Protein & Peptide Letters

In the past two decades molecular biomarkers have achieved importance in the field of medicine as diagnostic molecules for the diagnosis and prognosis stratification of the diseases. Proteomics has proven to be a promising tool for discovering biomarkers linked to several diseases, including endocrine dysfunctions. Furthermore the use of protemic technologies represents an interesting tool in the characterization of the pathophysiology of several endocrine diseases, permitting to unveil the molecular machinaries involved in the hormonal action and regulation. In this thematic issue, three review articles, five minireview articles, one systematic review article and one letter are published. At first, Maffezzoni et al., reviewed and summarized the current state in the field of the identification of new potential biomarkers for acromegaly and GH use/abuse [1]. Both acromegaly and growth hormone deficiency (GHD) are pathological conditions related to important comorbidities that, in addition to having stringent diagnostic criteria, require valid markers for the definition of treatment, treatment monitoring and follow-up. Although GH and IGF-I are widely validated biomarkers of acromegaly and GHD, their role is not completely exhaustive or suitable for clinical classification and follow-up. Therefore, new biological markers for acromegaly and GH replacement therapy are strongly needed. Furthermore two different isoforms of the GH Receptor (GHR) were described, that differ for the presence or the absence of transcription of the exon 3 of the GHR gene. Both the isoforms produce a functioning receptor, but the exon 3-deleted isoforms (d3-GHR) has a higher sensitivity to endogenous and recombinant GH as compared to the full-length isoform (fl- GHR). Chiloiro et al. evaluated in their review article the role of GH receptor polymorphisms in skeletal fragility in patients affected by GHD and acromegaly [2]. Triple-A Syndrome (TAS) is a rare autosomal recessive disorder characterized by three cardinal symptoms: alacrimia, achalasia and adrenal insufficiency due to ACTH insensitivity. Early identification of the syndrome is challenging, given the rarity of the condition and high phenotypic heterogeneity even among members of the same family. In their mini-review, Pogliaghi et al. aimed to summarize the current knowledge of clinical and molecular profile of patients with TAS and recommendations for the diagnosis, management, and follow-up of patients [3]. Proteomics study have been performed moreover in endocrine cancers, aimed to identify and characterize functional proteins that drive the transformation of malignancy, and to discover biomarkers to detect early-stage cancer, predict prognosis, identify novel therapeutical targets, in a perspective of personalized medicine. Pancreatic neuroendocrine tumors (PanNETs) are rare tumors having usually an indolent behavior, but sometimes with unpredictable aggressiveness. Diagnostic and prognostic markers, but also new therapeutic targets of PanNETs, are still lacking. Bianchi et al. conducted a systematic review to summarize the state of the art of proteomics in PanNETs [4]. Proteomic techniques have been widely applied in the last decade in the field of andrology. The entire sperm proteome carry crucial information for fertilization and embryo development. Altogether, 6871 proteins have been reported in spermatozoa so far. In their letter Cannarella et al. underlined the role of the sperm proteome in fertilization, preimplantation embryo development and paternal epigenetic inheritance [5]. Seminal plasma proteome has moreover a pivotal role in fertilization, reflecting moreover the androgenic status of sexual accessory glands. In their mini-review Grande et al. discuss the role of seminal proteins as a seminal fingerprint of male hypogonadism, usefull for the diagnosis of patients with moderate grades of testosterone reduction and in the follow-up of testosterone replacement treatment [6]. Apart from testosterone, Leydig cells secretes two other hormones: 25-OH vitamin D and Insulin-like factor 3 (INSL3). INSL3 has endocrine anabolic functions on the bone-skeletal muscle unit. INSL3 moreover reflects the number and the differentiation state of Leydig cells and therefore it represents the ideal marker of Leydig cell function. Facondo et al. in their minireview focused on the role of INSL3 as a marker of both Leydig cell function and testis-bone-skeletal muscle network [7]. Osteoporosis is a systemic skeletal disease characterized by low bone mass and microarchitectural deterioration of bone tissue. Biomarkers of bone turnover have been used for years in bone disease management. Recently, new approaches for the detection of bone physiology and pathology biomarkers have been proposed, among which proteomics, with particular interest in osteoporosis. Porcelli et al. reviewed current knowledge on proteomics applied to osteoporosis in vivo [8]. During the last decade, the disclosure of systemic effects of osteocalcin (OCN) in its undercarboxylated form contributed to switch the concept of bone from a merely structural apparatus to a fully endocrine organ involved in the regulation of systemic functions. De Toni et al. in their review article, discuss current experimental data supporting the inter-organ communication roles of this protein are discussed, including the possible significance the OCN as a template agonist on its receptor GPRC6A, for the development of novel therapeutic and pharmacological approaches for the treatment of dismetabolic states [9]. Adipose tissue is nowadays considered a complex organ with lots of endocrine and metabolic functions. Both adipocytes and surrounding resident cells (macrophages, endothelial cells and others) produce a huge number of molecules, or adipokines, with endocrine or paracrine functions, that regulate various aspects of metabolism whose clinical relevance is emerging. In their mini-review Parrettini et al. summarized scientific evidence about adipokines' effects on human metabolism, by focusing on their role on either Metabolic Syndrome, or insulin-resistance in pregnancy, or finally, reproductive function disorders [10]. In summary, experts from the differents fields of endocrinology have reviewed the current advances in their specific area in this special thematic issue of Protein & Peptide Letters. As the guest editors, we wish that this diverse collection of articles will gain broad readership. We would like to express our sincere gratitude to all authors and referees for their contribution to this issue. We would also like to extend our appreciation to the Editor-in-Chief, Professor Ben M. Dunn and the editorial staff of Protein & Peptide Letters, especially Ms. Syeda Mehak Fatima, for their excellent support and for providing us with the opportunity to pursue this hot topic issue.

