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Proteinoids - thermal proteins - are produced by heating amino acids to their melting point and initiation of polymerisation to produce polymeric chains. Proteinoids swell in aqueous solution into hollow microspheres. The proteinoid microspheres produce endogenous burst of electrical potential spikes and change patterns of their electrical activity in response to illumination. The microspheres can interconnect by pores and tubes and form networks with a programmable growth. We speculate on how ensembles of the proteinoid microspheres can be developed into unconventional computing devices., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Microfluidic chip electrophoresis has been widely employed for separation of various biochemical species owing to its advantages of low sample consumption, low cost, fast analysis, high throughput, and integration capability. In this article, we reviewed the development of four different modes of microfluidics-based electrophoresis technologies including capillary electrophoresis, gel electrophoresis, dielectrophoresis, and field (electric) flow fractionation. Coupling detection schemes on microfluidic electrophoresis platform were also reviewed such as optical, electrochemical, and mass spectrometry method. We further discussed the innovative applications of microfluidic electrophoresis for biomacromolecules (nucleic acids and proteins), biochemical small molecules (amino acids, metabolites, ions, etc.), and bioparticles (cells and pathogens) analysis. The future direction of microfluidic chip electrophoresis was predicted., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology-a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations-using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Here we report two phase modulated NMR experiments: PM-2D HN(CACBHB) and PM-2D HN(HB), that use 1 H β chemical shifts to rapidly identify amino acid type in proteins. The magnetization on the 1 H β spins during the experiments is allowed to evolve for a fixed evolution period that results in phase modulation (positive or negative) of the cross peaks corresponding to various amino acid residues on their 2D HN projections, resembling a typical 2D [ 1 H- 15 N]-HSQC spectrum. All amino acids except glycine can be categorized into three discernible groups based on their 1 H β chemical shifts, resulting in unique phase patterns at different fixed evolution periods for 1 H β , thus facilitating their identification. Remarkably, the PM-2D HN(HB) stands out among all amino acid type identification NMR techniques for its applicability with cost-effective and most routinely employed 15 N-labeled protein samples for NMR studies. Furthermore, when combined effectively with the 13 C β chemical shift-based phase modulated NMR method (PM-2D HN(CACB)), these methods resolved the identification of large groups of amino acids into relatively smaller groups. Moreover, these techniques can accelerate the sequence-specific sequential resonance assignment (SSRA) process and would help in fast tracking of assigned NMR signals exhibiting chemical shift perturbation on the 2D [ 1 H- 15 N]-HSQC spectrum of proteins during various experiments (e.g., temperature change, pH change, and protein or ligand or cofactor binding) as well as in site-directed mutagenesis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Amino acid propensities for protein secondary structures are vital for protein structure prediction, understanding folding, and design, and have been studied using various theoretical and experimental methods. Traditional assessments of average propensities using statistical methods have been done on relatively smaller dataset for only a few secondary structures. They also involve averaging out the environmental factors and lack insights into consistency of preferences across diverse protein structures. While a few studies have explored variations in propensities across protein structural classes and folds, exploration of such variations across protein structures remains to be carried out. In this work, we have revised the average propensities for all six different secondary structures, namely α-helix, β-strand, 3 10 -helix, π-helix, turn and coil, analyzing the most exhaustive dataset available till date using two robust secondary structure assignment algorithms, DSSP and STRIDE. The propensities evaluated here can serve as a standard reference. Moreover, we present here, for the first time, the propensities within individual protein structures and investigated how the preferences of residues and more interestingly, of their groups formed based on their structural features, vary across different unique structures. We devised a novel approach- the minimal set analysis, based on the propensity distribution of residues, which along with the group propensities led us to the conclusion that a residue's preference for a specific secondary structure is primarily dictated by its side chain's structural features. The findings in this study provide a more insightful picture of residues propensities and can be useful in protein folding and design studies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Solid-phase fluorescence excitation-emission matrix (SPF-EEM) spectroscopy has potential for non-extractive, non-destructive, and non-contact analytical measurements of powder and solid-state samples, as well as front-face EEM spectroscopy for suspensions of high optical density. However, as there is no unified measurement method for SPF spectroscopy, comparing samples measured in different research fields is difficult. Therefore, this study designs a cell that can be created by a 3D printer and examines reproducibility on measuring fluorescent powders. The developed cell is applied to proteins (ovalbumin, BSA, gliadin, gluten, powdered collagen, casein), amino acids (tryptophan, tyrosine, and phenylalanine), soybean ingredients (daidzein, and genistein), and fluorescent chemicals (rhodamine B, fluorescein sodium salt, pyrene, and quinine sulfate dihydrate) and their spectra are compared with those in the solution states. When samples are refilled into the cell three times, the cell exhibits high reproducibility in terms of fluorescence peak wavelength and intensity. The solid proteins exhibit peaks attributed to the fluorescent amino acid residues, and broad peaks which are not detected for the proteins in the solution states. Powdered rhodamine B and fluorescein sodium salt do not exhibit fluorescence, possibly due to the inner-filter effect (IFE). Some non-colored molecules also exhibit loss of fluorescence or a remarkable difference between the solid and solution states, possibly due to the interaction of the fluorescent structure with the surrounding local environment, similar to the solvent effect, which is possibly affected by the molecular proximity, three-dimensional structure, and moisture absorption capacity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

