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Health is fundamental for the development of individuals and evolution of species. In that sense, for human societies is relevant to understand how the human body has developed molecular strategies to maintain health. In the present review, we summarize diverse evidence that support the role of peptides in this endeavor. Of particular interest to the present review are antimicrobial peptides (AMP) and cell-penetrating peptides (CPP). Different experimental evidence indicates that AMP/CPP are able to regulate autophagy, which in turn regulates the immune system response. AMP also assists in the establishment of the microbiota, which in turn is critical for different behavioral and health aspects of humans. Thus, AMP and CPP are multifunctional peptides that regulate two aspects of our bodies that are fundamental to our health: autophagy and microbiota. While it is now clear the multifunctional nature of these peptides, we are still in the early stages of the development of computational strategies aimed to assist experimentalists in identifying selective multifunctional AMP/CPP to control nonhealthy conditions. For instance, both AMP and CPP are computationally characterized as amphipatic and cationic, yet none of these features are relevant to differentiate these peptides from non-AMP or non-CPP. The present review aims to highlight current knowledge that may facilitate the development of AMP’s design tools for preventing or treating illness.

Predicting protein&ndash, protein interactions (PPI) represents an important challenge in structural bioinformatics. Current computational methods display different degrees of accuracy when predicting these interactions. Different factors were proposed to help improve these predictions, including choosing the proper descriptors of proteins to represent these interactions, among others. In the current work, we provide a representative protein structure that is amenable to PPI classification using machine learning approaches, referred to as residue cluster classes. Through sampling and optimization, we identified the best algorithm&ndash, parameter pair to classify PPI from more than 360 different training sets. We tested these classifiers against PPI datasets that were not included in the training set but shared sequence similarity with proteins in the training set to reproduce the situation of most proteins sharing sequence similarity with others. We identified a model with almost no PPI error (96&ndash, 99% of correctly classified instances) and showed that residue cluster classes of protein pairs displayed a distinct pattern between positive and negative protein interactions. Our results indicated that residue cluster classes are structural features relevant to model PPI and provide a novel tool to mathematically model the protein structure/function relationship.

Cells adapt to different stress conditions, such as the antibiotics presence. This adaptation sometimes is achieved by changing relevant protein positions, of which the mutability is limited by structural constrains. Understanding the basis of these constrains represent an important challenge for both basic science and potential biotechnological applications. To study these constraints, we performed a systematic saturation mutagenesis of the transmembrane region of HokC, a toxin used by Escherichia coli to control its own population, and observed that 92% of single-point mutations are tolerated and that all the non-tolerated mutations have compensatory mutations that reverse their effect. We provide experimental evidence that HokC accumulates multiple compensatory mutations that are found as correlated mutations in the HokC family multiple sequence alignment. In agreement with these observations, transmembrane proteins show higher probability to present correlated mutations and are less densely packed locally than globular proteins, previous mutagenesis results on transmembrane proteins further support our observations on the high tolerability to mutations of transmembrane regions of proteins. Thus, our experimental results reveal the HokC transmembrane region high tolerance to loss-of-function mutations that is associated with low sequence conservation and high rate of correlated mutations in the HokC family sequences alignment, which are features shared with other transmembrane proteins.

The emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity, hence, the development of computer-assisted procedures to identify antibiotic activity in human-targeted compounds may assist in preventing the emergence of resistant microbes. In this regard, it is worth noting that while most antibiotics used to treat human infectious diseases are non-peptidic compounds, most known antimicrobials nowadays are peptides, therefore all computer-based models aimed to predict antimicrobials either use small datasets of non-peptidic compounds rendering predictions with poor reliability or they predict antimicrobial peptides that are not currently used in humans. Here we report a machine-learning-based approach trained to identify gut antimicrobial compounds, a unique aspect of our model is the use of heterologous training sets, in which peptide and non-peptide antimicrobial compounds were used to increase the size of the training data set. Our results show that combining peptide and non-peptide antimicrobial compounds rendered the best classification of gut antimicrobial compounds. Furthermore, this classification model was tested on the latest human-approved drugs expecting to identify antibiotics with broad-spectrum activity and our results show that the model rendered predictions consistent with current knowledge about broad-spectrum antibiotics. Therefore, heterologous machine learning rendered an efficient computational approach to classify antimicrobial compounds.

