Nicole Balasco

KCTD proteins exhibit significant structural complexity, arising from their modular organization, oligomerization, and intricate biological partnerships. Although their biological importance has been assessed for two decades, the biochemical basis of their activities is only partially understood. This is certainly due to the limited structural information that was available until a few years ago. Fortunately, some recent insightful structural studies and the advent of machine-learning-based approaches are rapidly changing the scenario. By surveying the literature and structural databases and integrating this information with ad hoc 3D-structure predictions, we provide a detailed view of the structural biology of these proteins at different levels: individual domains, full-length oligomers, functional hetero-oligomers formed by different family members, and complexes with functional partners. Collectively, these surveys and analyses provide insights into the family’s evolutionary history and its structure–function relationships. The family-wide coverage of structural information also indicates the extent to which structural similarities are reflected in functional analogies. Finally, the potential functional implications of the intricate architecture of these multimeric proteins and the tendency of their members to hetero-oligomerize are discussed from a functional perspective.

KCTD (K-potassium Channel Tetramerization Domain-containing) proteins form an emerging class of proteins involved in a wide range of physiological and pathological processes. The involvement of these proteins in diverse biological functions is often a characteristic shared within family clades. Among them, the multifunctional roles of the cluster members 1B, which include KCTD1 and KCTD15, are particularly intriguing. Several studies have shown that these proteins participate in various signaling pathways and are linked to diseases such as cancer, genetic disorders, and obesity. In this proceedings paper, after a brief review of recent findings on the various (mis)functional roles of these proteins, we analyze available structural data and illustrate how such data have helped uncover structure-function relationships. A summary of the key open questions in the field is also included.

Collagen is a family of large, fibrous biomacromolecules common in animals, distinguished by unique molecular, structural, and functional properties. Despite the relatively low complexity of their sequences and the repetitive conformation of the triple helix, which is the defining feature of this family, unraveling sequence–stability and structure–function relationships in this group of proteins remains a challenging task. Considering the importance of the structural aspects in collagen chain recognition and selection, we reviewed our current knowledge of the heterotrimeric structures of non-collagenous (NC) regions that lack the triple helix sequence motif, Gly-X-Y, and are crucial for the correct folding of the functional states of these proteins. This study was conducted by simultaneously surveying the current literature, mining the structural database, and making predictions of the three-dimensional structure of these domains using highly reliable approaches based on machine learning techniques, such as AlphaFold. The combination of experimental structural data and predictive analyses offers some interesting clues about the structural features of heterotrimers formed by collagen NC regions. Structural studies carried out in the last decade show that for fibrillar collagens (types I, V, XI, and mixed V/XI), key factors include the formation of specific disulfide bridges and electrostatic interaction patterns. In the subgroup of collagens whose heterotrimers create supramolecular networks (types IV and VIII), available structural information provides a solid ground for the definition of the basis of the molecular and supramolecular organization. Very recent AlphaFold predictions and structural analyses of type VI collagen offer strong evidence of the specific domains in the NC region of the protein that are involved in chain selection and their staggering. Insightful crystallographic studies have also revealed some fundamental elements of the chain selection process in type IX collagen. Collectively, the data reported here indicate that, although some aspects (particularly the quantification of the relative contribution of the NC and triple helix regions to correct collagen folding) are yet to be fully understood, the available structural information provides a solid foundation for future studies aimed at precisely defining sequence–structure–function relationships in collagens.

An integrated computational–experimental pipeline identifies peptide fragments from the PDB that self-assemble into cross-β nanostructures with tunable functional properties, enabling bioinspired material design.

The human CHCHD4 protein, which is a prototypical family member, carries a coiled–coil–helix–coiled–coil–helix motif that is stabilized by two disulfide bonds. Using its CPC sequence motif, CHCHD4 plays a key role in mitochondrial metabolism, cell survival, and response to stress conditions, controlling the mitochondrial import of diversified protein substrates that are specifically recognized through an interplay between covalent and non-covalent interactions. In the present review, we provide an updated and comprehensive analysis of CHCHD4 substrates controlled by its redox activities. A particular emphasis has been placed on the molecular and structural aspects of these partnerships. The literature survey has been integrated with the mining of structural databases reporting either experimental structures (Protein Data Bank) or structures predicted by AlphaFold, which provide protein three-dimensional models using machine learning-based approaches. In providing an updated view of the thirty-four CHCHD4 substrates that have been experimentally validated, our analyses highlight the notion that this protein can operate on a variety of structurally diversified substrates. Although in most cases, CHCHD4 plays a crucial role in the formation of disulfide bridges that stabilize helix–coil–helix motifs of its substrates, significant variations on this common theme are observed, especially for substrates that have been more recently identified.

