SODA: prediction of protein solubility from disorder and aggregation propensity

Computational Identification and Functional Analysis of Potentially Pathogenic nsSNPs in the NLRP3 Gene Linked to Alzheimer's Disease

Single Nucleotide Polymorphisms (SNPs) are key in understanding complex diseases. Nonsynonymous single-nucleotide polymorphisms (nsSNPs) occur in protein-coding regions, potentially altering amino acid sequences, protein structure and function. Computational methods are vital for distinguishing deleterious nsSNPs from neutral ones. We investigated the role of NLRP3 gene in neuroinflammation associated with Alzheimer's disease (AD) pathogenesis. A total of 893 missense (nsSNPs) were obtained from the dbSNP database and subjected to rigorous filtering using bioinformatics tools like SIFT, Align GVGD, PolyPhen-2, and PANTHER to identify potentially damaging variants. Of these, 18 nsSNPs were consistently predicted to have deleterious effects across all tools. Notably, 16 of these variants exhibited reduced protein stability, while only 4 were predicted to be buried within the protein structure. Among the identified nsSNPs, rs180177442 (R262L and R262P), rs201875324 (T659I), and rs139814109 (T897M) were classified as high-risk variants due to their significant deleterious impact, probable damaging effects, and association with decreased protein stability. Molecular docking and simulation analyses were conducted utilizing Memantine, a standard drug utilized in AD treatment, to investigate potential interactions with the altered protein structures. Additional clinical and genetic investigations are necessary to elucidate the underlying mechanisms that link NLRP3 polymorphisms with the initiation of AD.

Challenges in Peptide Solubilization - Amyloids Case Study

Peptide science has been a rapidly growing research field because of the enormous potential application of these biocompatible and bioactive molecules. However, many factors limit the widespread use of peptides in medicine, and low solubility is among the most common problems that hamper drug development in the early stages of research. Solubility is a crucial, albeit poorly understood, feature that determines peptide behavior. Several different solubility predictors have been proposed, and many strategies and protocols have been reported to dissolve peptides, but none of them is a one-size-fits-all method for solubilization of even the same peptide. In this review, we look for the reasons behind the difficulties in dissolving peptides, analyze the factors influencing peptide aggregation, conduct a critical analysis of solubilization strategies and protocols available in the literature, and give some tips on how to deal with the so-called difficult sequences. We focus on amyloids, which are particularly difficult to dissolve and handle such as amyloid beta (Aβ), insulin, and phenol-soluble modulins (PSMs).

Analysis of highly frequent point mutations in glycoprotein C, glycoprotein N, and nucleoprotein of CCHFV

Crimean-Congo hemorrhagic fever virus (CCHFV) is classified among top 10 priority pathogens by World Health Organization. CCHFV belongs to Bunyaviridae family and negative sense ssRNA genome composed of three RNA segments: L, M, and S. RNA viruses show higher mutation rate as compared to DNA viruses. To gain deeper understanding of impact of point mutations in CCHFV M and S segment, mutation profiling, homology modeling, and molecular dynamic (MD) simulation were performed. Structural glycoproteins (glycoprotein C [Gc] and glycoprotein N [Gn]) of CCHFV are important for host-virus interaction and genome packaging, whereas CCHFV nucleoprotein (NP) is crucial for viral replication. Hence, current study is focused on evaluation of eight mutations in structural glycoproteins (Gc: 7 and Gn: 1) of M segment and seven mutations in NP of S segment. All these mutations were highly frequent, with mutation frequency between 0.81 and 1.0 and found to be persistent in the recent strains of CCHFV. Solubility analysis predicted that selected point mutations reduce solubility of Gc protein and increase solubility of Gn and NP proteins. MD simulation study deciphered that A1046V and G1158E in Gc protein, I778T in Gn protein, and H195R in NP protein displayed large deviation and fluctuation, and affected intramolecular interactions. In conclusion, we observed that point mutations could impact structure, stability, and host-virus interaction of protein, and might lead to evolution of new strains for better survival and drug resistance.

Elucidating the Impact of Deleterious Mutations on IGHG1 and Their Association with Huntington's Disease

