CHARACTERIZING THE SPACE OF INTERATOMIC DISTANCE DISTRIBUTION FUNCTIONS CONSISTENT WITH SOLUTION SCATTERING DATA

2010 ◽  
Vol 08 (02) ◽  
pp. 315-335 ◽  
Author(s):  
PARITOSH A. KAVATHEKAR ◽  
BRUCE A. CRAIG ◽  
ALAN M. FRIEDMAN ◽  
CHRIS BAILEY-KELLOGG ◽  
DEVIN J. BALKCOM
Keyword(s):  
Three Dimensional ◽  
Protein Molecule ◽  
Linear Constraints ◽  
Distribution Functions ◽  
Distance Distribution ◽  
Scattering Data ◽  
Dimensional Structure ◽  
Programming Approach ◽  
X Rays ◽  
Wide Range

Scattering of neutrons and X-rays from molecules in solution offers alternative approaches to the study of a wide range of macromolecular structures in their solution state without crystallization. We study one part of the problem of elucidating three-dimensional structure from solution scattering data, determining the distribution of interatomic distances, P(r), where r is the distance between two atoms in the protein molecule. This problem is known to be ill-conditioned: for a single observed diffraction pattern, there may be many consistent distance distribution functions, and there is a risk of overfitting the observed scattering data. We propose a new approach to avoiding this problem: accepting the validity of multiple alternative P(r) curves rather than seeking a single "best." We place linear constraints to ensure that a computed P(r) is consistent with the experimental data. The constraints enforce smoothness in the P(r) curve, ensure that the P(r) curve is a probability distribution, and allow for experimental error. We use these constraints to precisely describe the space of all consistent P(r) curves as a polytope of histogram values or Fourier coefficients. We develop a linear programming approach to sampling the space of consistent, realistic P(r) curves. On both experimental and simulated scattering data, our approach efficiently generates ensembles of such curves that display substantial diversity.

MRPC (Missing Regions in Polypeptide Chains): a knowledgebase

2019 ◽  
Vol 52 (6) ◽  
pp. 1422-1426
Author(s):  
Rajendran Santhosh ◽  
Namrata Bankoti ◽  
Adgonda Malgonnavar Padmashri ◽  
Daliah Michael ◽  
Jeyaraman Jeyakanthan ◽  
...  
Keyword(s):  
Protein Structures ◽  
Three Dimensional ◽  
Protein Molecule ◽  
Data Bank ◽  
Protein Crystal ◽  
Dimensional Structure ◽  
Protein Structure Analysis ◽  
Three Dimensional Structure ◽  
X Ray Crystallography ◽  
Polypeptide Chains

Missing regions in protein crystal structures are those regions that cannot be resolved, mainly owing to poor electron density (if the three-dimensional structure was solved using X-ray crystallography). These missing regions are known to have high B factors and could represent loops with a possibility of being part of an active site of the protein molecule. Thus, they are likely to provide valuable information and play a crucial role in the design of inhibitors and drugs and in protein structure analysis. In view of this, an online database, Missing Regions in Polypeptide Chains (MRPC), has been developed which provides information about the missing regions in protein structures available in the Protein Data Bank. In addition, the new database has an option for users to obtain the above data for non-homologous protein structures (25 and 90%). A user-friendly graphical interface with various options has been incorporated, with a provision to view the three-dimensional structure of the protein along with the missing regions using JSmol. The MRPC database is updated regularly (currently once every three months) and can be accessed freely at the URL http://cluster.physics.iisc.ac.in/mrpc.

scholarly journals BLASTing away preconceptions in crystallization trials

2019 ◽  
Vol 75 (3) ◽  
pp. 184-192 ◽  
Author(s):  
Gabriel Jan Abrahams ◽  
Janet Newman
Keyword(s):  
Protein Data Bank ◽  
Sequence Similarity ◽  
Three Dimensional ◽  
Protein Sequences ◽  
Protein Molecule ◽  
Data Bank ◽  
Dimensional Structure ◽  
Critical Step ◽  
Quantitative Verification ◽  
Better Than

