Allosteric Activation of RhoA Complexed with p115-RhoGEF Deciphered by Conformational Dynamics.

The Ras homologue family member A (RhoA) is a member of the Rho family, a subgroup of the Ras superfamily. RhoA interacts with the 115 kDa guanine nucleotide exchange factor (p115-RhoGEF), which assists in activation and binding with downstream effectors. Here, we use molecular dynamics (MD) simulations and essential dynamics analysis of the inactive RhoA-GDP and active RhoA-GTP, when bound to p115-RhoGEF to decipher the mechanism of RhoA activation at the structural level. We observe that inactive RhoA-GDP maintains its position near the catalytic site on the Dbl homology (DH) domain of p115-RhoGEF through the interaction of its Switch I region with the DH domain. We further show that the active RhoA-GTP is engaged in more interactions with the p115-RhoGEF membrane-bound Pleckstrin homology (PH) domain as compared to RhoA-GDP. We hypothesize that the role of the interactions between the active RhoA-GTP and the PH domain is to help release it from the DH domain upon activation. Our results support this premise, and our simulations uncover the beginning of this process and provide structural details. They also point to allosteric communication pathways that take part in RhoA activation to promote and strengthen the interaction between the active RhoA-GTP and the PH domain. Allosteric regulation also occurs among other members of the Rho superfamily. Collectively, we suggest that in the activation process, the role of the RhoA-GTP interaction with the PH domain is to release RhoA-GTP from the DH domain after activation, making it available to downstream effectors.

Active and Inactive Cdc42 Differ in Their Insert Region Conformational Dynamics.

Cell division control protein 42 homolog (Cdc42) protein, a Ras superfamily GTPase, regulates cellular activities, including cancer progression. Using all-atom molecular dynamics (MD) simulations and essential dynamic analysis, we investigated the structure and dynamics of the catalytic domains of GDP-bound (inactive) and GTP-bound (active) Cdc42 in solution. We discovered substantial differences in the dynamics of the inactive and active forms, particularly in the "insert region" (residues 122-135), which plays a role in Cdc42 activation and binding to effectors. The insert region has larger conformational flexibility in the GDP-bound Cdc42 than in the GTP-bound Cdc42. The G2 loop and switch I at the effector lobe of the catalytic domain exhibit large conformational changes in both the GDP- and the GTP-bound systems, but in the GTP-bound Cdc42, the switch I interactions with GTP are retained. Oncogenic mutations were identified in the Ras superfamily. In Cdc42, the G12V and Q61L mutations decrease the GTPase activity. We simulated these mutations in both GDP- and GTP-bound Cdc42. Although the overall structural organization is quite similar between the wild type and the mutants, there are small differences in the conformational dynamics, especially in the two switch regions. Taken together, the G12V and Q61L mutations may play a role similar to their K-Ras counterparts in nucleotide binding and activation. The conformational differences, which are mainly in the insert region and, to a lesser extent, in the switch regions flanking the nucleotide binding site, can shed light on binding and activation. We propose that the differences are due to a network of hydrogen bonds that gets disrupted when Cdc42 is bound to GDP, a disruption that does not exist in other Rho GTPases. The differences in the dynamics between the two Cdc42 states suggest that the inactive conformation has reduced ability to bind to effectors.

Protein hormone fragmentation in intercellular signaling: hormones as nested information systems.

This study explores the hypothesis that protein hormones are nested information systems in which initial products of gene transcription, and their subsequent protein fragments, before and after secretion and initial target cell action, play additional physiological regulatory roles. The study produced four tools and key results: (1) a problem approach that proceeds, with examples and suggestions for in vivo organismal functional tests for peptide-protein interactions, from proteolytic breakdown prediction to models of hormone fragment modulation of protein-protein binding motifs in unrelated proteins; (2) a catalog of 461 known soluble human protein hormones and their predicted fragmentation patterns; (3) an analysis of the predicted proteolytic patterns of the canonical protein hormone transcripts demonstrating near-universal persistence of 9 ± 7 peptides of 8 ± 8 amino acids even after cleavage with 24 proteases from four protease classes; and (4) a coincidence analysis of the predicted proteolysis locations and the 1939 exon junctions within the transcripts that shows an excess (P < 0.001) of predicted proteolysis within 10 residues, especially at the exonal junction (P < 0.01). It appears all protein hormone transcripts generate multiple fragments the size of peptide hormones or protein-protein binding domains that may alter intracellular or extracellular functions by acting as modulators of metabolic enzymes, transduction factors, protein binding proteins, or hormone receptors. High proteolytic frequency at exonal junctions suggests proteolysis has evolved, as a complement to gene exon fusion, to extract structures or functions within single exons or protein segments to simplify the genome by discarding archaic one-exon genes.