Huntington's disease (HD) is a chronic, inherited neurodegenerative condition marked by chorea, dementia, and changes in personality. The primary cause of HD is a mutation characterized by the expansion of a triplet repeat (CAG) within the huntingtin gene located on chromosome 4. Despite substantial progress in elucidating the molecular and cellular mechanisms of HD, an effective treatment for this disorder is not available so far. In recent years, researchers have been interested in studying cerebrospinal fluid (CSF) as a source of biomarkers that could aid in the diagnosis and therapeutic development of this disorder. Immunoglobulin heavy constant gamma 1 (IGHG1) is one of the CSF proteins found to increase significantly in HD. Considering this, it is reasonable to study the potential involvement of deleterious mutations in IGHG1 in the pathogenesis of this disorder. In this study, we explored the potential impact of deleterious mutations on IGHG1 and their subsequent association with HD. We evaluated 126 single-point amino acid substitutions for their impact on the structure and functionality of the IGHG1 protein while exploiting multiple computational resources such as SIFT, PolyPhen-2, FATHMM, SNPs&Go mCSM, DynaMut2, MAESTROweb, PremPS, MutPred2, and PhD-SNP. The sequence- and structure-based tools highlighted 10 amino acid substitutions that were deleterious and destabilizing. Subsequently, out of these 10 mutations, eight variants (Y32C, Y32D, P34S, V39E, C83R, C83Y, V85M, and H87Q) were identified as pathogenic by disease phenotype predictors. Finally, two pathogenic variants (Y32C and P34S) were found to reduce the solubility of the protein, suggesting their propensity to form protein aggregates. These variants also exhibited higher residual frustration within the protein structure. Considering these findings, the study hypothesized that the identified variants of IGHG1 may compromise its function and potentially contribute to HD pathogenesis.

Predicting the effects of mutations on protein solubility using graph convolution network and protein language model representation

Solubility is one of the most important properties of protein. Protein solubility can be greatly changed by single amino acid mutations and the reduced protein solubility could lead to diseases. Since experimental methods to determine solubility are time-consuming and expensive, in-silico methods have been developed to predict the protein solubility changes caused by mutations mostly through protein evolution information. However, these methods are slow since it takes long time to obtain evolution information through multiple sequence alignment. In addition, these methods are of low performance because they do not fully utilize protein 3D structures due to a lack of experimental structures for most proteins. Here, we proposed a sequence-based method DeepMutSol to predict solubility change from residual mutations based on the Graph Convolutional Neural Network (GCN), where the protein graph was initiated according to predicted protein structure from Alphafold2, and the nodes (residues) were represented by protein language embeddings. To circumvent the small data of solubility changes, we further pretrained the model over absolute protein solubility. DeepMutSol was shown to outperform state-of-the-art methods in benchmark tests. In addition, we applied the method to clinically relevant genes from the ClinVar database and the predicted solubility changes were shown able to separate pathogenic mutations. All of the data sets and the source code are available at https://github.com/biomed-AI/DeepMutSol.

Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation

Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a challenging task as indicated by the poor scores of normalized Correct Prediction Ratio (CPR). Part of the challenge stems from the fact that there is no three-dimensional (3D) structures for the wild-type and mutant proteins. This work integrates persistent Laplacians and pre-trained Transformer for the task. The Transformer, pretrained with hundreds of millions of protein sequences, embeds wild-type and mutant sequences, while persistent Laplacians track the topological invariant change and homotopic shape evolution induced by mutations in 3D protein structures, which are rendered from AlphaFold2. The resulting machine learning model was trained on an extensive data set labeled with three solubility types. Our model outperforms all existing predictive methods and improves the state-of-the-art up to 15%.

Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation

Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite of tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a challenging task as indicated by the poor scores of normalized Correct Prediction Ratio (CPR). Part of the challenge stems from the fact that there is no three-dimensional (3D) structures for the wild-type and mutant proteins. This work integrates persistent Laplacians and pre-trained Transformer for the task. The Transformer, pretrained with hunderds of millions of protein sequences, embeds wild-type and mutant sequences, while persistent Laplacians track the topological invariant change and homotopic shape evolution induced by mutations in 3D protein structures, which are rendered from AlphaFold2. The resulting machine learning model was trained on an extensive data set labeled with three solubility types. Our model outperforms all existing predictive methods and improves the state-of-the-art up to 15%.

SARS-CoV-2 vaccine breakthrough infections (VBI) by Omicron variant (B.1.1.529) and consequences in structural and functional impact

This study investigated the efficacy of existing vaccines against hospitalization and infection due to the Omicron variant of COVID-19, particularly for those who received two doses of Moderna or Pfizer vaccines and one dose of Johnson & Johnson vaccine or who were vaccinated more than five months before. A total of 36 variants in Omicron's spike protein, targeted by all three vaccinations, have made antibodies less effective at neutralizing the virus. The genotyping of the SARS-CoV-2 viral sequence revealed clinically significant variants such as E484K in three genetic mutations (T95I, D614G, and del142-144). A woman showed two of these mutations, indicating a potential risk of infection after successful immunization, as recently reported by Hacisuleyman (2021). We examine the effects of mutations on domains (NID, RBM, and SD2) found at the interfaces of the spike domains Omicron B.1.1529, Delta/B.1.1529, Alpha/B.1.1.7, VUM B.1.526, B.1.575.2, and B.1.1214 (formerly VOI Iota). We tested the affinity of Omicron for ACE2 and found that the wild- and mutant-spike proteins were using atomistic molecular dynamics simulations. According to the binding free energies calculated during mutagenesis, the ACE2 bound Omicron spikes more strongly than the wild strain SARS-CoV-2. T95I, D614G, and E484K are three substitutions that significantly contribute to RBD, corresponding to ACE2 binding energies and a doubling of the electrostatic potential of Omicron spike proteins. The Omicron appears to bind to ACE2 with greater affinity, increasing its infectivity and transmissibility. The spike virus was designed to strengthen antibody immune evasion through binding while boosting receptor binding by enhancing IgG and IgM antibodies that stimulate human β-cell, as opposed to the wild strain, which has more vital stimulation of both antibodies.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Delineating the impact of pathogenic mutations on the conformational dynamics of HDL's vital protein ApoA1: a combined computational and molecular dynamic simulation approach

