A hot spot of binding energy in a hormone-receptor interface

Biparatopic binding of ISB 1442 to CD38 in trans enables increased cell antibody density and increased avidity

ISB 1442 is a bispecific biparatopic antibody in clinical development to treat hematological malignancies. It consists of two adjacent anti-CD38 arms targeting non-overlapping epitopes that preferentially drive binding to tumor cells and a low-affinity anti-CD47 arm to enable avidity-induced blocking of proximal CD47 receptors. We previously reported the pharmacology of ISB 1442, designed to reestablish synthetic immunity in CD38+ hematological malignancies. Here, we describe the discovery, optimization and characterization of the ISB 1442 antigen binding fragment (Fab) arms, their assembly to 2 + 1 format, and present the high-resolution co-crystal structures of the two anti-CD38 Fabs, in complex with CD38. This, with biophysical and functional assays, elucidated the underlying mechanism of action of ISB 1442. In solution phase, ISB 1442 forms a 2:2 complex with CD38 as determined by size-exclusion chromatography with multi-angle light scattering and electron microscopy. The predicted antibody-antigen stoichiometries at different CD38 surface densities were experimentally validated by surface plasmon resonance and cell binding assays. The specific design and structural features of ISB 1442 enable: 1) enhanced trans binding to adjacent CD38 molecules to increase Fc density at the cancer cell surface; 2) prevention of avid cis binding to monomeric CD38 to minimize blockade by soluble shed CD38; and 3) greater binding avidity, with a slower off-rate at high CD38 density, for increased specificity. The superior CD38 targeting of ISB 1442, at both high and low receptor densities, by its biparatopic design, will enhance proximal CD47 blockade and thus counteract a major tumor escape mechanism in multiple myeloma patients.

PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models

Protein-protein interactions (PPIs) refer to the phenomenon of protein binding through various types of bonds to execute biological functions. These interactions are critical for understanding biological mechanisms and drug research. Among these, the protein binding interface is a critical region involved in protein-protein interactions, particularly the hotspot residues on it that play a key role in protein interactions. Current deep learning methods trained on large-scale data can characterize proteins to a certain extent, but they often struggle to adequately capture information about protein binding interfaces. To address this limitation, we propose the PPI-Graphomer module, which integrates pretrained features from large-scale language models and inverse folding models. This approach enhances the characterization of protein binding interfaces by defining edge relationships and interface masks on the basis of molecular interaction information. Our model outperforms existing methods across multiple benchmark datasets and demonstrates strong generalization capabilities.

A Computational Approach for Designing a Peptide-Based Acetyl-CoA Synthetase 2 Inhibitor: A New Horizon for Anticancer Development

Acetyl-CoA Synthetase 2 (ACSS2) has emerged as a new target for anticancer development owing to its high expression in various tumours and its enhancement of malignancy. Stressing the growing interest in peptide-derived drugs featuring better selectivity and efficacy, a computational protocol was applied to design a peptide inhibitor for ACSS2. Herein, 3600 peptide sequences derived from ACSS2 nucleotide motif were generated by classifying the 20 amino acids into six physiochemical groups. De novo modeling maintained essential binding interactions, and a refined library of 16 peptides was derived using Support Vector Machine filters to ensure proper bioavailability, toxicity, and therapeutic relevance. Structural and folding predictions, along with molecular docking, identified the top candidate, Pep16, which demonstrated significantly higher binding affinity (91.1 ± 1.6 kcal/mol) compared to a known inhibitor (53.7 ± 0.7 kcal/mol). Further molecular dynamics simulations and binding free energy calculations revealed that Pep16 enhances ACSS2 conformational variability, occupies a larger binding interface, and achieved firm binding. MM/GBSA analysis highlighted key electrostatic interactions with specific ACSS2 residues, including ARG 373, ARG 526, ARG 628, ARG 631, and LYS 632. Overall, Pep16 appears to lock the ACSS2 nucleotide pocket into a compact, rigid conformation, potentially blocking ATP binding and catalytic activity, and may serve as a novel specific ACSS2 inhibitor. Though, we urge further research to confirm and compare its therapeutic potential to existing inhibitors. We also believe that this systematic methodology would represent an indispensable tool for prospective peptide-based drug discovery.

Strategies for in Silico Drug Discovery to Modulate Macromolecular Interactions Altered by Mutations

Most human diseases have genetic components, frequently single nucleotide variants (SNVs), which alter the wild type characteristics of macromolecules and their interactions. A straightforward approach for correcting such SNVs-related alterations is to seek small molecules, potential drugs, that can eliminate disease-causing effects. Certain disorders are caused by altered protein-protein interactions, for example, Snyder-Robinson syndrome, the therapy for which focuses on the development of small molecules that restore the wild type homodimerization of spermine synthase. Other disorders originate from altered protein-nucleic acid interactions, as in the case of cancer; in these cases, the elimination of disease-causing effects requires small molecules that eliminate the effect of mutation and restore wild type p53-DNA affinity. Overall, especially for complex diseases, pathogenic mutations frequently alter macromolecular interactions. This effect can be direct, i.e., the alteration of wild type affinity and specificity, or indirect via alterations in the concentration of the binding partners. Here, we outline progress made in methods and strategies to computationally identify small molecules capable of altering macromolecular interactions in a desired manner, reducing or increasing the binding affinity, and eliminating the disease-causing effect. When applicable, we provide examples of the outlined general strategy. Successful cases are presented at the end of the work.

Recent progress and future challenges in structure-based protein-protein interaction prediction

Protein-protein interactions (PPIs) play a fundamental role in cellular processes, and understanding these interactions is crucial for advances in both basic biological science and biomedical applications. This review presents an overview of recent progress in computational methods for modeling protein complexes and predicting PPIs based on 3D structures, focusing on the transformative role of artificial intelligence-based approaches. We further discuss the expanding biomedical applications of PPI research, including the elucidation of disease mechanisms, drug discovery, and therapeutic design. Despite these advances, significant challenges remain in predicting host-pathogen interactions, interactions between intrinsically disordered regions, and interactions related to immune responses. These challenges are worthwhile for future explorations and represent the frontier of research in this field.

