A. Perez

Limited protection against early-life cytomegalovirus infection results from deficiency of cytotoxic CD8T cells

This study shows that deficiency of cytotoxic CD8 T cells in early life leads to limited protection against cytomegalovirus infection.

MELD-Adapt: On-the-Fly Belief Updating in Integrative Molecular Dynamics

MELD-Adapt dynamically evaluates data reliability to improve structure prediction accuracy by adjusting initial beliefs and identifying reliable data fractions.

Multiple endocrine defects in adult-onset Sprouty1/2/4 triple knockout mice

This study investigates adult-onset Sprouty1/2/4 triple knockout mice, revealing endocrine defects without increased tumor incidence.

Protein Retrieval via Integrative Molecular Ensembles (PRIME) through extended similarity indices

PRIME improves identification of native-like protein structures from MD simulations by using extended similarity indices, achieving lower RMSD than traditional centroid-based methods.

FtsZ-mediated spatial-temporal control over septal cell wall synthesis

This paper shows FtsZ treadmilling acts as a template and conveyor to organize cell wall synthesis during bacterial division, with roles changing over time.

Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein- Protein Binding Candidates in High-Throughput Protein Libraries

This paper introduces a computational pipeline using AlphaFold2 to prioritize protein-protein binding candidates from high-throughput libraries and identify binding epitopes.

GENERALIST: An efficient generative model for protein sequence families

The paper introduces GENERALIST, a generative model for protein sequence families that accurately captures amino acid covariation and predicts stable sequences.

A consensus view on the folding mechanism of protein G, L and their mutants

The paper uses simulations and Bayesian inference to analyze the folding mechanisms of protein G, L, and their mutants, identifying distinct pathways and validating results with experimental data.

AlphaFold encodes the principles to identify high affinity peptide binders

This study applies AlphaFold to identify high-affinity peptide binders by predicting their bound and unbound states in competitive binding scenarios.

Structure determination of protein-peptide complexes from NMR chemical shift data using MELD.

The paper develops a hybrid MELD-NMR approach to determine structures of flexible protein-polypeptide complexes using only NMR chemical shift data, achieving accurate results for ET domain-BET protein interactions.

Structural biases in disordered proteins are prevalent in the cell

The paper shows that structural biases in intrinsically disordered proteins persist in human cells and can respond to intracellular changes.

Hematopoietic recovery after transplantation is primarily derived from the stochastic contribution of hematopoietic stem cells

The paper tracks clonal diversity in hematopoietic stem cells after transplantation and finds that HSCs contribute stochastically to early recovery rather than through two distinct phases.

Improving the analysis of biological ensembles through extended similarity measures

The paper introduces extended similarity measures to improve structural ensemble analysis of macromolecules, achieving linear computational complexity and better clustering performance.

MELD-DNA: A new tool for capturing protein-DNA binding

MELD-DNA predicts protein-DNA binding structures by sampling multiple modes, identifying preferred binding modes, and providing sequence preferences.

CryoFold: Ab-initio structure determination from electron density maps using molecular dynamics

CryoFold determines protein structures from electron density maps using molecular dynamics and physical knowledge, producing ensembles of low-energy conformations validated experimentally.

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

The paper evaluates cryo-EM model quality using benchmark maps, showing high accuracy and reproducibility across 13 teams and recommending multiple metrics for model assessment.

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

This study combines mechanistic and machine learning models to optimize tryptophan metabolism in yeast, achieving a 106% increase in tryptophan accumulation through predictive engineering.

A machine learning approach predicts essential genes and pharmacological targets in cancer

This paper introduces ECLIPSE, a machine learning model that predicts essential genes and pharmacological targets in cancer by integrating genomic, cell line, and experimental features, showing better differentiation of approved cancer drugs from non-approved ones.

High accuracy protein structures from minimal sparse paramagnetic solid-state NMR restraints

The paper presents a method combining sparse paramagnetic solid-state NMR restraints with physics-based simulations to accurately determine protein structures quickly.