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Head-Gordon, T.

Publications and source records attributed to Head-Gordon, T..

11 recordsLinked to original sources

CAFE: A Co-folding Approach for Fragment Exploration of Allosteric and Cryptic Binding Sites

Co-folding models hold immense potential for allosteric drug discovery, but have been severely hampered by their systematic bias toward orthosteric ligand binding. While fragment screening has been proposed for allosteric binding site discovery, we show that co-folding models still suffer from memorization in which chemically simpler fragments also default to canonical orthosteric binding sites. To overcome these limitations, we introduce CAFE (Co-folding Approach for Fragment Exploration), a co-folding protocol that uses competitive orthosteric blockers to divert fragments into non-canonical sites as illustrated here with the Boltz-2 co-folding model. Using ADP as an orthosteric blocker for the kinase family, we find CAFE substantially increases the allosteric binding site exploration for fragments, with notably strong absolute binding free energies that match or exceed those of known crystallographic poses, without post-hoc refinement of the Boltz-2 prediction. We also show that CAFE identifies cryptic binding pockets undetected by conventional pocket prediction tools, some of which are more thermodynamically favorable than the allosteric or orthosteric pockets. To demonstrate generality, we apply CAFE using Type I orthosteric blockers for kinase proteins, known orthosteric ligands as blockers for non-kinase proteins in the RAS-MAPK signaling pathway, and for virtual screening campaigns using fragment libraries for new fragments that selectively engage allosteric and cryptic binding sites. CAFE establishes orthosteric blocking and fragment screening as a training-free, inference-time protocol that helps overcome some of the limitations of current co-folding models while elevating their great promise for allosteric and cryptic binding drug discovery.

molecular biology↗

Competing effects modulate the rate of poly(A) RNA deadenylation in a biomolecular condensate

The unique solvent milieu found in biomolecular condensates can control cellular enzymatic reactions and shift reaction kinetics by modulating reactant concentrations, structural dynamics, and enzyme activities. Here we explore the interplay of multiple regulatory factors within a condensate to control poly(A) RNA deadenylation, the first and rate-limiting step in mRNA turnover. The deadenylase CNOT7, a subunit of the CCR4-NOT deadenylation complex, localizes to cytoplasmic RNA granules and shows increased degradation activity in vitro in condensates formed by the C-terminal low complexity disordered region of CAPRIN1, a component of RNA granules. We use a combination of enzymatic assays, kinetic modeling, microscopy, Nuclear Magnetic Resonance (NMR) spectroscopy, and molecular dynamics simulations to deconvolute and define the components that underlie this enhancement. We found that enzyme and RNA are concentrated in condensates relative to buffer, which increases CNOT7 activity, while the equilibrium between CNOT7s active and inactive states remains unchanged. The concentration-dependent increase in enzymatic rates is counterbalanced by a substantial decrease in the enzymes catalytic efficiency, likely due to slower diffusion of CNOT7 and RNA within the condensates, which lessens the probability of enzyme-substrate complex formation. Molecular dynamics simulations reveal CNOT7-CAPRIN1 interactions that rely on conserved CAPRIN1 sequence features, hinting at an evolutionarily conserved role for CAPRIN1 condensation. With this quantitative kinetic analysis, we describe the multifaceted mechanism behind regulation of CNOT7 deadenylation by a condensate environment. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/736149v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@51cd47org.highwire.dtl.DTLVardef@7bfccaorg.highwire.dtl.DTLVardef@114375org.highwire.dtl.DTLVardef@163f4ef_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

Conformational Ensembles of the Disordered 4E-BP2:eIF4E Complex Restrained by smFRET Experiments

Eukaryotic cap-dependent translation initiation is regulated by binding of the predominantly folded eukaryotic initiation factor 4E (eIF4E) to the intrinsically disordered eIF4E binding proteins (4E-BPs). Here, we report full-length atomistic conformational ensembles generated by IDPConformerGenerator and optimized by X-EISDv2 workflow for both apo 4E-BP2, the neuronal 4E-BP, and 4E-BP2 in complex with eIF4E, using data from single-molecule fluorescence and nuclear magnetic resonance (NMR), together with select coordinates from a 4E-BP1:eIF4E crystal structure. Structural sampling within dynamic complexes is often under-appreciated, with NMR and crystal structure data for 4E-BP:eIF4E suggesting different degrees of structural heterogeneity. Our ensemble models validated by solution spectroscopy data enable comparison of free 4E-BP2 and its complex with eIF4E. This shows a delocalization of contacts around canonical regions, which supports previous findings of unidirectional conditional occupancy of the binding sites. Two new contact regions emerged: one between the disordered N-termini of eIF4E and 4E-BP2, which may play an allosteric role in tuning the binding affinity, and the other between the C-terminus of 4E-BP2 and an extended region of eIF4E, which is consistent with the extended, dynamic binding interface that we reported previously. These results support a model of translation regulation in which the dynamic 4E-BP2:eIF4E complex facilitates accessibility of regulatory sites of 4E-BP2 when bound.

