{"entity": "journal", "iuid": "6a8c425213894ee7b4916a06402839e6", "timestamp": "2026-08-20T21:30:36.665Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/Protein%20J.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/Protein%20J"}}, "title": "Protein J", "issn": "1875-8355", "issn-l": null, "publications_count": 2, "publications": [{"entity": "publication", "iuid": "e9ac179c52f34be2bf1476a0d6861db5", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/e9ac179c52f34be2bf1476a0d6861db5.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/e9ac179c52f34be2bf1476a0d6861db5"}}, "title": "On the Path to Optimal Alchemistry.", "authors": [{"family": "Lundborg", "given": "Magnus", "initials": "M"}, {"family": "Lidmar", "given": "Jack", "initials": "J"}, {"family": "Hess", "given": "Berk", "initials": "B"}], "type": "journal article", "published": "2023-10-00", "journal": {"title": "Protein J", "issn": "1875-8355", "volume": "42", "issue": "5", "pages": "477-489", "issn-l": null}, "abstract": "Alchemical free energy calculations have become a standard and widely used tool, in particular for calculating and comparing binding affinities of drugs. Although methods to compute such free energies have improved significantly over the last decades, the choice of path between the end states of interest is usually still the same as two decades ago. We will show that there is a fundamentally arbitrary, implicit choice of parametrization of this path. To address this, the notion of the length of a path or a metric is required. A metric recently introduced in the context of the accelerated weight histogram method also proves to be very useful here. We demonstrate that this metric can not only improve the efficiency of sampling along a given path, but that it can also be used to improve the actual choice of path. For a set of relevant use cases, the combination of these improvements can increase the efficiency of alchemical free energy calculations by up to a factor 16.", "doi": "10.1007/s10930-023-10137-1", "pmid": "37651042", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC10480267"}, {"db": "pii", "key": "10.1007/s10930-023-10137-1"}], "notes": [], "created": "2026-08-20T06:40:13.938Z", "modified": "2026-08-20T06:40:13.996Z"}, {"entity": "publication", "iuid": "ccefc040f04b48969c2df248758fa3a3", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/ccefc040f04b48969c2df248758fa3a3.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/ccefc040f04b48969c2df248758fa3a3"}}, "title": "A Brief History of Protein Sorting Prediction.", "authors": [{"family": "Nielsen", "given": "Henrik", "initials": "H", "orcid": "0000-0002-9412-9643", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/95aab73521844700a8137cfc2cb287b0.json"}}, {"family": "Tsirigos", "given": "Konstantinos D", "initials": "KD"}, {"family": "Brunak", "given": "S\u00f8ren", "initials": "S"}, {"family": "von Heijne", "given": "Gunnar", "initials": "G"}], "type": "journal article", "published": "2019-06-00", "journal": {"title": "Protein J", "issn": "1875-8355", "volume": "38", "issue": "3", "pages": "200-216", "issn-l": null}, "abstract": "Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.", "doi": "10.1007/s10930-019-09838-3", "pmid": "31119599", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC6589146"}, {"db": "pii", "key": "10.1007/s10930-019-09838-3"}], "notes": [], "created": "2026-08-20T06:40:11.923Z", "modified": "2026-08-20T06:40:12.014Z"}], "created": "2026-08-20T06:40:11.984Z", "modified": "2026-08-20T06:40:11.984Z"}