Protein Design Research Articles

Leveraging the Thermodynamics of Protein Conformations in Drug Discovery.

As the name implies, structure-based drug design requires confidence in the holo complex structure. The ability to clarify which protein conformation to use when ambiguity arises would be incredibly useful. We present a large scale validation of the computational method Protein Reorganization Free Energy Perturbation (PReorg-FEP) and demonstrate its quantitative accuracy in selecting the correct protein conformation among candidate models in apo or ligand induced states for 14 different systems. These candidate conformations are pulled from various drug discovery related campaigns: cryptic conformations induced by novel hits in lead identification, binding site rearrangement during lead optimization, and conflicting structural biology models. We also show an example of a pH-dependent conformational change, relevant to protein design.

Designing host-associated microbiomes using the consumer/resource model.

Generative models are extremely popular in modern biology. They have been used to model the variation of protein sequences, entire genomes, and RNA sequencing profiles. Importantly, generative models have been used to extrapolate and interpolate to unobserved regimes of data to design biological systems with desired properties. For example, there has been a boom in machine-learning models aiding in the design of proteins with user-specified structures or functions. Host-associated microbiomes play important roles in animal health and disease, as well as the productivity and environmental footprint of livestock species. However, there are no generative models of host-associated microbiomes. One chief reason is that off-the-shelf machine-learning models are data hungry, and microbiome studies usually deal with large variability and small sample sizes. Moreover, microbiome compositions are heavily context dependent, with characteristics of the host and the abiotic environment leading to distinct patterns in host-microbiome associations. Consequently, off-the-shelf generative modeling has not been successfully applied to microbiomes.To address these challenges, we develop a generative model for host-associated microbiomes derived from the consumer/resource (C/R) framework. This derivation allows us to fit the model to readily available cross-sectional microbiome profile data. Using data from three animal hosts, we show that this mechanistic generative model has several salient features: the model identifies a latent space that represents variables that determine the growth and, therefore, relative abundances of microbial species. Probabilistic modeling of variation in this latent space allows us to generate realistic in silico microbial communities. The model can assign probabilities to microbiomes, thereby allowing us to discriminate between dissimilar ecosystems. Importantly, the model predictively captures host-associated microbiomes and the corresponding hosts' phenotypes, enabling the design of microbial communities associated with user-specified host characteristics.

Comprehensive Analysis and Application Research of Advanced Computational Algorithms in Protein Folding Simulations

Protein engineering stands at the forefront of biotechnology, aiming to modify natural proteins or create new ones tailored to specific functional requirements. The three-dimensional structures of proteins, particularly their folding patterns, are critical in defining their biological roles. Accurate prediction and detailed examination of these protein folding structures are crucial in protein engineering. The close relationship between protein structure and function highlights the importance of understanding protein folding dynamics to successfully manipulate protein designs for intended uses. Genetic algorithms (GA), taking inspiration from natural evolutionary principles, employ a heuristic search approach that integrates elements of randomness. In contrast, simulated annealing (SA) leverages stochastic optimization techniques based on the Monte Carlo method, theoretically capable of approximating the global optimum with a high degree of accuracy. Additionally, generalized ensemble methods are increasingly used to explore protein folding processes. This paper explores the fundamental principles and practical applications of these algorithms in simulating protein folding dynamics, aiming to enhance the methodologies used in protein engineering. This exploration not only aids in the refinement of protein design but also extends the potential applications of engineered proteins in various scientific and industrial fields.

Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists.

Despite progress in designing protein-binding proteins, the shape matching of designs to targets is lower than in many native protein complexes, and design efforts have failed for the tumor necrosis factor receptor 1 (TNFR1) and other protein targets with relatively flat and polar surfaces. We hypothesized that free diffusion from random noise could generate shape-matched binders for challenging targets and tested this approach on TNFR1. We obtain designs with low picomolar affinity whose specificity can be completely switched to other family members using partial diffusion. Designs function as antagonists or as superagonists when presented at higher valency for OX40 and 4-1BB. The ability to design high-affinity and high-specificity antagonists and agonists for pharmacologically important targets in silico presages a coming era in protein design in which binders are made by computation rather than immunization or random screening approaches.

Design of a Water-Soluble CD20 Antigen with Computational Epitope Scaffolding.

The poor solubility of integral membrane proteins in water frequently hinders studies with these proteins, presenting challenges for structure determination and binding screens. For instance, the transmembrane protein CD20, which is an important target for treating B-cell malignancies, is not soluble in water and cannot be easily screened against potential protein binders with techniques like phage display or yeast display. Here, we use de novo protein design to create a water-soluble mimic of the CD20 dimer ("soluble CD20"). Soluble CD20 replaces the central transmembrane helix of CD20 with a water-soluble helix that dimerizes to form a coiled coil that structurally matches the dimer interface of native CD20 and presents the central extracellular loop of CD20 in a binding competent conformation. Unlike peptides derived from CD20, soluble CD20 binds tightly to monoclonal antibodies that recognize quaternary epitopes on the extracellular face of CD20. We demonstrate that soluble CD20 is easy to produce, remains folded above 60°C, and is compatible with binder screening via yeast display. Our results highlight the ability of computational protein design to scaffold conformational epitopes from membrane proteins for use in binding and protein engineering studies.