Apolipoprotein A1 (ApoA1), is the important component of high-density lipoproteins (HDL), that has key role in HDL biogenesis, cholesterol trafficking, and reverse cholesterol transport (RCT). Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in ApoA1 have been linked to cardiovascular diseases and amyloidosis as they alter the protein's native structure and function. Therefore in this study, we attempted to understand the molecular pathogenicity profile of nsSNPs of ApoA1 using various computational approaches. We used state-of-the-art computational methods to thoroughly investigate the 295 ApoA1 nsSNPs at sequence and structural levels. Seven nsSNPs (L13R, L84R, L84P, L99P, R173P, L187P, and L238P) out of 295 were classified as the most deleterious and destabilizing. In order to estimate the effect of such destabilizing mutations on the protein conformation, all-atom molecular dynamics simulations (MDS) of ApoA1 wild-type (WT), L99P and R173P for 100 ns, was carried out using GROMACS 5.0.1 package. The MD simulation investigation revealed significant structural alterations in L99P and R173P. In addition, they had changed principal component analysis and electrostatic surface potential, decreased structural compactness, and intramolecular hydrogen bonds, which supported the rationale underpinning ApoA1 dysfunction with such mutations. This work sheds light on ApoA1 dysfunction due to single amino acid alterations, and offers new insight into the molecular basis of ApoA1-related diseases progression.Communicated by Ramaswamy H. Sarma.

Mutational analysis of catalytic site domain of CCHFV L RNA segment

INTRODUCTION

Crimean-Congo haemorrhagic fever virus (CCHFV) has tripartite RNA genome and is endemic in various countries of Asia, Africa and Europe.

METHOD

The present study is focused on mutation profiling of CCHFV L segment and phylogenetic clustering of protein dataset into six CCHFV genotypes.

RESULTS

Phylogenetic tree rooted with NCBI reference sequence (YP_325663.1) indicated less divergence from genotype III and the sequences belonging to same genotypes have shown less divergence among each other. Mutation frequency at 729 mutated positions was calculated and 563, 49, 33, 46 and 38 amino acid positions were found to be mutated at mutation frequency intervals of 0-0.2, 0.21-0.4, 0.41-0.6, 0.61-0.8 and 0.81-1.0 respectively. Thirty-eight highly frequent mutations (0.81-1.0 interval) were found in all genotypes and mapping in L segment (encoded for RdRp) revealed four mutations (V2074I, I2134T/A, V2148A and Q2695H/R) in catalytic site domain and no mutation in OTU domain. Molecular dynamic simulation and in silico analysis showed that catalytic site domain displayed large deviation and fluctuation upon introduction of these point mutations.

CONCLUSION

Overall study provides strong evidence that OTU domain is highly conserved and less prone to mutation whereas point mutations recorded in catalytic domain have affected the stability of protein and were found to be persistent in the large population.

The EDN1 Missense Variant rs5370G > T Regulates Adaptation and Maladaptation under Hypobaric Hypoxia

Endothelin 1 (EDN1) encodes a potent endogenous vasoconstrictor, ET1, to maintain vascular homeostasis and redistribution of tissue blood flow during exercise. One of the EDN1 missense polymorphisms, rs5370 G/T, has strongly been associated with cardiopulmonary diseases. This study investigated the impact of rs5370 polymorphism in high-altitude pulmonary oedema (HAPE) disorder or maladaptation and adaptation physiology in a well-characterized case-control study of high-altitude and low-altitude populations comprising 310 samples each of HAPE-patients, HAPE-free controls and native highlanders. The rs5370 polymorphism was genotyped, and the gene expression and plasma level of EDN1 were evaluated. The functional relevance of each allele was investigated in the human embryonic kidney 293 cell line after exposure to hypoxia and computationally. The T allele was significantly more prevalent in HAPE-p compared to HAPE-f and HLs. The EDN1 gene expression and ET1 bio-level were significantly elevated in HAPE-p compared to controls. Compared to the G allele, the T allele was significantly associated with elevated levels of ET-1 in all three study groups and cells exposed to hypoxia. The in silico studies further confirmed the stabilizing effect of the T allele on the structural integrity and function of ET1 protein. The ET1 rs5370 T allele is associated with an increased concentration of ET-1 in vivo and in vitro, establishing it as a potent marker in the adaptation/maladaptation physiology under the high-altitude environment. This could also be pertinent in endurance exercises at high altitudes.