Chemically Engineered Peptide Efficiently Blocks Malaria Parasite Entry into Red Blood Cells

Chemical peptide engineering, enabled by residue insertion, backbone cyclization, and incorporation of an additional disulfide bond, led to a unique cyclic peptide that efficiently inhibits the invasion of red blood cells by malaria parasites. The engineered peptide exhibits a 20-fold enhanced affinity toward its receptor (PfAMA1) compared to the native peptide ligand (PfRON2), as determined by surface plasmon resonance. In-vitro parasite growth inhibition assay revealed augmented potency of the engineered peptide. The structure of the PfAMA1-cyclic peptide complex, predicted by the deep learning-based structure prediction tool ColabFold-AlphaFold2 with protein-cyclic peptide complex offset, provided valuable insights into the observed activity of the peptide analogs. Rational editing of the peptide backbone and side chain described here proved to be an effective strategy for designing peptide-based inhibitors to interfere with disease-related protein-protein interactions.

Indels allow antiviral proteins to evolve functional novelty inaccessible by missense mutations

Antiviral proteins often evolve rapidly at virus-binding interfaces to defend against new viruses. We investigated whether antiviral adaptation via missense mutations might face limits, which insertion or deletion mutations (indels) could overcome. Using high-throughput saturation missense mutagenesis, we identify one such case of a nearly insurmountable evolutionary challenge: the human anti-retroviral protein TRIM5α requires more than five missense mutations in its specificity-determining v1 loop to restrict a divergent simian immunodeficiency virus (SIV). However, through a novel saturating indel scanning methodology, we find that duplicating just one amino acid in v1 enables human TRIM5α to potently restrict SIV in a single evolutionary step. Moreover, natural primate TRIM5α v1 loops have evolved indels that confer novel antiviral specificities. Thus, indels enable antiviral proteins to overcome viral challenges otherwise inaccessible by missense mutations. Our findings reveal the potential of often-overlooked indel mutations in driving protein innovation.

Decoding the functional impact of the cancer genome through protein-protein interactions

Acquisition of genomic mutations enables cancer cells to gain fitness advantages under selective pressure and, ultimately, leads to oncogenic transformation. Interestingly, driver mutations, even within the same gene, can yield distinct phenotypes and clinical outcomes, necessitating a mutation-focused approach. Conversely, cellular functions are governed by molecular machines and signalling networks that are mostly controlled by protein-protein interactions (PPIs). The functional impact of individual genomic alterations could be transmitted through regulated nodes and hubs of PPIs. Oncogenic mutations may lead to modified residues of proteins, enabling interactions with other proteins that the wild-type protein does not typically interact with, or preventing interactions with proteins that the wild-type protein usually interacts with. This can result in the rewiring of molecular signalling cascades and the acquisition of an oncogenic phenotype. Here, we review the altered PPIs driven by oncogenic mutations, discuss technologies for monitoring PPIs and provide a functional analysis of mutation-directed PPIs. These driver mutation-enabled PPIs and mutation-perturbed PPIs present a new paradigm for the development of tumour-specific therapeutics. The intersection of cancer variants and altered PPI interfaces represents a new frontier for understanding oncogenic rewiring and developing tumour-selective therapeutic strategies.

Peptide design to control protein-protein interactions

Targeting of protein-protein interactions has become of huge interest in every aspect of medicinal and biological sciences. The control of protein interactions selectively offers the opportunity to control biological processes while limiting off target effects. This interest has massively increased with the development of cryo-EM and protein structure prediction with tools such as RosettaFold and AlphaFold. When designing molecules to control protein interactions, either inhibition or stabilisation, a starting point is commonly peptide design. This tutorial review describes that process, highlighting the selection of an initial sequence with and without structural information. Subsequently, methods for how the sequence can be analysed for key residues and how this information can be used to optimise the ligand efficiency are highlighted. Finally a discussion on how peptides can be further modified to increase their affinity and cell permeability, improving their drug-like properties, is presented.

Position-Regulated Electrostatic Interactions for Single Amino Acid Revealed by Aspartic Acid-Scanning Mutagenesis

We have examined in this contribution the electrostatic interactions between single arginine and aspartic acid by analyzing the peptide-peptide binding characteristics involving arginine-aspartic acid, arginine-glycine, arginine-tryptophan and tryptophan-glycine interactions. The results of aspartic acid mutagenesis revealed that the interactions between arginine and aspartic acid have significant dependence on the position and composition of amino acids. While the primary interaction can be attributed to arginine-tryptophan contacts originated from the indole moieties with the main chains of 14-mers containing N-H and C=O moieties, pronounced enhancement could be identified in association with the electrostatic side-chain-side-chain interactions between arginine and aspartic acid. An optimal separation of 2~4 amino acids between two adjacent aspartic acid and tryptophan binding sites can be identified to achieve maximal enhancement of binding interactions. Such observed separation dependence may be utilized to unravel cooperative effects in heterogeneous interactions between single pair of amino acids.

Restoring adapter protein complex 4 function with small molecules: an in silico approach to spastic paraplegia 50

This study focuses on spastic paraplegia type 50 (SPG50), an adapter protein complex 4 deficiency syndrome caused by mutations in the adapter protein complex 4 subunit mu-1 (AP4M1) gene, and on the downstream alterations of the AP4M1 protein. We applied a battery of heterogeneous computational resources, encompassing two in-house tools described here for the first time, to (a) assess the druggability potential of AP4M1, (b) characterize SPG50-associated mutations and their 3D scenario, (c) identify mutation-tailored drug candidates for SPG50, and (d) elucidate their mechanisms of action by means of structural considerations on homology models of the adapter protein complex 4 core. Altogether, the collected results indicate R367Q as the mutation with the most promising potential of being corrected by small-molecule drugs, and the flavonoid rutin as best candidate for this purpose. Rutin shows promise in rescuing the interaction between the AP4M1 and adapter protein complex subunit beta-1 (AP4B1) subunits by means of a glue-like mode of action. Overall, this approach offers a framework that could be systematically applied to the investigation of mutation-wise molecular mechanisms in different hereditary spastic paraplegias, too.