The Renin–Angiotensin System (RAS) consists of a complex enzymatic cascade which produces diverse angiotensin peptides. Over the past few years, experimental and clinical evidences indicate that these peptides have actions that go beyond the cardiovascular and renal systems. Several studies have associated cerebrovascular, neurodegenerative and psychiatric diseases with important alterations in systemic and local/tissue RAS. For this reason, in this thematic issue “Renin-Angiotensin System: Role in cerebrovascular, neurodegenerative and psychiatric disease”, we covered the relation between some of the cerebrovascular, neurodegenerative and psychiatric diseases with key components of RAS, besides address the most important aspects of its interactions and implications for research progress, drug developments and new treatments in a near future. It is known that RAS components affect blood pressure, cerebral blood flow, neuroinflammation and oxidative stress. Overactivity of the classical axis, composed by angiotensin (Ang) converting enzyme (ACE)/Ang II/AT1 receptor axis, may promote brain tissue damage. In contrast, stimulation of AT2 receptors in the brain has been demonstrated to elicit beneficial effects. Likewise, activation of the counter-regulatory RAS axis composed by ACE2/Ang-(1-7)/Mas receptor seems to elicit neuroprotection. In a mini-review, Mohite et al. [1] integrated basic and clinical studies showing the associations between RAS and mood disorders. Animal studies on mood disorders models - either depression or mania - were focused on the reversal of behavioral and/or cognitive symptoms through ACE inhibitors (ACEi) or Angiotensin Receptor Blockers (ARBs). ACE polymorphisms were linked to mood disorders and suicidal behavior, while hypertension was associated with neurocognitive deficits in mood disorders, which was reversed with RAS inhibition. Low levels of RAS components (renin activity or aldosterone) and mood symptoms improvement with ACEi and ARBs were also observed in mood disorders. Machado and colleagues [2] reviewed the participation of the RAS components in Huntington’s disease (HD). They showed that alterations in brain RAS components are seen in this disease, suggesting that this system participates in the pathophysiology of HD, although the results are still inconclusive and need further investigation. Kangussu et al. [3] summarize and discuss recent findings related to the modulation of RAS components in cerebrovascular diseases (CVD). Despite CVD being a leading cause of morbidity and mortality worldwide, therapy for these conditions are very limited and the precise mechanisms underlying CVD are still a matter of debate. Clinical trials with ACEi or ARBs, like in AD, have generally shown a beneficial role in patients with stroke, leading to a reduction of brain damage with limitation of stroke area, less neuroinflammation and oxidative stress, but the mechanisms of this neuroprotection are also not fully elucidated. Unfortunately, evidence on the beneficial role of ACE2/Ang/(1-7)/Mas receptor axis in CVD was almost exclusively provided by animal models. Therefore, studies with patients are necessary to investigate the potential therapeutic role of the alternative RAS axis in CVD. de Melo and Almeida-Santos [4] described that the ACE2/Ang-(1-7)/Mas receptor pathway induces anxiolytic and antidepressive effects. Overall, these studies reiterate the strong and under-researched connection between RAS and mood disorders, which justifies further investigation into this system as a possible new therapeutic target in the management of anxiety, depression and other neuropsychiatric disorders. In Alzheimer's disease (AD), a neurodegenerative disease, Ribeiro and colleagues [5] discusses the pathophysiology of the disease and its possible interaction with RAS, looking for potential therapeutic approaches. The link between AD and clinical conditions related to classical RAS axis hyperactivation (hypertension, heart failure, and chronic kidney disease) supports the hypothesized role of this system in AD. Additionally, RAS-targeting drugs, like ACEi or ARBs, seem to exert beneficial effects on AD. Results of randomized controlled trials testing ACEi or ARBs in AD are awaited to elucidate whether AD-RAS interaction has implications on AD therapeutics. Sassi and colleagues [6] did a systematic review of the studies evaluating the involvement of RAS in patients with migraine. There is evidence from genetic studies exploring the relation between migraine and RAS-related genes and from clinical trials evaluating the efficacy of ACEi and ARBs in migraine prophylaxis. RAS seems to play a role in the pathophysiology of migraine, but more direct evidences are still missing. Completing our thematic issue, Barbosa et al. [7] investigated the modulation of RAS components in Bipolar Disorder (BD) through a systematic review. BD patients presented higher plasma renin activity in comparison with controls, but the studies evaluating the RAS molecules in BD are very scarce and heterogeneous. The literature suggests a potential role of RAS in BD, although further studies are necessary to investigate this relationship. In summary, although, after a period of vast and important discoveries about RAS, some researchers considered this an exhausted subject, today we know that it is a very complex system, with multiple effectors, present in all tissues and with influence on several pathophysiology aspects. Thus, many studies will still be needed to better understand and characterize the RAS components, with the objective to expand strategies for the treatment of cerebrovascular, neurodegenerative and psychiatric disease.

