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Functional peptides play crucial roles in various biological processes and hold significant potential in many fields such as drug discovery and biotechnology. Accurately predicting the functions of peptides is essential for understanding their diverse effects and designing peptide-based therapeutics. Here, we propose CELA-MFP, a deep learning framework that incorporates feature Contrastive Enhancement and Label Adaptation for predicting Multi-Functional therapeutic Peptides. CELA-MFP utilizes a protein language model (pLM) to extract features from peptide sequences, which are then fed into a Transformer decoder for function prediction, effectively modeling correlations between different functions. To enhance the representation of each peptide sequence, contrastive learning is employed during training. Experimental results demonstrate that CELA-MFP outperforms state-of-the-art methods on most evaluation metrics for two widely used datasets, MFBP and MFTP. The interpretability of CELA-MFP is demonstrated by visualizing attention patterns in pLM and Transformer decoder. Finally, a user-friendly online server for predicting multi-functional peptides is established as the implementation of the proposed CELA-MFP and can be freely accessed at http://dreamai.cmii.online/CELA-MFP., (© The Author(s) 2024. Published by Oxford University Press.)

Motivation: Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information., Results: In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein-protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite., Availability and Implementation: The datasets and source codes can be accessed at https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/., (© The Author(s) 2023. Published by Oxford University Press.)

The prolific spread of COVID-19 caused by a novel coronavirus (SARS-CoV-2) from its epicenter in Wuhan, China, to every nook and cranny of the world after December 2019, jeopardize the prevailing health system in the world and has raised serious concerns about human safety. Multi-directional efforts are made to design small molecule inhibitors, and vaccines and many other therapeutic options are practiced, but their final therapeutic potential is still to be tested. Using the old drug or vaccine or peptides could aid this process to avoid such long experimental procedures. Hence, here, we have repurposed a small peptide (ATLQAIAS) from the previous study, which reported the inhibitory effects of this peptide. We used in silico mutagenesis approach to design more peptides from the native wild peptide, which revealed that substitutions (T2W, T2Y, L3R, and A5W) could increase the binding affinity of the peptide towards the 3CLpro. Furthermore, using MD simulation and free energy calculation confirmed its dynamics stability and stronger binding affinities. Per-residue energy decomposition analysis revealed that the specified substitution significantly increased the binding affinity at the residue level. Our wide-ranging analyses of binding affinities disclosed that our designed peptide owns the potential to hinder the SARS-CoV-2 and will reduce the progression of SARS-CoV-2-borne pneumonia. Our research strongly suggests the experimental and clinical validation of these peptides to curtail the recent corona outbreak., (© 2021. International Association of Scientists in the Interdisciplinary Areas.)

The anti-cancer targets play a crucial role in the signaling processes of cells, and therefore, it becomes nearly impossible to engage these targets without affecting the native cellular function. Thus, an approach has been taken to develop an anti-cancer Scanner (ACPS) tool aimed toward the recognition of anti-cancer marks in the form of peptides. The proposed ACPS tool allows fast fingerprinting of the anti-cancer targets having extreme significance in the current bioinformatics research. There already exist some tools that offer these features on a single platform; however, the performance of ACPS was compared with the preexisting online tools and was observed that ACPS offers greater than 95% accuracy that is comparatively much higher. The anti-cancer marked sequences of proteins supplied by the operators are scanned against the anti-cancer target datasets via ACPS and provide precision-based anti-cancer peptides. The proposed tool has been contrived in PERL programming language, and this tool is the extended version of A-CaMP codes, which are highly scalable having an extensible application in cancer biology with robust coding architecture. The availability of tools like ACPS will greatly benefit researchers in the field of oncology and structure-based drug design., (© 2020 John Wiley & Sons Ltd.)

Helicobacter pylori ( H. pylori ) is one of the most extensively studied Gram-negative bacteria due to its implication in gastric cancer. The oncogenicity of H. pylori is associated with cytotoxin-associated gene A (CagA), which is injected into epithelial cells lining the stomach. Both the C - and N -termini of CagA are involved in the interaction with several host proteins, thereby disrupting vital cellular functions, such as cell adhesion, cell cycle, intracellular signal transduction, and cytoskeletal structure. The N -terminus of CagA interacts with the tumor-suppressing protein, apoptosis-stimulating protein of p53 (ASPP2), subsequently disrupting the apoptotic function of tumor suppressor gene p53. Here, we present the in-depth molecular dynamic mechanism of the CagA-ASPP2 interaction and highlight hot-spot residues through in silico mutagenesis. Our findings are in agreement with previous studies and further suggest other residues that are crucial for the CagA-ASPP2 interaction. Furthermore, the ASPP2-binding pocket possesses potential druggability and could be engaged by decoy peptides, identified through a machine-learning system and suggested in this study. The binding affinities of these peptides with CagA were monitored through extensive computational procedures and reported herein. While CagA is crucial for the oncogenicity of H. pylori , our designed peptides possess the potential to inhibit CagA and restore the tumor suppressor function of ASPP2.

The human herpes simplex virus type 1 (HSV-1) is an extremely rampant human pathogen, and its infection could cause life-long diseases, including the central nervous system disorders. The glycoproteins of HSV-1 such as glycoprotein B, glycoprotein C, glycoprotein D, glycoprotein H, and glycoprotein L are highly involved in mediating the viral attachment and infection of the host cell. Therefore, immunoinformatic approaches followed by molecular dynamics simulation and systems biology has been used to analyze these glycoproteins in order to propose effective peptide-based vaccine candidates against the HSV-1 infection. The ElliPro and NetCTL.1.2 online tools were employed to forecast the B- and T-lymphocyte (CTL) epitopes for gB, gC, gD, gH, and gL. The 3D coordinates of these epitopes were modeled and docked against the human major histocompatibility complex molecule-1. The outcomes obtained from postdocking analysis along with TAP (Transporter associated with antigen processing), MHC binding, and C-terminal cleavage score assisted in the selection of potential epitopes. These epitopes were further subjected to molecular dynamics simulation and systems biology approach which showed significant results. On the basis of these substantial outcomes, peptides are proposed that could be used to provoke immunity against the HSV-1 infection., (© 2019 John Wiley & Sons A/S.)

Flexible alignment and docking studies were conducted for the three octapeptides, ATLQANEV, AVLQSGFR, and ATLQAIAS, that were cleavable by SARS-CoV Mpro. It has been observed that all pharmacophores of the three peptides overlap very well, and that ATLQANEV binds best with the receptor, followed by AVLQSGFR, and then ATLQAIAS. During the process of docking the octapeptides to the SARS enzyme, the residues of the catalytic dyad, i.e., His-41 and Cys-145 are actively involved in forming the hydrogen bonds, so is the center residue (Gln) of all the three octapeptides. The findings are fully consistent with experimental observations. The present studies suggest that the octapeptides ATLQANEV and ATLQAIAS, like AVLQSGFR, might also be the good starting points for designing potential drugs against SARS.

The cleavage mechanism of severe acute respiratory syndrome (SARS) coronavirus main proteinase (M(pro) or 3CL(pro)) for the octapeptide AVLQSGFR is studied using molecular mechanics (MM) and quantum mechanics (QM). The catalytic dyad His-41 and Cys-145 in the active pocket between domain I and II seem to polarize the pi-electron density of the peptide bond between Gln and Ser in the octapeptide, leading to an increase of positive charge on C(CO) of Gln and negative charge on N(NH) of Ser. The possibility of enhancing the chemical bond between Gln and Ser based on the "distorted key" theory [Anal. Biochem. 233 (1996) 1] is examined. The scissile peptide bond between Gln and Ser is found to be solidified through "hybrid peptide bond" by changing the carbonyl group CO of Gln to CH(2) or CF(2). This leads to a break of the pi-bond system for the peptide bond, making the octapeptide (AVLQSGFR) a "distorted key" and a potential starting system for the design of anti SARS drugs.

Recently, peptide-based drugs have gained unprecedented interest in discovering and developing antifungal drugs due to their high efficacy, broad-spectrum activity, low toxicity and few side effects. However, it is time-consuming and expensive to identify antifungal peptides (AFPs) experimentally. Therefore, computational methods for accurately predicting AFPs are highly required. In this work, we develop AFP-MFL, a novel deep learning model that predicts AFPs only relying on peptide sequences without using any structural information. AFP-MFL first constructs comprehensive feature profiles of AFPs, including contextual semantic information derived from a pre-trained protein language model, evolutionary information, and physicochemical properties. Subsequently, the co-attention mechanism is utilized to integrate contextual semantic information with evolutionary information and physicochemical properties separately. Extensive experiments show that AFP-MFL outperforms state-of-the-art models on four independent test datasets. Furthermore, the SHAP method is employed to explore each feature contribution to the AFPs prediction. Finally, a user-friendly web server of the proposed AFP-MFL is developed and freely accessible at http://inner.wei-group.net/AFPMFL/ , which can be considered as a powerful tool for the rapid screening and identification of novel AFPs. [ABSTRACT FROM AUTHOR]

Current biopharmaceutical drugs are mainly a class of peptides or proteins that play an essential role in the treatment of many diseases. Such peptides/proteins are usually thermally unstable and may lose their bioactivity when exposed to ambient conditions. Therefore, they are not suitable for long-term storage. Lyophilisation is the most common method to prolong shelf life of solid peptide/protein drugs; however, the freeze-drying process can lead to irreversible damage. In the present study, human interferon-alpha 2a (IFN-α2a) was selected as a model protein drug; four disaccharides (β-lactose, β-maltose, sucrose, and trehalose) were selected as bioactive protectants. We investigated the effects of different protectants on IFN-α2a under various ambient conditions (vacuum, dry state, and aqueous solution) using replica exchange molecular dynamics simulation. The protective effect of β-maltose on IFN-α2a was the highest in aqueous solution and dry state, β-lactose showed a poor protective effect in all three conditions, the performance of sucrose was good in all conditions, and trehalose showed a better protective effect under vacuum conditions and in aqueous solution. Disaccharides form H-bonds with water, thereby preventing water from the tertiary structure of proteins. Trehalose forms strong H-bonds with water which explains its extraordinary stability. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) infections have adopted the shape of a global pandemic now, demanding an urgent vaccine design. The current work reports contriving an anti-coronavirus peptide scanner tool to discern anti-coronavirus targets in the embodiment of peptides. The proffered CoronaPep tool features the fast fingerprinting of the anti-coronavirus target serving supreme prominence in the current bioinformatics research. The anti-coronavirus target protein sequences reported from the current outbreak are scanned against the anti-coronavirus target data-sets via CORONAPEP which provides precision-based anti-coronavirus peptides. This tool is specifically for the coronavirus data, which can predict peptides from the whole genome, or a gene or protein's list. Besides it is relatively fast, accurate, userfriendly and can generate maximum output from the limited information. The availability of tools like CORONAPEP will immeasurably perquisite researchers in the discipline of oncology and structure-based drug design. [ABSTRACT FROM AUTHOR]

High-risk human papillomaviruses (hrHPVs) are the most prevalent viruses in human diseases including cervical cancers. Expression of E6 protein has already been reported in cervical cancer cases, excluding normal tissues. Continuous expression of E6 protein is making it ideal to develop therapeutic vaccines against hrHPVs infection and cervical cancer. Therefore, we carried out a meta-analysis of multiple hrHPVs to predict the most potential prophylactic peptide vaccines. In this study, immunoinformatics approach was employed to predict antigenic epitopes of hrHPVs E6 proteins restricted to 12 Human HLAs to aid the development of peptide vaccines against hrHPVs. Conformational B-cell and CTL epitopes were predicted for hrHPVs E6 proteins using ElliPro and NetCTL. The potential of the predicted peptides were tested and validated by using systems biology approach considering experimental concentration. We also investigated the binding interactions of the antigenic CTL epitopes by using docking. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlighted the regions from 46–62 and 65–76 that could be the first choice for the development of prophylactic peptide vaccines against hrHPVs. To overcome the worldwide distribution, the predicted epitopes restricted to different HLAs could cover most of the vaccination and would help to explore the possibility of these epitopes for adaptive immunotherapy against HPVs infections. [ABSTRACT FROM AUTHOR]

Techniques of rare event dynamics were reviewed, including string methods, which will be implemented with the biochemical simulation packages. The existing methods were applied to study biological systems with relevance to drug design and drug metabolism. The rare event dynamics simulations were performed to understand the kinetic and thermodynamic free energy information on the drug binding sites in the M2 proton channel, the free energy of insertion and association of membrane proteins and membrane active peptides. Results give a theoretical framework to interpret and reconcile existing and often conflicting opinions. [ABSTRACT FROM AUTHOR]

Review: 34 refs. [ABSTRACT FROM AUTHOR]

The cleavage mechanism of severe acute respiratory syndrome (SARS) coronavirus main proteinase (Mpro or 3CLpro) for the octapeptide AVLQSGFR is studied using molecular mechanics (MM) and quantum mechanics (QM). The catalytic dyad His-41 and Cys-145 in the active pocket between domain I and II seem to polarize the π-electron density of the peptide bond between Gln and Ser in the octapeptide, leading to an increase of positive charge on C(CO) of Gln and negative charge on N(NH) of Ser. The possibility of enhancing the chemical bond between Gln and Ser based on the “distorted key” theory [Anal. Biochem. 233 (1996) 1] is examined. The scissile peptide bond between Gln and Ser is found to be solidified through “hybrid peptide bond” by changing the carbonyl group CO of Gln to CH2 or CF2. This leads to a break of the π-bond system for the peptide bond, making the octapeptide (AVLQSGFR) a “distorted key” and a potential starting system for the design of anti SARS drugs. [Copyright &y& Elsevier]