Patch-DCA: improved protein interface prediction by utilizing structural information and clustering DCA scores.

MOTIVATION: Over the past decade, there have been impressive advances in determining the 3D structures of protein complexes. However, there are still many complexes with unknown structures, even when the structures of the individual proteins are known. The advent of protein sequence information provides an opportunity to leverage evolutionary information to enhance the accuracy of protein-protein interface prediction. To this end, several statistical and machine learning methods have been proposed. In particular, direct coupling analysis has recently emerged as a promising approach for identification of protein contact maps from sequential information. However, the ability of these methods to detect protein-protein inter-residue contacts remains relatively limited. RESULTS: In this work, we propose a method to integrate sequential and co-evolution information with structural and functional information to increase the performance of protein-protein interface prediction. Further, we present a post-processing clustering method that improves the average relative F1 score by 70% and 24% and the average relative precision by 80% and 36% in comparison with two state-of-the-art methods, PSICOV and GREMLIN. AVAILABILITY AND IMPLEMENTATION: https://github.com/BioMLBoston/PatchDCA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrating Rigidity Analysis into the Exploration of Protein Conformational Pathways Using RRT* and MC.

To understand how proteins function on a cellular level, it is of paramount importance to understand their structures and dynamics, including the conformational changes they undergo to carry out their function. For the aforementioned reasons, the study of large conformational changes in proteins has been an interest to researchers for years. However, since some proteins experience rapid and transient conformational changes, it is hard to experimentally capture the intermediate structures. Additionally, computational brute force methods are computationally intractable, which makes it impossible to find these pathways which require a search in a high-dimensional, complex space. In our previous work, we implemented a hybrid algorithm that combines Monte-Carlo (MC) sampling and RRT*, a version of the Rapidly Exploring Random Trees (RRT) robotics-based method, to make the conformational exploration more accurate and efficient, and produce smooth conformational pathways. In this work, we integrated the rigidity analysis of proteins into our algorithm to guide the search to explore flexible regions. We demonstrate that rigidity analysis dramatically reduces the run time and accelerates convergence.

Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability.

Predicting how a point mutation alters a protein's stability can guide pharmaceutical drug design initiatives which aim to counter the effects of serious diseases. Conducting mutagenesis studies in physical proteins can give insights about the effects of amino acid substitutions, but such wet-lab work is prohibitive due to the time as well as financial resources needed to assess the effect of even a single amino acid substitution. Computational methods for predicting the effects of a mutation on a protein structure can complement wet-lab work, and varying approaches are available with promising accuracy rates. In this work we compare and assess the utility of several machine learning methods and their ability to predict the effects of single and double mutations. We in silico generate mutant protein structures, and compute several rigidity metrics for each of them. We use these as features for our Support Vector Regression (SVR), Random Forest (RF), and Deep Neural Network (DNN) methods. We validate the predictions of our in silico mutations against experimental Δ Δ G stability data, and attain Pearson Correlation values upwards of 0.71 for single mutations, and 0.81 for double mutations. We perform ablation studies to assess which features contribute most to a model's success, and also introduce a voting scheme to synthesize a single prediction from the individual predictions of the three models.

Detecting intermediate protein conformations using algebraic topology.

BACKGROUND: Understanding protein structure and dynamics is essential for understanding their function. This is a challenging task due to the high complexity of the conformational landscapes of proteins and their rugged energy levels. In particular, it is important to detect highly populated regions which could correspond to intermediate structures or local minima. RESULTS: We present a hierarchical clustering and algebraic topology based method that detects regions of interest in protein conformational space. The method is based on several techniques. We use coarse grained protein conformational search, efficient robust dimensionality reduction and topological analysis via persistent homology as the main tools. We use two dimensionality reduction methods as well, robust Principal Component Analysis (PCA) and Isomap, to generate a reduced representation of the data while preserving most of the variance in the data. CONCLUSIONS: Our hierarchical clustering method was able to produce compact, well separated clusters for all the tested examples.

Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression.

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on the three types of cancer-GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

Sequence dependence of C-end rule peptides in binding and activation of neuropilin-1 receptor.

