The de novo design of protein structures

Massively parallel identification of sequence motifs triggering ribosome-associated mRNA quality control

Decay of mRNAs can be triggered by ribosome slowdown at stretches of rare codons or positively charged amino acids. However, the full diversity of sequences that trigger co-translational mRNA decay is poorly understood. To comprehensively identify sequence motifs that trigger mRNA decay, we use a massively parallel reporter assay to measure the effect of all possible combinations of codon pairs on mRNA levels in S. cerevisiae. In addition to known mRNA-destabilizing sequences, we identify several dipeptide repeats whose translation reduces mRNA levels. These include combinations of positively charged and bulky residues, as well as proline-glycine and proline-aspartate dipeptide repeats. Genetic deletion of the ribosome collision sensor Hel2 rescues the mRNA effects of these motifs, suggesting that they trigger ribosome slowdown and activate the ribosome-associated quality control (RQC) pathway. Deep mutational scanning of an mRNA-destabilizing dipeptide repeat reveals a complex interplay between the charge, bulkiness, and location of amino acid residues in conferring mRNA instability. Finally, we show that the mRNA effects of codon pairs are predictive of the effects of endogenous sequences. Our work highlights the complexity of sequence motifs driving co-translational mRNA decay in eukaryotes, and presents a high throughput approach to dissect their requirements at the codon level.

Massively parallel identification of sequence motifs triggering ribosome-associated mRNA quality control

Decay of mRNAs can be triggered by ribosome slowdown at stretches of rare codons or positively charged amino acids. However, the full diversity of sequences that trigger co-translational mRNA decay is poorly understood. To comprehensively identify sequence motifs that trigger mRNA decay, we use a massively parallel reporter assay to measure the effect of all possible combinations of codon pairs on mRNA levels in S. cerevisiae. In addition to known mRNA-destabilizing sequences, we identify several dipeptide repeats whose translation reduces mRNA levels. These include combinations of positively charged and bulky residues, as well as proline-glycine and proline-aspartate dipeptide repeats. Genetic deletion of the ribosome collision sensor Hel2 rescues the mRNA effects of these motifs, suggesting that they trigger ribosome slowdown and activate the ribosome-associated quality control (RQC) pathway. Deep mutational scanning of an mRNA-destabilizing dipeptide repeat reveals a complex interplay between the charge, bulkiness, and location of amino acid residues in conferring mRNA instability. Finally, we show that the mRNA effects of codon pairs are predictive of the effects of endogenous sequences. Our work highlights the complexity of sequence motifs driving co-translational mRNA decay in eukaryotes, and presents a high throughput approach to dissect their requirements at the codon level.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

De novo metalloprotein design

Natural metalloproteins perform many functions - ranging from sensing to electron transfer and catalysis - in which the position and property of each ligand and metal, is dictated by protein structure. De novo protein design aims to define an amino acid sequence that encodes a specific structure and function, providing a critical test of the hypothetical inner workings of (metallo)proteins. To date, de novo metalloproteins have used simple, symmetric tertiary structures - uncomplicated by the large size and evolutionary marks of natural proteins - to interrogate structure-function hypotheses. In this Review, we discuss de novo design applications, such as proteins that induce complex, increasingly asymmetric ligand geometries to achieve function, as well as the use of more canonical ligand geometries to achieve stability. De novo design has been used to explore how proteins fine-tune redox potentials and catalyse both oxidative and hydrolytic reactions. With an increased understanding of structure-function relationships, functional proteins including O2-dependent oxidases, fast hydrolases, and multi-proton/multi-electron reductases, have been created. In addition, proteins can now be designed using xeno-biological metals or cofactors and principles from inorganic chemistry to derive new-to-nature functions. These results and the advances in computational protein design suggest a bright future for the de novo design of diverse, functional metalloproteins.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.

A Brief History of De Novo Protein Design: Minimal, Rational, and Computational

Protein design has come of age, but how will it mature? In the 1980s and the 1990s, the primary motivation for de novo protein design was to test our understanding of the informational aspect of the protein-folding problem; i.e., how does protein sequence determine protein structure and function? This necessitated minimal and rational design approaches whereby the placement of each residue in a design was reasoned using chemical principles and/or biochemical knowledge. At that time, though with some notable exceptions, the use of computers to aid design was not widespread. Over the past two decades, the tables have turned and computational protein design is firmly established. Here, I illustrate this progress through a timeline of de novo protein structures that have been solved to atomic resolution and deposited in the Protein Data Bank. From this, it is clear that the impact of rational and computational design has been considerable: More-complex and more-sophisticated designs are being targeted with many being resolved to atomic resolution. Furthermore, our ability to generate and manipulate synthetic proteins has advanced to a point where they are providing realistic alternatives to natural protein functions for applications both in vitro and in cells. Also, and increasingly, computational protein design is becoming accessible to non-specialists. This all begs the questions: Is there still a place for minimal and rational design approaches? And, what challenges lie ahead for the burgeoning field of de novo protein design as a whole?