Atomic molecular dynamics simulation advances of de novo-designed proteins.

Proteins are vital biological macromolecules that execute biological functions and form the core of synthetic biological systems. The history of de novo protein has evolved from initial successes in subordinate structural design to more intricate protein creation, challenging the complexities of natural proteins. Recent strides in protein design have leveraged computational methods to craft proteins for functions beyond their natural capabilities. Molecular dynamics (MD) simulations have emerged as a crucial tool for comprehending the structural and dynamic properties of de novo-designed proteins. In this study, we examined the pivotal role of MD simulations in elucidating the sampling methods, force field, water models, stability, and dynamics of de novo-designed proteins, highlighting their potential applications in diverse fields. The synergy between computational modeling and experimental validation continued to play a crucial role in the creation of novel proteins tailored for specific functions and applications.

ESM-scan-A tool to guide amino acid substitutions.

Protein structure prediction and (re)design have gone through a revolution in the last 3 years. The tremendous progress in these fields has been almost exclusively driven by readily available machine learning algorithms applied to protein folding and sequence design problems. Despite these advancements, predicting site-specific mutational effects on protein stability and function remains an unsolved problem. This is a persistent challenge, mainly because the free energy of large systems is very difficult to compute with absolute accuracy and subtle changes to protein structures are hard to capture with computational models. Here, we describe the implementation and use of ESM-Scan, which uses the ESM zero-shot predictor to scan entire protein sequences for preferential amino acid changes, thus enabling in silico deep mutational scanning experiments. We benchmark ESM-Scan on its predictive capabilities for stability and functionality of sequence changes using three publicly available datasets and proceed by experimentally testing the tool's performance on a challenging test case of a blue-light-activated diguanylate cyclase from Methylotenera species (MsLadC), where it accurately predicted the importance of a highly conserved residue in a region involved in allosteric product inhibition. Our experimental results show that the ESM-zero shot model is capable of inferring the effects of a set of amino acid substitutions in their correlation between predicted fitness and experimental results. ESM-Scan is publicly available at https://huggingface.co/spaces/thaidaev/zsp.

HyperMPNN-A general strategy to design thermostable proteins learned from hyperthermophiles.

Stability is a key factor to enable the use of recombinant proteins in therapeutic or biotechnological applications. Deep learning protein design approaches like ProteinMPNN have shown strong performance both in creating novel proteins or stabilizing existing ones. However, it is unlikely that the stability of the designs will significantly exceed that of the natural proteins in the training set, which are biophysically only marginally stable. Therefore, we collected predicted protein structures from hyperthermophiles, which differ substantially in their amino acid composition from mesophiles. Notably, ProteinMPNN fails to recover their unique amino acid composition. Here we show that a retrained network on predicted proteins from hyperthermophiles, termed HyperMPNN, not only recovers this unique amino acid composition but can also be applied to proteins from non-hyperthermophiles. Using this novel approach on a protein nanoparticle with a melting temperature of 65°C resulted in designs remaining stable at 95°C. In conclusion, we created a new way to design highly thermostable proteins through self-supervised learning on data from hyperthermophiles.

Computer-guided design of Z domain peptides with improved inhibition of VEGF.

Computational protein design is becoming increasingly helpful in the development of new protein therapeutics with enhanced efficacy, specificity, and minimal side effects, for precise modulation of biological pathways. In vascular biology, the interaction between vascular endothelial growth factor A (VEGFA) and its receptors (VEGFR1-R3) is a pivotal process underlying blood vessel growth. Dysregulation of this pathway contributes to diseases such as cancer and diabetic retinopathy. Existing VEGFA inhibitors are effective but have limitations, driving interest in peptide-based therapeutics. Peptide inhibitors offer advantages, including reduced toxicity, improved formulation flexibility, and enhanced stability. This study leverages computational tools, particularly ProteinMPNN and Rosetta, to design optimized peptide-based VEGFA inhibitors. Building on the existing peptide templates mini-Z-1 and Z-1-2, new sequences were computationally predicted and experimentally validated. A novel peptide with improved affinity (KD = 6.2 µM) compared to mini-Z-1 (KD = 9.3 µM) was found, requiring only one round of design and testing. The integration of ProteinMPNN and Rosetta enabled a rapid and cost-effective pipeline for designing potent VEGFA inhibitors, underscoring the potential of computational peptide design in developing next-generation therapeutics targeting angiogenesis-dependent diseases.

