Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era.

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

Engineering highly thermostable Cas12b via de novo structural analyses for one-pot detection of nucleic acids.

CRISPR-Cas-based diagnostics have the potential to elevate nucleic acid detection. CRISPR-Cas systems can be combined with a pre-amplification step in a one-pot reaction to simplify the workflow and reduce carryover contamination. Here, we report an engineered Cas12b with improved thermostability that falls within the optimal temperature range (60°C-65°C) of reverse transcription-loop-mediated isothermal amplification (RT-LAMP). Using de novo structural analyses, we introduce mutations to wild-type BrCas12b to tighten its hydrophobic cores, thereby enhancing thermostability. The one-pot detection assay utilizing the engineered BrCas12b, called SPLENDID (single-pot LAMP-mediated engineered BrCas12b for nucleic acid detection of infectious diseases), exhibits robust trans-cleavage activity up to 67°C in a one-pot setting. We validate SPLENDID clinically in 80 serum samples for hepatitis C virus (HCV) and 66 saliva samples for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with high specificity and accuracy. We obtain results in as little as 20 min, and with the extraction process, the entire assay can be performed within an hour.

CryoFold 2.0: Cryo-EM Structure Determination with MELD.

Cryo-electron microscopy data are becoming more prevalent and accessible at higher resolution levels, leading to the development of new computational tools to determine the atomic structure of macromolecules. However, while existing tools adapted from X-ray crystallography are suitable for the highest-resolution maps, new tools are needed for lower-resolution levels and to account for map heterogeneity. In this article, we introduce CryoFold 2.0, an integrative physics-based approach that combines Bayesian inference and the ability to handle multiple data sources with the molecular dynamics flexible fitting (MDFF) approach to determine the structures of macromolecules by using cryo-EM data. CryoFold 2.0 is incorporated into the MELD (modeling employing limited data) plugin, resulting in a pipeline that is more computationally efficient and accurate than running MELD or MDFF alone. The approach requires fewer computational resources and shorter simulation times than the original CryoFold, and it minimizes manual intervention. We demonstrate the effectiveness of the approach on eight different systems, highlighting its various benefits.

Ranking Peptide Binders by Affinity with AlphaFold.

AlphaFold has revolutionized structural biology by predicting highly accurate structures of proteins and their complexes with peptides and other proteins. However, for protein-peptide systems, we are also interested in identifying the highest affinity binder among a set of candidate peptides. We present a novel competitive binding assay using AlphaFold to predict structures of the receptor in the presence of two peptides. For systems in which the individual structures of the peptides are well predicted, the assay captures the higher affinity binder in the bound state, and the other peptide in the unbound form with statistical significance. We test the application on six protein receptors for which we have experimental binding affinities to several peptides. We find that the assay is best suited for identifying medium to strong peptide binders that adopt stable secondary structures upon binding.

Towards rational computational peptide design.

Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available-and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery.

Deciphering the Folding Mechanism of Proteins G and L and Their Mutants.

Much of our understanding of folding mechanisms comes from interpretations of experimental ϕ and ψ value analysis, relating the differences in stability of the transition state ensemble (TSE) and folded state. We introduce a unified approach combining simulations and Bayesian inference to provide atomistic detail for the folding mechanism of proteins G and L and their mutants. Proteins G and L fold to similar topologies despite low sequence similarity, but differ in their folding pathways. A fast folding redesign of protein G, NuG2, switches folding pathways and folds through a similar pathway with protein L. A redesign of protein L also leads to faster folding, respecting the original folding pathway. Our Bayesian inference approach starts from the same prior on all systems and correctly identifies the folding mechanism for each of the four proteins, a success of the force field and sampling strategy. The approach is computationally efficient and correctly identifies the TSE and intermediate structures along the folding pathway in good agreement with experiments. We complement our findings by using two orthogonal approaches that differ in computational cost and interpretability. Adaptive sampling MD combined with the Markov state model provides a kinetic model that confirms the more complex folding mechanism of protein G and its mutant. Finally, a novel fragment decomposition approach using AlphaFold identifies preferences for secondary structure element combinations that follow the order of events observed in the folding pathways.

Improvement of Unilateral Breast Hypoplasia With Oral Spironolactone in a Patient With Becker Nevus Syndrome.

