The design of idealized alpha/beta-barrels: analysis of beta-sheet closure requirements

The 8-fold parallel alpha/beta-barrel topology is encountered in proteins that display an impressive variety of functions, suggesting that this topology may be a rather nonspecific and stable folding motif. Consequently, this motif can be considered as an interesting framework to design novel proteins. It has been shown that the shape of the beta-sheet portion of the barrel can be approximated by a hyperboloid. This geometric object may therefore be used as a scaffold to construct an idealized eight-stranded beta-barrel. To facilitate the de novo design of such structures, a collection of modeling tools has been developed allowing secondary structure elements to be mapped onto the scaffold surface and rotation and translation operations to be performed about user defined axes while evaluating their contribution to the conformational energy of the system. These tools have been applied in a systematic study assessing the phi, psi requirements to design symmetric eight stranded beta barrels with optimal hydrogen bonding between adjacent beta-strands. It is observed that: (a) the beta-sheet structure can be closed without introducing irregular stagger between beta-strands and (b) the region of phi, psi dihedral angle space compatible with the formation of regular symmetric eight stranded beta-barrels coincides with the phi, psi region corresponding to average beta-strands in known protein structures, suggesting that barrel closure does not impose gross constraints on beta-strand geometry.     

How the Sequence of a Gene Specifies Structural Symmetry in Proteins

Internal symmetry is commonly observed in the majority of fundamental protein folds. Meanwhile, sufficient evidence suggests that nascent polypeptide chains of proteins have the potential to start the co-translational folding process and this process allows mRNA to contain additional information on protein structure. In this paper, we study the relationship between gene sequences and protein structures from the viewpoint of symmetry to explore how gene sequences code for structural symmetry in proteins. We found that, for a set of two-fold symmetric proteins from left-handed beta-helix fold, intragenic symmetry always exists in their corresponding gene sequences. Meanwhile, codon usage bias and local mRNA structure might be involved in modulating translation speed for the formation of structural symmetry: a major decrease of local codon usage bias in the middle of the codon sequence can be identified as a common feature; and major or consecutive decreases in local mRNA folding energy near the boundaries of the symmetric substructures can also be observed. The results suggest that gene duplication and fusion may be an evolutionarily conserved process for this protein fold. In addition, the usage of rare codons and the formation of higher order of secondary structure near the boundaries of symmetric substructures might have coevolved as conserved mechanisms to slow down translation elongation and to facilitate effective folding of symmetric substructures. These findings provide valuable insights into our understanding of the mechanisms of translation and its evolution, as well as the design of proteins via symmetric modules.     

Symmetric key structural residues in symmetric proteins with beta-trefoil fold

To understand how symmetric structures of many proteins are formed from asymmetric sequences, the proteins with two repeated beta-trefoil domains in Plant Cytotoxin B-chain family and all presently known beta-trefoil proteins are analyzed by structure-based multi-sequence alignments. The results show that all these proteins have similar key structural residues that are distributed symmetrically in their structures. These symmetric key structural residues are further analyzed in terms of inter-residues interaction numbers and B-factors. It is found that they can be distinguished from other residues and have significant propensities for structural framework. This indicates that these key structural residues may conduct the formation of symmetric structures although the sequences are asymmetric.     

对称蛋白质序列与结构关系研究

进化的观点认为,蛋白质结构的对称性是基因复制和融合的结果,但是由于在长期进化过程中的氨基酸突变,绝大多数现有的蛋白质序列都失去了这种直观的重复性特征。该文简要地回顾了国际上发展的寻找蛋白质序列中重复片段的方法,重点介绍了作者自己提出的分析蛋白质序列和结构对称性的方法以及在蛋白质对称结构形成机理方面的初步工作,并系统分析了各类对称折叠子的序列与结构关系,发现它们的序列都具有隐含的与结构相同的对称性,或者说序列的对称性决定结构的对称性。     

Data-driven computational protein design

Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design.     

The Mirror-Type Internal Symmetry in the Primary Structure of Proteins: Detection and Functional Role (Review)

This review summarized the data obtained by the author in studies on internal symmetry of the mirror type in primary structures of proteins. The methods for detection of symmetric segments in amino acid sequences are analyzed: (1) the method based on analysis of sequences of roots of amino acid codons; (2) the dot matrix method; (3) the method of internal symmetry scanning. The results of studies of internal symmetry in enzymes and signaling proteins are presented. The probable role of the internal symmetry in the structural-functional organization of proteins is discussed.     

