Family Of Homologous Proteins Research Articles

Does actin polymerization contribute to closure of the avian ductus arteriosus (Gallus gallus)?

The ductus arteriosus (DA) is a cardiovascular embryonic shunt present in all developing land vertebrates. This oxygen-sensitive blood vessel plays the important role of connecting the pulmonary artery to the aorta during development and directs blood flow toward the fetal respiratory organ (placenta in mammals or chorioallantois in birds and reptiles). Permanent vessel closure upon birth or hatch is necessary to establish proper circulation in the neonate. Increased arterial PO2 at birth or hatch stimulates DA closure via smooth muscle contraction. Smooth muscle contraction, such as in the DA, is governed by myosin light chain kinase (MLCK) stimulating increased cross-bridge cycling, and through increases in actin polymerization. The Ras homologous protein family (Rho) of GTPases are involved in calcium sensitization and contraction of vascular smooth muscle but have mostly been studied for their role in cell migration and signaling. Rho GTPases activate the Rho activated kinase (ROCK) pathway, which works in conjunction with MLCK to generate smooth muscle contraction as well as activating LIM kinase which inhibits the actin depolymerizer cofilin. Rac and Cdc42 GTPase pathways increase contractile strength in smooth muscle cells by activating the nucleator Arp2/3, leading to increased actin polymerization. The role of these GTPase pathways during closure of the DA is unknown. We examined the role of the Rho, Rac, and Cdc42 GTPase pathways leading to actin polymerization for their role in the closure of the avian DA in late-term chicken ( Gallus gallus) embryos. We hypothesized that inhibiting the pathways that lead to actin polymerization will inhibit tension development in the DA. Using a Danish Myo Technology wire myograph, DA physiology was examined when exposed to 25% oxygen followed by activators and inhibitors of the Rho, Rac, and Cdc42 GTPase pathways. Activation of Rho A pathways with CN01 (0.2 units/ml) produced contraction of the DA. Blocking ROCK pathways with Y-27632 (10 mM) removed the O2-sensitivity of the vessel, while the vessel was still able to respond to phenylephrine (100 mM). This suggests the Rho pathway is involved in maintaining baseline tension and contractile response to O2. Exposure to inhibitors of Rac (10 uM EHT 1864) and Cdc42 (via ML141) GTPases blocked the ability of vessels to contract in response to 25% oxygen, even after 3 hours of constant O2. Similarly, inhibition of the Arp2/3 complex (100 uM CK666) reduced the ability of the vessel to respond to oxygen. These findings suggest that Rho A, Rac, and Cdc42 pathways play a role in active tension of the chicken DA during development by potentially increasing actin polymerization. This research was funded by Grant R15HL14887 from NIH-NHLBI. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Mechanoregulation and function of calponin and transgelin.

It is well known that chemical energy can be converted to mechanical force in biological systems by motor proteins such as myosin ATPase. It is also broadly observed that constant/static mechanical signals potently induce cellular responses. However, the mechanisms that cells sense and convert the mechanical force into biochemical signals are not well understood. Calponin and transgelin are a family of homologous proteins that participate in the regulation of actin-activated myosin motor activity. An isoform of calponin, calponin 2, has been shown to regulate cytoskeleton-based cell motility functions under mechanical signaling. The expression of the calponin 2 gene and the turnover of calponin 2 protein are both under mechanoregulation. The regulation and function of calponin 2 has physiological and pathological significance, as shown in platelet adhesion, inflammatory arthritis, arterial atherosclerosis, calcific aortic valve disease, post-surgical fibrotic peritoneal adhesion, chronic proteinuria, ovarian insufficiency, and tumor metastasis. The levels of calponin 2 vary in different cell types, reflecting adaptations to specific tissue environments and functional states. The present review focuses on the mechanoregulation of calponin and transgelin family proteins to explore how cells sense steady tension and convert the force signal to biochemical activities. Our objective is to present a current knowledge basis for further investigations to establish the function and mechanisms of calponin and transgelin in cellular mechanoregulation.

Constraint-Induced Movement Therapy Promotes Myelin Remodeling and Motor Function by Mediating Sox2/Fyn Signals in Rats with Hemiplegic Cerebral Palsy.