The emergence of complex diseases is promoting a change from one-target to multitarget drugs and peptides are ideal molecules to fulfill this polypharmacologic role. Here we review current status in the design of polypharmacological peptides aimed to treat complex diseases, focusing on tuberculosis. In this sense, combining multiple activities in single molecules is a two-sided sword, as both positive and negative side effects might arise. These polypharmacologic compounds may be directed to regulate autophagy, a catabolic process that enables cells to eliminate intracellular microbes (xenophagy), such as Mycobacterium tuberculosis (MBT). Here we review some strategies to control MBT infection and propose that a peptide combining both antimicrobial and pro-autophagic activities would have a greater potential to limit MBT infection. This endeavor may complement the knowledge gained in understanding the mechanism of action of antibiotics and may lead to the design of better polypharmacological peptides to treat complex diseases such as tuberculosis.

Although most antibiotics act on cells that are actively dividing and non-dividing cells such as in microbe sporulation or cancer stem cells represent a new paradigm for the control of disease. In addition to their relevance to health, such antibiotics may promote our understanding of the relationship between the cell cycle and cell death. No antibiotic specifically acting on microbial cells arrested in their cell cycle has been identified until the present time. In this study we used an antimicrobial peptide derived from α-pheromone, IP-1, targeted against MATa Saccharomyces cerevisiae cells in order to assess its dependence on cell cycle arrest to kill cells. Analysis by flow cytometry and fluorescence microscopy of various null mutations of genes involved in biological processes activated by the pheromone pathway (the mitogen-activated protein kinase pathway, cell cycle arrest, cell proliferation, autophagy, calcium influx) showed that IP-1 requires arrest in G0/G1 in order to kill yeast cells. Isolating cells in different cell cycle phases by elutriation provided further evidence that entry into cell cycle arrest, and not into G1 phase, is necessary if our peptide is to kill yeast cells. We also describe a variant of IP-1 that does not activate the pheromone pathway and consequently does not kill yeast cells that express the pheromone’s receptor; the use of this variant peptide in combination with different cell cycle inhibitors that induce cell cycle arrest independently of the pheromone pathway confirmed that it is cell cycle arrest that is required for the cell death induced by this peptide in yeast. We show that the cell death induced by IP-1 differs from that induced by α-pheromone and depends on FIG1 in a way independent of the cell cycle arrest induced by the pheromone. Thus, IP-1 is the first molecule described that specifically kills microbial cells during cell cycle arrest, a subject of interest beyond the process of mating in yeast cells. The experimental system described in this study should be useful in the study of the mechanisms at play in the communication between cell cycle arrest and cell death on other organisms, hence promoting the development of new antibiotics.

Protein structure and protein function should be related, yet the nature of this relationship remains unsolved. Mapping the critical residues for protein function with protein structure features represents an opportunity to explore this relationship, yet two important limitations have precluded a proper analysis of the structure-function relationship of proteins: (i) the lack of a formal definition of what critical residues are and (ii) the lack of a systematic evaluation of methods and protein structure features. To address this problem, here we introduce an index to quantify the protein-function criticality of a residue based on experimental data and a strategy aimed to optimize both, descriptors of protein structure (physicochemical and centrality descriptors) and machine learning algorithms, to minimize the error in the classification of critical residues. We observed that both physicochemical and centrality descriptors of residues effectively relate protein structure and protein function, and that physicochemical descriptors better describe critical residues. We also show that critical residues are better classified when residue criticality is considered as a binary attribute (i.e., residues are considered critical or not critical). Using this binary annotation for critical residues 8 models rendered accurate and non-overlapping classification of critical residues, confirming the multi-factorial character of the structure-function relationship of proteins.

Cell penetrating peptides (CPP) and cationic antibacterial peptides (CAP) have similar physicochemical properties and yet it is not understood how such similar peptides display different activities. To address this question, we used Iztli peptide 1 (IP-1) because it has both CPP and CAP activities. Combining experimental and computational modeling of the internalization of IP-1, we show it is not internalized by receptor-mediated endocytosis, yet it permeates into many different cell types, including fungi and human cells. We also show that IP-1 makes pores in the presence of high electrical potential at the membrane, such as those found in bacteria and mitochondria. These results provide the basis to understand the functional redundancy of CPPs and CAPs.