A novel helical N-capping motif has been considered. It occurs in the βα-arches of right-handed βαβ-units and contains an N-cap residue in a sterically strained conformation. Moreover, this amino acid position contains almost no glycines, that could relieve strain. It was shown that the N-cap adopts this conformation as a result of the unusual convergence between the second and third amino acid positions of the α-helix (counting from the N-cap) and the second position of the preceding β-strand. This is achieved by the presence of glycines in the specified positions (i.e. positions i - 2, i + 2 and i + 3, if N-cap is i). The N-cap conformation is stabilized by a hydrogen bond between the backbone amide group in the second position of the α-helix and the carbonyl group in the first position of the β-strand. The occurrence of similar N-capping motifs in different types of βαβ-units was compared and their structural differences caused by the influence of the environment were described. Study results may be useful for protein design and ab initio prediction of the 3D protein structure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Physical compartments are essential for the origin of life. While lipid vesicles are commonly regarded as precursors of cell membranes, we propose a simpler and more primitive model based on proteinoids. Proteinoids are macromolecules formed by the thermal polymerization of amino acids, mimicking primitive proteins. They self-assemble into spherical microspheres in water. Under a temperature gradient, proteinoid microspheres (PM) dissolve and flow, forming microcapsules with thin shells. The mechanism of this process has not yet been elucidated. We hypothesize that it involves the interplay between the dissolution and flow of PM. We tested our hypothesis by applying forced flow to the PM and observing capsule formation. We found that neither heat nor flow alone can produce capsules, confirming our hypothesis. We conclude that flow-induced capsule formation is a general phenomenon and a plausible model for the origin of physical compartments in early life., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

A circular paper chromatographic method was developed for the separation of the amino acids in proteins into 7 subgroups. Butanol-acetic acid water (4+1+1) was used as the developing solvent. Eluted ninhydrin-stained aminograms gave rise to graphic profiles or numerical indexes based on absorbance percentages. The profiles can be used to compare protein-containing samples. Twenty different samples were studied through 190 comparisons of graphic profiles and coefficients of correlation, with only 4% misleading results. The method showed excellent reproducibility for the identification or differentiation of proteins and has the advantage of being performed with low-priced apparatus and reagents.

The NH(2)-terminal amino acid composition of the soluble and ribosomal proteins from Neurospora crassa mycelia and conidia was determined by the dinitrophenyl method. A nonrandom distribution of NH(2)-terminal amino acids was observed in the complex protein mixtures. Glycine, alanine, and serine accounted for 75% of the NH(2)-terminal amino acids, and glycine appeared most frequently in mature proteins of mycelia. The appearance of phenylalanine as one of the major NH(2)-termini in crude conidial fraction suggests that the composition of proteins may vary in different developmental stages.

