Saturation mutagenesis of a predicted ancestral Syk-family kinase

Allosteric modulation of NF1 GAP: Differential distributions of catalytically competent populations in loss-of-function and gain-of-function mutants

Neurofibromin (NF1), a Ras GTPase-activating protein (GAP), catalyzes Ras-mediated GTP hydrolysis and thereby negatively regulates the Ras/MAPK pathway. NF1 mutations can cause neurofibromatosis type 1 manifesting tumors, and neurodevelopmental disorders. Exactly how the missense mutations in the GAP-related domain of NF1 (NF1GRD) allosterically impact NF1 GAP to promote these distinct pathologies is unclear. Especially tantalizing is the question of how same-domain, same-residue NF1GRD variants exhibit distinct clinical phenotypes. Guided by clinical data, we take up this dilemma. We sampled the conformational ensembles of NF1GRD in complex with GTP-bound K-Ras4B by performing molecular dynamics simulations. Our results show that mutations in NF1GRD retain the active conformation of K-Ras4B but with biased propensities of the catalytically competent populations of K-Ras4B-NF1GRD complex. In agreement with clinical depiction and experimental tagging, compared to the wild type, NF1GRD E1356A and E1356V mutants effectively act through loss-of-function and gain-of-function mechanisms, leading to neurofibromatosis and developmental disorders, respectively. Allosteric modulation of NF1GRD GAP activity through biasing the conformational ensembles in the different states is further demonstrated by the diminished GAP activity by NF1GRD isoform 2, further manifesting propensities of conformational ensembles as powerful predictors of protein function. Taken together, our work identifies a NF1GRD hotspot that could allosterically tune GAP function, suggests targeting Ras oncogenic mutations by restoring NF1 catalytic activity, and offers a molecular mechanism for NF1 phenotypes determined by their distinct conformational propensities.

Conserved regulatory motifs in the juxtamembrane domain and kinase N-lobe revealed through deep mutational scanning of the MET receptor tyrosine kinase domain

MET is a receptor tyrosine kinase (RTK) responsible for initiating signaling pathways involved in development and wound repair. MET activation relies on ligand binding to the extracellular receptor, which prompts dimerization, intracellular phosphorylation, and recruitment of associated signaling proteins. Mutations, which are predominantly observed clinically in the intracellular juxtamembrane and kinase domains, can disrupt typical MET regulatory mechanisms. Understanding how juxtamembrane variants, such as exon 14 skipping (METΔEx14), and rare kinase domain mutations can increase signaling, often leading to cancer, remains a challenge. Here, we perform a parallel deep mutational scan (DMS) of the MET intracellular kinase domain in two fusion protein backgrounds: wild-type and METΔEx14. Our comparative approach has revealed a critical hydrophobic interaction between a juxtamembrane segment and the kinase ⍺C-helix, pointing to potential differences in regulatory mechanisms between MET and other RTKs. Additionally, we have uncovered a β5 motif that acts as a structural pivot for the kinase domain in MET and other TAM family of kinases. We also describe a number of previously unknown activating mutations, aiding the effort to annotate driver, passenger, and drug resistance mutations in the MET kinase domain.

Ancestral Reconstruction and the Evolution of Protein Energy Landscapes

A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.

ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers

Protein design holds immense potential for optimizing naturally occurring proteins, with broad applications in drug discovery, material design, and sustainability. However, computational methods for protein engineering are confronted with significant challenges, such as an expansive design space, sparse functional regions, and a scarcity of available labels. These issues are further exacerbated in practice by the fact most real-life design scenarios necessitate the simultaneous optimization of multiple properties. In this work, we introduce ProteinNPT, a non-parametric transformer variant tailored to protein sequences and particularly suited to label-scarce and multi-task learning settings. We first focus on the supervised fitness prediction setting and develop several cross-validation schemes which support robust performance assessment. We subsequently reimplement prior top-performing baselines, introduce several extensions of these baselines by integrating diverse branches of the protein engineering literature, and demonstrate that ProteinNPT consistently outperforms all of them across a diverse set of protein property prediction tasks. Finally, we demonstrate the value of our approach for iterative protein design across extensive in silico Bayesian optimization and conditional sampling experiments.