Evidence map›Paper›PMID 17397537›Full record

ArticleBMC structural biology2007

Atomic hydration potentials using a Monte Carlo Reference State (MCRS) for protein solvation modeling.

Sergei V Rakhmanov, Vsevolod J Makeev

Abstract readComparative Study
In one paragraph

Article in BMC structural biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Water Networks in Complexes between Proteins and FDA-Approved Drugs.Journal of chemical information and modeling · 2023
    Article
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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Sergei V RakhmanovInstitute of Genetics and Selection of Industrial Microorganisms, State Research Centre GosNIIgenetika, Moscow, Russia. sergeira@genetika.ru
Vsevolod J Makeev

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurate description of protein interaction with aqueous solvent is crucial for modeling of protein folding, protein-protein interaction, and drug design. Efforts to build a working description of solvation, both by continuous models and by molecular dynamics, yield controversial results. Specifically constructed knowledge-based potentials appear to be promising for accounting for the solvation at the molecular level, yet have not been used for this purpose.

resultsWe developed original knowledge-based potentials to study protein hydration at the level of atom contacts. The potentials were obtained using a new Monte Carlo reference state (MCRS), which simulates the expected probability density of atom-atom contacts via exhaustive sampling of structure space with random probes. Using the MCRS allowed us to calculate the expected atom contact densities with high resolution over a broad distance range including very short distances. Knowledge-based potentials for hydration of protein atoms of different types were obtained based on frequencies of their contacts at different distances with protein-bound water molecules, in a non-redundant training data base of 1776 proteins with known 3D structures. Protein hydration sites were predicted in a test set of 12 proteins with experimentally determined water locations. The MCRS greatly improves prediction of water locations over existing methods. In addition, the contribution of the energy of macromolecular solvation into total folding free energy was estimated, and tested in fold recognition experiments. The correct folds were preferred over all the misfolded decoys for the majority of proteins from the improved Rosetta decoy set based on the structure hydration energy alone.

conclusionMCRS atomic hydration potentials provide a detailed distance-dependent description of hydropathies of individual protein atoms. This allows placement of water molecules on the surface of proteins and in protein interfaces with much higher precision. The potentials provide a means to estimate the total solvation energy for a protein structure, in many cases achieving a successful fold recognition. Possible applications of atomic hydration potentials to structure verification, protein folding and stability, and protein-protein interactions are discussed.

Indexed as

Models, MolecularProtein FoldingBiophysical PhenomenaBiophysicsDatabases, ProteinMonte Carlo MethodOxygenProteinsSolventsStatic ElectricityWaterOxygenProteinsSolventsWater

Identifiers

PMID17397537
PMCPMC1852318

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.