scholarly journals Leveraging protein dynamics to identify cancer mutational hotspots in 3D-structures

2018 ◽  
Author(s):  
Sushant Kumar ◽  
Declan Clarke ◽  
Mark B. Gerstein
Keyword(s):  
Protein Dynamics ◽  
Large Scale ◽  
Protein Structures ◽  
Three Dimensional ◽  
Structural Data ◽  
Detection Methods ◽  
Hotspot Detection ◽  
Driver Genes ◽  
3D Structures ◽  
Mutational Hotspots

AbstractLarge-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence and clustering-based approaches. Some of these methods also employ three-dimensional protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite the essential role of dynamics in protein functionality. In this work, we present a framework to identify driver genes using a dynamics-based search of mutational hotspot communities. After partitioning 3D structures into distinct communities of residues using anisotropic network models, we map variants onto the partitioned structures. We then search for signals of positive selection among these residue communities to identify putative drivers. We applied our method using the TCGA pan-cancer atlas missense mutation catalog. Overall, our analyses predict one or more mutational hotspots within the resolved structures of 434 genes. Ontological and pathway enrichment analyses implicate genes with predicted hotspots to be enriched in biological processes associated with tumor progression. Additionally, a comparison between our approach and existing hotspot detection methods that use structural data suggests that the inclusion of dynamics significantly increases the sensitivity of driver detection.

scholarly journals Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures

2019 ◽  
Vol 116 (38) ◽  
pp. 18962-18970 ◽  
Author(s):  
Sushant Kumar ◽  
Declan Clarke ◽  
Mark B. Gerstein
Keyword(s):  
Protein Dynamics ◽  
Protein Function ◽  
Large Scale ◽  
Protein Structures ◽  
Structural Data ◽  
The Cancer Genome Atlas ◽  
Detection Methods ◽  
Hotspot Detection ◽  
Driver Genes ◽  
Mutational Hotspots

Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue–residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.

scholarly journals Simplified geometric representations of protein structures identify complementary interaction interfaces

2019 ◽  
Author(s):  
Caitlyn L. McCafferty ◽  
Edward M. Marcotte ◽  
David W. Taylor
Keyword(s):  
Conformational Changes ◽  
Protein Interaction ◽  
Protein Function ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Protein Complexes ◽  
Protein Structures ◽  
Molecular Properties ◽  
3D Structures ◽  
Geometric Representations

ABSTRACTProtein-protein interactions are critical to protein function, but three-dimensional (3D) arrangements of interacting proteins have proven hard to predict, even given the identities and 3D structures of the interacting partners. Specifically, identifying the relevant pairwise interaction surfaces remains difficult, often relying on shape complementarity with molecular docking while accounting for molecular motions to optimize rigid 3D translations and rotations. However, such approaches can be computationally expensive, and faster, less accurate approximations may prove useful for large-scale prediction and assembly of 3D structures of multi-protein complexes. We asked if a reduced representation of protein geometry retains enough information about molecular properties to predict pairwise protein interaction interfaces that are tolerant of limited structural rearrangements. Here, we describe a cuboid transformation of 3D protein accessible surfaces on which molecular properties such as charge, hydrophobicity, and mutation rate can be easily mapped, implemented in the MorphProt package. Pairs of surfaces are compared to rapidly assess partner-specific potential surface complementarity. On two available benchmarks of 85 overall known protein complexes, we observed F1 scores (a weighted combination of precision and recall) of 19-34% at correctly identifying protein interaction surfaces, comparable to more computationally intensive 3D docking methods in the annual Critical Assessment of PRedicted Interactions. Furthermore, we examined the effect of molecular motion through normal mode simulation on a benchmark receptor-ligand pair and observed no marked loss of predictive accuracy for distortions of up to 6 Å RMSD. Thus, a cuboid transformation of protein surfaces retains considerable information about surface complementarity, offers enhanced speed of comparison relative to more complex geometric representations, and exhibits tolerance to conformational changes.

scholarly journals Protein Structure Determination in Living Cells

2019 ◽  
Vol 20 (10) ◽  
pp. 2442 ◽  
Author(s):  
Teppei Ikeya ◽  
Peter Güntert ◽  
Yutaka Ito
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Structure Prediction ◽  
Structural Information ◽  
Nuclear Overhauser Effect ◽  
Protein Structures ◽  
Three Dimensional ◽  
Structural Data ◽  
Sample Tube ◽  
In Cells