Bayesian optimization with evolutionary and structure-based regularization for directed protein evolution

Background

Directed evolution (DE) is a technique for protein engineering that involves iterative rounds of mutagenesis and screening to search for sequences that optimize a given property, such as binding affinity to a specified target. Unfortunately, the underlying optimization problem is under-determined, and so mutations introduced to improve the specified property may come at the expense of unmeasured, but nevertheless important properties (ex. solubility, thermostability, etc). We address this issue by formulating DE as a regularized Bayesian optimization problem where the regularization term reflects evolutionary or structure-based constraints.

Results

We applied our approach to DE to three representative proteins, GB1, BRCA1, and SARS-CoV-2 Spike, and evaluated both evolutionary and structure-based regularization terms. The results of these experiments demonstrate that: (i) structure-based regularization usually leads to better designs (and never hurts), compared to the unregularized setting; (ii) evolutionary-based regularization tends to be least effective; and (iii) regularization leads to better designs because it effectively focuses the search in certain areas of sequence space, making better use of the experimental budget. Additionally, like previous work in Machine learning assisted DE, we find that our approach significantly reduces the experimental burden of DE, relative to model-free methods.

Conclusion

Introducing regularization into a Bayesian ML-assisted DE framework alters the exploratory patterns of the underlying optimization routine, and can shift variant selections towards those with a range of targeted and desirable properties. In particular, we find that structure-based regularization often improves variant selection compared to unregularized approaches, and never hurts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13015-021-00195-4.

An Evolutionary Profile Guided Greedy Parallel Replica-Exchange Monte Carlo Search Algorithm for Rapid Convergence in Protein Design

Protein design, also known as the inverse protein folding problem, is the identification of a protein sequence that folds into a target protein structure. Protein design is proved as an NP-hard problem. While researchers are working on designing heuristics with an emphasis on new scoring functions, we propose a replica-exchange Monte Carlo (REMC) search algorithm that ensures faster convergence using a greedy strategy. Using biological insights, we construct an evolutionary profile to encode the amino acid variability in different positions of the target protein from its structural homologs. The evolutionary profile guides the REMC search, and the greedy approach confirms appreciable exploration and exploitation of the sequence-structure fitness surface. We allow termination of a simulation trajectory once stagnant situation is detected. A series of sequence and structure level validations establish the goodness of our design. On a benchmark dataset, our algorithm reports an average root-mean-square deviation of 1.21Å between the target and the design proteins when modeled with an existing protein folding software. Besides, our algorithm assures 6.16 times overall speedup. In Molecular Dynamics simulations, we observe that four out of selected five design proteins report better to comparable stability to the corresponding target proteins.

De Novo-Designed β-Sheet Heme Proteins

The field of de novo protein design has met with considerable success over the past few decades. Heme, a cofactor, has often been introduced to impart a diverse array of functions to a protein, ranging from electron transport to respiration. In nature, heme is found to occur predominantly in α-helical structures over β-sheets, which has resulted in significant designs of heme proteins utilizing coiled-coil helices. By contrast, there are only a few known β-sheet proteins that bind heme and designs of β-sheets frequently result in amyloid-like aggregates. This review reflects on our success in designing a series of multistranded β-sheet heme binding peptides that are well folded in both aqueous and membrane-like environments. Initially, we designed a β-hairpin peptide that self-assembles to bind heme and performs peroxidase activity in membrane. The β-hairpin was optimized further to accommodate a heme binding pocket within multistranded β-sheets for catalysis and electron transfer in membranes. Furthermore, we de novo designed and characterized β-sheet peptides and miniproteins that are soluble in an aqueous environment capable of binding single and multiple hemes with high affinity and stability. Collectively, these studies highlight the substantial progress made toward the design of functional β-sheets.

De novo protein design, a retrospective

Proteins are molecular machines whose function depends on their ability to achieve complex folds with precisely defined structural and dynamic properties. The rational design of proteins from first-principles, or de novo, was once considered to be impossible, but today proteins with a variety of folds and functions have been realized. We review the evolution of the field from its earliest days, placing particular emphasis on how this endeavor has illuminated our understanding of the principles underlying the folding and function of natural proteins, and is informing the design of macromolecules with unprecedented structures and properties. An initial set of milestones in de novo protein design focused on the construction of sequences that folded in water and membranes to adopt folded conformations. The first proteins were designed from first-principles using very simple physical models. As computers became more powerful, the use of the rotamer approximation allowed one to discover amino acid sequences that stabilize the desired fold. As the crystallographic database of protein structures expanded in subsequent years, it became possible to construct proteins by assembling short backbone fragments that frequently recur in Nature. The second set of milestones in de novo design involves the discovery of complex functions. Proteins have been designed to bind a variety of metals, porphyrins, and other cofactors. The design of proteins that catalyze hydrolysis and oxygen-dependent reactions has progressed significantly. However, de novo design of catalysts for energetically demanding reactions, or even proteins that bind with high affinity and specificity to highly functionalized complex polar molecules remains an importnant challenge that is now being achieved. Finally, the protein design contributed significantly to our understanding of membrane protein folding and transport of ions across membranes. The area of membrane protein design, or more generally of biomimetic polymers that function in mixed or non-aqueous environments, is now becoming increasingly possible.