In silico analysis of the C-terminal domain of big defensin from the Pacific oyster

Defensins are antimicrobial peptides consisting of intramolecular disulphide bonds in a complex folded arrangement of two or three antiparallel β-sheets with or without an α-helical structure. They are produced by a vast range of organisms being constitutively expressed or induced in various tissues against different stimuli like infection, injury or other inflammatory factors. Two classes of invertebrate defensin exist, namely CS-αβ and big defensin, the latter being predominantly present in molluscs. Intriguingly, an invertebrate big defensin gene has been hypothesized as the most probable ancestor of vertebrate β-defensins. Here, conserved residues were identified for both big defensin and β-defensin. In silico mutation on conserved amino acid positions of the β-defensin-like domain of big defensin from Crassostrea gigas was carried out to understand the effects of mutation on the structure and function of the protein. R64A and E71A have been identified as deleterious as well as destabilizing for the protein. Changes in amino acid network and aggregation propensity were also observed upon mutating these two charged residues. 100 ns molecular dynamics simulations of wild-type, R64A and E71A structures revealed significant conformational changes in the case of mutants. Furthermore, molecular docking highlighted the significance of R64 in ligand interaction. In conclusion, these results provide the first in-depth understanding of the structural and functional importance imparted by two conserved charged residues in the C-terminal region of big defensin. It also enhances the existing knowledge about this antimicrobial peptide for application in therapeutics and other aspects of protein engineering.Communicated by Ramaswamy H. Sarma.

Infection Dynamics of ATG8 in Leishmania: Balancing Autophagy for Therapeutics

In many regions of the world, Leishmaniasis is a cause of substantial mortality and ailment. Due to impediment in available treatment, development of novel and effective treatments is indispensable. Significance of autophagy has been accentuated in infectious disease as well as in Leishmaniasis, and it is having capability to be manifested as a therapeutic target. By evincing autophagy as a novel therapeutic regime, this study emphasized on the critical role of ATG4.1-ATG8 and ATG5-ATG12 complexes in Leishmania species. The objective here was to identify ATG8 as a potential therapeutic target in Leishmania. R71T, P56E, R18P are the significant mutations which shows detrimental effect on ATG8 while Arg276, Arg73, Cys75 of ATG4.1 and Val88, Pro89, Glu116, Asn117, and Gly120 are interacting residues of ATG8. Along with this, we also bring into spotlight an enticing role of Thiabendazole derivatives that interferes with the survival mechanisms by targeting ATG8. Further, the study claims that thiabendazole can be a potential drug candidate to target autophagy process in the infectious disease Leishmaniasis.

NetSolP: predicting protein solubility in Escherichia coli using language models

MOTIVATION

Solubility and expression levels of proteins can be a limiting factor for large-scale studies and industrial production. By determining the solubility and expression directly from the protein sequence, the success rate of wet-lab experiments can be increased.

RESULTS

In this study, we focus on predicting the solubility and usability for purification of proteins expressed in Escherichia coli directly from the sequence. Our model NetSolP is based on deep learning protein language models called transformers and we show that it achieves state-of-the-art performance and improves extrapolation across datasets. As we find current methods are built on biased datasets, we curate existing datasets by using strict sequence-identity partitioning and ensure that there is minimal bias in the sequences.

AVAILABILITY AND IMPLEMENTATION

The predictor and data are available at https://services.healthtech.dtu.dk/service.php?NetSolP and the open-sourced code is available at https://github.com/tvinet/NetSolP-1.0.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

Impact of Deleterious Mutations on Structure, Function and Stability of Serum/Glucocorticoid Regulated Kinase 1: A Gene to Diseases Correlation

Serum and glucocorticoid-regulated kinase 1 (SGK1) is a Ser/Thr protein kinase involved in regulating cell survival, growth, proliferation, and migration. Its elevated expression and dysfunction are reported in breast, prostate, hepatocellular, lung adenoma, and renal carcinomas. We have analyzed the SGK1 mutations to explore their impact at the sequence and structure level by utilizing state-of-the-art computational approaches. Several pathogenic and destabilizing mutations were identified based on their impact on SGK1 and analyzed in detail. Three amino acid substitutions, K127M, T256A, and Y298A, in the kinase domain of SGK1 were identified and incorporated structurally into original coordinates of SGK1 to explore their time evolution impact using all-atom molecular dynamic (MD) simulations for 200 ns. MD results indicate substantial conformational alterations in SGK1, thus its functional loss, particularly upon T256A mutation. This study provides meaningful insights into SGK1 dysfunction upon mutation, leading to disease progression, including cancer, and neurodegeneration.