Hypoxic-ischemic brain injury in infants often leads to hemiplegic motor dysfunction. The mechanism of their motor dysfunction has been attributed to deficiencies of the transcription factor SRY (sex-determining region) box 2 (Sox2) or the non-receptor-type tyrosine kinase Fyn (involved in neuronal signal transduction), which causes a defect in myelin formation. Constraint-induced movement therapy (CIMT) following cerebral hypoxia-ischemia may stimulate myelin growth by regulating Sox2/Fyn, Ras homolog protein family A (RhoA), and Rho-associated kinase 2 (ROCK2) expression levels. This study investigated how Sox2/Fyn regulates myelin remodeling following CIMT to improve motor function in rats with hemiplegic cerebral palsy (HCP). To investigate the mechanism of Sox2 involvement in myelin growth and neural function in rats with HCP, Lentivirus-Sox2 adeno-associated virus and negative control-Lenti-Sox2 adeno-associated virus were injected into the lateral ventricle. The rats were divided into a control group and an HCP group with different interventions (CIMT, Lenti-Sox2 [LS], or negative control-Lenti-Sox2 [NS] treatment), yielding the HCP, HCP plus CIMT (HCP + CIMT), HCP + LS, HCP + LS + CIMT, HCP + NS, and HCP + NS + CIMT groups. Front-limb suspension and RotaRod tests, Golgi-Cox staining, transmission electron microscopy, immunofluorescence staining, Western blotting, and quantitative polymerase chain reaction experiments were used to analyze motor function, dendrite/axon area, myelin ultrastructure, and the levels of expression of oligodendrocytes and Sox2/Fyn/RhoA/ROCK2 in the motor cortex. The rats in the HCP + LS + CIMT group had better values for motor function, dendrite/axon area, myelin ultrastructure, oligodendrocytes, and Sox2/Fyn/RhoA/ROCK2 expression in the motor cortex than rats in the HCP and HCP + NS groups. The improvement of motor function and myelin remodeling, the expression of oligodendrocytes, and the expression of Sox2/Fyn/RhoA/ROCK2 in the HCP + LS group were similar to those in the HCP + CIMT group. CIMT might overcome RhoA/ROCK2 signaling by upregulating the transcription of Sox2 to Fyn in the brain to induce maturation and differentiation of oligodendrocytes, thereby promoting myelin remodeling and improving motor function in rats with HCP.Impact. The pathway mediated by Sox2/Fyn could be a promising therapeutic target for HCP.

ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design.

Deep generative models (DGMs) have shown great success in the understanding and data-driven design of proteins. Variational autoencoders (VAEs) are a popular DGM approach that can learn the correlated patterns of amino acid mutations within a multiple sequence alignment (MSA) of protein sequences and distill this information into a low-dimensional latent space to expose phylogenetic and functional relationships and guide generative protein design. Autoregressive (AR) models are another popular DGM approach that typically lacks a low-dimensional latent embedding but does not require training sequences to be aligned into an MSA and enable the design of variable length proteins. In this work, we propose ProtWave-VAE as a novel and lightweight DGM, employing an information maximizing VAE with a dilated convolution encoder and an autoregressive WaveNet decoder. This architecture blends the strengths of the VAE and AR paradigms in enabling training over unaligned sequence data and the conditional generative design of variable length sequences from an interpretable, low-dimensional learned latent space. We evaluated the model's ability to infer patterns and design rules within alignment-free homologous protein family sequences and to design novel synthetic proteins in four diverse protein families. We show that our model can infer meaningful functional and phylogenetic embeddings within latent spaces and make highly accurate predictions within semisupervised downstream fitness prediction tasks. In an application to the C-terminal SH3 domain in the Sho1 transmembrane osmosensing receptor in baker's yeast, we subject ProtWave-VAE-designed sequences to experimental gene synthesis and select-seq assays for the osmosensing function to show that the model enables synthetic protein design, conditional C-terminus diversification, and engineering of the osmosensing function into SH3 paralogues.