Proteins are an important components that makes up all the cells and tissues of the human body. Proteins play an important role in the life activities of cells and organisms, such as the principal catalytic agents, structural elements, signal transmitters, transporters and molecular machines. However, proteins cannot show their biological functions as individuals. They need to interact with other molecules, such as other proteins, RNAs, DNAs and small molecule compounds, to carry out their cellular roles.

Studying the varied interactions related to proteins has very important biological implications. Basically, we can investigate these interactions through biological wet experiments. These experiments are extremely valuable and have contributed greatly to our knowledge of protein interaction mechanisms. But these experiments are usually not only time-consuming but also costly. Therefore, there is an urgent need for low-cost and time-saving computational methods. During recent years, new complex network methods, deep learning and machine learning techniques have rapidly developed and achieved an amazing level of performance in many area, such as protein–protein interaction prediction, protein–RNA interaction prediction and drug-target association analysis. This progress could significantly improve our understanding in the field of protein interactions prediction.

The following paragraphs are brief descriptions of five manuscripts presenting recent findings which use computational techniques for the research of protein interactions in this special issue, representing only a small portion of current research. The identification of Drug Target Interactions (DTIs) is a significant part of drug discovery and medical research. To better identify the DTIs, researchers have developed a variety of computational methods for effectively predicting DTIs. Ding et al. retrospectively describe the identification methods of DTIs, including web-based methods and machine learning-based methods, and briefly introduced the advantages and disadvantages of various models [1].

Deep Learning (DL) helps us theoretically identify and quantify relationships that experts have not yet discovered. It is a rapidly evolving field of machine learning and widely used in many other fields. At present, Deep Learning of protein research has emerged, so Shi et al. reviewed the application of this technology in protein-related interactions prediction [2]. Wan et al. summarized the widely used computational methods for predicting site-specific proteins as well as site-specific nucleases predicted by off-target sites, and provided a series of online predictive tools based on their characteristics and computational methods [3].

Semi-supervised learning without negative data sets is an important issue in machine learning, mainly using a small amount of marked data and a large amount of unmarked data to identify unknown data. Zhong et al. reviewed several computational models of lncRNA-protein interaction prediction based on semi-supervised learning, and introduced their main contents in summary [4].

Self-Interacting Proteins (SIPs) have significant roles in cellular functions and the evolution of protein interaction networks. Qu et al. first introduced the concept and functions of SIPs, then they reviewed some excellent computational models that have been designed for SIPs prediction [5].

At last we would like to thank all the authors who contribute their original work to this issue and the reviewers for their valuable comments. We would also like to express our sincere gratitude to the Editor-in-Chief, Professor Ben M. Dunn and the editorial staff of Protein & Peptide Letters, especially Mehak Fatima, for their excellent support and providing us with this opportunity to hold this hot topic issue successfully.