"Ste2p is a G protein-coupled receptor (GPCR) in Saccharomyces cerevisiae that mediates mating by responding to the alpha-mating factor pheromone. Ste2p belongs to a subfamily of GPCRs with no global sequence similarity to GPCRs of known atomic three-dimensional structure, yet it shares functional similarities with many of these. To deepen our understanding of the structure-function relationship of this receptor, we built an atomic threedimensional homology-based model of Ste2p that was used to simulate the docking of the alpha pheromone. The Ste2p model is in general agreement with the available experimental data and allowed us to propose that the interface between Ste2p and alpha pheromone is formed by 26 residues, most of which are polar residues located at the three extracellular loops and helices HI, H5, and H6. This interface does not include Ile190, a highly conserved residue among fungal species, located at the second extracellular loop and therefore a potential binding site residue. By performing mutagenesis of STE2 at this position we observed only a small effect of this residue in receptor signaling. Hence, the Ste2p model presented here is consistent in general with current experimental data and constitutes a framework to test hypothesis about the structure-function relationship of this receptor."

Antimicrobial peptides (AMPs) may display the ability to penetrate cells, which may be relevant for their antibiotic activity. To investigate the relevance of the penetrating activity for the antibiotic activity of AMPs, here we describe a method based on the combined use of confocal microscopy and computational modeling coupled with cell death kinetics.

Antimicrobial peptides (AMPs) may display the ability to penetrate cells, which may be relevant for their antibiotic activity. To investigate the relevance of the penetrating activity for the antibiotic activity of AMPs, here we describe a method based on the combined use of confocal microscopy and computational modeling coupled with cell death kinetics.

The emergence of complex diseases is promoting a change from one-target to multitarget drugs and peptides are ideal molecules to fulfill this polypharmacologic role. Here we review current status in the design of polypharmacological peptides aimed to treat complex diseases, focusing on tuberculosis. In this sense, combining multiple activities in single molecules is a two-sided sword, as both positive and negative side effects might arise. These polypharmacologic compounds may be directed to regulate autophagy, a catabolic process that enables cells to eliminate intracellular microbes (xenophagy), such as Mycobacterium tuberculosis (MBT). Here we review some strategies to control MBT infection and propose that a peptide combining both antimicrobial and pro-autophagic activities would have a greater potential to limit MBT infection. This endeavor may complement the knowledge gained in understanding the mechanism of action of antibiotics and may lead to the design of better polypharmacological peptides to treat complex diseases such as tuberculosis.

Netrin-1 receptors UNC5H (UNC5H1-4) were originally proposed to mediate the chemorepulsive activity of netrin-1 during axonal guidance processes. However, UNC5H receptors were more recently described as dependence receptors and, as such, able to trigger apoptosis in the absence of netrin-1. They were also proposed as putative tumor suppressors. Here, we show that UNC5H2 physically interacts with the serine/threonine kinase death-associated protein kinase (DAP-kinase) both in cell culture and in embryonic mouse brains. This interaction occurs in part through the respective death domains of UNC5H2 and DAP-kinase. Moreover, part of UNC5H2 proapoptotic activity occurs through this interaction because UNC5H2-induced cell death is partly impaired in the presence of dominant-negative mutants of DAP-kinase or in DAP-kinase mutant murine embryonic fibroblast cells. In the absence of netrin-1, UNC5H2 reduces DAP-kinase autophosphorylation on Ser308 and increases the catalytic activity of the kinase while netrin-1 blocks UNC5H2-dependent DAP-kinase activation. Thus, the pair netrin-1/UNC5H2 may regulate cell fate by controlling the proapoptotic kinase activity of DAP-kinase.

Programmed cell death (pcd) may take the form of apoptosis or of nonapoptotic pcd. Whereas cysteine aspartyl-specific proteases (caspases) mediate apoptosis, the mediators of nonapoptotic cell death programs are much less well characterized. Here we report that alternative, nonapoptotic pcd induced by the neurokinin-1 receptor (NK1R) activated by its ligand Substance P, is mediated by a MAPK phosphorylation cascade recruited by the scaffold protein arrestin 2. The activation of the protein kinases Raf-1, MEK2, and ERK2 is essential for this form of nonapoptotic pcd, leading to the phosphorylation of the orphan nuclear receptor Nur77. NK1R-mediated cell death was inhibited by a dominant negative form of arrestin 2, Raf-1, or Nur77, by MEK1/2-specific inhibitors, and by RNA interference directed against ERK2 or MEK2 but not ERK1 or MEK1 and against Nur77. The MAPK pathway is also activated in neurons in primary culture undergoing NK1R-mediated death, since the MEK inhibitor PD98059 inhibited Substance P-induced death in primary striatal neurons. These results suggest that Nur77, which is regulated by a MAPK pathway activated via arrestin 2, modulates NK1R-mediated nonapoptotic pcd.

