Mapping Local Conformational Landscapes of Proteins in Solution

The design of functional proteins using tensorized energy calculations

In protein design, the energy associated with a huge number of sequence-conformer perturbations has to be routinely estimated. Hence, enhancing the throughput and accuracy of these energy calculations can profoundly improve design success rates and enable tackling more complex design problems. In this work, we explore the possibility of tensorizing the energy calculations and apply them in a protein design framework. We use this framework to design enhanced proteins with anti-cancer and radio-tracing functions. Particularly, we designed multispecific binders against ligands of the epidermal growth factor receptor (EGFR), where the tested design could inhibit EGFR activity in vitro and in vivo. We also used this method to design high-affinity Cu2+ binders that were stable in serum and could be readily loaded with copper-64 radionuclide. The resulting molecules show superior functional properties for their respective applications and demonstrate the generalizable potential of the described protein design approach.

From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

Probing conformational landscapes and mechanisms of allosteric communication in the functional states of the ABL kinase domain using multiscale simulations and network-based mutational profiling of allosteric residue potentials

In the current study, multiscale simulation approaches and dynamic network methods are employed to examine the dynamic and energetic details of conformational landscapes and allosteric interactions in the ABL kinase domain that determine the kinase functions. Using a plethora of synergistic computational approaches, we elucidate how conformational transitions between the active and inactive ABL states can employ allosteric regulatory switches to modulate intramolecular communication networks between the ATP site, the substrate binding region, and the allosteric binding pocket. A perturbation-based network approach that implements mutational profiling of allosteric residue propensities and communications in the ABL states is proposed. Consistent with biophysical experiments, the results reveal functionally significant shifts of the allosteric interaction networks in which preferential communication paths between the ATP binding site and substrate regions in the active ABL state become suppressed in the closed inactive ABL form, which in turn features favorable allosteric coupling between the ATP site and the allosteric binding pocket. By integrating the results of atomistic simulations with dimensionality reduction methods and Markov state models, we analyze the mechanistic role of macrostates and characterize kinetic transitions between the ABL conformational states. Using network-based mutational scanning of allosteric residue propensities, this study provides a comprehensive computational analysis of long-range communications in the ABL kinase domain and identifies conserved regulatory hotspots that modulate kinase activity and allosteric crosstalk between the allosteric pocket, ATP binding site, and substrate binding regions.

A glutamine-based single α-helix scaffold to target globular proteins

The binding of intrinsically disordered proteins to globular ones can require the folding of motifs into α-helices. These interactions offer opportunities for therapeutic intervention but their modulation with small molecules is challenging because they bury large surfaces. Linear peptides that display the residues that are key for binding can be targeted to globular proteins when they form stable helices, which in most cases requires their chemical modification. Here we present rules to design peptides that fold into single α-helices by instead concatenating glutamine side chain to main chain hydrogen bonds recently discovered in polyglutamine helices. The resulting peptides are uncharged, contain only natural amino acids, and their sequences can be optimized to interact with specific targets. Our results provide design rules to obtain single α-helices for a wide range of applications in protein engineering and drug design.

A topological refactoring design strategy yields highly stable granulopoietic proteins

Protein therapeutics frequently face major challenges, including complicated production, instability, poor solubility, and aggregation. De novo protein design can readily address these challenges. Here, we demonstrate the utility of a topological refactoring strategy to design novel granulopoietic proteins starting from the granulocyte-colony stimulating factor (G-CSF) structure. We change a protein fold by rearranging the sequence and optimising it towards the new fold. Testing four designs, we obtain two that possess nanomolar activity, the most active of which is highly thermostable and protease-resistant, and matches its designed structure to atomic accuracy. While the designs possess starkly different sequence and structure from the native G-CSF, they show specific activity in differentiating primary human haematopoietic stem cells into mature neutrophils. The designs also show significant and specific activity in vivo. Our topological refactoring approach is largely independent of sequence or structural context, and is therefore applicable to a wide range of protein targets.