The absence of the evolutionary state of the Prokaryote would imply a polyphyletic origin of proteins and that LUCA, the ancestor of bacteria and that of archaea were progenotes

I analysed the similarity gradient observed in protein families – of phylogenetically deep fundamental traits - of bacteria and archaea, ranging from cases such as the core of the DNA replication apparatus where there is no sequence similarity between the proteins involved, to cases in which, as in the translation initiation factors, only some proteins involved would be homologs, to cases such as for aminoacyl-tRNA synthetases in which most of the proteins involved would be homologs. This pattern of similarity between bacteria and archaea would seem to be a very clear indication of a transitional evolutionary stage that preceded both the Last Bacterial Common Ancestor and the Last Archaeal Common Ancestor, i.e. progenotic stages. Indeed, this similarity pattern would seem to exemplify an ongoing transition as all the evolutionary phases would be represented in it. Instead, in the cellular stage it is expected that these evolutionary phases should have already been overcome, i.e. completed, and therefore no longer detectable. In fact, if we had really been in the presence of the prokaryotic stage then we should not have observed this similarity pattern in proteins involved in defining the ancestral characters of bacteria and archaea, as the completion of the different cellular structures should have required a very low number of proteins to be late evolved in lineages leading to bacteria and archaea. Indeed, the already reached state of the Prokaryote would have determined complete cellular structures therefore a total absence of proteins to evolve independently in the two main phyletic lineages and able to complete the evolution of a particular character already evidently in a definitive state, which, on the other hand, does not appear to have been the case. All this would have prevented the formation of this pattern of similarity which instead would appear to be real. In conclusion, the existence of this pattern of similarity observed in the families of homologous proteins of bacteria and archaea would imply the absence of the evolutionary stage of the Prokaryote and consequently a progenotic status to be assigned to the LUCA. Indeed, the LUCA stage would have been a stage of evolutionary transition because it is belatedly marked by the presence of all the different evolutionary phases, evidently more easily interpretable within the definition of progenote than that of genote precisely because they are inherent in an evolutionary transition and not to an evolution that has already been achieved. Finally, I discuss the importance of these arguments for the polyphyletic origin of proteins.

ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction.

The latent features extracted from the multiple sequence alignments (MSAs) of homologous protein families are useful for identifying residue-residue contacts, predicting mutation effects, shaping protein evolution, etc. Over the past three decades, a growing body of supervised and unsupervised machine learning methods have been applied to this field, yielding fruitful results. Here, we propose a novel self-supervised model, called encoder-transformation layer-decoder (ETLD) architecture, capable of capturing protein sequence latent features directly from MSAs. Compared to the typical autoencoder model, ETLD introduces a transformation layer with the ability to learn inter-site couplings, which can be used to parse out the two-dimensional residue-residue contacts map after a simple mathematical derivation or an additional supervised neural network. ETLD retains the process of encoding and decoding sequences, and the predicted probabilities of amino acids at each site can be further used to construct the mutation landscapes for mutation effects prediction, outperforming advanced models such as GEMME, DeepSequence and EVmutation in general. Overall, ETLD is a highly interpretable unsupervised model with great potential for improvement and can be further combined with supervised methods for more extensive and accurate predictions.

Unsupervised hierarchical clustering using the learning dynamics of restricted Boltzmann machines.

Data sets in the real world are often complex and to some degree hierarchical, with groups and subgroups of data sharing common characteristics at different levels of abstraction. Understanding and uncovering the hidden structure of these data sets is an important task that has many practical applications. To address this challenge, we present a general method for building relational data trees by exploiting the learning dynamics of the restricted Boltzmann machine. Our method is based on the mean-field approach, derived from the Plefka expansion, and developed in the context of disordered systems. It is designed to be easily interpretable. We tested our method in an artificially created hierarchical data set and on three different real-world data sets (images of digits, mutations in the human genome, and a homologous family of proteins). The method is able to automatically identify the hierarchical structure of the data. This could be useful in the study of homologous protein sequences, where the relationships between proteins are critical for understanding their function and evolution.

GraphscoreDTA: optimized graph neural network for protein-ligand binding affinity prediction.

