Predicting Protein-Peptide Complex Structures by Accounting for Peptide Flexibility and the Physicochemical Environment

Anti-obesity and anti-diabetic bioactive peptides: A comprehensive review of their sources, properties, and techno-functional challenges

The scourge of obesity arising from obesogens and poor dieting still ravages our planet as half of the global population may be overweight and obese by 2035. This metabolic disorder is intertwined with type 2 diabetes (T2D), both of which warrant alternative therapeutic options other than clinically approved drugs like orlistat with their tendency of abuse and side effects. In this review, we comprehensively describe the global obesity problem and its connection to T2D. Obesity, overconsumption of fats, the mechanism of fat digestion, obesogenic gut microbiota, inhibition of fat digestion, and natural anti-obesity compounds are discussed. Similar discussions are made for diabetes with regard to glucose regulation, the diabetic gut microbiota, and insulinotropic compounds. The sources and production of anti-obesity bioactive peptides (AOBPs) and anti-diabetic bioactive peptides (ADBPs) are also described while explaining their structure-function relationships, gastrointestinal behaviors, and action mechanisms. Finally, the techno-functional applications of AOBPs and ADBPs are highlighted.

Cell-specific polymerization-driven biomolecular condensate formation fine-tunes root tissue morphogenesis

Formation of biomolecular condensates can be driven by weak multivalent interactions and emergent polymerization. However, the mechanism of polymerization-mediated condensate formation is less studied. We found lateral root cap cell (LRC)-specific SUPPRESSOR OF RPS4-RLD1 (SRFR1) condensates fine-tune primary root development. Polymerization of the SRFR1 N-terminal domain is required for both LRC condensate formation and optimal root growth. Surprisingly, the first intrinsically disordered region (IDR1) of SRFR1 can be functionally substituted by a specific group of intrinsically disordered proteins known as dehydrins. This finding facilitated the identification of functional segments in the IDR1 of SRFR1, a generalizable strategy to decode unknown IDRs. With this functional information we further improved root growth by modifying the SRFR1 condensation module, providing a strategy to improve plant growth and resilience.

Quantified instant conjugation of peptides on a nanogold surface for tunable ice recrystallization inhibition

The adverse effects of recrystallization limit the application of cryopreservation in many fields. Peptide-based materials play an essential role in the antifreezing area because of their excellent biocompatibility and abundant ice-binding sites. Peptide-gold nanoparticle conjugates can effectively reduce time and material costs through metal-thiol interactions, but controlled modification remains an outstanding issue, which makes it difficult to elucidate the antifreezing effects of antifreeze peptides at different densities and lengths. In this study, we developed an instant peptide capping on gold nanoparticles with butanol-assisted dehydration and provided a controllable quantitative coupling within a certain range. This chemical dehydration makes it possible to fabricate peptide-gold nanoparticle conjugates in large batches at minute levels. Based on this, the influence of the peptide density and sequence length on the antifreezing behaviors of the conjugates was investigated. The results evidenced that the antifreezing property of the flexible peptide conjugated on a rigid core is related to both the density and length of the peptide. In a certain range, the density is proportional to the antifreeze, while the length is negatively correlated with it. We proposed a rapidly controllable method for synthesizing peptide-gold nanoparticle conjugates, which may provide a universal approach for the development of subsequent recrystallization-inhibiting materials.

Improved Peptide Docking with Privileged Knowledge Distillation using Deep Learning

Protein-peptide interactions play a key role in biological processes. Understanding the interactions that occur within a receptor-peptide complex can help in discovering and altering their biological functions. Various computational methods for modeling the structures of receptor-peptide complexes have been developed. Recently, accurate structure prediction enabled by deep learning methods has significantly advanced the field of structural biology. AlphaFold (AF) is among the top-performing structure prediction methods and has highly accurate structure modeling performance on single-chain targets. Shortly after the release of AlphaFold, AlphaFold-Multimer (AFM) was developed in a similar fashion as AF for prediction of protein complex structures. AFM has achieved competitive performance in modeling protein-peptide interactions compared to previous computational methods; however, still further improvement is needed. Here, we present DistPepFold, which improves protein-peptide complex docking using an AFM-based architecture through a privileged knowledge distillation approach. DistPepFold leverages a teacher model that uses native interaction information during training and transfers its knowledge to a student model through a teacher-student distillation process. We evaluated DistPepFold's docking performance on two protein-peptide complex datasets and showed that DistPepFold outperforms AFM. Furthermore, we demonstrate that the student model was able to learn from the teacher model to make structural improvements based on AFM predictions.

Insights into Chemical Changes Causing Transient Potential Patterns during Cobalt Electrodeposition: An Operando SHINERS Investigation

Transient potential oscillations in a self-organized system involve a sequence of mass-transfer-limited chemical reactions. Often, these oscillations determine the microstructure of the electrodeposited metallic films. In this study, two distinct potential oscillations have been observed during galvanostatic deposition of cobalt in the presence of butynediol. Understanding the underlying chemical reactions in these potential oscillations is essential for designing efficient electrodeposition systems. Operando shell-isolated nanoparticle-enhanced Raman spectroscopy is deployed to record these chemical changes, and we present direct spectroscopic evidence of adsorbed hydrogen scavenging by butynediol, Co(OH)₂ formation, and removal limited by mass transfer of butynediol and protons. The potential oscillatory patterns have four distinguishable segments associated with mass-transfer limitation of either proton or butynediol. These observations improve our understanding of the oscillatory behavior in metal electrodeposition.

Effect Produced by a Cyclooctyne Derivative on Both Infarct Area and Left Ventricular Pressure via Calcium Channel Activation

BACKGROUND

There are reports which indicate that some cyclooctyne derivatives may exert changes in cardiovascular system; however, its molecular mechanism is not very clear.

OBJECTIVE

The aim of this study was to evaluate the biological activity of four cyclooctyne derivatives (compounds 1: to 4: ) produced on infarct area and left ventricular pressure.

METHODS

Biological activity produced by cyclooctyne derivatives on infarct area was determinate using an ischemia/reperfusion injury model. In addition, to characterize the molecular mechanism of this effect, the following strategies were carried out as follows; i) biological activity produced by cyclooctyne derivative (compound 4: ) on either perfusion pressure or left ventricular pressure was evaluated using an isolated rat heart; ii) theoretical interaction of cyclooctyne derivative with calcium channel (1t0j protein surface) using a docking model.

RESULTS

The results showed that cyclooctyne derivative (compound 4: ) decrease infarct area of in a dose-dependent manner compared with compound 1: to 3: . Besides, this cyclooctyne derivative increase both perfusion pressure and left ventricular pressure which was inhibited by nifedipine. Other theoretical data suggests that cyclooctyne derivative could interact with some aminoacid residues (Met₈₃, Ile₈₅, Ser₈₆, Leu₁₀₈, Glu₁₁₄) involved in 1t0j protein surface.

CONCLUSIONS

All these data indicate that cyclooctyne derivative increase left ventricular pressure via calcium channel activation and this phenomenon could be translated as a decrease of infarct area.

Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction

While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how the revolution can be transferred to advances in structure-based drug discovery. Because the lessons from the recent GPCR dock challenge were inconclusive primarily due to the size of the dataset, in this work we further elaborated on 70 diverse GPCR complexes bound to either small molecules or peptides to investigate the best-practice modeling and docking strategies for GPCR drug discovery. From our quantitative analysis, it is shown that substantial improvements in docking and virtual screening have been possible by the advance in DL-based protein structure predictions with respect to the expected results from the combination of best pre-DL tools. The success rate of docking on DL-based model structures approaches that of cross-docking on experimental structures, showing over 30% improvement from the best pre-DL protocols. This amount of performance could be achieved only when two modeling points were considered properly: 1) correct functional-state modeling of receptors and 2) receptor-flexible docking. Best-practice modeling strategies and the model confidence estimation metric suggested in this work may serve as a guideline for future computer-aided GPCR drug discovery scenarios.

Matching protein surface structural patches for high-resolution blind peptide docking

Modeling interactions between short peptides and their receptors is a challenging docking problem due to the peptide flexibility, resulting in a formidable sampling problem of peptide conformation in addition to its orientation. Alternatively, the peptide can be viewed as a piece that complements the receptor monomer structure. Here, we show that the peptide conformation can be determined based on the receptor backbone only and sampled using local structural motifs found in solved protein monomers and interfaces, independent of sequence similarity. This approach outperforms current peptide docking protocols and promotes new directions for peptide interface design.

Peptide docking can be perceived as a subproblem of protein–protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein–peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide–protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide–protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.