Computational protein design with backbone plasticity

Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.

Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence

Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.

Algorithms for protein design

Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.

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 general-purpose protein design framework based on mining sequence-structure relationships in known protein structures

Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.

Structural Basis of Design and Engineering for Advanced Plant Optogenetics

In optogenetics, light-sensitive proteins are specifically expressed in target cells and light is used to precisely control the activity of these proteins at high spatiotemporal resolution. Optogenetics initially used naturally occurring photoreceptors to control neural circuits, but has expanded to include carefully designed and engineered photoreceptors. Several optogenetic constructs are based on plant photoreceptors, but their application to plant systems has been limited. Here, we present perspectives on the development of plant optogenetics, considering different levels of design complexity. We discuss how general principles of light-driven signal transduction can be coupled with approaches for engineering protein folding to develop novel optogenetic tools. Finally, we explore how the use of computation, networks, circular permutation, and directed evolution could enrich optogenetics.

Unintended specificity of an engineered ligand-binding protein facilitated by unpredicted plasticity of the protein fold

Attempts to create novel ligand-binding proteins often focus on formation of a binding pocket with shape complementarity against the desired ligand (particularly for compounds that lack distinct polar moieties). Although designed proteins often exhibit binding of the desired ligand, in some cases they display unintended recognition behavior. One such designed protein, that was originally intended to bind tetrahydrocannabinol (THC), was found instead to display binding of 25-hydroxy-cholecalciferol (25-D3) and was subjected to biochemical characterization, further selections for enhanced 25-D3 binding affinity and crystallographic analyses. The deviation in specificity is due in part to unexpected altertion of its conformation, corresponding to a significant change of the orientation of an α-helix and an equally large movement of a loop, both of which flank the designed ligand-binding pocket. Those changes led to engineered protein constructs that exhibit significantly more contacts and complementarity towards the 25-D3 ligand than the initial designed protein had been predicted to form towards its intended THC ligand. Molecular dynamics simulations imply that the initial computationally designed mutations may contribute to the movement of the helix. These analyses collectively indicate that accurate prediction and control of backbone dynamics conformation, through a combination of improved conformational sampling and/or de novo structure design, represents a key area of further development for the design and optimization of engineered ligand-binding proteins.

Development and structural characterization of an engineered multi-copper oxidase reporter of protein-protein interactions

Protein–protein interactions (PPIs) are ubiquitous in almost all biological processes and are often corrupted in diseased states. A detailed understanding of PPIs is therefore key to understanding cellular physiology and can yield attractive therapeutic targets. Here, we describe the development and structural characterization of novel Escherichia coli CueO multi-copper oxidase variants engineered to recapitulate protein–protein interactions with commensurate modulation of their enzymatic activities. The fully integrated single-protein sensors were developed through modular grafting of ligand-specific peptides into a highly compliant and flexible methionine-rich loop of CueO. Sensitive detection of diverse ligand classes exemplified by antibodies, an E3 ligase, MDM2 proto-oncogene (MDM2), and protease (SplB from Staphylococcus aureus) was achieved in a simple mix and measure homogeneous format with visually observable colorimetric readouts. Therapeutic antagonism of MDM2 by small molecules and peptides in clinical development for treatment of cancer patients was assayed using the MDM2-binding CueO enzyme. Structural characterization of the free and MDM2-bound CueO variant provided functional insight into signal-transducing mechanisms of the engineered enzymes and highlighted the robustness of CueO as a stable and compliant scaffold for multiple applications.

N-Lipidated Amino Acids and Peptides Immobilizedon Cellulose Able to Split Amide Bonds

N-lipidated short peptides and amino acids immobilized on the cellulose were used as catalysts cleaved amide bonds under biomimetic conditions. In order to select catalytically most active derivatives a library of 156 N-lipidated amino acids, dipeptides and tripeptides immobilized on cellulose was obtained. The library was synthesized from serine, histidine and glutamic acid peptides N-acylated with heptanoic, octanoic, hexadecanoic and (E)-octadec-9-enoic acids. Catalytic efficiency was monitored by spectrophotometric determination of p-nitroaniline formed by the hydrolysis of a 0.1 M solution of Z-Leu-NP. The most active 8 structures contained tripeptide fragment with 1-3 serine residues. It has been found that incorporation of metal ions into catalytic pockets increase the activity of the synzymes. The structures of the 17 most active catalysts selected from the library of complexes obtained with Cu²⁺ ion varied from 16 derivatives complexed with Zn²⁺ ion. For all of them, a very high reaction rate during the preliminary phase of measurements was followed by a substantial slowdown after 1 h. The catalytic activity gradually diminished after subsequent re-use. HPLC analysis of amide bond splitting confirmed that substrate consumption proceeded in two stages. In the preliminary stage 24–40% of the substrate was rapidly hydrolysed followed by the substantially lower reaction rate. Nevertheless, using the most competent synzymes product of hydrolysis was formed with a yield of 60–83% after 48h under mild and strictly biomimetic conditions.