Computational approaches for identifying the protein-ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein-ligand binding affinity and achieve significant performance improvement. However, protein-ligand binding affinity prediction still has fundamental challenges. One challenge is that the mutual information between proteins and ligands is hard to capture. Another challenge is how to find and highlight the important atoms of the ligands and residues of the proteins. To solve these limitations, we develop a novel graph neural network strategy with the Vina distance optimization terms (GraphscoreDTA) for predicting protein-ligand binding affinity, which takes the combination of graph neural network, bitransport information mechanism and physics-based distance terms into account for the first time. Unlike other methods, GraphscoreDTA can not only effectively capture the protein-ligand pairs' mutual information but also highlight the important atoms of the ligands and residues of the proteins. The results show that GraphscoreDTA significantly outperforms existing methods on multiple test sets. Furthermore, the tests of drug-target selectivity on the cyclin-dependent kinase and the homologous protein families demonstrate that GraphscoreDTA is a reliable tool for protein-ligand binding affinity prediction. The resource codes are available at https://github.com/CSUBioGroup/GraphscoreDTA.

Molecular basis of protein-protein interactions in a homologous set of Drosophila neuronal proteins using machine learning.

A set of highly homologous proteins have been found in Drosophila melanogaster to play a critical role in the neuromorphogenesis of their nervous system. These proteins, the Dpr and the DIPs (defective probosci's extension response-Dpr interacting proteins) of 21 and 11 members, respectively, can form 231 complexes. Only a subset of 57 complexes, referred to as cognate partners, can bind with high affinity and are detected by SPR experiments. The remaining complexes were either undetected or below a certain threshold leaving uncertainties regarding their actual affinity. Taking into account similarity, phylogenetic classification, and binding experiments, the proteins still have been mapped as cognate and non-cognate partners. To accurately classify DIP-Dpr complexes and to elucidate the molecular basis for their interactions, we resorted to machine learning (ML) approaches to handle this large number of protein-protein complexes and interactions. Gathering information from evolutionary, structural, and biochemical properties in input features, we obtained good accuracy with two different algorithms: linear discriminant analysis (88%) and random forest (98%). To test the robustness of our method, we used the trained models for other DIPs and Dprs recovered from thirteen different Drosophila species. For each species, the prediction accuracy matched values obtained with Drosophila melanogaster, confirming the robustness of our models. This specific methodology to develop ML models to classify binding and non-binding partners accurately amongst homologous protein families is transferable to any relevant biological system and could help find the binding partners of new family members (mutants, newly identified/designed...).

Deep-learning generative models enable design of synthetic orthologs of a signaling protein.

Understanding the relationships between protein sequence and function is critical for rational protein engineering. We have developed a deep learning model based on a variational autoencoders (VAE) to perform data-driven learning of these relationships within homologous protein families. In an application to the C-terminal SH3 domain of the high osmolarity signaling protein Sho1 (Sho1-SH3) in budding yeast, our trained VAE latent space accurately discriminates Sho1-SH3 orthologs and paralogs and enables the generative design of novel synthetic proteins predicted to possess high osmosensing function. Experimental gene synthesis and in vivo functional assays by a quantitative next-generation sequencing-based selective assay demonstrates the capability of the model to design more than 500 novel synthetic proteins possessing similar or superior function to natural sequences while sharing at minimum 60% sequence similarity with any known natural SH3 domains. Our results demonstrate the potential of deep learning models to learn the rules of protein design from extant natural proteins and use these rules to rationally design novel synthetic proteins with high functionality.

An interpretable machine learning model for selectivity of small-molecules against homologous protein family.

Aim: In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side effects. Methods: We have developed a multitask deep learning model for predicting the selectivity on closely related homologs of the target protein. The model has been tested on the Janus-activated kinaseand dopamine receptor families of proteins. Results & conclusion: The feature-based representation (extended connectivity fingerprint 4) with Extreme Gradient Boosting performed better when compared with deep neural network models in most of the evaluation metrics. Both the Extreme Gradient Boostingand deep neural networkmodels outperformed the graph-based models. Furthermore, to decipher the model decision on selectivity, the important fragments associated with each homologous protein were identified.

A Study of Combined Genotype Effects of SHCBP1 on Wool Quality Traits in Chinese Merino.