Crystallization is in many cases a critical step for solving the three-dimensional structure of a protein molecule. Determining which set of chemicals to use in the initial screen is typically agnostic of the protein under investigation; however, crystallization efficiency could potentially be improved if this were not the case. Previous work has assumed that sequence similarity may provide useful information about appropriate crystallization cocktails; however, the authors are not aware of any quantitative verification of this assumption. This research investigates whether, given current information, one can detect any correlation between sequence similarity and crystallization cocktails. BLAST was used to quantitate the similarity between protein sequences in the Protein Data Bank, and this was compared with three estimations of the chemical similarities of the respective crystallization cocktails. No correlation was detected between proteins of similar (but not identical) sequence and their crystallization cocktails, suggesting that methods of determining screens based on this assumption are unlikely to result in screens that are better than those currently in use.

Structural modeling and role of HAX-1 as a positive allosteric modulator of human serine protease HtrA2

2019 ◽  
Vol 476 (20) ◽  
pp. 2965-2980
Author(s):  
Lalith K. Chaganti ◽  
Shubhankar Dutta ◽  
Raja Reddy Kuppili ◽  
Mriganka Mandal ◽  
Kakoli Bose
Keyword(s):  
Pdz Domain ◽  
Limited Proteolysis ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Multifunctional Protein ◽  
Promising Therapeutic Target ◽  
Wide Range ◽  
Structural Details ◽  
Structural Architecture ◽  
First Time

Abstract HAX-1, a multifunctional protein involved in cell proliferation, calcium homeostasis, and regulation of apoptosis, is a promising therapeutic target. It regulates apoptosis through multiple pathways, understanding of which is limited by the obscurity of its structural details and its intricate interaction with its cellular partners. Therefore, using computational modeling, biochemical, functional enzymology and spectroscopic tools, we predicted the structure of HAX-1 as well as delineated its interaction with one of it pro-apoptotic partner, HtrA2. In this study, three-dimensional structure of HAX-1 was predicted by threading and ab initio tools that were validated using limited proteolysis and fluorescence quenching studies. Our pull-down studies distinctly demonstrate that the interaction of HtrA2 with HAX-1 is directly through its protease domain and not via the conventional PDZ domain. Enzymology studies further depicted that HAX-1 acts as an allosteric activator of HtrA2. This ‘allosteric regulation’ offers promising opportunities for the specific control and functional modulation of a wide range of biological processes associated with HtrA2. Hence, this study for the first time dissects the structural architecture of HAX-1 and elucidates its role in PDZ-independent activation of HtrA2.

scholarly journals Life Experience and DNA Polymorphisms Influence the Brain Epigenome

2013 ◽  
Vol 40 (5) ◽  
pp. 726-727
Author(s):  
Michael O. Poulter
Keyword(s):  
Life Experience ◽  
Cancer Susceptibility ◽  
Methylation Status ◽  
Three Dimensional ◽  
Covalent Modification ◽  
Dna Polymorphisms ◽  
Dimensional Structure ◽  
Fetal Alcohol ◽  
Wide Range ◽  
Biological Outcomes

Although not strictly fitting the category of translational neuroscience, I believe the implications of this study where it was found that variability in DNA sequence, a single nucleotide polymorphism (SNP), can influence the epigenetic status of DNA and this is influenced by childhood trauma should be of wide interest.Epigenetics is a burgeoning field of study that seeks to understand how alterations in DNA structure influence a wide range of biological outcomes ranging from cancer susceptibility to behaviour. Across this spectrum, two basic kinds of structure are most often examined. The first is covalent modification of DNA by methylation and second is the interaction between DNA binding proteins (histones for example) and DNA. Both influence the three dimensional structure of DNA and therefore gene expression. Importantly these dynamics are thought to be influenced by environmental conditions that may be positive or detrimental. For example, fetal alcohol syndrome has been shown to alter the methylation status of DNA accounting for the facial/cranial abnormalities that are often observed in these patients.