Evolutionary approaches in protein engineering towards biomaterial construction

The tailoring of proteins for specific applications by evolutionary methods is a highly active area of research. Rational design and directed evolution are the two main strategies to reengineer proteins or create chimeric structures. Rational engineering is often limited by insufficient knowledge about proteins' structure-function relationships; directed evolution overcomes this restriction but poses challenges in the screening of candidates. A combination of these protein engineering approaches will allow us to create protein variants with a wide range of desired properties. Herein, we focus on the application of these approaches towards the generation of protein biomaterials that are known for biodegradability, biocompatibility and biofunctionality, from combinations of natural, synthetic, or engineered proteins and protein domains. Potential applications depend on the enhancement of biofunctional, mechanical, or other desired properties. Examples include scaffolds for tissue engineering, thermostable enzymes for industrial biocatalysis, and other therapeutic applications.

A Structure-Based Drug Discovery Paradigm

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

The ascent of man(made oxidoreductases)

Though established 40 years ago, the field of de novo protein design has recently come of age, with new designs exhibiting an unprecedented level of sophistication in structure and function. With respect to catalysis, de novo enzymes promise to revolutionise the industrial production of useful chemicals and materials, while providing new biomolecules as plug-and-play components in the metabolic pathways of living cells. To this end, there are now de novo metalloenzymes that are assembled in vivo, including the recently reported C45 maquette, which can catalyse a variety of substrate oxidations with efficiencies rivalling those of closely related natural enzymes. Here we explore the successful design of this de novo enzyme, which was designed to minimise the undesirable complexity of natural proteins using a minimalistic bottom-up approach.

Recent advances in automated protein design and its future challenges

INTRODUCTION

Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

Fragment-Based Ligand Designing

Fragment-based drug design strategies have been used in drug discovery since it was first demonstrated using experimental structural biology techniques such as nuclear magnetic resonance (NMR) and X-ray crystallography. The underlying idea is that existing or new chemical entities with known desirable properties may serve both as tool compounds and as starting points for hit-to-lead expansion. Despite the recent advancements, there remain challenges to overcome, such as assembly of the synthetically feasible structures, development of scoring functions to correlate structure and their activities, and fine tuning of the promising molecules. This chapter first covers the theoretical background needed to understand the concepts and the challenges related to the field of study, followed by the description of important protocols and related software. Case studies are presented to demonstrate practical applications.

Optimising minimal building blocks for addressable self-assembly

Addressable structures are characterised by the set of unique components from which they are built and by the specific location that each component occupies. For an addressable structure to self-assemble, its constituent building blocks must be encoded with sufficient information to define their positions with respect to each other and to enable them to navigate to those positions. DNA, with its vast scope for encoding specific interactions, has been successfully used to synthesise addressable systems of several hundred components. In this work we examine the complementary question of the minimal requirements for building blocks to undergo addressable self-assembly driven by a controlled temperature quench. Our testbed is an idealised model of cubic particles patterned with attractive interactions. We introduce a scheme for optimising the interactions using a variant of basin-hopping and a negative design principle. The designed building blocks are tested dynamically in simple target structures to establish how their complexity affects the limits of reliable self-assembly.

Toward high-resolution computational design of the structure and function of helical membrane proteins

The computational design of α-helical membrane proteins is still in its infancy but has already made great progress. De novo design allows stable, specific and active minimal oligomeric systems to be obtained. Computational reengineering can improve the stability and function of naturally occurring membrane proteins. Currently, the major hurdle for the field is the experimental characterization of the designs. The emergence of new structural methods for membrane proteins will accelerate progress.

Insights from molecular dynamics simulations for computational protein design

A grand challenge in the field of structural biology is to design and engineer proteins that exhibit targeted functions. Although much success on this front has been achieved, design success rates remain low, an ever-present reminder of our limited understanding of the relationship between amino acid sequences and the structures they adopt. In addition to experimental techniques and rational design strategies, computational methods have been employed to aid in the design and engineering of proteins. Molecular dynamics (MD) is one such method that simulates the motions of proteins according to classical dynamics. Here, we review how insights into protein dynamics derived from MD simulations have influenced the design of proteins. One of the greatest strengths of MD is its capacity to reveal information beyond what is available in the static structures deposited in the Protein Data Bank. In this regard simulations can be used to directly guide protein design by providing atomistic details of the dynamic molecular interactions contributing to protein stability and function. MD simulations can also be used as a virtual screening tool to rank, select, identify, and assess potential designs. MD is uniquely poised to inform protein design efforts where the application requires realistic models of protein dynamics and atomic level descriptions of the relationship between dynamics and function. Here, we review cases where MD simulations was used to modulate protein stability and protein function by providing information regarding the conformation(s), conformational transitions, interactions, and dynamics that govern stability and function. In addition, we discuss cases where conformations from protein folding/unfolding simulations have been exploited for protein design, yielding novel outcomes that could not be obtained from static structures.