biophysics↗

LinkLlama: Enabling Large Language Model for Chemically Reasonable Linker Design

Fragment-based drug discovery (FBDD) relies heavily on the design of chemically viable linkers to connect fragments binding to different pocket regions into potent lead molecules. While recent generative models have advanced spatial fragment linking, they frequently produce linkers characterized by high torsional strain and non-drug-like motifs. In this work, we present LinkLlama, a fine-tuned Meta Llama 3 model that bridges the gap between text-based generation and 3D spatial awareness. By accepting natural language prompts that specify geometric constraints, such as distances and angles, alongside physicochemical targets like Lipinskis rules and rotatable bond limits, LinkLlama generates highly tailored molecules for the input fragments. Leveraging the inherent chemical grammar captured through supervised fine-tuning on a curated corpus of drug-like molecules from ChEMBL, the model prioritizes chemical validity without requiring complex reinforcement learning loops. Benchmarking on the ZINC and HiQBind datasets demonstrates that LinkLlama maintains competitive geometric fidelity compared to strictly 3D-aware models while achieving a two-fold increase in the proportion of chemically reasonable designs. This rising success rate, jumping from 35% to over 80%, is defined by strict adherence to comprehensive structural filters including PAINS, non-drug-like chemical patterns and complex ring systems. We further illustrate the models versatility through prospective case studies in novel small-molecule scaffold hopping and PROTAC linker design, validated via molecular docking and molecular dynamics simulations against known crystal poses. Ultimately, LinkLlama demonstrates that large language models can overcome the structural pitfalls of purely 3D-generative methods, offering a highly controllable and chemically robust framework to accelerate linker design and drug discovery in general.

bioinformatics↗

KinConfBench: A Curated Benchmark for Cofolding Models on Kinase Conformational States

Protein kinases are critical drug targets, requiring therapeutics that can modulate their active and inactive conformational states. While cofolding models can generate global folds directly from kinase sequences and ligand SMILES strings, these models have not yet been tested on their ability to recover ligand induced-fit conformational states of the kinase proteins. Here, we introduce KinConfBench, a curated benchmark of 2,225 high-quality human kinase chains to evaluate the ability of three state-of-the-art cofolding models--Boltz-2, Chai-1, and Protenix--to recover both canonical and rare conformational states. We show that geometric success metrics of a ligand pose in the active site does not correlate strongly with the correct kinase conformational state, motivating a new set of dynamical benchmarks for assessing cofolding models. While all three cofolding models achieve [~]65-75% prediction accuracy for kinase conformational classification, they exhibit severe mode collapse when performing multiple inferences, show negligible structural diversity in sampling induced-fit motions, and display a prevalent "apo-drift" in which all three cofolding models predominately predict the kinase to be in its ligand-free state. Our results highlight that capturing ligand-induced protein conformational diversity, not just geometric fit, is critical for next-generation structure-based drug discovery.

biophysics↗

DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery

Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-effective drug discovery pipelines. However there is currently a lack of objective assessments of LLM performance to ascertain their advantages and limitations over traditional drug discovery platforms. To tackle this emergent problem, we have developed DrugPlayGround, a framework to evaluate and benchmark LLM performance for generating meaningful text-based descriptions of physiochemical drug characteristics, drug synergism, drug-protein interactions, and the physiological response to perturbations introduced by drug molecules. Moreover, DrugPlayGround is designed to work with domain experts to provide detailed explanations for justifying the predictions of LLMs, thereby testing LLMs for chemical and biological reasoning capabilities to push their greater use at the frontier of drug discovery at all of its stages.

bioinformatics↗

IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder

Although machine learning has transformed protein structure prediction of folded protein ground states with remarkable accuracy, intrinsically disordered proteins and regions (IDPs/IDRs) are defined by diverse and dynamical structural ensembles that are predicted with low confidence by algorithms such as AlphaFold and RoseTTAFold. We present a new machine learning method, IDPForge (Intrinsically Disordered Protein, FOlded and disordered Region GEnerator), that exploits a transformer protein language diffusion model to create all-atom IDP ensembles and IDR disordered ensembles that maintains the folded domains. IDPForge does not require sequence-specific training, back transformations from coarse-grained representations, nor ensemble reweighting, as in general the created IDP/IDR conformational ensembles show good agreement with bot NMR and SAXS solution experimental data, and options for biasing with experimental restraints are provided if desired. We envision that IDPForge with these diverse capabilities will facilitate integrative and structural studies for proteins that contain intrinsic disorder, and is available as an open source resource for general use.

biophysics↗

AlphaFlex: Ensembles of the human proteome representing disordered regions

More than two thirds of proteins in the human proteome are predicted to contain intrinsically disordered regions (IDRs), which lack stable folded structure. IDRs are critical for biological regulation and organization, as targets for post-translational modifications, and as mediators of biomolecular condensates. To address the pressing need for better structural models enabling functional insight, we developed AlphaFlex to model fully atomistic conformer ensembles for proteins predicted to have IDRs, modeled in the context of AlphaFold folded domains and an implicit bilayer for transmembrane proteins. The AlphaFlex resource provides conformational ensembles of human proteins from the AlphaFold database with identified IDRs in the Protein Ensemble Database that is mirrored in UniProt. This transformative resource of AlphaFlex ensembles provides physically and biologically relevant full-length models for IDR proteins, including scaffold proteins, those with IDR:folded-domain interactions, regulatory and condensate proteins requiring exposed binding elements, conditionally folding IDRs, and transmembrane proteins containing IDRs.

