Multiple mechanisms of self-association of chemokine receptors CXCR4 and CCR5 demonstrated by deep mutagenesis

Membrane Heteroreceptor Complexes as Second-Order Protein Modulators: A Novel Integrative Mechanism through Allosteric Receptor-Receptor Interactions

Bioluminescence and fluorescence resonance energy transfer (BRET and FRET) together with the proximity ligation method revealed the existence of G-protein-coupled receptors, Ionotropic and Receptor tyrosine kinase heterocomplexes, e.g., A2AR-D2R, GABAA-D5R, and FGFR1-5-HT1AR heterocomplexes. Molecular integration takes place through allosteric receptor-receptor interactions in heteroreceptor complexes of synaptic and extra-synaptic regions. It involves the modulation of receptor protomer recognition, signaling and trafficking, as well as the modulation of behavioral responses. Allosteric receptor-receptor interactions in hetero-complexes give rise to concepts like meta-modulation and protein modulation. The introduction of receptor-receptor interactions was the origin of the concept of meta-modulation provided by Katz and Edwards in 1999, which stood for the fine-tuning or modulation of nerve cell transmission. In 2000-2010, Ribeiro and Sebastiao, based on a series of papers, provided strong support for their view that adenosine can meta-modulate (fine-tune) synaptic transmission through adenosine receptors. However, another term should also be considered: protein modulation, which is the key feature of allosteric receptor-receptor interactions leading to learning and consolidation by novel adapter proteins to memory. Finally, it must be underlined that allosteric receptor-receptor interactions and their involvement both in brain disease and its treatment are of high interest. Their pathophysiological relevance has been obtained, especially for major depressive disorder, cocaine use disorder, and Parkinson's disease.

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.