Molecular recognition of peptides and proteins by cucurbit[n]urils: systems and applications

The development of methodology for attaching ligand binding sites to proteins of interest has accelerated biomedical science. Such protein tags have widespread applications as well as properties that significantly limit their utility. This review describes the mechanisms and applications of supramolecular systems comprising the synthetic receptors cucurbit[7]uril (Q7) or cucurbit[8]uril (Q8) and their polypeptide ligands. Molecular recognition of peptides and proteins occurs at sites of 1-3 amino acids with high selectivity and affinity via several distinct mechanisms, which are supported by extensive thermodynamic and structural studies in aqueous media. The commercial availability, low cost, high stability, and biocompatibility of these synthetic receptors has led to the development of myriad applications. This comprehensive review compiles the molecular recognition studies and the resulting applications with the goals of providing a valuable resource to the community and inspiring the next generation of innovation.

Structure-Based Design of Bicyclic Helical Peptides That Target the Oncogene β-Catenin

The inhibition of intracellular protein-protein interactions is challenging, in particular, when involved interfaces lack pronounced cavities. The transcriptional co-activator protein and oncogene β-catenin is a prime example of such a challenging target. Despite extensive targeting efforts, available high-affinity binders comprise only large molecular weight inhibitors. This hampers the further development of therapeutically useful compounds. Herein, we report the design of a considerably smaller peptidomimetic scaffold derived from the α-helical β-catenin-binding motif of Axin. Sequence maturation and bicyclization provided a stitched peptide with an unprecedented crosslink architecture. The binding mode and site were confirmed by a crystal structure. Further derivatization yielded a β-catenin inhibitor with single-digit micromolar activity in a cell-based assay. This study sheds light on how to design helix mimetics with reduced molecular weight thereby improving their biological activity.

Tau Protein and Tauopathies: Exploring Tau Protein-Protein and Microtubule Interactions, Cross-Interactions and Therapeutic Strategies

Tau, a microtubule-associated protein (MAP), is essential to maintaining neuronal stability and function in the healthy brain. However, aberrant modifications and pathological aggregations of Tau are implicated in various neurodegenerative disorders, collectively known as tauopathies. The most common Tauopathy is Alzheimer's Disease (AD) counting nowadays more than 60 million patients worldwide. This comprehensive review delves into the multifaceted realm of Tau protein, puzzling out its intricate involvement in both physiological and pathological roles. Emphasis is put on Tau Protein-Protein Interactions (PPIs), depicting its interaction with tubulin, microtubules and its cross-interaction with other proteins such as Aβ1-42, α-synuclein, and the chaperone machinery. In the realm of therapeutic strategies, an overview of diverse possibilities is presented with their relative clinical progresses. The focus is mostly addressed to Tau protein aggregation inhibitors including recent small molecules, short peptides and peptidomimetics with specific focus on compounds that showed a double anti aggregative activity on both Tau protein and Aβ amyloid peptide. This review amalgamates current knowledge on Tau protein and evolving therapeutic strategies, providing a comprehensive resource for researchers seeking to deepen their understanding of the Tau protein and for scientists involved in the development of new peptide-based anti-aggregative Tau compounds.

Categorization of hotspots into three types - weak, moderate and strong to distinguish protein-protein versus protein-peptide interactions

Protein-protein and protein-peptide interactions (PPI and PPepI) belong to a similar category of interactions, yet seemingly subtle differences exist among them. To characterize differences between protein-protein (PP) and protein-peptide (PPep) interactions, we have focussed on two important classes of residues-hotspot and anchor residues. Using implicit solvation-based free energy calculations, a very large-scale alanine scanning has been performed on benchmark datasets, consisting of over 5700 interface residues. The differences in the two categories are more pronounced, if the data were divided into three distinct types, namely - weak hotspots (having binding free energy loss upon Ala mutation, ΔΔG, ∼2-10 kcal/mol), moderate hotspots (ΔΔG, ∼10-20 kcal/mol) and strong hotspots (ΔΔG ≥ ∼20 kcal/mol). The analysis suggests that for PPI, weak hotspots are predominantly populated by polar and hydrophobic residues. The distribution shifts towards charged and polar residues for moderate hotspot and charged residues (principally Arg) are overwhelmingly present in the strong hotspot. On the other hand, in the PPepI dataset, the distribution shifts from predominantly hydrophobic and polar (in the weak type) to almost similar preference for polar, hydrophobic and charged residues (in moderate type) and finally the charged residue (Arg) and Trp are mostly occupied in the strong type. The preferred anchor residues in both categories are Arg, Tyr and Leu, possessing bulky side chain and which also strike a delicate balance between side chain flexibility and rigidity. The present knowledge should aid in effective design of biologics, by augmentation or disruption of PPIs with peptides or peptidomimetics.Communicated by Ramaswamy H. Sarma.

Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?

Protein-protein interactions (PPIs) are central to the cellular signaling and regulatory networks that underlie many physiological and pathophysiological processes. It is challenging to target PPIs using traditional small molecule or peptide-based approaches due to the frequent lack of well-defined binding pockets at the large and flat PPI interfaces. Synthetic polymers offer an opportunity to circumvent these challenges by providing unparalleled flexibility in tuning their physiochemical properties to achieve the desired binding properties. In this review, we summarize the current state of the field pertaining to polymer-protein interactions in solution, highlighting various polyelectrolyte systems, their tunable parameters, and their characterization. We provide an outlook on how these architectures can be improved by incorporating sequence control, foldability, and machine learning to mimic proteins at every structural level. Advances in these directions will enable the design of more specific protein-binding polymers and provide an effective strategy for targeting dynamic proteins, such as intrinsically disordered proteins.