biochemistry↗

Local Disordered Region Sampling (LDRS) for Ensemble Modeling of Proteins with Experimentally Undetermined or Low Confidence Prediction Segments

STRUCTURED ABSTRACTO_ST_ABSSUMMARYC_ST_ABSThe Local Disordered Region Sampling (LDRS, pronounced loaders) tool, developed for the IDPConformerGenerator platform (Teixeira et al. 2022), provides a method for generating all-atom conformations of intrinsically disordered regions (IDRs) at N- and C-termini of and in loops or linkers between folded regions of an existing protein structure. These disordered elements often lead to missing coordinates in experimental structures or low confidence in predicted structures. Requiring only a pre-existing PDB structure of the protein with missing coordinates or with predicted confidence scores and its full-length primary sequence, LDRS will automatically generate physically meaningful conformational ensembles of the missing flexible regions to complete the full-length protein. The capabilities of the LDRS tool of IDPConformerGenerator include modeling phosphorylation sites using enhanced Monte Carlo Side Chain Entropy (MC-SCE) (Bhowmick and Head-Gordon 2015), transmembrane proteins within an all-atom bilayer, and multi-chain complexes. The modeling capacity of LDRS capitalizes on the modularity, ability to be used as a library and via command-line, and computational speed of the IDPConformerGenerator platform. AVAILABILITY AND IMPLEMENTATIONThe LDRS module is part of the IDPConformerGenerator modeling suite, which can be downloaded from GitHub at https://github.com/julie-forman-kay-lab/IDPConformerGenerator. IDPConformerGenerator is written in Python and works on Linux, Microsoft Windows, and Mac OS versions that support DSSP. Users can utilize LDRSs Python API for scripting the same way they can use any part of IDPConformerGenerators API, by importing functions from the idpconfgen.ldrs_helper library. Otherwise, LDRS can be used as a command line interface application within IDPConformerGenerator. Full documentation is available within the command-line interface (CLI) as well as on IDPConformerGenerators official documentation pages (https://idpconformergenerator.readthedocs.io/en/latest/). CONTACTFor support with LDRS please contact Zi Hao (Nemo) Liu via nemo.liu@sickkids.ca or submit an issue in the IDPConformerGenerator repository on GitHub (https://github.com/julie-forman-kay-lab/IDPConformerGenerator/issues). SUPPLEMENTARY INFORMATIONThe supplementary information document contains, or links to, all the conformer ensembles generated for this publication, the generalized Python scripts using the LDRS Python API, figures of detailed methods, fractional secondary structure information, torsion angle sampling, and the time required to generate the different protein cases.

biophysics↗

IDPConformerGenerator: A Flexible Software Suite for Sampling Conformational Space of Disordered Protein States

The power of structural information for informing biological mechanism is clear for stable folded macromolecules, but similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here we present IDPConformerGenerator, a flexible, modular open source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi ({varphi}), psi ({psi}), and omega ({omega}) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank, and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal to Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

biophysics↗

Integrating smFRET, SAXS and NMR data to infer structural ensembles of an intrinsically-disordered protein

Intrinsically disordered proteins (IDPs) have fluctuating heterogeneous conformations, which makes structural characterization challenging, but of great interest, since their conformational ensembles are the link between their sequences and functions. An accurate description of IDP conformational ensembles depends crucially on the amount and quality of the experimental data, how it is integrated, and if it supports a consistent structural picture. We have used an integrative modelling approach to understand how conformational restraints imposed by the most common structural techniques for IDPs: Nuclear Magnetic Resonance (NMR) spectroscopy, Small-angle X-ray Scattering (SAXS), and single-molecule Forster Resonance Energy Transfer (smFRET) reach concordance on structural ensembles for Sic1 and phosphorylated Sic1 (pSic1). To resolve apparent discrepancies between smFRET and SAXS, we integrated SAXS data with non-smFRET (NMR) data and reserved the new smFRET data for Sic1 and pSic1 as an independent validation. The consistency of the SAXS/NMR restrained ensembles with smFRET, which was not guaranteed a priori, indicates that the perturbative effects of NMR or smFRET labels on the Sic1 and pSic1 ensembles are minimal. Furthermore, the mutual agreement with such a diverse set of experimental data suggest that details of the generated ensembles can now be examined with a high degree of confidence to reveal distinguishing features of Sic1 vs. pSic1. From the experimentally well supported ensembles, we find they are consistent with independent biophysical models of Sic1s ultrasensitive binding to its partner Cdc4. Our results underscore the importance of integrative modelling in calculating and drawing biological conclusions from IDP conformational ensembles.

biophysics↗