Skokowa et al. reconstruct the fold of a granulopoietic cytokine, resulting in de novo, hyperstable, highly active proteins with therapeutic potential for treating several neutropenia disorders.

The copper-linked Escherichia coli AZY operon: Structure, metal binding, and a possible physiological role in copper delivery

The Escherichia coli yobA-yebZ-yebY (AZY) operon encodes the proteins YobA, YebZ, and YebY. YobA and YebZ are homologs of the CopC periplasmic copper-binding protein and the CopD putative copper importer, respectively, whereas YebY belongs to the uncharacterized Domain of Unknown Function 2511 family. Despite numerous studies of E. coli copper homeostasis and the existence of the AZY operon in a range of bacteria, the operon's proteins and their functional roles have not been explored. In this study, we present the first biochemical and functional studies of the AZY proteins. Biochemical characterization and structural modeling indicate that YobA binds a single Cu2+ ion with high affinity. Bioinformatics analysis shows that YebY is widespread and encoded either in AZY operons or in other genetic contexts unrelated to copper homeostasis. We also determined the 1.8 Å resolution crystal structure of E. coli YebY, which closely resembles that of the lantibiotic self-resistance protein MlbQ. Two strictly conserved cysteine residues form a disulfide bond, consistent with the observed periplasmic localization of YebY. Upon treatment with reductants, YebY binds Cu+ and Cu2+ with low affinity, as demonstrated by metal-binding analysis and tryptophan fluorescence. Finally, genetic manipulations show that the AZY operon is not involved in copper tolerance or antioxidant defense. Instead, YebY and YobA are required for the activity of the copper-related NADH dehydrogenase II. These results are consistent with a potential role of the AZY operon in copper delivery to membrane proteins.

Integrative Structural Biology in the Era of Accurate Structure Prediction

Characterizing the three-dimensional structure of macromolecules is central to understanding their function. Traditionally, structures of proteins and their complexes have been determined using experimental techniques such as X-ray crystallography, NMR, or cryo-electron microscopy-applied individually or in an integrative manner. Meanwhile, however, computational methods for protein structure prediction have been improving their accuracy, gradually, then suddenly, with the breakthrough advance by AlphaFold2, whose models of monomeric proteins are often as accurate as experimental structures. This breakthrough foreshadows a new era of computational methods that can build accurate models for most monomeric proteins. Here, we envision how such accurate modeling methods can combine with experimental structural biology techniques, enhancing integrative structural biology. We highlight the challenges that arise when considering multiple structural conformations, protein complexes, and polymorphic assemblies. These challenges will motivate further developments, both in modeling programs and in methods to solve experimental structures, towards better and quicker investigation of structure-function relationships.

Design of novel granulopoietic proteins by topological rescaffolding

Computational protein design is rapidly becoming more powerful, and improving the accuracy of computational methods would greatly streamline protein engineering by eliminating the need for empirical optimization in the laboratory. In this work, we set out to design novel granulopoietic agents using a rescaffolding strategy with the goal of achieving simpler and more stable proteins. All of the 4 experimentally tested designs were folded, monomeric, and stable, while the 2 determined structures agreed with the design models within less than 2.5 Å. Despite the lack of significant topological or sequence similarity to their natural granulopoietic counterpart, 2 designs bound to the granulocyte colony-stimulating factor (G-CSF) receptor and exhibited potent, but delayed, in vitro proliferative activity in a G-CSF-dependent cell line. Interestingly, the designs also induced proliferation and differentiation of primary human hematopoietic stem cells into mature granulocytes, highlighting the utility of our approach to develop highly active therapeutic leads purely based on computational design.

De novo designed cytokines that activate the G-CSF receptor show that the receptor-binding information can be encoded onto stable, miniaturised protein scaffolds that possess potent granulopoietic activity; such novel proteins provide for ideal candidates for protein-based therapeutics.