Proteins are biomolecules characterized by uncommon chemical and physicochemical complexities coupled with extreme responsiveness to even minor chemical modifications or environmental variations. Since the shape that proteins assume is fundamental for their function, understanding the chemical and structural bases that drive their three-dimensional structures represents the central problem for an atomic-level interpretation of biology. Not surprisingly, this question has progressively become the Holy Grail of structural biology (the folding problem). From this perspective, we initially describe and discuss the different formulations of the folding problem. In the present manuscript, the folding problem is framed from a historical perspective, effectively highlighting the progress made in the last lustrum. We chronologically summarize the major contributions that traditional methodologies provide in approaching this multifaceted problem. We then describe the recent advent and evolution of predictive approaches based on machine learning techniques that are revolutionizing the field by pointing out the potentialities and limitations of this approach. In the final part of the perspective, we illustrate the contribution that computational approaches will make in current structural biology to overcome the limitations of the reductionist approach of studying individual molecules to afford the atomic-level characterization of entire cellular compartments.

Human CHCHD4 is the prototype of the proteins carrying a coiled-coil-helix-coiled-coil-helix motif that is stabilized by two disulfide bonds. Using its CPC sequence motif, CHCHD4 plays a key role in mitochondrial metabolism, cell survival, and response to stress conditions controlling the mitochondrial import of diversified protein substrates that are specifically recognized through an interplay between covalent and non-covalent interactions. In the present review, we provide an updated and comprehensive analysis of CHCHD4 substrates controlled by its redox activities. A particular emphasis has been given to the molecular and structural aspects of these partnerships. The literature survey has been integrated with the mining of structural databases reporting either experimental (Protein Data Bank) or structures predicted by AlphaFold, which provide protein three-dimensional models using machine learning-based approaches. In providing an updated view of the thirty-four CHCHD4 substrates that have been experimentally validated, our analyses highlight the notion that this protein can operate on a variety of structurally diversified substrates. Although in most cases, CHCHD4 plays a crucial role in the formation of disulfide bridges that stabilize in helix-coil-helix motifs of its substrates, significant variations on this common theme are observed, especially for substrates that have been more recently identified.

KCTD ((K)potassium Channel Tetramerization Domain-containing) proteins constitute an emerging class of proteins involved in fundamental physio-pathological processes. In these proteins, the BTB domain, which represents the defining element of the family, may have the dual role of promoting oligomerization and favoring functionally important partnerships with different interactors. Here, by exploiting the potential of recently developed methodologies for protein structure prediction, we report a comprehensive analysis of the interactions of all KCTD proteins with their most common partner Cullin 3 (Cul3). The data here presented demonstrate the impressive ability of this approach to discriminate between KCTDs that interact with Cul3 and those that do not. Indeed, reliable and stable models of the complexes were only obtained for the 15 members of the family that are known to interact with Cul3. The generation of three-dimensional models for all KCTD–Cul3 complexes provides interesting clues on the determinants of the structural basis of this partnership as clear structural differences emerged between KCTDs that bind or do not bind Cul3. Finally, the availability of accurate three-dimensional models for KCTD–Cul3 interactions may be valuable for the ad hoc design and development of compounds targeting specific KCTDs that are involved in several common diseases.

A detailed comprehension of MHC-epitope recognition is essential for the design and development of new antigens that could be effectively used in immunotherapy. Yet, the high variability of the peptide together with the large abundance of MHC variants binding makes the process highly specific and large-scale characterizations extremely challenging by standard experimental techniques. Taking advantage of the striking predictive accuracy of AlphaFold, we report a structural and dynamic-based strategy to gain insights into the molecular basis that drives the recognition and interaction of MHC class I in the immune response triggered by pathogens and/or tumor-derived peptides. Here, we investigated at the atomic level the recognition of E7 and TRP-2 epitopes to their known receptors, thus offering a structural explanation for the different binding preferences of the studied receptors for specific residues in certain positions of the antigen sequences. Moreover, our analysis provides clues on the determinants that dictate the affinity of the same epitope with different receptors. Collectively, the data here presented indicate the reliability of the approach that can be straightforwardly extended to a large number of related systems.