Protein engineering is an emerging area that has expanded our understanding of protein folding and laid the groundwork for the creation of unprecedented structures with unique functions. We previously designed the first native-like pore-forming protein, small globular protein (SGP). We show here that this artificially engineered protein has membrane-disrupting properties and anti-tumor activity in several cancer animal models. We propose and validate a mechanism for the selectivity of SGP toward cell membranes in tumors. SGP is the prototype for a new class of artificial proteins designed for therapeutic applications.

Accumulation of misfolded proteins and alterations in Ca2+ homeostasis in the endoplasmic reticulum (ER) causes ER stress and leads to cell death. However, the signal-transducing events that connect ER stress to cell death pathways are incompletely understood. To discern the pathway by which ER stress-induced cell death proceeds, we performed studies on Apaf-1−/− (null) fibroblasts that are known to be relatively resistant to apoptotic insults that induce the intrinsic apoptotic pathway. While these cells were resistant to cell death initiated by proapoptotic stimuli such as tamoxifen, they were susceptible to apoptosis induced by thapsigargin and brefeldin-A, both of which induce ER stress. This pathway was inhibited by catalytic mutants of caspase-12 and caspase-9 and by a peptide inhibitor of caspase-9 but not by caspase-8 inhibitors. Cleavage of caspases and poly(ADP-ribose) polymerase was observed in cell-free extracts lacking cytochrome c that were isolated from thapsigargin or brefeldin-treated cells. To define the molecular requirements for this Apaf-1 and cytochrome c-independent apoptosis pathway further, we developed a cell-free system of ER stress-induced apoptosis; the addition of microsomes prepared from ER stress-induced cells to a normal cell extract lacking mitochondria or cytochromec resulted in processing of caspases. Immunodepletion experiments suggested that caspase-12 was one of the microsomal components required to activate downstream caspases. Thus, ER stress-induced programmed cell death defines a novel, mitochondrial and Apaf-1-independent, intrinsic apoptotic pathway.

We describe the cloning and characterization of a rat single transmembrane protein that is homologous to the common neurotrophin receptor p75NTR in its death domain and the transmembrane region but dissimilar outside these regions. We have dubbed this protein PLAIDD, for p75-like apoptosis-inducing death domain protein. PLAIDD messenger RNA, which is ubiquitously distributed, is highly expressed in the embryo, but downregulated in adult tissues. Alternative splicing within the extracellular region of PLAIDD generates four RNA species, but only two of them are translated, PLAIDD_L and PLAIDD_S (long and short isoforms, respectively). While the amino acid sequence of the intracellular region of PLAIDD displays 41% identity with the intracellular region of p75NTR, the extracellular region of PLAIDD does not reveal any homology with p75NTR. Overexpression of each isoform of PLAIDD led to cytotoxicity in superior cervical ganglion neurons and in human embryonic kidney 293T cells. Both isoforms of PLAIDD could be co-immunoprecipitated with p75NTR, suggesting an interaction between these molecules.

