RESUMO
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.
Assuntos
Metilação de DNA , Regulação Neoplásica da Expressão Gênica , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Neoplasias/genética , Neoplasias/classificação , Transcriptoma/genética , Glioblastoma/genética , Glioblastoma/classificação , Neoplasias do Colo/genética , Neoplasias do Colo/classificação , Perfilação da Expressão Gênica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise por Conglomerados , Biomarcadores Tumorais/genéticaRESUMO
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.
Assuntos
Simulação de Dinâmica Molecular , Regulação Alostérica , Fatores de Troca de Nucleotídeo Guanina Rho , Domínios Proteicos , Guanosina Trifosfato/metabolismoRESUMO
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.
Assuntos
Mutação INDEL , Proteínas , Proteínas/genética , Proteínas/química , Substituição de Aminoácidos , Mutação , Aminoácidos/genéticaRESUMO
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.
Assuntos
Proteínas/química , Algoritmos , Animais , Biologia Computacional , Humanos , Conformação ProteicaRESUMO
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.
Assuntos
Comunicação Celular/fisiologia , Hormônios/metabolismo , Proteólise , Sequência de Aminoácidos , Aminoácidos/metabolismo , Animais , Hormônios/química , Humanos , Fragmentos de Peptídeos/química , Fragmentos de Peptídeos/metabolismo , Mapas de Interação de Proteínas/fisiologia , Processamento de Proteína Pós-Traducional , Proteínas/química , Proteínas/metabolismo , Transdução de Sinais/fisiologiaRESUMO
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.
Assuntos
Simulação de Dinâmica Molecular , Proteína cdc42 de Ligação ao GTP , Sítios de Ligação , Domínio Catalítico , Guanosina Difosfato , Guanosina Trifosfato , Proteína cdc42 de Ligação ao GTP/genética , Proteína cdc42 de Ligação ao GTP/metabolismo , Proteínas rasRESUMO
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.
Assuntos
Algoritmos , Biologia Computacional , Sequência de Aminoácidos , Análise por Conglomerados , ProteínasRESUMO
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.
Assuntos
Árvores de Decisões , Mutação , Redes Neurais de Computação , Proteínas/química , Máquina de Vetores de Suporte , Substituição de Aminoácidos , Simulação por Computador , Conformação Proteica , Estabilidade Proteica , TermodinâmicaRESUMO
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.
Assuntos
Biologia Computacional/métodos , Conformação Proteica , Proteínas , Análise por Conglomerados , Análise de Componente Principal , Proteínas/química , Proteínas/metabolismoRESUMO
Discriminating native-like structures from false positives with high accuracy is one of the biggest challenges in protein-protein docking. While there is an agreement on the existence of a relationship between various favorable intermolecular interactions (e.g., Van der Waals, electrostatic, and desolvation forces) and the similarity of a conformation to its native structure, the precise nature of this relationship is not known. Existing protein-protein docking methods typically formulate this relationship as a weighted sum of selected terms and calibrate their weights by using a training set to evaluate and rank candidate complexes. Despite improvements in the predictive power of recent docking methods, producing a large number of false positives by even state-of-the-art methods often leads to failure in predicting the correct binding of many complexes. With the aid of machine learning methods, we tested several approaches that not only rank candidate structures relative to each other but also predict how similar each candidate is to the native conformation. We trained a two-layer neural network, a multilayer neural network, and a network of Restricted Boltzmann Machines against extensive data sets of unbound complexes generated by RosettaDock and PyDock. We validated these methods with a set of refinement candidate structures. We were able to predict the root mean squared deviations (RMSDs) of protein complexes with a very small, often less than 1.5 Å, error margin when trained with structures that have RMSD values of up to 7 Å. In our most recent experiments with the protein samples having RMSD values up to 27 Å, the average prediction error was still relatively small, attesting to the potential of our approach in predicting the correct binding of protein-protein complexes.