Background: Autohydrolysis is an extensively investigated pretreatment method due to its environmental friendliness. During autohydrolysis, most xylan from hemicellulose can be converted into xylooligosaccharides (XOS), and cellulose in the autohydrolyzed residues can be transformed into glucose after enzymatic hydrolysis. Both of these are value-added biochemicals in the biorefining process. In this work, paper mulberry (PM), which contains abundant protein, was utilized as a raw material to coproduce XOS and single-cell protein (SCP) through autohydrolysis and fermentation technologies. Results: The results showed that 8.3 g of XOS and 1.8 g of amino acids could be recovered in the autohydrolysate (based on 100 g raw material) after autohydrolysis (170 °C, 1 h). Moreover, 5.7 g of low-DP XOS along with 1.8 g of amino acids could be further obtained from the autohydrolysate after hydrolysis with endo-β-1-4-xylanase. In addition, 20.1 g of fermentable monosaccharides was recovered after hydrolyzing the autohydrolyzed PM with cellulase, which can be used to produce 4.8 g of SCP after fermentation with Candida utilis. Conclusion: As a valuable application of PM, a novel process is proposed to coproduce amino acid-rich XOS and SCP through autohydrolysis. The carbohydrate of PM is effectively converted to high value-added products. [ABSTRACT FROM AUTHOR]

Photo- and chemical crosslinking of proteins have offered various avenues for studying protein structure and protein interactions with biomolecules. Conventional photoactivatable groups generally lack reaction selectivity toward amino acid residues. New photoactivatable groups reacting with selected residues have emerged recently, increasing crosslinking efficiency and facilitating crosslink identification. Traditional chemical crosslinking usually employs highly reactive functional groups, while recent advance has developed latent reactive groups with reactivity triggered by proximity, which reduce spurious crosslinks and improve biocompatibility. The employment of these residue selective chemical functional groups, activated by light or proximity, in small molecule crosslinkers and in genetically encoded unnatural amino acids is summarized. Together with new software development in identifying protein crosslinks, residue selective crosslinking has enhanced the research of elusive protein-protein interactions in vitro, in cell lysate, and in live cells. Residue selective crosslinking is expected to expand to other methods for the investigation of various protein-biomolecule interactions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Proteinoids, or thermal proteins, are inorganic entities formed by heating amino acids to their melting point and commencing polymerisation to form polymeric chains. Typically, their diameters range from 1μm to 10μm. Some amino acids incorporated into proteinoid chains are more hydrophobic than others, leading proteinoids to cluster together when they are present in aqueous solutions at specific concentrations, allowing them to grow into microspheres. The peculiar structure of proteinoids composed of linked amino acids endows them with unique properties, including action-potential like spiking of electrical potential. These unique properties make ensembles of proteinoid microspheres a promising substrate for designing future artificial brains and unconventional computing devices. To evaluate a potential of proteinoid microspheres for unconventional electronic devices we measure and analyse the data-transfer capacities of proteinoid microspheres. In experimental laboratory conditions we demonstrate that the transfer function of proteinoids microspheres is a nontrivial phenomenon, which might be due to the wide range of proteinoid shapes, sizes, and structures., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2023. Published by Elsevier B.V. All rights reserved.)

We have developed a Triangular Spatial Relationship (TSR)-based computational method for protein structure comparison and motif discovery that is both sequence and structure alignment-free. A protein 3D structure is modeled by all possible triangles that are constructed with every three C α atoms of amino acids as vertices. Every triangle is represented using an integer (a key). The keys are calculated by a rule-based formula which is a function of a representative length, a representative angle, and the vertex labels associated with amino acids. A 3D structure is thereby represented by a vector of integers (TSR keys). Global or local structure comparisons are achieved by computing all keys or a set of keys, respectively. Many enzymatic reactions and notable marketed drugs are highly stereospecific. Thus, in this paper, we propose a modified key calculation formula by including a mechanism for discriminating mirror-image keys to capture stereo geometry. We assign a positive or a negative sign to the integers representing mirror-image keys. Applying the new key calculation function provides the ability to further discriminate mirror-image keys that were previously considered identical. As the result, applying the mirror-image discrimination capability (i) significantly increases the number of distinct keys; (ii) decreases the number of common keys; (iii) decreases structural similarity; (iv) increases the opportunity to identify specific keys for each type of the receptors. The specific keys identified in this study for the cases of without (not applying) and with (applying) mirror-image discrimination can be considered as the structure signatures that exclusively belong to a certain type of receptors. Applying mirror-image discrimination introduces stereospecificity to keys for allowing more precise modeling of ligand - target interactions. The development of mirror-image TSR keys of C α atom, in conjunction with the integration of C α TSR keys with all-atom TSR keys for amino acids and drugs, will lead to a new and promising computational method for aiding drug design and discovery., Competing Interests: Conflict of Interest The authors declare no conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Ramachandran plots, which describe protein structures by plotting the dihedral angle pairs of the backbone on a two-dimensional plane, have played an important role in structural biology over the past few decades. However, despite continued discovery of new protein structures to date, the Ramachandran plot is still constructed by only a small number of data points, and further it cannot reflect the steric information of proteins. Here, we investigated the secondary structure of proteins in terms of static and dynamic characteristics. As for static feature, the Ramachandran plot was revisited for the dataset consisting of 9,148 non-redundant high-resolution protein structures released in the protein data bank until April 1, 2022. By calculating amino acid propensities, it was found that the proportion of secondary structures with respect to residue depth is directly related to their hydrophobicity. As for dynamic feature, normal mode analysis (NMA) based on an elastic network model (ENM) was carried out for the dataset using our KOSMOS web server (http://bioengineering.skku.ac.kr/kosmos/). All ENM-based NMA results were stored in the KOSMOS database, allowing researchers to use them in various ways. In this process, it was commonly found that high B-factors appeared at the edge of the alpha helix region, which was elucidated by introducing residue depth. In addition, by investigating the change in dihedral angle, it was possible to quantitatively survey the contribution of structural change of protein on the Ramachandran plot. In conclusion, our statistical analysis of protein characteristics will provide insight into a range of protein structural studies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga., Competing Interests: The authors have declared that no competing interests exist.