Strategies for designing non-natural enzymes and binders

The design of tailor-made enzymes is a major goal in biochemical research that can result in wide-range applications and will lead to a better understanding of how proteins fold and function. In this review we highlight recent advances in enzyme and small molecule binder design. A focus is placed on novel strategies for the design of scaffolds, developments in computational methods, and recent applications of these techniques on receptors, sensors, and enzymes. Further, the integration of computational and experimental methodologies is discussed. The outlined examples of designed enzymes and binders for various purposes highlight the importance of this topic and underline the need for tailor-made proteins.

ISAMBARD: an open-source computational environment for biomolecular analysis, modelling and design

Motivation

The rational design of biomolecules is becoming a reality. However, further computational tools are needed to facilitate and accelerate this, and to make it accessible to more users.

Results

Here we introduce ISAMBARD, a tool for structural analysis, model building and rational design of biomolecules. ISAMBARD is open-source, modular, computationally scalable and intuitive to use. These features allow non-experts to explore biomolecular design in silico. ISAMBARD addresses a standing issue in protein design, namely, how to introduce backbone variability in a controlled manner. This is achieved through the generalization of tools for parametric modelling, describing the overall shape of proteins geometrically, and without input from experimentally determined structures. This will allow backbone conformations for entire folds and assemblies not observed in nature to be generated de novo, that is, to access the ‘dark matter of protein-fold space’. We anticipate that ISAMBARD will find broad applications in biomolecular design, biotechnology and synthetic biology.

Availability and implementation

A current stable build can be downloaded from the python package index (https://pypi.python.org/pypi/isambard/) with development builds available on GitHub (https://github.com/woolfson-group/) along with documentation, tutorial material and all the scripts used to generate the data described in this paper.

Supplementary information

Supplementary data are available at Bioinformatics online.

TetraBASE: A Side Chain-Independent Statistical Energy for Designing Realistically Packed Protein Backbones

To construct backbone structures of high designability is a primary aspect of computational protein design. We report here a side chain-independent statistical energy that aims at realistic modeling of through-space packing of polypeptide backbones. To mitigate the lack of explicit amino acid side chains, the model treats the interbackbone site packing as being dependent on peptide local conformation. In addition, new variables suitable for statistical analysis, one for relative orientation and another for distance, have been introduced to represent the intersite geometry based on the asymmetrical tetrahedron organization of distinct chemical groups surrounding the Cα-carbon atoms. The resulting tetrahedron-based backbone statistical energy (tetraBASE) model has been used to optimize the tertiary organizations of secondary structure elements (SSEs) of designated types with Monte Caro simulated annealing, starting from artificial initial configurations. The tetraBASE minimum energy structures can reproduce SSE packing frequently observed in native proteins with atomic root-mean-square deviations of 1-2 Å. The model has also been tested by examining the stability of native SSE arrangements under tetraBASE. The results suggest that tetraBASE model can be used to effectively represent interbackbone packing when designing backbone structures without explicitly knowing side chain types.

Combining Rosetta with molecular dynamics (MD): A benchmark of the MD-based ensemble protein design

Computational protein design is a set of procedures for computing amino acid sequences that will fold into a specified structure. Rosetta Design, a commonly used software for protein design, allows for the effective identification of sequences compatible with a given backbone structure, while molecular dynamics (MD) simulations can thoroughly sample near-native conformations. We benchmarked a procedure in which Rosetta design is started on MD-derived structural ensembles and showed that such a combined approach generates 20-30% more diverse sequences than currently available methods with only a slight increase in computation time. Importantly, the increase in diversity is achieved without a loss in the quality of the designed sequences assessed by their resemblance to natural sequences. We demonstrate that the MD-based procedure is also applicable to de novo design tasks started from backbone structures without any sequence information. In addition, we implemented a protocol that can be used to assess the stability of designed models and to select the best candidates for experimental validation. In sum our results demonstrate that the MD ensemble-based flexible backbone design can be a viable method for protein design, especially for tasks that require a large pool of diverse sequences.

Unraveling co-translational protein folding: Concepts and methods

Advances in techniques such as nuclear magnetic resonance spectroscopy, cryo-electron microscopy, and single-molecule and time-resolved fluorescent approaches are transforming our ability to study co-translational protein folding both in vivo in living cells and in vitro in reconstituted cell-free translation systems. These approaches provide comprehensive information on the spatial organization and dynamics of nascent polypeptide chains and the kinetics of co-translational protein folding. This information has led to an improved understanding of the process of protein folding in living cells and should allow remaining key questions in the field, such as what structures are formed within nascent chains during protein synthesis and when, to be answered. Ultimately, studies using these techniques will facilitate development of a unified concept of protein folding, a process that is essential for proper cell function and organism viability. This review describes current methods for analysis of co-translational protein folding with an emphasis on some of the recently developed techniques that allow monitoring of co-translational protein folding in real-time.