PPI-hotspotID for detecting protein-protein interaction hot spots from the free protein structure

Experimental detection of residues critical for protein-protein interactions (PPI) is a time-consuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspotID, a novel method for identifying PPI-hot spots using the free protein structure, and validated it on the largest collection of experimentally confirmed PPI-hot spots to date. We explored the possibility of detecting PPI-hot spots using (i) FTMap in the PPI mode, which identifies hot spots on protein-protein interfaces from the free protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer. PPI-hotspotID yielded better performance than FTMap and SPOTONE, a webserver for predicting PPI-hot spots given the protein sequence. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-hotspotID yielded better performance than either method alone. Furthermore, we experimentally verified several PPI-hotspotID-predicted PPI-hot spots of eukaryotic elongation factor 2. Notably, PPI-hotspotID can reveal PPI-hot spots not obvious from complex structures, including those in indirect contact with binding partners. PPI-hotspotID serves as a valuable tool for understanding PPI mechanisms and aiding drug design. It is available as a web server (https://ppihotspotid.limlab.dnsalias.org/) and open-source code (https://github.com/wrigjz/ppihotspotid/).

A Fragment-Based Competitive 19F LB-NMR Platform For Hotspot-Directed Ligand Profiling

Ligand binding hotspots are regions of protein surfaces that form particularly favourable interactions with small molecule pharmacophores. Targeting interactions with these hotspots maximises the efficiency of ligand binding. Existing methods are capable of identifying hotspots but often lack assays to quantify ligand binding and direct elaboration at these sites. Herein, we describe a fragment-based competitive 19F Ligand Based NMR (LB-NMR) screening platform that enables routine, quantitative ligand profiling focused at ligand-binding hotspots. As a proof of concept, the method was applied to 4'-phosphopantetheine adenylyltransferase (PPAT) from Mycobacterium abscessus (Mabs). X-ray crystallographic characterisation of the hits from a 960-member fragment screen identified three ligand-binding hotspots across the PPAT active site. From the fragment hits a collection of 19F reporter candidates were designed and synthesised. By rigorous prioritisation and use of optimisation workflows, a single 19F reporter molecule was generated for each hotspot. Profiling the binding of a set of structurally characterised ligands by competitive 19F LB-NMR with this suite of 19F reporters recapitulated the binding affinity and site ID assignments made by ITC and X-ray crystallography. This quantitative mapping of ligand binding events at hotspot level resolution establishes the utility of the fragment-based competitive 19F LB-NMR screening platform for hotspot-directed ligand profiling.

TPPU_DSF: A Web Application to Calculate Thermodynamic Parameters Using DSF Data

Here we present TPPU_DSF (https://maciasnmr.net/tppu_dsf/). This is a free and open-source web application that opens, converts, fits, and calculates the thermodynamic parameters of protein unfolding from standard differential scanning fluorimetry (DSF) data in an automated manner. The software has several applications. In the context of screening compound libraries for protein binders, obtaining thermodynamic parameters provides a more robust approach to detecting hits than the changes in the melting temperature (Tm) alone, thereby helping to increase the number of positive hits in screening campaigns. Moreover, changes in ΔGuo indicate protein response to binding at lower compound concentrations than those in the Tm, thereby reducing the costs associated with the amounts of protein and compounds required for the assays. Also, by adding thermodynamic information to the Tm comparison, the software can contribute to the optimization of protein constructs and buffer conditions, a common practice before structural and functional projects.

Dual-Mechanism Peptide SR25 has Broad Antimicrobial Activity and Potential Application for Healing Bacteria-infected Diabetic Wounds

The rise of antibiotic resistance poses a significant public health crisis, particularly due to limited antimicrobial options for the treatment of infections with Gram-negative pathogens. Here, an antimicrobial peptide (AMP) SR25 is characterized, which effectively kills both Gram-negative and Gram-positive bacteria through a unique dual-targeting mechanism without detectable resistance. Meanwhile, an SR25-functionalized hydrogel is developed for the efficient treatment of infected diabetic wounds. SR25 is obtained through genome mining from an uncultured bovine enteric actinomycete named Nonomuraea Jilinensis sp. nov. Investigations reveal that SR25 has two independent cellular targets, disrupting bacterial membrane integrity and restraining the activity of succinate:quinone oxidoreductase (SQR). In a diabetic mice wound infection model, the SR25-incorporated hydrogel exhibits high efficacy against mixed infections of Escherichia coli (E. coli) and methicillin-resistant Staphylococcus aureus (MRSA), accelerating wound healing. Overall, these findings demonstrate the therapeutic potential of SR25 and highlight the value of mining drugs with multiple mechanisms from uncultured animal commensals for combating challenging bacterial pathogens.

AI-based IsAb2.0 for antibody design

Therapeutic antibody design has garnered widespread attention, highlighting its interdisciplinary importance. Advancements in technology emphasize the critical role of designing nanobodies and humanized antibodies in antibody engineering. However, current experimental methods are costly and time-consuming. Computational approaches, while progressing, faced limitations due to insufficient structural data and the absence of a standardized protocol. To tackle these challenges, our lab previously developed IsAb1.0, an in silico antibody design protocol. Yet, IsAb1.0 lacked accuracy, had a complex procedure, and required extensive antibody bioinformation. Moreover, it overlooked nanobody and humanized antibody design, hindering therapeutic antibody development. Building upon IsAb1.0, we enhanced our design protocol with artificial intelligence methods to create IsAb2.0. IsAb2.0 utilized AlphaFold-Multimer (2.3/3.0) for accurate modeling and complex construction without templates and employed the precise FlexddG method for in silico antibody optimization. Validated through optimization of a humanized nanobody J3 (HuJ3) targeting HIV-1 gp120, IsAb2.0 predicted five mutations that can improve HuJ3-gp120 binding affinity. These predictions were confirmed by commercial software and validated through binding and neutralization assays. IsAb2.0 streamlined antibody design, offering insights into future techniques to accelerate immunotherapy development.

Rational design of small-sized peptidomimetic inhibitors disrupting protein-protein interaction

Protein-protein interactions are fundamental to nearly all biological processes. Due to their structural flexibility, peptides have emerged as promising candidates for developing inhibitors targeting large and planar PPI interfaces. However, their limited drug-like properties pose challenges. Hence, rational modifications based on peptide structures are anticipated to expedite the innovation of peptide-based therapeutics. This review comprehensively examines the design strategies for developing small-sized peptidomimetic inhibitors targeting PPI interfaces, which predominantly encompass two primary categories: peptidomimetics with abbreviated sequences and low molecular weights and peptidomimetics mimicking secondary structural conformations. We have also meticulously detailed several instances of designing and optimizing small-sized peptidomimetics targeting PPIs, including MLL1-WDR5, PD-1/PD-L1, and Bak/Bcl-xL, among others, to elucidate the potential application prospects of these design strategies. Hopefully, this review will provide valuable insights and inspiration for the future development of PPI small-sized peptidomimetic inhibitors in pharmaceutical research endeavors.