During the past decades, computational method has become an important strategy in the understanding and study of biological sciences. With the more and more proteomics data generation, it is urgent to develop and apply various computational resources and methods to handle these protein data. In this thematic issue, two review articles, seven research papers and one letter are published. At first, Chen et al. [1] reviewed the development of machine learning methods applied in bacteriophage virion protein identification. The phage virion proteins play important roles in virus capsid and budding. Several works have developed to identify the proteins. Chen et al. work provides a summary of the field. Subsequently, Han et al. [2] focused on the proteinprotein interaction. They summarized many useful tools and visualization programs including databases, computational methods for annotating and analyzing biological networks. In most of proteins, disordered regions are important regions for protein function. Xie and Feng [3] developed a support vector machine -based method to identify the six classes of intrinsically disordered proteins. Four kinds of descriptors were extracted to formulate protein samples. In cross-validation, their model could produce a very high accuracy. It is very important for protein classification to formulate a protein sample with a valid mathematical descriptor. Zhang and Kong [4] introduced an amino acid properties selection technique and applied it in protein fold classification. Comparison with published results showed that their model is better. Multiple sequence alignment is a fundamental task in computational biology. Traditional methods do not consider the different conserve regions in protein sequences, which results in a poor alignment. Zhan et al. [5] developed a novel refinement method to solve the problem. Their method is based on splitting-splicing vertically, namely separating a sequence vertically into three parts, and then performing alignment. Protein fold is also a fundamental topic in protein study. Li et al. [6] focused on the influence of palindromes in mRNA on protein folding rate. They found that palindromes play an important role in protein folding. Another two papers [7, 8] also focused on protein folding. Li et al. [8] used the effective cumulative torsion angles model to predict protein folding rate. This is an important progress for protein folding rate prediction from physical insight. Especially, their model based on such physical parameter can produce better results than other published methods. Xiao et al. [7] presented a machine learning model to predict protein quaternary structure. The gene ontology information was used in their model. The machine learning method was also applied in protein function prediction. Cell wall lytic enzyme is a potential protein drug for anti-bacteria. Endolysin and autolysin are two kinds of enzymes which could destroy cell wall. Xu et al. [9] introduced a computational method to discriminate the two kinds of enzymes. By using feature selection technique, they dramatically improved the accuracy of proposed model. Wang et al. [10] developed a model to recognize cleavage sites of neuropeptide precursors. In 5-fold cross-validation, their model could produce the accuracy of >90%. In summary, computational methods have been widely applied in protein function and structure prediction. We hope more accurate tools can appear for providing convenience to most of the scholars.

Recombinant Proteins: Emerging Production Trends and Applications

A wide range of solvent accessibility methods to investigate structural features and conformational changes of macro-molecules in solution are described in the articles in this issue. These methods have grown substantially of the past few decades, and are highly complementary to other more widely used structural elucidation techniques for biological macromolecules, such as x-ray diffraction, cryoelectron microscopy, and nuclear magnetic resonance. While these more widely known methods are all excellent techniques for studying protein structure, they often require protein engineering, high concentrations of sample, or non-physiological sample environments, and in addition, can be limited by the size and/or complexity of the molecule. Solvent accessibility methods have their own unique advantages and disadvantages, and are increasingly used as stand-alone methods or as part of a suite of tools to characterize macromolecules. The concept of footprinting refers to the method of delineating interactions between macromolecules, i.e., the "footprint" of one molecule on another. Some of the earliest footprinting methods used enzymatic digestion to define the areas on nucleic acid which were solvent protected by binding partners, i.e., a protein protected a specific region on the nucleic acid from digestion, thereby delineating a "footprint" on the nucleic acid [1]. Chemical methods for determining solvent accessibility, such as methylation, were developed around the same time, and had the advantage of using a smaller probe, thus yielding higher resolution footprints. Many variations on enzymatic and chemical footprinting for both nucleic acid and proteins have been developed in the last several decades. Protein footprinting in particular, has historically been deployed using proteases [2] and antibodies [3] as well as other modification methods such as hydroxyl radical footprinting using reagents [4]. More recently, hydroxyl radical footprinting has been developed using an external energy source for the generation of hydroxyl radicals, as in electron [5], laser-induced [6], X-ray [7, 8], and very recently, plasma-induced radical formation [9]. The hydroxyl radical, in particular, is an excellent probe for determining protein-protein and protein-nucleic acid interactions, because of its small size and high reactivity. The method is variously referred to as Radical Probe Mass Spectrometry (RP-MS), Hydroxyl Radical Footprinting (HRF), Fast Photochemical Oxidative Footprinting (FPOP), X-ray Footprinting (XRF), or X-ray Footrpinting with Mass Spectrometry (XF-MS) depending on the radical source and method. In this thematic issue, the first review article is by Maleknia & Downard and covers the development of RP-MS over the last two decades, describing the technological advances of the different methods of hydroxyl generation through the years. In contrast to the hydroxyl radical reaction with protein sidechains, the method of Hydrogen Exchange Mass Spectrometry (HDX-MS), as described in the second review by Benhaim et al., relies on the modification of protein backbone amide groups. This method is well-developed, increasingly used in industrial applications, and provides a natural complement to the other side-chain labeling footprinting methods described in this issue. FPOP is reviewed by Shi & Gross, and describes the recent development of a lab-based radical generating method using an excimer laser to induce radical formation. A review by Garcia et al., then describes the application of the method for characterization of biotherapeutics. Specific applications to membrane proteins are described in the review by Gupta, while Bohon describes the history of the development of an X-ray footprinting beamline at the Brookhaven National Laboratory on the East Coast of the United States. In the research article section, Leser et al., describe a novel approach for oxidative footprinting, a "lab on a chip" method which enables many different chemical environments to be probed quickly and simultaneously, and will be of particular interest for biotechnology applications. Morton et al., describe the new development of an X-ray footprinting beamline at Lawrence Berkeley national Laboratory on the West Coast of the United States.