Design of Redox-Active Peptides: Towards Functional Materials

In nature, the majority of processes that occur in the cell involve the cycling of electrons and protons, changing the reduction and oxidation state of substrates to alter their chemical reactivity and usefulness in vivo. One of the most relevant examples of these processes is the electron transport chain, a series of oxidoreductase proteins that shuttle electrons through well-defined pathways, concurrently moving protons across the cell membrane. Inspired by these processes, researchers have sought to develop materials to mimic natural systems for a number of applications, including fuel production. The most common cofactors found in proteins to carry out electron transfer are iron sulfur clusters and porphyrin-like molecules. Both types have been studied within natural proteins, such as in photosynthetic machinery or soluble electron carriers; in parallel, an extensive literature has developed over recent years attempting to model and study these cofactors within peptide-based materials. This chapter will focus on major designs that have significantly advanced the field.

Evaluating the accuracy of protein design using native secondary sub-structures

BACKGROUND

According to structure-dependent function of proteins, two main challenging problems called Protein Structure Prediction (PSP) and Inverse Protein Folding (IPF) are investigated. In spite of IPF essential applications, it has not been investigated as much as PSP problem. In fact, the ultimate goal of IPF problem or protein design is to create proteins with enhanced properties or even novel functions. One of the major computational challenges in protein design is its large sequence space, namely searching through all plausible sequences is impossible. Inasmuch as, protein secondary structure represents an appropriate primary scaffold of the protein conformation, undoubtedly studying the Protein Secondary Structure Inverse Folding (PSSIF) problem is a quantum leap forward in protein design, as it can reduce the search space. In this paper, a novel genetic algorithm which uses native secondary sub-structures is proposed to solve PSSIF problem. In essence, evolutionary information can lead the algorithm to design appropriate amino acid sequences respective to the target secondary structures. Furthermore, they can be folded to tertiary structures almost similar to their reference 3D structures.

RESULTS

The proposed algorithm called GAPSSIF benefits from evolutionary information obtained by solved proteins in the PDB. Therefore, we construct a repository of protein secondary sub-structures to accelerate convergence of the algorithm. The secondary structure of designed sequences by GAPSSIF is comparable with those obtained by Evolver and EvoDesign. Although we do not explicitly consider tertiary structure features through the algorithm, the structural similarity of native and designed sequences declares acceptable values.

CONCLUSIONS

Using the evolutionary information of native structures can significantly improve the quality of designed sequences. In fact, the combination of this information and effective features such as solvent accessibility and torsion angles leads IPF problem to an efficient solution. GAPSSIF can be downloaded at http://bioinformatics.aut.ac.ir/GAPSSIF/ .

Energy Minimization of Discrete Protein Titration State Models Using Graph Theory

There are several applications in computational biophysics that require the optimization of discrete interacting states, for example, amino acid titration states, ligand oxidation states, or discrete rotamer angles. Such optimization can be very time-consuming as it scales exponentially in the number of sites to be optimized. In this paper, we describe a new polynomial time algorithm for optimization of discrete states in macromolecular systems. This algorithm was adapted from image processing and uses techniques from discrete mathematics and graph theory to restate the optimization problem in terms of "maximum flow-minimum cut" graph analysis. The interaction energy graph, a graph in which vertices (amino acids) and edges (interactions) are weighted with their respective energies, is transformed into a flow network in which the value of the minimum cut in the network equals the minimum free energy of the protein and the cut itself encodes the state that achieves the minimum free energy. Because of its deterministic nature and polynomial time performance, this algorithm has the potential to allow for the ionization state of larger proteins to be discovered.

Design strategies for self-assembly of discrete targets

Both biological and artificial self-assembly processes can take place by a range of different schemes, from the successive addition of identical building blocks to hierarchical sequences of intermediates, all the way to the fully addressable limit in which each component is unique. In this paper, we introduce an idealized model of cubic particles with patterned faces that allows self-assembly strategies to be compared and tested. We consider a simple octameric target, starting with the minimal requirements for successful self-assembly and comparing the benefits and limitations of more sophisticated hierarchical and addressable schemes. Simulations are performed using a hybrid dynamical Monte Carlo protocol that allows self-assembling clusters to rearrange internally while still providing Stokes-Einstein-like diffusion of aggregates of different sizes. Our simulations explicitly capture the thermodynamic, dynamic, and steric challenges typically faced by self-assembly processes, including competition between multiple partially completed structures. Self-assembly pathways are extracted from the simulation trajectories by a fully extendable scheme for identifying structural fragments, which are then assembled into history diagrams for successfully completed target structures. For the simple target, a one-component assembly scheme is most efficient and robust overall, but hierarchical and addressable strategies can have an advantage under some conditions if high yield is a priority.