基于蛋白质受体的药物分子计算机辅助设计策略

药物分子计算机辅助设计是一种在计算机或者理论上通过构建具有一定潜在药理活性的新化学实体的分子模拟方法。近十几年来,高通量组学技术的快速发展为生物和化学药物分子设计提供了良好的数据支撑和研究契机。另外,现代社会对生物制药合理性以及作用机理理解的要求越来越高,行业普遍要求药物需要有高效、无毒或者低毒以及靶向性强等特点。随着越来越多与药物靶点相关的蛋白质结构通过实验方法解析出来,基于蛋白质受体的药物分子设计方法可行性进一步提高,其方法也变得越来越重要。基于蛋白质受体的药物分子设计方法,一般是以蛋白质以及配体的三维结构出发进行分析,这让药物分子先导物的发现更加理性化。随着相关实验数据的积累以及深度学习等算法的发展,从而可以进行更加科学的药物分子设计,这在一定程度上加快了新药研发的进程,并更有利于探索相应的分子机理。本文对基于蛋白质受体的药物分子设计方法的常用策略进行系统的回顾、总结和展望。     

Automated minimization of steric clashes in protein structures

Molecular modeling of proteins including homology modeling, structure determination, and knowledge-based protein design requires tools to evaluate and refine three-dimensional protein structures. Steric clash is one of the artifacts prevalent in low-resolution structures and homology models. Steric clashes arise due to the unnatural overlap of any two nonbonding atoms in a protein structure. Usually, removal of severe steric clashes in some structures is challenging since many existing refinement programs do not accept structures with severe steric clashes. Here, we present a quantitative approach of identifying steric clashes in proteins by defining clashes based on the Van der Waals repulsion energy of the clashing atoms. We also define a metric for quantitative estimation of the severity of clashes in proteins by performing statistical analysis of clashes in high-resolution protein structures. We describe a rapid, automated, and robust protocol, Chiron, which efficiently resolves severe clashes in low-resolution structures and homology models with minimal perturbation in the protein backbone. Benchmark studies highlight the efficiency and robustness of Chiron compared with other widely used methods. We provide Chiron as an automated web server to evaluate and resolve clashes in protein structures that can be further used for more accurate protein design.     

Design and structure of two new protein cages illustrate successes and ongoing challenges in protein engineering

In recent years, new protein engineering methods have produced more than a dozen symmetric, self‐assembling protein cages whose structures have been validated to match their design models with near‐atomic accuracy. However, many protein cage designs that are tested in the lab do not form the desired assembly, and improving the success rate of design has been a point of recent emphasis. Here we present two protein structures solved by X‐ray crystallography of designed protein oligomers that form two‐component cages with tetrahedral symmetry. To improve on the past tendency toward poorly soluble protein, we used a computational protocol that favors the formation of hydrogen‐bonding networks over exclusively hydrophobic interactions to stabilize the designed protein–protein interfaces. Preliminary characterization showed highly soluble expression, and solution studies indicated successful cage formation by both designed proteins. For one of the designs, a crystal structure confirmed at high resolution that the intended tetrahedral cage was formed, though several flipped amino acid side chain rotamers resulted in an interface that deviates from the precise hydrogen‐bonding pattern that was intended. A structure of the other designed cage showed that, under the conditions where crystals were obtained, a noncage structure was formed wherein a porous 3D protein network in space group I2₁3 is generated by an off‐target twofold homomeric interface. These results illustrate some of the ongoing challenges of developing computational methods for polar interface design, and add two potentially valuable new entries to the growing list of engineered protein materials for downstream applications.     

Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.     

Benchmarking a computational design method for the incorporation of metal ion‐binding sites at symmetric protein interfaces

The design of novel metal‐ion binding sites along symmetric axes in protein oligomers could provide new avenues for metalloenzyme design, construction of protein‐based nanomaterials and novel ion transport systems. Here, we describe a computational design method, symmetric protein recursive ion‐cofactor sampling (SyPRIS), for locating constellations of backbone positions within oligomeric protein structures that are capable of supporting desired symmetrically coordinated metal ion(s) chelated by sidechains (chelant model). Using SyPRIS on a curated benchmark set of protein structures with symmetric metal binding sites, we found high recovery of native metal coordinating rotamers: in 65 of the 67 (97.0%) cases, native rotamers featured in the best scoring model while in the remaining cases native rotamers were found within the top three scoring models. In a second test, chelant models were crossmatched against protein structures with identical cyclic symmetry. In addition to recovering all native placements, 10.4% (8939/86013) of the non‐native placements, had acceptable geometric compatibility scores. Discrimination between native and non‐native metal site placements was further enhanced upon constrained energy minimization using the Rosetta energy function. Upon sequence design of the surrounding first‐shell residues, we found further stabilization of native placements and a small but significant (1.7%) number of non‐native placement‐based sites with favorable Rosetta energies, indicating their designability in existing protein interfaces. The generality of the SyPRIS approach allows design of novel symmetric metal sites including with non‐natural amino acid sidechains, and should enable the predictive incorporation of a variety of metal‐containing cofactors at symmetric protein interfaces.     