The cellular ribosome shows a naturally evolved strong preference for the synthesis of proteins with standard amino acids. An in-depth understanding of the translation process enables scientists to go beyond this natural limitation and engineer translating systems capable of synthesizing proteins with artificially designed and synthesized non-standard amino acids (nsAA) featuring more bulky sidechains. The sidechains can be functional groups, with chosen biophysical or chemical activities, that enable the direct application of these proteins. Alternatively, the sidechains can be designed to contain highly reactive groups: enabling the ready formation of conjugates via a covalent bond between the sidechain and other chemicals or biomolecules. This co-translational incorporation of nsAAs into proteins allows for a vast number of possible applications. In this paper, we first systematically summarized the advances in the engineering of the translation system. Subsequently, we reviewed the extensive applications of these nsAA-containing proteins (after chemical modification) by discussing representative reports on how they can be utilized for different purposes. Finally, we discussed the direction of further studies which could be undertaken to improve the current technology utilized in incorporating nsAAs in order to use them to their full potential and improve accessibility across disciplines.

Proteins play important roles in living organisms, and their function is directly linked with their structure. Due to the growing gap between the number of proteins being discovered and their functional characterization (in particular as a result of experimental limitations), reliable prediction of protein function through computational means has become crucial. This paper reviews the machine learning techniques used in the literature, following their evolution from simple algorithms such as logistic regression to more advanced methods like support vector machines and modern deep neural networks. Hyperparameter optimization methods adopted to boost prediction performance are presented. In parallel, the metamorphosis in the features used by these algorithms from classical physicochemical properties and amino acid composition, up to text-derived features from biomedical literature and learned feature representations using autoencoders, together with feature selection and dimensionality reduction techniques, are also reviewed. The success stories in the application of these techniques to both general and specific protein function prediction are discussed., (© 2019 Wiley Periodicals, Inc.)

1. A new diagonal etectrophoretic technique for determining the order of the lysine-containing tryptic peptides of a protein is described. The protein is converted into its trifluoracetyl derivative, digested enzymatically (or chemically), and the resulting peptides separated by paper electrophoresis. The paper is then treated with ammonia vapour, which re-exposes the ε-amino groups of the lysine residues, and submitted to a second electrophoresis at right angles to the first direction. Peptides containing lysine residues, together with the N-terminal peptide of the protein, are found to lie off a diagonal formed by all other peptides, whence they may be readily purified. A study of these peptides enables the order of the lysine-containing tryptic peptides in the protein to be deduced. 2. The technique has been successfully tested with insulin. 3. When the method was applied to porcine pepsin, the four tryptic peptides isolated were easily ordered and the carboxyl-terminal sequence of the protein shown to be Arg-Gln-Tyr-TyrThr-Val-Phe-Asp-Arg-Ala-Asn-Asn-Lys-Val-Gly-Leu-Ala-Pro-Val-Ala. The thee basic ammo acid residues in the molecule are thus found clustering towards the C-terminus of the polypeptide chain. 4. A common ancestral gene for porcine pepsin and bovine (calf) rennin is suggested by the close homology between the C-terminal sequents of the two proteins. [ABSTRACT FROM AUTHOR]