The primary aim is to cover the most exciting and promising new areas of research in peptide science as applied to therapeutic discovery and development. The strengths of peptides as drug targets are their high potency, specificity and good safety profile. These positive attributes of peptides along with advances in drug delivery technologies have afforded a renewed interest in the discovery, optimization and development of peptides as therapeutics. In this themed issue, the most compelling indications and targets for peptide therapeutics are reviewed and discussed with the intent to demonstrate that peptides have broad applicability in many therapeutic areas. Target selection for peptide therapeutics is challenging due to the inherent properties of peptides. Therefore, identifying a clear differentiation strategy for a new peptide program over a small molecule or antibody program from the outset is critical for successful navigation of drug development hurdles. The latest techniques for overcoming some of the perceived limitations of peptides to increase their druggability will be reviewed and discussed. Emerging technologies for stabilizing peptides for sufficient in vivo half-life will be described and evaluated, as well as novel technologies for getting peptides across cell membranes to reach intracellular targets and across the blood brain barrier to reach central nervous system targets. Capitalizing on the strengths of peptides in this issue, the latest advancements in therapeutic peptidic hormones, peptides targeting protein-protein interactions especially for oncology indications, venom peptides as therapeutics as well as diagnostics, and antimicrobial peptide progress will be covered with emphasis on the distinctive properties peptides have compared to other therapeutics.

Proteins are the material basis of life, and the basic organic matter that constitutes cells. In cells, proteins are the principal catalytic agents, structural elements, signal transmitters, transporters and molecular machines. But individual proteins do not function alone, they must interact with other molecules, such as proteins, RNAs, DNAs and small molecule compounds, to carry out their cellular roles. Studying these interactions has very important biological implications. Typically, interactions of proteins are identified and characterized by a number of biochemical or biophysical experimental methods. Although these experiments are very valuable and contribute significantly to our understanding of the mechanisms of protein interactions, technical challenges make these experiments both labor intensive and time consuming. Therefore, reliable computational methods are more and more widely recognized by researchers. In recent years, computational techniques have been successfully applied to predict the interactions between protein and various kinds of molecules. Especially, protein–protein interactions prediction, protein–RNA interactions prediction and protein–drug interactions prediction based on heterogeneous biological data have become critical topics in the search of functions of proteins and therapeutic targets for various diseases. With the development and application of many new computational techniques, such as machine learning, deep learning, new feature extraction methods and network methods, a great progress has been made in the field of protein interactions prediction. The following pages of this issue are four papers presenting current computational models for various interactions related to proteins, representing only a small portion of current research. The following is a brief description of these papers. In the opening paper of this issue, Xu et al. gave the process for prediction of posttranslational modification sites including feature construction, algorithms, evaluation measurements and online web-servers. There are two types of posttranslational modifications in proteins, and this paper summarized the methods and steps in the prediction of single and multiple PTM sites, respectively [1]. Hasan et al. discussed the latest developments and challenges in protein S-sulfenylation site prediction from available data sets, algorithms and accessible services, and demonstrated the limitations and future prospects of computational prediction tools [2]. One of the most important steps in developing functional genomics and proteomics prediction methods is to represent biological sequences in fixed-length digital form, which can be further analyzed using machine learning algorithms. Several software tools have been developed to apply these algorithms, and Zhao et al. reviewed the technical aspects of these software tools [3]. Zhao et al. introduced a semi-supervised learning algorithm prediction model called random walk for lncRNA-protein association prediction. This approach successfully avoided the difficulty of extracting negative data sets and features and achieved better results than previous algorithms [4]. We would like to express our sincere gratitude to all authors and reviewers for their contribution to this issue. We would also like to extend our appreciation to the Editor-in-Chief, Professor Ben M. Dunn and the editorial staff of Protein & Peptide Letters for their excellent support and for providing us with the opportunity to pursue this hot topic issue.