14-3-3 Protein-Protein Interactions: From Mechanistic Understanding to Their Small-Molecule Stabilization

Protein-protein interactions (PPIs) are of utmost importance for maintenance of cellular homeostasis. Herein, a central role can be found for 14-3-3 proteins. These hub-proteins are known to bind hundreds of interaction partners, thereby regulating their activity, localization, and/or stabilization. Due to their ability to bind a large variety of client proteins, studies of 14-3-3 protein complexes flourished over the last decades, aiming to gain greater molecular understanding of these complexes and their role in health and disease. Because of their crucial role within the cell, 14-3-3 protein complexes are recognized as highly interesting therapeutic targets, encouraging the discovery of small molecule modulators of these PPIs. We discuss various examples of 14-3-3-mediated regulation of its binding partners on a mechanistic level, highlighting the versatile and multi-functional role of 14-3-3 within the cell. Furthermore, an overview is given on the development of stabilizers of 14-3-3 protein complexes, from initially used natural products to fragment-based approaches. These studies show the potential of 14-3-3 PPI stabilizers as novel agents in drug discovery and as tool compounds to gain greater molecular understanding of the role of 14-3-3-based protein regulation.

Unraveling the folding-assisted unbinding mechanism of TCF with its binding partner β-catenin

This study utilizes molecular dynamics simulations aided with multiple walker parallel bias metadynamics to investigate the TCF unbinding mechanism from the β-catenin interface. The results, consistent with experimental binding affinity calculations, unveil a folding-assisted unbinding mechanism.

Molecular glues and bifunctional compounds: Therapeutic modalities based on induced proximity

This Perspective explores molecular glues and bifunctional compounds-proximity-inducing compounds-and offers a framework to understand and exploit their similarity to hotspots, missense mutations, and posttranslational modifications (PTMs). This view is also shown to be relevant to intramolecular glues, where compounds induce contacts between distinct domains of the same protein. A historical perspective of these compounds is presented that shows the field has come full circle from molecular glues targeting native proteins, to bifunctionals targeting fusion proteins, and back to molecular glues and bifunctionals targeting native proteins. Modern screening methods and data analyses with pre-selected target proteins are shown to yield either cooperative molecular glues or bifunctional compounds that induce proximity, thereby enabling novel functional outcomes.

The Rapid Evolution of De Novo Proteins in Structure and Complex

Recent studies in the rice genome-wide have established that de novo genes, evolving from noncoding sequences, enhance protein diversity through a stepwise process. However, the pattern and rate of their evolution in protein structure over time remain unclear. Here, we addressed these issues within a surprisingly short evolutionary timescale (<1 million years for 97% of Oryza de novo genes) with comparative approaches to gene duplicates. We found that de novo genes evolve faster than gene duplicates in the intrinsically disordered regions (such as random coils), secondary structure elements (such as α helix and β strand), hydrophobicity, and molecular recognition features. In de novo proteins, specifically, we observed an 8% to 14% decay in random coils and intrinsically disordered region lengths and a 2.3% to 6.5% increase in structured elements, hydrophobicity, and molecular recognition features, per million years on average. These patterns of structural evolution align with changes in amino acid composition over time as well. We also revealed higher positive charges but smaller molecular weights for de novo proteins than duplicates. Tertiary structure predictions showed that most de novo proteins, though not typically well folded on their own, readily form low-energy and compact complexes with other proteins facilitated by extensive residue contacts and conformational flexibility, suggesting a faster-binding scenario in de novo proteins to promote interaction. These analyses illuminate a rapid evolution of protein structure in de novo genes in rice genomes, originating from noncoding sequences, highlighting their quick transformation into active, protein complex-forming components within a remarkably short evolutionary timeframe.

Assessing the functional impact of protein binding site definition

Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data, this approach also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also shows that methods for protein binding interface predictions should perform above approximately F-score = 0.7 accuracy level to capture the biological function of a protein.

Molecular basis for antibody recognition of multiple drug-peptide/MHC complexes

The HapImmuneTM platform exploits covalent inhibitors as haptens for creating major histocompatibility complex (MHC)-presented tumor-specific neoantigens by design, combining targeted therapies with immunotherapy for the treatment of drug-resistant cancers. A HapImmune antibody, R023, recognizes multiple sotorasib-conjugated KRAS(G12C) peptides presented by different human leukocyte antigens (HLAs). This high specificity to sotorasib, coupled with broad HLA-binding capability, enables such antibodies, when reformatted as T cell engagers, to potently and selectively kill sotorasib-resistant KRAS(G12C) cancer cells expressing different HLAs upon sotorasib treatment. The loosening of HLA restriction could increase the patient population that can benefit from this therapeutic approach. To understand the molecular basis for its unconventional binding capability, we used single-particle cryogenic electron microscopy to determine the structures of R023 bound to multiple sotorasib-peptide conjugates presented by different HLAs. R023 forms a pocket for sotorasib between the VH and VL domains, binds HLAs in an unconventional, angled way, with VL making most contacts with them, and makes few contacts with the peptide moieties. This binding mode enables the antibody to accommodate different hapten-peptide conjugates and to adjust its conformation to different HLAs presenting hapten-peptides. Deep mutational scanning validated the structures and revealed distinct levels of mutation tolerance by sotorasib- and HLA-binding residues. Together, our structural information and sequence landscape analysis reveal key features for achieving MHC-restricted recognition of multiple hapten-peptide antigens, which will inform the development of next-generation therapeutic antibodies.

Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning

Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed. First, we extracted features of these hot spots' solid solvent-accessible surface area (ASA) and secondary structure, and then the mean, variance, energy and autocorrelation function values of the first three intrinsic modal components (IMFs) of these conventional features were extracted as new features via the empirical modal decomposition algorithm (EMD). A total of 218 dimensional features were obtained. For feature selection, we used the maximum correlation minimum redundancy sequence forward selection method (mRMR-SFS) to obtain an optimal 11-dimensional-feature subset. To address the issue of data imbalance, we used the SMOTE-Tomek algorithm to balance positive and negative samples and finally used cat gradient boosting (CatBoost) to construct our hot spot prediction model for protein-DNA binding interfaces. Our method performs well on the test set, with AUC, MCC and F1 score values of 0.847, 0.543 and 0.772, respectively. After a comparative evaluation, EC-PDH outperforms the existing state-of-the-art methods in identifying hot spots.

Indels allow antiviral proteins to evolve functional novelty inaccessible by missense mutations

Antiviral proteins often evolve rapidly at virus-binding interfaces to defend against new viruses. We investigated whether antiviral adaptation via missense mutations might face limits, which insertion or deletion mutations (indels) could overcome. We report one such case of a nearly insurmountable evolutionary challenge: the human anti-retroviral protein TRIM5α requires more than five missense mutations in its specificity-determining v1 loop to restrict a divergent simian immunodeficiency virus (SIV). However, duplicating just one amino acid in v1 enables human TRIM5α to potently restrict SIV in a single evolutionary step. Moreover, natural primate TRIM5α v1 loops have evolved indels that confer novel antiviral specificities. Thus, indels enable antiviral proteins to overcome viral challenges inaccessible by missense mutations, revealing the potential of these often-overlooked mutations in driving protein innovation.

Cold Spot SCANNER: Colab Notebook for predicting cold spots in protein-protein interfaces

BACKGROUND

Protein-protein interactions (PPIs) are conveyed through binding interfaces or surface patches on proteins that become buried upon binding. Structural and biophysical analysis of many protein-protein interfaces revealed certain unique features of these surfaces that determine the energetics of interactions and play a critical role in protein evolution. One of the significant aspects of binding interfaces is the presence of binding hot spots, where mutations are highly deleterious for binding. Conversely, binding cold spots are positions occupied by suboptimal amino acids and several mutations in such positions could lead to affinity enhancement. While there are many software programs for identification of hot spot positions, there is currently a lack of software for cold spot detection.

RESULTS

In this paper, we present Cold Spot SCANNER, a Colab Notebook, which scans a PPI binding interface and identifies cold spots resulting from cavities, unfavorable charge-charge, and unfavorable charge-hydrophobic interactions. The software offers a Py3DMOL-based interface that allows users to visualize cold spots in the context of the protein structure and generates a zip file containing the results for easy download.

CONCLUSIONS

Cold spot identification is of great importance to protein engineering studies and provides a useful insight into protein evolution. Cold Spot SCANNER is open to all users without login requirements and can be accessible at: https://colab.

RESEARCH

google.com/github/sagagugit/Cold-Spot-Scanner/blob/main/Cold_Spot_Scanner.ipynb .

SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein-Protein Interfaces

Protein-protein interactions mediate most molecular processes in the cell, offering a significant opportunity to expand the set of known druggable targets. Unfortunately, targeting these interactions can be challenging due to their typically flat and featureless interaction surfaces, which often change as the complex forms. Such surface changes may reveal hidden (cryptic) druggable pockets. Here, we analyze a set of well-characterized protein-protein interactions harboring cryptic pockets and investigate the predictive power of current computational methods. Based on our observations, we developed a new computational strategy, SWISH-X (SWISH Expanded), which combines the established cryptic pocket identification capabilities of SWISH with the rapid temperature range exploration of OPES MultiThermal. SWISH-X is able to reliably identify cryptic pockets at protein-protein interfaces while retaining its predictive power for revealing cryptic pockets in isolated proteins, such as TEM-1 β-lactamase.

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

Prediction of virus-host interactions and identification of hot spot residues of DENV-2 and SH3 domain interactions

Dengue virus, particularly serotype 2 (DENV-2), poses a significant global health threat, and understanding the molecular basis of its interactions with host cell proteins is imperative for developing targeted therapeutic strategies. This study elucidated the interactions between proline-enriched motifs and Src homology 3 (SH3) domain. The SH3 domain is pivotal in mediating protein-protein interactions, particularly by recognizing and binding to proline-rich regions in partner proteins. Through a computational pipeline, we analyzed the interactions and binding modes of proline-enriched motifs with SH3 domains, identified new potential DENV-2 interactions with the SH3 domain, and revealed potential hot spot residues, underscoring their significance in the viral life cycle. This comprehensive analysis provides crucial insights into the molecular basis of DENV-2 infection, highlighting conserved and serotype-specific interactions. The identified hot spot residues offer potential targets for therapeutic intervention, laying the foundation for developing antiviral strategies against Dengue virus infection. These findings contribute to the broader understanding of viral-host interactions and provide a roadmap for future research on Dengue virus pathogenesis and treatment.

An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1

The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.

Inhibition of protein-protein interactions using biodegradable depsipeptide nanoassemblies

Although peptides notoriously have poor intrinsic pharmacokinetic properties, it is well-known that nanostructures with excellent pharmacokinetic properties can be designed. Noticing that peptide inhibitors are generally nonpolar, here, we consolidate the peptide inhibitor targeting intracellular protein-protein interactions (PPIs) as an integral part of biodegradable self-assembled depsipeptide nanostructures (SdPNs). Because the peptide inhibitor has the dual role of PPI inhibition and self-assembly in this design, problems associated with the poor pharmacokinetics of peptides and encapsulation/entrapment processes can be overcome. Optimized SdPNs displayed better tumor targeting and PPI inhibition properties than the comparable small molecule inhibitor in vivo. Kinetics of PPI inhibition for SdPNs were gradual and controllable in contrast to the rapid inhibition kinetics of the small molecule. Because SdPN is modular, any appropriate peptide inhibitor can be incorporated into the platform without concern for the poor pharmacokinetic properties of the peptide.