SHCBP1 (Shc SH2-domain binding protein 1) is a member of the Src and collagen homolog (Shc) protein family and is closely associated with multiple signaling pathways that play important roles during hair follicle induction, morphogenesis, and cycling. The purpose of this study was to investigate SHCBP1 gene expression, polymorphisms, and the association between SHCBP1 and wool quality traits in Chinese Merino sheep. The SHCBP1 gene was shown, by qPCR, to be ubiquitously expressed in sheep tissues and differentially expressed in the adult skin of Chinese Merino and Suffolk sheep. Four SNPs (termed SHCBP1SNPs 1-4) were identified by Sanger sequencing and were located in exon 2, intron 9, intron 12, and exon 13 of the sheep SHCBP1 gene, respectively. SHCBP1SNPs 3 and 4 (rs411176240 and rs160910635) were significantly associated with wool crimp (P < 0.05). The combined polymorphism (SHCBP1SNP3-SHCBP1SNP4) was significantly associated with wool crimp (P < 0.05). Bioinformatics analysis showed that the SNPs associated with wool crimp (SHCBP1SNPs 3 and 4) might affect the pre-mRNA splicing by creating binding sites for serine-arginine-rich proteins and that SHCBP1SNP4 might alter the SHCBP1 mRNA and protein secondary structure. Our results suggest that SHCBP1 influences wool crimp and SHCBP1SNPs 3 and 4 might be useful markers for marker-assisted selection and sheep breeding.

SCD2-mediated cooperative activation of IRF3-IRF9 regulatory circuit controls type I interferon transcriptome in CD4+ T cells

Type I interferons (type I-IFN) are critical for the host defense to viral infection, and at the same time, the dysregulation of type I-IFN responses leads to autoinflammation or autoimmunity. Recently, we reported that the decrease in monounsaturated fatty acid caused by the genetic deletion of Scd2 is essential for the activation of type I-IFN signaling in CD4+ Th1 cells. Although interferon regulatory factor (IRF) is a family of homologous proteins that control the transcription of type I-IFN and interferon stimulated genes (ISGs), the member of the IRF family that is responsible for the type I-IFN responses induced by targeting of SCD2 remains unclear. Here, we report that the deletion of Scd2 triggered IRF3 activation for type I-IFN production, resulting in the nuclear translocation of IRF9 to induce ISG transcriptome in Th1 cells. These data led us to hypothesize that IRF9 plays an essential role in the transcriptional regulation of ISGs in Scd2-deleted (sgScd2) Th1 cells. By employing ChIP-seq analyses, we found a substantial percentage of the IRF9 target genes were shared by sgScd2 and IFNβ-treated Th1 cells. Importantly, our detailed analyses identify a unique feature of IRF9 binding in sgScd2 Th1 cells that were not observed in IFNβ-treated Th1 cells. In addition, our combined analyses of transcriptome and IRF9 ChIP-seq revealed that the autoimmunity related genes, which increase in patient with SLE, were selectively increased in sgScd2 Th1 cells. Thus, our findings provide novel mechanistic insights into the process of fatty acid metabolism that is essential for the type I-IFN response and the activation of the IRF family in CD4+ T cells.

Flap sub-domain dynamics of serine-threonine phosphatase (Stp1) of Staphylococcus aureus: an accelerated molecular dynamics simulation study

Vancomycin and daptomycin are commonly used glycopeptide antibiotics to cure Gram-positive staphylococcal infections. The clinical isolates of mutant Staphylococcus aureus strains, Methicillin-Resistant (MRSA) and Vancomycin-Resistant (VRSA), have developed resistance against these antibiotics. A recently discovered Serine/threonine phosphatase (Stp1) is an Mn+2 containing protein at the active site with a flap sub-domain that participates in the phospho-signaling system of bacterial cell wall formation. The flap sub-domain probably regulates substrates recruitment and release with an extra Mn+2, possibly highly flexible as in the other homologous family of proteins. In this study, the flap sub-domain has been sampled with conventional and accelerated molecular dynamics (cMD and aMD) simulations to get other sub-optimal conformational states of the protein that are nearly impossible to observe through experimental methods. Trajectory analysis has shown that protein remained static in cMD while dynamic in aMD with RMSD of ∼2Å and ∼3Å, respectively. Accelerated MD has shown greater flexibility of ∼4 Å in the flap sub-domain, while cMD only captured a deviation of ∼ 2 Å. Later, the dynamic cross-correlation map (DCCM) confirmed that the flap sub-domain is significantly more flexible than the other part of the structure, indicating its role in substrate regulation. Secondary structure transition in the flap sub-domain, i.e. 3-10 helix and turn (PRO159 - ILE163) region of the flap sub-domain shifted into α-helix, which is a more stable structure. Further, the trajectory has been clustered, and conformational states extracted, which may be exploited in structure-based antibiotics discovery.