Three-dimensional reconstruction of liquid phases in disordered mesopores usingin situsmall-angle scattering

2013 ◽  
Vol 46 (2) ◽  
pp. 493-504 ◽  
Author(s):  
Cedric J. Gommes
Keyword(s):  
Mesoporous Materials ◽  
Small Angle ◽  
Solid Phase ◽  
Gaussian Random Field ◽  
Three Dimensional ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Wetting Transition ◽  
X Rays ◽  
Angle Scattering

Small-angle scattering of X-rays (SAXS) or neutrons is one of the few experimental methods currently available for thein situanalysis of phenomena in mesoporous materials at the mesoscopic scale. In the case of disordered mesoporous materials, however, the main difficulty of the method lies in the data analysis. A stochastic model is presented, which enables one to reconstruct the three-dimensional nanostructure of liquids confined in disordered mesopores starting from small-angle scattering data. This so-called plurigaussian model is a multi-phase generalization of clipped Gaussian random field models. Its potential is illustrated through the synchrotron SAXS analysis of a gel permeated with a critical nitrobenzene/hexane solution that is progressively cooled below its consolute temperature. The reconstruction brings to light a wetting transition whereby the nanostructure of the pore-filling liquids passes from wetting layers that uniformly cover the solid phase of the gel to plugs that locally occlude the pores. Using the plurigaussian model, the dewetting phenomenon is analyzed quantitatively at the nanometre scale in terms of changing specific interface areas, contact angle and specific length of the triple line.

Interpretation of small-angle scattering data of inhomogeneous ellipsoids

2004 ◽  
Vol 37 (5) ◽  
pp. 815-822 ◽  
Author(s):  
Gerhard Fritz ◽  
Alexander Bergmann
Keyword(s):  
Cross Sections ◽  
Small Angle ◽  
Theoretical Calculations ◽  
Negative Contrast ◽  
Small Angle Scattering ◽  
Distribution Functions ◽  
Distance Distribution ◽  
Scattering Data ◽  
Elliptical Cross ◽  
Angle Scattering

Small-angle scattering data of inhomogeneous ellipsoidal particles are discussed in terms of their pair distance distribution functionsp(r). Special attention is given to the determination of core and shell thicknesses and axis ratios as well as to large distances within the particles, since cross terms between parts of positive and negative contrast within the particle can produce misleading results, similar to homogeneous particles or Janus particles. Cross-section pair distance distribution functionspc(r) of cylinders with elliptical cross sections show similar behaviour. Theoretical calculations are compared with small-angle X-ray and neutron scattering (SAXS and SANS) data of cetyltrimethylammonium bromide in aqueous KCl solutions.

scholarly journals SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction

2021 ◽  
Vol 11 ◽  
Author(s):  
Shudong Wang ◽  
Dayan Liu ◽  
Mao Ding ◽  
Zhenzhen Du ◽  
Yue Zhong ◽  
...  
Keyword(s):  
Neural Network ◽  
Binding Affinity ◽  
Three Dimensional ◽  
Model Performance ◽  
Protein Molecule ◽  
Stochastic Gradient Descent ◽  
Dimensional Structure ◽  
Scoring Functions ◽  
Ligand Complex ◽  
The Stability

Deep learning methods, which can predict the binding affinity of a drug–target protein interaction, reduce the time and cost of drug discovery. In this study, we propose a novel deep convolutional neural network called SE-OnionNet, with two squeeze-and-excitation (SE) modules, to computationally predict the binding affinity of a protein–ligand complex. The OnionNet is used to extract a feature map from the three-dimensional structure of a protein–drug molecular complex. The SE module is added to the second and third convolutional layers to improve the non-linear expression of the network to improve model performance. Three different optimizers, stochastic gradient descent (SGD), Adam, and Adagrad, were also used to improve the performance of the model. A majority of protein–molecule complexes were used for training, and the comparative assessment of scoring functions (CASF-2016) was used as the benchmark. Experimental results show that our model performs better than OnionNet, Pafnucy, and AutoDock Vina. Finally, we chose the macrophage migration inhibitor factor (PDB ID: 6cbg) to test the stability and robustness of the model. We found that the prediction results were not affected by the docking position, and thus, our model is of acceptable robustness.

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