Neuropilin-1 (NRP-1) is a hub receptor that plays an essential role in angiogenesis and vascular permeability. It is over-expressed in the new blood vessels grown by tumor cells and is a target for anti-tumor treatments. Peptides that expose the consensus sequence R/K/XXR/K at the C-terminus (C-end rule or CendR peptides) bind to NRP-1 and are internalized into the cell. We used peptide phage display binding assays and molecular dynamics (MD) simulations to study the potential role of the central residues of CendR peptides in binding and activation of the NRP-1 receptor. The high stability of RPAR-receptor domain complex stems from the formation of a characteristic pattern of three hydrogen bonds between the peptide C-terminus and the residues in the NRP-1 loop III. Any changes in the peptide structure that fail to preserve this triad result in a less-stable complex. We performed a systematic study of RXXR mutants, where X=A/D/S/R/P, in order to test the effect of replacement of A or P on the binding capabilities. Our results, both experimental and computational, show that RRAR, RDAR, RPDR, RPRR and RPPR are capable of binding NRP-1. However, only RPPR and RPRR segments form an optimal organization around loop III with low potential energy. In other analogs, the absence of these stabilizing interactions always results in higher potential energy of the complexes. The binding of RPAR analogs does not guarantee receptor activation; only stable complexes that are properly stabilized via loop III appear able to trigger NRP-1 activation.

A conservation and biophysics guided stochastic approach to refining docked multimeric proteins.

BACKGROUND: We introduce a protein docking refinement method that accepts complexes consisting of any number of monomeric units. The method uses a scoring function based on a tight coupling between evolutionary conservation, geometry and physico-chemical interactions. Understanding the role of protein complexes in the basic biology of organisms heavily relies on the detection of protein complexes and their structures. Different computational docking methods are developed for this purpose, however, these methods are often not accurate and their results need to be further refined to improve the geometry and the energy of the resulting complexes. Also, despite the fact that complexes in nature often have more than two monomers, most docking methods focus on dimers since the computational complexity increases exponentially due to the addition of monomeric units. RESULTS: Our results show that the refinement scheme can efficiently handle complexes with more than two monomers by biasing the results towards complexes with native interactions, filtering out false positive results. Our refined complexes have better IRMSDs with respect to the known complexes and lower energies than those initial docked structures. CONCLUSIONS: Evolutionary conservation information allows us to bias our results towards possible functional interfaces, and the probabilistic selection scheme helps us to escape local energy minima. We aim to incorporate our refinement method in a larger framework which also enables docking of multimeric complexes given only monomeric structures.

The 6th Computational Structural Bioinformatics Workshop.

INTRODUCTION: The rapid accumulation of macromolecular structures presents a unique set of challenges and opportunities in the analysis, comparison, modeling, and prediction of macromolecular structures and interactions. The 6th Computational Structural Bioinformatics Workshop (CSBW) was held in Philadelphia on October 4, 2012. This issue includes eleven papers selected from the work presented at the CSBW of 2012. In "Four-body atomic potential for modeling protein-ligand binding affinity: application to enzyme-inhibitor binding energy prediction", a predictive model of free-energy was built to evaluate the binding between a protein and a ligand. In "Unbiased, scalable sampling of protein loop conformations from probabilistic priors", a new Markov chain Monte Carlo algorithm was proposed to generate unbiased conformations of closed protein loops from probabilistic priors. In "Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce", authors demonstrate an enhanced method by cutting a long RNA sequence into smaller chunks at strategically selected points, and distributing the tasks to multiple processors. In "A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction", the authors present an evolutionary search algorithm to obtain a discrete representation of the protein energy surface in terms of an ensemble of conformations representing local energy minima. In "Estimating loop length from Cryo-EM images at medium resolutions", the authors developed a computational geometry method to simplify the points along the skeleton to measure loop length in 3D images. The paper "A conservation and rigidity based method for detecting critical protein residues" presents a method that combines the rigidity and the evolutionary conservation in detection of the critical residues. In "A conservation and biophysics guided stochastic approach to refining docked multimeric proteins", the authors introduce a refinement method that accepts complexes consisting of any number of monomeric units. In "Elucidating the ensemble of functionally-relevant transitions in protein systems with a robotics-inspired method," the authors present a robotics-inspired tree-based method to sample energetically-credible conformational pathways connecting diverse functional states in multimodal proteins. The paper "An aggregate analysis of many predicted structures to reduce errors in protein structure comparison caused by conformational flexibility" applies protein structure prediction algorithms to enhance the classification of homologous proteins according to their binding preferences. In "DINC: A new AutoDock-based protocol for docking large ligands", an enhanced method was demonstrated for docking large ligands. The paper "Modeling protein conformational transitions by a combination of coarse-grained normal mode analysis and robotics-inspired methods" presents an efficient approach involving robotics concepts.