Numerous long-standing questions in origins-of-life research center on the history of biopolymers. For example, how and why did nature select the polypeptide backbone and proteinaceous side chains? Depsipeptides, containing both ester and amide linkages, have been proposed as ancestors of polypeptides. In this paper, we investigate cationic depsipeptides that form under mild dry-down reactions. We compare the oligomerization of various cationic amino acids, including the cationic proteinaceous amino acids (lysine, Lys; arginine, Arg; and histidine, His), along with nonproteinaceous analogs of Lys harboring fewer methylene groups in their side chains. These analogs, which have been discussed as potential prebiotic alternatives to Lys, are ornithine, 2,4-diaminobutyric acid, and 2,3-diaminopropionic acid (Orn, Dab, and Dpr). We observe that the proteinaceous amino acids condense more extensively than these nonproteinaceous amino acids. Orn and Dab readily cyclize into lactams, while Dab and Dpr condense less efficiently. Furthermore, the proteinaceous amino acids exhibit more selective oligomerization through their α-amines relative to their side-chain groups. This selectivity results in predominantly linear depsipeptides in which the amino acids are α-amine-linked, analogous to today's proteins. These results suggest a chemical basis for the selection of Lys, Arg, and His over other cationic amino acids for incorporation into proto-proteins on the early Earth. Given that electrostatics are key elements of protein-RNA and protein-DNA interactions in extant life, we hypothesize that cationic side chains incorporated into proto-peptides, as reported in this study, served in a variety of functions with ancestral nucleic acid polymers in the early stages of life., Competing Interests: The authors declare no conflict of interest., (Copyright © 2019 the Author(s). Published by PNAS.)

Protein phosphorylation plays an important role in many significant cellular processes, and has thus gained tremendous interest in the field of proteomics. The electro-active tyrosine residue, as an important receptor of phosphorylation in proteins, exhibits electro-inactivity after being phosphorylated on the hydroxy group of its aromatic ring. In this study, the electrochemical oxidation of tyrosine on indium tin oxide (ITO) electrodes was catalyzed with an electron mediator Os(bpy)32+(bpy = 2,2′-bipyridine) and was employed as a signal reporter to differentially detect non-phosphorylated and phosphorylated peptides. A short, tyrosine-containing peptide glu-glu-glu-glu-glu-tyr (EY-6) was immobilized on an ITO surface using the layer-by-layer self-assembly method, and was detected by cyclic voltammetry in an Os(bpy)32+solution. The limit of detection was about 0.23 μg mL−1EY-6 in solution. The phosphorylated peptide glu-glu-glu-glu-glu-tyr-OP (EY-6P) did not produce an appreciable oxidation current on the electrode. Surface plasmon resonance measurements revealed that the amount of EY-6 and EY-6P adsorbed on the sensor chip surface was 269 and 378 pg mm−2, respectively. The poly(glu, tyr) (4 : 1) peptide, a protein tyrosine kinase substrate, was also detected by the same approach, with a detection limit of 0.65 μg mL−1. This new approach offers the possibility of label-free and on-chip detection of protein tyrosine kinase activity. [ABSTRACT FROM AUTHOR]

It is well known that proteins are built up from an alphabet of 20 different amino acid types. These suffice to enable the protein to fold into its operative form relevant to its required functional roles. For carrying out these allotted functions, there may in some cases be a need for post-translational modifications and it has been established that an additional three types of amino acid have at some point been recruited into this process. But it still remains the case that the 20 residue types referred to are the major building blocks in all terrestrial proteins, and probably "universally". Given this fact, it is surprising that no satisfactory answer has been given to the two questions: "why 20?" and "why just these 20?". Furthermore, a suggestion is made as to how these 20 map to the codon repertoire which in principle has the capacity to cater for 64 different residue types. Attempts are made in this paper to answer these questions by employing a combination of quantum chemical and chemoinformatic tools which are applied to the standard 20 amino acid types as well as 3 "non-standard" types found in nature, a set of fictitious but feasible analog structures designed to test the need for greater coverage of function space and the collection of candidate alternative structures found either on meteorites or in experiments designed to reconstruct pre-life scenarios., Competing Interests: The author has declared that no competing interests exist.