In 1843, the nephew of Napoleon Bonaparte (i.e. Charles Lucien Bonaparte) established for the first time the proteinaceous nature of snake venom, a complex substance secreted by glands to paralyze and kill either for predatory or defensive purposes. Snake venoms represent a cocktail of thousands of diverse molecules, mainly proteins accounting for up to 95% of venom’s dry weight. These proteins are mainly enzymes such as oxidoreductases e.g. catalase and oxidase; transferases such as ALT; hydrolases including phospholipases, phosphodiesterases and hyaluronidases; lyases; (metalloproteinases) as well as peptides (e.g. cardiotoxins, cytotoxin, hemotoxins alias hemorrhagins such as mucrotoxin or PLA2; postsynaptic neurotoxins, such as cobratoxin or taipoxin). In addition to free amino acids, there are nucleosides, lipids, carbohydrates and metals coupled to proteins, which account for the 5% of venom’s dry weight [1]. The difference in chemical constituents of venoms among various species is both topographical and onto-genic. Now-a-days, Omics technologies are contributing to a qualitative and quantitative progress in the road map towards reconstruction of the natural history of venoms, which is of great interest in diverse fields such as evolutionary ecology and biotechnology [2]. Snake venoms exert potent, selective and pleiotropic biological functions, which can lead to deleterious/toxic effects or beneficial/exploitable solutions for biotechnological/medicinal applications [3]. It is worth noting that animal venoms have been used for a long time in folk medicine, mostly in Asian and Middle-East countries and their applications are well documented as diuretic, cardiotonic, anesthetic or anti-cancers agents. For a bit more than four decades, venoms and toxins of animals have been extensively explored for the management of human ailments like hypertension, diabetes, long-lasting pains, cancers and HIV [4]. Therefore, the complex chemical cocktail of venoms represents a rich source of discovery and development of lead compounds. An increasing number of in vitro and in vivo investigations have revealed the anti-carcinogenic properties of snake venoms and purified toxins in various types of cancer (e.g. colorectal, cervical and breast cancers), produced mostly via the blocking of progress of cancer, stopping of cell cycle, initiation of oxidative-stress mediated apoptosis, inhibition of pro-inflammatory cytokines, and suppression of cancer metastasis. Thereby, anti-angiogenic effects by inhibiting Vascular Endothelial Growth Factors (VEGF) were displayed by the Disintegrin Lebein isolated from snake venom [5-7]. Interestingly, those venoms or purified venom toxins may be either an alternative or a complementary alternative to the currently available chemotherapeutic agents [8]. The current and future challenges towards encapsulation of venoms or purified proteins in functionalized nanoparticles (e.g. biocompatible and biodegradable liposomes/nanovesicles) are entailing in enhancing the anti-cancer potential while minimizing the side effects (e.g. efficacy versus systemic or local toxicities) of free venom or free pure venom proteins [8]. Various novel nanoformulations are being tested, and their transfer from the benchmark to the bedside will certainly constitute a new hope for more accurate theranostic strategies. The crucial emphasis of our special thematic issue of Protein and Peptide Letters concerns the theranostic applications of venom proteins isolated from different organisms (e.g. snakes, frogs, bees, spiders, scorpions, jellyfish, leeches, snails) to diagnostic procedures like hemostasis testing and in therapeutics as anti-thrombotic agents in peripheral arterial thrombosis, deep-vein thrombosis, myocardial infarction, stroke, HIV, diabetes, neurodegenerative disorders, hypertension and cancers. We are thankful to all the Venomics researchers for their valuable contribution to this Special Issue. The stout efforts and promise of the researchers always made the advancement of science to bring innovative perspectives and expectation for the management of emerging chronic and complex disorders

The health threat caused by kidney diseases such as kidney cancer, chronic kidney disease and acute kidney failure, have a high prevalence and represent a large burden of health care cost. Intensive research to develop novel strategies on various aspects have been conducted in order to detect kidney function and treat kidney disease. One potential risk of Chronic Kidney Disease (CKD) is end-stage renal disease, which requires costly renal replacement therapy such as dialysis and transplantation. Kidney disease is conventionally assessed in terms of both overall renal function (glomerular filtration rate) and the presence of kidney damage ascertained by either kidney biopsy or other markers of kidney damage such as proteinuria, abnormal urinary sediment and abnormalities on imaging studies. Urinary proteome analysis has improved the current state of the art of CKD detection and prediction. Additionally, in attempt to prevent or retard the progression of further kidney damage from CKD, kidney specific drug targeting or renal drug delivery systems are used as an attractive strategy to reduce unwanted side effects and to improve drug efficacy in the kidney tissue. Renal Cell Carcinoma (RCC) is the most prevailing kidney cancer. On one hand, targeted therapies dramatically improved progression-free survival towards RCC patients compared with other traditional chemotherapies and radiotherapies; on the other hand, immunotherapy has become an effective approach in the management of RCC patient with metastatic disease because RCC has a high resistance of both chemotherapies and radiotherapies. An increase in interesting preliminary results is being reported in aforementioned subtopics. As witnesses and practitioners in this exciting area, however, we notice that the related communication in public domain is still limited due to the relatively small number of scientists involved. Thus, we feel the necessity to compile a timely issue about the special topic “Protein and Peptide Studies in Kidney Disease”, covering state-of-the-art research papers and expert reviews from this field. We are pleased that Protein & Peptide Letters is willing to realize the idea with us. The opening paper of this issue by Dr. Wu and co-author discusses several approaches to prolong the half-life of protein and peptide drugs based on the unique structures of the glomerular capillary wall and mechanisms of glomerular filtration, such as increasing the size and hydrodynamic diameter; increasing the negative charge to delay the filtration; and increasing plasma protein binding to decrease plasma clearance [1]. Next, Dr. Xu and coauthors describe current low molecular weight proteins and peptide carriers for kidney targeted drug delivery systems, and outline the current status as well as give an outlook on the further study [2]. The following two papers discuss the treatment of RCC. RCC, a type of kidney cancers, is one of the common malignant tumors and accounts for approximately 4% of all cancers with high rates of morbidity and mortality. Dr. Yang and coauthors discuss the role of therapeutic drug monitoring in the individual targeted treatment of RCC [3]. The breakthroughs in the understanding of tumor immune biology and the development of newer generation of cancer immunotherapies have provided a new strategy for battling tumors. Recent progress of the use of immunotherapy with different agents for RCC are presented by Dr. Gong and coauthors. In addition, the combination of different novel immunotherapeutic agents and other immune therapies are also discussed [4]. The last review of this issue by Dr. Zeng and co-worker provide an overview of the varied probes from small molecular to nanoparticle-based sensors that have been applied to the detection of various proteins from bench to bedside [5]. The quantitative measurement of protein and peptides will be benefit for the diagnosis and monitoring of kidney diseases in the future. In summary, experts from different fields have showcased new results and expressed their opinions in this special thematic issue of Protein & Peptide Letters. As the guest editor, I wish that this diverse collection of valuable intellectual contributions will positively influence this emerging area and gain broad readership. At the end, we would like to express our sincerest gratitude to all authors and referees for their invaluable contribution to this issue. We would also like to extend our appreciation to Editor-in-Chief, Professor Ben M. Dunn and the staff of Protein & Peptide Letters, especially Ms. Rukhshanda Rehman, for their excellent support and for providing us with the opportunity to pursue this exciting project.

