The fitness landscape of HIV-1 gag: advanced modeling approaches and validation of model predictions by in vitro testing.
about
The evolution within usE Pluribus Unum: 50 Years of Research, Millions of Viruses, and One Goal--Tailored Acceleration of AAV EvolutionDeep sequencing of protease inhibitor resistant HIV patient isolates reveals patterns of correlated mutations in Gag and proteaseBenchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models.Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness.Learning maximum entropy models from finite-size data sets: A fast data-driven algorithm allows sampling from the posterior distribution.Improving landscape inference by integrating heterogeneous data in the inverse Ising problem.Molecular and epidemiological characterization of HIV-1 subtypes among Libyan patients.Inference of Epistatic Effects Leading to Entrenchment and Drug Resistance in HIV-1 Protease.Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1.ACE: adaptive cluster expansion for maximum entropy graphical model inference.Preexisting compensatory amino acids compromise fitness costs of a HIV-1 T cell escape mutation.Scaling laws describe memories of host-pathogen riposte in the HIV population.Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity.Analysis of T cell responses to chimpanzee adenovirus vectors encoding HIV gag-pol-nef antigenPopulation genomics of intrapatient HIV-1 evolutionRelative rate and location of intra-host HIV evolution to evade cellular immunity are predictable.Inferring interaction partners from protein sequences.On the (un)predictability of a large intragenic fitness landscape.HIV-1 vaccine immunogen design strategies.Brief Report: Selection of HIV-1 Variants With Higher Transmission Potential by 1% Tenofovir Gel Microbicide.Mutation effects predicted from sequence co-variation.Identification of drug resistance mutations in HIV from constraints on natural evolution.Co-evolution techniques are reshaping the way we do structural bioinformatics.Error catastrophe and phase transition in the empirical fitness landscape of HIV.Fitness landscape of the human immunodeficiency virus envelope protein that is targeted by antibodies.Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs.Mapping mutational effects along the evolutionary landscape of HIV envelope.An introduction to the maximum entropy approach and its application to inference problems in biology.Co-evolution networks of HIV/HCV are modular with direct association to structure and function
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P2860
The fitness landscape of HIV-1 gag: advanced modeling approaches and validation of model predictions by in vitro testing.
description
2014 nî lūn-bûn
@nan
2014 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
The fitness landscape of HIV-1 ...... edictions by in vitro testing.
@ast
The fitness landscape of HIV-1 ...... edictions by in vitro testing.
@en
The fitness landscape of HIV-1 ...... edictions by in vitro testing.
@nl
type
label
The fitness landscape of HIV-1 ...... edictions by in vitro testing.
@ast
The fitness landscape of HIV-1 ...... edictions by in vitro testing.
@en
The fitness landscape of HIV-1 ...... edictions by in vitro testing.
@nl
prefLabel
The fitness landscape of HIV-1 ...... edictions by in vitro testing.
@ast
The fitness landscape of HIV-1 ...... edictions by in vitro testing.
@en
The fitness landscape of HIV-1 ...... edictions by in vitro testing.
@nl
P2093
P2860
P50
P1476
The fitness landscape of HIV-1 ...... edictions by in vitro testing.
@en
P2093
Arup Chakraborty
Jaclyn K Mann
Saleha Omarjee
P2860
P304
P356
10.1371/JOURNAL.PCBI.1003776
P577
2014-08-07T00:00:00Z