A conservation and rigidity based method for detecting critical protein residues.

BACKGROUND: Certain amino acids in proteins play a critical role in determining their structural stability and function. Examples include flexible regions such as hinges which allow domain motion, and highly conserved residues on functional interfaces which allow interactions with other proteins. Detecting these regions can aid in the analysis and simulation of protein rigidity and conformational changes, and helps characterizing protein binding and docking. We present an analysis of critical residues in proteins using a combination of two complementary techniques. One method performs in-silico mutations and analyzes the protein's rigidity to infer the role of a point substitution to Glycine or Alanine. The other method uses evolutionary conservation to find functional interfaces in proteins. RESULTS: We applied the two methods to a dataset of proteins, including biomolecules with experimentally known critical residues as determined by the free energy of unfolding. Our results show that the combination of the two methods can detect the vast majority of critical residues in tested proteins. CONCLUSIONS: Our results show that the combination of the two methods has the potential to detect more information than each method separately. Future work will provide a confidence level for the criticalness of a residue to improve the accuracy of our method and eliminate false positives. Once the combined methods are integrated into one scoring function, it can be applied to other domains such as estimating functional interfaces.

Elucidating the Structural Impacts of Protein InDels.

The effects of amino acid insertions and deletions (InDels) remain a rather under-explored area of structural biology. These variations oftentimes are the cause of numerous disease phenotypes. In spite of this, research to study InDels and their structural significance remains limited, primarily due to a lack of experimental information and computational methods. In this work, we fill this gap by modeling InDels computationally; we investigate the rigidity differences between the wildtype and a mutant variant with one or more InDels. Further, we compare how structural effects due to InDels differ from the effects of amino acid substitutions, which are another type of amino acid mutation. We finish by performing a correlation analysis between our rigidity-based metrics and wet lab data for their ability to infer the effects of InDels on protein fitness.

Binding of a C-end rule peptide to the neuropilin-1 receptor: a molecular modeling approach.

Neuropilin-1 (NRP-1) is a receptor that plays an essential role in angiogenesis, vascular permeability, and nervous system development. Previous studies have shown that peptides with an N-terminal Arg, especially peptides with the four-residue consensus sequence R/K/XXR/K, bind to NRP-1 cell surfaces. Peptides containing such consensus sequences promote binding and internalization into cells, while blocking the C-terminal Arg (or Lys) prevents the internalization. In this study, we use molecular dynamics simulations to model the structural properties of the NRP-1 complex with a prototypic CendR peptide, RPAR. Our simulations show that RPAR binds NRP-1 through specific interactions of the RPAR C-terminus: three hydrogen bonds and a salt bridge anchor the ligand in the receptor pocket. The modeling results were used as the starting point for a systematic computational study of new RPAR analogues based on chemical modifications of their natural amino acids. Comparison of the structural properties of the new peptide-receptor complexes with the original organization suggests that some of the analogues can increase the binding affinity while reducing the natural sensitivity of RXXR to endogenous proteases.

Multi-scale characterization of the energy landscape of proteins with application to the C3D/Efb-C complex.

We present a novel multi-level methodology to explore and characterize the low energy landscape and the thermodynamics of proteins. Traditional conformational search methods typically explore only a small portion of the conformational space of proteins and are hard to apply to large proteins due to the large amount of calculations required. In our multi-scale approach, we first provide an initial characterization of the equilibrium state ensemble of a protein using an efficient computational conformational sampling method. We then enrich the obtained ensemble by performing short Molecular Dynamics (MD) simulations on selected conformations from the ensembles as starting points. To facilitate the analysis of the results, we project the resulting conformations on a low-dimensional landscape to efficiently focus on important interactions and examine low energy regions. This methodology provides a more extensive sampling of the low energy landscape than an MD simulation starting from a single crystal structure as it explores multiple trajectories of the protein. This enables us to obtain a broader view of the dynamics of proteins and it can help in understanding complex binding, improving docking results and more. In this work, we apply the methodology to provide an extensive characterization of the bound complexes of the C3d fragment of human Complement component C3 and one of its powerful bacterial inhibitors, the inhibitory domain of Staphylococcus aureus extra-cellular fibrinogen-binding domain (Efb-C) and two of its mutants. We characterize several important interactions along the binding interface and define low free energy regions in the three complexes. Proteins 2010. (c) 2009 Wiley-Liss, Inc.

