Multi-state design of flexible proteins predicts sequences optimal for conformational change

Systematic analysis of the relationship between fold-dependent flexibility and artificial intelligence protein structure prediction

Artificial Intelligence (AI)-based deep learning methods for predicting protein structures are reshaping knowledge development and scientific discovery. Recent large-scale application of AI models for protein structure prediction has changed perceptions about complicated biological problems and empowered a new generation of structure-based hypothesis testing. It is well-recognized that proteins have a modular organization according to archetypal folds. However, it is yet to be determined if predicted structures are tuned to one conformation of flexible proteins or if they represent average conformations. Further, whether or not the answer is protein fold-dependent. Therefore, in this study, we analyzed 2878 proteins with at least ten distinct experimental structures available, from which we can estimate protein topological rigidity verses heterogeneity from experimental measurements. We found that AlphaFold v2 (AF2) predictions consistently return one specific form to high accuracy, with 99.68% of distinct folds (n = 623 out of 628) having an experimental structure within 2.5Å RMSD from a predicted structure. Yet, 27.70% and 10.82% of folds (174 and 68 out of 628 folds) have at least one experimental structure over 2.5Å and 5Å RMSD, respectively, from their AI-predicted structure. This information is important for how researchers apply and interpret the output of AF2 and similar tools. Additionally, it enabled us to score fold types according to how homogeneous versus heterogeneous their conformations are. Importantly, folds with high heterogeneity are enriched among proteins which regulate vital biological processes including immune cell differentiation, immune activation, and metabolism. This result demonstrates that a large amount of protein fold flexibility has already been experimentally measured, is vital for critical cellular processes, and is currently unaccounted for in structure prediction databases. Therefore, the structure-prediction revolution begets the protein dynamics revolution!

An integrative approach to protein sequence design through multiobjective optimization

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the two-state design problem of the foldswitching protein RfaH as an in-depth case study, and PapD and calmodulin as examples of higher-dimensional design problems, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications.

Design of stimulus-responsive two-state hinge proteins

In nature, proteins that switch between two conformations in response to environmental stimuli structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Designing proteins with two distinct but fully structured conformations is a challenge for protein design as it requires sculpting an energy landscape with two distinct minima. Here we describe the design of "hinge" proteins that populate one designed state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, double electron-electron resonance spectroscopy, and binding measurements demonstrate that despite the significant structural differences the two states are designed with atomic level accuracy and that the conformational and binding equilibria are closely coupled.

Model of ligand-triggered information transmission in G-protein coupled receptor complexes

We present a model for the effects of ligands on information transmission in G-Protein Coupled Receptor (GPCR) complexes. The model is built ab initio entirely on principles of statistical mechanics and tenets of information transmission theory and was validated in part using agonist-induced effector activity and signaling bias for the angiotensin- and adrenergic-mediated signaling pathways, with in vitro observations of phosphorylation sites on the C tail of the GPCR complex, and single-cell information-transmission experiments. The model extends traditional kinetic models that form the basis for many existing models of GPCR signaling. It is based on maximizing the rates of entropy production and information transmission through the GPCR complex. The model predicts that (1) phosphatase-catalyzed reactions, as opposed to kinase-catalyzed reactions, on the C-tail and internal loops of the GPCR are responsible for controlling the signaling activity, (2) signaling favors the statistical balance of the number of switches in the ON state and the number in the OFF state, and (3) biased-signaling response depends discontinuously on ligand concentration.

ProtGPT2 is a deep unsupervised language model for protein design

Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Recent progress in Transformer-based architectures has enabled the implementation of language models capable of generating text with human-like capabilities. Here, motivated by this success, we describe ProtGPT2, a language model trained on the protein space that generates de novo protein sequences following the principles of natural ones. The generated proteins display natural amino acid propensities, while disorder predictions indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences. Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space. AlphaFold prediction of ProtGPT2-sequences yields well-folded non-idealized structures with embodiments and large loops and reveals topologies not captured in current structure databases. ProtGPT2 generates sequences in a matter of seconds and is freely available.

The present and the future of protein biosensor engineering

Protein biosensors play increasingly important roles in cell and neurobiology and have the potential to revolutionise the way clinical and industrial analytics are performed. The gradual transition from multicomponent biosensors to fully integrated single chain allosteric biosensors has brought the field closer to commercial applications. We evaluate various approaches for converting constitutively active protein reporter domains into analyte operated switches. We discuss the paucity of the natural receptors that undergo conformational changes sufficiently large to control the activity of allosteric reporter domains. This problem can be overcome by constructing artificial versions of such receptors. The design path to such receptors involves the construction of Chemically Induced Dimerisation systems (CIDs) that can be configured to operate single and two-component biosensors.

Design principles of protein switches

Protein switches perform essential roles in many biological processes and are exciting targets for de novo protein design, which aims to produce proteins of arbitrary shape and functionality. However, the biophysical requirements for switch function - multiple conformational states, fine-tuned energetics, and stimuli-responsiveness - pose a formidable challenge for design by computation (or intuition). A variety of methods have been developed toward tackling this challenge, usually taking inspiration from the wealth of sequence and structural information available for naturally occurring protein switches. More recently, modular switches have been designed computationally, and new methods have emerged for sampling unexplored structure space, providing promising new avenues toward the generation of purpose-built switches and de novo signaling systems for cellular engineering.

Computational Design of Miniprotein Binders

Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.

How much can physics do for protein design?

Physics and physical chemistry are an important thread in computational protein design, complementary to knowledge-based tools. They provide molecular mechanics scoring functions that need little or no ad hoc parameter readjustment, methods to thoroughly sample equilibrium ensembles, and different levels of approximation for conformational flexibility. They led recently to the successful redesign of a small protein using a physics-based folded state energy. Adaptive Monte Carlo or molecular dynamics schemes were discovered where protein variants are populated as per their ligand-binding free energy or catalytic efficiency. Molecular dynamics have been used for backbone flexibility. Implicit solvent models have been refined, polarizable force fields applied, and many physical insights obtained.

