ForliLab

Excited to be included amongst all this incredible science. Check out the paper: www.nature.com/articles/s42...

2025 Editors' Highlights The Editors and Editorial Board Members of Communications Chemistry are pleased to launch a Collection featuring some of their favourite articles published in ...

Explore some of our favourite articles published in Communications Chemistry in 2025:

www.nature.com/collections/...

#ChemSky

AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of AlphaFold The ability of proteins to adopt multiple conformations is fundamental to their biological function. With the advent of AlphaFold, machine learning (ML)-based methods have extended their capabilities to more broadly sample this intrinsic conformational diversity. However, the extent to which ML approaches can independently generate ensembles of diverse and biologically relevant conformations remains an open question. We sought to tackle this challenge by developing AlphaFold-RandomWalk (AF-RW) and AlphaFold-Ensemble (AF-Ensemble), novel ML-based methods to generate diverse protein conformations. As opposed to traditional approaches which rely on modifying the input multiple sequence alignment, AF-RW systematically adds noise to the weights of the model on a per-target basis, significantly increasing the conformational diversity of predicted models compared to conventional methods. AF-Ensemble takes the complementary approach fine-tuning an ensemble of models to produce diversity from a set of two-state systems. Additionally, both methods were incorporated into an automated, multistage computational pipeline that seeds unbiased molecular dynamics simulations from ML-generated conformations to efficiently sample alternative conformations. When evaluated on a diverse set of ten proteins, our pipeline provided useful, MD-guided hypotheses for determining biologically meaningful alternative conformations. Moreover, simulations seeded from diverse ML-generated conformations provided a reasonable approximation to the free energy landscape of two challenging protein targets, K-Ras and ribose-binding protein. Overall, our work highlights the potential of combining diverse conformations generated by perturbing the weights of AF with molecular dynamics simulations to efficiently probe protein conformational heterogeneity.

AlphaFold is great, but proteins aren’t single structures.
In our new paper, we introduce AlphaFold-RandomWalk and AlphaFold-Ensemble to sample alternative conformations directly from AlphaFold.
Paper: pubs.acs.org/doi/10.1021/...
and GitHub: github.com/forlilab/pafmd

Meeko is finally published in JCIM! Check out the paper (pubs.acs.org/doi/full/10....) and GitHub (github.com/forlilab/Meeko) for all your docking preparation needs!

Congratulations to Dr. Ishan Taneja on successfully defending his thesis “Computational Methods for Sampling Alternative Protein Conformations and Modeling Desolvation Upon Protein-Ligand Binding”! He is officially a "Team Forli" graduate!!

Scripps Research scientists honored on Clarivate’s Highly Cited Researchers list

Congratulations to eleven Scripps Research scientists named to the global list of #HighlyCited2025 Researchers by @clarivate.com, which recognizes individuals who exemplify research excellence and broad influence across fields including chemistry, microbiology, neuroscience and more.

Meeko: molecule parameterization and software interoperability for docking and beyond Molecule parametrization is an essential requirement to guarantee the accuracy of simulations. Parametrization includes a proper perception of chemical properties such as bonds, formal charges and pro...

Check out the preprint: chemrxiv.org/engage/chemrxiv/article-details/68c054243e708a7649fa21d3
Checkout the full package at github.com/forlilab/Meeko
Meeko can be installed via conda or PyPI.

Excited to announce a preprint describing our software package Meeko! Meeko is a Python package that uses RDKit for receptor and ligand preparation, including protonation, bond order, and connectivity and processing of docking results. It is customizable and suitable for high-throughput workflows.

Post-Translational Modifications Remodel Proteome-Wide Ligandability Post-translational modifications (PTMs) vastly expand the diversity of human proteome, dynamically reshaping protein activity, interactions, and localization in response to environmental, pharmacologi...

Excited to share a new preprint from the lab. We show that PTMs like phosphorylation & glycosylation dynamically reshape proteome-wide ligandability in cells, including proteins like KRAS. Great collaboration with the Huang Lab, @forlilab.bsky.social and BMS. www.biorxiv.org/content/10.1...

Huge congratulations to Dr. Althea Hansel-Harris on successfully defending her thesis, "Virtual Drug Discovery for Cancer Biology"! Best of luck as you return to medical school at UCSD — we’re excited to see all the amazing things you’ll accomplish! #dr #futuredrdr

A Quantitative Analysis of Ligand Binding at the Protein-Lipid Bilayer Interface

For a lay summary of our findings, check out our blog post: communities.springernature.com/posts/a-quan... (5/5)

Our analysis of this dataset revealed that ligands which bind at the protein-lipid interface have distinct properties compared to those that bind to soluble proteins and that atomic properties of these molecules vary significantly depending on their depth in the bilayer (4/5).

What’s in LILAC-DB?
✅ 413 structures from the PDB
✅ 141 unique ligands
✅ 105 membrane proteins
✅ Curated ligand–protein–lipid interface annotations (3/5)

This resource sheds light on drug design targeting lipid-exposed sites, informing drug discovery against otherwise challenging targets! The dataset is available to download here: zenodo.org/records/1483... (2/5)

A quantitative analysis of ligand binding at the protein-lipid bilayer interface - Communications Chemistry Targeting membrane proteins at sites embedded in the lipid bilayer offers the potential to discover ligands for undruggable targets; however, ligand binding at the protein-membrane interface remains u...

Excited to share our new paper: www.nature.com/articles/s42...! We've compiled the LILAC-DB, a dataset of small molecules bound to membrane proteins at the protein-lipid bilayer interface. (1/5)

Another big publication from @sijiewang.bsky.social this week. Check out our efforts to use mRNA display to screen for covalent binding inhibitors of Staphylococcus aureus serine hydrolases. pubs.acs.org/articlesonre...

A great read for these dire times