To date, in-cell NMR has elucidated various aspects of protein behaviour by associating structures in physiological conditions. Meanwhile, current studies of this method mostly have deduced protein states in cells exclusively based on ‘indirect’ structural information from peak patterns and chemical shift changes but not ‘direct’ data explicitly including interatomic distances and angles. To fully understand the functions and physical properties of proteins inside cells, it is indispensable to obtain explicit structural data or determine three-dimensional (3D) structures of proteins in cells. Whilst the short lifetime of cells in a sample tube, low sample concentrations, and massive background signals make it difficult to observe NMR signals from proteins inside cells, several methodological advances help to overcome the problems. Paramagnetic effects have an outstanding potential for in-cell structural analysis. The combination of a limited amount of experimental in-cell data with software for ab initio protein structure prediction opens an avenue to visualise 3D protein structures inside cells. Conventional nuclear Overhauser effect spectroscopy (NOESY)-based structure determination is advantageous to elucidate the conformations of side-chain atoms of proteins as well as global structures. In this article, we review current progress for the structure analysis of proteins in living systems and discuss the feasibility of its future works.

scholarly journals 2D vs. 3D Change Detection Using Aerial Imagery to Support Crisis Management of Large-Scale Events

2018 ◽  
Vol 10 (12) ◽  
pp. 2054 ◽  
Author(s):  
Veronika Gstaiger ◽  
Jiaojiao Tian ◽  
Ralph Kiefl ◽  
Franz Kurz
Keyword(s):  
Change Detection ◽  
Crisis Management ◽  
Large Scale ◽  
Three Dimensional ◽  
Aerial Imagery ◽  
Added Value ◽  
Detection Methods ◽  
Protestant Church ◽  
Existing Structures ◽  
3D Information

Large-scale events represent a special challenge for crisis management. To ensure that participants can enjoy an event safely and carefree, it must be comprehensively prepared and attentively monitored. Remote sensing can provide valuable information to identify potential risks and take appropriate measures in order to prevent a disaster, or initiate emergency aid measures as quickly as possible in the event of an emergency. Especially, three-dimensional (3D) information that is derived using photogrammetry can be used to analyze the terrain and map existing structures that are set up at short notice. Using aerial imagery acquired during a German music festival in 2016 and the celebration of the German Protestant Church Assembly of 2017, the authors compare two-dimensional (2D) and novel fusion-based 3D change detection methods, and discuss their suitability for supporting large-scale events during the relevant phases of crisis management. This study serves to find out what added value the use of 3D change information can provide for on-site crisis management. Based on the results, an operational, fully automatic processor for crisis management operations and corresponding products for end users can be developed.

scholarly journals Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics

2018 ◽  
Vol 19 (11) ◽  
pp. 3401 ◽  
Author(s):  
Ashutosh Srivastava ◽  
Tetsuro Nagai ◽  
Arpita Srivastava ◽  
Osamu Miyashita ◽  
Florence Tama
Keyword(s):  
Protein Dynamics ◽  
Computational Methods ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
X Ray ◽  
X Ray Crystallography ◽  
Insight Into

Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes.

scholarly journals Streamlined use of protein structures in variant analysis

2021 ◽  
Author(s):  
Sandeep Kaur ◽  
Neblina Sikta ◽  
Andrea Schafferhans ◽  
Nicola Bordin ◽  
Mark J. Cowley ◽  
...  
Keyword(s):  
Protein Function ◽  
Molecular Mechanisms ◽  
Structural Information ◽  
Protein Structures ◽  
Structural Data ◽  
Supplementary Information ◽  
3D Structures ◽  
Link Type ◽  
Variant Analysis ◽  
Many Sources

AbstractMotivationVariant analysis is a core task in bioinformatics that requires integrating data from many sources. This process can be helped by using 3D structures of proteins, which can provide a spatial context that can provide insight into how variants affect function. Many available tools can help with mapping variants onto structures; but each has specific restrictions, with the result that many researchers fail to benefit from valuable insights that could be gained from structural data.ResultsTo address this, we have created a streamlined system for incorporating 3D structures into variant analysis. Variants can be easily specified via URLs that are easily readable and writable, and use the notation recommended by the Human Genome Variation Society (HGVS). For example, ‘https://aquaria.app/SARS-CoV-2/S/?N501Y’ specifies the N501Y variant of SARS-CoV-2 S protein. In addition to mapping variants onto structures, our system provides summary information from multiple external resources, including COSMIC, CATH-FunVar, and PredictProtein. Furthermore, our system identifies and summarizes structures containing the variant, as well as the variant-position. Our system supports essentially any mutation for any well-studied protein, and uses all available structural data — including models inferred via very remote homology — integrated into a system that is fast and simple to use. By giving researchers easy, streamlined access to a wealth of structural information during variant analysis, our system will help in revealing novel insights into the molecular mechanisms underlying protein function in health and disease.AvailabilityOur resource is freely available at the project home page (https://aquaria.app). After peer review, the code will be openly available via a GPL version 2 license at https://github.com/ODonoghueLab/Aquaria. PSSH2, the database of sequence-to-structure alignments, is also freely available for download at https://zenodo.org/record/[email protected] informationNone.