Contribution of Phe-7 to Tat-dependent export of β-lactamase in Xanthomonas campestris

Strains of Xanthomonas campestris pv. campestris isolated in Taiwan are commonly resistant to ampicillin owing to the constitutive expression of a chromosomally encoded β-lactamase that is secreted into the periplasm. In this study, we found that levels of β-lactamase vary among X. campestris pv. campestris strains, a difference that can be attributed to amino acid substitutions at least at positions 7 and 206, with the former having the major impact. Bioinformatic and PCR analyses indicated that X. campestris pv. campestris possesses tatABC genes and that the signal peptide of X. campestris pv. campestris pre-Bla contains the typical twin-arginine motif (N-R-R-Q-F-L at amino acid residues 3 to 8 in strain X. campestris pv. campestris strain 11), suggesting that Bla is secreted via the Tat pathway. To assess the importance of Phe(7) in the efficient export of X. campestris pv. campestris Bla, we prepared mutant constructs containing amino acid substitutions and monitored their expression by measuring enzyme activity and detecting Bla protein by Western blotting. The results indicate that replacement of Phe(7) with Leu severely inhibited Bla export whereas replacement with Pro almost abolished it. Although a change to Arg caused moderate inhibition of export, replacement with Tyr had no effect. These results suggest that for efficient export of Bla by X. campestris pv. campestris, the aromatic-aromatic interactions and stability of protein structure around the twin-arginine motif are important, since only proteins that can attain a folded state in the cytoplasm are competent for export via the Tat pathway.

Investigating diproline segments in proteins: occurrences, conformation and classification

The covalent linkage between the side-chain and the backbone nitrogen atom of proline leads to the formation of the five-membered pyrrolidine ring and hence restriction of the backbone torsional angle ϕ to values of -60 °± 30° for the L-proline. Diproline segments constitute a chain fragment with considerably reduced conformational choices. In the current study, the conformational states for the diproline segment (( L) Pro-( L) Pro) found in proteins has been investigated with an emphasis on the cis and trans states for the Pro-Pro peptide bond. The occurrence of diproline segments in turns and other secondary structures has been studied and compared to that of Xaa-Pro-Yaa segments in proteins which gives us a better understanding on the restriction imposed on other residues by the diproline segment and the single proline residue. The study indicates that P(II) -P(II) and P(II) -α are the most favorable conformational states for the diproline segment. The analysis on Xaa-Pro-Yaa sequences reveals that the Xaa-Pro peptide bond exists preferably as the trans conformer rather than the cis conformer. The present study may lead to a better understanding of the behavior of proline occurring in diproline segments which can facilitate various designed diproline-based synthetic templates for biological and structural studies.

De novo drug design

Computer-assisted molecular design supports drug discovery by suggesting novel chemotypes and compound modifications for lead structure optimization. While the aspect of synthetic feasibility of the automatically designed compounds has been neglected for a long time, we are currently witnessing an increased interest in this topic. Here, we review state-of-the-art software for de novo drug design with a special emphasis on fragment-based techniques that generate druglike, synthetically accessible compounds. The importance of scoring functions that can be used to predict compound reactivity and potency is highlighted, and several promising solutions are discussed. Recent practical validation studies are presented that have already demonstrated that rule-based fragment assembly can result in novel synthesizable compounds with druglike properties and a desired biological activity.

A polypeptide "building block" for the β-trefoil fold identified by "top-down symmetric deconstruction"

Fibroblast growth factor-1, a member of the 3-fold symmetric β-trefoil fold, was subjected to a series of symmetric constraint mutations in a process termed "top-down symmetric deconstruction." The mutations enforced a cumulative exact 3-fold symmetry upon symmetrically equivalent positions within the protein and were combined with a stability screen. This process culminated in a β-trefoil protein with exact 3-fold primary-structure symmetry that exhibited excellent folding and stability properties. Subsequent fragmentation of the repeating primary-structure motif yielded a 42-residue polypeptide capable of spontaneous assembly as a homotrimer, producing a thermostable β-trefoil architecture. The results show that despite pronounced reduction in sequence complexity, pure symmetry in the design of a foldable, thermostable β-trefoil fold is possible. The top-down symmetric deconstruction approach provides a novel alternative means to successfully identify a useful polypeptide "building block" for subsequent "bottom-up" de novo design of target protein architecture.

Systematic classification and analysis of themes in protein-DNA recognition

Protein-DNA recognition plays a central role in the regulation of gene expression. With the rapidly increasing number of protein-DNA complex structures available at atomic resolution in recent years, a systematic, complete, and intuitive framework to clarify the intrinsic relationship between the global binding modes of these complexes is needed. In this work, we modified, extended, and applied previously defined RNA-recognition themes to describe protein-DNA recognition and used a protocol that incorporates automatic methods into manual inspection to plant a comprehensive classification tree for currently available high-quality protein-DNA structures. Further, a nonredundant (representative) data set consisting of 200 thematically diverse complexes was extracted from the leaves of the classification tree by using a locally sensitive interface comparison algorithm. On the basis of the representative data set, various physical and chemical properties associated with protein-DNA interactions were analyzed using empirical or semiempirical methods. We also examined the individual energetic components involved in protein-DNA interactions and highlighted the importance of conformational entropy, which has been almost completely ignored in previous studies of protein-DNA binding energy.