Becker nevus (BN) is a benign cutaneous smooth muscle hamartoma that presents with a hyperpigmented patch or plaque with or without hypertrichosis.1 BN may be associated with ipsilateral breast hypoplasia or other musculoskeletal abnormalities, an association which has been termed Becker nevus syndrome (BNS).

Modelling peptide-protein complexes: docking, simulations and machine learning.

Peptides mediate up to 40% of protein interactions, their high specificity and ability to bind in places where small molecules cannot make them potential drug candidates. However, predicting peptide-protein complexes remains more challenging than protein-protein or protein-small molecule interactions, in part due to the high flexibility peptides have. In this review, we look at the advances in docking, molecular simulations and machine learning to tackle problems related to peptides such as predicting structures, binding affinities or even kinetics. We specifically focus on explaining the number of docking programmes and force fields used in molecular simulations, so a prospective user can have an educated guess as to why choose one modelling tool or another to address their scientific questions.

Basaloid Follicular Hamartoma: An Additional Criterion of Nevoid Basal Cell Carcinoma Syndrome.

ABSTRACT: Basaloid follicular hamartoma (BFH) is a rare, benign follicular neoplasm which typically presents as brown to skin-colored papules on the face, scalp, and trunk. Histologically, BFH consists of cords and strands of basaloid cells forming cystic structures with scant stroma and should be distinguished from infundibulocystic basal cell carcinoma to avoid overly aggressive treatment. Although BFH has been found to be associated with distinct syndromes, including alopecia, myasthenia gravis, and cystic fibrosis, there is often clinical, histopathologic, and genetic overlap with nevoid basal cell carcinoma syndrome (NBCCS). In this article, we describe a case of a 13-year-old patient with NBCCS who presented with multiple BFHs and propose that it its inclusion into the diagnostic criteria for NBCCS be considered.

Improving the analysis of biological ensembles through extended similarity measures.

We present new algorithms to classify structural ensembles of macromolecules based on the recently proposed extended similarity measures. Molecular dynamics provides a wealth of structural information on systems of biological interest. As computer power increases, we capture larger ensembles and larger conformational transitions between states. Typically, structural clustering provides the statistical mechanics treatment of the system to identify relevant biological states. The key advantage of our approach is that the newly introduced extended similarity indices reduce the computational complexity of assessing the similarity of a set of structures from O(N2) to O(N). Here we take advantage of this favorable cost to develop several highly efficient techniques, including a linear-scaling algorithm to determine the medoid of a set (which we effectively use to select the most representative structure of a cluster). Moreover, we use our extended similarity indices as a linkage criterion in a novel hierarchical agglomerative clustering algorithm. We apply these new metrics to analyze the ensembles of several systems of biological interest such as folding and binding of macromolecules (peptide, protein, DNA-protein). In particular, we design a new workflow that is capable of identifying the most important conformations contributing to the protein folding process. We show excellent performance in the resulting clusters (surpassing traditional linkage criteria), along with faster performance and an efficient cost-function to identify when to merge clusters.

The benefit of poor mixing: kinetics of coacervation.

Complex coacervation has become a prominent area of research in the fields of food science, personal care, drug stabilization, and more. However, little has been reported on the kinetics of assembly of coacervation itself. Here, we describe a simple, low-cost way of looking at the kinetics of coacervation by creating poorly mixed samples. In particular, we examine how polymer chain length, the patterning and symmetry of charges on the oppositely charged polyelectrolytes, and the presence of salt and a zwitterionic buffer affect the kinetics of complex coacervation. Our results suggest an interesting relationship between the time for equilibration and the order of addition of polymers with asymmetric patterns of charge. Furthermore, we demonstrated that increasing polymer chain length resulted in a non-monotonic trend in the sample equilibration times as a result of opposing factors such as excluded volume and diffusion. We also observed differences in the rate of sample equilibration based on the presence of a neutral, zwitterionic buffer, as well as the presence and identity of added salt, consistent with previous reports of salt-specific effects on the rheology of complex coacervates. While not a replacement for more advanced characterization strategies, this turbidity-based method could serve as a screening tool to identify interesting and unique phenomena for further study.

An integrative approach identifies dysregulated long non-coding RNAs as microRNA decoys during nevus to melanoma transformation.