In developed countries, subfertility has become an increasing problem. Infertility is the inability to conceive after 12 months of regular, unprotected intercourse. It is a worldwide problem, affecting 15% of couples that have unprotected intercourse. In general, 50% of infertility cases are due to a solely female factor, pure male factor accounts for 20-30% of the problem, and the remaining 20-30% is due to a combination of both male and female factors. Again, one of the common complications of pregnancy is spontaneous pregnancy loss which occurs in an estimated 5-15% of pregnancies. Although pregnancy loss has been associated to various hematologic, anatomic, hormonal, immune, and genetic defects, in 30% of the cases, screening tests performed in cases of Repeated Pregnancy Loss (RPL) give negative results. The last 10 to 15 years have witnessed rapid advances in approaches for the analyses of DNA sequence and structure. The amount of information currently available about the genomes in human and animal reproduction has increased dramatically in the past few years and the rate of its accumulation will continue to increase. Also, proteomics has been widely used over recent years in almost all relevant biological samples (tissues, fluids, cells) to depict both physiological and pathological states, and has yielded important results, including the significance of posttranslational modifications in sperm and the intense immunological-inflammatory processes that take place during ovulation and implantation. Proteomics represents a state-of-the-art, technology-driven science, which, in a high-throughput mode, studies proteins and their post-translational modifications and interactions. This approach provides the opportunity to elucidate complex biological processes and conditions, including fertilization, embryo implantation and differentiation and pregnancy. Proteomics is providing useful insights into physiology of human reproduction and it is leading to the identification of numerous proteins in biological tissues and fluids related to human reproduction that may be potential biomarkers and/or treatment targets. In the area of reproduction, this approach seems to be the most promising and powerful platform that has been recently applied in order to widely study the physiology and pathophysiology of reproduction and to identify novel markers of diseases. In the diagnostic of male infertility, semen analysis represents the cornerstone for the assessment of the male partner in subfertile couples. Many studies, however, criticized the thresholds used to define male factor infertility using sperm concentration, motility and morphology, the three classical sperm parameters measured by all laboratories. Additional markers of male fertility have been proposed including the evaluation of anti-sperm antibodies, biochemical tests, kinematic parameters, sperm DNA integrity and oxidative stress markers. However, due to the lack of a standardized and universally accepted assays and the expensive or time-consuming techniques, these parameters did not reveal a role in the clinical workout of male infertility. Therefore, several sources of clinical biomarkers have been proposed for female reproductive tract disease and cervical mucus represents the most promising source of biomarkers reflecting the health of the male tract. This special issue of Protein & Peptide Letters, entitled “Protein and peptide markers in reproduction” intends to showcase new results and express the opinions of a selection of specialists who have expanded the field with their recent discoveries. In the opening paper of this issue, Dr. Bibancos and co-authors discuss the potential use of proteomic platforms in the workflow of couple infertility in clinical practice. Beyond the identification of the oocytes and embryos with the best developmental potentials in assisted reproductive techniques or prediction of a pregnancy’s outcome, it may add significant information for the diagnosis of both male and female infertility [1]. The most abundant sperm nuclear proteins are protamins. Dr. Oliva, in addition to briefly reviewing the studies available so far about protamine alterations and male infertility, provided a very comprehensive protocol for the successful implementation of protamine extraction and analysis into the laboratory routine, including protocol variations for specific procedure applications [2]. The post-genomic approaches in the study of male sperm function, consisting of different methodologies for concurrently testicular transcriptome studies, protein compositional analysis and metabolomics findings, have been reviewed by Dr. Dipresa [3]. Dr. Luca summarized some of the recent findings regarding the immunomodulatory role of Sertoli cells, and especially the key Sertoli cell regulatory proteins that are associated with the well-defined immune privileged status of the testis [4]. Dr. De Toni analyzed the most recent cues about the molecular bases of the known process of sperm thermotaxis, and the possible pathophysiological and therapeutic repercussions deriving from the characterization of sperm proteins acting as thermosensors [5]. Furthermore, the expression in sperm surface of specific nicotine receptors might represent a marker of sperm smokingrelated damage. Dr. La Vignera discussed in his manuscript the mechanism by which nicotine alters spermatozoa and the expression pattern of nicotinic receptors subunits in human spermatozoa [6]. In addition Dr. Di Nicuolo reviewed the role of inflammosome in female reproduction and namely in RPL [7]. Dr. Fernandez-Hermida in her review article collected relevant knowledge about the physicochemical properties and functions of the cervical mucus, including its important role as a clinical marker of female fertility, and draws attention to cervical mucus as the best source of potential protein biomarkers to assess the health of female genital tract [8]. Proteomic platforms have been applied to identify novel markers of disease in seminal plasma. Dr. Grande reported a research article aimed to identify, for the first time, novel seminal biomarkers for the male tract infection by Enterococcus faecalis, using proteomic profiling, in order to understand the effect of E. faecalis on the physiopathology of male reproduction [9]. Among the proposed markers of Male Accessory Gland Inflammation (MAGI), soluble urokinase-type plasminogen activator receptor (suPAR) has been recently proposed as a reliable and sensitive marker. In the study by Milardi et al., the role of suPAR as a protein marker of MAGI was confirmed in hypogonadism, supporting the hypothesis that hypogonadism induces a state of inflammation in male accessory glands which is involved in male infertility [10]. In summary, experts from the field of male and female reproduction have presented new results and reviewed the current advances in their specific area in this special thematic issue of Protein & Peptide Letters. As the guest editors, we wish that this diverse collection of articles will gain broad readership. The protein and peptide analysis provides a good starting point to discover new reproduction-related proteins; however, further functional analyses should be conducted to gain a specific role for fertility traits in human reproduction metabolism. The comprehensive proteomic profiling of human reproduction and its pathology constitutes a major challenge for the international scientific community. We would like to express our sincere gratitude to all authors and referees for their contribution to this issue. We would also like to extend our appreciation to the Editor-in-Chief, Professor Ben M. Dunn and the editorial staff of Protein & Peptide Letters, especially Ms Amna Sajid, for their excellent support and for providing us with the opportunity to pursue this hot topic issue.