Experimental support for the evolution of symmetric protein architecture from a simple peptide motif

The majority of protein architectures exhibit elements of structural symmetry, and "gene duplication and fusion" is the evolutionary mechanism generally hypothesized to be responsible for their emergence from simple peptide motifs. Despite the central importance of the gene duplication and fusion hypothesis, experimental support for a plausible evolutionary pathway for a specific protein architecture has yet to be effectively demonstrated. To address this question, a unique "top-down symmetric deconstruction" strategy was utilized to successfully identify a simple peptide motif capable of recapitulating, via gene duplication and fusion processes, a symmetric protein architecture (the threefold symmetric β-trefoil fold). The folding properties of intermediary forms in this deconstruction agree precisely with a previously proposed "conserved architecture" model for symmetric protein evolution. Furthermore, a route through foldable sequence-space between the simple peptide motif and extant protein fold is demonstrated. These results provide compelling experimental support for a plausible evolutionary pathway of symmetric protein architecture via gene duplication and fusion processes.

Computer-aided design of functional protein interactions

Predictive methods for the computational design of proteins search for amino acid sequences adopting desired structures that perform specific functions. Typically, design of 'function' is formulated as engineering new and altered binding activities into proteins. Progress in the design of functional protein-protein interactions is directed toward engineering proteins to precisely control biological processes by specifically recognizing desired interaction partners while avoiding competitors. The field is aiming for strategies to harness recent advances in high-resolution computational modeling-particularly those exploiting protein conformational variability-to engineer new functions and incorporate many functional requirements simultaneously.

Analysis on conservation of disulphide bonds and their structural features in homologous protein domain families

Background

Disulphide bridges are well known to play key roles in stability, folding and functions of proteins. Introduction or deletion of disulphides by site-directed mutagenesis have produced varying effects on stability and folding depending upon the protein and location of disulphide in the 3-D structure. Given the lack of complete understanding it is worthwhile to learn from an analysis of extent of conservation of disulphides in homologous proteins. We have also addressed the question of what structural interactions replaces a disulphide in a homologue in another homologue.

Results

Using a dataset involving 34,752 pairwise comparisons of homologous protein domains corresponding to 300 protein domain families of known 3-D structures, we provide a comprehensive analysis of extent of conservation of disulphide bridges and their structural features. We report that only 54% of all the disulphide bonds compared between the homologous pairs are conserved, even if, a small fraction of the non-conserved disulphides do include cytoplasmic proteins. Also, only about one fourth of the distinct disulphides are conserved in all the members in protein families. We note that while conservation of disulphide is common in many families, disulphide bond mutations are quite prevalent. Interestingly, we note that there is no clear relationship between sequence identity between two homologous proteins and disulphide bond conservation. Our analysis on structural features at the sites where cysteines forming disulphide in one homologue are replaced by non-Cys residues show that the elimination of a disulphide in a homologue need not always result in stabilizing interactions between equivalent residues.

Conclusion

We observe that in the homologous proteins, disulphide bonds are conserved only to a modest extent. Very interestingly, we note that extent of conservation of disulphide in homologous proteins is unrelated to the overall sequence identity between homologues. The non-conserved disulphides are often associated with variable structural features that were recruited to be associated with differentiation or specialisation of protein function.

Physics of proteins

Globular proteins are a key component of the network of life. Over many decades much experimental data on proteins have been gathered, yet theoretical progress has been somewhat limited. We show that the results accumulated over the years inexorably lead to a unified framework for understanding proteins. The framework predicts the existence of a fixed menu of folds determined by geometry, clarifies the role of the amino acid sequence in selecting the native-state structure from this menu, and explains the propensity for amyloid formation. The experimental data and the new approach reveal an astonishing simplicity underlying the protein problem.

A toxic monomeric conformer of the polyglutamine protein

Polyglutamine (polyQ) diseases are classified as conformational neurodegenerative diseases, like Alzheimer and Parkinson diseases, and they are caused by proteins with an abnormally expanded polyQ stretch. However, conformational changes of the expanded polyQ protein and the toxic conformers formed during aggregation have remained poorly understood despite their important role in pathogenesis. Here we show that a beta-sheet conformational transition of the expanded polyQ protein monomer precedes its assembly into beta-sheet-rich amyloid-like fibrils. Microinjection of the various polyQ protein conformers into cultured cells revealed that the soluble beta-sheet monomer causes cytotoxicity. The polyQ-binding peptide QBP1 prevents the toxic beta-sheet conformational transition of the expanded polyQ protein monomer. We conclude that the toxic conformational transition, and not simply the aggregation process itself, is a therapeutic target for polyQ diseases and possibly for conformational diseases in general.

Analycys: A database for conservation and conformation of disulphide bonds in homologous protein domains

Disulphide bonds in proteins are known to play diverse roles ranging from folding to structure to function. Thorough knowledge of the conservation status and structural state of the disulphide bonds will help in understanding of the differences in homologous proteins. Here we present a database for the analysis of conservation and conformation of disulphide bonds in SCOP structural families. This database has a wide range of applications including mapping of disulphide bond mutation patterns, identification of disulphide bonds important for folding and stabilization, modeling of protein tertiary structures and in protein engineering. The database can be accessed at: http://bioinformatics.univ-reunion.fr/analycys/.