Assuntos
Aprendizado de Máquina , Redes Neurais de Computação , Conformação Proteica , Proteínas/química , Animais , Humanos , Simulação de Acoplamento Molecular , Ligação ProteicaRESUMO
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.
Assuntos
Sítios de Ligação/genética , Proteínas/genética , Aminoácidos/genética , Biologia Computacional/métodos , Modelos Moleculares , Ligação Proteica/genética , Mapas de Interação de Proteínas/genéticaRESUMO
In recent years there has been increasing interest in nanostructure design based on the self-assembly properties of proteins and polymers. Nanodesign requires the ability to predictably manipulate the properties of the self-assembly of autonomous building blocks, which can fold or aggregate into preferred conformational states. The design includes functional synthetic materials and biological macromolecules. Autonomous biological building blocks with available 3D structures provide an extremely rich and useful resource. Structural databases contain large libraries of protein molecules and their building blocks with a range of sizes, shapes, surfaces, and chemical properties. The introduction of engineered synthetic residues or short peptides into these building blocks can greatly expand the available chemical space and enhance the desired properties. Herein, we summarize a protocol for designing nanostructures consisting of self-assembling building blocks, based on our recent works. We focus on the principles of nanostructure design with naturally occurring proteins and synthetic amino acids, as well as hybrid materials made of amyloids and synthetic polymers.
Assuntos
Aminoácidos , Modelos Moleculares , Nanoestruturas , Peptídeos , Conformação Proteica , Engenharia de Proteínas/métodos , Proteínas , Aminoácidos/química , Amiloide/química , Amiloide/genética , Simulação por Computador , Bases de Dados de Proteínas , Mutação , Peptídeos/química , Peptídeos/genética , Proteínas/química , Proteínas/genéticaRESUMO
One of the major challenges for protein docking methods is to accurately discriminate native-like structures from false positives. Docking methods are often inaccurate and the results have to be refined and re-ranked to obtain native-like complexes and remove outliers. In a previous work, we introduced AccuRefiner, a machine learning based tool for refining protein-protein complexes. Given a docked complex, the refinement tool produces a small set of refined versions of the input complex, with lower root-mean-square-deviation (RMSD) of atomic positions with respect to the native structure. The method employs a unique ranking tool that accurately predicts the RMSD of docked complexes with respect to the native structure. In this work, we use a deep learning network with a similar set of features and five layers. We show that a properly trained deep learning network can accurately predict the RMSD of a docked complex with 1.40 Å error margin on average, by approximating the complex relationship between a wide set of scoring function terms and the RMSD of a docked structure. The network was trained on 35000 unbound docking complexes generated by RosettaDock. We tested our method on 25 different putative docked complexes produced also by RosettaDock for five proteins that were not included in the training data. The results demonstrate that the high accuracy of the ranking tool enables AccuRefiner to consistently choose the refinement candidates with lower RMSD values compared to the coarsely docked input structures.
Assuntos
Simulação de Acoplamento Molecular/métodos , Proteínas/química , Bases de Dados de Proteínas , Redes Neurais de Computação , Conformação Proteica , Proteínas/metabolismoRESUMO
One of the major challenges for protein-protein docking methods is to accurately discriminate nativelike structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm. Our method achieved predicting RMSDs of unbound docked complexes with 0.4Å error margin.
Assuntos
Biologia Computacional/métodos , Conformação Proteica , Proteínas/química , Algoritmos , Simulação de Acoplamento Molecular/métodos , Ligação Proteica , SoftwareRESUMO
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.
Assuntos
Modelos Moleculares , Neuropilina-1/metabolismo , Peptídeos/metabolismo , Conformação Proteica , Sítios de Ligação/genética , Técnicas de Visualização da Superfície Celular , Ligação de Hidrogênio , Simulação de Dinâmica Molecular , Mutação/genética , Peptídeos/genética , Ligação Proteica , Estabilidade ProteicaRESUMO
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.
Assuntos
Biologia Computacional/métodos , Proteínas/química , Proteínas/metabolismo , Algoritmos , Modelos Moleculares , Conformação ProteicaRESUMO
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.