PON-Sol2: Prediction of Effects of Variants on Protein Solubility

Genetic variations have a multitude of effects on proteins. A substantial number of variations affect protein-solvent interactions, either aggregation or solubility. Aggregation is often related to structural alterations, whereas solubilizable proteins in the solid phase can be made again soluble by dilution. Solubility is a central protein property and when reduced can lead to diseases. We developed a prediction method, PON-Sol2, to identify amino acid substitutions that increase, decrease, or have no effect on the protein solubility. The method is a machine learning tool utilizing gradient boosting algorithm and was trained on a large dataset of variants with different outcomes after the selection of features among a large number of tested properties. The method is fast and has high performance. The normalized correct prediction rate for three states is 0.656, and the normalized GC2 score is 0.312 in 10-fold cross-validation. The corresponding numbers in the blind test were 0.545 and 0.157. The performance was superior in comparison to previous methods. The PON-Sol2 predictor is freely available. It can be used to predict the solubility effects of variants for any organism, even in large-scale projects.

Structural genomics approach to investigate deleterious impact of nsSNPs in conserved telomere maintenance component 1

Conserved telomere maintenance component 1 (CTC1) is an important component of the CST (CTC1-STN1-TEN1) complex, involved in maintaining the stability of telomeric DNA. Several non-synonymous single-nucleotide polymorphisms (nsSNPs) in CTC1 have been reported to cause Coats plus syndrome and Dyskeratosis congenital diseases. Here, we have performed sequence and structure analyses of nsSNPs of CTC1 using state-of-the-art computational methods. The structure-based study focuses on the C-terminal OB-fold region of CTC1. There are 11 pathogenic mutations identified, and detailed structural analyses were performed. These mutations cause a significant disruption of noncovalent interactions, which may be a possible reason for CTC1 instability and consequent diseases. To see the impact of such mutations on the protein conformation, all-atom molecular dynamics (MD) simulations of CTC1-wild-type (WT) and two of the selected mutations, R806C and R806L for 200 ns, were carried out. A significant conformational change in the structure of the R806C mutant was observed. This study provides a valuable direction to understand the molecular basis of CTC1 dysfunction in disease progression, including Coats plus syndrome.

Impact of non-synonymous mutations on the structure and function of telomeric repeat binding factor 1

Telomeric repeat binding factor 1 (TRF1) is one of the major components of the shelterin complex. It directly binds to the telomere and controls its function by regulating the telomerase acting on it. Several variations are reported in the TRF1 gene; some are associated with variety of diseases. Here, we have studied the structural and functional significance of these variations in the TRFH domain of TRF1. We have used cutting-edge computational methods such as SIFT, PolyPhen-2, PROVEAN, Mutation Assessor, mCSM, SDM, STRUM, MAESTRO, and DUET to predict the effects of 124 mutations in the TRFH domain of TRF1. Out of 124 mutations, we have identified 12 deleterious mutations with high confidence based on their prediction. To see the impact of the finally selected mutations on the structure and stability of TRF1, all-atom molecular dynamics (MD) simulations on TRF1-Wild type (WT), L79R and P150R mutants for 200 ns were carried out. A significant conformational change in the structure of the P150R mutant was observed. Our integrated computational study provides a comprehensive understanding of structural changes in TRF1 incurred due to the mutations and subsequent function, leading to the progression of many diseases.Communicated by Ramaswamy H. Sarma.

Solubility and Aggregation of Selected Proteins Interpreted on the Basis of Hydrophobicity Distribution

Protein solubility is based on the compatibility of the specific protein surface with the polar aquatic environment. The exposure of polar residues to the protein surface promotes the protein’s solubility in the polar environment. The aquatic environment also influences the folding process by favoring the centralization of hydrophobic residues with the simultaneous exposure to polar residues. The degree of compatibility of the residue distribution, with the model of the concentration of hydrophobic residues in the center of the molecule, with the simultaneous exposure of polar residues is determined by the sequence of amino acids in the chain. The fuzzy oil drop model enables the quantification of the degree of compatibility of the hydrophobicity distribution observed in the protein to a form fully consistent with the Gaussian 3D function, which expresses an idealized distribution that meets the preferences of the polar water environment. The varied degrees of compatibility of the distribution observed with the idealized one allow the prediction of preferences to interactions with molecules of different polarity, including water molecules in particular. This paper analyzes a set of proteins with different levels of hydrophobicity distribution in the context of the solubility of a given protein and the possibility of complex formation.