Tracing conformational changes in proteins.

BACKGROUND: Many proteins undergo extensive conformational changes as part of their functionality. Tracing these changes is important for understanding the way these proteins function. Traditional biophysics-based conformational search methods require a large number of calculations and are hard to apply to large-scale conformational motions. RESULTS: In this work we investigate the application of a robotics-inspired method, using backbone and limited side chain representation and a coarse grained energy function to trace large-scale conformational motions. We tested the algorithm on four well known medium to large proteins and we show that even with relatively little information we are able to trace low-energy conformational pathways efficiently. The conformational pathways produced by our methods can be further filtered and refined to produce more useful information on the way proteins function under physiological conditions. CONCLUSIONS: The proposed method effectively captures large-scale conformational changes and produces pathways that are consistent with experimental data and other computational studies. The method represents an important first step towards a larger scale modeling of more complex biological systems.

De novo tubular nanostructure design based on self-assembly of beta-helical protein motifs.

We present an approach for designing self-assembled nanostructures from naturally occurring building block segments obtained from native protein structures. We focus on structural motifs from left-handed beta-helical proteins. We selected 17 motifs. Copies of each of the motifs are stacked one atop the other. The obtained structures were simulated for long periods by using Molecular Dynamics to test their ability to retain their organization over time. We observed that a structural model based on the self-assembly of a motif from E. coli galactoside acetyltransferase produced a very stable tube. We studied the interactions that help maintain the conformational stability of the systems, focusing on the role of specific amino acids at specific positions. Analysis of these systems and a mutational study of selected candidates revealed that the presence of proline and glycine residues in the loops of beta-helical structures greatly enhances the structural stability of the systems.

Principles of nanostructure design with protein building blocks.

Currently there is increasing interest in nanostructures and their design. Nanostructure design involves the ability to predictably manipulate the properties of the self-assembly of autonomous units. Autonomous units have preferred conformational states. The units can be synthetic material science-based or derived from functional biological macromolecules. Autonomous biological building blocks with available structures provide an extremely rich and useful resource for design. For proteins, the structural databases contain large libraries of protein molecules and their building blocks with a range of shapes, surfaces, and chemical properties. The introduction of engineered synthetic residues or short peptides into these can expand the available chemical space and enhance the desired properties. Here we focus on the principles of nanostructure design with protein building blocks.

A hierarchical protein folding scheme based on the building block folding model.

The building block protein folding model states that the native protein structure is the product of a combinatorial assembly of relatively structurally independent contiguous parts of the protein that possess a hydrophobic core, i.e., building blocks (BBs). According to this model, our group proposed a three-stage scheme for a feasible time-wise semi ab-intio protein structure prediction. Given a protein sequence, at the first stage of the prediction scheme, we propose cutting the sequence into structurally assigned BBs. Next, we perform a combinatorial assembly and attempt to predict the relative three-dimensional arrangement of the BBs. In the third stage, we refine and rank the assemblies. The scheme has proven to be very promising in reducing the complexity of the protein folding problem and gaining insight into the protein folding process. In this chapter, we describe the different stages of the scheme and discuss a possible application of the model to protein design.

Methods for Detecting Critical Residues in Proteins.

In proteins, certain amino acids may play a critical role in determining their structure and function. Examples include flexible regions, which allow domain motions, and highly conserved residues on functional interfaces, which play a role in binding and interaction with other proteins. Detecting these regions facilitates the analysis and simulation of protein rigidity and conformational changes, and aids in characterizing protein-protein binding. We present a protocol that combines graph-theory rigidity analysis and machine-learning-based methods for predicting critical residues in proteins. Our approach combines amino-acid specific information and data obtained by two complementary methods. One method, KINARI, performs graph-based analysis to find rigid clusters of amino acids in a protein, while the other method relies on evolutionary conservation scores to find functional interfaces in proteins. Our machine learning model combines both methods, in addition to amino acid type and solvent-accessible surface area.