Single-molecule analysis of actin filament debranching by cofilin and GMF

Eukaryotic cells contain branched actin networks that are essential for endocytosis, motility, and other key cellular processes. These networks, which are formed by filamentous actin and the Arp2/3 complex, must subsequently be debranched to allow network remodeling and to recycle the Arp2/3 complex. Debranching appears to be catalyzed by two different members of the actin depolymerizing factor homology protein family: cofilin and glial maturation factor (GMF). However, their mechanisms of debranching are only partially understood. Here, we used single-molecule fluorescence imaging of Arp2/3 complex and actin filaments under physiological ionic conditions to observe debranching by GMF and cofilin. We demonstrate that cofilin, like GMF, is an authentic debrancher independent of its filament-severing activity and that the debranching activities of the two proteins are additive. While GMF binds directly to the Arp2/3 complex, cofilin selectively accumulates on branch-junction daughter filaments in tropomyosin-decorated networks just prior to debranching events. Quantitative comparison of debranching rates with the known kinetics of cofilin-actin binding suggests that cofilin occupancy of a particular single actin site at the branch junction is sufficient to trigger debranching. In rare cases in which the order of departure could be resolved during GMF- or cofilin-induced debranching, the Arp2/3 complex left the branch junction bound to the pointed end of the daughter filament, suggesting that both GMF and cofilin can work by destabilizing the mother filament-Arp2/3 complex interface. Taken together, these observations suggest that GMF and cofilin promote debranching by distinct yet complementary mechanisms.

Understanding the role of RTL8 proteins in Ubiquilin‐2 biology

The brain expressed ubiquilins (UBQLNs) 1, 2 and 4 are a family of ubiquitin adaptor proteins that participate broadly in protein quality control (PQC) pathways, including the ubiquitin proteasome system (UPS). One family member, UBQLN2, plays a role in numerous neurodegenerative diseases including ALS/FTD and Huntington’s disease. UBQLN2 typically resides in the cytoplasm but can translocate to the nucleus under proteotoxic stress and in disease to facilitate nuclear PQC. How UBQLN2 translocates to the nucleus and clears aberrant nuclear proteins, however, is not well understood. In a mass spectrometry screen to discover UBQLN2 interactors, we identified a family of small (13 kDa), highly homologous uncharacterized family of proteins, retrotransposon Gag‐like 8 (RTL8). We then confirmed the interaction between UBQLN2 and a representative RTL8 family member, RTL8A, both in vitro using recombinant proteins and in vivo using mouse brain tissue. We further show that RTL8A promotes nuclear translocation of UBQLN2. The robust effect of RTL8A on nuclear translocation of UBQLN2 does not extend to the other brain‐expressed ubiquilins, UBQLN1 and UBQLN4. Depletion of all RTL8 proteins in human cell lines perturbs nuclear translocation of UBQLN2 following heat shock. Finally, compared to UBQLN1 and UBQLN4, UBQLN2 preferentially stabilizes endogenous RTL8 levels in cells, supporting functional heterogeneity among UBQLNs. As novel UBQLN2 interactors that recruit UBQLN2 to specific subnuclear compartments, RTL8 proteins may regulate the role of UBQLN2 in nuclear PQC.

Prediction of Protein Sites and Physicochemical Properties Related to Functional Specificity.