scholarly journals Systematic analysis of mutation distribution in three dimensional protein structures identifies cancer driver genes

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Akihiro Fujimoto ◽  
Yukinori Okada ◽  
Keith A. Boroevich ◽  
Tatsuhiko Tsunoda ◽  
Hiroaki Taniguchi ◽  
...  
Keyword(s):  
Protein Structures ◽  
Three Dimensional ◽  
Driver Genes ◽  
Systematic Analysis ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Mutation Distribution

scholarly journals DeeplyTough: Learning Structural Comparison of Protein Binding Sites

2019 ◽  
Author(s):  
Martin Simonovsky ◽  
Joshua Meyers
Keyword(s):  
Protein Binding ◽  
Binding Sites ◽  
Protein Function ◽  
Large Scale ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Binding Sites ◽  
Data Driven Approach ◽  
Benchmark Datasets ◽  
New Perspective

AbstractMotivationProtein binding site comparison (pocket matching) is of importance in drug discovery. Identification of similar binding sites can help guide efforts for hit finding, understanding polypharmacology and characterization of protein function. The design of pocket matching methods has traditionally involved much intuition, and has employed a broad variety of algorithms and representations of the input protein structures. We regard the high heterogeneity of past work and the recent availability of large-scale benchmarks as an indicator that a data-driven approach may provide a new perspective.ResultsWe propose DeeplyTough, a convolutional neural network that encodes a three-dimensional representation of protein binding sites into descriptor vectors that may be compared efficiently in an alignment-free manner by computing pairwise Euclidean distances. The network is trained with supervision: (i) to provide similar pockets with similar descriptors, (ii) to separate the descriptors of dissimilar pockets by a minimum margin, and (iii) to achieve robustness to nuisance variations. We evaluate our method using three large-scale benchmark datasets, on which it demonstrates excellent performance for held-out data coming from the training distribution and competitive performance when the trained network is required to generalize to datasets constructed independently.Availabilityhttps://github.com/BenevolentAI/[email protected],[email protected]

scholarly journals Large-scale determination of previously unsolved protein structures using evolutionary information

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Sergey Ovchinnikov ◽  
Lisa Kinch ◽  
Hahnbeom Park ◽  
Yuxing Liao ◽  
Jimin Pei ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Large Scale ◽  
De Novo ◽  
Structural Information ◽  
Sequence Similarity ◽  
Protein Structures ◽  
Three Dimensional ◽  
Public Access ◽  
Evolutionary Information ◽  
Blind Test

The prediction of the structures of proteins without detectable sequence similarity to any protein of known structure remains an outstanding scientific challenge. Here we report significant progress in this area. We first describe de novo blind structure predictions of unprecendented accuracy we made for two proteins in large families in the recent CASP11 blind test of protein structure prediction methods by incorporating residue–residue co-evolution information in the Rosetta structure prediction program. We then describe the use of this method to generate structure models for 58 of the 121 large protein families in prokaryotes for which three-dimensional structures are not available. These models, which are posted online for public access, provide structural information for the over 400,000 proteins belonging to the 58 families and suggest hypotheses about mechanism for the subset for which the function is known, and hypotheses about function for the remainder.

Analyzing Internal Motion of Proteins From Viewpoint of Robot Kinematics: Formulation of Group Forced Response Method

2012 ◽  
Author(s):  
Keisuke Arikawa
Keyword(s):  
Protein Structures ◽  
Computational Cost ◽  
Three Dimensional ◽  
Structural Data ◽  
Simple Calculation ◽  
Forced Response ◽  
Internal Motion ◽  
Robot Kinematics ◽  
Internal Motions ◽  
Applied Forces

An analogous relationship exists between the kinematic structures of proteins and robotic mechanisms. Hence, using this analogy, we attempt to understand the internal motions of proteins from the perspective of robot kinematics. In this study, we propose a method called group forced response (GFR) method for predicting the internal motion of proteins on the basis of their three-dimensional structural data (PDB data). In this method, we apply forces in static equilibrium to groups of atoms (e.g., secondary structures, domains, and subunits) and not to specific atoms. Furthermore, we predict the internal motion of proteins by analyzing the relative motion caused among groups by the applied forces. First, we show a method for approximately modeling protein structures as a robotic mechanism and the basic kinematic equations of the model. Next, the GFR method is formulated (e.g., Jacobian matrix for group motions, magnitude of forces applied to groups, and decomposition of motions into modes according to structural compliances). Finally, we present example applications of the proposed method in real protein structures. Despite the approximations in the model, low computational cost, and use of simple calculation parameters, the results almost agree with measured internal motions.

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