Design of high specificity binders for peptide-MHC-I complexes.

Class I MHC molecules present peptides derived from intracellular antigens on the cell surface for immune surveillance, and specific targeting of these peptide-MHC (pMHC) complexes could have considerable utility for treating diseases. Such targeting is challenging as it requires readout of the few outward facing peptide antigen residues and the avoidance of extensive contacts with the MHC carrier which is present on almost all cells. Here we describe the use of deep learning-based protein design tools to de novo design small proteins that arc above the peptide binding groove of pMHC complexes and make extensive contacts with the peptide. We identify specific binders for ten target pMHCs which when displayed on yeast bind the on-target pMHC tetramer but not closely related peptides. For five targets, incorporation of designs into chimeric antigen receptors leads to T-cell activation by the cognate pMHC complexes well above the background from complexes with peptides derived from proteome. Our approach can generate high specificity binders starting from either experimental or predicted structures of the target pMHC complexes, and should be widely useful for both protein and cell based pMHC targeting.

A strategy to design protein-based antagonists against type I cytokine receptors.

Excessive cytokine signaling resulting from dysregulation of a cytokine or its receptor can be a main driver of cancer, autoimmune, or hematopoietic disorders. Here, we leverage protein design to create tailored cytokine receptor blockers with idealized properties. Specifically, we aimed to tackle the granulocyte-colony stimulating factor receptor (G-CSFR), a mediator of different types of leukemia and autoinflammatory diseases. By modifying designed G-CSFR binders, we engineered hyper-stable proteins that function as nanomolar signaling antagonists. X-ray crystallography showed atomic-level agreement with the experimental structure of an exemplary design. Furthermore, the most potent design blocks G-CSFR in acute myeloid leukemia cells and primary human hematopoietic stem cells. Thus, the resulting designs can be used for inhibiting or homing to G-CSFR-expressing cells. Our results also demonstrate that similarly designed cytokine mimics can be used to derive antagonists to tackle other type I cytokine receptors.

De novo protein design in the age of artificial intelligence

Proteins with specific functions and characteristics play a crucial role in biomedicine and nanotechnology. De novo protein design enables the customization of sequences to produce proteins with desired structures that do not exist in the nature. In recent years, with the rapid development of artificial intelligence (AI), deep learning-based generative models have increasingly become powerful tools, enabling the design of functional proteins with atomic-level precision. This article provides an overview of the evolution of de novo protein design, with focus on the latest algorithmic models, and then analyzes existing challenges such as low design success rates, insufficient accuracy, and dependence on experimental validation. Furthermore, this article discusses the future trends in protein design, aiming to provide insights for researchers and practitioners in this field.

Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins.

The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures advances our understanding about how life's molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine-learning based protein structure prediction. Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins that are applied in organic synthesis. Importantly, both technologies also have a profound impact on drug discovery as any therapeutic protein target can now be modelled allowing the de novo design of peptide binders or the identification of small molecule ligands through the in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design enabled by this transformative shift in protein science.

Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins

The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures advances our understanding about how life’s molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine‐learning based protein structure prediction. Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins that are applied in organic synthesis. Importantly, both technologies also have a profound impact on drug discovery as any therapeutic protein target can now be modelled allowing the de novo design of peptide binders or the identification of small molecule ligands through the in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design enabled by this transformative shift in protein science.

Boltz-1 Democratizing Biomolecular Interaction Modeling.

Understanding biomolecular interactions is fundamental to advancing fields like drug discovery and protein design. In this paper, we introduce Boltz-1, an open-source deep learning model incorporating innovations in model architecture, speed optimization, and data processing achieving A lpha F old 3-level accuracy in predicting the 3D structures of biomolecular complexes. Boltz-1 demonstrates a performance on-par with state-of-the-art commercial models on a range of diverse benchmarks, setting a new benchmark for commercially accessible tools in structural biology. By releasing the training and inference code, model weights, datasets, and benchmarks under the MIT open license, we aim to foster global collaboration, accelerate discoveries, and provide a robust platform for advancing biomolecular modeling.

Accurate de novo design of high-affinity protein binding macrocycles using deep learning.