Communication between cells is a ubiquitous feature of cell populations and is frequently realized by secretion and detection of signaling molecules. Direct visualization of the resulting complex gradients between secreting and receiving cells is often impossible due to the small size of diffusing molecules and because such visualization requires experimental perturbations such as attachment of fluorescent markers, which can change diffusion properties. We designed a method to estimate such extracellular concentration profiles in vivo by using spatiotemporal mathematical models derived from microscopic analysis. This method is applied to populations of thousands of haploid yeast cells during mating in order to quantify the extracellular distributions of the pheromone α-factor and the activity of the aspartyl protease Bar1. We demonstrate that Bar1 limits the range of the extracellular pheromone signal and is critical in establishing α-factor concentration gradients, which is crucial for effective mating. Moreover, haploid populations of wild type yeast cells, but not BAR1 deletion strains, create a pheromone pattern in which cells differentially grow and mate, with low pheromone regions where cells continue to bud and regions with higher pheromone levels and gradients where cells conjugate to form diploids. However, this effect seems to be exclusive to high-density cultures. Our results show a new role of Bar1 protease regulating the pheromone distribution within larger populations and not only locally inside an ascus or among few cells. As a consequence, wild type populations have not only higher mating efficiency, but also higher growth rates than mixed MAT a bar1Δ/MAT α cultures. We provide an explanation of how a rapidly diffusing molecule can be exploited by cells to provide spatial information that divides the population into different transcriptional programs and phenotypes., Author Summary Haploid budding yeast cells cannot actively move to find a mating partner, like some flagellated bacteria do. Instead they must grow a so-called shmoo – a mating projection – precisely into the direction of a potential partner. They communicate with each other by releasing pheromones into their environment, which are sensed by cells of the opposite mating type. This serves the localization of nearby cells and initiates growth arrest and mating. Paradoxically, yeast cells also secrete the protease Bar1 that destroys pheromones. To visualize the resulting pheromone distribution and understand the effect on mating efficiency, we combined fluorescence imaging and mathematical modeling. We observed that the controlled destruction of pheromones by the yeast cells is beneficial to communication since it causes relatively higher pheromone concentrations in areas where cells are dense and vanishing pheromone concentrations elsewhere. This allows the population to maintain two different cellular behaviors at the same time, i.e. mating and continued growth, a behavior which disappears when we genetically delete the gene for the pheromone-destroying protein.

The endoplasmic reticulum (ER) is the site of assembly of polypeptide chains destined for secretion or routing into various subcellular compartments. It also regulates cellular responses to stress and intracellular Ca2+ levels. A variety of toxic insults can result in ER stress that ultimately leads to apoptosis. Apoptosis is initiated by the activation of members of the caspase family and serves as a central mechanism in the cell death process. The present study was carried out to determine the role of caspases in triggering ER stress-induced cell death. Treatment of cells with ER stress inducers such as brefeldin-A or thapsigargin induces the expression of caspase-12 protein and also leads to translocation of cytosolic caspase-7 to the ER surface. Caspase-12, like most other members of the caspase family, requires cleavage of the prodomain to activate its proapoptotic form. Caspase-7 associates with caspase-12 and cleaves the prodomain to generate active caspase-12, resulting in increased cell death. We propose that any cellular insult that causes prolonged ER stress may induce apoptosis through caspase-7-mediated caspase-12 activation. The data underscore the involvement of ER and caspases associated with it in the ER stress-induced apoptotic process.

We have previously described a novel cancer chemotherapeutic approach based on the induction of apoptosis in targeted cells by homing pro-apoptotic peptides. In order to improve this approach we developed a computational method (approach for detecting potential apoptotic peptides, APAP) to detect short PAPs, based on the prediction of the helical content of peptides, the hydrophobic moment, and the isoelectric point. PAPs are toxic against bacteria and mitochondria, but not against mammalian cells when applied extracellularly. Among other peptides, substance P was identified as a PAP and subsequently demonstrated to be a pro-apoptotic peptide experimentally. APAP thus provides a method to detect and ultimately improve pro-apoptotic peptides for chemotherapy.

Tuberculosis (TB) is a world-leading infectious disease caused by Mycobacterium tuberculosis (Mtb). The current treatment lasts 6 months and has contributed to the development of multidrug resistant (MDR) strains that nowadays cause almost half a million deaths around the globe. Forty years of research have rendered only 1 new drug to treat the new MDR strains. In the current review we present emerging trends to treat TB particularly focused on natural and synthetic peptides. The ability of some of these peptides to display multifunctional roles in TB treatment, particularly immune system modulation through autophagy and direct antimicrobial activity against Mtb, may present advantages to control the impact of this disease. We review the mechanisms of action relevant in the development of multifunctional peptides that may lead to evaluate new ways to treat TB, a disease that has accompanied human society for centuries

There were a number of errors in the legends for Figures 1, 3, S2, S5, and S9. The complete, correct Figure legends can be found here: http://www.plosone.org/corrections/pone.0040125.001.cn.tif