BioCARS: Synchrotron facility for probing structural dynamics of biological macromolecules

A major goal in biomedical science is to move beyond static images of proteins and other biological macromolecules to the internal dynamics underlying their function. This level of study is necessary to understand how these molecules work and to engineer new functions and modulators of function. Stemming from a visionary commitment to this problem by Keith Moffat decades ago, a community of structural biologists has now enabled a set of x-ray scattering technologies for observing intramolecular dynamics in biological macromolecules at atomic resolution and over the broad range of timescales over which motions are functionally relevant. Many of these techniques are provided by BioCARS, a cutting-edge synchrotron radiation facility built under Moffat leadership and located at the Advanced Photon Source at Argonne National Laboratory. BioCARS enables experimental studies of molecular dynamics with time resolutions spanning from 100 ps to seconds and provides both time-resolved x-ray crystallography and small- and wide-angle x-ray scattering. Structural changes can be initiated by several methods-UV/Vis pumping with tunable picosecond and nanosecond laser pulses, substrate diffusion, and global perturbations, such as electric field and temperature jumps. Studies of dynamics typically involve subtle perturbations to molecular structures, requiring specialized computational techniques for data processing and interpretation. In this review, we present the challenges in experimental macromolecular dynamics and describe the current state of experimental capabilities at this facility. As Moffat imagined years ago, BioCARS is now positioned to catalyze the scientific community to make fundamental advances in understanding proteins and other complex biological macromolecules.

Progression of albuminuria and podocyte injury in focal segmental glomerulosclerosis inhibited by enhanced glycosphingolipid GM3 via valproic acid

Focal segmental glomerulosclerosis, characterized by decreased numbers of podocytes in glomeruli, is a common cause of refractory nephrotic syndrome. Recently, we showed that enhanced glycosphingolipid GM3 expression after administration of valproic acid, an upregulator of ST3GAL5/St3gal5, was effective in preventing albuminuria and podocyte injury. We also revealed the molecular mechanism for this preventive effect, which involves GM3 directly binding nephrin that then act together in glycolipid-enriched membrane (GEM) fractions under normal conditions and in non-GEM fractions under nephrin injury conditions. Kidney disease is frequently referred to as a "silent killer" because it is often difficult to detect subjective symptoms. Thus, primary treatment for these diseases is initiated after the onset of disease progression. Consequently, the efficacy of enhanced levels of GM3 induced by valproic acid needs to be evaluated after the onset of the disease with severe albuminuria such as focal segmental glomerulosclerosis. Here, we report the therapeutic effect of enhanced GM3 expression induced via administration of valproic acid on albuminuria and podocyte injury after the onset focal segmental glomerulosclerosis in anti-nephrin antibody treated mice. Our findings suggest elevated levels of GM3 following treatment with valproic acid has therapeutic utility for kidney disease associated with severe albuminuria and podocyte injury.

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions

Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.

Protein-DNA interface hotspots prediction based on fusion features of embeddings of protein language model and handcrafted features

The identification of hotspot residues at the protein-DNA binding interfaces plays a crucial role in various aspects such as drug discovery and disease treatment. Although experimental methods such as alanine scanning mutagenesis have been developed to determine the hotspot residues on protein-DNA interfaces, they are both inefficient and costly. Therefore, it is highly necessary to develop efficient and accurate computational methods for predicting hotspot residues. Several computational methods have been developed, however, they are mainly based on hand-crafted features which may not be able to represent all the information of proteins. In this regard, we propose a model called PDH-EH, which utilizes fused features of embeddings extracted from a protein language model (PLM) and handcrafted features. After we extracted the total 1141 dimensional features, we used mRMR to select the optimal feature subset. Based on the optimal feature subset, several different learning algorithms such as Random Forest, Support Vector Machine, and XGBoost were used to build the models. The cross-validation results on the training dataset show that the model built by using Random Forest achieves the highest AUROC. Further evaluation on the independent test set shows that our model outperforms the existing state-of-the-art models. Moreover, the effectiveness and interpretability of embeddings extracted from PLM were demonstrated in our analysis. The codes and datasets used in this study are available at: https://github.com/lixiangli01/PDH-EH.

FASTDock: A Pipeline for Allosteric Drug Discovery

Allostery is involved in innumerable biological processes and plays a fundamental role in human disease. Thus, the exploration of allosteric modulation is crucial for research on biological mechanisms and in the development of novel therapeutics. The development of small-molecule allosteric effectors can be used as tools to probe biological mechanisms of interest. One of the main limitations in targeting allosteric sites is the difficulty in uncovering them for specific receptors. Furthermore, upon discovery of novel allosteric modulation, early lead generation is made more difficult as compared to that at orthosteric sites because there is likely no information about the types of molecules that can bind at the site. In the work described here, we present a novel drug discovery pipeline, FASTDock, which allows one to uncover ligandable sites as well as small molecules that target the given site without requiring pre-existing knowledge of ligands that can bind in the targeted site. By using a hierarchical screening strategy, this method has the potential to enable high-throughput screens of an exceptionally large database of targeted ligand space.

In silico molecular and functional characterization of a dual function antimicrobial peptide, hepcidin (GIFT-Hep), isolated from genetically improved farmed tilapia (GIFT, Oreochromis niloticus)

BACKGROUND

Antimicrobial peptides (AMPs), innate immune response molecules in organisms, are also known for their dual functionality, exemplified by hepcidin-an immunomodulator and iron regulator. Identifying and studying various AMPs from fish species can provide valuable insights into the immune profiles of aquaculturally significant fish, which can be made use of in its culture.

RESULTS

Hepcidin, a dual-function antimicrobial peptide, was isolated from the gill tissue of Genetically Improved Farmed Tilapia (GIFT-Hep). GIFT-Hep consists of a 90 amino acid pre-propeptide with a 24-mer signal, a 40-mer propeptide, and a 26-mer mature peptide region. The mature peptide had a molecular weight of 3015.61 Da, a theoretical pI of 8.78, a net charge of +4.25, and a protein-binding potential of 2.06 kcal/mol. Four disulfide bonds were formed by eight cysteine residues in the mature region. The presence of positively charged arginine residues renders the peptide 50% hydrophobic. Molecular analysis of GIFT-Hep revealed the presence of a furin propeptide convertase motif, RX(K/R)R, which facilitates trimming of the peptide to yield the mature GIFT-Hep. The hypothetical iron regulatory sequence, QSHLSL, was also identified in the mature peptide. In silico predictions about the characteristics of GIFT-Hep, such as charge, hydrophobicity, high surface accessibility, transmembrane helical regions, hydrophobic faces, hot spots, and cell-penetrating properties, suggest that the peptide functions as an iron regulatory antimicrobial agent.