Principles and Methods in Computational Membrane Protein Design

After decades of progress in computational protein design, the design of proteins folding and functioning in lipid membranes appears today as the next frontier. Some notable successes in the de novo design of simplified model membrane protein systems have helped articulate fundamental principles of protein folding, architecture and interaction in the hydrophobic lipid environment. These principles are reviewed here, together with the computational methods and approaches that were used to identify them. We provide an overview of the methodological innovations in the generation of new protein structures and functions and in the development of membrane-specific energy functions. We highlight the opportunities offered by new machine learning approaches applied to protein design, and by new experimental characterization techniques applied to membrane proteins. Although membrane protein design is in its infancy, it appears more reachable than previously thought.

Rosetta design with co-evolutionary information retains protein function

Computational protein design has the ambitious goal of crafting novel proteins that address challenges in biology and medicine. To overcome these challenges, the computational protein modeling suite Rosetta has been tailored to address various protein design tasks. Recently, statistical methods have been developed that identify correlated mutations between residues in a multiple sequence alignment of homologous proteins. These subtle inter-dependencies in the occupancy of residue positions throughout evolution are crucial for protein function, but we found that three current Rosetta design approaches fail to recover these co-evolutionary couplings. Thus, we developed the Rosetta method ResCue (residue-coupling enhanced) that leverages co-evolutionary information to favor sequences which recapitulate correlated mutations, as observed in nature. To assess the protocols via recapitulation designs, we compiled a benchmark of ten proteins each represented by two, structurally diverse states. We could demonstrate that ResCue designed sequences with an average sequence recovery rate of 70%, whereas three other protocols reached not more than 50%, on average. Our approach had higher recovery rates also for functionally important residues, which were studied in detail. This improvement has only a minor negative effect on the fitness of the designed sequences as assessed by Rosetta energy. In conclusion, our findings support the idea that informing protocols with co-evolutionary signals helps to design stable and native-like proteins that are compatible with the different conformational states required for a complex function.

In homologous proteins, functionally or structurally important residues are strongly conserved. Thus, the consideration of conservation signals during protein design protocols can help to create sequences that are more native-like. However, the number of conserved residues is small in many proteins and not all important residues can be captured by conservation analysis. Residues are forming networks whose composition is dictated by protein structure function and thus is visible through the co-evolutionary analysis. Nowadays, advanced methods allow us to deduce these networks from multiple sequence alignments. Thus, we have implemented the novel Rosetta method termed ‘ResCue’ that informs the design protocol with co-evolutionary signals. Recapitulation designs based on ten difficult benchmarks made clear that this protocol creates sequences that are more native-like than three other, state-of-the-art design protocols.

Predicting susceptibility to SARS-CoV-2 infection based on structural differences in ACE2 across species

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the global pandemic of coronavirus disease-2019 (COVID-19). SARS-CoV-2 is a zoonotic disease, but little is known about variations in species susceptibility that could identify potential reservoir species, animal models, and the risk to pets, wildlife, and livestock. Certain species, such as domestic cats and tigers, are susceptible to SARS-CoV-2 infection, while other species such as mice and chickens are not. Most animal species, including those in close contact with humans, have unknown susceptibility. Hence, methods to predict the infection risk of animal species are urgently needed. SARS-CoV-2 spike protein binding to angiotensin-converting enzyme 2 (ACE2) is critical for viral cell entry and infection. Here we integrate species differences in susceptibility with multiple in-depth structural analyses to identify key ACE2 amino acid positions including 30, 83, 90, 322, and 354 that distinguish susceptible from resistant species. Using differences in these residues across species, we developed a susceptibility score that predicts an elevated risk of SARS-CoV-2 infection for multiple species including horses and camels. We also demonstrate that SARS-CoV-2 is nearly optimal for binding ACE2 of humans compared to other animals, which may underlie the highly contagious transmissibility of this virus among humans. Taken together, our findings define potential ACE2 and SARS-CoV-2 residues for therapeutic targeting and identification of animal species on which to focus research and protection measures for environmental and public health.

Driving Protein Conformational Cycles in Physiology and Disease: "Frustrated" Amino Acid Interaction Networks Define Dynamic Energy Landscapes: Amino Acid Interaction Networks Change Progressively Along Alpha Tryptophan Synthase's Catalytic Cycle

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or "conformational cycle," required for function is proposed. It is suggested that the free-energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free-energy landscape. Some network connections are consistent in every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states.

Which animals are at risk? Predicting species susceptibility to Covid-19

In only a few months, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic, leaving physicians, scientists, and public health officials racing to understand, treat, and contain this zoonotic disease. SARS-CoV-2 has made the leap from animals to humans, but little is known about variations in species susceptibility that could identify potential reservoir species, animal models, and the risk to pets, wildlife, and livestock. While there is evidence that certain species, such as cats, are susceptible, the vast majority of animal species, including those in close contact with humans, have unknown susceptibility. Hence, methods to predict their infection risk are urgently needed. SARS-CoV-2 spike protein binding to angiotensin converting enzyme 2 (ACE2) is critical for viral cell entry and infection. Here we identified key ACE2 residues that distinguish susceptible from resistant species using in-depth sequence and structural analyses of ACE2 and its binding to SARS-CoV-2. Our findings have important implications for identification of ACE2 and SARS-CoV-2 residues for therapeutic targeting and identification of animal species with increased susceptibility for infection on which to focus research and protection measures for environmental and public health.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.