Global warming, diminishing fossil fuel resources and growing energy requirements have kindled the research on alternative fuels. Moreover, cleaner production and sustainable consumption of industrial products have become forefront research areas in recent years. The special issue “Recent trends in biofuels and bio-industry” focusses on the emerging trends in this area. Original research articles presenting the cornerstone technologies to address the bottleneck issues in biofuel and bio-industrial processes, and critical review articles analyzing the challenges and discussing the future opportunities are welcomed. The submissions focusing on the characterization/engineering of novel proteins/enzymes of industrial/biofuel importance, identification/engineering/synthesis of metabolic pathways for biofuels and industrial chemicals, would be highly appreciated.

In the last decade, materials synthesis and processing under conditions with low energy consumptions have been gathering attractive interests for building sustainable societies. Because biominerals are precisely-designed composite materials made from the combinations of inorganic minerals such as calcium carbonate, silica, hydroxyapatite and organic template molecules such as proteins and peptides under ambient conditions, biomimetic production of organic-inorganic hybrid nanomaterials would be one of the approaches to provide a variety of morphologies and arrangements of inorganic components for fabrications and applications. So far, many scientists/chemists have devoted their great efforts to control morphologies of inorganic precipitates using organic compounds such as polymers, surfactants, and so on. Proteins and peptides are promising organic template molecules for use in the biomimetic process because they confer several advantages: 1) proteins and peptides can offer milder conditions for the production, 2) proteins and peptides can impose on the size, shape, and crystal structure of the inorganic products, 3) proteins and peptides can offer the potential to produce materials with highly specific or multiple functions. Therefore, the rapid progress in the development of synthesis and processing of organic-inorganic hybrid materials is evident and the compilation of the subtopics listed below would appear to be timely. This theme issue intends to offer for understanding new knowledge on protein- and/or peptide-associated materials and propose unprecedented and fundamental concepts as well as some directions to their practical applications. The theme issue included in the following potential subtopics, but are not limited to:

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