Julie C. Mitchell

An automated decision‐tree approach to predicting protein interaction hot spots

Protein–protein interactions can be altered by mutating one or more “hot spots,” the subset of residues that account for most of the interfaces binding free energy. The identification of hot spots requires a significant experimental effort, highlighting the practical value of hot spot predictions. We present two knowledge‐based models that improve the ability to predict hot spots: K‐FADE uses shape specificity features calculated by the Fast Atomic Density Evaluation (FADE) program, and K‐CON uses biochemical contact features. The combined K‐FADE/CON (KFC) model displays better overall predictive accuracy than computational alanine scanning (Robetta–Ala). In addition, because these methods predict different subsets of known hot spots, a large and significant increase in accuracy is achieved by combining KFC and Robetta–Ala. The KFC analysis is applied to the calmodulin (CaM)/smooth muscle myosin light chain kinase (smMLCK) interface, and to the bone morphogenetic protein‐2 (BMP‐2)/BMP receptor‐type I (BMPR‐IA) interface. The results indicate a strong correlation between KFC hot spot predictions and mutations that significantly reduce the binding affinity of the interface. Proteins 2007.

Community-wide assessment of protein-interface modeling suggests improvements to design methodology

The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

KFC2: a knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features.

Hot spots constitute a small fraction of protein–protein interface residues, yet they account for a large fraction of the binding affinity. Based on our previous method (KFC), we present two new methods (KFC2a and KFC2b) that outperform other methods at hot spot prediction. A number of improvements were made in developing these new methods. First, we created a training data set that contained a similar number of hot spot and non‐hot spot residues. In addition, we generated 47 different features, and different numbers of features were used to train the models to avoid over‐fitting. Finally, two feature combinations were selected: One (used in KFC2a) is composed of eight features that are mainly related to solvent accessible surface area and local plasticity; the other (KFC2b) is composed of seven features, only two of which are identical to those used in KFC2a. The two models were built using support vector machines (SVM). The two KFC2 models were then tested on a mixed independent test set, and compared with other methods such as Robetta, FOLDEF, HotPoint, MINERVA, and KFC. KFC2a showed the highest predictive accuracy for hot spot residues (True Positive Rate: TPR = 0.85); however, the false positive rate was somewhat higher than for other models. KFC2b showed the best predictive accuracy for hot spot residues (True Positive Rate: TPR = 0.62) among all methods other than KFC2a, and the False Positive Rate (FPR = 0.15) was comparable with other highly predictive methods. Proteins 2011.

KFC Server: interactive forecasting of protein interaction hot spots

The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model—a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interfaces; binding free energy. The server facilitates the automated analysis of a user submitted protein–protein or protein–DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org.

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

Community‐wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community‐wide assessment of methods to predict the effects of mutations on protein–protein interactions. Twenty‐two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side‐chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large‐scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies. Proteins 2013; 81:1980–1987.

Generating Uniform Incremental Grids on SO(3) Using the Hopf Fibration

The problem of generating uniform deterministic samples over the rotation group, SO(3), is fundamental to computational biology, chemistry, physics, and numerous branches of computer science. We present the best-known method to date for constructing incremental, deterministic grids on SO(3); it provides: (1) the lowest metric distortion for grid neighbor edges, (2) optimal dispersion-reduction with each additional sample, (3) explicit neighborhood structure, and (4) equivolumetric partition of SO(3) by the grid cells. We also demonstrate the use of the sequence on motion planning problems.

Rapid atomic density methods for molecular shape characterization.

Two methods for rapid characterization of molecular shape are presented. Both techniques are based on the density of atoms near the molecular surface. The Fast Atomic Density Evaluation (FADE) algorithm uses fast Fourier transforms to quickly estimate densities. The Pairwise Atomic Density Reverse Engineering (PADRE) method derives modified density measures from the relationship between atomic density and total potentials. While many shape-characterization techniques define shape relative to a surface, the descriptors returned by FADE and PADRE can measure local geometry from points within the three-dimensional space surrounding a molecule. The methods can be used to find crevices and protrusions near the surface of a molecule and to test shape complementarity at the interface between docking molecules.

Structural analysis and modeling reveals new mechanisms governing ESCRT-III spiral filament assembly

Cryo-EM and molecular dynamics simulations reveal unexpected flexibility in individual monomers and a stable interface between monomers in the spiral filaments formed by the ESCRT-III subunit Vps32/CHMP4B.

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues.

Mitochondrial COQ9 is a lipid-binding protein that associates with COQ7 to enable coenzyme Q biosynthesis

Significance Coenzyme Q (CoQ) is a requisite component of the mitochondrial oxidative phosphorylation machinery that produces more than 90% of cellular ATP. Despite the discovery of CoQ more than 50 years ago, many aspects of its biosynthesis remain obscure. These include the functions of uncharacterized CoQ-related proteins whose disruption can cause human diseases. Our work reveals that one such protein, COQ9, is a lipid-binding protein that enables CoQ biosynthesis through its physical and functional interaction with COQ7, and via its stabilization of the entire CoQ biosynthetic complex. Unexpectedly, COQ9 achieves these functions by repurposing an ancient bacterial fold typically used for transcriptional regulation. Collectively, our work adds new insight into a core component of the CoQ biosynthesis process. Coenzyme Q (CoQ) is an isoprenylated quinone that is essential for cellular respiration and is synthesized in mitochondria by the combined action of at least nine proteins (COQ1–9). Although most COQ proteins are known to catalyze modifications to CoQ precursors, the biochemical role of COQ9 remains unclear. Here, we report that a disease-related COQ9 mutation leads to extensive disruption of the CoQ protein biosynthetic complex in a mouse model, and that COQ9 specifically interacts with COQ7 through a series of conserved residues. Toward understanding how COQ9 can perform these functions, we solved the crystal structure of Homo sapiens COQ9 at 2.4 Å. Unexpectedly, our structure reveals that COQ9 has structural homology to the TFR family of bacterial transcriptional regulators, but that it adopts an atypical TFR dimer orientation and is not predicted to bind DNA. Our structure also reveals a lipid-binding site, and mass spectrometry-based analyses of purified COQ9 demonstrate that it associates with multiple lipid species, including CoQ itself. The conserved COQ9 residues necessary for its interaction with COQ7 comprise a surface patch around the lipid-binding site, suggesting that COQ9 might serve to present its bound lipid to COQ7. Collectively, our data define COQ9 as the first, to our knowledge, mammalian TFR structural homolog and suggest that its lipid-binding capacity and association with COQ7 are key features for enabling CoQ biosynthesis.