Specificity Determining Positions (SDPs) are protein sites responsible for functional specificity within a family of homologous proteins. These positions are extracted from a family’s multiple sequence alignment and complement the fully conserved positions as predictors of functional sites. SDP analysis is now routinely used for locating these specificity-related sites in families of proteins of biomedical or biotechnological interest with the aim of mutating them to switch specificities or design new ones. There are many different approaches for detecting these positions in multiple sequence alignments. Nevertheless, existing methods report the potential SDP positions but they do not provide any clue on the physicochemical basis behind the functional specificity, which has to be inferred a-posteriori by manually inspecting these positions in the alignment. In this work, a new methodology is presented that, concomitantly with the detection of the SDPs, automatically provides information on the amino-acid physicochemical properties more related to the change in specificity. This new method is applied to two different multiple sequence alignments of homologous of the well-studied RasH protein representing different cases of functional specificity and the results discussed in detail.

Research advances and prospects of legume lectins

Lectins are widely distributed proteins having ability of binding selectively and reversibly with carbohydrates moieties and glycoconjugates. Although lectins have been reported from different biological sources, the legume lectins are the best-characterized family of plant lectins. Legume lectins are a large family of homologous proteins with considerable similarity in amino acid sequence and their tertiary structures. Despite having strong sequence conservation, these lectins show remarkable variability in carbohydrate specificity and quaternary structures. The ability of legume lectins in recognizing glycans and glycoconjugates on cells and other intracellular structures make them a valuable research tool in glycomic research. Due to variability in binding with glycans, glycoconjugates and multiple biological functions, legume lectins are the subject of intense research for their diverse application in different fields such as glycobiology, biomedical research and crop improvement. The present review specially focuses on structural and functional characteristics of legume lectins along with their potential areas of application.

Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.

During their evolution, proteins explore sequence space via an interplay between random mutations and phenotypic selection. Here, we build upon recent progress in reconstructing data-driven fitness landscapes for families of homologous proteins, to propose stochastic models of experimental protein evolution. These models predict quantitatively important features of experimentally evolved sequence libraries, like fitness distributions and position-specific mutational spectra. They also allow us to efficiently simulate sequence libraries for a vast array of combinations of experimental parameters like sequence divergence, selection strength, and library size. We showcase the potential of the approach in reanalyzing two recent experiments to determine protein structure from signals of epistasis emerging in experimental sequence libraries. To be detectable, these signals require sufficiently large and sufficiently diverged libraries. Our modeling framework offers a quantitative explanation for different outcomes of recently published experiments. Furthermore, we can forecast the outcome of time- and resource-intensive evolution experiments, opening thereby a way to computationally optimize experimental protocols.

Maturation of a postsynaptic domain: Role of small Rho GTPases in organising nicotinic acetylcholine receptor aggregates at the vertebrate neuromuscular junction.

The neuromuscular junction (NMJ) is the peripheral synapse formed between a motor axon and a skeletal muscle fibre that allows muscle contraction and the coordinated movement in many species. A main hallmark of the mature NMJ is the assembly of nicotinic acetylcholine receptor (nAChR) aggregates in the muscle postsynaptic domain, that distributes in perfect apposition to presynaptic motor terminals. To assemble its unique functional architecture, initial embryonic NMJs undergo an early postnatal maturation process characterised by the transformation of homogenous nAChR-containing plaques to elaborate and branched pretzel-like structures. In spite of a detailed morphological characterisation, the molecular mechanisms controlling the intracellular scaffolding that organises a postsynaptic domain at the mature NMJ have not been fully elucidated. In this review, we integrate evidence of key processes and molecules that have shed light on our current understanding of the NMJ maturation process. On the one hand, we consider in vitro studies revealing the potential role of podosome-like structures to define discrete low nAChR-containing regions to consolidate a plaque-to-pretzel transition at the NMJ. On the other hand, we focus on in vitro and in vivo evidence demonstrating that members of the Ras homologous (Rho) protein family of small GTPases (small Rho GTPases) play indispensable roles on NMJ maturation by regulating the stability of nAChR aggregates. We combine this evidence to propose that small Rho GTPases are key players in the assembly of podosome-like structures that drive the postsynaptic maturation of vertebrate NMJs.