In this paper, a novel method to compute side chain conformational variations for a protein molecule tunnel (or channel) is proposed. From the conformational variations, we compute the flexibly deformed shapes of the initial tunnel, and present a way to compute the maximum size of the ligand that can pass through the deformed tunnel. By using the two types of graphs corresponding to amino acids and their side chain rotamers, the suggested algorithm classifies amino acids and rotamers which possibly have collisions. Based on the divide and conquer technique, local side chain conformations are computed first, and then a global conformation is generated by combining them. With the exception of certain cases, experimental results show that the algorithm finds up to 327,680 valid side chain conformations from 12⁸~12 33 conformation candidates within three seconds.

Motivation: Single amino acid variations (SAVs) in protein-protein interaction (PPI) sites play critical roles in diseases. PPI sites (interfaces) have a small subset of residues called hot spots that contribute significantly to the binding energy, and they may form clusters called hot regions. Singlet hot spots are the single amino acid hot spots outside of the hot regions. The distribution of SAVs on the interface residues may be related to their disease association., Results: We performed statistical and structural analyses of SAVs with literature curated experimental thermodynamics data, and demonstrated that SAVs which destabilize PPIs are more likely to be found in singlet hot spots rather than hot regions and energetically less important interface residues. In contrast, non-hot spot residues are significantly enriched in neutral SAVs, which do not affect PPI stability. Surprisingly, we observed that singlet hot spots tend to be enriched in disease-causing SAVs, while benign SAVs significantly occur in non-hot spot residues. Our work demonstrates that SAVs in singlet hot spot residues have significant effect on protein stability and function., Availability and Implementation: The dataset used in this paper is available as Supplementary Material. The data can be found at http://prism.ccbb.ku.edu.tr/data/sav/ as well., Supplementary Information: Supplementary data are available at Bioinformatics online.

Co-evolution between pairs of residues in a multiple sequence alignment (MSA) of homologous proteins has long been proposed as an indicator of structural contacts. Recently, several methods, such as direct-coupling analysis (DCA) and MetaPSICOV, have been shown to achieve impressive rates of contact prediction by taking advantage of considerable sequence data. In this paper, we show that prediction success rates are highly sensitive to the structural definition of a contact, with more permissive definitions (i.e., those classifying more pairs as true contacts) naturally leading to higher positive predictive rates, but at the expense of the amount of structural information contributed by each contact. Thus, the remaining limitations of contact prediction algorithms are most noticeable in conjunction with geometrically restrictive contacts-precisely those that contribute more information in structure prediction. We suggest that to improve prediction rates for such "informative" contacts one could combine co-evolution scores with additional indicators of contact likelihood. Specifically, we find that when a pair of co-varying positions in an MSA is occupied by residue pairs with favorable statistical contact energies, that pair is more likely to represent a true contact. We show that combining a contact potential metric with DCA or MetaPSICOV performs considerably better than DCA or MetaPSICOV alone, respectively. This is true regardless of contact definition, but especially true for stricter and more informative contact definitions. In summary, this work outlines some remaining challenges to be addressed in contact prediction and proposes and validates a promising direction towards improvement., Competing Interests: The authors have declared that no competing interests exist.

Amino acid analysis, commonly done by acid hydrolysis of proteins and HPLC analysis, faces one major problem: incomplete hydrolysis of stable amino acids and degradation of unstable amino acids are causing amino acid losses. As a result, amino acid recovery of unknown samples cannot be estimated. Some methods have been reported for correction of these factors in the past. This paper shows an improved and integrated method to overcome this problem by using stillage as an exemplary unknown sample material. Amino acid recovery from an unknown sample can be estimated by standard addition of a known protein. If the sample does not cause matrix effects during amino acid hydrolysis, recoveries of the standard protein are transferable to the sample. If the sample does cause matrix effects correction of amino acid losses can instead be done by determination of hydrolysis kinetics. Therefore, first order kinetics were used for amino acids that undergo degradation during hydrolysis. For all stable amino acids higher order kinetics were used, a novel approach to determine hydrolysis kinetics. The presented method can be a helpful tool for scientists who want to optimize amino acid analysis of a particular biomass substrate., (Copyright © 2017 Elsevier Inc. All rights reserved.)