinal Change in the Photosynthetic Capacity of Tropical Trees: A Case Study from Ecuador and a Pantropical Literature Analysis

In tropical mountains, trees are the dominant life form from sea level to above 4,000-m altitude under highly variable thermal conditions (range of mean annual temperatures: <8 to >28°C). How light-saturated net photosynthesis of tropical trees adapts to variation in temperature, atmospheric CO₂ concentration, and further environmental factors, that change along elevation gradients, is not precisely known. With gas exchange measurements in mature trees, we determined light-saturated net photosynthesis at ambient temperature (T) and [CO₂] (A _sat) of 40 tree species from 21 families in tropical mountain forests at 1000-, 2000-, and 3000-m elevation in southern Ecuador. We tested the hypothesis that stand-level averages of A _sat and leaf dark respiration (R _D) per leaf area remain constant with elevation. Stand-level means of A _sat were 8.8, 11.3, and 7.2?μmol?CO₂?m^?2?s^?1; those of R _D 0.8, 0.6, and 0.7?μmol?CO₂?m^?2?s^?1 at 1000-, 2000-, and 3000-m elevation, respectively, with no significant altitudinal trend. We obtained coefficients of among-species variation in A _sat and R _D of 20–53% (n?=?10–16 tree species per stand). Examining our data in the context of a pan-tropical A _sat data base for mature tropical trees (c. 170 species from 18 sites at variable elevation) revealed that area-based A _sat decreases in tropical mountains by, on average, 1.3?μmol?CO₂?m^?2?s^?1?per?km altitude increase (or by 0.2?μmol?CO₂?m^?2?s^?1 per K temperature decrease). The A _sat decrease occurred despite an increase in leaf mass per area with altitude. Local geological and soil fertility conditions and related foliar N and P concentrations considerably influenced the altitudinal A _sat patterns. We conclude that elevation is an important influencing factor of the photosynthetic activity of tropical trees. Lowered A _sat together with a reduced stand leaf area decrease canopy C gain with elevation in tropical mountains.     

Self-organizing ontology of biochemically relevant small molecules

Background  

The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest.     

MetaboLights: towards a new COSMOS of metabolomics data management

Exciting funding initiatives are emerging in Europe and the US for metabolomics data production, storage, dissemination and analysis. This is based on a rich ecosystem of resources around the world, which has been build during the past ten years, including but not limited to resources such as MassBank in Japan and the Human Metabolome Database in Canada. Now, the European Bioinformatics Institute has launched MetaboLights, a database for metabolomics experiments and the associated metadata (http://www.ebi.ac.uk/metabolights). It is the first comprehensive, cross-species, cross-platform metabolomics database maintained by one of the major open access data providers in molecular biology. In October, the European COSMOS consortium will start its work on Metabolomics data standardization, publication and dissemination workflows. The NIH in the US is establishing 6?C8 metabolomics services cores as well as a national metabolomics repository. This communication reports about MetaboLights as a new resource for Metabolomics research, summarises the related developments and outlines how they may consolidate the knowledge management in this third large omics field next to proteomics and genomics.     

An information-carrying and knowledge-producing molecular machine. A Monte-Carlo simulation

The concept called Knowledge is a measure of the quality of genetically transferred information. Its usefulness is demonstrated quantitatively in a Monte-Carlo simulation on critical steps in a origin of life model. The model describes the origin of a bio-like genetic apparatus by a long sequence of physical-chemical steps: it starts with the presence of a self-replicating oligomer and a specifically structured environment in time and space that allow for the formation of aggregates such as assembler-hairpins-devices and, at a later stage, an assembler-hairpins-enzyme device—a first translation machine.