Potential aggregation hot spots in recombinant human keratinocyte growth factor: a computational study

The recombinant human keratinocyte growth factor (rhKGF) is a highly aggregation-prone therapeutic protein. The high aggregation liability of rhKGF is manifested by loss of the monomeric state, and accumulation of the aggregated species even at moderate temperatures. Here, we analyzed the rhKGF for its vulnerability toward aggregation by detection of aggregation-prone regions (APRs) using several sequence-based computational tools including TANGO, ZipperDB, AGGRESCAN, Zyggregator, Camsol, PASTA, SALSA, WALTZ, SODA, Amylpred, AMYPDB, and structure-based tools including SolubiS, CamSol structurally corrected, Aggrescan3D and spatial aggregation propensity (SAP) algorithm. The sequence-based prediction of APRs in rhKGF indicated that they are mainly located at positions 10-30, 40-60, 61-66, 88-120, and 130-140. Mapping on the rhKGF structure revealed that most of these residues including F16-R25, I43, E45, R47-I56, F61, Y62, N66, L88-E91, E108-F110, A112, N114, T131, and H133-T140 are surface-exposed in the native state which can promote aggregation without major unfolding event, or the conformational change may occur in the oligomers. The other regions are buried in the native state and their contribution to non-native aggregation is mediated by a preceding unfolding event. The structure-based prediction of APRs using the SAP tool limited the number of identified APRs to the dynamically-exposed hydrophobic residues including V12, A50, V51, L88, I89, L90, I118, L135, and I139 mediating the native-state aggregation. Our analysis of APRs in rhKGF identified the regions determining the intrinsic aggregation propensity of the rhKGF which are the candidate positions for engineering the rhKGF to reduce its aggregation tendency.Communicated by Ramaswamy H. Sarma.

A multidimensional computational exploration of congenital myasthenic syndrome causing mutations in human choline acetyltransferase

Missense mutations of human choline acetyltransferase (CHAT) are mainly associated with congenital myasthenic syndrome (CMS). To date, several pathogenic mutations have been reported, but due to the rarity and genetic complexity of CMS and difficult genotype-phenotype correlations, the CHAT mutations, and their consequences are underexplored. In this study, we systematically sift through the available genetic data in search of previously unreported pathogenic mutations and use a dynamic in silico model to provide structural explanations for the pathogenicity of the reported deleterious and undetermined variants. Through rigorous multiparameter analyses, we conclude that mutations can affect CHAT through a variety of different mechanisms: by disrupting the secondary structure, by perturbing the P-loop through long-range allosteric interactions, by disrupting the domain connecting loop, and by affecting the phosphorylation process. This study provides the first dynamic look at how mutations affect the structure and catalytic activity in CHAT and highlights the need for further genomic research to better understand the pathology of CHAT.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods

There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.

iAMY-SCM: Improved prediction and analysis of amyloid proteins using a scoring card method with propensity scores of dipeptides

Fast, accurate identification and characterization of amyloid proteins at a large-scale is essential for understating their role in therapeutic intervention strategies. As a matter of fact, there exist only one in silico model for amyloid protein identification using the random forest (RF) model in conjunction with various feature types namely the RFAmy. However, it suffers from low interpretability for biologists. Thus, it is highly desirable to develop a simple and easily interpretable prediction method with robust accuracy as compared to the existing complicated model. In this study, we propose iAMY-SCM, the first scoring card method-based predictor for predicting and analyzing amyloid proteins. Herein, the iAMY-SCM made use of a simple weighted-sum function in conjunction with the propensity scores of dipeptides for the amyloid protein identification. Cross-validation results indicated that iAMY-SCM provided an accuracy of 0.895 that corresponded to 10-22% higher performance than that of widely used machine learning models. Furthermore, iAMY-SCM achieving an accuracy of 0.827 as evaluated by an independent test, which was found to be comparable to that of RFAmy and was approximately 9-13% higher than widely used machine learning models. Furthermore, the analysis of estimated propensity scores of amino acids and dipeptides were performed to provide insights into the biophysical and biochemical properties of amyloid proteins. As such, this demonstrates that the proposed iAMY-SCM is efficient and reliable in terms of simplicity, interpretability and implementation. To facilitate ease of use of the proposed iAMY-SCM, a user-friendly and publicly accessible web server at http://camt.pythonanywhere.com/iAMY-SCM has been established. We anticipate that that iAMY-SCM will be an important tool for facilitating the large-scale prediction and characterization of amyloid protein.