Computer-based design of novel protein structures

Over the past 10 years there has been tremendous success in the area of computational protein design. Protein design software has been used to stabilize proteins, solubilize membrane proteins, design intermolecular interactions, and design new protein structures. A key motivation for these studies is that they test our understanding of protein energetics and structure. De novo design of novel structures is a particularly rigorous test because the protein backbone must be designed in addition to the amino acid side chains. A priori it is not guaranteed that the target backbone is even designable. To address this issue, researchers have developed a variety of methods for generating protein-like scaffolds and for optimizing the protein backbone in conjunction with the amino acid sequence. These protocols have been used to design proteins from scratch and to explore sequence space for naturally occurring protein folds.

Theodor Bücher Lecture. Metabolomics, modelling and machine learning in systems biology - towards an understanding of the languages of cells. Delivered on 3 July 2005 at the 30th FEBS Congress and the 9th IUBMB conference in Budapest

The newly emerging field of systems biology involves a judicious interplay between high-throughput 'wet' experimentation, computational modelling and technology development, coupled to the world of ideas and theory. This interplay involves iterative cycles, such that systems biology is not at all confined to hypothesis-dependent studies, with intelligent, principled, hypothesis-generating studies being of high importance and consequently very far from aimless fishing expeditions. I seek to illustrate each of these facets. Novel technology development in metabolomics can increase substantially the dynamic range and number of metabolites that one can detect, and these can be exploited as disease markers and in the consequent and principled generation of hypotheses that are consistent with the data and achieve this in a value-free manner. Much of classical biochemistry and signalling pathway analysis has concentrated on the analyses of changes in the concentrations of intermediates, with 'local' equations - such as that of Michaelis and Menten v=(Vmax x S)/(S+K m) - that describe individual steps being based solely on the instantaneous values of these concentrations. Recent work using single cells (that are not subject to the intellectually unsupportable averaging of the variable displayed by heterogeneous cells possessing nonlinear kinetics) has led to the recognition that some protein signalling pathways may encode their signals not (just) as concentrations (AM or amplitude-modulated in a radio analogy) but via changes in the dynamics of those concentrations (the signals are FM or frequency-modulated). This contributes in principle to a straightforward solution of the crosstalk problem, leads to a profound reassessment of how to understand the downstream effects of dynamic changes in the concentrations of elements in these pathways, and stresses the role of signal processing (and not merely the intermediates) in biological signalling. It is this signal processing that lies at the heart of understanding the languages of cells. The resolution of many of the modern and postgenomic problems of biochemistry requires the development of a myriad of new technologies (and maybe a new culture), and thus regular input from the physical sciences, engineering, mathematics and computer science. One solution, that we are adopting in the Manchester Interdisciplinary Biocentre (http://www.mib.ac.uk/) and the Manchester Centre for Integrative Systems Biology (http://www.mcisb.org/), is thus to colocate individuals with the necessary combinations of skills. Novel disciplines that require such an integrative approach continue to emerge. These include fields such as chemical genomics, synthetic biology, distributed computational environments for biological data and modelling, single cell diagnostics/bionanotechnology, and computational linguistics/text mining.

Geometry of proteins: hydrogen bonding, sterics, and marginally compact tubes

The functionality of proteins is governed by their structure in the native state. Protein structures are made up of emergent building blocks of helices and almost planar sheets. A simple coarse-grained geometrical model of a flexible tube barely subject to compaction provides a unified framework for understanding the common character of globular proteins. We argue that a recent critique of the tube idea is not well founded.

Computer-based de novo design of drug-like molecules

Ever since the first automated de novo design techniques were conceived only 15 years ago, the computer-based design of hit and lead structure candidates has emerged as a complementary approach to high-throughput screening. Although many challenges remain, de novo design supports drug discovery projects by generating novel pharmaceutically active agents with desired properties in a cost- and time-efficient manner. In this review, we outline the various design concepts and highlight current developments in computer-based de novo design.

Redesigning symmetry-related "mini-core" regions of FGF-1 to increase primary structure symmetry: thermodynamic and functional consequences of structural symmetry

Previous reports detailing mutational effects within the hydrophobic core of human acidic fibroblast growth factor (FGF-1) have shown that a symmetric primary structure constraint is compatible with a stably folded protein. In the present report, we investigate symmetrically related pairs of buried hydrophobic residues in FGF-1 (termed "mini-cores") that are not part of the central core. The effect upon the stability and function of FGF-1 mutations designed to increase primary structure symmetry within these "mini-core" regions was evaluated. At symmetry-related positions 22, 64, and 108, the wild-type protein contains either Tyr or Phe side chains. The results show that either residue can be readily accommodated at these positions. At symmetry-related positions 42, 83, and 130, the wild-type protein contains either Cys or Ile side chains. While positions 42 and 130 can readily accommodate either Cys or Ile side chains, position 83 is substantially destabilized by substitution by Ile. Tertiary structure asymmetry in the vicinity of position 83 appears responsible for the inability to accommodate an Ile side chain at this position, and is known to contribute to functional half-life. A mutant form of FGF-1 with enforced primary structure symmetry at positions 22, 64, and 108 (all Tyr) and 42, 83, and 130 (all Cys) is shown to be more stable than the reference FGF-1 protein. The results support the hypothesis that a symmetric primary structure within a symmetric protein superfold represents a solution to achieving a foldable, stable polypeptide, and highlight the role that function may play in the evolution of asymmetry within symmetric superfolds.