The development of macrocyclic binders to therapeutic proteins typically relies on large-scale screening methods that are resource-intensive and provide little control over binding mode. Despite considerable progress in physics-based methods for peptide design and deep-learning methods for protein design, there are currently no robust approaches for de novo design of protein-binding macrocycles. Here, we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic peptide binders against protein targets of interest. We test 20 or fewer designed macrocycles against each of four diverse proteins and obtain medium to high-affinity binders against all selected targets. Designs against MCL1 and MDM2 demonstrate K D between 1-10 μM, and the best anti-GABARAP macrocycle binds with a K D of 6 nM and a sub-nanomolar IC 50 in vitro . For one of the targets, RbtA, we obtain a high-affinity binder with K D < 10 nM despite starting from the target sequence alone due to the lack of an experimentally determined target structure. X-ray structures determined for macrocycle-bound MCL1, GABARAP, and RbtA complexes match very closely with the computational design models, with three out of the four structures demonstrating Ca RMSD of less than 1.5 Å to the design models. In contrast to library screening approaches for which determining binding mode can be a major bottleneck, the binding modes of RFpeptides-generated macrocycles are known by design, which should greatly facilitate downstream optimization. RFpeptides thus provides a powerful framework for rapid and custom design of macrocyclic peptides for diagnostic and therapeutic applications.

Protein Design by Integrating Machine Learning and Quantum-Encoded Optimization

The protein design problem involves finding polypeptide sequences folding into a given three-dimensional structure. Its rigorous algorithmic solution is computationally demanding, involving a nested search in sequence and structure spaces. Structure searches can now be bypassed thanks to recent machine-learning breakthroughs, which have enabled accurate and rapid structure predictions. Similarly, sequence searches might be entirely transformed by the advent of quantum annealing machines and by the required new encodings of the search problem, which could be performative even on classical machines. In this work, we introduce a general protein design scheme where algorithmic and technological advancements in machine learning and quantum-inspired algorithms can be integrated, and an optimal physics-based scoring function is iteratively learned. In this first proof-of-concept application, we apply the iterative method to a lattice protein model amenable to exhaustive benchmarks, finding that it can rapidly learn a physics-based scoring function and achieve promising design performances. Strikingly, our quantum-inspired reformulation outperforms conventional sequence optimization even when adopted on classical machines. The scheme is general and can be extended, e.g., to encompass off-lattice models, and it can integrate progress on various computational platforms, thus representing a new paradigm approach for protein design. Published by the American Physical Society 2024

Designing proteins: Mimicking natural protein sequence heterogeneity.

This study presents an enhanced protein design algorithm that aims to emulate natural heterogeneity of protein sequences. Initial analysis revealed that natural proteins exhibit a permutation composition lower than the theoretical maximum, suggesting a selective utilization of the 20-letter amino acid alphabet. By not constraining the amino acid composition of the protein sequence but instead allowing random reshuffling of the composition, the resulting design algorithm generates sequences that maintain lower permutation compositions in equilibrium, aligning closely with natural proteins. Folding free energy computations demonstrated that the designed sequences refold to their native structures with high precision, except for proteins with large disordered regions. In addition, direct coupling analysis showed a strong correlation between predicted and actual protein contacts, with accuracy exceeding 82% for a large number of top pairs (>4L). The algorithm also resolved biases in previous designs, ensuring a more accurate representation of protein interactions. Overall, it not only mimics the natural heterogeneity of proteins but also ensures correct folding, marking a significant advancement in protein design and engineering.

Current Status of Computational Approaches for Small Molecule Drug Discovery.

2024 has been an exciting year for computational sciences, with the Nobel Prize in Physics awarded for "artificial neural network" and the Nobel Prize in Chemistry presented for "protein structure prediction and design". Given the rapid advancements in Computer-Aided Drug Design (CADD) and Artificial Intelligence in Drug Discovery (AIDD), a document summarizing their current standing and future directions would be timely and relevant to the readership of Journal of Medicinal Chemistry. This piece of commentary aims to highlight recent developments, key challenges, and potential synergies between these fields, contributing to ongoing discussions in the literature and scientific blogs.

Adaptation of Aglycosylated Monoclonal Antibodies for Improved Production in Komagataella phaffii.

Monoclonal antibodies (mAbs) are a major class of biopharmaceuticals manufactured by well-established processes using Chinese Hamster Ovary (CHO) cells. Next-generation biomanufacturing using alternative hosts like Komagataella phaffii could improve the accessibility of these medicines, address broad societal goals for sustainability, and offer financial advantages for accelerated development of new products. Antibodies produced by K. phaffii, however, may manifest unique molecular quality attributes, like host-dependent, product-related variants, that could raise potential concerns for clinical use. We demonstrate here conservative modifications to the amino acid sequence of aglycosylated antibodies based on the human IgG1 isotype that minimize product-related variations when secreted by K. phaffii. A combination of 2-3 changes of amino acids reduced variations across six different aglycosylated versions of commercial mAbs. Expression of a modified sequence of NIST mAb in both K. phaffii and CHO cells showed comparable biophysical properties and molecular variations. These results suggest a path toward the production of high-quality mAbs that could be expressed interchangeably by either yeast or mammalian cells. Improving molecular designs of proteins to enable a range of manufacturing strategies for well-characterized biopharmaceuticals could accelerate global accessibility and innovations.