CONCLUSIONS

This study reports a hepcidin antimicrobial peptide with both HAMP1 and HAMP2 properties isolated from genetically improved farmed tilapia, and further evaluation of the properties will prove the feasibility of GIFT-Hep being used as a therapeutant in aquaculture.

Crystal Packing Reveals a Potential Autoinhibited KRAS Dimer Interface and a Strategy for Small-Molecule Inhibition of RAS Signaling

KRAS GTPases harbor oncogenic mutations in more than 25% of human tumors. KRAS is considered to be largely undruggable due to the lack of a suitable small-molecule binding site. Here, we report a unique crystal structure of His-tagged KRASG12D that reveals a remarkable conformational change. The Switch I loop of one His-KRASG12D structure extends into the Switch I/II pocket of another His-KRASG12D in an adjacent unit cell to create an elaborate interface that is reminiscent of high-affinity protein-protein complexes. We explore the contributions of amino acids at this interface using alanine-scanning studies with alchemical free energy perturbation calculations based on explicit-solvent molecular dynamics simulations. Several interface amino acids were found to be hot spots as they contributed more than 1.5 kcal/mol to the protein-protein interaction. Computational analysis of the complex revealed the presence of two large binding pockets that possess physicochemical features typically found in pockets considered druggable. Small-molecule binding to these pockets may stabilize this autoinhibited structure of KRAS if it exists in cells to provide a new strategy to inhibit RAS signaling.

Modeling and monitoring the effects of three partly conserved Ile residues in the dimerization domain of a Mip-like virulence factor from Escherichia coli

FKBP22, an Escherichia coli-made peptidyl-prolyl cis-trans isomerase, has shown considerable homology with Mip-like virulence factors. While the C-terminal domain of this enzyme is used for executing catalytic function and binding inhibitor, the N-terminal domain is employed for its dimerization. To precisely determine the underlying factors of FKBP22 dimerization, its structural model, developed using a suitable template, was carefully inspected. The data show that the dimeric FKBP22, like dimeric Mip proteins, has a V-like shape. Further, it dimerizes using 40 amino acid residues including Ile 9, Ile 17, Ile 42, and Ile 65. All of the above Ile residues except Ile 9 are partly conserved in the Mip-like proteins. To confirm the roles of the partly conserved Ile residues, three FKBP22 mutants, constructed by substituting them with an Ala residue, were studied as well. The results together indicate that Ile 65 has little role in maintaining the dimeric state or enzymatic activity of FKBP22. Conversely, both Ile 17 and Ile 42 are essential for preserving the structure, enzymatic activity, and dimerization ability of FKBP22. Ile 42 in particular looks more essential to FKBP22. However, none of these two Ile residues is required for binding the cognate inhibitor. Additional computational studies also indicated the change of V-shape and the dimeric state of FKBP22 due to the Ala substitution at position 42. The ways Ile 17 and Ile 42 protect the structure, function, and dimerization of FKBP22 have been discussed at length.Communicated by Ramaswamy H. Sarma.

FPGA Implementation of IIR Notch and Anti-Notch Filters With an Application to Localization of Protein Hot-Spots

In this paper, high-speed second-order infinite impulse response (IIR) notch filter (NF) and anti-notch filter (ANF) are designed and realized on hardware. The improvement in speed of operation for the NF is then achieved by using the re-timing concept. The ANF is designed to specify a stability margin and minimize the amplitude area. Next, an improved approach is proposed for the detection of protein hot-spot locations using the designed second-order IIR ANF. The analytical and experimental results reported in this paper show that the proposed approach provides better hot-spot prediction compared to the reported classical filtering techniques based on the IIR Chebyshev filter and S-transform. The proposed approach also yields consistency in prediction hot-spots compared to the results based on biological methodologies. Furthermore, the presented technique reveals some new "potential" hot-spots. The proposed filters are simulated and synthesized using the Xilinx Vivado 18.3 software platform with Zynq-7000 Series (ZedBoard Zynq Evaluation and Development Kit xc7z020clg484-1) FPGA family.

Prediction of hot spots towards drug discovery by protein sequence embedding with 1D convolutional neural network

Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mostly focused on using machine learning methods to predict hot spots from known interface residues, which artificially extract the corresponding features of amino acid residues from sequence, structure, evolution, energy, and other information to train and test machine learning models. The process is cumbersome, time-consuming and laborious to some extent. This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protein hotspot residues. In order to obtain large data samples, this work integrates and extracts data from the datasets of ASEdb, BID, SKEMPI and dbMPIKT to generate a new dataset, and adopts the SMOTE algorithm to expand positive samples to form the training set. The experimental results show that the method achieves an F1 score of 0.82 on the test set. Compared with other hot spot prediction methods, our model achieved better prediction performance.

Epitopes, paratopes, and other topes 30 years on: Understanding what we are talking about

The question of which protein antigens, such as HLA class I or class II molecules, will bind, and how well, to a given antibody is often assumed to depend exclusively on the details of protein surface structure. These structures are usually based on static models resulting from X-ray crystallography. While these notions are useful, the ultimate causal factors determining how well a given antigen binds a given antibody are based in thermodynamics and can include atomic mobility and the time-varying conformations of proteins. In this article, fundamental biophysical principles of antibody-antigen interaction are discussed, concepts critical for a deeper understanding of the pertinent molecular phenomena are highlighted, and common misunderstandings are identified and debunked.

An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins

The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between holo-RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7-nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in-silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7-nsp12 complexation, with one of the lead peptides giving an IC50 of 25μM . Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof-of-concept of an approach for rational discovery of peptide inhibitors of SARS-CoV-2 protein-protein interactions.

Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library Screening

Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries' display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.