Helix packing motif common to the crystal structures of two undecapeptides containing dehydrophenylalanine residues: Implications for the de novo design of helical bundle super secondary structural modules

De novo designed peptide based super secondary structures are expected to provide scaffolds for the incorporation of functional sites as in proteins. Self-association of peptide helices of similar screw sense, mediated by weak interactions, has been probed by the crystal structure determination of two closely related peptides: Ac-Gly1-Ala2-Delta Phe3-Leu4-Val5-DeltaPhe6-Leu7-Val8-DeltaPhe9-Ala10-Gly11-NH2 (I) and Ac-Gly1-Ala2-DeltaPhe3-Leu4-Ala5-DeltaPhe6-Leu7-Ala8-DeltaPhe9-Ala10-Gly11-NH2 (II). The crystal structures determined to atomic resolution and refined to R factors 8.12 and 4.01%, respectively, reveal right-handed 3(10)-helical conformations for both peptides. CD has also revealed the preferential formation of right-handed 3(10)-helical conformations for both molecules. Our aim was to critically analyze the packing of the helices in the solid state with a view to elicit clues for the design of super secondary structural motifs such as two, three, and four helical bundles based on helix-helix interactions. An important finding is that a packing motif could be identified common to both the structures, in which a given peptide helix is surrounded by six other helices reminiscent of transmembrane seven helical bundles. The outer helices are oriented either parallel or antiparallel to the central helix. The helices interact laterally through a combination of N--H...O, C--H...O, and C--H...pi hydrogen bonds. Layers of interacting leucine residues are seen in both peptide crystal structures. The packing of the peptide helices in the solid state appears to provide valuable leads for the design of super secondary structural modules such as two, three, or four helix bundles by connecting adjacent antiparallel helices through suitable linkers such as tetraglycine segments.

Unified perspective on proteins: a physics approach

We study a physical system which, while devoid of the complexity one usually associates with proteins, nevertheless displays a remarkable array of proteinlike properties. The constructive hypothesis that this striking resemblance is not accidental not only leads to a unified framework for understanding protein folding, amyloid formation, and protein interactions but also has implications for natural selection.

Accommodation of a highly symmetric core within a symmetric protein superfold

An alternative core packing group, involving a set of five positions, has been introduced into human acidic FGF-1. This alternative group was designed so as to constrain the primary structure within the core region to the same threefold symmetry present in the tertiary structure of the protein fold (the beta-trefoil superfold). The alternative core is essentially indistinguishable from the WT core with regard to structure, stability, and folding kinetics. The results show that the beta-trefoil superfold is compatible with a threefold symmetric constraint on the core region, as might be the case if the superfold arose as a result of gene duplication/fusion events. Furthermore, this new core arrangement can form the basis of a structural "building block" that can greatly simplify the de novo design of beta-trefoil proteins by using symmetric structural complementarity. Remaining asymmetry within the core appears to be related to asymmetry in the tertiary structure associated with receptor and heparin binding functionality of the growth factor.

Molecular organization of amyloid protofilament-like assembly of betabellin 15D: helical array of beta-sandwiches

Betabellin is a 32-residue peptide engineered to fold into a four-stranded antiparallel beta-sheet protein. Upon air oxidation, the betabellin peptides can fold and assemble into a disulfide-bridged homodimer, or beta-sandwich, of 64 residues. Recent biophysical and ultrastructural studies indicate that betabellin 15D (B15D) (a homodimer of HSLTAKIpkLTFSIAphTYTCAVpkYTAKVSH, where p = DPro, k = DLys, and h = DHis) forms unbranched, 35-A wide assemblies that resemble the protofilaments of amyloid fibers. In the present study, we have analyzed in detail the X-ray diffraction patterns of B15D prepared from acetonitrile. The fiber diffraction analysis indicated that the B15D fibril was composed of a double helix defined by the selection rule l = n + 7m (where l is even, and n and m are any integers), and having a 199-A period and pitch, 28-A rise per unit, and 10-A radius. This helical model is equivalent to a reverse-handed, single helix with half the period and defined by the selection rule l = -3n + 7m (where l is any integer). The asymmetric unit is the single B15D beta-sandwich molecule. These results suggest that the betabellin assembly that models the protofilaments of amyloid fibers is made up of discrete subunits on a helical array. Multiple intersheet hydrogen bonds in the axial direction and intersandwich polar interactions in the lateral direction stabilize the array.