Posts Tagged ‘gpmtg’

Multistep nucleation of nanocrystals in aqueous solution : Nature Chemistry : Nature Research

Monday, October 2nd, 2017

Multistep nucleation of nanocrystals in…solution
http://www.Nature.com/nchem/journal/v9/n1/full/nchem.2618.html Creation of Au-Au pair, expelling water, drives cluster formation

QT:{{”
To explain how the gold-rich aqueous phase forms and breaks down in Figures 1A and 2A, we
performed ab-initio calculations of a hydrated gold atom pair (Figure 4). This hydrated atom pair
becomes ionized when brought closer together: the left gold atom plus two nearby water molecules
form a linear cationic coordination complex, [Au(H2O)2]+1, while the right gold atom becomes an
anion surrounded by a simple hydration shell. Other (square planar and linear) complexes involving
chloride and hydroxide ligands may also participate, depending on pH (26, 33) (Section SI2). For
nanoclusters to form inside the gold-rich aqueous phase, pairs of gold atoms within it must be partially
dehydrated. In our calculations (Figure 4), this dehydration is delayed by a 7.6 kcal/mol (12.9 kBT)
energy barrier required to breakdown the linear cationic complex (close to the gold anion).
“}}

also:
https://arxiv.org/abs/1605.04632

Protein Structural Memory Influences Ligand Binding Mode(s) and Unbinding Rates – Journal of Chemical Theory and Computation (ACS Publications)

Tuesday, September 5th, 2017

Structural Memory Influences Ligand-Binding Mode http://pubs.ACS.org/doi/abs/10.1021/acs.jctc.5b01052 Rearrangement of #solvation layer is ~100x slower than unbinding

J Chem Theory Comput. 2016 Mar 8;12(3):1393-9. doi:
10.1021/acs.jctc.5b01052. Epub 2016 Feb 3.

Xu M, Caflisch A, Hamm P.

Protein-structure-guided discovery of functional mutations across 19 cancer types : Nature Genetics : Nature Research

Sunday, December 11th, 2016

Protein-structure-guided discovery of functional mutations across 19 #cancer types http://www.nature.com/ng/journal/v48/n8/abs/ng.3586.html Cancer3D relates SNVs to drugs

http://www.nature.com/ng/journal/v48/n8/abs/ng.3586.html

Skhizein: a really nice animation on today’s grpmtg

Friday, December 9th, 2016

https://vimeo.com/36824575

PLOS Computational Biology: PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

Monday, November 28th, 2016

PredictSNP…Consensus Classifier for Prediction of Disease-Related
Mutations http://journals.PLOS.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003440 Demo of various #ensemble approaches

Frustration in biomolecules | Quarterly Reviews of Biophysics | Cambridge Core

Tuesday, November 1st, 2016

#Frustration in biomolecules
https://www.cambridge.org/core/journals/quarterly-reviews-of-biophysics/article/frustration-in-biomolecules/DECEA176849986FC11DB079C1EB4B24A Reviews the field: how large molecules pay a local price to achieve global stability

PLOS Computational Biology: PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions

Sunday, October 9th, 2016

PredictSNP2: A Unified Platform http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004962 Ensembles many scores for the impact of non-coding variants, including #FunSeq

Elucidating Molecular Motion through Structural and Dynamic Filters of Energy-Minimized Conformer Ensembles

Friday, September 30th, 2016

Elucidating Molecular Motion (compatible w. #NMR relaxation times) through…Filters of Energy-Minimized…Ensembles
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3983377

Predicting peptide binding sites on protein surfaces by clustering chemical interactions – Yan – 2014 – Journal of Computational Chemistry – Wiley Online Library

Monday, July 4th, 2016

Predicting peptide binding sites on protein surfaces by ACCLUSTER http://onlinelibrary.wiley.com/doi/10.1002/jcc.23771/abstract #Chemical interactions out perform pure #packing

Learning the Sequence Determinants of Alternative Splicing from Millions of Random Sequences: Cell

Sunday, April 24th, 2016

Learning the…Determinants of Alternative #Splicing [in a largely linear model] from Millions of Random Sequences
http://www.cell.com/cell/abstract/S0092-8674(15)01271-4

** Rosenberg et al Cell. 2015

Builds a model of splicing using a library of randomized sequence Also, builds a generalized model for predicting effect of a SNP in the Geuvadis RNAseq
7mer model does well with lots of data