Effects of external interactions on protein sequence-structure relations of beta-trefoil fold

Proteins with symmetric structures are ideal models to investigate the sequence-structure relations. We investigate proteins with beta-trefoil fold and find they have different degrees of sequence symmetries although they show similar symmetric structures. To understand this, we calculate the strength of interactions of the beta-trefoil folds with surrounding environments and find the low degrees of sequence symmetries are often correlated with large external interactions. Our results give an additional confirmation of Anfinsen's thermodynamic hypothesis that protein structures are not only determined by their sequences but also by their surrounding environments. We suggest the external interactions should be considered additionally in protein structure prediction through ab initio folding.     

Designing supramolecular protein assemblies

Many natural proteins self-assemble, either to fulfill their biological function or as part of a pathogenic process. Biological assembly phenomena such as amyloidogenesis, domain swapping and symmetric oligomerization are inspiring new strategies for designing proteins that self-assemble to form supramolecular complexes. Recent advances include the design of novel proteins that assemble into filaments, symmetric cages and regular arrays.     

Computational design of membrane proteins

Membrane proteins are involved in a wide variety of cellular processes, and are typically part of the first interaction a cell has with extracellular molecules. As a result, these proteins comprise a majority of known drug targets. Membrane proteins are among the most difficult proteins to obtain and characterize, and a structure-based understanding of their properties can be difficult to elucidate. Notwithstanding, the design of membrane proteins can provide stringent tests of our understanding of these crucial biological systems, as well as introduce novel or targeted functionalities. Computational design methods have been particularly helpful in addressing these issues, and this review discusses recent studies that tailor membrane proteins to display specific structures or functions and examines how redesigned membrane proteins are being used to facilitate structural and functional studies.     

Determining membrane protein structures: still a challenge!

Determination of structures and dynamics events of transmembrane proteins is important for the understanding of their function. Analysis of such events requires high-resolution 3D structures of the different conformations coupled with molecular dynamics analyses describing the conformational pathways. However, the solution of 3D structures of transmembrane proteins at atomic level remains a particular challenge for structural biochemists--the need for purified and functional transmembrane proteins causes a 'bottleneck'. There are various ways to obtain 3D structures: X-ray diffraction, electron microscopy, NMR and modelling; these methods are not used exclusively of each other, and the chosen combination depends on several criteria. Progress in this field will improve knowledge of ligand-induced activation and inhibition of membrane proteins in addition to aiding the design of membrane-protein-targeted drugs.     

Target selection and determination of function in structural genomics

The first crucial step in any structural genomics project is the selection and prioritization of target proteins for structure determination. There may be a number of selection criteria to be satisfied, including that the proteins have novel folds, that they be representatives of large families for which no structure is known, and so on. The better the selection at this stage, the greater is the value of the structures obtained at the end of the experimental process. This value can be further enhanced once the protein structures have been solved if the functions of the given proteins can also be determined. Here we describe the methods used at either end of the experimental process: firstly, sensitive sequence comparison techniques for selecting a high-quality list of target proteins, and secondly the various computational methods that can be applied to the eventual 3D structures to determine the most likely biochemical function of the proteins in question.     

Prediction of tertiary structures in 'scorpion-toxin' type proteins.

In the three-dimensional architecture of macromolecules, the structural stability and proper folding manifest due to cooperative packing interaction of various segments. Hydrophobicity is the major factor stabilizing protein-protein associations. In the disulfide-containing proteins, S-S bonds are integral part of structural motifs and large part of the protein-folding problem can be reduced to identifying and understanding motifs and subdomains of these proteins. Identifying such a motif with S-S bonds in 'scorpion-toxin' type proteins, and from model-building studies, five tertiary structural models for these type of proteins can be proposed. These canonical structural models can be refined by regular minimum energy and computer simulation methods to arrive at the final tertiary structures. Such 'models' can be of considerable use i) in understanding the biochemical reaction mechanisms in the structure-function relationships, ii) structure determination by X-ray methods (molecular replacement method), iii) drug design etc.     

An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality.