In silico models for designing and discovering novel anticancer peptides

Antimicrobial and Cell-Penetrating Peptides: How to Understand Two Distinct Functions Despite Similar Physicochemical Properties

Antimicrobial and cell-penetrating peptides are both classes of membrane-active peptides sharing similar physicochemical properties. Both kinds of peptides have attracted much attention owing to their specific features. AMPs disrupt cell membranes of bacteria and display urgently needed antibiotic substances with alternative modes of action. Since the multidrug resistance of bacterial pathogens is a more and more raising concern, AMPs have gained much interest during the past years. On the other side, CPPs enter eukaryotic cells without substantially affecting the plasma membrane. They can be used as drug delivery platforms and have proven their usefulness in various applications. However, although both groups of peptides are quite similar, their intrinsic activity is often different, and responsible factors are still in discussion. The aim of this chapter is to summarize and shed light on recent findings and concepts dealing with differences and similarities of AMPs and CPPs and to understand these different functions.

ImmunoSPdb: an archive of immunosuppressive peptides

Immunosuppression proved as a captivating therapy in several autoimmune disorders, asthma as well as in organ transplantation. Immunosuppressive peptides are specific for reducing efficacy of immune system with wide range of therapeutic implementations. `ImmunoSPdb’ is a comprehensive, manually curated database of around 500 experimentally verified immunosuppressive peptides compiled from 79 research article and 32 patents. The current version comprises of 553 entries providing extensive information including peptide name, sequence, chirality, chemical modification, origin, nature of peptide, its target as well as mechanism of action, amino acid frequency and composition, etc. Data analysis revealed that most of the immunosuppressive peptides are linear (91%), are shorter in length i.e. up to 20 amino acids (62%) and have L form of amino acids (81%). About 30% peptide are either chemically modified or have end terminal modification. Most of the peptides either are derived from proteins (41%) or naturally (27%) exist. Blockage of potassium ion channel (24%) is one a major target for immunosuppressive peptides. In addition, we have annotated tertiary structure by using PEPstrMOD and I-TASSER. Many user-friendly, web-based tools have been integrated to facilitate searching, browsing and analyzing the data. We have developed a user-friendly responsive website to assist a wide range of users.

ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

Motivation

Anti-cancer peptides (ACPs) have recently emerged as promising therapeutic agents for cancer treatment. Due to the avalanche of protein sequence data in the post-genomic era, there is an urgent need to develop automated computational methods to enable fast and accurate identification of novel ACPs within the vast number of candidate proteins and peptides.

Results

To address this, we propose a novel predictor named Anti-Cancer peptide Predictor with Feature representation Learning (ACPred-FL) for accurate prediction of ACPs based on sequence information. More specifically, we develop an effective feature representation learning model, with which we can extract and learn a set of informative features from a pool of support vector machine-based models trained using sequence-based feature descriptors. By doing so, the class label information of data samples is fully utilized. To improve the feature representation, we further employ a two-step feature selection technique, resulting in a most informative five-dimensional feature vector for the final peptide representation. Experimental results show that such five features provide the most discriminative power for identifying ACPs than currently available feature descriptors, highlighting the effectiveness of the proposed feature representation learning approach. The developed ACPred-FL method significantly outperforms state-of-the-art methods.

Availability and implementation

The web-server of ACPred-FL is available at http://server.malab.cn/ACPred-FL.

Supplementary information

Supplementary data are available at Bioinformatics online.

Advances with support vector machines for novel drug discovery

INTRODUCTION

Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or inactive) to the construction of regression models and the ranking/virtual screening of databased compounds. Areas covered: Herein, the authors consider some of the applications of SVMs in medicinal chemistry, illustrating their main advantages and disadvantages, as well as trends in their utilization, via the available published literature. The aim of this review is to provide an up-to-date review of the recent applications of SVMs in drug discovery as described by the literature, thereby highlighting their strengths, weaknesses, and future challenges. Expert opinion: Techniques based on SVMs are considered as powerful approaches in early drug discovery. The ability of SVMs to classify active or inactive compounds has enabled the prioritization of substances for virtual screening. Indeed, one of the main advantages of SVMs is related to their potential in the analysis of nonlinear problems. However, despite successes in employing SVMs, the challenges of improving accuracy remain.

Identifying anticancer peptides by using a generalized chaos game representation

We generalize chaos game representation (CGR) to higher dimensional spaces while maintaining its bijection, keeping such method sufficiently representative and mathematically rigorous compare to previous attempts. We first state and prove the asymptotic property of CGR and our generalized chaos game representation (GCGR) method. The prediction follows that the dissimilarity of sequences which possess identical subsequences but distinct positions would be lowered exponentially by the length of the identical subsequence; this effect was taking place unbeknownst to researchers. By shining a spotlight on it now, we show the effect fundamentally supports (G)CGR as a similarity measure or feature extraction technique. We develop two feature extraction techniques: GCGR-Centroid and GCGR-Variance. We use the GCGR-Centroid to analyze the similarity between protein sequences by using the datasets 9 ND5, 24 TF and 50 beta-globin proteins. We obtain consistent results compared with previous studies which proves the significance thereof. Finally, by utilizing support vector machines, we train the anticancer peptide prediction model by using both GCGR-Centroid and GCGR-Variance, and achieve a significantly higher prediction performance by employing the 3 well-studied anticancer peptide datasets.

Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. In spite of advancements in cancer therapy, the low success rate and recurrence of tumor has necessitated the ongoing search for new therapeutic agents. Aside from drugs based on small molecules and protein-based biopharmaceuticals, there has been an intense effort geared towards the development of peptide-based therapeutics owing to its favorable and intrinsic properties of being relatively small, highly selective, potent, safe and low in production costs. In spite of these advantages, there are several inherent weaknesses that are in need of attention in the design and development of therapeutic peptides. An abundance of data on bioactive and therapeutic peptides have been accumulated over the years and the burgeoning area of artificial intelligence has set the stage for the lucrative utilization of machine learning to make sense of these large and high-dimensional data. This review summarizes the current state-of-the-art on the application of machine learning for studying the bioactivity of anticancer peptides along with future outlook of the field. Data and R codes used in the analysis herein are available on GitHub at https://github.com/Shoombuatong2527/anticancer-peptides-review.

A Novel Hybrid Sequence-Based Model for Identifying Anticancer Peptides

Cancer is a serious health issue worldwide. Traditional treatment methods focus on killing cancer cells by using anticancer drugs or radiation therapy, but the cost of these methods is quite high, and in addition there are side effects. With the discovery of anticancer peptides, great progress has been made in cancer treatment. For the purpose of prompting the application of anticancer peptides in cancer treatment, it is necessary to use computational methods to identify anticancer peptides (ACPs). In this paper, we propose a sequence-based model for identifying ACPs (SAP). In our proposed SAP, the peptide is represented by 400D features or 400D features with g-gap dipeptide features, and then the unrelated features are pruned using the maximum relevance-maximum distance method. The experimental results demonstrate that our model performs better than some existing methods. Furthermore, our model has also been extended to other classifiers, and the performance is stable compared with some state-of-the-art works.

iACP: a sequence-based tool for identifying anticancer peptides

Cancer remains a major killer worldwide. Traditional methods of cancer treatment are expensive and have some deleterious side effects on normal cells. Fortunately, the discovery of anticancer peptides (ACPs) has paved a new way for cancer treatment. With the explosive growth of peptide sequences generated in the post genomic age, it is highly desired to develop computational methods for rapidly and effectively identifying ACPs, so as to speed up their application in treating cancer. Here we report a sequence-based predictor called iACP developed by the approach of optimizing the g-gap dipeptide components. It was demonstrated by rigorous cross-validations that the new predictor remarkably outperformed the existing predictors for the same purpose in both overall accuracy and stability. For the convenience of most experimental scientists, a publicly accessible web-server for iACP has been established at http://lin.uestc.edu.cn/server/iACP, by which users can easily obtain their desired results.

MLACP: machine-learning-based prediction of anticancer peptides

Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html.

Evaluation of peptide designing strategy against subunit reassociation in mucin 1: A steered molecular dynamics approach

Subunit reassociation in mucin 1, a breast cancer tumor marker, is reported as one of the critical factors for its cytoplasmic activation. Inhibition of its heterodimeric association would therefore result in loss of its function and alter disease progression. The present study aimed at evaluating peptide inhibitor designing strategies that may serve as antagonist against this receptor-ligand alliance. Several peptides and their derivatives were designed based on native residues, subunit interface, hydrogen bonding and secondary structure. Docking studies with the peptides were carried on the receptor subunit and their binding affinities were evaluated using steered molecular dynamics simulation and umbrella sampling. Our results showed that among all the different classes of peptides evaluated, the receptor based peptide showed the highest binding affinity. This result was concurrent with the experimental observation that the receptor-ligand alliance in mucin 1 is highly specific. Our results also show that peptide ligand against this subunit association is only stabilized through native residue inter-protein interaction irrespective of the peptide structure, peptide length and number of hydrogen bonds. Consistency in binding affinity, pull force and free energy barrier was observed with only the receptor derived peptides which resulted in favorable interprotein interactions at the interface. Several observations were made and discussed which will eventually lead to designing efficient peptide inhibitors against mucin 1 heterodimeric subunit reassociation.

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space

Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers.

Peptides with Dual Antimicrobial and Anticancer Activities

In recent years, the number of people suffering from cancer and multi-resistant infections has increased, such that both diseases are already seen as current and future major causes of death. Moreover, chronic infections are one of the main causes of cancer, due to the instability in the immune system that allows cancer cells to proliferate. Likewise, the physical debility associated with cancer or with anticancer therapy itself often paves the way for opportunistic infections. It is urgent to develop new therapeutic methods, with higher efficiency and lower side effects. Antimicrobial peptides (AMPs) are found in the innate immune system of a wide range of organisms. Identified as the most promising alternative to conventional molecules used nowadays against infections, some of them have been shown to have dual activity, both as antimicrobial and anticancer peptides (ACPs). Highly cationic and amphipathic, they have demonstrated efficacy against both conditions, with the number of nature-driven or synthetically designed peptides increasing year by year. With similar properties, AMPs that can also act as ACPs are viewed as future chemotherapeutic drugs, with the advantage of low propensity to resistance, which started this paradigm in the pharmaceutical market. These peptides have already been described as molecules presenting killing mechanisms at the membrane level, but also acting toward intracellular targets, which increases their success compartively to one-target specific drugs. This review will approach the desirable characteristics of small peptides that demonstrated dual activity against microbial infections and cancer, as well as the peptides engaged in clinical trials.

HemoPred: a web server for predicting the hemolytic activity of peptides

AIM

Toxicity arising from hemolytic activity of peptides hinders its further progress as drug candidates.

MATERIALS & METHODS

This study describes a sequence-based predictor based on a random forest classifier using amino acid composition, dipeptide composition and physicochemical descriptors (named HemoPred).

RESULTS

This approach could outperform previously reported method and typical classification methods (e.g., support vector machine and decision tree) verified by fivefold cross-validation and external validation with accuracy and Matthews correlation coefficient in excess of 95% and 0.91, respectively. Results revealed the importance of hydrophobic and Cys residues on α-helix and β-sheet, respectively, on the hemolytic activity.

CONCLUSION

A sequence-based predictor which is publicly available as the web service of HemoPred, is proposed to predict and analyze the hemolytic activity of peptides.

Enzyme-Assisted Discovery of Antioxidant Peptides from Edible Marine Invertebrates: A Review

Marine invertebrates, such as oysters, mussels, clams, scallop, jellyfishes, squids, prawns, sea cucumbers and sea squirts, are consumed as foods. These edible marine invertebrates are sources of potent bioactive peptides. The last two decades have seen a surge of interest in the discovery of antioxidant peptides from edible marine invertebrates. Enzymatic hydrolysis is an efficient strategy commonly used for releasing antioxidant peptides from food proteins. A growing number of antioxidant peptide sequences have been identified from the enzymatic hydrolysates of edible marine invertebrates. Antioxidant peptides have potential applications in food, pharmaceuticals and cosmetics. In this review, we first give a brief overview of the current state of progress of antioxidant peptide research, with special attention to marine antioxidant peptides. We then focus on 22 investigations which identified 32 antioxidant peptides from enzymatic hydrolysates of edible marine invertebrates. Strategies adopted by various research groups in the purification and identification of the antioxidant peptides will be summarized. Structural characteristic of the peptide sequences in relation to their antioxidant activities will be reviewed. Potential applications of the peptide sequences and future research prospects will also be discussed.

In Silico Design of Anticancer Peptides

In the past few years, small peptides having anticancer properties have emerged as a potential avenue for cancer therapy. Compared to current anti-cancer chemotherapeutic drugs (or small molecules), anticancer peptides (ACPs) have numerous advantageous properties, such as high specificity, low production cost, high tumor penetration, ease of synthesis and modification. However, in wet lab setups, identification and characterization of novel ACPs is a time-consuming and labor-intensive process. Therefore, in silico designing of anticancer peptides is beneficial, prior to their synthesis and characterization. This approach is less time consuming and more cost-effective. In this chapter, we discuss a web-based tool, AntiCP (http://crdd.osdd.net/raghava/anticp/), for designing ACPs.

Computational resources and tools for antimicrobial peptides

Antimicrobial peptides (AMPs), as evolutionarily conserved components of innate immune system, protect against pathogens including bacteria, fungi, viruses, and parasites. In general, AMPs are relatively small peptides (<10 kDa) with cationic nature and amphipathic structure and have modes of action different from traditional antibiotics. Up to now, there are more than 19 000 AMPs that have been reported, including those isolated from nature sources or by synthesis. They have been considered to be promising substitutes of conventional antibiotics in the quest to address the increasing occurrence of antibiotic resistance. However, most AMPs have modest direct antimicrobial activity, and their mechanisms of action, as well as their structure-activity relationships, are still poorly understood. Computational strategies are invaluable assets to provide insight into the activity of AMPs and thus exploit their potential as a new generation of antimicrobials. This article reviews the advances of AMP databases and computational tools for the prediction and design of new active AMPs. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.

A machine-learning approach for predicting palmitoylation sites from integrated sequence-based features

Palmitoylation is the covalent attachment of lipids to amino acid residues in proteins. As an important form of protein posttranslational modification, it increases the hydrophobicity of proteins, which contributes to the protein transportation, organelle localization, and functions, therefore plays an important role in a variety of cell biological processes. Identification of palmitoylation sites is necessary for understanding protein-protein interaction, protein stability, and activity. Since conventional experimental techniques to determine palmitoylation sites in proteins are both labor intensive and costly, a fast and accurate computational approach to predict palmitoylation sites from protein sequences is in urgent need. In this study, a support vector machine (SVM)-based method was proposed through integrating PSI-BLAST profile, physicochemical properties, [Formula: see text]-mer amino acid compositions (AACs), and [Formula: see text]-mer pseudo AACs into the principal feature vector. A recursive feature selection scheme was subsequently implemented to single out the most discriminative features. Finally, an SVM method was implemented to predict palmitoylation sites in proteins based on the optimal features. The proposed method achieved an accuracy of 99.41% and Matthews Correlation Coefficient of 0.9773 for a benchmark dataset. The result indicates the efficiency and accuracy of our method in prediction of palmitoylation sites based on protein sequences.

A General Strategy for Facile Synthesis and In Situ Screening of Self-Assembled Polymer-Peptide Nanomaterials

A universal strategy for efficient, mild, and purification-free synthesis and in situ screening of functional polymer-peptide nanomaterials is described. More than 1000 polymer-peptide conjugates (PPCs) with various chemical structures, compositions, and therapeutic efficacy are created. According to this strategy, the structure-function relationship of the PPCs is revealed, and the antitumor efficacies of the top performing PPCs are evaluated in vivo.

Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a K-means clustering algorithm

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.

Prediction of anticancer peptides against MCF-7 breast cancer cells from the peptidomes of Achatina fulica mucus fractions

Several reports have shown antimicrobial and anticancer activities of mucous glycoproteins extracted from the giant African snail Achatina fulica. Anticancer properties of the snail mucous peptides remain incompletely revealed. The aim of this study was to predict anticancer peptides from A. fulica mucus. Two of HPLC-separated mucous fractions (F2 and F5) showed in vitro cytotoxicity against the breast cancer cell line (MCF-7) and normal epithelium cell line (Vero). According to the mass spectrometric analysis, 404 and 424 peptides from the F2 and F5 fractions were identified. Our comprehensive bioinformatics workflow predicted 16 putative cationic and amphipathic anticancer peptides with diverse structures from these two peptidome data. These peptides would be promising molecules for new anti-breast cancer drug development.

AVP-IC50 Pred: Multiple machine learning techniques-based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50)

Peptide-based antiviral therapeutics has gradually paved their way into mainstream drug discovery research. Experimental determination of peptides' antiviral activity as expressed by their IC50 values involves a lot of effort. Therefore, we have developed "AVP-IC50 Pred," a regression-based algorithm to predict the antiviral activity in terms of IC50 values (μM). A total of 759 non-redundant peptides from AVPdb and HIPdb were divided into a training/test set having 683 peptides (T(683)) and a validation set with 76 independent peptides (V(76)) for evaluation. We utilized important peptide sequence features like amino-acid compositions, binary profile of N8-C8 residues, physicochemical properties and their hybrids. Four different machine learning techniques (MLTs) namely Support vector machine, Random Forest, Instance-based classifier, and K-Star were employed. During 10-fold cross validation, we achieved maximum Pearson correlation coefficients (PCCs) of 0.66, 0.64, 0.56, 0.55, respectively, for the above MLTs using the best combination of feature sets. All the predictive models also performed well on the independent validation dataset and achieved maximum PCCs of 0.74, 0.68, 0.59, 0.57, respectively, on the best combination of feature sets. The AVP-IC50 Pred web server is anticipated to assist the researchers working on antiviral therapeutics by enabling them to computationally screen many compounds and focus experimental validation on the most promising set of peptides, thus reducing cost and time efforts. The server is available at http://crdd.osdd.net/servers/ic50avp.

Structure-activity relationship study of novel peptoids that mimic the structure of antimicrobial peptides

The constant emergence of new bacterial strains that resist the effectiveness of marketed antimicrobials has led to an urgent demand for and intensive research on new classes of compounds to combat bacterial infections. Antimicrobial peptoids comprise one group of potential candidates for antimicrobial drug development. The present study highlights a library of 22 cationic amphipathic peptoids designed to target bacteria. All the peptoids share an overall net charge of +4 and are 8 to 9 residues long; however, the hydrophobicity and charge distribution along the abiotic backbone varied, thus allowing an examination of the structure-activity relationship within the library. In addition, the toxicity profiles of all peptoids were assessed in human red blood cells (hRBCs) and HeLa cells, revealing the low toxicity exerted by the majority of the peptoids. The structural optimization also identified two peptoid candidates, 3 and 4, with high selectivity ratios of 4 to 32 and 8 to 64, respectively, and a concentration-dependent bactericidal mode of action against Gram-negative Escherichia coli.

In silico and in vitro studies of cytotoxic activity of different peptides derived from vesicular stomatitis virus G protein

OBJECTIVES

This study aims at exploring cytotoxic activity of different peptides derived from VSVG protein against MCF-7 and MDA-MB-231 breast cancer cell lines and human embryonic kidney normal cell (HEK 293).

MATERIALS AND METHODS

The ANTICP web server was used to predict anticancer peptides. The cytotoxic activity of peptides with high score (P26, P7) and low score (P19) was examined by MTT and DNA fragmentation assays.

RESULTS

The results obtained from ANTICP web server demonstrated that 4 out of 48 peptides (P26, P7, P10, and P16) had anticancer activity. P26 and P7 peptides of these 4 peptides were detected to have high cytotoxic activity against MCF-7 cells with CC50 values of 98,280 µg/ml and MDA-MB231 cells with CC50 100,550 µg/ml, respectively. In addition, the results showed that amino acid residues of these 4 peptides were located near fusion domain.

CONCLUSION

The results confirmed that P26 and P7 peptides might induce membrane damage and initiate apoptosis. The present study suggested that P26 and P7 peptides could be appropriate candidates for further studies as cytotoxic agents and modifications in the residue at positions 70-280 might potentially produce a more efficient VSVG protein in gene therapy.

AHTPDB: a comprehensive platform for analysis and presentation of antihypertensive peptides

AHTPDB (http://crdd.osdd.net/raghava/ahtpdb/) is a manually curated database of experimentally validated antihypertensive peptides. Information pertaining to peptides with antihypertensive activity was collected from research articles and from various peptide repositories. These peptides were derived from 35 major sources that include milk, egg, fish, pork, chicken, soybean, etc. In AHTPDB, most of the peptides belong to a family of angiotensin-I converting enzyme inhibiting peptides. The current release of AHTPDB contains 5978 peptide entries among which 1694 are unique peptides. Each entry provides detailed information about a peptide like sequence, inhibitory concentration (IC₅₀), toxicity/bitterness value, source, length, molecular mass and information related to purification of peptides. In addition, the database provides structural information of these peptides that includes predicted tertiary and secondary structures. A user-friendly web interface with various tools has been developed to retrieve and analyse the data. It is anticipated that AHTPDB will be a useful and unique resource for the researchers working in the field of antihypertensive peptides.

Bio/chemoinformatics in India: an outlook

With the advent of significant establishment and development of Internet facilities and computational infrastructure, an overview on bio/chemoinformatics is presented along with its multidisciplinary facts, promises and challenges. The Government of India has paved the way for more profound research in biological field with the use of computational facilities and schemes/projects to collaborate with scientists from different disciplines. Simultaneously, the growth of available biomedical data has provided fresh insight into the nature of redundant and compensatory data. Today, bioinformatics research in India is characterized by a powerful grid computing systems, great variety of biological questions addressed and the close collaborations between scientists and clinicians, with a full spectrum of focuses ranging from database building and methods development to biological discoveries. In fact, this outlook provides a resourceful platform highlighting the funding agencies, institutes and industries working in this direction, which would certainly be of great help to students seeking their career in bioinformatics. Thus, in short, this review highlights the current bio/chemoinformatics trend, educations, status, diverse applicability and demands for further development.

Designing of peptides with desired half-life in intestine-like environment

BACKGROUND

In past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life.

RESULTS

In this study, we have used 10mer (HL10) and 16mer (HL16) peptides dataset to develop prediction models for peptide half-life in intestine-like environment. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R2 of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. Secondly, models developed on HL16 dataset showed maximum R/R2 of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Based on above models, we have developed a web server named HLP (Half Life Prediction), for predicting and designing peptides with desired half-life. The web server provides three facilities; i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides.

CONCLUSION

In summary, this study describes a web server 'HLP' that has been developed for assisting scientific community for predicting intestinal half-life of peptides and to design mutant peptides with better half-life and physicochemical properties. HLP models were trained using a dataset of peptides whose half-lives have been determined experimentally in crude intestinal proteases preparation. Thus, HLP server will help in designing peptides possessing the potential to be administered via oral route (http://www.imtech.res.in/raghava/hlp/).

Identification and structural characterization of a new pro-apoptotic cyclic octapeptide cyclosaplin from somatic seedlings of Santalum album L

Small cyclic peptides exhibiting potent biological activity have great potential for anticancer therapy. An antiproliferative cyclic octapeptide, cyclosaplin was purified from somatic seedlings of Santalum album L. (sandalwood) using gel filtration and RP-HPLC separation process. The molecular mass of purified peptide was found to be 858 Da and the sequence was determined by MALDI-ToF-PSD-MS as 'RLGDGCTR' (cyclic). The cytotoxic activity of the peptide was tested against human breast cancer (MDA-MB-231) cell line in a dose and time-dependent manner. The purified peptide exhibited significant antiproliferative activity with an IC50 2.06 μg/mL. In a mechanistic approach, apoptosis was observed in differential microscopic studies for peptide treated MDA-MB-231 cells, which was further confirmed by mitochondrial membrane potential, DNA fragmentation assay, cell cycle analysis and caspase 3 activities. The modeling and docking experiments revealed strong affinity (kcal/mol) of peptide toward EGFR and procaspase 3. The co-localization studies revealed that the peptide sensitizes MDA-MB-231 cells by possibly binding to EGFR and induces apoptosis. This unique cyclic octapeptide revealed to be a favorable candidate for development of anticancer agents.