Insight into the protein solubility driving forces with neural attention

Protein solubility is a key aspect for many biotechnological, biomedical and industrial processes, such as the production of active proteins and antibodies. In addition, understanding the molecular determinants of the solubility of proteins may be crucial to shed light on the molecular mechanisms of diseases caused by aggregation processes such as amyloidosis. Here we present SKADE, a novel Neural Network protein solubility predictor and we show how it can provide novel insight into the protein solubility mechanisms, thanks to its neural attention architecture. First, we show that SKADE positively compares with state of the art tools while using just the protein sequence as input. Then, thanks to the neural attention mechanism, we use SKADE to investigate the patterns learned during training and we analyse its decision process. We use this peculiarity to show that, while the attention profiles do not correlate with obvious sequence aspects such as biophysical properties of the aminoacids, they suggest that N- and C-termini are the most relevant regions for solubility prediction and are predictive for complex emergent properties such as aggregation-prone regions involved in beta-amyloidosis and contact density. Moreover, SKADE is able to identify mutations that increase or decrease the overall solubility of the protein, allowing it to be used to perform large scale in-silico mutagenesis of proteins in order to maximize their solubility.

The solubility of proteins is a crucial biophysical aspect when it comes to understanding many human diseases and to improve the industrial processes for protein production. Due to its relevance, computational methods have been devised in order to study and possibly optimize the solubility of proteins. In this work we apply a deep-learning technique, called neural attention to predict protein solubility while “opening” the model itself to interpretability, even though Machine Learning models are usually considered black boxes. Thank to the attention mechanism, we show that i) our model implicitly learns complex patterns related to emergent, protein folding-related, aspects such as to recognize β-amyloidosis regions and that ii) the N-and C-termini are the regions with the highes signal fro solubility prediction. When it comes to enhancing the solubility of proteins, we, for the first time, propose to investigate the synergistic effects of tandem mutations instead of “single” mutations, suggesting that this could minimize the number of required proposed mutations.

Matching pH values for antibody stabilization and crystallization suggest rationale for accelerated development of biotherapeutic drugs

Monoclonal antibodies (mAbs) are currently leading products in the global biopharmaceutical market. Multiple mAbs are in clinical development and novel biotherapeutic protein scaffolds, based on the canonical immunoglobulin G (IgG) fold, are emerging as treatment options for various medical conditions. However, fast approvals for biotherapeutics are challenging to achieve, because of difficult scientific development procedures and complex regulatory processes. Selecting molecular entities with superior physicochemical properties that proceed into clinical trials and the identification of stable formulations are crucial developability aspects. It is widely accepted that the solution pH has critical influences on both the protein's colloidal stability and its crystallization behavior. Furthermore, proteins usually crystallize best at solution conditions that enable high protein solubility, purity, stability, and monodispersity. Therefore, we hypothesize that the solution pH value is a central parameter that is linking together protein formulation, protein crystallization, and thermal protein stability. In order to experimentally test this hypothesis, we have investigated the effect of the solution pH on the thermal stabilities and crystallizabilities for three different mAbs. Combining biophysical measurements with high throughput protein (HTP) crystallization trials we observed a correlation in the buffer pH values for eminent mAb stability and successful crystallization. Specifically, differential scanning fluorimetry (DSF) was used to determine pH values that exert highest thermal mAb stabilities and additionally led to the identification of unfolding temperatures of individual mAb domains. Independently performed crystallization trials with the same mAbs resulted in their successful crystallization at pH values that displayed highest thermal stabilities. In summary, the presented results suggest a strategy how protein crystallization could be used as a screening method for the development of biotherapeutic protein formulations with improved in vitro stabilities.

Structural Analysis and Conformational Dynamics of STN1 Gene Mutations Involved in Coat Plus Syndrome

The human CST complex (CTC1-STN1-TEN1) is associated with telomere functions including genome stability. We have systemically analyzed the sequence of STN and performed structure analysis to establish its association with the Coat Plus (CP) syndrome. Many deleterious non-synonymous SNPs have been identified and subjected for structure analysis to find their pathogenic association and aggregation propensity. A 100-ns all-atom molecular dynamics simulation of WT, R135T, and D157Y structures revealed significant conformational changes in the case of mutants. Changes in hydrogen bonds, secondary structure, and principal component analysis further support the structural basis of STN1 dysfunction in such mutations. Free energy landscape analysis revealed the presence of multiple energy minima, suggesting that R135T and D157Y mutations destabilize and alter the conformational dynamics of STN1 and thus may be associated with the CP syndrome. Our study provides a valuable direction to understand the molecular basis of CP syndrome and offer a newer therapeutics approach to address CP syndrome.