Mounting evidence supports a role for dysregulated long non-coding RNAs (lncRNA) in the development of many cancers. A recently discovered function of lncRNAs is to act as microRNA (miR) decoys or competing endogenous RNAs, which sequester specific miRs and relieve negative regulation of mRNA expression by miRs. Although a large number of non-coding RNAs are thought to function as competing endogenous RNAs, miR-sequestering lncRNAs involved in nevus to melanoma transformation remain largely unknown. In this study, we applied a bioinformatics approach to a unique dataset of benign melanocytic nevi and primary melanomas of the skin in order to fill this research gap. We modified a previously published miR target prediction algorithm, RNAhybrid, and improved its search efficiency. We reported the presence of many lncRNAs and miRs deregulated when transitioning from a senescence-like state of nevi to melanoma. We provided evidence of a relatively new and understudied mechanism of gene regulation during this process and identified for the first time lncRNAs (n = 122) that may potentially function as miR decoys as well as their target miRs during nevus to melanoma transformation. The knowledge presented here can be employed for developing biomarkers for diagnostic and risk stratification purposes.

Melanoma Ex Blue Nevus With GNA11 Mutation and BAP1 Loss: Case Report and Review of the Literature.

Cutaneous melanomas may demonstrate a variety of histopathological features and genetic abnormalities. Melanomas that arise in the setting of blue nevi, also known as "malignant blue nevus" or melanoma ex blue nevus (MBN), share a similar histopathological and mutational profile with uveal melanoma. Most uveal melanomas show characteristic GNA11 or GNAQ mutations; additional BAP1 mutation or loss is associated with the highest risk of metastasis and worst prognosis. However, the significance of BAP1 loss in melanomas ex blue nevus remains unclear. We present a case of MBN arising from the scalp of a 21-year-old woman. The diagnosis was established on histopathological findings demonstrating a markedly atypical melanocytic proliferation with increased mitotic activity, necrosis, and a focus of angiolymphatic invasion. Immunohistochemical analysis demonstrated the absence of BAP1 nuclear expression within tumor cells. Next generation sequencing detected GNA11 Q209L mutation and BAP1 loss (chromosome 3p region loss), supporting the diagnosis. We reviewed another 21 MBN cases with reported BAP1 status from the literature. MBN with BAP1 loss presented at a younger average age (41 vs. 61 years), demonstrated larger average lesion thickness (9.0 vs. 7.3 mm), and had a higher rate of metastasis (50% vs. 33%) compared with BAP1-retained MBN. BAP1 expression studies may assist in the diagnosis and management of MBN, but further research is needed.

Designing Electrostatic Interactions via Polyelectrolyte Monomer Sequence.

Charged polymers are ubiquitous in biological systems because electrostatic interactions can drive complicated structure formation and respond to environmental parameters such as ionic strength and pH. In these systems, function emerges from sophisticated molecular design; for example, intrinsically disordered proteins leverage specific sequences of monomeric charges to control the formation and function of intracellular compartments known as membraneless organelles. The role of a charged monomer sequence in dictating the strength of electrostatic interactions remains poorly understood despite extensive evidence that sequence is a powerful tool biology uses to tune soft materials. In this article, we use a combination of theory, experiment, and simulation to establish the physical principles governing sequence-driven control of electrostatic interactions. We predict how arbitrary sequences of charge give rise to drastic changes in electrostatic interactions and correspondingly phase behavior. We generalize a transfer matrix formalism that describes a phase separation phenomenon known as "complex coacervation" and provide a theoretical framework to predict the phase behavior of charge sequences. This work thus provides insights into both how charge sequence is used in biology and how it could be used to engineer properties of synthetic polymer systems.

Sequence and entropy-based control of complex coacervates.

Biomacromolecules rely on the precise placement of monomers to encode information for structure, function, and physiology. Efforts to emulate this complexity via the synthetic control of chemical sequence in polymers are finding success; however, there is little understanding of how to translate monomer sequence to physical material properties. Here we establish design rules for implementing this sequence-control in materials known as complex coacervates. These materials are formed by the associative phase separation of oppositely charged polyelectrolytes into polyelectrolyte dense (coacervate) and polyelectrolyte dilute (supernatant) phases. We demonstrate that patterns of charges can profoundly affect the charge-charge associations that drive this process. Furthermore, we establish the physical origin of this pattern-dependent interaction: there is a nuanced combination of structural changes in the dense coacervate phase and a 1D confinement of counterions due to patterns along polymers in the supernatant phase.