Eugene I. Shakhnovich

Direct estimation of genotype fitness from time series

The paper introduces a method to directly estimate genotype fitness from time series data without assumptions on epistasis or iterative optimization, demonstrating its effectiveness across simulations and experimental datasets.

Complete Enzyme Clustering Enhances Coenzyme Q Biosynthesisvia Substrate Channeling

The paper investigates how metabolon clustering enhances coenzyme Q biosynthesis through substrate channeling using simulations and experiments.

Biophysical fitness landscape design traps viral evolution

This paper designs biophysical fitness landscapes to control viral evolution, demonstrating suppression of SARS-CoV-2 escape variants through optimized antibody ensembles.

Chaperonin recognition of protein dynamics drives drug resistance

The paper shows that chaperonin GroEL/S promotes drug resistance by recognizing protein dynamics, displacing inhibitors and restoring active enzyme function.

Co-targeting Metabolic Neighbours Constraints Bacterial Adaptive Evolution

This study identifies a dual-targeting strategy for bacterial adaptive evolution by inhibiting two metabolic enzymes in the folate pathway, demonstrating constrained evolutionary trajectories with Arbutin.

CASPULE: A computational tool to study sticker spacer polymer condensates

CASPULE simulates and analyzes sticker-spacer polymer condensates using a novel force field and provides statistical tools to characterize cluster size distributions.

Constrained Evolutionary Funnels Shape Viral Immune Escape

The paper develops a probabilistic framework to predict viral immune escape trajectories, showing that escape is constrained to specific pathways despite vast mutational space.

Evolutionary dynamics under phenotypic uncertainty

This paper develops a new mathematical framework, ProP Gen, to model evolutionary dynamics under phenotypic uncertainty and shows it explains phenomena like phenotypic buoying and improves predictions of bacterial persistence.

A General Framework for Injecting BiophysicalPriors into Protein Embeddings

This paper introduces a framework that integrates biophysical priors into protein embeddings to improve DeltaDeltaG prediction, showing that general-purpose encoders can outperform specialized models.

Phase diagrams for biophysical fitness landscape design

The paper derives an analytical theory for fitness landscape design (FLD) and validates it with experimental data on antibody binding affinities to influenza proteins.

Cotranslational Protein Folding Through Non-Native Structural Intermediates

This study computationally predicts and experimentally validates a vectorial hierarchy of cotranslational protein folding intermediates stabilized by non-native hydrophobic interactions.

Few-Shot Viral Variant Detection via Bayesian Active Learning and Biophysics

This paper introduces a few-shot learning framework for detecting high-fitness viral variants using Bayesian active learning and biophysics, showing it can identify variants up to fivefold faster with fewer experiments.

Spatial Clustering of Interface Residues Enhances Few-Shot Prediction of Viral Protein Binding

The paper introduces PROXICLUST, a method that uses spatial clustering of interface residues to predict viral protein binding affinities across larger mutation spaces using smaller datasets.

The Origin of the Ionic-strength Dependent Reentrant Behavior in Liquid-Liquid Phase Separation of Uncharged IDPs

The paper explains the salt-dependent reentrant behavior in liquid-liquid phase separation of uncharged IDPs using analytical theory and simulations.

More Structures, Less Accuracy: ESM3's Binding Prediction Paradox

This study shows that using consistent structures improves ESM3's binding prediction, but using different relaxed mutant structures reduces performance.

Entropy Changes in Water Networks Promote Protein Denaturation

This study identifies entropy changes in water networks as a mechanism for ion-induced protein denaturation, revealing an 'inverse hydrophobic effect' that promotes unfolding.

Epistasis meets pleiotropy in shaping biophysical protein subspaces associated with antimicrobial resistance

The paper analyzes how mutations affect biophysical subspaces of DHFR enzymes, revealing epistatic and pleiotropic interactions across different phenotypic dimensions.

Empowering systems-guided drug target discovery with metabolic and structural analysis

This study combines machine learning with metabolic and structural analysis to identify specific drug targets from metabolomics data, using a case study of a DHFR-targeting antibiotic.

Universal activation mechanism of class A GPCRs

The paper identifies a universal activation mechanism for class A GPCRs by analyzing high-resolution structures, linking ligand binding to G protein coupling through TM6 movement.