Increased stability at alkaline pH should be a valuable attribute for the utilization of penicillin acylase in bioreactors employed to convert penicillins into 6-aminopenicillanic acid, a precursor of semisynthetic penicillins. In these systems, base is added for pH control, which results in local alkaline conditions that promote enzyme inactivation. Hydrolysis and synthesis reactions are also pH dependent. Here, we report work in which the gene coding for Escherichia coli penicillin acylase was subjected to oligonucleotide-directed random mutagenesis at regions coding for amino acids predicted to be at the surface of the enzyme. The resulting mutant library, cloned in E. coli, was screened by a filter paper assay of the colonies for the presence of penicillin acylase activity with enhanced stability at alkaline pH. Characterization of one of the selected clones revealed the presence of a mutation, Trp431-Arg, which would presumably alter the surface charge of the protein. In vitro experiments demonstrated a near twofold increase in the half-life of the mutant enzyme when stored at pH 8.5 as compared with the wild-type enzyme, with a comparable specific activity at several pH values. In general, the mutant displayed increased stability toward the basic side in the pH-stability profile. (c) 1995 John Wiley & Sons, Inc.

Hunter-killer peptides combine two activities in a single polypeptide that work in an independent fashion like many other multi-functional, multi-domain proteins. We hypothesize that emergent functions may result from the combination of two or more activities in a single protein domain and that could be a mechanism selected in nature to form moonlighting proteins. We designed moonlighting peptides using the two mechanisms proposed to be involved in the evolution of such molecules (i.e., to mutate non-functional residues and the use of natively unfolded peptides). We observed that our moonlighting peptides exhibited two activities that together rendered a new function that induces cell death in yeast. Thus, we propose that moonlighting in proteins promotes emergent properties providing a further level of complexity in living organisms so far unappreciated.

Exhaustive prediction of physicochemical properties of peptide sequences is used in different areas of biological research. One example is the identification of selective cationic antibacterial peptides (SCAPs), which may be used in the treatment of different diseases. Due to the discrete nature of peptide sequences, the physicochemical properties calculation is considered a high-performance computing problem. A competitive solution for this class of problems is to embed algorithms into dedicated hardware. In the present work we present the adaptation, design and implementation of an algorithm for SCAPs prediction into a Field Programmable Gate Array (FPGA) platform. Four physicochemical properties codes useful in the identification of peptide sequences with potential selective antibacterial activity were implemented into an FPGA board. The speed-up gained in a single-copy implementation was up to 108 times compared with a single Intel processor cycle for cycle. The inherent scalability of our design allows for replication of this code into multiple FPGA cards and consequently improvements in speed are possible. Our results show the first embedded SCAPs prediction solution described and constitutes the grounds to efficiently perform the exhaustive analysis of the sequence-physicochemical properties relationship of peptides.

A long-standing goal in biology is to establish the link between function, structure, and dynamics of proteins. Considering that protein function at the molecular level is understood by the ability of proteins to bind to other molecules, the limited structural data of proteins in association with other bio-molecules represents a major hurdle to understanding protein function at the structural level. Recent reports show that protein function can be linked to protein structure and dynamics through network centrality analysis, suggesting that the structures of proteins bound to natural ligands may be inferred computationally. In the present work, a new method is described to discriminate protein conformations relevant to the specific recognition of a ligand. The method relies on a scoring system that matches critical residues with central residues in different structures of a given protein. Central residues are the most traversed residues with the same frequency in networks derived from protein structures. We tested our method in a set of 24 different proteins and more than 260,000 structures of these in the absence of a ligand or bound to it. To illustrate the usefulness of our method in the study of the structure/dynamics/function relationship of proteins, we analyzed mutants of the yeast TATA-binding protein with impaired DNA binding. Our results indicate that critical residues for an interaction are preferentially found as central residues of protein structures in complex with a ligand. Thus, our scoring system effectively distinguishes protein conformations relevant to the function of interest., Author Summary Proteins participate in most of the doings of the cells through a variety of interactions. There is an intimate relationship between the function of a protein and its three-dimensional structure, but understanding this relationship remains an unsolved problem, in part due to the limited information on protein structures bound to other biological molecules. On the other hand, thousands of protein structures in the unbound or free form, are made public every year and these differ from those of the bound structures. How to predict the protein structure in the bound form may assist researchers in understanding the structure/function relationship. Here we report that protein structures bound to other molecules tend to present, as central amino acids, those that are critical for binding other molecules. This feature allowed us to identify the protein structures known to be involved in protein interactions from a screening of thousands of structures derived from the free form.