Structural and functional impact of non-synonymous SNPs in the CST complex subunit TEN1: structural genomics approach

TEN1 protein is a key component of CST complex, implicated in maintaining the telomere homeostasis, and provides stability to the eukaryotic genome. Mutations in TEN1 gene have higher chances of deleterious impact; thus, interpreting the number of mutations and their consequential impact on the structure, stability, and function is essentially important. Here, we have investigated the structural and functional consequences of nsSNPs in the TEN1 gene. A wide array of sequence- and structure-based computational prediction tools were employed to identify the effects of 78 nsSNPs on the structure and function of TEN1 protein and to identify the deleterious nsSNPs. These deleterious or destabilizing nsSNPs are scattered throughout the structure of TEN1. However, major mutations were observed in the α1-helix (12–16 residues) and β5-strand (88–96 residues). We further observed that mutations at the C-terminal region were having higher tendency to form aggregate. In-depth structural analysis of these mutations reveals that the pathogenicity of these mutations are driven mainly through larger structural changes because of alterations in non-covalent interactions. This work provides a blueprint to pinpoint the possible consequences of pathogenic mutations in the CST complex subunit TEN1.

Protein engineering approaches for antibody fragments: directed evolution and rational design approaches

The number of therapeutic antibodies in preclinical, clinical, or approved phases has been increasing exponentially, mostly due to their known successes. Development of antibody engineering methods has substantially hastened the development of therapeutic antibodies. A variety of protein engineering techniques can be applied to antibodies to improve their afinity and/or biophysical properties such as solubility and stability. Antibody fragments (where all or some parts of constant regions are eliminated while the essential antigen binding region is preserved) are more suitable for protein engineering techniques because there are many in vitro screening technologies available for antibody fragments but not full-length antibodies. Improvement of biophysical characteristics is important in the early development phase because most antibodies fail at the later stage of development and this leads to loss of resources and time. Here, we review directed evolution and rational design methods to improve antibody properties. Recent developments in rational design approaches and antibody display technologies, and especially phage display, which was recently awarded the 2018 Nobel Prize, are discussed to be used in antibody research and development.

Bacterial inclusion bodies are industrially exploitable amyloids

Understanding the structure, functionalities and biology of functional amyloids is an issue of emerging interest. Inclusion bodies, namely protein clusters formed in recombinant bacteria during protein production processes, have emerged as unanticipated, highly tunable models for the scrutiny of the physiology and architecture of functional amyloids. Based on an amyloidal skeleton combined with varying amounts of native or native-like protein forms, bacterial inclusion bodies exhibit an unusual arrangement that confers mechanical stability, biological activity and conditional protein release, being thus exploitable as versatile biomaterials. The applicability of inclusion bodies in biotechnology as enriched sources of protein and reusable catalysts, and in biomedicine as biocompatible topographies, nanopills or mimetics of endocrine secretory granules has been largely validated. Beyond these uses, the dissection of how recombinant bacteria manage the aggregation of functional protein species into structures of highly variable complexity offers insights about unsuspected connections between protein quality (conformational status compatible with functionality) and cell physiology.

In vitro and in silico assessment of the developability of a designed monoclonal antibody library

Despite major advances in antibody discovery technologies, the successful development of monoclonal antibodies (mAbs) into effective therapeutic and diagnostic agents can often be impeded by developability liabilities, such as poor expression, low solubility, high viscosity and aggregation. Therefore, strategies to predict at the early phases of antibody development the risk of late-stage failure of antibody candidates are highly valuable. In this work, we employ the in silico solubility predictor CamSol to design a library of 17 variants of a humanized mAb predicted to span a broad range of solubility values, and we examine their developability potential with a battery of commonly used in vitro and in silico assays. Our results demonstrate the ability of CamSol to rationally enhance mAb developability, and provide a quantitative comparison of in vitro developability measurements with each other and with more resource-intensive solubility measurements, as well as with in silico predictors that offer a potentially faster and cheaper alternative. We observed a strong correlation between predicted and experimentally determined solubility values, as well as with measurements obtained using a panel of in vitro developability assays that probe non-specific interactions. These results indicate that computational methods have the potential to reduce or eliminate the need of carrying out laborious in vitro quality controls for large numbers of lead candidates. Overall, our study provides support to the emerging view that the implementation of in silico tools in antibody discovery campaigns can ensure rapid and early selection of antibodies with optimal developability potential.

RFAmyloid: A Web Server for Predicting Amyloid Proteins

Amyloid is an insoluble fibrous protein and its mis-aggregation can lead to some diseases, such as Alzheimer’s disease and Creutzfeldt–Jakob’s disease. Therefore, the identification of amyloid is essential for the discovery and understanding of disease. We established a novel predictor called RFAmy based on random forest to identify amyloid, and it employed SVMProt 188-D feature extraction method based on protein composition and physicochemical properties and pse-in-one feature extraction method based on amino acid composition, autocorrelation pseudo acid composition, profile-based features and predicted structures features. In the ten-fold cross-validation test, RFAmy’s overall accuracy was 89.19% and F-measure was 0.891. Results were obtained by comparison experiments with other feature, classifiers, and existing methods. This shows the effectiveness of RFAmy in predicting amyloid protein. The RFAmy proposed in this paper can be accessed through the URL http://server.malab.cn/RFAmyloid/.